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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
</ul>
<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
</ul>
<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
'label2' => 'Validation: Focus on CpG-rich regions',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><span>Premium Methyl UDI-UMI Adapters Module - Set A includes 24 m</span>ethylated full-length adapters <span>with <strong>unique dual indexes</strong> (UDI) and<strong> unique molecular identifiers</strong> (UMI) for library multiplexing with the <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> and other BS-seq applications</span><span>. The use of </span>UDIs (unique i5 and i7 barcodes) is<span> highly recommended to mitigate errors introduced by read misassignment. The use of UMIs (9-base) is hilghy recommended for quantitave analysis or low-frequency variant detection.</span></p>
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<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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<p><span>Premium Methyl UDI-UMI Adapters Module - Set B includes 24 m</span>ethylated full-length adapters <span>with <strong>unique dual indexes</strong> (UDI) and<strong> unique molecular identifiers</strong><span> </span>(UMI) for library multiplexing with the<span> </span><a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a><span> </span>and other BS-seq applications</span><span>. The use of </span>UDIs (unique i5 and i7 barcodes) is<span> highly recommended to mitigate errors introduced by read misassignment. The use of UMIs (9-base) is hilghy recommended for quantitave analysis or low-frequency variant detection.</span></p>
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<p><span></span></p>
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<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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<p>Diagenode’s MicroChIP DiaPure columns have been optimized for the purification and elution of very low amounts of DNA. This rapid method has been validated for epigenetic applications like low input ChIP (e.g. using the True MicroChIP kit) and CUT&Tag (e.g. using Diagenode’s pA-Tn5), but is also compatible with many other applications. The DNA can be eluted at high concentrations in volumes down to 6 μl and it is suitable for any downstream application (e.g. NGS).</p>
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<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
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<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
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<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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<p><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to 7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with reliable results</li>
<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> widely adopted</li>
<li>As little as 25 ng for any vertebrate species</li>
<li>Differentially methylated site analysis <span>using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<div class="extra-spaced">
<h2></h2>
</div>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
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<h4>RRBS Service includes:</h4>
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<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration </li>
<li>Assessment of DNA quality</li>
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<tr>
<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
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<li>MspI digestion</li>
<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</li>
<li>Bisulfite conversion</li>
<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<tr>
<td style="width: 264px;"><strong>Deep sequencing</strong></td>
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<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7 million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage (on average) for human samples</li>
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<th style="width: 262px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
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<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
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<tr>
<td style="width: 262px;"><strong>Differential methylation analysis<br /></strong></td>
<td style="width: 624px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, …)</li>
<li>Clustering analysis</li>
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</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Gene ontology terms analysis</strong></p>
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<td style="width: 624px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
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</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
'label2' => 'How it works?',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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<script src="chrome-extension://hhojmcideegachlhfgfdhailpfhgknjm/web_accessible_resources/index.js"></script>
<script src="chrome-extension://hhojmcideegachlhfgfdhailpfhgknjm/web_accessible_resources/index.js"></script>',
'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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'label2' => 'How it works?',
'info2' => '<center><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/workflow-RRBS_2021.png" width="700px" alt="Premium RRBS kit " caption="false" /></center>
<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<script src="chrome-extension://hhojmcideegachlhfgfdhailpfhgknjm/web_accessible_resources/index.js"></script>',
'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div class="large-12 columns">
<div style="text-align: justify;" class="small-12 medium-8 large-8 columns">
<h2>Complete solutions for DNA methylation studies</h2>
<p>Whether you are experienced or new to the field of DNA methylation, Diagenode has everything you need to make your assay as easy and convenient as possible while ensuring consistent data between samples and experiments. Diagenode offers sonication instruments, reagent kits, high quality antibodies, and high-throughput automation capability to address all of your specific DNA methylation analysis requirements.</p>
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<div class="small-12 medium-4 large-4 columns text-center"><a href="../landing-pages/dna-methylation-grant-applications"><img src="https://www.diagenode.com/img/banners/banner-dna-grant.png" alt="" /></a></div>
<div style="text-align: justify;" class="small-12 medium-12 large-12 columns">
<p>DNA methylation was the first discovered epigenetic mark and is the most widely studied topic in epigenetics. <em>In vivo</em>, DNA is methylated following DNA replication and is involved in a number of biological processes including the regulation of imprinted genes, X chromosome inactivation. and tumor suppressor gene silencing in cancer cells. Methylation often occurs in cytosine-guanine rich regions of DNA (CpG islands), which are commonly upstream of promoter regions.</p>
</div>
<div class="small-12 medium-12 large-12 columns"><br /><br />
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#dnamethyl"><i class="fa fa-caret-right"></i> Learn more</a>
<div id="dnamethyl" class="content">5-methylcytosine (5-mC) has been known for a long time as the only modification of DNA for epigenetic regulation. In 2009, however, Kriaucionis discovered a second methylated cytosine, 5-hydroxymethylcytosine (5-hmC). The so-called 6th base, is generated by enzymatic conversion of 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine by the TET family of oxygenases. Early reports suggested that 5-hmC may represent an intermediate of active demethylation in a new pathway which demethylates DNA, converting 5-mC to cytosine. Recent evidence fuel this hypothesis suggesting that further oxidation of the hydroxymethyl group leads to a formyl or carboxyl group followed by either deformylation or decarboxylation. The formyl and carboxyl groups of 5-formylcytosine (5-fC) and 5-carboxylcytosine (5-caC) could be enzymatically removed without excision of the base.
<p class="text-center"><img src="https://www.diagenode.com/img/categories/kits_dna/dna_methylation_variants.jpg" /></p>
</div>
</li>
</ul>
<br />
<h2>Main DNA methylation technologies</h2>
<p style="text-align: justify;">Overview of the <span style="font-weight: 400;">three main approaches for studying DNA methylation.</span></p>
<div class="row">
<ol>
<li style="font-weight: 400;"><span style="font-weight: 400;">Chemical modification with bisulfite – Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Enrichment of methylated DNA (including MeDIP and MBD)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Treatment with methylation-sensitive or dependent restriction enzymes</span></li>
</ol>
<p><span style="font-weight: 400;"> </span></p>
<div class="row">
<table>
<thead>
<tr>
<th></th>
<th>Description</th>
<th width="350">Features</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td><span style="font-weight: 400;">Chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at single nucleotide resolution.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Single nucleotide resolution</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Quantitative analysis - methylation rate (%)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Gold standard and well studied</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><b>Methylated DNA enrichment</b></td>
<td><span style="font-weight: 400;">(Hydroxy-)Methylated DNA is enriched by using specific antibodies (hMeDIP or MeDIP) or proteins (MBD) that specifically bind methylated CpG sites in fragmented genomic DNA.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Resolution depends on the fragment size of the enriched methylated DNA (300 bp)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Qualitative analysis</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><strong>Restriction enzyme-based digestion</strong></td>
<td><span style="font-weight: 400;">Use of (hydroxy)methylation-sensitive or (hydroxy)methylation-dependent restriction enzymes for DNA methylation analysis at specific sites.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Determination of methylation status is limited by the enzyme recognition site</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Easy to use</span></li>
</ul>
</td>
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<div style="text-align: justify;" class="large-12 columns">Bisulfite modification of DNA is the most commonly used, "<strong>gold standard</strong>" method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. T<span style="font-weight: 400;">his technology is based on the chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at the singe nucleotide level.</span></div>
<div style="text-align: justify;" class="large-12 columns"></div>
<div style="text-align: justify;" class="large-12 columns">Various analyses can be performed on the altered sequence to retrieve this information: bisulfite sequencing, pyrosequencing, methylation-specific PCR, high resolution melting curve analysis, microarray-based approaches, and next-generation sequencing.
<h3>How it works</h3>
Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected (see Figure 1).
<p class="text-center"><img src="https://www.diagenode.com/img/applications/bisulfite.png" /><br />Figure 1: Overview of bisulfite conversion of DNA</p>
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<p>Sodium bisulfite conversion of genomic DNA is the most commonly used method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. It enables <span>to differentiate and detect unmethylated versus methylated cytosines. This procedure can then be followed either by <strong>PCR amplification</strong> or <strong>next generation sequencing</strong> to reveal the methylation status of every cytosine in gene specific amplification or whole genome amplification.</span></p>
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<h2>How it works</h2>
<p style="text-align: left;">Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, while methylated cytosines remain unchanged. <span>The DNA is then amplified by PCR where the uracils are converted to thymines. </span></p>
<p style="text-align: center;"><span></span></p>
<p><img src="https://www.diagenode.com/img/categories/bisulfite-conversion/bisulfite-conversion-acgautac.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<h2>Advantages</h2>
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<li><i class="fa fa-arrow-circle-right"></i><strong> </strong><strong>Single nucleotide</strong> resolution</li>
<li><i class="fa fa-arrow-circle-right"></i><strong> Gene-specific </strong>and <strong>genome-wide</strong><span> analyses</span></li>
<li><i class="fa fa-arrow-circle-right"></i><strong> NGS</strong><span> </span>compatible</li>
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<h2>Downstream analysis techniques</h2>
<ul class="square">
<li>Reduced Representation Bisulfite Sequencing (RRBS) with our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS Kit V2</a></li>
<li>Bisulfite conversion with our <a href="https://www.diagenode.com/en/p/premium-bisulfite-kit-50-rxns">Premium Bisulfite Kit</a> followed by qPCR, Sanger, Pyrosequencing</li>
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'name' => 'Pesticide-induced transgenerational alterations of genome-wide DNA methylation patterns in the pancreas of Xenopus tropicalis correlate with metabolic phenotypes',
'authors' => 'Roza M. et al. ',
'description' => '<p><span>The unsustainable use of manmade chemicals poses significant threats to biodiversity and human health. Emerging evidence highlights the potential of certain chemicals to cause transgenerational impacts on metabolic health. Here, we investigate male transmitted epigenetic transgenerational effects of the anti-androgenic herbicide linuron in the pancreas of </span><em>Xenopus tropicalis</em><span><span> </span>frogs, and their association with metabolic phenotypes. Reduced representation bisulfite sequencing (RRBS) was used to assess genome-wide DNA methylation patterns in the pancreas of adult male F2 generation ancestrally exposed to environmentally relevant linuron levels (44 ± 4.7 μg/L). We identified 1117 differentially methylated regions (DMRs) distributed across the<span> </span></span><em>X. tropicalis</em><span><span> </span>genome, revealing potential regulatory mechanisms underlying metabolic disturbances. DMRs were identified in genes crucial for pancreatic function, including calcium signalling (</span><em>clstn2, cacna1d</em><span><span> </span>and<span> </span></span><em>cadps2</em><span>), genes associated with type 2 diabetes (</span><em>tcf7l2</em><span><span> </span>and<span> </span></span><em>adcy5</em><span>) and a biomarker for pancreatic ductal adenocarcinoma (</span><em>plec</em><span>). Correlation analysis revealed associations between DNA methylation levels in these genes and metabolic phenotypes, indicating epigenetic regulation of glucose metabolism. Moreover, differential methylation in genes related to histone modifications suggests alterations in the epigenetic machinery. These findings underscore the long-term consequences of environmental contamination on pancreatic function and raise concerns about the health risks associated with transgenerational effects of pesticides.</span></p>',
'date' => '2024-10-05',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S030438942402034X',
'doi' => 'https://doi.org/10.1016/j.jhazmat.2024.135455',
'modified' => '2024-09-02 10:08:08',
'created' => '2024-09-02 10:08:08',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 2 => array(
'id' => '4962',
'name' => 'Triphenyl Phosphate Alters Methyltransferase Expression and Induces Genome-Wide Aberrant DNA Methylation in Zebrafish Larvae',
'authors' => 'Negi C.K. et al.',
'description' => '<p><span>Emerging environmental contaminants, organophosphate flame retardants (OPFRs), pose significant threats to ecosystems and human health. Despite numerous studies reporting the toxic effects of OPFRs, research on their epigenetic alterations remains limited. In this study, we investigated the effects of exposure to 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), and triphenyl phosphate (TPHP) on DNA methylation patterns during zebrafish embryonic development. We assessed general toxicity and morphological changes, measured global DNA methylation and hydroxymethylation levels, and evaluated DNA methyltransferase (DNMT) enzyme activity, as well as mRNA expression of DNMTs and ten-eleven translocation (TET) methylcytosine dioxygenase genes. Additionally, we analyzed genome-wide methylation patterns in zebrafish larvae using reduced-representation bisulfite sequencing. Our morphological assessment revealed no general toxicity, but a statistically significant yet subtle decrease in body length following exposure to TMPP and EHDPP, along with a reduction in head height after TPHP exposure, was observed. Eye diameter and head width were unaffected by any of the OPFRs. There were no significant changes in global DNA methylation levels in any exposure group, and TMPP showed no clear effect on DNMT expression. However, EHDPP significantly decreased only DNMT1 expression, while TPHP exposure reduced the expression of several DNMT orthologues and TETs in zebrafish larvae, leading to genome-wide aberrant DNA methylation. Differential methylation occurred primarily in introns (43%) and intergenic regions (37%), with 9% and 10% occurring in exons and promoter regions, respectively. Pathway enrichment analysis of differentially methylated region-associated genes indicated that TPHP exposure enhanced several biological and molecular functions corresponding to metabolism and neurological development. KEGG enrichment analysis further revealed TPHP-mediated potential effects on several signaling pathways including TGFβ, cytokine, and insulin signaling. This study identifies specific changes in DNA methylation in zebrafish larvae after TPHP exposure and brings novel insights into the epigenetic mode of action of TPHP.</span></p>',
'date' => '2024-08-29',
'pmid' => 'https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.4c00223',
'doi' => 'https://doi.org/10.1021/acs.chemrestox.4c00223',
'modified' => '2024-09-02 10:16:01',
'created' => '2024-09-02 10:16:01',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 3 => array(
'id' => '4934',
'name' => 'Differential DNA methylation in iPSC-derived dopaminergic neurons: a step forward on the role of SNORD116 microdeletion in the pathophysiology of addictive behavior in Prader-Willi syndrome',
'authors' => 'Salles J. et al.',
'description' => '<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Introduction</h3>
<p>A microdeletion including the<span> </span><i>SNORD116</i><span> </span>gene (<i>SNORD116</i><span> </span>MD) has been shown to drive the Prader-Willi syndrome (PWS) features. PWS is a neurodevelopmental disorder clinically characterized by endocrine impairment, intellectual disability and psychiatric symptoms such as a lack of emotional regulation, impulsivity, and intense temper tantrums with outbursts. In addition, this syndrome is associated with a nutritional trajectory characterized by addiction-like behavior around food in adulthood. PWS is related to the genetic loss of expression of a minimal region that plays a potential role in epigenetic regulation. Nevertheless, the role of the<span> </span><i>SNORD116</i><span> </span>MD in DNA methylation, as well as the impact of the oxytocin (OXT) on it, have never been investigated in human neurons.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Methods</h3>
<p>We studied the methylation marks in induced pluripotent stem-derived dopaminergic neurons carrying a<span> </span><i>SNORD116</i><span> </span>MD in comparison with those from an age-matched adult healthy control. We also performed identical neuron differentiation in the presence of OXT. We performed a genome-wide DNA methylation analysis from the iPSC-derived dopaminergic neurons by reduced-representation bisulfite sequencing. In addition, we performed RNA sequencing analysis in these iPSC-derived dopaminergic neurons differentiated with or without OXT.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Results</h3>
<p>The analysis revealed that 153,826 cytosines were differentially methylated between<span> </span><i>SNORD116</i><span> </span>MD neurons and control neurons. Among the differentially methylated genes, we determined a list of genes also differentially expressed. Enrichment analysis of this list encompassed the dopaminergic system with<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i>.<span> </span><i>COMT</i><span> </span>displayed hypermethylation and under-expression in<span> </span><i>SNORD116</i><span> </span>MD, and<span> </span><i>SLC6A3</i><span> </span>displayed hypomethylation and over-expression in<span> </span><i>SNORD116</i><span> </span>MD. RT-qPCR confirmed significant over-expression of<span> </span><i>SLC6A3</i><span> </span>in<span> </span><i>SNORD116 MD</i><span> </span>neurons. Moreover, the expression of this gene was significantly decreased in the case of OXT adjunction during the differentiation.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Conclusion</h3>
<p><i>SNORD116</i><span> </span>MD dopaminergic neurons displayed differential methylation and expression in the<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i><span> </span>genes, which are related to dopaminergic clearance.</p>',
'date' => '2024-04-02',
'pmid' => 'https://www.nature.com/articles/s41380-024-02542-4',
'doi' => 'https://doi.org/10.1038/s41380-024-02542-4',
'modified' => '2024-04-09 00:46:18',
'created' => '2024-04-09 00:46:18',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 4 => array(
'id' => '4913',
'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
'authors' => 'Oganezovi N. et al.',
'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
'date' => '2024-01-30',
'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
'modified' => '2024-03-28 11:30:49',
'created' => '2024-02-22 12:28:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4898',
'name' => 'DNA methylome, R-loop and clinical exome profiling of patients with sporadic amyotrophic lateral sclerosis.',
'authors' => 'Feró, O. et al.',
'description' => '<p><span>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the death of motor neurons, the aetiology of which is essentially unknown. Here, we present an integrative epigenomic study in blood samples from seven clinically characterised sporadic ALS patients to elucidate molecular factors associated with the disease. We used clinical exome sequencing (CES) to study DNA variants, DNA-RNA hybrid immunoprecipitation sequencing (DRIP-seq) to assess R-loop distribution, and reduced representation bisulfite sequencing (RRBS) to examine DNA methylation changes. The above datasets were combined to create a comprehensive repository of genetic and epigenetic changes associated with the ALS cases studied. This repository is well-suited to unveil new correlations within individual patients and across the entire patient cohort. The molecular attributes described here are expected to guide further mechanistic studies on ALS, shedding light on the underlying genetic causes and facilitating the development of new epigenetic therapies to combat this life-threatening disease.</span></p>',
'date' => '2024-01-24',
'pmid' => 'https://www.nature.com/articles/s41597-024-02985-y',
'doi' => 'https://doi.org/10.1038/s41597-024-02985-y',
'modified' => '2024-01-29 10:11:43',
'created' => '2024-01-29 10:11:43',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4896',
'name' => 'Gestational Caloric Restriction Alters Adipose Tissue Methylome and Offspring’s Metabolic Profile in a Swine Model',
'authors' => 'Mas-Pares B. et al.',
'description' => '<p><span>Limited nutrient supply to the fetus results in physiologic and metabolic adaptations that have unfavorable consequences in the offspring. In a swine animal model, we aimed to study the effects of gestational caloric restriction and early postnatal metformin administration on offspring’s adipose tissue epigenetics and their association with morphometric and metabolic variables. Sows were either underfed (30% restriction of total food) or kept under standard diet during gestation, and piglets were randomly assigned at birth to receive metformin (n = 16 per group) or vehicle treatment (n = 16 per group) throughout lactation. DNA methylation and gene expression were assessed in the retroperitoneal adipose tissue of piglets at weaning. Results showed that gestational caloric restriction had a negative effect on the metabolic profile of the piglets, increased the expression of inflammatory markers in the adipose tissue, and changed the methylation of several genes related to metabolism. Metformin treatment resulted in positive changes in the adipocyte morphology and regulated the methylation of several genes related to atherosclerosis, insulin, and fatty acids signaling pathways. The methylation and gene expression of the differentially methylated </span><span class="html-italic">FASN</span><span>,<span> </span></span><span class="html-italic">SLC5A10</span><span>,<span> </span></span><span class="html-italic">COL5A1</span><span>, and<span> </span></span><span class="html-italic">PRKCZ</span><span><span> </span>genes in adipose tissue associated with the metabolic profile in the piglets born to underfed sows. In conclusion, our swine model showed that caloric restriction during pregnancy was associated with impaired inflammatory and DNA methylation markers in the offspring’s adipose tissue that could predispose the offspring to later metabolic abnormalities. Early metformin administration could modulate the size of adipocytes and the DNA methylation changes.</span></p>',
'date' => '2024-01-17',
'pmid' => 'https://www.mdpi.com/1422-0067/25/2/1128',
'doi' => 'https://doi.org/10.3390/ijms25021128',
'modified' => '2024-01-22 13:45:24',
'created' => '2024-01-22 13:45:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4890',
'name' => 'Diagnostic Algorithm to Subclassify Atypical Spitzoid Tumors in Low and High Risk According to Their Methylation Status',
'authors' => 'Gonzales-Munoz J.F. et al.',
'description' => '<p><span>Current diagnostic algorithms are insufficient for the optimal clinical and therapeutic management of cutaneous spitzoid tumors, particularly atypical spitzoid tumors (AST). Therefore, it is crucial to identify new markers that allow for reliable and reproducible diagnostic assessment and can also be used as a predictive tool to anticipate the individual malignant potential of each patient, leading to tailored individual therapy. Using Reduced Representation Bisulfite Sequencing (RRBS), we studied genome–wide methylation profiles of a series of Spitz nevi (SN), spitzoid melanoma (SM), and AST. We established a diagnostic algorithm based on the methylation status of seven cg sites located in </span><span class="html-italic">TETK4P2</span><span><span> </span>(Tektin 4 Pseudogene 2),<span> </span></span><span class="html-italic">MYO1D</span><span><span> </span>(Myosin ID), and<span> </span></span><span class="html-italic">PMF1-BGLAP</span><span><span> </span>(PMF1-BGLAP Readthrough), which allows the distinction between SN and SM but is also capable of subclassifying AST according to their similarity to the methylation levels of Spitz nevi or spitzoid melanoma. Thus, our epigenetic algorithm can predict the risk level of AST and predict its potential clinical outcomes.</span></p>',
'date' => '2023-12-25',
'pmid' => 'https://www.mdpi.com/1422-0067/25/1/318',
'doi' => 'https://doi.org/10.3390/ijms25010318',
'modified' => '2024-01-02 11:11:57',
'created' => '2024-01-02 11:11:57',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4808',
'name' => 'Knockout of TRDMT1 methyltransferase affects DNA methylome inglioblastoma cells.',
'authors' => 'Zabek T. et al.',
'description' => '<p><strong class="sub-title">Purpose:<span> </span></strong>We have previously shown that TRDMT1 methyltransferase is a regulator of chemotherapy-associated responses in glioblastoma cells. Despite the fact that glioblastoma, a common and malignant brain tumor, is widely characterized in terms of genetic and epigenetic markers, there are no data on TRDMT1-related changes in 5-methylcytosine pools in the genome. In the present study, the effect of TRDMT1 gene knockout (KO) on DNA methylome was analyzed.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>CRISPR-based approach was used to obtain TRDMT1 KO glioblastoma cells. Total 5-methylcytosine levels in DNA, DNMT1 pools and DNMT activity were studied using ELISA. Reduced representation bisulfite sequencing (RRBS) was considered to comprehensively evaluate DNA methylome in glioblastoma cells with TRDMT1 KO.</p>
<p><strong class="sub-title">Results:<span> </span></strong>TRDMT1 KO cells were characterized by decreased levels of total 5-methylcytosine in DNA and DNMT1, and DNMT activity. RRBS-based methylome analysis revealed statistically significant differences in methylation-relevant DMS-linked genes in control cells compared to TRDMT1 KO cells. TRDMT1 KO-associated changes in DNA methylome may affect the activity of several processes and pathways such as telomere maintenance, cell cycle and longevity regulating pathway, proteostasis, DNA and RNA biology.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>TRDMT1 may be suggested as a novel modulator of gene expression by changes in DNA methylome that may affect cancer cell fates during chemotherapy. We postulate that the levels and mutation status of TRDMT1 should be considered as a prognostic marker and carefully monitored during glioblastoma progression.</p>',
'date' => '2023-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37169948',
'doi' => '10.1007/s11060-023-04304-8',
'modified' => '2023-06-15 08:50:24',
'created' => '2023-06-13 21:11:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4786',
'name' => 'Sperm DNA methylation is predominantly stable in mice offspring bornafter transplantation of long-term cultured spermatogonial stem cells.',
'authors' => 'Serrano J. B.et al.',
'description' => '<p>BACKGROUND: Spermatogonial stem cell transplantation (SSCT) is proposed as a fertility therapy for childhood cancer survivors. SSCT starts with cryopreserving a testicular biopsy prior to gonadotoxic treatments such as cancer treatments. When the childhood cancer survivor reaches adulthood and desires biological children, the biopsy is thawed and SSCs are propagated in vitro and subsequently auto-transplanted back into their testis. However, culturing stress during long-term propagation can result in epigenetic changes in the SSCs, such as DNA methylation alterations, and might be inherited by future generations born after SSCT. Therefore, SSCT requires a detailed preclinical epigenetic assessment of the derived offspring before this novel cell therapy is clinically implemented. With this aim, the DNA methylation status of sperm from SSCT-derived offspring, with in vitro propagated SSCs, was investigated in a multi-generational mouse model using reduced-representation bisulfite sequencing. RESULTS: Although there were some methylation differences, they represent less than 0.5\% of the total CpGs and methylated regions, in all generations. Unsupervised clustering of all samples showed no distinct grouping based on their pattern of methylation differences. After selecting the few single genes that are significantly altered in multiple generations of SSCT offspring compared to control, we validated the results with quantitative Bisulfite Sanger sequencing and RT-qPCRin various organs. Differential methylation was confirmed only for Tal2, being hypomethylated in sperm of SSCT offspring and presenting higher gene expression in ovaries of SSCT F1 offspring compared to control F1. CONCLUSIONS: We found no major differences in DNA methylation between SSCT-derived offspring and control, both in F1 and F2 sperm. The reassuring outcomes from our study are a prerequisite for promising translation of SSCT to the human situation.</p>',
'date' => '2023-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37029425',
'doi' => '10.1186/s13148-023-01469-x',
'modified' => '2023-06-12 08:55:47',
'created' => '2023-05-05 12:34:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4760',
'name' => 'DNA methylation changes from primary cultures through senescence-bypassin Syrian hamster fetal cells initially exposed to benzo[a]pyrene.',
'authors' => 'Desaulniers D. et al.',
'description' => '<p>Current chemical testing strategies are limited in their ability to detect non-genotoxic carcinogens (NGTxC). Epigenetic anomalies develop during carcinogenesis regardless of whether the molecular initiating event is associated with genotoxic (GTxC) or NGTxC events; therefore, epigenetic markers may be harnessed to develop new approach methodologies that improve the detection of both types of carcinogens. This study used Syrian hamster fetal cells to establish the chronology of carcinogen-induced DNA methylation changes from primary cells until senescence-bypass as an essential carcinogenic step. Cells exposed to solvent control for 7 days were compared to naïve primary cultures, to cells exposed for 7 days to benzo[a]pyrene, and to cells at the subsequent transformation stages: normal colonies, morphologically transformed colonies, senescence, senescence-bypass, and sustained proliferation in vitro. DNA methylation changes identified by reduced representation bisulphite sequencing were minimal at day-7. Profound DNA methylation changes arose during cellular senescence and some of these early differentially methylated regions (DMRs) were preserved through the final sustained proliferation stage. A set of these DMRs (e.g., Pou4f1, Aifm3, B3galnt2, Bhlhe22, Gja8, Klf17, and L1l) were validated by pyrosequencing and their reproducibility was confirmed across multiple clones obtained from a different laboratory. These DNA methylation changes could serve as biomarkers to enhance objectivity and mechanistic understanding of cell transformation and could be used to predict senescence-bypass and chemical carcinogenicity.</p>',
'date' => '2023-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36754249',
'doi' => '10.1016/j.tox.2023.153451',
'modified' => '2023-04-17 09:08:16',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4616',
'name' => 'Myelodysplastic Syndrome associated TET2 mutations affect NK cellfunction and genome methylation.',
'authors' => 'Boy M. et al.',
'description' => '<p>Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders, representing high risk of progression to acute myeloid leukaemia, and frequently associated to somatic mutations, notably in the epigenetic regulator TET2. Natural Killer (NK) cells play a role in the anti-leukemic immune response via their cytolytic activity. Here we show that patients with MDS clones harbouring mutations in the TET2 gene are characterised by phenotypic defects in their circulating NK cells. Remarkably, NK cells and MDS clones from the same patient share the TET2 genotype, and the NK cells are characterised by increased methylation of genomic DNA and reduced expression of Killer Immunoglobulin-like receptors (KIR), perforin, and TNF-α. In vitro inhibition of TET2 in NK cells of healthy donors reduces their cytotoxicity, supporting its critical role in NK cell function. Conversely, NK cells from patients treated with azacytidine (#NCT02985190; https://clinicaltrials.gov/ ) show increased KIR and cytolytic protein expression, and IFN-γ production. Altogether, our findings show that, in addition to their oncogenic consequences in the myeloid cell subsets, TET2 mutations contribute to repressing NK-cell function in MDS patients.</p>',
'date' => '2023-02-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36737440',
'doi' => '10.1038/s41467-023-36193-w',
'modified' => '2023-04-04 08:43:27',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4588',
'name' => 'Epigenetics and stroke: role of DNA methylation and effect of aging onblood-brain barrier recovery.',
'authors' => 'Phillips C. et al.',
'description' => '<p>Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., , ), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.</p>',
'date' => '2023-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36711725',
'doi' => '10.21203/rs.3.rs-2444060/v1',
'modified' => '2023-04-11 10:01:44',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4761',
'name' => 'Development of DNA methylation-based epigenetic age predictors inloblolly pine (Pinus taeda).',
'authors' => 'Gardner S. T. et al.',
'description' => '<p>Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6\% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.</p>',
'date' => '2023-01-01',
'pmid' => 'https://doi.org/10.1101%2F2022.01.27.477887',
'doi' => '10.1111/1755-0998.13698',
'modified' => '2023-04-17 09:09:49',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4729',
'name' => 'Molecular toxicity study on glyphosate, Roundup MON 52276 and alow-dose pesticide mixture administered to adult Female rats for 90 days',
'authors' => 'Mesnage Robin and Antoniou Michael N.',
'description' => '<p>We describe a comprehensive repository describing a collection of data from a range of studies investigating the molecular mechanisms of toxicity of glyphosate, the glyphosate-based herbicide commercial formulation Roundup, and a mixture of glyphosate and 5 other most frequently used pesticides (azoxystrobin, boscalid, chlorpyrifos, imidacloprid and thiabendazole) present as residues in food products in Europe. The data were obtained by analysing tissues from rats exposed to the pesticides for 90 days via drinking water. The administration of the mixture of six pesticides was chosen to mimic a possible human exposure scenario. We compared conventional methods used in regulatory toxicity studies to evaluate the safety of pesticide exposure (gross pathology, serum biochemistry) to new molecular profiling methods encompassing the analysis of the caecum and blood metabolome, liver transcriptome, liver DNA methylation, liver small RNA profiles, and caecum metagenome of the exposed animals. Altogether, these investigations provided in-depth molecular profiling in laboratory animals exposed to pesticides revealing metabolic perturbations that would remain undetected by standard regulatory biochemical measures. Our results highlight how multi-omics phenotyping can be used to improve the predictability of health risk assessment from exposure to toxic chemicals to better protect public health.</p>',
'date' => '2022-12-01',
'pmid' => 'https://doi.org/10.1080%2F26895293.2022.2156626',
'doi' => '10.1080/26895293.2022.2156626',
'modified' => '2023-03-07 09:09:33',
'created' => '2023-02-28 12:19:11',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4652',
'name' => 'Differential methylation patterns in lean and obese non-alcoholicsteatohepatitis-associated hepatocellular carcinoma.',
'authors' => 'Hymel Emma et al.',
'description' => '<p>BACKGROUND: Nonalcoholic fatty liver disease affects about 24\% of the world's population and may progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). While more common in those that are obese, NASH-HCC can develop in lean individuals. The mechanisms by which HCC develops and the role of epigenetic changes in the context of obesity and normal weight are not well understood. METHODS: In this study, we used previously generated mouse models of lean and obese HCC using a choline deficient/high trans-fat/fructose/cholesterol diet and a choline supplemented/high trans-fat/fructose/cholesterol diet, respectively, to evaluate methylation differences in HCC progression in lean versus obese mice. Differentially methylated regions were determined using reduced representation bisulfite sequencing. RESULTS: A larger number of differentially methylated regions (DMRs) were seen in NASH-HCC progression in the obese mice compared to the non-obese mice. No overlap existed in the DMRs with the largest methylation differences between the two models. In lean NASH-HCC, methylation differences were seen in genes involved with cancer progression and prognosis (including HCC), such as CHCHD2, FSCN1, and ZDHHC12, and lipid metabolism, including PNPLA6 and LDLRAP1. In obese NASH- HCC, methylation differences were seen in genes known to be associated with HCC, including RNF217, GJA8, PTPRE, PSAPL1, and LRRC8D. Genes involved in Wnt-signaling pathways were enriched in hypomethylated DMRs in the obese NASH-HCC. CONCLUSIONS: These data suggest that differential methylation may play a role in hepatocarcinogenesis in lean versus obese NASH. Hypomethylation of Wnt signaling pathway-related genes in obese mice may drive progression of HCC, while progression of HCC in lean mice may be driven through other signaling pathways, including lipid metabolism.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36474183',
'doi' => '10.1186/s12885-022-10389-7',
'modified' => '2023-03-13 08:50:33',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4628',
'name' => 'Altered DNA methylation in estrogen-responsive repetitive sequences ofspermatozoa of infertile men with shortened anogenital distance.',
'authors' => 'Stenz L. et al.',
'description' => '<p>BACKGROUND: It has been suggested that antenatal exposure to environmental endocrine disruptors is responsible for adverse trends in male reproductive health, including male infertility, impaired semen quality, cryptorchidism and testicular cancer, a condition known as testicular dysgenesis syndrome. Anogenital distance (AGD) is an anthropomorphic measure of antenatal exposure to endocrine disruptors, with higher exposure levels leading to shortened AGD. We hypothesized that exposure to endocrine disruptors could lead to changes in DNA methylation during early embryonic development, which could then persist in the sperm of infertile men with shortened AGD. RESULTS: Using fluorescence activated cell sorting based on staining with either YO-PRO-1 (YOPRO) or chromomycin-3 (CMA3), we isolated four sperm fractions from eleven infertile men with short AGD and ten healthy semen donors. We examined DNA methylation in these sorted spermatozoa using reduced representation bisulfite sequencing. We found that fractions of spermatozoa from infertile men stained with CMA3 or YOPRO were more likely to contain transposable elements harboring an estrogen receptor response element (ERE). Abnormal sperm (as judged by high CMA3 or YOPRO staining) from infertile men shows substantial hypomethylation in estrogenic Alu sequences. Conversely, normal sperm fractions (as judged by low CMA3 or YO-PRO-1 staining) of either healthy donors or infertile patients were more likely to contain hypermethylated Alu sequences with ERE. CONCLUSIONS: Shortened AGD, as related to previous exposure to endocrine disruptors, and male infertility are accompanied by increased presence of hormonal response elements in the differentially methylated regulatory sequences of the genome of sperm fractions characterized by chromatin decondensation and apoptosis.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36572941',
'doi' => '10.1186/s13148-022-01409-1',
'modified' => '2023-03-28 09:09:22',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4537',
'name' => 'Epigenetic Alterations of Repeated Relapses in Patient-matchedChildhood Ependymomas.',
'authors' => 'Zhao Sibo et al.',
'description' => '<p>Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.</p>',
'date' => '2022-11-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36335125',
'doi' => '10.1038/s41467-022-34514-z',
'modified' => '2022-11-25 08:55:12',
'created' => '2022-11-24 08:49:52',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4441',
'name' => 'Epigenetic Suppression of the IL-7 Pathway in ProgressiveGlioblastoma.',
'authors' => 'Tompa M. et al.',
'description' => '<p>BACKGROUND: Immune evasion in glioblastoma (GBM) shields cancer cells from cytotoxic immune response. METHODS: We investigated CpG methylation in promoters, genes, and pathways in 22 pairs of formalin-fixed paraffin-embedded sequential (FFPE) GBM using restricted resolution bisulfite sequencing (RRBS) and bioinformatic analyses. RESULTS: Gene ontology revealed hypermethylation in elements of the innate and adaptive immune system when recurrent GBM samples (GBM) were compared to control (CG) and primary GBM samples (GBM). Higher methylation levels of the IL-7 signaling pathway and response to IL-7 were found in GBM suggesting a progressive blockade of the IL-7 driven T cell response in sequential GBM. Analyses of the Cancer Genome Atlas array-based data confirmed hypermethylation of the IL-7 pathway in recurrent compared with primary GBM. We also quantified DNA CpG methylation in promoter and gene regions of the IL-7 ligand and IL-7 α-receptor subunit in individual samples of a large RRBS-based sequential cohort of GBM in a Viennese database and found significantly higher methylation levels in the IL-7 receptor α-subunit in GBM compared with GBM. CONCLUSIONS: This study revealed the progressive suppression of the IL-7 receptor-mediated pathway as a means of immune evasion by GBM and thereby highlighted it as a new treatment target.</p>',
'date' => '2022-09-01',
'pmid' => 'https://doi.org/10.3390%2Fbiomedicines10092174',
'doi' => '10.3390/biomedicines10092174',
'modified' => '2022-10-14 16:32:44',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4371',
'name' => 'DNA methylation may affect beef tenderness through signal transduction inBos indicus.',
'authors' => 'de Souza M. M. et al.',
'description' => '<p>BACKGROUND: Beef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus. RESULTS: Comparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity. CONCLUSIONS: We present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.</p>',
'date' => '2022-05-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1415533%2Fv1',
'doi' => '10.1186/s13072-022-00449-4',
'modified' => '2022-08-04 16:05:03',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4401',
'name' => 'Folic Acid Treatment Directly Influences the Genetic andEpigenetic Regulation along with the Associated CellularMaintenance Processes of HT-29 and SW480 Colorectal CancerCell Lines.',
'authors' => 'Zsigrai S. et al.',
'description' => '<p>Folic acid (FA) is a synthetic form of vitamin B9, generally used as a nutritional supplement and an adjunctive medication in cancer therapy. FA is involved in genetic and epigenetic regulation; therefore, it has a dual modulatory role in established neoplasms. We aimed to investigate the effect of short-term (72 h) FA supplementation on colorectal cancer; hence, HT-29 and SW480 cells were exposed to different FA concentrations (0, 100, 10,000 ng/mL). HT-29 cell proliferation and viability levels elevated after 100 ng/mL but decreased for 10,000 ng/mL FA. Additionally, a significant ( ≤ 0.05) improvement of genomic stability was detected in HT-29 cells with micronucleus scoring and comet assay. Conversely, the FA treatment did not alter these parameters in SW480 samples. RRBS results highlighted that DNA methylation changes were bidirectional in both cells, mainly affecting carcinogenesis-related pathways. Based on the microarray analysis, promoter methylation status was in accordance with FA-induced expression alterations of 27 genes. Our study demonstrates that the FA effect was highly dependent on the cell type, which can be attributed to the distinct molecular background and the different expression of proliferation- and DNA-repair-associated genes (, , , ). Moreover, new aspects of FA-regulated DNA methylation and consecutive gene expression were revealed.</p>',
'date' => '2022-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35406592',
'doi' => '10.3390/cancers14071820',
'modified' => '2022-08-11 14:41:59',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4405',
'name' => 'Complex regulatory role of DNA methylation in caste- and age-specificexpression of a termite',
'authors' => 'Harrison Mark C. et al. ',
'description' => '<p>The reproductive castes of eusocial insects are often characterised by extreme lifespans and reproductive output, indicating an absence of the fecundity/longevity trade-off. The role of DNA methylation in the regulation of caste- and age-specific gene expression in eusocial insects is controversial. While some studies find a clear link to caste formation in honeybees and ants, others find no correlation when replication is increased across independent colonies. Although recent studies have identified transcription patterns involved in the maintenance of high reproduction throughout the long lives of queens, the role of DNA methylation in the regulation of these genes is unknown. We carried out a comparative analysis of DNA methylation in the regulation of caste-specific transcription and its importance for the regulation of fertility and longevity in queens of the higher termite, Macrotermes natalensis. We found evidence for significant, well-regulated changes in DNA methylation in mature compared to young queens, especially in several genes related to ageing and fecundity in mature queens. We also found a strong link between methylation and caste-specific alternative splicing. This study reveals a complex regulatory role of fat body DNA methylation both in the division of labour in termites, and during the reproductive maturation of queens.</p>',
'date' => '2022-03-01',
'pmid' => 'https://doi.org/10.1101%2F2022.03.08.483442',
'doi' => '10.1101/2022.03.08.483442',
'modified' => '2022-08-11 15:01:34',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => array(
'id' => '4229',
'name' => 'When left does not seem right: epigenetic and bioelectric differencesbetween left- and right-sided breast cancer.',
'authors' => 'Sofía, Masuelli and Sebastián, Real and Emanuel, Campoy andBranham, María Teresita and Marzese, Diego Matías andMatthew, Salomon and De Blas, Gerardo and Rodolfo, Arias andMichael, Levin and María, Roqué',
'description' => '<p>BACKGROUND: During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms. METHODS: We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts. RESULTS: We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count. CONCLUSIONS: Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1020823%2Fv1',
'doi' => '10.1186/s10020-022-00440-5',
'modified' => '2022-05-19 16:03:56',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '4248',
'name' => 'Comparative Toxicogenomics of Glyphosate and Roundup Herbicidesby Mammalian Stem Cell-Based Genotoxicity Assays andMolecular Profiling in Sprague-Dawley Rats.',
'authors' => 'Mesnage R. et al.',
'description' => '<p>Whether glyphosate-based herbicides (GBHs) are more potent than glyphosate alone at activating cellular mechanisms, which drive carcinogenesis remain controversial. As GBHs are more cytotoxic than glyphosate, we reasoned they may also be more capable of activating carcinogenic pathways. We tested this hypothesis by comparing the effects of glyphosate with Roundup GBHs both in vitro and in vivo. First, glyphosate was compared with representative GBHs, namely MON 52276 (European Union), MON 76473 (United Kingdom), and MON 76207 (United States) using the mammalian stem cell-based ToxTracker system. Here, MON 52276 and MON 76473, but not glyphosate and MON 76207, activated oxidative stress and unfolded protein responses. Second, molecular profiling of liver was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (at 0.5, 50, and 175 mg/kg bw/day glyphosate) for 90 days. MON 52276 but not glyphosate increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and circadian rhythm regulation. Genes most affected in liver were similarly altered in kidneys. Small RNA profiling in liver showed decreased amounts of miR-22 and miR-17 from MON 52276 ingestion. Glyphosate decreased miR-30, whereas miR-10 levels were increased. DNA methylation profiling of liver revealed 5727 and 4496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed animals, respectively. Apurinic/apyrimidinic DNA damage formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup formulations cause more biological changes linked with carcinogenesis than glyphosate.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.1093%2Ftoxsci%2Fkfab143',
'doi' => '10.1093/toxsci/kfab143',
'modified' => '2022-05-20 09:32:37',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 24 => array(
'id' => '4368',
'name' => 'Glucose-6-phosphate dehydrogenase and MEG3 controls hypoxia-inducedexpression of serum response factor (SRF) and SRF-dependent genes inpulmonary smooth muscle cell.',
'authors' => 'Kitagawa A. et al.',
'description' => '<p>Although hypoxia induces aberrant gene expression and dedifferentiation of smooth muscle cells (SMCs), mechanisms that alter dedifferentiation gene expression by hypoxia remain unclear. Therefore, we aimed to gain insight into the hypoxia-controlled gene expression in SMCs. We conducted studies using SMCs cultured in 3\% oxygen (hypoxia) and the lungs of mice exposed to 10\% oxygen (hypoxia). Our results suggest hypoxia upregulated expression of transcription factor CP2-like protein1, krüppel-like factor 4, and E2f transcription factor 1 enriched genes including basonuclin 2 (Bcn2), serum response factor (Srf), polycomb 3 (Cbx8), homeobox D9 (Hoxd9), lysine demethylase 1A (Kdm1a), etc. Additionally, we found that silencing glucose-6-phosphate dehydrogenase (G6PD) expression and inhibiting G6PD activity downregulated Srf transcript and hypomethylation of SMC genes (Myocd, Myh11, and Cnn1) and concomitantly increased their expression in the lungs of hypoxic mice. Furthermore, G6PD inhibition hypomethylated MEG3, a long non-coding RNA, gene and upregulated MEG3 expression in the lungs of hypoxic mice and in hypoxic SMCs. Silencing MEG3 expression in SMC mitigated the hypoxia-induced transcription of SRF. These findings collectively demonstrate that MEG3 and G6PD codependently regulate Srf expression in hypoxic SMCs. Moreover, G6PD inhibition upregulated SRF-MYOCD-driven gene expression, determinant of a differentiated SMC phenotype.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35491127',
'doi' => '10.1540/jsmr.58.34',
'modified' => '2022-08-04 16:21:02',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 25 => array(
'id' => '4114',
'name' => 'The ETS transcription factor ERF controls the exit from the naïve pluripotent state in a MAPK-dependent manner',
'authors' => 'Maria Vega-Sendino et. al.',
'description' => '<p><span>The naïve epiblast transitions to a pluripotent primed state during embryo implantation. Despite the relevance of the FGF pathway during this period, little is known about the downstream effectors regulating this signaling. Here, we examined the molecular mechanisms coordinating the naïve to primed transition by using inducible ESC to genetically eliminate all RAS proteins. We show that differentiated RAS</span><sup>KO</sup><span><span> </span>ESC remain trapped in an intermediate state of pluripotency with naïve-associated features. Elimination of the transcription factor ERF overcomes the developmental blockage of RAS-deficient cells by naïve enhancer decommissioning. Mechanistically, ERF regulates NANOG expression and ensures naïve pluripotency by strengthening naïve transcription factor binding at ESC enhancers. Moreover, ERF negatively regulates the expression of the methyltransferase DNMT3B, which participates in the extinction of the naïve transcriptional program. Collectively, we demonstrated an essential role for ERF controlling the exit from naïve pluripotency in a MAPK-dependent manner during the progression to primed pluripotency.</span></p>',
'date' => '2021-10-01',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34597136/',
'doi' => '10.1126/sciadv.abg8306',
'modified' => '2022-05-19 16:05:11',
'created' => '2021-10-06 08:45:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 26 => array(
'id' => '4230',
'name' => 'Adaptive Convergence of Methylomes Reveals Epigenetic Driversand Boosters of Repeated Relapses in Patient-matched ChildhoodEpendymomas and Identifies Targets for Anti-RecurrenceTherapies',
'authors' => 'Zhao S. et al.',
'description' => '<p>Ependymoma (EPN) is the third most common brain tumor in children and frequently recurs. Here, we report an integrated longitudinal analysis of epigenetic, genetic and tumorigenic changes in 30 patient-matched repeated relapses obtained from 10 pediatric patients to understand the mechanism of recurrences. Genome-wide DNA methylation analysis revealed stable molecular subtypes and convergent epigenetic reprogramming during serial relapses of the 5 RELA and 5 PFA EPNs that paralleled with elevated patient-derived orthotopic xenograft (PDOX) (13/27) formation in the late relapses. Differentially methylated CpGs (DMCs) preexisted in the primary tumors and persisted in the relapses (driver DMCs) were detected, ranging from 51 hypo-methylated in RELA to 148 hyper-methylated DMCs in PFA tumors; while newly acquired DMCs sustained in all the relapses but was absent in the primary tumors (booster DMCs) ranged from 38- 323 hyper-methylated DMCs in RELA and PFA EPNs, respectively. Integrated analysis of these DMC associated DNA methylation regions (DMRs) and RNAseq in both patient and PDOX tumors identified a small fraction of the differentially expressed genes (4.6±4.4\% in RELA and 4.5±1.1\% in PFA) as regulated by driver DMRs (e.g., up-regulated CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and booster DMRs (including the sole upregulated PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). Most these genes were novel to EPN relapses. Seven DMCs in RELA and 22 in PFA tumors were also identified as potential relapse predictors. Finally, integrating DNA methylation with histone modification identified LSD1 as a relapse driver gene. Combined treatment of a novel inhibitor SYC-836 with radiation significantly prolonged survival times in two PDOX models of recurrent PFA. This high-resolution epigenetic and genetic roadmap of EPN relapse and our 13 new PDOX models should significantly facilitate biological and preclinical studies of pediatric EPN recurrences.</p>',
'date' => '2021-10-01',
'pmid' => 'https://www.researchsquare.com/article/rs-908607/v1',
'doi' => '10.21203/rs.3.rs-908607/v1',
'modified' => '2022-05-19 16:48:13',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 27 => array(
'id' => '4115',
'name' => 'Genome-Wide Epigenomic Analyses in Patients With Nociceptive and Neuropathic Chronic Pain Subtypes Reveals Alterations in Methylation of Genes Involved in the Neuro-Musculoskeletal System',
'authors' => 'Stenz et al',
'description' => '<p><span>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs were seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</span></p>',
'date' => '2021-09-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34547430/',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-19 16:05:36',
'created' => '2021-10-22 19:01:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 28 => array(
'id' => '4299',
'name' => 'Genome-wide epigenomic analyses in patients with nociceptive andneuropathic chronic pain subtypes reveals alterations in methylation ofgenes involved in the neuro-musculoskeletal system.',
'authors' => 'Stenz Ludwig et al.',
'description' => '<p>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</p>',
'date' => '2021-09-01',
'pmid' => 'https://doi.org/10.1016%2Fj.jpain.2021.09.001',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-30 09:41:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 29 => array(
'id' => '4383',
'name' => 'Biobehavioral organization shapes the immune epigenome in infant rhesusMacaques (Macaca mulatta).',
'authors' => 'Baxter A. et al.',
'description' => '<p>How individuals respond to and cope with stress is linked with their health and well-being. It is presumed that early stress responsiveness helps shape the health of the developing organism, but the relationship between stress responsiveness and early immune function during development is not well-known. We hypothesized that stress responsiveness may shape epigenetic regulation of immune genes in infancy. We investigated whether aspects of behavioral responsiveness and hypothalamic-pituitary adrenal stress-response were associated with epigenome-wide immune cell DNA methylation patterns in 154 infant rhesus monkeys (3-4 months old). Infants' behavioral and physiological responses were collected during a standardized biobehavioral assessment, which included temporary relocation and separation from their mother and social group. Genome-wide DNA methylation was quantified using restricted representation bisulfite sequencing (RRBS) from blood DNA collected 2-hours post-separation. Epigenome-wide analyses were conducted using simple regression, multiple regression controlling for immune cell counts, and permutation regression, all corrected for false discovery rate. Across the variables analyzed, there were 20,368 unique sites (in 9,040 genes) at which methylation was significantly associated with at least one behavioral responsiveness or cortisol measure across the three analyses. There were significant associations in 442 genes in the Immune System Process ontology category, and 94 genes in the Inflammation mediated by chemokine and cytokine signaling gene pathway. Out of 35 candidate genes that were selected for further investigation, there were 13 genes with at least one site at which methylation was significantly associated with behavioral responsiveness or cortisol, including two intron sites in the glucocorticoid receptor gene, at which methylation was negatively correlated with emotional behavior the day following the social separation (Day 2 Emotionality; β = -0.39, q < 0.001) and cortisol response following a relocation stressor (Sample 1; β = -0.33, q < 0.001). We conclude that biobehavioral stress responsiveness may correlate with the developing epigenome, and that DNA methylation of immune cells may be a mechanism by which patterns of stress response affect health and immune functioning.</p>',
'date' => '2021-08-01',
'pmid' => 'https://doi.org/10.1016%2Fj.bbi.2021.06.006',
'doi' => '10.1016/j.bbi.2021.06.006',
'modified' => '2022-08-04 15:54:12',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 30 => array(
'id' => '4112',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus',
'authors' => 'Sara Zocher, Rupert W. Overall, Mathias Lesche, Andreas Dahl & Gerd Kempermann',
'description' => '<p><span>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</span></p>',
'date' => '2021-06-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34162876/',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-05-19 16:06:20',
'created' => '2021-09-06 08:02:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 31 => array(
'id' => '4111',
'name' => 'Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes',
'authors' => 'Luca Pagliaroli, Abel Fothi, Ester Nespoli, Istvan Liko, Borbala Veto, Piroska Devay, Flora Szeri, Bastian Hengerer, Csaba Barta, Tamas Aranyi',
'description' => '<p>Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.</p>',
'date' => '2021-06-09',
'pmid' => 'https://www.mdpi.com/2073-4409/10/6/1442',
'doi' => '10.3390/cells10061442',
'modified' => '2022-05-19 16:06:47',
'created' => '2021-08-27 11:27:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 32 => array(
'id' => '4324',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape inthe aged mouse hippocampus',
'authors' => 'Zocher S. et al. ',
'description' => '<p>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</p>',
'date' => '2021-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34162876',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-08-03 15:56:05',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 33 => array(
'id' => '4419',
'name' => 'Pathophysiological adaptations of resistance arteries in rat offspringexposed in utero to maternal obesity is associated with sex-specificepigenetic alterations.',
'authors' => 'Payen Cyrielle et al.',
'description' => '<p>BACKGROUND/OBJECTIVES: Maternal obesity impacts vascular functions linked to metabolic disorders in offspring, leading to cardiovascular diseases during adulthood. Even if the relation between prenatal conditioning of cardiovascular diseases by maternal obesity and vascular function begins to be documented, little is known about resistance arteries. They are of particular interest because of their specific role in the regulation of local blood flow. Then our study aims to determine if maternal obesity can directly program fetal vascular dysfunction of resistance arteries, independently of metabolic disorders. METHODS: With a model of rats exposed in utero to mild maternal diet-induced obesity (OMO), we investigated third-order mesenteric arteries of 4-month old rats in absence of metabolic disorders. The methylation profile of these vessels was determined by reduced representation bisulfite sequencing (RRBS). Vascular structure and reactivity were investigated using histomorphometry analysis and wire-myography. The metabolic function was evaluated by insulin and glucose tolerance tests, plasma lipid profile, and adipose tissue analysis. RESULTS: At 4 months of age, small mesenteric arteries of OMO presented specific epigenetic modulations of matrix metalloproteinases (MMPs), collagens, and potassium channels genes in association with an outward remodeling and perturbations in the endothelium-dependent vasodilation pathways (greater contribution of EDHFs pathway in OMO males compared to control rats, and greater implication of PGI in OMO females compared to control rats). These vascular modifications were detected in absence of metabolic disorders. CONCLUSIONS: Our study reports a specific methylation profile of resistance arteries associated with vascular remodeling and vasodilation balance perturbations in offspring exposed in utero to maternal obesity, in absence of metabolic dysfunctions.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33637953',
'doi' => '10.1038/s41366-021-00777-7',
'modified' => '2022-09-28 08:51:40',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 34 => array(
'id' => '4175',
'name' => 'Multi-omics phenotyping of the gut-liver axis reveals metabolicperturbations from a low-dose pesticide mixture in rats.',
'authors' => 'Mesnage, Robin et al.',
'description' => '<p>Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33854195',
'doi' => '10.1038/s42003-021-01990-w',
'modified' => '2021-12-21 16:12:25',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 35 => array(
'id' => '4356',
'name' => 'Muscle allele-specific expression QTLs may affect meat quality traitsin Bos indicus.',
'authors' => 'Bruscadin J.J. et al.',
'description' => '<p>Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33795794',
'doi' => '10.1038/s41598-021-86782-2',
'modified' => '2022-08-03 16:44:51',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 36 => array(
'id' => '4173',
'name' => 'The insecticide permethrin induces transgenerational behavioral changeslinked to transcriptomic and epigenetic alterations in zebrafish (Daniorerio).',
'authors' => 'Blanc, Mélanie et al.',
'description' => '<p>The pyrethroid insecticide permethrin is widely used for agricultural and domestic purposes. Previous data indicated that it acts as a developmental neurotoxicant and can induce transgenerational effects in non-target organisms. However, associated underlying mechanisms remain unclear. The aim of this study was to investigate permethrin-related transgenerational effects in the zebrafish model, and to identify possible molecular mechanisms underlying inheritance. Zebrafish (F0) were exposed to permethrin during early-life (2 h post-fertilization up to 28 days). The F1 and F2 offspring generations were obtained by pairing exposed F0 males and females, and were bred unexposed. Locomotor and anxiety behavior were investigated, together with transcriptomic and epigenomic (DNA methylation) changes in brains. Permethrin exposed F0 fish were hypoactive at adulthood, while males from the F1 and F2 generations showed a specific decrease in anxiety-like behavior. In F0, transcriptomic data showed enrichment in pathways related to glutamatergic synapse activity, which may partly underlie the behavioral effects. In F1 and F2 males, dysregulation of similar pathways was observed, including a subset of differentially methylated regions that were inherited from the F0 to the F2 generation and indicated stable dysregulation of glutamatergic signaling. Altogether, the present results provide novel evidence on the transgenerational neurotoxic effects of permethrin, as well as mechanistic insight: a transient exposure induces persistent transcriptional and DNA methylation changes that may translate into transgenerational alteration of glutamatergic signaling and, thus, into behavioral alterations.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33752003',
'doi' => '10.1016/j.scitotenv.2021.146404',
'modified' => '2021-12-21 16:02:21',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 37 => array(
'id' => '4142',
'name' => 'The aging DNA methylome reveals environment-by-aging interactions in amodel teleost',
'authors' => 'Bertucci, E. M. et al.',
'description' => '<p>The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4\% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.</p>',
'date' => '2021-03-01',
'pmid' => 'https://doi.org/10.1101%2F2021.03.01.433371',
'doi' => '10.1101/2021.03.01.433371',
'modified' => '2022-05-19 16:07:18',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 38 => array(
'id' => '4155',
'name' => 'Perturbed DNA methylation by sustained overexpression of Gadd45b induces chromatin disorganization, DNA strand breaks and dopaminergic neurondeath in mice',
'authors' => 'Ravel-Godreuil, C. et al.',
'description' => '<p>Heterochromatin disorganization is a key hallmark of aging and DNA methylation state is currently the main molecular predictor of chronological age. The most frequent neurodegenerative diseases like Parkinson disease and Alzheimer’s disease are age-related but how the aging process and chromatin alterations are linked to neurodegeneration is unknown. Here, we investigated the consequences of viral overexpression of Gadd45b, a multifactorial protein involved in active DNA demethylation, in the midbrain of wild-type mice. Gadd45b overexpression induces global and stable changes in DNA methylation, particularly on gene bodies of genes related to neuronal functions. DNA methylation changes were accompanied by perturbed H3K9me3-marked heterochromatin and increased DNA damage. Prolonged Gadd45b expression resulted in dopaminergic neuron degeneration accompanied by altered expression of candidate genes related to heterochromatin maintenance, DNA methylation or Parkinson disease. Gadd45b overexpression rendered midbrain dopaminergic neurons more vulnerable to acute oxidative stress. Heterochromatin disorganization and DNA demethylation resulted in derepression of mostly young LINE-1 transposable elements, a potential source of DNA damage, prior to Gadd45b-induced neurodegeneration. Our data implicate that alterations in DNA methylation and heterochromatin organization, LINE-1 derepression and DNA damage can represent important contributors in the pathogenic mechanisms of dopaminergic neuron degeneration with potential implications for Parkinson disease.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2020.06.23.158014',
'doi' => '10.1101/2020.06.23.158014',
'modified' => '2022-05-19 16:07:48',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 39 => array(
'id' => '4189',
'name' => 'The Identification of a Novel Fucosidosis-Associated Mutation: A Case of a5-Year-Old Polish Girl with Two Additional Rare Chromosomal Aberrations andAffected DNA Methylation Patterns.',
'authors' => 'Domin A. et al. ',
'description' => '<p>Fucosidosis is a rare neurodegenerative autosomal recessive disorder, which manifests as progressive neurological and psychomotor deterioration, growth retardation, skin and skeletal abnormalities, intellectual disability and coarsening of facial features. It is caused by biallelic mutations in encoding the α-L-fucosidase enzyme, which in turn is responsible for degradation of fucose-containing glycoproteins and glycolipids. mutations lead to severe reduction or even loss of α-L-fucosidase enzyme activity. This results in incomplete breakdown of fucose-containing compounds leading to their deposition in different tissues and, consequently, disease progression. To date, 36 pathogenic variants in associated with fucosidosis have been documented. Among these are three splice site variants. Here, we report a novel fucosidosis-related 9-base-pair deletion (NG_013346.1:g.10233_10241delACAGGTAAG) affecting the exon 3/intron 3 junction within a sequence. This novel pathogenic variant was identified in a five-year-old Polish girl with a well-defined pattern of fucosidosis symptoms. Since it is postulated that other genetic, nongenetic or environmental factors can also contribute to fucosidosis pathogenesis, we performed further analysis and found two rare de novo chromosomal aberrations in the girl's genome involving a 15q11.1-11.2 microdeletion and an Xq22.2 gain. These abnormalities were associated with genome-wide changes in DNA methylation status in the epigenome of blood cells.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33435586',
'doi' => '10.3390/genes12010074',
'modified' => '2022-05-19 16:08:10',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 40 => array(
'id' => '4357',
'name' => 'Developmental cannabidiol exposure increases anxiety and modifiesgenome-wide brain DNA methylation in adult female mice.',
'authors' => 'Wanner N. M. et al. ',
'description' => '<p>BACKGROUND: Use of cannabidiol (CBD), the primary non-psychoactive compound found in cannabis, has recently risen dramatically, while relatively little is known about the underlying molecular mechanisms of its effects. Previous work indicates that direct CBD exposure strongly impacts the brain, with anxiolytic, antidepressant, antipsychotic, and other effects being observed in animal and human studies. The epigenome, particularly DNA methylation, is responsive to environmental input and can direct persistent patterns of gene regulation impacting phenotype. Epigenetic perturbation is particularly impactful during embryogenesis, when exogenous exposures can disrupt critical resetting of epigenetic marks and impart phenotypic effects lasting into adulthood. The impact of prenatal CBD exposure has not been evaluated; however, studies using the psychomimetic cannabinoid Δ9-tetrahydrocannabinol (THC) have identified detrimental effects on psychological outcomes in developmentally exposed adult offspring. We hypothesized that developmental CBD exposure would have similar negative effects on behavior mediated in part by the epigenome. Nulliparous female wild-type Agouti viable yellow (A) mice were exposed to 20 mg/kg CBD or vehicle daily from two weeks prior to mating through gestation and lactation. Coat color shifts, a readout of DNA methylation at the Agouti locus in this strain, were measured in F1 A/a offspring. Young adult F1 a/a offspring were then subjected to tests of working spatial memory and anxiety/compulsive behavior. Reduced-representation bisulfite sequencing was performed on both F0 and F1 cerebral cortex and F1 hippocampus to identify genome-wide changes in DNA methylation for direct and developmental exposure, respectively. RESULTS: F1 offspring exposed to CBD during development exhibited increased anxiety and improved memory behavior in a sex-specific manner. Further, while no significant coat color shift was observed in A/a offspring, thousands of differentially methylated loci (DMLs) were identified in both brain regions with functional enrichment for neurogenesis, substance use phenotypes, and other psychologically relevant terms. CONCLUSIONS: These findings demonstrate for the first time that despite positive effects of direct exposure, developmental CBD is associated with mixed behavioral outcomes and perturbation of the brain epigenome.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33407853',
'doi' => '10.1186/s13148-020-00993-4',
'modified' => '2022-08-03 17:04:44',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 41 => array(
'id' => '4208',
'name' => 'Hepatic transcriptome and DNA methylation patterns following perinataland chronic BPS exposure in male mice.',
'authors' => 'Brulport A. et al. ',
'description' => '<p>BACKGROUND: Bisphenol S (BPS) is a common bisphenol A (BPA) substitute, since BPA is virtually banned worldwide. However, BPS and BPA have both endocrine disrupting properties. Their effects appear mostly in adulthood following perinatal exposures. The objective of the present study was to investigate the impact of perinatal and chronic exposure to BPS at the low dose of 1.5 μg/kg body weight/day on the transcriptome and methylome of the liver in 23 weeks-old C57BL6/J male mice. RESULTS: This multi-omic study highlights a major impact of BPS on gene expression (374 significant deregulated genes) and Gene Set Enrichment Analysis show an enrichment focused on several biological pathways related to metabolic liver regulation. BPS exposure also induces a hypomethylation in 58.5\% of the differentially methylated regions (DMR). Systematic connections were not found between gene expression and methylation profile excepted for 18 genes, including 4 genes involved in lipid metabolism pathways (Fasn, Hmgcr, Elovl6, Lpin1), which were downregulated and featured differentially methylated CpGs in their exons or introns. CONCLUSIONS: This descriptive study shows an impact of BPS on biological pathways mainly related to an integrative disruption of metabolism (energy metabolism, detoxification, protein and steroid metabolism) and, like most high-throughput studies, contributes to the identification of potential exposure biomarkers.</p>',
'date' => '2020-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33297965',
'doi' => '10.1186/s12864-020-07294-3',
'modified' => '2022-01-13 14:57:00',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 42 => array(
'id' => '4033',
'name' => 'Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans',
'authors' => 'Neeraj K. Sharma, Mary E. Comeau, Dennis Montoya, Matteo Pellegrini, Timothy D. Howard, Carl D. Langefeld, Swapan K. Das',
'description' => '<p><span>Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in Type 2 Diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans from AAGMEx cohort, we performed<span> </span></span><em>cis</em><span>-expression quantitative trait methylation (</span><em>cis</em><span>-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated CpG-regions for 82 transcripts (FDR-P<0.05). The strongest eQTM locus was observed for the proopiomelanocortin (</span><em>POMC</em><span>; ρ= -0.632, P= 4.70X10</span><sup>-27</sup><span>) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 CpG regions associated (FDR-P <0.05) with Matsuda index, S</span><sub>I</sub><span><span> </span>and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, S</span><sub>I</sub><span><span> </span>and/or BMI in EWAS. These associated genes include<span> </span></span><em>FERMT3</em><span>,<span> </span></span><em>ITGAM</em><span>,<span> </span></span><em>ITGAX</em><span>, and<span> </span></span><em>POMC</em><span>. In summary, applying an integrative multi-omics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.</span></p>',
'date' => '2020-09-20',
'pmid' => 'https://diabetes.diabetesjournals.org/content/early/2020/09/03/db20-0117',
'doi' => '10.2337/db20-0117',
'modified' => '2022-05-19 16:08:46',
'created' => '2020-10-22 10:55:58',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 43 => array(
'id' => '4020',
'name' => 'DNA CpG methylation in sequential glioblastoma specimens.',
'authors' => 'Kraboth, Z and Galik, B and Tompa, M and Kajtar, B and Urban, P andGyenesei, A and Miseta, A and Kalman, B',
'description' => '<p>PURPOSE: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples. METHODS: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters. RESULTS: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport. CONCLUSION: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.</p>',
'date' => '2020-08-10',
'pmid' => 'http://www.pubmed.gov/32779022',
'doi' => '10.1007/s00432-020-03349-w',
'modified' => '2022-05-19 16:09:06',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 44 => array(
'id' => '3989',
'name' => 'Early Life Exposure to Environmentally Relevant Levels of Endocrine Disruptors Drive Multigenerational and Transgenerational Epigenetic Changes in a Fish Model',
'authors' => 'Major Kaley M., DeCourten Bethany M., Li Jie, Britton Monica, Settles Matthew L., Mehinto Alvine C., Connon Richard E., Brander Susanne M.',
'description' => '<p>The inland silverside, Menidia beryllina, is a euryhaline fish and a model organism in ecotoxicology. We previously showed that exposure to picomolar (ng/L) levels of endocrine disrupting chemicals (EDCs) can cause a variety of effects in M. beryllina, from changes in gene expression to phenotypic alterations. Here we explore the potential for early life exposure to EDCs to modify the epigenome in silversides, with a focus on multi- and transgenerational effects. EDCs included contaminants of emerging concern (the pyrethroid insecticide bifenthrin and the synthetic progestin levonorgestrel), as well as a commonly detected synthetic estrogen (ethinylestradiol), and a synthetic androgen (trenbolone) at exposure levels ranging from 3 to 10 ng/L. In a multigenerational experiment, we exposed parental silversides to EDCs from fertilization until 21 days post hatch (dph). Then we assessed DNA methylation patterns for three generations (F0, F1, and F2) in whole body larval fish using reduced representation bisulfite sequencing (RRBS). We found significant (α = 0.05) differences in promoter and/or gene body methylation in treatment fish relative to controls for all EDCs and all generations indicating that both multigenerational (F1) and transgenerational (F2) effects that were caused by strict inheritance of DNA methylation alterations and the dysregulation of epigenetic control mechanisms. Using gene ontology and pathway analyses, we found enrichment in biological processes and pathways representative of growth and development, immune function, reproduction, pigmentation, epigenetic regulation, stress response and repair (including pathways important in carcinogenesis). Further, we found that a subset of potentially EDC responsive genes (EDCRGs) were differentially methylated across all treatments and generations and included hormone receptors, genes involved in steroidogenesis, prostaglandin synthesis, sexual development, DNA methylation, protein metabolism and synthesis, cell signaling, and neurodevelopment. The analysis of EDCRGs provided additional evidence that differential methylation is inherited by the offspring of EDC-treated animals, sometimes in the F2 generation that was never exposed. These findings show that low, environmentally relevant levels of EDCs can cause altered methylation in genes that are functionally relevant to impaired phenotypes documented in EDC-exposed animals and that EDC exposure has the potential to affect epigenetic regulation in future generations of fish that have never been exposed.</p>',
'date' => '2020-06-24',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fmars.2020.00471/full',
'doi' => '10.3389/fmars.2020.00471',
'modified' => '2022-05-19 16:09:23',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 45 => array(
'id' => '3983',
'name' => 'Chronic cannabidiol alters genome-wide DNA methylation in adult mouse hippocampus: epigenetic implications for psychiatric disease.',
'authors' => 'Wanner NM, Colwell M, Drown C, Faulk C',
'description' => '<p>Cannabidiol (CBD) is the primary non-psychoactive compound found in cannabis (Cannabis sativa) and an increasingly popular dietary supplement as a result of widespread availability of CBD-containing products. CBD is FDA-approved for the treatment of epilepsy and exhibits anxiolytic, antipsychotic, prosocial, and other behavioral effects in animal and human studies, however, the underlying mechanisms governing these phenotypes are still being elucidated. The epigenome, particularly DNA methylation, is responsive to environmental input and can govern persistent patterns of gene regulation affecting phenotype across the life course. In order to understand the epigenomic activity of chronic cannabidiol exposure in the adult brain, 12-week-old male C57BL/6 mice were exposed to either 20 mg/kg CBD or vehicle daily by oral administration for fourteen days. Hippocampal tissue was collected and reduced-representation bisulfite sequencing (RRBS) was performed. Analyses revealed 3,323 differentially methylated loci (DMLs) in CBD-exposed animals with a small skew toward global hypomethylation. Genes for cell adhesion and migration, dendritic spine development, and excitatory postsynaptic potential were found to be enriched in a gene ontology term analysis of DML-containing genes, and disease ontology enrichment revealed an overrepresentation of DMLs in gene sets associated with autism spectrum disorder, schizophrenia, and other phenotypes. These results suggest that the epigenome may be a key substrate for CBD's behavioral effects and provides a wealth of gene regulatory information for further study. This article is protected by copyright. All rights reserved.</p>',
'date' => '2020-06-24',
'pmid' => 'http://www.pubmed.gov/32579259',
'doi' => '10.1002/em.22396',
'modified' => '2022-05-19 16:09:42',
'created' => '2020-08-21 16:41:39',
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[maximum depth reached]
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(int) 46 => array(
'id' => '3885',
'name' => 'Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease',
'authors' => 'Guan Y, Liu H, Ma Z, Li SY, Park J, Sheng X, Susztak K',
'description' => '<p>BACKGROUND: Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing methylation. METHODS: We generated mice with genetic deletion of and in nephron progenitor cells () and kidney tubule cells (). We characterized mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. RESULTS: mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that and play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of and resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by and were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with and deletion. CONCLUSIONS: Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.</p>',
'date' => '2020-03-03',
'pmid' => 'http://www.pubmed.gov/32127410',
'doi' => '10.1681/ASN.2019080797',
'modified' => '2022-05-19 16:10:07',
'created' => '2020-03-13 13:45:54',
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[maximum depth reached]
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(int) 47 => array(
'id' => '3877',
'name' => 'Rheumatoid Arthritis Patients, Both Newly Diagnosed and Methotrexate Treated, Show More DNA Methylation Differences in CD4+ Memory Than in CD4+ Naïve T Cells',
'authors' => 'Guderud Kari, Sunde Line H., Flåm Siri T., Mæhlen Marthe T., Mjaavatten Maria D., Lillegraven Siri, Aga Anna-Birgitte, Evenrød Ida M., Norli Ellen S., Andreassen Bettina K., Franzenburg Sören, Franke Andre, Haavardsholm Espen A., Rayner Simon, Gervin Kris',
'description' => '<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes pain and swelling of multiple joints in the body. The underlying disease mechanisms are believed to involve a complex interplay between common genetic and environmental factors. The heritability of RA has been estimated to be ~50% for anti-citrullinated protein antibody (ACPA) positive RA and ~20% for ACPA negative RA in a large familial aggregation study (1). Genome-wide association studies (GWAS) have identified more than 100 RA risk loci, mostly conferring risk to ACPA positive RA, marked by lead single nucleotide polymorphisms (SNPs) across various populations (2). The risk SNPs have small effect sizes, and only explain parts of heritability in RA. Environmental and epigenetic factors are also thought to be involved in the RA disease pathogenesis (3) of which smoking is the only established environmental risk factor (4, 5). Epigenetic modifications are important for regulation and maintenance of cell type specific biological functions, and alterations in the epigenome have been found to be associated with RA (6). The most studied epigenetic modification in humans is DNA methylation of cytosine followed by a guanine at so-called CpG sites (CpGs). CpGs are often clustered in regions called CpG islands (CGIs), which frequently overlap gene promoters (7). DNA methylation in promotor regions is usually negatively correlated with transcription of the nearby gene (8). A wide range of immune cells has been implicated in the pathogenesis of RA. One of the most widely used drugs for treatment of RA, methotrexate (MTX) (9), acts as an immunosuppressant in proliferating cells (10), and of these, the most relevant cell population for RA is CD4+ T cells (11). Interestingly, the RA risk loci are enriched in accessible chromatin regions (H3K4me3 peaks) in T cells, including both CD4+ naïve and CD4+ memory T cells (2). Studies have identified cell type specific DNA methylation differences in B (CD19+) and T (CD3+) lymphocytes (12, 13), as well as CD4+ T cells subsets (14, 15) isolated from RA patients compared to healthy controls. However, memory and naïve CD4+ T cells also display distinct genome-wide and gene-specific DNA methylation patterns as a result of normal differentiation (16); hence analyses of bulk T cells may be confounded by different proportions of naïve and memory T cells. Given the recent observations that CD4+ T cell subset distributions are abnormal both in treatment naïve RA patients and in RA patients who has undergone MTX treatment (17) methylation profiles for distinct CD4+ T cell subpopulations should be investigated separately. Methylation levels have so far only been assessed by array-based methods in RA, however reduced representation bisulfite sequencing (RRBS) using next generation sequencers allows for an interrogation of even more CpG sites. RRBS enriches for CpG dinucleotides by utilizes the restriction enzyme MspI (C∧CGG) to digest the DNA sample before bisulfite conversion and sequencing. In this study, we aimed to investigate whether we could detect DNA methylation differences in primary naïve and memory CD4+ T cells from RA patients. To do this, we conducted an epigenome-wide association study using RRBS on isolated T cell populations from two different RA cohorts; (1) disease modifying anti-rheumatic drug (DMARD) naïve RA patients with active disease and (2) MTX-treated RA patients who had been in remission for >12 months. The two cohorts were compared to matched healthy controls.</p>',
'date' => '2020-02-14',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fimmu.2020.00194/full',
'doi' => '10.3389/fimmu.2020.00194',
'modified' => '2022-05-19 16:10:24',
'created' => '2020-03-13 13:45:54',
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(int) 48 => array(
'id' => '3794',
'name' => 'Obesogen effect of bisphenol S alters mRNA expression and DNA methylation profiling in male mouse liver',
'authors' => 'Brulport Axelle, Vaiman Daniel, Chagnon Marie-Christine, Le Corre Ludovic',
'description' => '<p>Environmental pollution is increasingly considered an important factor involved in the obesity incidence. Endocrine disruptors (EDs) are important actors in the concept of DOHaD (Developmental Origins of Health and Disease), where epigenetic mechanisms play crucial roles. Bisphenol A (BPA), a monomer used in the manufacture of plastics and resins is one of the most studied obesogenic endocrine disruptor. Bisphenol S (BPS), a BPA substitute, has the same obesogenic properties, acting at low doses with a sex-specific effect following perinatal exposure. Since the liver is a major organ in regulating body lipid homeostasis, we investigated gene expression and DNA methylation under low-dose BPS exposure. The BPS obesogenic effect was associated with an increase of hepatic triglyceride content. These physiological disturbances were accompanied by genome-wide changes in gene expression (1366 genes significantly modified more than 1.5-fold). Gene ontology analysis revealed alteration of gene cascades involved in protein translation and complement regulation. It was associated with hepatic DNA hypomethylation in autosomes and hypermethylation in sex chromosomes. Although no systematic correlation has been found between gene repression and hypermethylation, several genes related to liver metabolism were either hypermethylated (Acsl4, Gpr40, Cel, Pparδ, Abca6, Ces3a, Sgms2) or hypomethylated (Soga1, Gpihbp1, Nr1d2, Mlxipl, Rps6kb2, Esrrb, Thra, Cidec). In specific cases (Hapln4, ApoA4, Cidec, genes involved in lipid metabolism and liver fibrosis) mRNA upregulation was associated with hypomethylation. In conclusion, we show for the first time wide disruptive physiological effects of low-dose of BPS, which raises the question of its harmlessness as an industrial substitute for BPA.</p>',
'date' => '2019-10-15',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/31683443',
'doi' => '10.1016/j.chemosphere.2019.125092',
'modified' => '2022-05-19 16:10:42',
'created' => '2019-12-02 15:25:44',
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(int) 49 => array(
'id' => '3674',
'name' => 'Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA.',
'authors' => 'Mayorga L, Salassa BN, Marzese DM, Loos MA, Eiroa HD, Lubieniecki F, García Samartino C, Romano PS, Roqué M',
'description' => '<p>Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a "pro-survival" adaptive state that seems to be triggered by the differential methylation of nuclear genes.</p>',
'date' => '2019-04-01',
'pmid' => 'http://www.pubmed.gov/30673822',
'doi' => '10.1007/s00018-019-03008-5',
'modified' => '2022-05-19 16:10:59',
'created' => '2019-06-21 14:55:31',
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'id' => '3416',
'name' => 'Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination.',
'authors' => 'Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG',
'description' => '<p>Bacillus Calmette-Guérin (BCG) is the only licensed vaccine for tuberculosis (TB) and induces highly variable protection against pulmonary disease in different countries. We hypothesised that DNA methylation is one of the molecular mechanisms driving variability in BCG-induced immune responses. DNA methylation in peripheral blood mononuclear cells (PBMC) from BCG vaccinated infants was measured and comparisons made between low and high BCG-specific cytokine responders. We found 318 genes and 67 pathways with distinct patterns of DNA methylation, including immune pathways, e.g. for T cell activation, that are known to directly affect immune responses. We also highlight signalling pathways that could indirectly affect the BCG-induced immune response: potassium and calcium channel, muscarinic acetylcholine receptor, G Protein coupled receptor (GPCR), glutamate signalling and WNT pathways. This study suggests that in addition to immune pathways, cellular processes drive vaccine-induced immune responses. Our results highlight mechanisms that require consideration when designing new TB vaccines.</p>',
'date' => '2018-08-30',
'pmid' => 'http://www.pubmed.gov/30166570',
'doi' => '10.1038/s41598-018-31537-9',
'modified' => '2022-05-19 16:11:19',
'created' => '2018-12-04 09:51:07',
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(int) 51 => array(
'id' => '3322',
'name' => 'In Situ Fixation Redefines Quiescence and Early Activation of Skeletal Muscle Stem Cells',
'authors' => 'Machado L. et al.',
'description' => '<div class="abstract">
<h2 class="sectionTitle" tabindex="0">Summary</h2>
<div class="content">
<p>State of the art techniques have been developed to isolate and analyze cells from various tissues, aiming to capture their <em>in vivo</em> state. However, the majority of cell isolation protocols involve lengthy mechanical and enzymatic dissociation steps followed by flow cytometry, exposing cells to stress and disrupting their physiological niche. Focusing on adult skeletal muscle stem cells, we have developed a protocol that circumvents the impact of isolation procedures and captures cells in their native quiescent state. We show that current isolation protocols induce major transcriptional changes accompanied by specific histone modifications while having negligible effects on DNA methylation. In addition to proposing a protocol to avoid isolation-induced artifacts, our study reveals previously undetected quiescence and early activation genes of potential biological interest.</p>
</div>
</div>',
'date' => '2017-11-14',
'pmid' => 'http://www.cell.com/cell-reports/abstract/S2211-1247(17)31543-7',
'doi' => '',
'modified' => '2022-05-19 16:11:43',
'created' => '2018-02-02 16:36:37',
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(int) 52 => array(
'id' => '3286',
'name' => 'DNMT3B overexpression contributes to aberrant DNA methylation and MYC-driven tumor maintenance in T-ALL and Burkitt’s lymphoma',
'authors' => 'Poole et al.',
'description' => '<p>Aberrant DNA methylation is a hallmark of cancer. However, our understanding of how tumor cell-specific DNA methylation patterns are established and maintained is limited. Here, we report that in T-cell acute lymphoblastic leukemia (T-ALL) and Burkitt’s lymphoma the <em>MYC </em>oncogene causes overexpression of DNA methyltransferase (DNMT) 1 and 3B, which contributes to tumor maintenance. By utilizing a tetracycline-regulated <em>MYC </em>transgene in a mouse T-ALL (EμSRα-tTA;tet-o- MYC) and human Burkitt’s lymphoma (P493-6) model, we demonstrated that DNMT1 and DNMT3B expression depend on high MYC levels, and that their transcription decreased upon MYC-inactivation. Chromatin immunoprecipitation indicated that MYC binds to the <em>DNMT1 </em>and <em>DNMT3B </em>promoters, implicating a direct transcriptional regulation. Hence, shRNA-mediated knock-down of endogenous MYC in human T-ALL and Burkitt’s lymphoma cell lines, downregulated DNMT3B expression. Knock-down and pharmacologic inhibition of DNMT3B in T-ALL reduced cell proliferation associated with genome-wide changes in DNA methylation, indicating a tumor promoter function during tumor maintenance. We provide novel evidence that MYC directly deregulates the expression of both <em>de novo </em>and maintenance DNMTs, showing that MYC controls DNA methylation in a genome-wide fashion. Our finding that a coordinated interplay between the components of the DNA methylating machinery contributes to MYC-driven tumor maintenance highlights the potential of specific DNMTs for targeted therapies.</p>',
'date' => '2017-08-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/29100357',
'doi' => '10.18632/oncotarget.20176',
'modified' => '2022-05-19 16:12:01',
'created' => '2017-11-10 11:44:30',
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(int) 53 => array(
'id' => '3063',
'name' => 'DNA methylation and alcohol use disorders: Progress and challenges',
'authors' => 'Zhang H. and Gelernter J.',
'description' => '<section class="article-section article-body-section" id="ajad12465-sec-0001">
<h3>Background and Objectives</h3>
<p>Risk for alcohol use disorders (AUDs) is influenced by gene–environment interactions. Environmental factors can affect gene expression through epigenetic mechanisms such as DNA methylation. This review outlines the findings regarding the association of DNA methylation and AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0002">
<h3>Methods</h3>
<p>We searched PubMed (by April 2016) and identified 29 studies that examined the association of DNA methylation and AUDs. We also evaluated the methods used in these studies.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0003">
<h3>Results</h3>
<p>Two studies demonstrated elevated global (repetitive element) DNA methylation levels in AUD subjects. Fifteen candidate gene studies showed hypermethylation of promoter regions of six genes (<em>AVP</em>, <em>DNMT3B</em>, <em>HERP</em>, <em>HTR3A</em>, <em>OPRM1</em>, and <em>SNCA</em>) or hypomethylation of the <em>GDAP1</em> promoter region in AUD subjects. Five genome-wide DNA methylation studies demonstrated widespread DNA methylation changes across the genome in AUD subjects. Six studies showed significant correlations of DNA methylation with gene expression in AUD subjects. Three studies revealed interactive effects of genetic variation and DNA methylation on susceptibility to AUDs. Most studies analyzed AUD-associated DNA methylation changes in the peripheral blood; a few studies examined DNA methylation changes in postmortem brains of AUD subjects.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0004">
<h3>Discussion and Conclusions</h3>
<p>Chronic alcohol consumption may result in DNA methylation changes, leading to neuroadaptations that may underlie some of the mechanisms of AUD risk and persistence. Future studies are needed to confirm the few existing results, and then to elucidate whether DNA methylation changes are the cause or consequence of AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0005">
<h3>Scientific Significance</h3>
<p>DNA methylation profiles may be used to assess AUD status or monitor AUD treatment response. (Am J Addict 2016;XX:1–14)</p>
</section>',
'date' => '2016-10-19',
'pmid' => 'http://onlinelibrary.wiley.com/doi/10.1111/ajad.12465/abstract?campaign=wolsavedsearch',
'doi' => '',
'modified' => '2022-05-19 16:12:16',
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'name' => 'Diagenode® Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage',
'authors' => 'Anne-Clémence Veillard, Paul Datlinger, Miklos Laczik, Sharon Squazzo & Christoph Bock',
'description' => '<p>Reduced representation bisulfite sequencing (RRBS) enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of whole-genome sequencing. The Diagenode Premium RRBS kit makes this technology widely available and provides high coverage (up to 4 million CpGs in human samples). Multiplexing prior to bisulfite conversion allows processing of 96 samples per experiment, enabling studies of large cohorts.</p>',
'date' => '2016-01-28',
'pmid' => 'http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html',
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
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<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
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<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
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<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
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<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p>将 <input name="data[Cart][quantity]" placeholder="1" value="1" min="1" style="width:60px;display:inline" type="number" id="CartQuantity" required="required"/> <strong> Premium RRBS kit V2 <br /> RRBS for low DNA amounts and accurate analysis</strong> 添加至我的购物车。</p>
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<p>将 <input name="data[Cart][quantity]" placeholder="1" value="1" min="1" style="width:60px;display:inline" type="number" id="CartQuantity" required="required"/> <strong> Premium RRBS kit V2 x96<br /> RRBS for low DNA amounts and accurate analysis</strong> 添加至我的购物车。</p>
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'id' => '3224',
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'name' => 'Premium RRBS kit V2 x96<br /> RRBS for low DNA amounts and accurate analysis',
'description' => '<p><a href="https://www.diagenode.com/files/products/kits/RRBS-KIT-V2_manual.pdf"><img src="https://www.diagenode.com/img/buttons/bt-manual.png" /></a></p>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<script src="chrome-extension://hhojmcideegachlhfgfdhailpfhgknjm/web_accessible_resources/index.js"></script>',
'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
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<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>RRBS workflow allow you to:</p>
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<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
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<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
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<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
</ul>
<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
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<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
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<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
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<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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<div class="extra-spaced">
<p><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to 7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
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<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
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<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> widely adopted</li>
<li>As little as 25 ng for any vertebrate species</li>
<li>Differentially methylated site analysis <span>using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<div class="extra-spaced">
<h2></h2>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
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<h4>RRBS Service includes:</h4>
<table style="width: 905px;">
<tbody>
<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration </li>
<li>Assessment of DNA quality</li>
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</td>
</tr>
<tr>
<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
<td style="width: 636px;">
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<li>MspI digestion</li>
<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</li>
<li>Bisulfite conversion</li>
<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<td style="width: 264px;"><strong>Deep sequencing</strong></td>
<td style="width: 636px;">
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<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7 million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage (on average) for human samples</li>
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<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
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<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, …)</li>
<li>Clustering analysis</li>
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<li>Get functional insights</li>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div class="large-12 columns">
<div style="text-align: justify;" class="small-12 medium-8 large-8 columns">
<h2>Complete solutions for DNA methylation studies</h2>
<p>Whether you are experienced or new to the field of DNA methylation, Diagenode has everything you need to make your assay as easy and convenient as possible while ensuring consistent data between samples and experiments. Diagenode offers sonication instruments, reagent kits, high quality antibodies, and high-throughput automation capability to address all of your specific DNA methylation analysis requirements.</p>
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<div style="text-align: justify;" class="small-12 medium-12 large-12 columns">
<p>DNA methylation was the first discovered epigenetic mark and is the most widely studied topic in epigenetics. <em>In vivo</em>, DNA is methylated following DNA replication and is involved in a number of biological processes including the regulation of imprinted genes, X chromosome inactivation. and tumor suppressor gene silencing in cancer cells. Methylation often occurs in cytosine-guanine rich regions of DNA (CpG islands), which are commonly upstream of promoter regions.</p>
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<div class="small-12 medium-12 large-12 columns"><br /><br />
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#dnamethyl"><i class="fa fa-caret-right"></i> Learn more</a>
<div id="dnamethyl" class="content">5-methylcytosine (5-mC) has been known for a long time as the only modification of DNA for epigenetic regulation. In 2009, however, Kriaucionis discovered a second methylated cytosine, 5-hydroxymethylcytosine (5-hmC). The so-called 6th base, is generated by enzymatic conversion of 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine by the TET family of oxygenases. Early reports suggested that 5-hmC may represent an intermediate of active demethylation in a new pathway which demethylates DNA, converting 5-mC to cytosine. Recent evidence fuel this hypothesis suggesting that further oxidation of the hydroxymethyl group leads to a formyl or carboxyl group followed by either deformylation or decarboxylation. The formyl and carboxyl groups of 5-formylcytosine (5-fC) and 5-carboxylcytosine (5-caC) could be enzymatically removed without excision of the base.
<p class="text-center"><img src="https://www.diagenode.com/img/categories/kits_dna/dna_methylation_variants.jpg" /></p>
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</li>
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<br />
<h2>Main DNA methylation technologies</h2>
<p style="text-align: justify;">Overview of the <span style="font-weight: 400;">three main approaches for studying DNA methylation.</span></p>
<div class="row">
<ol>
<li style="font-weight: 400;"><span style="font-weight: 400;">Chemical modification with bisulfite – Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Enrichment of methylated DNA (including MeDIP and MBD)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Treatment with methylation-sensitive or dependent restriction enzymes</span></li>
</ol>
<p><span style="font-weight: 400;"> </span></p>
<div class="row">
<table>
<thead>
<tr>
<th></th>
<th>Description</th>
<th width="350">Features</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td><span style="font-weight: 400;">Chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at single nucleotide resolution.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Single nucleotide resolution</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Quantitative analysis - methylation rate (%)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Gold standard and well studied</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><b>Methylated DNA enrichment</b></td>
<td><span style="font-weight: 400;">(Hydroxy-)Methylated DNA is enriched by using specific antibodies (hMeDIP or MeDIP) or proteins (MBD) that specifically bind methylated CpG sites in fragmented genomic DNA.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Resolution depends on the fragment size of the enriched methylated DNA (300 bp)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Qualitative analysis</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><strong>Restriction enzyme-based digestion</strong></td>
<td><span style="font-weight: 400;">Use of (hydroxy)methylation-sensitive or (hydroxy)methylation-dependent restriction enzymes for DNA methylation analysis at specific sites.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Determination of methylation status is limited by the enzyme recognition site</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Easy to use</span></li>
</ul>
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<div style="text-align: justify;" class="large-12 columns">Bisulfite modification of DNA is the most commonly used, "<strong>gold standard</strong>" method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. T<span style="font-weight: 400;">his technology is based on the chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at the singe nucleotide level.</span></div>
<div style="text-align: justify;" class="large-12 columns"></div>
<div style="text-align: justify;" class="large-12 columns">Various analyses can be performed on the altered sequence to retrieve this information: bisulfite sequencing, pyrosequencing, methylation-specific PCR, high resolution melting curve analysis, microarray-based approaches, and next-generation sequencing.
<h3>How it works</h3>
Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected (see Figure 1).
<p class="text-center"><img src="https://www.diagenode.com/img/applications/bisulfite.png" /><br />Figure 1: Overview of bisulfite conversion of DNA</p>
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<p>Sodium bisulfite conversion of genomic DNA is the most commonly used method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. It enables <span>to differentiate and detect unmethylated versus methylated cytosines. This procedure can then be followed either by <strong>PCR amplification</strong> or <strong>next generation sequencing</strong> to reveal the methylation status of every cytosine in gene specific amplification or whole genome amplification.</span></p>
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<h2>How it works</h2>
<p style="text-align: left;">Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, while methylated cytosines remain unchanged. <span>The DNA is then amplified by PCR where the uracils are converted to thymines. </span></p>
<p style="text-align: center;"><span></span></p>
<p><img src="https://www.diagenode.com/img/categories/bisulfite-conversion/bisulfite-conversion-acgautac.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<h2>Advantages</h2>
<ul class="nobullet" style="font-size: 19px;">
<li><i class="fa fa-arrow-circle-right"></i><strong> </strong><strong>Single nucleotide</strong> resolution</li>
<li><i class="fa fa-arrow-circle-right"></i><strong> Gene-specific </strong>and <strong>genome-wide</strong><span> analyses</span></li>
<li><i class="fa fa-arrow-circle-right"></i><strong> NGS</strong><span> </span>compatible</li>
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<h2>Downstream analysis techniques</h2>
<ul class="square">
<li>Reduced Representation Bisulfite Sequencing (RRBS) with our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS Kit V2</a></li>
<li>Bisulfite conversion with our <a href="https://www.diagenode.com/en/p/premium-bisulfite-kit-50-rxns">Premium Bisulfite Kit</a> followed by qPCR, Sanger, Pyrosequencing</li>
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'description' => '<p>DNA methylation is an important epigenetic mark with broad relevance in development and disease. Reduced representation bisulfite sequencing (RRBS) enables genome-scale DNA methylation analysis with all the advantages of sequencing technology (accuracy, robustness, flexibility) at a fraction of the cost of whole genome bisulfite sequencing. Diagenode Premium RRBS kit makes this powerful technology readily available to any interested laboratory. Its optimized protocol provides genomic coverage for 3.5 to 4 million CpG dinucleotides in the human genome, which is 5-10 fold higher than the coverage of commercially available DNA methylation microarrays. The method covers not only CpG islands and promoter regions, but also a broad sampling of other functional elements including enhancers, CpG island shores and non-coding RNAs. Finally, its optimized workflow with multiplexing prior to bisulfite conversion allows for convenient processing of up to 96 samples in a single experiment, thus enabling epigenome-wide association studies in large cohorts and in any vertebrate species using an optimized and easy-to-use kit.</p>',
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'type' => 'Manual',
'url' => 'files/products/kits/RRBS-KIT-V2_manual.pdf',
'slug' => 'premium-rrbs-kit-v2-manual',
'meta_keywords' => '',
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'modified' => '2021-10-29 18:14:50',
'created' => '2021-10-29 18:14:50',
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(int) 3 => array(
'id' => '1156',
'name' => 'Sequence of the methylated spike-in control in FASTA format – Premium RRBS kit v2',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit v2 in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the <a href="../../files/products/kits/RRBS-KIT-V2_manual.pdf">Premium RRBS Kit v2 Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the <a href="../p/premium-rrbs-kit-V2-x24">Premium RRBS Kit v2 page</a> :</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_v2_methylated_control.fa',
'slug' => 'rrbs-v2-methylated-control-fa',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2022-06-22 15:15:36',
'created' => '2022-06-22 15:15:36',
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(int) 4 => array(
'id' => '1157',
'name' => 'Sequence of the unmethylated spike-in control in FASTA format – Premium RRBS kit v2',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit v2 in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/RRBS-KIT-V2_manual.pdf">Premium RRBS Kit v2 Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-V2-x24">Premium RRBS Kit v2 page</a><span> </span>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_v2_unmethylated_control.fa',
'slug' => 'rrbs-v2-unmethylated-control-fa',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2022-06-22 15:16:45',
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(int) 5 => array(
'id' => '1158',
'name' => 'Positions of the unmethylated cytosines in the sequence of the methylated control in BED format – Premium RRBS kit v2',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit v2 in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/RRBS-KIT-V2_manual.pdf">Premium RRBS Kit v2 Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-V2-x24">Premium RRBS Kit v2 page</a><span> </span>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_v2_methylated_spike_in_unmeth_positions.bed',
'slug' => 'rrbs-v2-methylated-spike-in-unmeth-positions',
'meta_keywords' => '',
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'modified' => '2022-06-22 15:18:20',
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[maximum depth reached]
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(int) 6 => array(
'id' => '1159',
'name' => 'Premium RRBS kit V2 - Spike-in controls analysis to estimate bisulfite conversion efficiency',
'description' => '<p>This document describes how to process demultiplexed RRBS V2 sequencing data to extract the information from the spike-in controls included in the Premium RRBS Kit v2 in order to estimate the bisulfite conversion efficiency.</p>
<p>Sequences of the methylated and unmethylated spike-in controls, as well as the positions of the unmethylated cytosines present in the methylated spike-in control, can be downloaded from the ‘Documents’ section of the Premium RRBS Kit v2 page <a href="../p/premium-rrbs-kit-V2-x24">https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24</a>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => 'Manual',
'url' => 'files/products/kits/Premium RRBS-spikein-controls-manual.pdf',
'slug' => 'premium-rrbs-spike-in-controls-manual',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2022-06-29 14:34:07',
'created' => '2022-06-29 14:34:07',
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'Feature' => array(),
'Image' => array(
(int) 0 => array(
'id' => '1778',
'name' => 'product/kits/methyl-kit-icon.png',
'alt' => 'Methylation kit icon',
'modified' => '2019-04-23 15:17:01',
'created' => '2018-03-15 15:52:12',
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[maximum depth reached]
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'Protocol' => array(),
'Publication' => array(
(int) 0 => array(
'id' => '4986',
'name' => 'Multi-omics characterization of chronic social defeat stress recall-activated engram nuclei in Arc-GFP mice',
'authors' => 'Monika Chanu Chongtham et al.',
'description' => '<p><span>Susceptibility to chronic social stressors often results in the development of mental health disorders including major depressive and anxiety disorders. In contrast, some individuals remain resilient even after repeated stress exposure. Understanding the molecular drivers behind these divergent phenotypic outcomes is crucial. However, previous studies using the chronic social defeat (CSD) stress model have been limited by the use of bulk tissues investigating single omics domains. To overcome these limitations, here, we applied the CSD mouse model to Arc-GFP mice for investigating the mechanistic divergence between susceptibility and resilience, specifically in stress recall-activated engram nuclei. By conducting an in-depth analysis of the less-known differential methylome landscape in the ventral hippocampal engrams, we noted unique phenotype-specific alterations in multiple biological processes with an overrepresentation of GTPase-related mechanisms. Interestingly, the differentially methylated regions were enriched in ETS transcription factor binding sites (TFBSs), important targets of the Ras-ETS signaling pathway. This differential methylation in the ETS TFBSs could form the basis of persisting stress effects long after stressor exposure. Furthermore, by integrating the methylome modifications with transcriptomic alterations, we resolved the GTPase-related mechanisms differentially activated in the resilient and susceptible phenotypes with alterations in endocytosis overrepresented in the susceptible phenotype. Overall, our findings implicate critical avenues for future therapeutic applications.</span></p>',
'date' => '2024-10-09',
'pmid' => 'https://www.researchsquare.com/article/rs-4643912/v1',
'doi' => 'https://doi.org/10.21203/rs.3.rs-4643912/v1',
'modified' => '2024-10-14 09:08:24',
'created' => '2024-10-14 09:08:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 1 => array(
'id' => '4959',
'name' => 'Pesticide-induced transgenerational alterations of genome-wide DNA methylation patterns in the pancreas of Xenopus tropicalis correlate with metabolic phenotypes',
'authors' => 'Roza M. et al. ',
'description' => '<p><span>The unsustainable use of manmade chemicals poses significant threats to biodiversity and human health. Emerging evidence highlights the potential of certain chemicals to cause transgenerational impacts on metabolic health. Here, we investigate male transmitted epigenetic transgenerational effects of the anti-androgenic herbicide linuron in the pancreas of </span><em>Xenopus tropicalis</em><span><span> </span>frogs, and their association with metabolic phenotypes. Reduced representation bisulfite sequencing (RRBS) was used to assess genome-wide DNA methylation patterns in the pancreas of adult male F2 generation ancestrally exposed to environmentally relevant linuron levels (44 ± 4.7 μg/L). We identified 1117 differentially methylated regions (DMRs) distributed across the<span> </span></span><em>X. tropicalis</em><span><span> </span>genome, revealing potential regulatory mechanisms underlying metabolic disturbances. DMRs were identified in genes crucial for pancreatic function, including calcium signalling (</span><em>clstn2, cacna1d</em><span><span> </span>and<span> </span></span><em>cadps2</em><span>), genes associated with type 2 diabetes (</span><em>tcf7l2</em><span><span> </span>and<span> </span></span><em>adcy5</em><span>) and a biomarker for pancreatic ductal adenocarcinoma (</span><em>plec</em><span>). Correlation analysis revealed associations between DNA methylation levels in these genes and metabolic phenotypes, indicating epigenetic regulation of glucose metabolism. Moreover, differential methylation in genes related to histone modifications suggests alterations in the epigenetic machinery. These findings underscore the long-term consequences of environmental contamination on pancreatic function and raise concerns about the health risks associated with transgenerational effects of pesticides.</span></p>',
'date' => '2024-10-05',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S030438942402034X',
'doi' => 'https://doi.org/10.1016/j.jhazmat.2024.135455',
'modified' => '2024-09-02 10:08:08',
'created' => '2024-09-02 10:08:08',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 2 => array(
'id' => '4962',
'name' => 'Triphenyl Phosphate Alters Methyltransferase Expression and Induces Genome-Wide Aberrant DNA Methylation in Zebrafish Larvae',
'authors' => 'Negi C.K. et al.',
'description' => '<p><span>Emerging environmental contaminants, organophosphate flame retardants (OPFRs), pose significant threats to ecosystems and human health. Despite numerous studies reporting the toxic effects of OPFRs, research on their epigenetic alterations remains limited. In this study, we investigated the effects of exposure to 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), and triphenyl phosphate (TPHP) on DNA methylation patterns during zebrafish embryonic development. We assessed general toxicity and morphological changes, measured global DNA methylation and hydroxymethylation levels, and evaluated DNA methyltransferase (DNMT) enzyme activity, as well as mRNA expression of DNMTs and ten-eleven translocation (TET) methylcytosine dioxygenase genes. Additionally, we analyzed genome-wide methylation patterns in zebrafish larvae using reduced-representation bisulfite sequencing. Our morphological assessment revealed no general toxicity, but a statistically significant yet subtle decrease in body length following exposure to TMPP and EHDPP, along with a reduction in head height after TPHP exposure, was observed. Eye diameter and head width were unaffected by any of the OPFRs. There were no significant changes in global DNA methylation levels in any exposure group, and TMPP showed no clear effect on DNMT expression. However, EHDPP significantly decreased only DNMT1 expression, while TPHP exposure reduced the expression of several DNMT orthologues and TETs in zebrafish larvae, leading to genome-wide aberrant DNA methylation. Differential methylation occurred primarily in introns (43%) and intergenic regions (37%), with 9% and 10% occurring in exons and promoter regions, respectively. Pathway enrichment analysis of differentially methylated region-associated genes indicated that TPHP exposure enhanced several biological and molecular functions corresponding to metabolism and neurological development. KEGG enrichment analysis further revealed TPHP-mediated potential effects on several signaling pathways including TGFβ, cytokine, and insulin signaling. This study identifies specific changes in DNA methylation in zebrafish larvae after TPHP exposure and brings novel insights into the epigenetic mode of action of TPHP.</span></p>',
'date' => '2024-08-29',
'pmid' => 'https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.4c00223',
'doi' => 'https://doi.org/10.1021/acs.chemrestox.4c00223',
'modified' => '2024-09-02 10:16:01',
'created' => '2024-09-02 10:16:01',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 3 => array(
'id' => '4934',
'name' => 'Differential DNA methylation in iPSC-derived dopaminergic neurons: a step forward on the role of SNORD116 microdeletion in the pathophysiology of addictive behavior in Prader-Willi syndrome',
'authors' => 'Salles J. et al.',
'description' => '<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Introduction</h3>
<p>A microdeletion including the<span> </span><i>SNORD116</i><span> </span>gene (<i>SNORD116</i><span> </span>MD) has been shown to drive the Prader-Willi syndrome (PWS) features. PWS is a neurodevelopmental disorder clinically characterized by endocrine impairment, intellectual disability and psychiatric symptoms such as a lack of emotional regulation, impulsivity, and intense temper tantrums with outbursts. In addition, this syndrome is associated with a nutritional trajectory characterized by addiction-like behavior around food in adulthood. PWS is related to the genetic loss of expression of a minimal region that plays a potential role in epigenetic regulation. Nevertheless, the role of the<span> </span><i>SNORD116</i><span> </span>MD in DNA methylation, as well as the impact of the oxytocin (OXT) on it, have never been investigated in human neurons.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Methods</h3>
<p>We studied the methylation marks in induced pluripotent stem-derived dopaminergic neurons carrying a<span> </span><i>SNORD116</i><span> </span>MD in comparison with those from an age-matched adult healthy control. We also performed identical neuron differentiation in the presence of OXT. We performed a genome-wide DNA methylation analysis from the iPSC-derived dopaminergic neurons by reduced-representation bisulfite sequencing. In addition, we performed RNA sequencing analysis in these iPSC-derived dopaminergic neurons differentiated with or without OXT.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Results</h3>
<p>The analysis revealed that 153,826 cytosines were differentially methylated between<span> </span><i>SNORD116</i><span> </span>MD neurons and control neurons. Among the differentially methylated genes, we determined a list of genes also differentially expressed. Enrichment analysis of this list encompassed the dopaminergic system with<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i>.<span> </span><i>COMT</i><span> </span>displayed hypermethylation and under-expression in<span> </span><i>SNORD116</i><span> </span>MD, and<span> </span><i>SLC6A3</i><span> </span>displayed hypomethylation and over-expression in<span> </span><i>SNORD116</i><span> </span>MD. RT-qPCR confirmed significant over-expression of<span> </span><i>SLC6A3</i><span> </span>in<span> </span><i>SNORD116 MD</i><span> </span>neurons. Moreover, the expression of this gene was significantly decreased in the case of OXT adjunction during the differentiation.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Conclusion</h3>
<p><i>SNORD116</i><span> </span>MD dopaminergic neurons displayed differential methylation and expression in the<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i><span> </span>genes, which are related to dopaminergic clearance.</p>',
'date' => '2024-04-02',
'pmid' => 'https://www.nature.com/articles/s41380-024-02542-4',
'doi' => 'https://doi.org/10.1038/s41380-024-02542-4',
'modified' => '2024-04-09 00:46:18',
'created' => '2024-04-09 00:46:18',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 4 => array(
'id' => '4913',
'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
'authors' => 'Oganezovi N. et al.',
'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
'date' => '2024-01-30',
'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
'modified' => '2024-03-28 11:30:49',
'created' => '2024-02-22 12:28:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4898',
'name' => 'DNA methylome, R-loop and clinical exome profiling of patients with sporadic amyotrophic lateral sclerosis.',
'authors' => 'Feró, O. et al.',
'description' => '<p><span>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the death of motor neurons, the aetiology of which is essentially unknown. Here, we present an integrative epigenomic study in blood samples from seven clinically characterised sporadic ALS patients to elucidate molecular factors associated with the disease. We used clinical exome sequencing (CES) to study DNA variants, DNA-RNA hybrid immunoprecipitation sequencing (DRIP-seq) to assess R-loop distribution, and reduced representation bisulfite sequencing (RRBS) to examine DNA methylation changes. The above datasets were combined to create a comprehensive repository of genetic and epigenetic changes associated with the ALS cases studied. This repository is well-suited to unveil new correlations within individual patients and across the entire patient cohort. The molecular attributes described here are expected to guide further mechanistic studies on ALS, shedding light on the underlying genetic causes and facilitating the development of new epigenetic therapies to combat this life-threatening disease.</span></p>',
'date' => '2024-01-24',
'pmid' => 'https://www.nature.com/articles/s41597-024-02985-y',
'doi' => 'https://doi.org/10.1038/s41597-024-02985-y',
'modified' => '2024-01-29 10:11:43',
'created' => '2024-01-29 10:11:43',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4896',
'name' => 'Gestational Caloric Restriction Alters Adipose Tissue Methylome and Offspring’s Metabolic Profile in a Swine Model',
'authors' => 'Mas-Pares B. et al.',
'description' => '<p><span>Limited nutrient supply to the fetus results in physiologic and metabolic adaptations that have unfavorable consequences in the offspring. In a swine animal model, we aimed to study the effects of gestational caloric restriction and early postnatal metformin administration on offspring’s adipose tissue epigenetics and their association with morphometric and metabolic variables. Sows were either underfed (30% restriction of total food) or kept under standard diet during gestation, and piglets were randomly assigned at birth to receive metformin (n = 16 per group) or vehicle treatment (n = 16 per group) throughout lactation. DNA methylation and gene expression were assessed in the retroperitoneal adipose tissue of piglets at weaning. Results showed that gestational caloric restriction had a negative effect on the metabolic profile of the piglets, increased the expression of inflammatory markers in the adipose tissue, and changed the methylation of several genes related to metabolism. Metformin treatment resulted in positive changes in the adipocyte morphology and regulated the methylation of several genes related to atherosclerosis, insulin, and fatty acids signaling pathways. The methylation and gene expression of the differentially methylated </span><span class="html-italic">FASN</span><span>,<span> </span></span><span class="html-italic">SLC5A10</span><span>,<span> </span></span><span class="html-italic">COL5A1</span><span>, and<span> </span></span><span class="html-italic">PRKCZ</span><span><span> </span>genes in adipose tissue associated with the metabolic profile in the piglets born to underfed sows. In conclusion, our swine model showed that caloric restriction during pregnancy was associated with impaired inflammatory and DNA methylation markers in the offspring’s adipose tissue that could predispose the offspring to later metabolic abnormalities. Early metformin administration could modulate the size of adipocytes and the DNA methylation changes.</span></p>',
'date' => '2024-01-17',
'pmid' => 'https://www.mdpi.com/1422-0067/25/2/1128',
'doi' => 'https://doi.org/10.3390/ijms25021128',
'modified' => '2024-01-22 13:45:24',
'created' => '2024-01-22 13:45:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4890',
'name' => 'Diagnostic Algorithm to Subclassify Atypical Spitzoid Tumors in Low and High Risk According to Their Methylation Status',
'authors' => 'Gonzales-Munoz J.F. et al.',
'description' => '<p><span>Current diagnostic algorithms are insufficient for the optimal clinical and therapeutic management of cutaneous spitzoid tumors, particularly atypical spitzoid tumors (AST). Therefore, it is crucial to identify new markers that allow for reliable and reproducible diagnostic assessment and can also be used as a predictive tool to anticipate the individual malignant potential of each patient, leading to tailored individual therapy. Using Reduced Representation Bisulfite Sequencing (RRBS), we studied genome–wide methylation profiles of a series of Spitz nevi (SN), spitzoid melanoma (SM), and AST. We established a diagnostic algorithm based on the methylation status of seven cg sites located in </span><span class="html-italic">TETK4P2</span><span><span> </span>(Tektin 4 Pseudogene 2),<span> </span></span><span class="html-italic">MYO1D</span><span><span> </span>(Myosin ID), and<span> </span></span><span class="html-italic">PMF1-BGLAP</span><span><span> </span>(PMF1-BGLAP Readthrough), which allows the distinction between SN and SM but is also capable of subclassifying AST according to their similarity to the methylation levels of Spitz nevi or spitzoid melanoma. Thus, our epigenetic algorithm can predict the risk level of AST and predict its potential clinical outcomes.</span></p>',
'date' => '2023-12-25',
'pmid' => 'https://www.mdpi.com/1422-0067/25/1/318',
'doi' => 'https://doi.org/10.3390/ijms25010318',
'modified' => '2024-01-02 11:11:57',
'created' => '2024-01-02 11:11:57',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4808',
'name' => 'Knockout of TRDMT1 methyltransferase affects DNA methylome inglioblastoma cells.',
'authors' => 'Zabek T. et al.',
'description' => '<p><strong class="sub-title">Purpose:<span> </span></strong>We have previously shown that TRDMT1 methyltransferase is a regulator of chemotherapy-associated responses in glioblastoma cells. Despite the fact that glioblastoma, a common and malignant brain tumor, is widely characterized in terms of genetic and epigenetic markers, there are no data on TRDMT1-related changes in 5-methylcytosine pools in the genome. In the present study, the effect of TRDMT1 gene knockout (KO) on DNA methylome was analyzed.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>CRISPR-based approach was used to obtain TRDMT1 KO glioblastoma cells. Total 5-methylcytosine levels in DNA, DNMT1 pools and DNMT activity were studied using ELISA. Reduced representation bisulfite sequencing (RRBS) was considered to comprehensively evaluate DNA methylome in glioblastoma cells with TRDMT1 KO.</p>
<p><strong class="sub-title">Results:<span> </span></strong>TRDMT1 KO cells were characterized by decreased levels of total 5-methylcytosine in DNA and DNMT1, and DNMT activity. RRBS-based methylome analysis revealed statistically significant differences in methylation-relevant DMS-linked genes in control cells compared to TRDMT1 KO cells. TRDMT1 KO-associated changes in DNA methylome may affect the activity of several processes and pathways such as telomere maintenance, cell cycle and longevity regulating pathway, proteostasis, DNA and RNA biology.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>TRDMT1 may be suggested as a novel modulator of gene expression by changes in DNA methylome that may affect cancer cell fates during chemotherapy. We postulate that the levels and mutation status of TRDMT1 should be considered as a prognostic marker and carefully monitored during glioblastoma progression.</p>',
'date' => '2023-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37169948',
'doi' => '10.1007/s11060-023-04304-8',
'modified' => '2023-06-15 08:50:24',
'created' => '2023-06-13 21:11:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4786',
'name' => 'Sperm DNA methylation is predominantly stable in mice offspring bornafter transplantation of long-term cultured spermatogonial stem cells.',
'authors' => 'Serrano J. B.et al.',
'description' => '<p>BACKGROUND: Spermatogonial stem cell transplantation (SSCT) is proposed as a fertility therapy for childhood cancer survivors. SSCT starts with cryopreserving a testicular biopsy prior to gonadotoxic treatments such as cancer treatments. When the childhood cancer survivor reaches adulthood and desires biological children, the biopsy is thawed and SSCs are propagated in vitro and subsequently auto-transplanted back into their testis. However, culturing stress during long-term propagation can result in epigenetic changes in the SSCs, such as DNA methylation alterations, and might be inherited by future generations born after SSCT. Therefore, SSCT requires a detailed preclinical epigenetic assessment of the derived offspring before this novel cell therapy is clinically implemented. With this aim, the DNA methylation status of sperm from SSCT-derived offspring, with in vitro propagated SSCs, was investigated in a multi-generational mouse model using reduced-representation bisulfite sequencing. RESULTS: Although there were some methylation differences, they represent less than 0.5\% of the total CpGs and methylated regions, in all generations. Unsupervised clustering of all samples showed no distinct grouping based on their pattern of methylation differences. After selecting the few single genes that are significantly altered in multiple generations of SSCT offspring compared to control, we validated the results with quantitative Bisulfite Sanger sequencing and RT-qPCRin various organs. Differential methylation was confirmed only for Tal2, being hypomethylated in sperm of SSCT offspring and presenting higher gene expression in ovaries of SSCT F1 offspring compared to control F1. CONCLUSIONS: We found no major differences in DNA methylation between SSCT-derived offspring and control, both in F1 and F2 sperm. The reassuring outcomes from our study are a prerequisite for promising translation of SSCT to the human situation.</p>',
'date' => '2023-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37029425',
'doi' => '10.1186/s13148-023-01469-x',
'modified' => '2023-06-12 08:55:47',
'created' => '2023-05-05 12:34:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4760',
'name' => 'DNA methylation changes from primary cultures through senescence-bypassin Syrian hamster fetal cells initially exposed to benzo[a]pyrene.',
'authors' => 'Desaulniers D. et al.',
'description' => '<p>Current chemical testing strategies are limited in their ability to detect non-genotoxic carcinogens (NGTxC). Epigenetic anomalies develop during carcinogenesis regardless of whether the molecular initiating event is associated with genotoxic (GTxC) or NGTxC events; therefore, epigenetic markers may be harnessed to develop new approach methodologies that improve the detection of both types of carcinogens. This study used Syrian hamster fetal cells to establish the chronology of carcinogen-induced DNA methylation changes from primary cells until senescence-bypass as an essential carcinogenic step. Cells exposed to solvent control for 7 days were compared to naïve primary cultures, to cells exposed for 7 days to benzo[a]pyrene, and to cells at the subsequent transformation stages: normal colonies, morphologically transformed colonies, senescence, senescence-bypass, and sustained proliferation in vitro. DNA methylation changes identified by reduced representation bisulphite sequencing were minimal at day-7. Profound DNA methylation changes arose during cellular senescence and some of these early differentially methylated regions (DMRs) were preserved through the final sustained proliferation stage. A set of these DMRs (e.g., Pou4f1, Aifm3, B3galnt2, Bhlhe22, Gja8, Klf17, and L1l) were validated by pyrosequencing and their reproducibility was confirmed across multiple clones obtained from a different laboratory. These DNA methylation changes could serve as biomarkers to enhance objectivity and mechanistic understanding of cell transformation and could be used to predict senescence-bypass and chemical carcinogenicity.</p>',
'date' => '2023-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36754249',
'doi' => '10.1016/j.tox.2023.153451',
'modified' => '2023-04-17 09:08:16',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4616',
'name' => 'Myelodysplastic Syndrome associated TET2 mutations affect NK cellfunction and genome methylation.',
'authors' => 'Boy M. et al.',
'description' => '<p>Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders, representing high risk of progression to acute myeloid leukaemia, and frequently associated to somatic mutations, notably in the epigenetic regulator TET2. Natural Killer (NK) cells play a role in the anti-leukemic immune response via their cytolytic activity. Here we show that patients with MDS clones harbouring mutations in the TET2 gene are characterised by phenotypic defects in their circulating NK cells. Remarkably, NK cells and MDS clones from the same patient share the TET2 genotype, and the NK cells are characterised by increased methylation of genomic DNA and reduced expression of Killer Immunoglobulin-like receptors (KIR), perforin, and TNF-α. In vitro inhibition of TET2 in NK cells of healthy donors reduces their cytotoxicity, supporting its critical role in NK cell function. Conversely, NK cells from patients treated with azacytidine (#NCT02985190; https://clinicaltrials.gov/ ) show increased KIR and cytolytic protein expression, and IFN-γ production. Altogether, our findings show that, in addition to their oncogenic consequences in the myeloid cell subsets, TET2 mutations contribute to repressing NK-cell function in MDS patients.</p>',
'date' => '2023-02-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36737440',
'doi' => '10.1038/s41467-023-36193-w',
'modified' => '2023-04-04 08:43:27',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4588',
'name' => 'Epigenetics and stroke: role of DNA methylation and effect of aging onblood-brain barrier recovery.',
'authors' => 'Phillips C. et al.',
'description' => '<p>Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., , ), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.</p>',
'date' => '2023-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36711725',
'doi' => '10.21203/rs.3.rs-2444060/v1',
'modified' => '2023-04-11 10:01:44',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4761',
'name' => 'Development of DNA methylation-based epigenetic age predictors inloblolly pine (Pinus taeda).',
'authors' => 'Gardner S. T. et al.',
'description' => '<p>Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6\% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.</p>',
'date' => '2023-01-01',
'pmid' => 'https://doi.org/10.1101%2F2022.01.27.477887',
'doi' => '10.1111/1755-0998.13698',
'modified' => '2023-04-17 09:09:49',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4729',
'name' => 'Molecular toxicity study on glyphosate, Roundup MON 52276 and alow-dose pesticide mixture administered to adult Female rats for 90 days',
'authors' => 'Mesnage Robin and Antoniou Michael N.',
'description' => '<p>We describe a comprehensive repository describing a collection of data from a range of studies investigating the molecular mechanisms of toxicity of glyphosate, the glyphosate-based herbicide commercial formulation Roundup, and a mixture of glyphosate and 5 other most frequently used pesticides (azoxystrobin, boscalid, chlorpyrifos, imidacloprid and thiabendazole) present as residues in food products in Europe. The data were obtained by analysing tissues from rats exposed to the pesticides for 90 days via drinking water. The administration of the mixture of six pesticides was chosen to mimic a possible human exposure scenario. We compared conventional methods used in regulatory toxicity studies to evaluate the safety of pesticide exposure (gross pathology, serum biochemistry) to new molecular profiling methods encompassing the analysis of the caecum and blood metabolome, liver transcriptome, liver DNA methylation, liver small RNA profiles, and caecum metagenome of the exposed animals. Altogether, these investigations provided in-depth molecular profiling in laboratory animals exposed to pesticides revealing metabolic perturbations that would remain undetected by standard regulatory biochemical measures. Our results highlight how multi-omics phenotyping can be used to improve the predictability of health risk assessment from exposure to toxic chemicals to better protect public health.</p>',
'date' => '2022-12-01',
'pmid' => 'https://doi.org/10.1080%2F26895293.2022.2156626',
'doi' => '10.1080/26895293.2022.2156626',
'modified' => '2023-03-07 09:09:33',
'created' => '2023-02-28 12:19:11',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4652',
'name' => 'Differential methylation patterns in lean and obese non-alcoholicsteatohepatitis-associated hepatocellular carcinoma.',
'authors' => 'Hymel Emma et al.',
'description' => '<p>BACKGROUND: Nonalcoholic fatty liver disease affects about 24\% of the world's population and may progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). While more common in those that are obese, NASH-HCC can develop in lean individuals. The mechanisms by which HCC develops and the role of epigenetic changes in the context of obesity and normal weight are not well understood. METHODS: In this study, we used previously generated mouse models of lean and obese HCC using a choline deficient/high trans-fat/fructose/cholesterol diet and a choline supplemented/high trans-fat/fructose/cholesterol diet, respectively, to evaluate methylation differences in HCC progression in lean versus obese mice. Differentially methylated regions were determined using reduced representation bisulfite sequencing. RESULTS: A larger number of differentially methylated regions (DMRs) were seen in NASH-HCC progression in the obese mice compared to the non-obese mice. No overlap existed in the DMRs with the largest methylation differences between the two models. In lean NASH-HCC, methylation differences were seen in genes involved with cancer progression and prognosis (including HCC), such as CHCHD2, FSCN1, and ZDHHC12, and lipid metabolism, including PNPLA6 and LDLRAP1. In obese NASH- HCC, methylation differences were seen in genes known to be associated with HCC, including RNF217, GJA8, PTPRE, PSAPL1, and LRRC8D. Genes involved in Wnt-signaling pathways were enriched in hypomethylated DMRs in the obese NASH-HCC. CONCLUSIONS: These data suggest that differential methylation may play a role in hepatocarcinogenesis in lean versus obese NASH. Hypomethylation of Wnt signaling pathway-related genes in obese mice may drive progression of HCC, while progression of HCC in lean mice may be driven through other signaling pathways, including lipid metabolism.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36474183',
'doi' => '10.1186/s12885-022-10389-7',
'modified' => '2023-03-13 08:50:33',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4628',
'name' => 'Altered DNA methylation in estrogen-responsive repetitive sequences ofspermatozoa of infertile men with shortened anogenital distance.',
'authors' => 'Stenz L. et al.',
'description' => '<p>BACKGROUND: It has been suggested that antenatal exposure to environmental endocrine disruptors is responsible for adverse trends in male reproductive health, including male infertility, impaired semen quality, cryptorchidism and testicular cancer, a condition known as testicular dysgenesis syndrome. Anogenital distance (AGD) is an anthropomorphic measure of antenatal exposure to endocrine disruptors, with higher exposure levels leading to shortened AGD. We hypothesized that exposure to endocrine disruptors could lead to changes in DNA methylation during early embryonic development, which could then persist in the sperm of infertile men with shortened AGD. RESULTS: Using fluorescence activated cell sorting based on staining with either YO-PRO-1 (YOPRO) or chromomycin-3 (CMA3), we isolated four sperm fractions from eleven infertile men with short AGD and ten healthy semen donors. We examined DNA methylation in these sorted spermatozoa using reduced representation bisulfite sequencing. We found that fractions of spermatozoa from infertile men stained with CMA3 or YOPRO were more likely to contain transposable elements harboring an estrogen receptor response element (ERE). Abnormal sperm (as judged by high CMA3 or YOPRO staining) from infertile men shows substantial hypomethylation in estrogenic Alu sequences. Conversely, normal sperm fractions (as judged by low CMA3 or YO-PRO-1 staining) of either healthy donors or infertile patients were more likely to contain hypermethylated Alu sequences with ERE. CONCLUSIONS: Shortened AGD, as related to previous exposure to endocrine disruptors, and male infertility are accompanied by increased presence of hormonal response elements in the differentially methylated regulatory sequences of the genome of sperm fractions characterized by chromatin decondensation and apoptosis.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36572941',
'doi' => '10.1186/s13148-022-01409-1',
'modified' => '2023-03-28 09:09:22',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4537',
'name' => 'Epigenetic Alterations of Repeated Relapses in Patient-matchedChildhood Ependymomas.',
'authors' => 'Zhao Sibo et al.',
'description' => '<p>Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.</p>',
'date' => '2022-11-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36335125',
'doi' => '10.1038/s41467-022-34514-z',
'modified' => '2022-11-25 08:55:12',
'created' => '2022-11-24 08:49:52',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4441',
'name' => 'Epigenetic Suppression of the IL-7 Pathway in ProgressiveGlioblastoma.',
'authors' => 'Tompa M. et al.',
'description' => '<p>BACKGROUND: Immune evasion in glioblastoma (GBM) shields cancer cells from cytotoxic immune response. METHODS: We investigated CpG methylation in promoters, genes, and pathways in 22 pairs of formalin-fixed paraffin-embedded sequential (FFPE) GBM using restricted resolution bisulfite sequencing (RRBS) and bioinformatic analyses. RESULTS: Gene ontology revealed hypermethylation in elements of the innate and adaptive immune system when recurrent GBM samples (GBM) were compared to control (CG) and primary GBM samples (GBM). Higher methylation levels of the IL-7 signaling pathway and response to IL-7 were found in GBM suggesting a progressive blockade of the IL-7 driven T cell response in sequential GBM. Analyses of the Cancer Genome Atlas array-based data confirmed hypermethylation of the IL-7 pathway in recurrent compared with primary GBM. We also quantified DNA CpG methylation in promoter and gene regions of the IL-7 ligand and IL-7 α-receptor subunit in individual samples of a large RRBS-based sequential cohort of GBM in a Viennese database and found significantly higher methylation levels in the IL-7 receptor α-subunit in GBM compared with GBM. CONCLUSIONS: This study revealed the progressive suppression of the IL-7 receptor-mediated pathway as a means of immune evasion by GBM and thereby highlighted it as a new treatment target.</p>',
'date' => '2022-09-01',
'pmid' => 'https://doi.org/10.3390%2Fbiomedicines10092174',
'doi' => '10.3390/biomedicines10092174',
'modified' => '2022-10-14 16:32:44',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4371',
'name' => 'DNA methylation may affect beef tenderness through signal transduction inBos indicus.',
'authors' => 'de Souza M. M. et al.',
'description' => '<p>BACKGROUND: Beef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus. RESULTS: Comparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity. CONCLUSIONS: We present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.</p>',
'date' => '2022-05-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1415533%2Fv1',
'doi' => '10.1186/s13072-022-00449-4',
'modified' => '2022-08-04 16:05:03',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4401',
'name' => 'Folic Acid Treatment Directly Influences the Genetic andEpigenetic Regulation along with the Associated CellularMaintenance Processes of HT-29 and SW480 Colorectal CancerCell Lines.',
'authors' => 'Zsigrai S. et al.',
'description' => '<p>Folic acid (FA) is a synthetic form of vitamin B9, generally used as a nutritional supplement and an adjunctive medication in cancer therapy. FA is involved in genetic and epigenetic regulation; therefore, it has a dual modulatory role in established neoplasms. We aimed to investigate the effect of short-term (72 h) FA supplementation on colorectal cancer; hence, HT-29 and SW480 cells were exposed to different FA concentrations (0, 100, 10,000 ng/mL). HT-29 cell proliferation and viability levels elevated after 100 ng/mL but decreased for 10,000 ng/mL FA. Additionally, a significant ( ≤ 0.05) improvement of genomic stability was detected in HT-29 cells with micronucleus scoring and comet assay. Conversely, the FA treatment did not alter these parameters in SW480 samples. RRBS results highlighted that DNA methylation changes were bidirectional in both cells, mainly affecting carcinogenesis-related pathways. Based on the microarray analysis, promoter methylation status was in accordance with FA-induced expression alterations of 27 genes. Our study demonstrates that the FA effect was highly dependent on the cell type, which can be attributed to the distinct molecular background and the different expression of proliferation- and DNA-repair-associated genes (, , , ). Moreover, new aspects of FA-regulated DNA methylation and consecutive gene expression were revealed.</p>',
'date' => '2022-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35406592',
'doi' => '10.3390/cancers14071820',
'modified' => '2022-08-11 14:41:59',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4405',
'name' => 'Complex regulatory role of DNA methylation in caste- and age-specificexpression of a termite',
'authors' => 'Harrison Mark C. et al. ',
'description' => '<p>The reproductive castes of eusocial insects are often characterised by extreme lifespans and reproductive output, indicating an absence of the fecundity/longevity trade-off. The role of DNA methylation in the regulation of caste- and age-specific gene expression in eusocial insects is controversial. While some studies find a clear link to caste formation in honeybees and ants, others find no correlation when replication is increased across independent colonies. Although recent studies have identified transcription patterns involved in the maintenance of high reproduction throughout the long lives of queens, the role of DNA methylation in the regulation of these genes is unknown. We carried out a comparative analysis of DNA methylation in the regulation of caste-specific transcription and its importance for the regulation of fertility and longevity in queens of the higher termite, Macrotermes natalensis. We found evidence for significant, well-regulated changes in DNA methylation in mature compared to young queens, especially in several genes related to ageing and fecundity in mature queens. We also found a strong link between methylation and caste-specific alternative splicing. This study reveals a complex regulatory role of fat body DNA methylation both in the division of labour in termites, and during the reproductive maturation of queens.</p>',
'date' => '2022-03-01',
'pmid' => 'https://doi.org/10.1101%2F2022.03.08.483442',
'doi' => '10.1101/2022.03.08.483442',
'modified' => '2022-08-11 15:01:34',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => array(
'id' => '4229',
'name' => 'When left does not seem right: epigenetic and bioelectric differencesbetween left- and right-sided breast cancer.',
'authors' => 'Sofía, Masuelli and Sebastián, Real and Emanuel, Campoy andBranham, María Teresita and Marzese, Diego Matías andMatthew, Salomon and De Blas, Gerardo and Rodolfo, Arias andMichael, Levin and María, Roqué',
'description' => '<p>BACKGROUND: During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms. METHODS: We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts. RESULTS: We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count. CONCLUSIONS: Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1020823%2Fv1',
'doi' => '10.1186/s10020-022-00440-5',
'modified' => '2022-05-19 16:03:56',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '4248',
'name' => 'Comparative Toxicogenomics of Glyphosate and Roundup Herbicidesby Mammalian Stem Cell-Based Genotoxicity Assays andMolecular Profiling in Sprague-Dawley Rats.',
'authors' => 'Mesnage R. et al.',
'description' => '<p>Whether glyphosate-based herbicides (GBHs) are more potent than glyphosate alone at activating cellular mechanisms, which drive carcinogenesis remain controversial. As GBHs are more cytotoxic than glyphosate, we reasoned they may also be more capable of activating carcinogenic pathways. We tested this hypothesis by comparing the effects of glyphosate with Roundup GBHs both in vitro and in vivo. First, glyphosate was compared with representative GBHs, namely MON 52276 (European Union), MON 76473 (United Kingdom), and MON 76207 (United States) using the mammalian stem cell-based ToxTracker system. Here, MON 52276 and MON 76473, but not glyphosate and MON 76207, activated oxidative stress and unfolded protein responses. Second, molecular profiling of liver was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (at 0.5, 50, and 175 mg/kg bw/day glyphosate) for 90 days. MON 52276 but not glyphosate increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and circadian rhythm regulation. Genes most affected in liver were similarly altered in kidneys. Small RNA profiling in liver showed decreased amounts of miR-22 and miR-17 from MON 52276 ingestion. Glyphosate decreased miR-30, whereas miR-10 levels were increased. DNA methylation profiling of liver revealed 5727 and 4496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed animals, respectively. Apurinic/apyrimidinic DNA damage formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup formulations cause more biological changes linked with carcinogenesis than glyphosate.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.1093%2Ftoxsci%2Fkfab143',
'doi' => '10.1093/toxsci/kfab143',
'modified' => '2022-05-20 09:32:37',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 24 => array(
'id' => '4368',
'name' => 'Glucose-6-phosphate dehydrogenase and MEG3 controls hypoxia-inducedexpression of serum response factor (SRF) and SRF-dependent genes inpulmonary smooth muscle cell.',
'authors' => 'Kitagawa A. et al.',
'description' => '<p>Although hypoxia induces aberrant gene expression and dedifferentiation of smooth muscle cells (SMCs), mechanisms that alter dedifferentiation gene expression by hypoxia remain unclear. Therefore, we aimed to gain insight into the hypoxia-controlled gene expression in SMCs. We conducted studies using SMCs cultured in 3\% oxygen (hypoxia) and the lungs of mice exposed to 10\% oxygen (hypoxia). Our results suggest hypoxia upregulated expression of transcription factor CP2-like protein1, krüppel-like factor 4, and E2f transcription factor 1 enriched genes including basonuclin 2 (Bcn2), serum response factor (Srf), polycomb 3 (Cbx8), homeobox D9 (Hoxd9), lysine demethylase 1A (Kdm1a), etc. Additionally, we found that silencing glucose-6-phosphate dehydrogenase (G6PD) expression and inhibiting G6PD activity downregulated Srf transcript and hypomethylation of SMC genes (Myocd, Myh11, and Cnn1) and concomitantly increased their expression in the lungs of hypoxic mice. Furthermore, G6PD inhibition hypomethylated MEG3, a long non-coding RNA, gene and upregulated MEG3 expression in the lungs of hypoxic mice and in hypoxic SMCs. Silencing MEG3 expression in SMC mitigated the hypoxia-induced transcription of SRF. These findings collectively demonstrate that MEG3 and G6PD codependently regulate Srf expression in hypoxic SMCs. Moreover, G6PD inhibition upregulated SRF-MYOCD-driven gene expression, determinant of a differentiated SMC phenotype.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35491127',
'doi' => '10.1540/jsmr.58.34',
'modified' => '2022-08-04 16:21:02',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 25 => array(
'id' => '4114',
'name' => 'The ETS transcription factor ERF controls the exit from the naïve pluripotent state in a MAPK-dependent manner',
'authors' => 'Maria Vega-Sendino et. al.',
'description' => '<p><span>The naïve epiblast transitions to a pluripotent primed state during embryo implantation. Despite the relevance of the FGF pathway during this period, little is known about the downstream effectors regulating this signaling. Here, we examined the molecular mechanisms coordinating the naïve to primed transition by using inducible ESC to genetically eliminate all RAS proteins. We show that differentiated RAS</span><sup>KO</sup><span><span> </span>ESC remain trapped in an intermediate state of pluripotency with naïve-associated features. Elimination of the transcription factor ERF overcomes the developmental blockage of RAS-deficient cells by naïve enhancer decommissioning. Mechanistically, ERF regulates NANOG expression and ensures naïve pluripotency by strengthening naïve transcription factor binding at ESC enhancers. Moreover, ERF negatively regulates the expression of the methyltransferase DNMT3B, which participates in the extinction of the naïve transcriptional program. Collectively, we demonstrated an essential role for ERF controlling the exit from naïve pluripotency in a MAPK-dependent manner during the progression to primed pluripotency.</span></p>',
'date' => '2021-10-01',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34597136/',
'doi' => '10.1126/sciadv.abg8306',
'modified' => '2022-05-19 16:05:11',
'created' => '2021-10-06 08:45:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 26 => array(
'id' => '4230',
'name' => 'Adaptive Convergence of Methylomes Reveals Epigenetic Driversand Boosters of Repeated Relapses in Patient-matched ChildhoodEpendymomas and Identifies Targets for Anti-RecurrenceTherapies',
'authors' => 'Zhao S. et al.',
'description' => '<p>Ependymoma (EPN) is the third most common brain tumor in children and frequently recurs. Here, we report an integrated longitudinal analysis of epigenetic, genetic and tumorigenic changes in 30 patient-matched repeated relapses obtained from 10 pediatric patients to understand the mechanism of recurrences. Genome-wide DNA methylation analysis revealed stable molecular subtypes and convergent epigenetic reprogramming during serial relapses of the 5 RELA and 5 PFA EPNs that paralleled with elevated patient-derived orthotopic xenograft (PDOX) (13/27) formation in the late relapses. Differentially methylated CpGs (DMCs) preexisted in the primary tumors and persisted in the relapses (driver DMCs) were detected, ranging from 51 hypo-methylated in RELA to 148 hyper-methylated DMCs in PFA tumors; while newly acquired DMCs sustained in all the relapses but was absent in the primary tumors (booster DMCs) ranged from 38- 323 hyper-methylated DMCs in RELA and PFA EPNs, respectively. Integrated analysis of these DMC associated DNA methylation regions (DMRs) and RNAseq in both patient and PDOX tumors identified a small fraction of the differentially expressed genes (4.6±4.4\% in RELA and 4.5±1.1\% in PFA) as regulated by driver DMRs (e.g., up-regulated CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and booster DMRs (including the sole upregulated PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). Most these genes were novel to EPN relapses. Seven DMCs in RELA and 22 in PFA tumors were also identified as potential relapse predictors. Finally, integrating DNA methylation with histone modification identified LSD1 as a relapse driver gene. Combined treatment of a novel inhibitor SYC-836 with radiation significantly prolonged survival times in two PDOX models of recurrent PFA. This high-resolution epigenetic and genetic roadmap of EPN relapse and our 13 new PDOX models should significantly facilitate biological and preclinical studies of pediatric EPN recurrences.</p>',
'date' => '2021-10-01',
'pmid' => 'https://www.researchsquare.com/article/rs-908607/v1',
'doi' => '10.21203/rs.3.rs-908607/v1',
'modified' => '2022-05-19 16:48:13',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 27 => array(
'id' => '4115',
'name' => 'Genome-Wide Epigenomic Analyses in Patients With Nociceptive and Neuropathic Chronic Pain Subtypes Reveals Alterations in Methylation of Genes Involved in the Neuro-Musculoskeletal System',
'authors' => 'Stenz et al',
'description' => '<p><span>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs were seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</span></p>',
'date' => '2021-09-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34547430/',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-19 16:05:36',
'created' => '2021-10-22 19:01:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 28 => array(
'id' => '4299',
'name' => 'Genome-wide epigenomic analyses in patients with nociceptive andneuropathic chronic pain subtypes reveals alterations in methylation ofgenes involved in the neuro-musculoskeletal system.',
'authors' => 'Stenz Ludwig et al.',
'description' => '<p>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</p>',
'date' => '2021-09-01',
'pmid' => 'https://doi.org/10.1016%2Fj.jpain.2021.09.001',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-30 09:41:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 29 => array(
'id' => '4383',
'name' => 'Biobehavioral organization shapes the immune epigenome in infant rhesusMacaques (Macaca mulatta).',
'authors' => 'Baxter A. et al.',
'description' => '<p>How individuals respond to and cope with stress is linked with their health and well-being. It is presumed that early stress responsiveness helps shape the health of the developing organism, but the relationship between stress responsiveness and early immune function during development is not well-known. We hypothesized that stress responsiveness may shape epigenetic regulation of immune genes in infancy. We investigated whether aspects of behavioral responsiveness and hypothalamic-pituitary adrenal stress-response were associated with epigenome-wide immune cell DNA methylation patterns in 154 infant rhesus monkeys (3-4 months old). Infants' behavioral and physiological responses were collected during a standardized biobehavioral assessment, which included temporary relocation and separation from their mother and social group. Genome-wide DNA methylation was quantified using restricted representation bisulfite sequencing (RRBS) from blood DNA collected 2-hours post-separation. Epigenome-wide analyses were conducted using simple regression, multiple regression controlling for immune cell counts, and permutation regression, all corrected for false discovery rate. Across the variables analyzed, there were 20,368 unique sites (in 9,040 genes) at which methylation was significantly associated with at least one behavioral responsiveness or cortisol measure across the three analyses. There were significant associations in 442 genes in the Immune System Process ontology category, and 94 genes in the Inflammation mediated by chemokine and cytokine signaling gene pathway. Out of 35 candidate genes that were selected for further investigation, there were 13 genes with at least one site at which methylation was significantly associated with behavioral responsiveness or cortisol, including two intron sites in the glucocorticoid receptor gene, at which methylation was negatively correlated with emotional behavior the day following the social separation (Day 2 Emotionality; β = -0.39, q < 0.001) and cortisol response following a relocation stressor (Sample 1; β = -0.33, q < 0.001). We conclude that biobehavioral stress responsiveness may correlate with the developing epigenome, and that DNA methylation of immune cells may be a mechanism by which patterns of stress response affect health and immune functioning.</p>',
'date' => '2021-08-01',
'pmid' => 'https://doi.org/10.1016%2Fj.bbi.2021.06.006',
'doi' => '10.1016/j.bbi.2021.06.006',
'modified' => '2022-08-04 15:54:12',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 30 => array(
'id' => '4112',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus',
'authors' => 'Sara Zocher, Rupert W. Overall, Mathias Lesche, Andreas Dahl & Gerd Kempermann',
'description' => '<p><span>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</span></p>',
'date' => '2021-06-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34162876/',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-05-19 16:06:20',
'created' => '2021-09-06 08:02:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 31 => array(
'id' => '4111',
'name' => 'Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes',
'authors' => 'Luca Pagliaroli, Abel Fothi, Ester Nespoli, Istvan Liko, Borbala Veto, Piroska Devay, Flora Szeri, Bastian Hengerer, Csaba Barta, Tamas Aranyi',
'description' => '<p>Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.</p>',
'date' => '2021-06-09',
'pmid' => 'https://www.mdpi.com/2073-4409/10/6/1442',
'doi' => '10.3390/cells10061442',
'modified' => '2022-05-19 16:06:47',
'created' => '2021-08-27 11:27:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 32 => array(
'id' => '4324',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape inthe aged mouse hippocampus',
'authors' => 'Zocher S. et al. ',
'description' => '<p>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</p>',
'date' => '2021-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34162876',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-08-03 15:56:05',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 33 => array(
'id' => '4419',
'name' => 'Pathophysiological adaptations of resistance arteries in rat offspringexposed in utero to maternal obesity is associated with sex-specificepigenetic alterations.',
'authors' => 'Payen Cyrielle et al.',
'description' => '<p>BACKGROUND/OBJECTIVES: Maternal obesity impacts vascular functions linked to metabolic disorders in offspring, leading to cardiovascular diseases during adulthood. Even if the relation between prenatal conditioning of cardiovascular diseases by maternal obesity and vascular function begins to be documented, little is known about resistance arteries. They are of particular interest because of their specific role in the regulation of local blood flow. Then our study aims to determine if maternal obesity can directly program fetal vascular dysfunction of resistance arteries, independently of metabolic disorders. METHODS: With a model of rats exposed in utero to mild maternal diet-induced obesity (OMO), we investigated third-order mesenteric arteries of 4-month old rats in absence of metabolic disorders. The methylation profile of these vessels was determined by reduced representation bisulfite sequencing (RRBS). Vascular structure and reactivity were investigated using histomorphometry analysis and wire-myography. The metabolic function was evaluated by insulin and glucose tolerance tests, plasma lipid profile, and adipose tissue analysis. RESULTS: At 4 months of age, small mesenteric arteries of OMO presented specific epigenetic modulations of matrix metalloproteinases (MMPs), collagens, and potassium channels genes in association with an outward remodeling and perturbations in the endothelium-dependent vasodilation pathways (greater contribution of EDHFs pathway in OMO males compared to control rats, and greater implication of PGI in OMO females compared to control rats). These vascular modifications were detected in absence of metabolic disorders. CONCLUSIONS: Our study reports a specific methylation profile of resistance arteries associated with vascular remodeling and vasodilation balance perturbations in offspring exposed in utero to maternal obesity, in absence of metabolic dysfunctions.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33637953',
'doi' => '10.1038/s41366-021-00777-7',
'modified' => '2022-09-28 08:51:40',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 34 => array(
'id' => '4175',
'name' => 'Multi-omics phenotyping of the gut-liver axis reveals metabolicperturbations from a low-dose pesticide mixture in rats.',
'authors' => 'Mesnage, Robin et al.',
'description' => '<p>Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33854195',
'doi' => '10.1038/s42003-021-01990-w',
'modified' => '2021-12-21 16:12:25',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 35 => array(
'id' => '4356',
'name' => 'Muscle allele-specific expression QTLs may affect meat quality traitsin Bos indicus.',
'authors' => 'Bruscadin J.J. et al.',
'description' => '<p>Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33795794',
'doi' => '10.1038/s41598-021-86782-2',
'modified' => '2022-08-03 16:44:51',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 36 => array(
'id' => '4173',
'name' => 'The insecticide permethrin induces transgenerational behavioral changeslinked to transcriptomic and epigenetic alterations in zebrafish (Daniorerio).',
'authors' => 'Blanc, Mélanie et al.',
'description' => '<p>The pyrethroid insecticide permethrin is widely used for agricultural and domestic purposes. Previous data indicated that it acts as a developmental neurotoxicant and can induce transgenerational effects in non-target organisms. However, associated underlying mechanisms remain unclear. The aim of this study was to investigate permethrin-related transgenerational effects in the zebrafish model, and to identify possible molecular mechanisms underlying inheritance. Zebrafish (F0) were exposed to permethrin during early-life (2 h post-fertilization up to 28 days). The F1 and F2 offspring generations were obtained by pairing exposed F0 males and females, and were bred unexposed. Locomotor and anxiety behavior were investigated, together with transcriptomic and epigenomic (DNA methylation) changes in brains. Permethrin exposed F0 fish were hypoactive at adulthood, while males from the F1 and F2 generations showed a specific decrease in anxiety-like behavior. In F0, transcriptomic data showed enrichment in pathways related to glutamatergic synapse activity, which may partly underlie the behavioral effects. In F1 and F2 males, dysregulation of similar pathways was observed, including a subset of differentially methylated regions that were inherited from the F0 to the F2 generation and indicated stable dysregulation of glutamatergic signaling. Altogether, the present results provide novel evidence on the transgenerational neurotoxic effects of permethrin, as well as mechanistic insight: a transient exposure induces persistent transcriptional and DNA methylation changes that may translate into transgenerational alteration of glutamatergic signaling and, thus, into behavioral alterations.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33752003',
'doi' => '10.1016/j.scitotenv.2021.146404',
'modified' => '2021-12-21 16:02:21',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 37 => array(
'id' => '4142',
'name' => 'The aging DNA methylome reveals environment-by-aging interactions in amodel teleost',
'authors' => 'Bertucci, E. M. et al.',
'description' => '<p>The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4\% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.</p>',
'date' => '2021-03-01',
'pmid' => 'https://doi.org/10.1101%2F2021.03.01.433371',
'doi' => '10.1101/2021.03.01.433371',
'modified' => '2022-05-19 16:07:18',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 38 => array(
'id' => '4155',
'name' => 'Perturbed DNA methylation by sustained overexpression of Gadd45b induces chromatin disorganization, DNA strand breaks and dopaminergic neurondeath in mice',
'authors' => 'Ravel-Godreuil, C. et al.',
'description' => '<p>Heterochromatin disorganization is a key hallmark of aging and DNA methylation state is currently the main molecular predictor of chronological age. The most frequent neurodegenerative diseases like Parkinson disease and Alzheimer’s disease are age-related but how the aging process and chromatin alterations are linked to neurodegeneration is unknown. Here, we investigated the consequences of viral overexpression of Gadd45b, a multifactorial protein involved in active DNA demethylation, in the midbrain of wild-type mice. Gadd45b overexpression induces global and stable changes in DNA methylation, particularly on gene bodies of genes related to neuronal functions. DNA methylation changes were accompanied by perturbed H3K9me3-marked heterochromatin and increased DNA damage. Prolonged Gadd45b expression resulted in dopaminergic neuron degeneration accompanied by altered expression of candidate genes related to heterochromatin maintenance, DNA methylation or Parkinson disease. Gadd45b overexpression rendered midbrain dopaminergic neurons more vulnerable to acute oxidative stress. Heterochromatin disorganization and DNA demethylation resulted in derepression of mostly young LINE-1 transposable elements, a potential source of DNA damage, prior to Gadd45b-induced neurodegeneration. Our data implicate that alterations in DNA methylation and heterochromatin organization, LINE-1 derepression and DNA damage can represent important contributors in the pathogenic mechanisms of dopaminergic neuron degeneration with potential implications for Parkinson disease.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2020.06.23.158014',
'doi' => '10.1101/2020.06.23.158014',
'modified' => '2022-05-19 16:07:48',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 39 => array(
'id' => '4189',
'name' => 'The Identification of a Novel Fucosidosis-Associated Mutation: A Case of a5-Year-Old Polish Girl with Two Additional Rare Chromosomal Aberrations andAffected DNA Methylation Patterns.',
'authors' => 'Domin A. et al. ',
'description' => '<p>Fucosidosis is a rare neurodegenerative autosomal recessive disorder, which manifests as progressive neurological and psychomotor deterioration, growth retardation, skin and skeletal abnormalities, intellectual disability and coarsening of facial features. It is caused by biallelic mutations in encoding the α-L-fucosidase enzyme, which in turn is responsible for degradation of fucose-containing glycoproteins and glycolipids. mutations lead to severe reduction or even loss of α-L-fucosidase enzyme activity. This results in incomplete breakdown of fucose-containing compounds leading to their deposition in different tissues and, consequently, disease progression. To date, 36 pathogenic variants in associated with fucosidosis have been documented. Among these are three splice site variants. Here, we report a novel fucosidosis-related 9-base-pair deletion (NG_013346.1:g.10233_10241delACAGGTAAG) affecting the exon 3/intron 3 junction within a sequence. This novel pathogenic variant was identified in a five-year-old Polish girl with a well-defined pattern of fucosidosis symptoms. Since it is postulated that other genetic, nongenetic or environmental factors can also contribute to fucosidosis pathogenesis, we performed further analysis and found two rare de novo chromosomal aberrations in the girl's genome involving a 15q11.1-11.2 microdeletion and an Xq22.2 gain. These abnormalities were associated with genome-wide changes in DNA methylation status in the epigenome of blood cells.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33435586',
'doi' => '10.3390/genes12010074',
'modified' => '2022-05-19 16:08:10',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 40 => array(
'id' => '4357',
'name' => 'Developmental cannabidiol exposure increases anxiety and modifiesgenome-wide brain DNA methylation in adult female mice.',
'authors' => 'Wanner N. M. et al. ',
'description' => '<p>BACKGROUND: Use of cannabidiol (CBD), the primary non-psychoactive compound found in cannabis, has recently risen dramatically, while relatively little is known about the underlying molecular mechanisms of its effects. Previous work indicates that direct CBD exposure strongly impacts the brain, with anxiolytic, antidepressant, antipsychotic, and other effects being observed in animal and human studies. The epigenome, particularly DNA methylation, is responsive to environmental input and can direct persistent patterns of gene regulation impacting phenotype. Epigenetic perturbation is particularly impactful during embryogenesis, when exogenous exposures can disrupt critical resetting of epigenetic marks and impart phenotypic effects lasting into adulthood. The impact of prenatal CBD exposure has not been evaluated; however, studies using the psychomimetic cannabinoid Δ9-tetrahydrocannabinol (THC) have identified detrimental effects on psychological outcomes in developmentally exposed adult offspring. We hypothesized that developmental CBD exposure would have similar negative effects on behavior mediated in part by the epigenome. Nulliparous female wild-type Agouti viable yellow (A) mice were exposed to 20 mg/kg CBD or vehicle daily from two weeks prior to mating through gestation and lactation. Coat color shifts, a readout of DNA methylation at the Agouti locus in this strain, were measured in F1 A/a offspring. Young adult F1 a/a offspring were then subjected to tests of working spatial memory and anxiety/compulsive behavior. Reduced-representation bisulfite sequencing was performed on both F0 and F1 cerebral cortex and F1 hippocampus to identify genome-wide changes in DNA methylation for direct and developmental exposure, respectively. RESULTS: F1 offspring exposed to CBD during development exhibited increased anxiety and improved memory behavior in a sex-specific manner. Further, while no significant coat color shift was observed in A/a offspring, thousands of differentially methylated loci (DMLs) were identified in both brain regions with functional enrichment for neurogenesis, substance use phenotypes, and other psychologically relevant terms. CONCLUSIONS: These findings demonstrate for the first time that despite positive effects of direct exposure, developmental CBD is associated with mixed behavioral outcomes and perturbation of the brain epigenome.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33407853',
'doi' => '10.1186/s13148-020-00993-4',
'modified' => '2022-08-03 17:04:44',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 41 => array(
'id' => '4208',
'name' => 'Hepatic transcriptome and DNA methylation patterns following perinataland chronic BPS exposure in male mice.',
'authors' => 'Brulport A. et al. ',
'description' => '<p>BACKGROUND: Bisphenol S (BPS) is a common bisphenol A (BPA) substitute, since BPA is virtually banned worldwide. However, BPS and BPA have both endocrine disrupting properties. Their effects appear mostly in adulthood following perinatal exposures. The objective of the present study was to investigate the impact of perinatal and chronic exposure to BPS at the low dose of 1.5 μg/kg body weight/day on the transcriptome and methylome of the liver in 23 weeks-old C57BL6/J male mice. RESULTS: This multi-omic study highlights a major impact of BPS on gene expression (374 significant deregulated genes) and Gene Set Enrichment Analysis show an enrichment focused on several biological pathways related to metabolic liver regulation. BPS exposure also induces a hypomethylation in 58.5\% of the differentially methylated regions (DMR). Systematic connections were not found between gene expression and methylation profile excepted for 18 genes, including 4 genes involved in lipid metabolism pathways (Fasn, Hmgcr, Elovl6, Lpin1), which were downregulated and featured differentially methylated CpGs in their exons or introns. CONCLUSIONS: This descriptive study shows an impact of BPS on biological pathways mainly related to an integrative disruption of metabolism (energy metabolism, detoxification, protein and steroid metabolism) and, like most high-throughput studies, contributes to the identification of potential exposure biomarkers.</p>',
'date' => '2020-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33297965',
'doi' => '10.1186/s12864-020-07294-3',
'modified' => '2022-01-13 14:57:00',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 42 => array(
'id' => '4033',
'name' => 'Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans',
'authors' => 'Neeraj K. Sharma, Mary E. Comeau, Dennis Montoya, Matteo Pellegrini, Timothy D. Howard, Carl D. Langefeld, Swapan K. Das',
'description' => '<p><span>Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in Type 2 Diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans from AAGMEx cohort, we performed<span> </span></span><em>cis</em><span>-expression quantitative trait methylation (</span><em>cis</em><span>-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated CpG-regions for 82 transcripts (FDR-P<0.05). The strongest eQTM locus was observed for the proopiomelanocortin (</span><em>POMC</em><span>; ρ= -0.632, P= 4.70X10</span><sup>-27</sup><span>) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 CpG regions associated (FDR-P <0.05) with Matsuda index, S</span><sub>I</sub><span><span> </span>and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, S</span><sub>I</sub><span><span> </span>and/or BMI in EWAS. These associated genes include<span> </span></span><em>FERMT3</em><span>,<span> </span></span><em>ITGAM</em><span>,<span> </span></span><em>ITGAX</em><span>, and<span> </span></span><em>POMC</em><span>. In summary, applying an integrative multi-omics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.</span></p>',
'date' => '2020-09-20',
'pmid' => 'https://diabetes.diabetesjournals.org/content/early/2020/09/03/db20-0117',
'doi' => '10.2337/db20-0117',
'modified' => '2022-05-19 16:08:46',
'created' => '2020-10-22 10:55:58',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 43 => array(
'id' => '4020',
'name' => 'DNA CpG methylation in sequential glioblastoma specimens.',
'authors' => 'Kraboth, Z and Galik, B and Tompa, M and Kajtar, B and Urban, P andGyenesei, A and Miseta, A and Kalman, B',
'description' => '<p>PURPOSE: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples. METHODS: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters. RESULTS: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport. CONCLUSION: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.</p>',
'date' => '2020-08-10',
'pmid' => 'http://www.pubmed.gov/32779022',
'doi' => '10.1007/s00432-020-03349-w',
'modified' => '2022-05-19 16:09:06',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 44 => array(
'id' => '3989',
'name' => 'Early Life Exposure to Environmentally Relevant Levels of Endocrine Disruptors Drive Multigenerational and Transgenerational Epigenetic Changes in a Fish Model',
'authors' => 'Major Kaley M., DeCourten Bethany M., Li Jie, Britton Monica, Settles Matthew L., Mehinto Alvine C., Connon Richard E., Brander Susanne M.',
'description' => '<p>The inland silverside, Menidia beryllina, is a euryhaline fish and a model organism in ecotoxicology. We previously showed that exposure to picomolar (ng/L) levels of endocrine disrupting chemicals (EDCs) can cause a variety of effects in M. beryllina, from changes in gene expression to phenotypic alterations. Here we explore the potential for early life exposure to EDCs to modify the epigenome in silversides, with a focus on multi- and transgenerational effects. EDCs included contaminants of emerging concern (the pyrethroid insecticide bifenthrin and the synthetic progestin levonorgestrel), as well as a commonly detected synthetic estrogen (ethinylestradiol), and a synthetic androgen (trenbolone) at exposure levels ranging from 3 to 10 ng/L. In a multigenerational experiment, we exposed parental silversides to EDCs from fertilization until 21 days post hatch (dph). Then we assessed DNA methylation patterns for three generations (F0, F1, and F2) in whole body larval fish using reduced representation bisulfite sequencing (RRBS). We found significant (α = 0.05) differences in promoter and/or gene body methylation in treatment fish relative to controls for all EDCs and all generations indicating that both multigenerational (F1) and transgenerational (F2) effects that were caused by strict inheritance of DNA methylation alterations and the dysregulation of epigenetic control mechanisms. Using gene ontology and pathway analyses, we found enrichment in biological processes and pathways representative of growth and development, immune function, reproduction, pigmentation, epigenetic regulation, stress response and repair (including pathways important in carcinogenesis). Further, we found that a subset of potentially EDC responsive genes (EDCRGs) were differentially methylated across all treatments and generations and included hormone receptors, genes involved in steroidogenesis, prostaglandin synthesis, sexual development, DNA methylation, protein metabolism and synthesis, cell signaling, and neurodevelopment. The analysis of EDCRGs provided additional evidence that differential methylation is inherited by the offspring of EDC-treated animals, sometimes in the F2 generation that was never exposed. These findings show that low, environmentally relevant levels of EDCs can cause altered methylation in genes that are functionally relevant to impaired phenotypes documented in EDC-exposed animals and that EDC exposure has the potential to affect epigenetic regulation in future generations of fish that have never been exposed.</p>',
'date' => '2020-06-24',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fmars.2020.00471/full',
'doi' => '10.3389/fmars.2020.00471',
'modified' => '2022-05-19 16:09:23',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 45 => array(
'id' => '3983',
'name' => 'Chronic cannabidiol alters genome-wide DNA methylation in adult mouse hippocampus: epigenetic implications for psychiatric disease.',
'authors' => 'Wanner NM, Colwell M, Drown C, Faulk C',
'description' => '<p>Cannabidiol (CBD) is the primary non-psychoactive compound found in cannabis (Cannabis sativa) and an increasingly popular dietary supplement as a result of widespread availability of CBD-containing products. CBD is FDA-approved for the treatment of epilepsy and exhibits anxiolytic, antipsychotic, prosocial, and other behavioral effects in animal and human studies, however, the underlying mechanisms governing these phenotypes are still being elucidated. The epigenome, particularly DNA methylation, is responsive to environmental input and can govern persistent patterns of gene regulation affecting phenotype across the life course. In order to understand the epigenomic activity of chronic cannabidiol exposure in the adult brain, 12-week-old male C57BL/6 mice were exposed to either 20 mg/kg CBD or vehicle daily by oral administration for fourteen days. Hippocampal tissue was collected and reduced-representation bisulfite sequencing (RRBS) was performed. Analyses revealed 3,323 differentially methylated loci (DMLs) in CBD-exposed animals with a small skew toward global hypomethylation. Genes for cell adhesion and migration, dendritic spine development, and excitatory postsynaptic potential were found to be enriched in a gene ontology term analysis of DML-containing genes, and disease ontology enrichment revealed an overrepresentation of DMLs in gene sets associated with autism spectrum disorder, schizophrenia, and other phenotypes. These results suggest that the epigenome may be a key substrate for CBD's behavioral effects and provides a wealth of gene regulatory information for further study. This article is protected by copyright. All rights reserved.</p>',
'date' => '2020-06-24',
'pmid' => 'http://www.pubmed.gov/32579259',
'doi' => '10.1002/em.22396',
'modified' => '2022-05-19 16:09:42',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 46 => array(
'id' => '3885',
'name' => 'Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease',
'authors' => 'Guan Y, Liu H, Ma Z, Li SY, Park J, Sheng X, Susztak K',
'description' => '<p>BACKGROUND: Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing methylation. METHODS: We generated mice with genetic deletion of and in nephron progenitor cells () and kidney tubule cells (). We characterized mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. RESULTS: mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that and play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of and resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by and were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with and deletion. CONCLUSIONS: Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.</p>',
'date' => '2020-03-03',
'pmid' => 'http://www.pubmed.gov/32127410',
'doi' => '10.1681/ASN.2019080797',
'modified' => '2022-05-19 16:10:07',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 47 => array(
'id' => '3877',
'name' => 'Rheumatoid Arthritis Patients, Both Newly Diagnosed and Methotrexate Treated, Show More DNA Methylation Differences in CD4+ Memory Than in CD4+ Naïve T Cells',
'authors' => 'Guderud Kari, Sunde Line H., Flåm Siri T., Mæhlen Marthe T., Mjaavatten Maria D., Lillegraven Siri, Aga Anna-Birgitte, Evenrød Ida M., Norli Ellen S., Andreassen Bettina K., Franzenburg Sören, Franke Andre, Haavardsholm Espen A., Rayner Simon, Gervin Kris',
'description' => '<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes pain and swelling of multiple joints in the body. The underlying disease mechanisms are believed to involve a complex interplay between common genetic and environmental factors. The heritability of RA has been estimated to be ~50% for anti-citrullinated protein antibody (ACPA) positive RA and ~20% for ACPA negative RA in a large familial aggregation study (1). Genome-wide association studies (GWAS) have identified more than 100 RA risk loci, mostly conferring risk to ACPA positive RA, marked by lead single nucleotide polymorphisms (SNPs) across various populations (2). The risk SNPs have small effect sizes, and only explain parts of heritability in RA. Environmental and epigenetic factors are also thought to be involved in the RA disease pathogenesis (3) of which smoking is the only established environmental risk factor (4, 5). Epigenetic modifications are important for regulation and maintenance of cell type specific biological functions, and alterations in the epigenome have been found to be associated with RA (6). The most studied epigenetic modification in humans is DNA methylation of cytosine followed by a guanine at so-called CpG sites (CpGs). CpGs are often clustered in regions called CpG islands (CGIs), which frequently overlap gene promoters (7). DNA methylation in promotor regions is usually negatively correlated with transcription of the nearby gene (8). A wide range of immune cells has been implicated in the pathogenesis of RA. One of the most widely used drugs for treatment of RA, methotrexate (MTX) (9), acts as an immunosuppressant in proliferating cells (10), and of these, the most relevant cell population for RA is CD4+ T cells (11). Interestingly, the RA risk loci are enriched in accessible chromatin regions (H3K4me3 peaks) in T cells, including both CD4+ naïve and CD4+ memory T cells (2). Studies have identified cell type specific DNA methylation differences in B (CD19+) and T (CD3+) lymphocytes (12, 13), as well as CD4+ T cells subsets (14, 15) isolated from RA patients compared to healthy controls. However, memory and naïve CD4+ T cells also display distinct genome-wide and gene-specific DNA methylation patterns as a result of normal differentiation (16); hence analyses of bulk T cells may be confounded by different proportions of naïve and memory T cells. Given the recent observations that CD4+ T cell subset distributions are abnormal both in treatment naïve RA patients and in RA patients who has undergone MTX treatment (17) methylation profiles for distinct CD4+ T cell subpopulations should be investigated separately. Methylation levels have so far only been assessed by array-based methods in RA, however reduced representation bisulfite sequencing (RRBS) using next generation sequencers allows for an interrogation of even more CpG sites. RRBS enriches for CpG dinucleotides by utilizes the restriction enzyme MspI (C∧CGG) to digest the DNA sample before bisulfite conversion and sequencing. In this study, we aimed to investigate whether we could detect DNA methylation differences in primary naïve and memory CD4+ T cells from RA patients. To do this, we conducted an epigenome-wide association study using RRBS on isolated T cell populations from two different RA cohorts; (1) disease modifying anti-rheumatic drug (DMARD) naïve RA patients with active disease and (2) MTX-treated RA patients who had been in remission for >12 months. The two cohorts were compared to matched healthy controls.</p>',
'date' => '2020-02-14',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fimmu.2020.00194/full',
'doi' => '10.3389/fimmu.2020.00194',
'modified' => '2022-05-19 16:10:24',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 48 => array(
'id' => '3794',
'name' => 'Obesogen effect of bisphenol S alters mRNA expression and DNA methylation profiling in male mouse liver',
'authors' => 'Brulport Axelle, Vaiman Daniel, Chagnon Marie-Christine, Le Corre Ludovic',
'description' => '<p>Environmental pollution is increasingly considered an important factor involved in the obesity incidence. Endocrine disruptors (EDs) are important actors in the concept of DOHaD (Developmental Origins of Health and Disease), where epigenetic mechanisms play crucial roles. Bisphenol A (BPA), a monomer used in the manufacture of plastics and resins is one of the most studied obesogenic endocrine disruptor. Bisphenol S (BPS), a BPA substitute, has the same obesogenic properties, acting at low doses with a sex-specific effect following perinatal exposure. Since the liver is a major organ in regulating body lipid homeostasis, we investigated gene expression and DNA methylation under low-dose BPS exposure. The BPS obesogenic effect was associated with an increase of hepatic triglyceride content. These physiological disturbances were accompanied by genome-wide changes in gene expression (1366 genes significantly modified more than 1.5-fold). Gene ontology analysis revealed alteration of gene cascades involved in protein translation and complement regulation. It was associated with hepatic DNA hypomethylation in autosomes and hypermethylation in sex chromosomes. Although no systematic correlation has been found between gene repression and hypermethylation, several genes related to liver metabolism were either hypermethylated (Acsl4, Gpr40, Cel, Pparδ, Abca6, Ces3a, Sgms2) or hypomethylated (Soga1, Gpihbp1, Nr1d2, Mlxipl, Rps6kb2, Esrrb, Thra, Cidec). In specific cases (Hapln4, ApoA4, Cidec, genes involved in lipid metabolism and liver fibrosis) mRNA upregulation was associated with hypomethylation. In conclusion, we show for the first time wide disruptive physiological effects of low-dose of BPS, which raises the question of its harmlessness as an industrial substitute for BPA.</p>',
'date' => '2019-10-15',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/31683443',
'doi' => '10.1016/j.chemosphere.2019.125092',
'modified' => '2022-05-19 16:10:42',
'created' => '2019-12-02 15:25:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 49 => array(
'id' => '3674',
'name' => 'Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA.',
'authors' => 'Mayorga L, Salassa BN, Marzese DM, Loos MA, Eiroa HD, Lubieniecki F, García Samartino C, Romano PS, Roqué M',
'description' => '<p>Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a "pro-survival" adaptive state that seems to be triggered by the differential methylation of nuclear genes.</p>',
'date' => '2019-04-01',
'pmid' => 'http://www.pubmed.gov/30673822',
'doi' => '10.1007/s00018-019-03008-5',
'modified' => '2022-05-19 16:10:59',
'created' => '2019-06-21 14:55:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 50 => array(
'id' => '3416',
'name' => 'Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination.',
'authors' => 'Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG',
'description' => '<p>Bacillus Calmette-Guérin (BCG) is the only licensed vaccine for tuberculosis (TB) and induces highly variable protection against pulmonary disease in different countries. We hypothesised that DNA methylation is one of the molecular mechanisms driving variability in BCG-induced immune responses. DNA methylation in peripheral blood mononuclear cells (PBMC) from BCG vaccinated infants was measured and comparisons made between low and high BCG-specific cytokine responders. We found 318 genes and 67 pathways with distinct patterns of DNA methylation, including immune pathways, e.g. for T cell activation, that are known to directly affect immune responses. We also highlight signalling pathways that could indirectly affect the BCG-induced immune response: potassium and calcium channel, muscarinic acetylcholine receptor, G Protein coupled receptor (GPCR), glutamate signalling and WNT pathways. This study suggests that in addition to immune pathways, cellular processes drive vaccine-induced immune responses. Our results highlight mechanisms that require consideration when designing new TB vaccines.</p>',
'date' => '2018-08-30',
'pmid' => 'http://www.pubmed.gov/30166570',
'doi' => '10.1038/s41598-018-31537-9',
'modified' => '2022-05-19 16:11:19',
'created' => '2018-12-04 09:51:07',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 51 => array(
'id' => '3322',
'name' => 'In Situ Fixation Redefines Quiescence and Early Activation of Skeletal Muscle Stem Cells',
'authors' => 'Machado L. et al.',
'description' => '<div class="abstract">
<h2 class="sectionTitle" tabindex="0">Summary</h2>
<div class="content">
<p>State of the art techniques have been developed to isolate and analyze cells from various tissues, aiming to capture their <em>in vivo</em> state. However, the majority of cell isolation protocols involve lengthy mechanical and enzymatic dissociation steps followed by flow cytometry, exposing cells to stress and disrupting their physiological niche. Focusing on adult skeletal muscle stem cells, we have developed a protocol that circumvents the impact of isolation procedures and captures cells in their native quiescent state. We show that current isolation protocols induce major transcriptional changes accompanied by specific histone modifications while having negligible effects on DNA methylation. In addition to proposing a protocol to avoid isolation-induced artifacts, our study reveals previously undetected quiescence and early activation genes of potential biological interest.</p>
</div>
</div>',
'date' => '2017-11-14',
'pmid' => 'http://www.cell.com/cell-reports/abstract/S2211-1247(17)31543-7',
'doi' => '',
'modified' => '2022-05-19 16:11:43',
'created' => '2018-02-02 16:36:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 52 => array(
'id' => '3286',
'name' => 'DNMT3B overexpression contributes to aberrant DNA methylation and MYC-driven tumor maintenance in T-ALL and Burkitt’s lymphoma',
'authors' => 'Poole et al.',
'description' => '<p>Aberrant DNA methylation is a hallmark of cancer. However, our understanding of how tumor cell-specific DNA methylation patterns are established and maintained is limited. Here, we report that in T-cell acute lymphoblastic leukemia (T-ALL) and Burkitt’s lymphoma the <em>MYC </em>oncogene causes overexpression of DNA methyltransferase (DNMT) 1 and 3B, which contributes to tumor maintenance. By utilizing a tetracycline-regulated <em>MYC </em>transgene in a mouse T-ALL (EμSRα-tTA;tet-o- MYC) and human Burkitt’s lymphoma (P493-6) model, we demonstrated that DNMT1 and DNMT3B expression depend on high MYC levels, and that their transcription decreased upon MYC-inactivation. Chromatin immunoprecipitation indicated that MYC binds to the <em>DNMT1 </em>and <em>DNMT3B </em>promoters, implicating a direct transcriptional regulation. Hence, shRNA-mediated knock-down of endogenous MYC in human T-ALL and Burkitt’s lymphoma cell lines, downregulated DNMT3B expression. Knock-down and pharmacologic inhibition of DNMT3B in T-ALL reduced cell proliferation associated with genome-wide changes in DNA methylation, indicating a tumor promoter function during tumor maintenance. We provide novel evidence that MYC directly deregulates the expression of both <em>de novo </em>and maintenance DNMTs, showing that MYC controls DNA methylation in a genome-wide fashion. Our finding that a coordinated interplay between the components of the DNA methylating machinery contributes to MYC-driven tumor maintenance highlights the potential of specific DNMTs for targeted therapies.</p>',
'date' => '2017-08-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/29100357',
'doi' => '10.18632/oncotarget.20176',
'modified' => '2022-05-19 16:12:01',
'created' => '2017-11-10 11:44:30',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 53 => array(
'id' => '3063',
'name' => 'DNA methylation and alcohol use disorders: Progress and challenges',
'authors' => 'Zhang H. and Gelernter J.',
'description' => '<section class="article-section article-body-section" id="ajad12465-sec-0001">
<h3>Background and Objectives</h3>
<p>Risk for alcohol use disorders (AUDs) is influenced by gene–environment interactions. Environmental factors can affect gene expression through epigenetic mechanisms such as DNA methylation. This review outlines the findings regarding the association of DNA methylation and AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0002">
<h3>Methods</h3>
<p>We searched PubMed (by April 2016) and identified 29 studies that examined the association of DNA methylation and AUDs. We also evaluated the methods used in these studies.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0003">
<h3>Results</h3>
<p>Two studies demonstrated elevated global (repetitive element) DNA methylation levels in AUD subjects. Fifteen candidate gene studies showed hypermethylation of promoter regions of six genes (<em>AVP</em>, <em>DNMT3B</em>, <em>HERP</em>, <em>HTR3A</em>, <em>OPRM1</em>, and <em>SNCA</em>) or hypomethylation of the <em>GDAP1</em> promoter region in AUD subjects. Five genome-wide DNA methylation studies demonstrated widespread DNA methylation changes across the genome in AUD subjects. Six studies showed significant correlations of DNA methylation with gene expression in AUD subjects. Three studies revealed interactive effects of genetic variation and DNA methylation on susceptibility to AUDs. Most studies analyzed AUD-associated DNA methylation changes in the peripheral blood; a few studies examined DNA methylation changes in postmortem brains of AUD subjects.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0004">
<h3>Discussion and Conclusions</h3>
<p>Chronic alcohol consumption may result in DNA methylation changes, leading to neuroadaptations that may underlie some of the mechanisms of AUD risk and persistence. Future studies are needed to confirm the few existing results, and then to elucidate whether DNA methylation changes are the cause or consequence of AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0005">
<h3>Scientific Significance</h3>
<p>DNA methylation profiles may be used to assess AUD status or monitor AUD treatment response. (Am J Addict 2016;XX:1–14)</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
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<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
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<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
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<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
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<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
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<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
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<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
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<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
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<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
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<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
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<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
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<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
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<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
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<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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'label2' => 'How it works?',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="RRBS" id="QuoteEpigenomicsServiceRRBS" /><label for="QuoteEpigenomicsServiceRRBS">RRBS</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="WGBS" id="QuoteEpigenomicsServiceWGBS" /><label for="QuoteEpigenomicsServiceWGBS">WGBS</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="MeDIP-seq" id="QuoteEpigenomicsServiceMeDIPSeq" /><label for="QuoteEpigenomicsServiceMeDIPSeq">MeDIP-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Targeted DNA methylation analysis" id="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis" /><label for="QuoteEpigenomicsServiceTargetedDNAMethylationAnalysis">Targeted DNA methylation analysis</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium MethylationEPIC Array v2" id="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2" /><label for="QuoteEpigenomicsServiceInfiniumMethylationEPICArrayV2">Infinium MethylationEPIC Array v2</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Infinium Mouse Methylation Array" id="QuoteEpigenomicsServiceInfiniumMouseMethylationArray" /><label for="QuoteEpigenomicsServiceInfiniumMouseMethylationArray">Infinium Mouse Methylation Array</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="RNA-seq" id="QuoteEpigenomicsServiceRNASeq" /><label for="QuoteEpigenomicsServiceRNASeq">RNA-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Bioinformatics" id="QuoteEpigenomicsServiceBioinformatics" /><label for="QuoteEpigenomicsServiceBioinformatics">Bioinformatics</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Data mining" id="QuoteEpigenomicsServiceDataMining" /><label for="QuoteEpigenomicsServiceDataMining">Data mining</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="Human Methylome" id="QuoteEpigenomicsServiceHumanMethylome" /><label for="QuoteEpigenomicsServiceHumanMethylome">Human Methylome</label></div>
</div>
<div class="row collapse">
<div class="small-12 medium-12 large-3 columns">
<span class="prefix">Sample species</span>
</div>
<div class="small-12 medium-12 large-9 columns">
<input name="data[Quote][sample_species]" maxlength="510" type="text" id="QuoteSampleSpecies"/> </div>
</div>
<div class="row collapse">
<div class="small-12 medium-12 large-6 columns">
<span class="prefix">Total number of samples (including replicates)</span>
</div>
<div class="small-12 medium-12 large-6 columns">
<input name="data[Quote][number_samples]" maxlength="255" type="text" id="QuoteNumberSamples"/> </div>
</div>
<div class="row collapse">
<h2>Contact Information</h2>
<div class="small-3 large-2 columns">
<span class="prefix">First name <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][first_name]" placeholder="john" maxlength="255" type="text" id="QuoteFirstName" required="required"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Last name <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][last_name]" placeholder="doe" maxlength="255" type="text" id="QuoteLastName" required="required"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][company]" placeholder="Organisation / Institute" maxlength="255" type="text" id="QuoteCompany" required="required"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Phone number</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][phone_number]" placeholder="+1 862 209-4680" maxlength="255" type="text" id="QuotePhoneNumber"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">City</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][city]" placeholder="Denville" maxlength="255" type="text" id="QuoteCity"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Country <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<select name="data[Quote][country]" required="required" class="triggers" id="country_selector_quote-2836">
<option value="">-- select a country --</option>
<option value="AF">Afghanistan</option>
<option value="AX">Åland Islands</option>
<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
<option value="AR">Argentina</option>
<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
<option value="AZ">Azerbaijan</option>
<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d'Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
<option value="DK">Denmark</option>
<option value="DJ">Djibouti</option>
<option value="DM">Dominica</option>
<option value="DO">Dominican Republic</option>
<option value="EC">Ecuador</option>
<option value="EG">Egypt</option>
<option value="SV">El Salvador</option>
<option value="GQ">Equatorial Guinea</option>
<option value="ER">Eritrea</option>
<option value="EE">Estonia</option>
<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
<option value="FO">Faroe Islands</option>
<option value="FJ">Fiji</option>
<option value="FI">Finland</option>
<option value="FR">France</option>
<option value="GF">French Guiana</option>
<option value="PF">French Polynesia</option>
<option value="TF">French Southern Territories</option>
<option value="GA">Gabon</option>
<option value="GM">Gambia</option>
<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
<option value="GI">Gibraltar</option>
<option value="GR">Greece</option>
<option value="GL">Greenland</option>
<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
<option value="GG">Guernsey</option>
<option value="GN">Guinea</option>
<option value="GW">Guinea-Bissau</option>
<option value="GY">Guyana</option>
<option value="HT">Haiti</option>
<option value="HM">Heard Island and McDonald Islands</option>
<option value="VA">Holy See (Vatican City State)</option>
<option value="HN">Honduras</option>
<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
<option value="IS">Iceland</option>
<option value="IN">India</option>
<option value="ID">Indonesia</option>
<option value="IR">Iran, Islamic Republic of</option>
<option value="IQ">Iraq</option>
<option value="IE">Ireland</option>
<option value="IM">Isle of Man</option>
<option value="IL">Israel</option>
<option value="IT">Italy</option>
<option value="JM">Jamaica</option>
<option value="JP">Japan</option>
<option value="JE">Jersey</option>
<option value="JO">Jordan</option>
<option value="KZ">Kazakhstan</option>
<option value="KE">Kenya</option>
<option value="KI">Kiribati</option>
<option value="KP">Korea, Democratic People's Republic of</option>
<option value="KR">Korea, Republic of</option>
<option value="KW">Kuwait</option>
<option value="KG">Kyrgyzstan</option>
<option value="LA">Lao People's Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
<option value="LS">Lesotho</option>
<option value="LR">Liberia</option>
<option value="LY">Libya</option>
<option value="LI">Liechtenstein</option>
<option value="LT">Lithuania</option>
<option value="LU">Luxembourg</option>
<option value="MO">Macao</option>
<option value="MK">Macedonia, Republic of</option>
<option value="MG">Madagascar</option>
<option value="MW">Malawi</option>
<option value="MY">Malaysia</option>
<option value="MV">Maldives</option>
<option value="ML">Mali</option>
<option value="MT">Malta</option>
<option value="MH">Marshall Islands</option>
<option value="MQ">Martinique</option>
<option value="MR">Mauritania</option>
<option value="MU">Mauritius</option>
<option value="YT">Mayotte</option>
<option value="MX">Mexico</option>
<option value="FM">Micronesia, Federated States of</option>
<option value="MD">Moldova</option>
<option value="MC">Monaco</option>
<option value="MN">Mongolia</option>
<option value="ME">Montenegro</option>
<option value="MS">Montserrat</option>
<option value="MA">Morocco</option>
<option value="MZ">Mozambique</option>
<option value="MM">Myanmar</option>
<option value="NA">Namibia</option>
<option value="NR">Nauru</option>
<option value="NP">Nepal</option>
<option value="NL">Netherlands</option>
<option value="NC">New Caledonia</option>
<option value="NZ">New Zealand</option>
<option value="NI">Nicaragua</option>
<option value="NE">Niger</option>
<option value="NG">Nigeria</option>
<option value="NU">Niue</option>
<option value="NF">Norfolk Island</option>
<option value="MP">Northern Mariana Islands</option>
<option value="NO">Norway</option>
<option value="OM">Oman</option>
<option value="PK">Pakistan</option>
<option value="PW">Palau</option>
<option value="PS">Palestine, State of</option>
<option value="PA">Panama</option>
<option value="PG">Papua New Guinea</option>
<option value="PY">Paraguay</option>
<option value="PE">Peru</option>
<option value="PH">Philippines</option>
<option value="PN">Pitcairn</option>
<option value="PL">Poland</option>
<option value="PT">Portugal</option>
<option value="PR">Puerto Rico</option>
<option value="QA">Qatar</option>
<option value="RE">Réunion</option>
<option value="RO">Romania</option>
<option value="RU">Russian Federation</option>
<option value="RW">Rwanda</option>
<option value="BL">Saint Barthélemy</option>
<option value="SH">Saint Helena, Ascension and Tristan da Cunha</option>
<option value="KN">Saint Kitts and Nevis</option>
<option value="LC">Saint Lucia</option>
<option value="MF">Saint Martin (French part)</option>
<option value="PM">Saint Pierre and Miquelon</option>
<option value="VC">Saint Vincent and the Grenadines</option>
<option value="WS">Samoa</option>
<option value="SM">San Marino</option>
<option value="ST">Sao Tome and Principe</option>
<option value="SA">Saudi Arabia</option>
<option value="SN">Senegal</option>
<option value="RS">Serbia</option>
<option value="SC">Seychelles</option>
<option value="SL">Sierra Leone</option>
<option value="SG">Singapore</option>
<option value="SX">Sint Maarten (Dutch part)</option>
<option value="SK">Slovakia</option>
<option value="SI">Slovenia</option>
<option value="SB">Solomon Islands</option>
<option value="SO">Somalia</option>
<option value="ZA">South Africa</option>
<option value="GS">South Georgia and the South Sandwich Islands</option>
<option value="ES">Spain</option>
<option value="LK">Sri Lanka</option>
<option value="SD">Sudan</option>
<option value="SR">Suriname</option>
<option value="SS">South Sudan</option>
<option value="SJ">Svalbard and Jan Mayen</option>
<option value="SZ">Swaziland</option>
<option value="SE">Sweden</option>
<option value="CH">Switzerland</option>
<option value="SY">Syrian Arab Republic</option>
<option value="TW">Taiwan</option>
<option value="TJ">Tajikistan</option>
<option value="TZ">Tanzania</option>
<option value="TH">Thailand</option>
<option value="TL">Timor-Leste</option>
<option value="TG">Togo</option>
<option value="TK">Tokelau</option>
<option value="TO">Tonga</option>
<option value="TT">Trinidad and Tobago</option>
<option value="TN">Tunisia</option>
<option value="TR">Turkey</option>
<option value="TM">Turkmenistan</option>
<option value="TC">Turks and Caicos Islands</option>
<option value="TV">Tuvalu</option>
<option value="UG">Uganda</option>
<option value="UA">Ukraine</option>
<option value="AE">United Arab Emirates</option>
<option value="GB">United Kingdom</option>
<option value="US" selected="selected">United States</option>
<option value="UM">United States Minor Outlying Islands</option>
<option value="UY">Uruguay</option>
<option value="UZ">Uzbekistan</option>
<option value="VU">Vanuatu</option>
<option value="VE">Venezuela</option>
<option value="VN">Viet Nam</option>
<option value="VG">Virgin Islands, British</option>
<option value="VI">Virgin Islands, U.S.</option>
<option value="WF">Wallis and Futuna</option>
<option value="EH">Western Sahara</option>
<option value="YE">Yemen</option>
<option value="ZM">Zambia</option>
<option value="ZW">Zimbabwe</option>
</select><script>
$('#country_selector_quote-2836').selectize();
</script><br />
</div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">State</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][state]" id="state-2836" maxlength="3" type="text"/><br />
</div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Email <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][email]" placeholder="email@address.com" maxlength="255" type="email" id="QuoteEmail" required="required"/> </div>
</div>
<div class="row collapse" id="email_v">
<div class="small-3 large-2 columns">
<span class="prefix">Email verification<sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][email_v]" autocomplete="nope" type="text" id="QuoteEmailV"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Project</span>
</div>
<div class="small-9 large-10 columns">
<textarea name="data[Quote][comment]" placeholder="Describe your project" cols="30" rows="6" id="QuoteComment"></textarea> </div>
</div>
<!------------SERVICES PARTICULAR FORM START---------------->
<!------------DATA TO POPULATE REGARDING SPECIFIC SERVICES----->
<div class="row collapse">
<div class="small-3 large-2 columns">
</div>
<div class="small-9 large-10 columns">
<div class="recaptcha"><div id="recaptcha67418bcba615d"></div></div> </div>
</div>
<br />
<div class="row collapse">
<div class="small-3 large-2 columns">
</div>
<div class="small-9 large-10 columns">
<button id="submit_btn-2836" class="alert button expand" form="Quote-2836" type="submit">Contact me</button> </div>
</div>
</form><script>
var pardotFormHandlerURL = 'https://go.diagenode.com/l/928883/2022-10-10/36b1c';
function postToPardot(formAction, id) {
$('#pardot-form-handler').load(function(){
$('#Quote-' + id).attr('action', formAction);
$('#Quote-' + id).submit();
});
$('#pardot-form-handler').attr('src', pardotFormHandlerURL + '?' + $('#Quote-' + id).serialize());
}
$(document).ready(function() {
$('body').append('<iframe id="pardot-form-handler" height="0" width="0" style="position:absolute; left:-100000px; top:-100000px" src="javascript:false;"></iframe>');
$('#Quote-2836').attr('action','javascript:postToPardot(\'' + $('#Quote-2836').attr('action') + '\', 2836)');
});
$("#Quote-2836 #submit_btn-2836").click(function (e) {
if($(this).closest('form')[0].checkValidity()){
e.preventDefault();
//disable the submit button
$("#Quote-2836 #submit_btn-2836").attr("disabled", true);
$("#Quote-2836 #submit_btn-2836").html("Processing...");
//submit the form
$("#Quote-2836").submit();
}
})
</script>
<script>
if ($("#Quote-2836 #country_selector_quote-2836.selectized").val() == 'US') {
var val = $("#state-2836").val();
$("#Quote-2836 #state-2836").replaceWith('<select name="data[Quote][state]" id="state-2836" required><option disabled selected value> -- select a state -- </option><option value="AL">Alabama (AL)</option><option value="AK">Alaska (AK)</option><option value="AZ">Arizona (AZ)</option><option value="AR">Arkansas (AR)</option><option value="CA">California (CA)</option><option value="CO">Colorado (CO)</option><option value="CT">Connecticut (CT)</option><option value="DE">Delaware (DE)</option><option value="FL">Florida (FL)</option><option value="GA">Georgia (GA)</option><option value="HI">Hawaii (HI)</option><option value="ID">Idaho (ID)</option><option value="IL">Illinois (IL)</option><option value="IN">Indiana (IN)</option><option value="IA">Iowa (IA)</option><option value="KS">Kansas (KS)</option><option value="KY">Kentucky (KY)</option><option value="LA">Louisiana (LA)</option><option value="ME">Maine (ME)</option><option value="MD">Maryland (MD)</option><option value="MA">Massachusetts (MA)</option><option value="MI">Michigan (MI)</option><option value="MN">Minnesota (MN)</option><option value="MS">Mississippi (MS)</option><option value="MO">Missouri (MO)</option><option value="MT">Montana (MT)</option><option value="NE">Nebraska (NE)</option><option value="NV">Nevada (NV)</option><option value="NH">New Hampshire (NH)</option><option value="NJ">New Jersey (NJ)</option><option value="NM">New Mexico (NM)</option><option value="NY">New York (NY)</option><option value="NC">North Carolina (NC)</option><option value="ND">North Dakota (ND)</option><option value="OH">Ohio (OH)</option><option value="OK">Oklahoma (OK)</option><option value="OR">Oregon (OR)</option><option value="PA">Pennsylvania (PA)</option><option value="PR">Puerto Rico (PR)</option><option value="RI">Rhode Island (RI)</option><option value="SC">South Carolina (SC)</option><option value="SD">South Dakota (SD)</option><option value="TN">Tennessee (TN)</option><option value="TX">Texas (TX)</option><option value="UT">Utah (UT)</option><option value="VT">Vermont (VT)</option><option value="VA">Virginia (VA)</option><option value="WA">Washington (WA)</option><option value="WV">West Virginia (WV)</option><option value="WI">Wisconsin (WI)</option><option value="WY">Wyoming (WY)</option><option value="DC">District of Columbia (DC)</option><option value="AS">American Samoa (AS)</option><option value="GU">Guam (GU)</option><option value="MP">Northern Mariana Islands (MP)</option><option value="PR">Puerto Rico (PR)</option><option value="UM">United States Minor Outlying Islands (UM)</option><option value="VI">Virgin Islands (VI)</option></select>');
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'name' => 'Premium RRBS kit V2 x96<br /> RRBS for low DNA amounts and accurate analysis',
'description' => '<p><a href="https://www.diagenode.com/files/products/kits/RRBS-KIT-V2_manual.pdf"><img src="https://www.diagenode.com/img/buttons/bt-manual.png" /></a></p>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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'label2' => 'How it works?',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<p><a href="https://www.diagenode.com/files/products/kits/RRBS-KIT-V2_manual.pdf"><img src="https://www.diagenode.com/img/buttons/bt-manual.png" /></a></p>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
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<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p>Sequences of the methylated and unmethylated spike-in controls, as well as the positions of the unmethylated cytosines present in the methylated spike-in control, can be downloaded from the ‘Documents’ section of the Premium RRBS Kit v2 page <a href="../p/premium-rrbs-kit-V2-x24">https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24</a>:</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
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<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
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<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
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<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
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<li>Higher genome coverage (3,3%)</li>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><span>Premium Methyl UDI-UMI Adapters Module - Set A includes 24 m</span>ethylated full-length adapters <span>with <strong>unique dual indexes</strong> (UDI) and<strong> unique molecular identifiers</strong> (UMI) for library multiplexing with the <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> and other BS-seq applications</span><span>. The use of </span>UDIs (unique i5 and i7 barcodes) is<span> highly recommended to mitigate errors introduced by read misassignment. The use of UMIs (9-base) is hilghy recommended for quantitave analysis or low-frequency variant detection.</span></p>
<p><span>Read more about </span><a href="https://www.diagenode.com/en/categories/bisulfite-conversion" target="_blank">Diagenode's BS-seq tools</a><span>.</span><span> </span></p>
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<p><span></span></p>
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<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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'meta_title' => 'Premium Methyl UMI-UDI Adapters - Set A | Diagenode',
'meta_keywords' => 'methylated adpters, bisulfite conversion, BS-seq, Methyl-seq, library preparation, unique dual indexes (UDI), unique molecular identifiers (UMI)',
'meta_description' => 'Methylated full-length adapters with unique dual indexes and optional unique molecular identifiers for Methyl-Seq and other sensitive NGS applications',
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<p><span>Premium Methyl UDI-UMI Adapters Module - Set B includes 24 m</span>ethylated full-length adapters <span>with <strong>unique dual indexes</strong> (UDI) and<strong> unique molecular identifiers</strong><span> </span>(UMI) for library multiplexing with the<span> </span><a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a><span> </span>and other BS-seq applications</span><span>. The use of </span>UDIs (unique i5 and i7 barcodes) is<span> highly recommended to mitigate errors introduced by read misassignment. The use of UMIs (9-base) is hilghy recommended for quantitave analysis or low-frequency variant detection.</span></p>
<p><span>Read more about </span><a href="https://www.diagenode.com/en/categories/bisulfite-conversion" target="_blank">Diagenode's BS-seq tools</a><span>.</span><span> </span></p>
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'info1' => '<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given RRBS library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original DNA sample (true variants) can be distinguished from errors introduced during library preparation or sequencing.</span></p>
<p><span></span></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/construct_rrbs.png" /></p>
<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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<p>Diagenode’s MicroChIP DiaPure columns have been optimized for the purification and elution of very low amounts of DNA. This rapid method has been validated for epigenetic applications like low input ChIP (e.g. using the True MicroChIP kit) and CUT&Tag (e.g. using Diagenode’s pA-Tn5), but is also compatible with many other applications. The DNA can be eluted at high concentrations in volumes down to 6 μl and it is suitable for any downstream application (e.g. NGS).</p>
<p>Benefits of the MicroChIP DiaPure columns:</p>
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'info1' => '<h2 style="text-align: center;">MicroChIP DiaPure columns after ChIP</h2>
<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-igv-microchip.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
<p></p>
<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
<p>Successful CUT&Tag results showing a low background with high region-specific enrichment has been generated using 50.000 of K562 cells, 1 µg of H3K27me3 antibody (Diagenode, Cat. No. C15410069) and proteinA-Tn5 (1:250) (Diagenode, Cat. No. C01070001). 1 µg of IgG (Diagenode, Cat. No. C15410206) was used as negative control. Samples were purified using the MicroChIP DiaPure columns or phenol-chloroform purification. The below figure presenst the comparison of two purification methods.</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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'name' => 'RRBS Service (Reduced Representation Bisulfite Sequencing)',
'description' => '<p><a href="https://www.diagenode.com/en/quotes/quote?id=2836"><img alt="RRBS Service" src="https://www.diagenode.com/img/banners/b-page-promo-rrbs.png" /></a></p>
<div class="extra-spaced">
<p><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to 7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with reliable results</li>
<li>Capability to use gDNA inputs down to 25 ng</li>
<li>Use of UDIs enhance sequencing results</li>
<li>UMI technology eliminates PCR duplicates</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
</ul>
<h2>Complete end-to-end service</h2>
<ul class="square">
<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> widely adopted</li>
<li>As little as 25 ng for any vertebrate species</li>
<li>Differentially methylated site analysis <span>using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>
</div>
<div class="extra-spaced">
<h2></h2>
</div>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
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<div class="small-6 columns">
<h4>RRBS Service includes:</h4>
<table style="width: 905px;">
<tbody>
<tr>
<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration </li>
<li>Assessment of DNA quality</li>
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<tr>
<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
<td style="width: 636px;">
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<li>MspI digestion</li>
<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</li>
<li>Bisulfite conversion</li>
<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<tr>
<td style="width: 264px;"><strong>Deep sequencing</strong></td>
<td style="width: 636px;">
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<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7 million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage (on average) for human samples</li>
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<thead>
<tr>
<th style="width: 262px;">
<h4><strong>Analysis</strong></h4>
</th>
<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
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</td>
</tr>
<tr>
<td style="width: 262px;"><strong>Differential methylation analysis<br /></strong></td>
<td style="width: 624px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, …)</li>
<li>Clustering analysis</li>
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</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
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</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
'label2' => 'How it works?',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'meta_title' => 'Premium Reduced Representation Bisulfite Sequencing (RRBS) Kit V2 x24 | Diagenode',
'meta_keywords' => 'DNA methylation,methylation epigenetics methylation Bisulfite conversion Bisulfite-seq Bis-seq RRBS Reduced Representation Bisulfite Sequencing reduced representation bisulfite DNA methylation sequencing 5-mC 5-methylcytosine',
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div style="text-align: justify;" class="small-12 medium-8 large-8 columns">
<h2>Complete solutions for DNA methylation studies</h2>
<p>Whether you are experienced or new to the field of DNA methylation, Diagenode has everything you need to make your assay as easy and convenient as possible while ensuring consistent data between samples and experiments. Diagenode offers sonication instruments, reagent kits, high quality antibodies, and high-throughput automation capability to address all of your specific DNA methylation analysis requirements.</p>
</div>
<div class="small-12 medium-4 large-4 columns text-center"><a href="../landing-pages/dna-methylation-grant-applications"><img src="https://www.diagenode.com/img/banners/banner-dna-grant.png" alt="" /></a></div>
<div style="text-align: justify;" class="small-12 medium-12 large-12 columns">
<p>DNA methylation was the first discovered epigenetic mark and is the most widely studied topic in epigenetics. <em>In vivo</em>, DNA is methylated following DNA replication and is involved in a number of biological processes including the regulation of imprinted genes, X chromosome inactivation. and tumor suppressor gene silencing in cancer cells. Methylation often occurs in cytosine-guanine rich regions of DNA (CpG islands), which are commonly upstream of promoter regions.</p>
</div>
<div class="small-12 medium-12 large-12 columns"><br /><br />
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#dnamethyl"><i class="fa fa-caret-right"></i> Learn more</a>
<div id="dnamethyl" class="content">5-methylcytosine (5-mC) has been known for a long time as the only modification of DNA for epigenetic regulation. In 2009, however, Kriaucionis discovered a second methylated cytosine, 5-hydroxymethylcytosine (5-hmC). The so-called 6th base, is generated by enzymatic conversion of 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine by the TET family of oxygenases. Early reports suggested that 5-hmC may represent an intermediate of active demethylation in a new pathway which demethylates DNA, converting 5-mC to cytosine. Recent evidence fuel this hypothesis suggesting that further oxidation of the hydroxymethyl group leads to a formyl or carboxyl group followed by either deformylation or decarboxylation. The formyl and carboxyl groups of 5-formylcytosine (5-fC) and 5-carboxylcytosine (5-caC) could be enzymatically removed without excision of the base.
<p class="text-center"><img src="https://www.diagenode.com/img/categories/kits_dna/dna_methylation_variants.jpg" /></p>
</div>
</li>
</ul>
<br />
<h2>Main DNA methylation technologies</h2>
<p style="text-align: justify;">Overview of the <span style="font-weight: 400;">three main approaches for studying DNA methylation.</span></p>
<div class="row">
<ol>
<li style="font-weight: 400;"><span style="font-weight: 400;">Chemical modification with bisulfite – Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Enrichment of methylated DNA (including MeDIP and MBD)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Treatment with methylation-sensitive or dependent restriction enzymes</span></li>
</ol>
<p><span style="font-weight: 400;"> </span></p>
<div class="row">
<table>
<thead>
<tr>
<th></th>
<th>Description</th>
<th width="350">Features</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td><span style="font-weight: 400;">Chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at single nucleotide resolution.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Single nucleotide resolution</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Quantitative analysis - methylation rate (%)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Gold standard and well studied</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><b>Methylated DNA enrichment</b></td>
<td><span style="font-weight: 400;">(Hydroxy-)Methylated DNA is enriched by using specific antibodies (hMeDIP or MeDIP) or proteins (MBD) that specifically bind methylated CpG sites in fragmented genomic DNA.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Resolution depends on the fragment size of the enriched methylated DNA (300 bp)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Qualitative analysis</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><strong>Restriction enzyme-based digestion</strong></td>
<td><span style="font-weight: 400;">Use of (hydroxy)methylation-sensitive or (hydroxy)methylation-dependent restriction enzymes for DNA methylation analysis at specific sites.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Determination of methylation status is limited by the enzyme recognition site</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Easy to use</span></li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="row"></div>
</div>
</div>
<div class="large-12 columns"></div>
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<div style="text-align: justify;" class="large-12 columns">Bisulfite modification of DNA is the most commonly used, "<strong>gold standard</strong>" method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. T<span style="font-weight: 400;">his technology is based on the chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at the singe nucleotide level.</span></div>
<div style="text-align: justify;" class="large-12 columns"></div>
<div style="text-align: justify;" class="large-12 columns">Various analyses can be performed on the altered sequence to retrieve this information: bisulfite sequencing, pyrosequencing, methylation-specific PCR, high resolution melting curve analysis, microarray-based approaches, and next-generation sequencing.
<h3>How it works</h3>
Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected (see Figure 1).
<p class="text-center"><img src="https://www.diagenode.com/img/applications/bisulfite.png" /><br />Figure 1: Overview of bisulfite conversion of DNA</p>
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<p>Sodium bisulfite conversion of genomic DNA is the most commonly used method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. It enables <span>to differentiate and detect unmethylated versus methylated cytosines. This procedure can then be followed either by <strong>PCR amplification</strong> or <strong>next generation sequencing</strong> to reveal the methylation status of every cytosine in gene specific amplification or whole genome amplification.</span></p>
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<h2>How it works</h2>
<p style="text-align: left;">Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, while methylated cytosines remain unchanged. <span>The DNA is then amplified by PCR where the uracils are converted to thymines. </span></p>
<p style="text-align: center;"><span></span></p>
<p><img src="https://www.diagenode.com/img/categories/bisulfite-conversion/bisulfite-conversion-acgautac.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<h2>Advantages</h2>
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<li><i class="fa fa-arrow-circle-right"></i><strong> </strong><strong>Single nucleotide</strong> resolution</li>
<li><i class="fa fa-arrow-circle-right"></i><strong> Gene-specific </strong>and <strong>genome-wide</strong><span> analyses</span></li>
<li><i class="fa fa-arrow-circle-right"></i><strong> NGS</strong><span> </span>compatible</li>
</ul>
<h2>Downstream analysis techniques</h2>
<ul class="square">
<li>Reduced Representation Bisulfite Sequencing (RRBS) with our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS Kit V2</a></li>
<li>Bisulfite conversion with our <a href="https://www.diagenode.com/en/p/premium-bisulfite-kit-50-rxns">Premium Bisulfite Kit</a> followed by qPCR, Sanger, Pyrosequencing</li>
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'date' => '2024-08-29',
'pmid' => 'https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.4c00223',
'doi' => 'https://doi.org/10.1021/acs.chemrestox.4c00223',
'modified' => '2024-09-02 10:16:01',
'created' => '2024-09-02 10:16:01',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 3 => array(
'id' => '4934',
'name' => 'Differential DNA methylation in iPSC-derived dopaminergic neurons: a step forward on the role of SNORD116 microdeletion in the pathophysiology of addictive behavior in Prader-Willi syndrome',
'authors' => 'Salles J. et al.',
'description' => '<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Introduction</h3>
<p>A microdeletion including the<span> </span><i>SNORD116</i><span> </span>gene (<i>SNORD116</i><span> </span>MD) has been shown to drive the Prader-Willi syndrome (PWS) features. PWS is a neurodevelopmental disorder clinically characterized by endocrine impairment, intellectual disability and psychiatric symptoms such as a lack of emotional regulation, impulsivity, and intense temper tantrums with outbursts. In addition, this syndrome is associated with a nutritional trajectory characterized by addiction-like behavior around food in adulthood. PWS is related to the genetic loss of expression of a minimal region that plays a potential role in epigenetic regulation. Nevertheless, the role of the<span> </span><i>SNORD116</i><span> </span>MD in DNA methylation, as well as the impact of the oxytocin (OXT) on it, have never been investigated in human neurons.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Methods</h3>
<p>We studied the methylation marks in induced pluripotent stem-derived dopaminergic neurons carrying a<span> </span><i>SNORD116</i><span> </span>MD in comparison with those from an age-matched adult healthy control. We also performed identical neuron differentiation in the presence of OXT. We performed a genome-wide DNA methylation analysis from the iPSC-derived dopaminergic neurons by reduced-representation bisulfite sequencing. In addition, we performed RNA sequencing analysis in these iPSC-derived dopaminergic neurons differentiated with or without OXT.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Results</h3>
<p>The analysis revealed that 153,826 cytosines were differentially methylated between<span> </span><i>SNORD116</i><span> </span>MD neurons and control neurons. Among the differentially methylated genes, we determined a list of genes also differentially expressed. Enrichment analysis of this list encompassed the dopaminergic system with<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i>.<span> </span><i>COMT</i><span> </span>displayed hypermethylation and under-expression in<span> </span><i>SNORD116</i><span> </span>MD, and<span> </span><i>SLC6A3</i><span> </span>displayed hypomethylation and over-expression in<span> </span><i>SNORD116</i><span> </span>MD. RT-qPCR confirmed significant over-expression of<span> </span><i>SLC6A3</i><span> </span>in<span> </span><i>SNORD116 MD</i><span> </span>neurons. Moreover, the expression of this gene was significantly decreased in the case of OXT adjunction during the differentiation.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Conclusion</h3>
<p><i>SNORD116</i><span> </span>MD dopaminergic neurons displayed differential methylation and expression in the<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i><span> </span>genes, which are related to dopaminergic clearance.</p>',
'date' => '2024-04-02',
'pmid' => 'https://www.nature.com/articles/s41380-024-02542-4',
'doi' => 'https://doi.org/10.1038/s41380-024-02542-4',
'modified' => '2024-04-09 00:46:18',
'created' => '2024-04-09 00:46:18',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 4 => array(
'id' => '4913',
'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
'authors' => 'Oganezovi N. et al.',
'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
'date' => '2024-01-30',
'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
'modified' => '2024-03-28 11:30:49',
'created' => '2024-02-22 12:28:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4898',
'name' => 'DNA methylome, R-loop and clinical exome profiling of patients with sporadic amyotrophic lateral sclerosis.',
'authors' => 'Feró, O. et al.',
'description' => '<p><span>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the death of motor neurons, the aetiology of which is essentially unknown. Here, we present an integrative epigenomic study in blood samples from seven clinically characterised sporadic ALS patients to elucidate molecular factors associated with the disease. We used clinical exome sequencing (CES) to study DNA variants, DNA-RNA hybrid immunoprecipitation sequencing (DRIP-seq) to assess R-loop distribution, and reduced representation bisulfite sequencing (RRBS) to examine DNA methylation changes. The above datasets were combined to create a comprehensive repository of genetic and epigenetic changes associated with the ALS cases studied. This repository is well-suited to unveil new correlations within individual patients and across the entire patient cohort. The molecular attributes described here are expected to guide further mechanistic studies on ALS, shedding light on the underlying genetic causes and facilitating the development of new epigenetic therapies to combat this life-threatening disease.</span></p>',
'date' => '2024-01-24',
'pmid' => 'https://www.nature.com/articles/s41597-024-02985-y',
'doi' => 'https://doi.org/10.1038/s41597-024-02985-y',
'modified' => '2024-01-29 10:11:43',
'created' => '2024-01-29 10:11:43',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4896',
'name' => 'Gestational Caloric Restriction Alters Adipose Tissue Methylome and Offspring’s Metabolic Profile in a Swine Model',
'authors' => 'Mas-Pares B. et al.',
'description' => '<p><span>Limited nutrient supply to the fetus results in physiologic and metabolic adaptations that have unfavorable consequences in the offspring. In a swine animal model, we aimed to study the effects of gestational caloric restriction and early postnatal metformin administration on offspring’s adipose tissue epigenetics and their association with morphometric and metabolic variables. Sows were either underfed (30% restriction of total food) or kept under standard diet during gestation, and piglets were randomly assigned at birth to receive metformin (n = 16 per group) or vehicle treatment (n = 16 per group) throughout lactation. DNA methylation and gene expression were assessed in the retroperitoneal adipose tissue of piglets at weaning. Results showed that gestational caloric restriction had a negative effect on the metabolic profile of the piglets, increased the expression of inflammatory markers in the adipose tissue, and changed the methylation of several genes related to metabolism. Metformin treatment resulted in positive changes in the adipocyte morphology and regulated the methylation of several genes related to atherosclerosis, insulin, and fatty acids signaling pathways. The methylation and gene expression of the differentially methylated </span><span class="html-italic">FASN</span><span>,<span> </span></span><span class="html-italic">SLC5A10</span><span>,<span> </span></span><span class="html-italic">COL5A1</span><span>, and<span> </span></span><span class="html-italic">PRKCZ</span><span><span> </span>genes in adipose tissue associated with the metabolic profile in the piglets born to underfed sows. In conclusion, our swine model showed that caloric restriction during pregnancy was associated with impaired inflammatory and DNA methylation markers in the offspring’s adipose tissue that could predispose the offspring to later metabolic abnormalities. Early metformin administration could modulate the size of adipocytes and the DNA methylation changes.</span></p>',
'date' => '2024-01-17',
'pmid' => 'https://www.mdpi.com/1422-0067/25/2/1128',
'doi' => 'https://doi.org/10.3390/ijms25021128',
'modified' => '2024-01-22 13:45:24',
'created' => '2024-01-22 13:45:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4890',
'name' => 'Diagnostic Algorithm to Subclassify Atypical Spitzoid Tumors in Low and High Risk According to Their Methylation Status',
'authors' => 'Gonzales-Munoz J.F. et al.',
'description' => '<p><span>Current diagnostic algorithms are insufficient for the optimal clinical and therapeutic management of cutaneous spitzoid tumors, particularly atypical spitzoid tumors (AST). Therefore, it is crucial to identify new markers that allow for reliable and reproducible diagnostic assessment and can also be used as a predictive tool to anticipate the individual malignant potential of each patient, leading to tailored individual therapy. Using Reduced Representation Bisulfite Sequencing (RRBS), we studied genome–wide methylation profiles of a series of Spitz nevi (SN), spitzoid melanoma (SM), and AST. We established a diagnostic algorithm based on the methylation status of seven cg sites located in </span><span class="html-italic">TETK4P2</span><span><span> </span>(Tektin 4 Pseudogene 2),<span> </span></span><span class="html-italic">MYO1D</span><span><span> </span>(Myosin ID), and<span> </span></span><span class="html-italic">PMF1-BGLAP</span><span><span> </span>(PMF1-BGLAP Readthrough), which allows the distinction between SN and SM but is also capable of subclassifying AST according to their similarity to the methylation levels of Spitz nevi or spitzoid melanoma. Thus, our epigenetic algorithm can predict the risk level of AST and predict its potential clinical outcomes.</span></p>',
'date' => '2023-12-25',
'pmid' => 'https://www.mdpi.com/1422-0067/25/1/318',
'doi' => 'https://doi.org/10.3390/ijms25010318',
'modified' => '2024-01-02 11:11:57',
'created' => '2024-01-02 11:11:57',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4808',
'name' => 'Knockout of TRDMT1 methyltransferase affects DNA methylome inglioblastoma cells.',
'authors' => 'Zabek T. et al.',
'description' => '<p><strong class="sub-title">Purpose:<span> </span></strong>We have previously shown that TRDMT1 methyltransferase is a regulator of chemotherapy-associated responses in glioblastoma cells. Despite the fact that glioblastoma, a common and malignant brain tumor, is widely characterized in terms of genetic and epigenetic markers, there are no data on TRDMT1-related changes in 5-methylcytosine pools in the genome. In the present study, the effect of TRDMT1 gene knockout (KO) on DNA methylome was analyzed.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>CRISPR-based approach was used to obtain TRDMT1 KO glioblastoma cells. Total 5-methylcytosine levels in DNA, DNMT1 pools and DNMT activity were studied using ELISA. Reduced representation bisulfite sequencing (RRBS) was considered to comprehensively evaluate DNA methylome in glioblastoma cells with TRDMT1 KO.</p>
<p><strong class="sub-title">Results:<span> </span></strong>TRDMT1 KO cells were characterized by decreased levels of total 5-methylcytosine in DNA and DNMT1, and DNMT activity. RRBS-based methylome analysis revealed statistically significant differences in methylation-relevant DMS-linked genes in control cells compared to TRDMT1 KO cells. TRDMT1 KO-associated changes in DNA methylome may affect the activity of several processes and pathways such as telomere maintenance, cell cycle and longevity regulating pathway, proteostasis, DNA and RNA biology.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>TRDMT1 may be suggested as a novel modulator of gene expression by changes in DNA methylome that may affect cancer cell fates during chemotherapy. We postulate that the levels and mutation status of TRDMT1 should be considered as a prognostic marker and carefully monitored during glioblastoma progression.</p>',
'date' => '2023-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37169948',
'doi' => '10.1007/s11060-023-04304-8',
'modified' => '2023-06-15 08:50:24',
'created' => '2023-06-13 21:11:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4786',
'name' => 'Sperm DNA methylation is predominantly stable in mice offspring bornafter transplantation of long-term cultured spermatogonial stem cells.',
'authors' => 'Serrano J. B.et al.',
'description' => '<p>BACKGROUND: Spermatogonial stem cell transplantation (SSCT) is proposed as a fertility therapy for childhood cancer survivors. SSCT starts with cryopreserving a testicular biopsy prior to gonadotoxic treatments such as cancer treatments. When the childhood cancer survivor reaches adulthood and desires biological children, the biopsy is thawed and SSCs are propagated in vitro and subsequently auto-transplanted back into their testis. However, culturing stress during long-term propagation can result in epigenetic changes in the SSCs, such as DNA methylation alterations, and might be inherited by future generations born after SSCT. Therefore, SSCT requires a detailed preclinical epigenetic assessment of the derived offspring before this novel cell therapy is clinically implemented. With this aim, the DNA methylation status of sperm from SSCT-derived offspring, with in vitro propagated SSCs, was investigated in a multi-generational mouse model using reduced-representation bisulfite sequencing. RESULTS: Although there were some methylation differences, they represent less than 0.5\% of the total CpGs and methylated regions, in all generations. Unsupervised clustering of all samples showed no distinct grouping based on their pattern of methylation differences. After selecting the few single genes that are significantly altered in multiple generations of SSCT offspring compared to control, we validated the results with quantitative Bisulfite Sanger sequencing and RT-qPCRin various organs. Differential methylation was confirmed only for Tal2, being hypomethylated in sperm of SSCT offspring and presenting higher gene expression in ovaries of SSCT F1 offspring compared to control F1. CONCLUSIONS: We found no major differences in DNA methylation between SSCT-derived offspring and control, both in F1 and F2 sperm. The reassuring outcomes from our study are a prerequisite for promising translation of SSCT to the human situation.</p>',
'date' => '2023-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37029425',
'doi' => '10.1186/s13148-023-01469-x',
'modified' => '2023-06-12 08:55:47',
'created' => '2023-05-05 12:34:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4760',
'name' => 'DNA methylation changes from primary cultures through senescence-bypassin Syrian hamster fetal cells initially exposed to benzo[a]pyrene.',
'authors' => 'Desaulniers D. et al.',
'description' => '<p>Current chemical testing strategies are limited in their ability to detect non-genotoxic carcinogens (NGTxC). Epigenetic anomalies develop during carcinogenesis regardless of whether the molecular initiating event is associated with genotoxic (GTxC) or NGTxC events; therefore, epigenetic markers may be harnessed to develop new approach methodologies that improve the detection of both types of carcinogens. This study used Syrian hamster fetal cells to establish the chronology of carcinogen-induced DNA methylation changes from primary cells until senescence-bypass as an essential carcinogenic step. Cells exposed to solvent control for 7 days were compared to naïve primary cultures, to cells exposed for 7 days to benzo[a]pyrene, and to cells at the subsequent transformation stages: normal colonies, morphologically transformed colonies, senescence, senescence-bypass, and sustained proliferation in vitro. DNA methylation changes identified by reduced representation bisulphite sequencing were minimal at day-7. Profound DNA methylation changes arose during cellular senescence and some of these early differentially methylated regions (DMRs) were preserved through the final sustained proliferation stage. A set of these DMRs (e.g., Pou4f1, Aifm3, B3galnt2, Bhlhe22, Gja8, Klf17, and L1l) were validated by pyrosequencing and their reproducibility was confirmed across multiple clones obtained from a different laboratory. These DNA methylation changes could serve as biomarkers to enhance objectivity and mechanistic understanding of cell transformation and could be used to predict senescence-bypass and chemical carcinogenicity.</p>',
'date' => '2023-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36754249',
'doi' => '10.1016/j.tox.2023.153451',
'modified' => '2023-04-17 09:08:16',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4616',
'name' => 'Myelodysplastic Syndrome associated TET2 mutations affect NK cellfunction and genome methylation.',
'authors' => 'Boy M. et al.',
'description' => '<p>Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders, representing high risk of progression to acute myeloid leukaemia, and frequently associated to somatic mutations, notably in the epigenetic regulator TET2. Natural Killer (NK) cells play a role in the anti-leukemic immune response via their cytolytic activity. Here we show that patients with MDS clones harbouring mutations in the TET2 gene are characterised by phenotypic defects in their circulating NK cells. Remarkably, NK cells and MDS clones from the same patient share the TET2 genotype, and the NK cells are characterised by increased methylation of genomic DNA and reduced expression of Killer Immunoglobulin-like receptors (KIR), perforin, and TNF-α. In vitro inhibition of TET2 in NK cells of healthy donors reduces their cytotoxicity, supporting its critical role in NK cell function. Conversely, NK cells from patients treated with azacytidine (#NCT02985190; https://clinicaltrials.gov/ ) show increased KIR and cytolytic protein expression, and IFN-γ production. Altogether, our findings show that, in addition to their oncogenic consequences in the myeloid cell subsets, TET2 mutations contribute to repressing NK-cell function in MDS patients.</p>',
'date' => '2023-02-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36737440',
'doi' => '10.1038/s41467-023-36193-w',
'modified' => '2023-04-04 08:43:27',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4588',
'name' => 'Epigenetics and stroke: role of DNA methylation and effect of aging onblood-brain barrier recovery.',
'authors' => 'Phillips C. et al.',
'description' => '<p>Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., , ), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.</p>',
'date' => '2023-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36711725',
'doi' => '10.21203/rs.3.rs-2444060/v1',
'modified' => '2023-04-11 10:01:44',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4761',
'name' => 'Development of DNA methylation-based epigenetic age predictors inloblolly pine (Pinus taeda).',
'authors' => 'Gardner S. T. et al.',
'description' => '<p>Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6\% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.</p>',
'date' => '2023-01-01',
'pmid' => 'https://doi.org/10.1101%2F2022.01.27.477887',
'doi' => '10.1111/1755-0998.13698',
'modified' => '2023-04-17 09:09:49',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4729',
'name' => 'Molecular toxicity study on glyphosate, Roundup MON 52276 and alow-dose pesticide mixture administered to adult Female rats for 90 days',
'authors' => 'Mesnage Robin and Antoniou Michael N.',
'description' => '<p>We describe a comprehensive repository describing a collection of data from a range of studies investigating the molecular mechanisms of toxicity of glyphosate, the glyphosate-based herbicide commercial formulation Roundup, and a mixture of glyphosate and 5 other most frequently used pesticides (azoxystrobin, boscalid, chlorpyrifos, imidacloprid and thiabendazole) present as residues in food products in Europe. The data were obtained by analysing tissues from rats exposed to the pesticides for 90 days via drinking water. The administration of the mixture of six pesticides was chosen to mimic a possible human exposure scenario. We compared conventional methods used in regulatory toxicity studies to evaluate the safety of pesticide exposure (gross pathology, serum biochemistry) to new molecular profiling methods encompassing the analysis of the caecum and blood metabolome, liver transcriptome, liver DNA methylation, liver small RNA profiles, and caecum metagenome of the exposed animals. Altogether, these investigations provided in-depth molecular profiling in laboratory animals exposed to pesticides revealing metabolic perturbations that would remain undetected by standard regulatory biochemical measures. Our results highlight how multi-omics phenotyping can be used to improve the predictability of health risk assessment from exposure to toxic chemicals to better protect public health.</p>',
'date' => '2022-12-01',
'pmid' => 'https://doi.org/10.1080%2F26895293.2022.2156626',
'doi' => '10.1080/26895293.2022.2156626',
'modified' => '2023-03-07 09:09:33',
'created' => '2023-02-28 12:19:11',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4652',
'name' => 'Differential methylation patterns in lean and obese non-alcoholicsteatohepatitis-associated hepatocellular carcinoma.',
'authors' => 'Hymel Emma et al.',
'description' => '<p>BACKGROUND: Nonalcoholic fatty liver disease affects about 24\% of the world's population and may progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). While more common in those that are obese, NASH-HCC can develop in lean individuals. The mechanisms by which HCC develops and the role of epigenetic changes in the context of obesity and normal weight are not well understood. METHODS: In this study, we used previously generated mouse models of lean and obese HCC using a choline deficient/high trans-fat/fructose/cholesterol diet and a choline supplemented/high trans-fat/fructose/cholesterol diet, respectively, to evaluate methylation differences in HCC progression in lean versus obese mice. Differentially methylated regions were determined using reduced representation bisulfite sequencing. RESULTS: A larger number of differentially methylated regions (DMRs) were seen in NASH-HCC progression in the obese mice compared to the non-obese mice. No overlap existed in the DMRs with the largest methylation differences between the two models. In lean NASH-HCC, methylation differences were seen in genes involved with cancer progression and prognosis (including HCC), such as CHCHD2, FSCN1, and ZDHHC12, and lipid metabolism, including PNPLA6 and LDLRAP1. In obese NASH- HCC, methylation differences were seen in genes known to be associated with HCC, including RNF217, GJA8, PTPRE, PSAPL1, and LRRC8D. Genes involved in Wnt-signaling pathways were enriched in hypomethylated DMRs in the obese NASH-HCC. CONCLUSIONS: These data suggest that differential methylation may play a role in hepatocarcinogenesis in lean versus obese NASH. Hypomethylation of Wnt signaling pathway-related genes in obese mice may drive progression of HCC, while progression of HCC in lean mice may be driven through other signaling pathways, including lipid metabolism.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36474183',
'doi' => '10.1186/s12885-022-10389-7',
'modified' => '2023-03-13 08:50:33',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4628',
'name' => 'Altered DNA methylation in estrogen-responsive repetitive sequences ofspermatozoa of infertile men with shortened anogenital distance.',
'authors' => 'Stenz L. et al.',
'description' => '<p>BACKGROUND: It has been suggested that antenatal exposure to environmental endocrine disruptors is responsible for adverse trends in male reproductive health, including male infertility, impaired semen quality, cryptorchidism and testicular cancer, a condition known as testicular dysgenesis syndrome. Anogenital distance (AGD) is an anthropomorphic measure of antenatal exposure to endocrine disruptors, with higher exposure levels leading to shortened AGD. We hypothesized that exposure to endocrine disruptors could lead to changes in DNA methylation during early embryonic development, which could then persist in the sperm of infertile men with shortened AGD. RESULTS: Using fluorescence activated cell sorting based on staining with either YO-PRO-1 (YOPRO) or chromomycin-3 (CMA3), we isolated four sperm fractions from eleven infertile men with short AGD and ten healthy semen donors. We examined DNA methylation in these sorted spermatozoa using reduced representation bisulfite sequencing. We found that fractions of spermatozoa from infertile men stained with CMA3 or YOPRO were more likely to contain transposable elements harboring an estrogen receptor response element (ERE). Abnormal sperm (as judged by high CMA3 or YOPRO staining) from infertile men shows substantial hypomethylation in estrogenic Alu sequences. Conversely, normal sperm fractions (as judged by low CMA3 or YO-PRO-1 staining) of either healthy donors or infertile patients were more likely to contain hypermethylated Alu sequences with ERE. CONCLUSIONS: Shortened AGD, as related to previous exposure to endocrine disruptors, and male infertility are accompanied by increased presence of hormonal response elements in the differentially methylated regulatory sequences of the genome of sperm fractions characterized by chromatin decondensation and apoptosis.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36572941',
'doi' => '10.1186/s13148-022-01409-1',
'modified' => '2023-03-28 09:09:22',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4537',
'name' => 'Epigenetic Alterations of Repeated Relapses in Patient-matchedChildhood Ependymomas.',
'authors' => 'Zhao Sibo et al.',
'description' => '<p>Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.</p>',
'date' => '2022-11-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36335125',
'doi' => '10.1038/s41467-022-34514-z',
'modified' => '2022-11-25 08:55:12',
'created' => '2022-11-24 08:49:52',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4441',
'name' => 'Epigenetic Suppression of the IL-7 Pathway in ProgressiveGlioblastoma.',
'authors' => 'Tompa M. et al.',
'description' => '<p>BACKGROUND: Immune evasion in glioblastoma (GBM) shields cancer cells from cytotoxic immune response. METHODS: We investigated CpG methylation in promoters, genes, and pathways in 22 pairs of formalin-fixed paraffin-embedded sequential (FFPE) GBM using restricted resolution bisulfite sequencing (RRBS) and bioinformatic analyses. RESULTS: Gene ontology revealed hypermethylation in elements of the innate and adaptive immune system when recurrent GBM samples (GBM) were compared to control (CG) and primary GBM samples (GBM). Higher methylation levels of the IL-7 signaling pathway and response to IL-7 were found in GBM suggesting a progressive blockade of the IL-7 driven T cell response in sequential GBM. Analyses of the Cancer Genome Atlas array-based data confirmed hypermethylation of the IL-7 pathway in recurrent compared with primary GBM. We also quantified DNA CpG methylation in promoter and gene regions of the IL-7 ligand and IL-7 α-receptor subunit in individual samples of a large RRBS-based sequential cohort of GBM in a Viennese database and found significantly higher methylation levels in the IL-7 receptor α-subunit in GBM compared with GBM. CONCLUSIONS: This study revealed the progressive suppression of the IL-7 receptor-mediated pathway as a means of immune evasion by GBM and thereby highlighted it as a new treatment target.</p>',
'date' => '2022-09-01',
'pmid' => 'https://doi.org/10.3390%2Fbiomedicines10092174',
'doi' => '10.3390/biomedicines10092174',
'modified' => '2022-10-14 16:32:44',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4371',
'name' => 'DNA methylation may affect beef tenderness through signal transduction inBos indicus.',
'authors' => 'de Souza M. M. et al.',
'description' => '<p>BACKGROUND: Beef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus. RESULTS: Comparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity. CONCLUSIONS: We present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.</p>',
'date' => '2022-05-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1415533%2Fv1',
'doi' => '10.1186/s13072-022-00449-4',
'modified' => '2022-08-04 16:05:03',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4401',
'name' => 'Folic Acid Treatment Directly Influences the Genetic andEpigenetic Regulation along with the Associated CellularMaintenance Processes of HT-29 and SW480 Colorectal CancerCell Lines.',
'authors' => 'Zsigrai S. et al.',
'description' => '<p>Folic acid (FA) is a synthetic form of vitamin B9, generally used as a nutritional supplement and an adjunctive medication in cancer therapy. FA is involved in genetic and epigenetic regulation; therefore, it has a dual modulatory role in established neoplasms. We aimed to investigate the effect of short-term (72 h) FA supplementation on colorectal cancer; hence, HT-29 and SW480 cells were exposed to different FA concentrations (0, 100, 10,000 ng/mL). HT-29 cell proliferation and viability levels elevated after 100 ng/mL but decreased for 10,000 ng/mL FA. Additionally, a significant ( ≤ 0.05) improvement of genomic stability was detected in HT-29 cells with micronucleus scoring and comet assay. Conversely, the FA treatment did not alter these parameters in SW480 samples. RRBS results highlighted that DNA methylation changes were bidirectional in both cells, mainly affecting carcinogenesis-related pathways. Based on the microarray analysis, promoter methylation status was in accordance with FA-induced expression alterations of 27 genes. Our study demonstrates that the FA effect was highly dependent on the cell type, which can be attributed to the distinct molecular background and the different expression of proliferation- and DNA-repair-associated genes (, , , ). Moreover, new aspects of FA-regulated DNA methylation and consecutive gene expression were revealed.</p>',
'date' => '2022-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35406592',
'doi' => '10.3390/cancers14071820',
'modified' => '2022-08-11 14:41:59',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4405',
'name' => 'Complex regulatory role of DNA methylation in caste- and age-specificexpression of a termite',
'authors' => 'Harrison Mark C. et al. ',
'description' => '<p>The reproductive castes of eusocial insects are often characterised by extreme lifespans and reproductive output, indicating an absence of the fecundity/longevity trade-off. The role of DNA methylation in the regulation of caste- and age-specific gene expression in eusocial insects is controversial. While some studies find a clear link to caste formation in honeybees and ants, others find no correlation when replication is increased across independent colonies. Although recent studies have identified transcription patterns involved in the maintenance of high reproduction throughout the long lives of queens, the role of DNA methylation in the regulation of these genes is unknown. We carried out a comparative analysis of DNA methylation in the regulation of caste-specific transcription and its importance for the regulation of fertility and longevity in queens of the higher termite, Macrotermes natalensis. We found evidence for significant, well-regulated changes in DNA methylation in mature compared to young queens, especially in several genes related to ageing and fecundity in mature queens. We also found a strong link between methylation and caste-specific alternative splicing. This study reveals a complex regulatory role of fat body DNA methylation both in the division of labour in termites, and during the reproductive maturation of queens.</p>',
'date' => '2022-03-01',
'pmid' => 'https://doi.org/10.1101%2F2022.03.08.483442',
'doi' => '10.1101/2022.03.08.483442',
'modified' => '2022-08-11 15:01:34',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => array(
'id' => '4229',
'name' => 'When left does not seem right: epigenetic and bioelectric differencesbetween left- and right-sided breast cancer.',
'authors' => 'Sofía, Masuelli and Sebastián, Real and Emanuel, Campoy andBranham, María Teresita and Marzese, Diego Matías andMatthew, Salomon and De Blas, Gerardo and Rodolfo, Arias andMichael, Levin and María, Roqué',
'description' => '<p>BACKGROUND: During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms. METHODS: We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts. RESULTS: We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count. CONCLUSIONS: Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1020823%2Fv1',
'doi' => '10.1186/s10020-022-00440-5',
'modified' => '2022-05-19 16:03:56',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '4248',
'name' => 'Comparative Toxicogenomics of Glyphosate and Roundup Herbicidesby Mammalian Stem Cell-Based Genotoxicity Assays andMolecular Profiling in Sprague-Dawley Rats.',
'authors' => 'Mesnage R. et al.',
'description' => '<p>Whether glyphosate-based herbicides (GBHs) are more potent than glyphosate alone at activating cellular mechanisms, which drive carcinogenesis remain controversial. As GBHs are more cytotoxic than glyphosate, we reasoned they may also be more capable of activating carcinogenic pathways. We tested this hypothesis by comparing the effects of glyphosate with Roundup GBHs both in vitro and in vivo. First, glyphosate was compared with representative GBHs, namely MON 52276 (European Union), MON 76473 (United Kingdom), and MON 76207 (United States) using the mammalian stem cell-based ToxTracker system. Here, MON 52276 and MON 76473, but not glyphosate and MON 76207, activated oxidative stress and unfolded protein responses. Second, molecular profiling of liver was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (at 0.5, 50, and 175 mg/kg bw/day glyphosate) for 90 days. MON 52276 but not glyphosate increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and circadian rhythm regulation. Genes most affected in liver were similarly altered in kidneys. Small RNA profiling in liver showed decreased amounts of miR-22 and miR-17 from MON 52276 ingestion. Glyphosate decreased miR-30, whereas miR-10 levels were increased. DNA methylation profiling of liver revealed 5727 and 4496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed animals, respectively. Apurinic/apyrimidinic DNA damage formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup formulations cause more biological changes linked with carcinogenesis than glyphosate.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.1093%2Ftoxsci%2Fkfab143',
'doi' => '10.1093/toxsci/kfab143',
'modified' => '2022-05-20 09:32:37',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 24 => array(
'id' => '4368',
'name' => 'Glucose-6-phosphate dehydrogenase and MEG3 controls hypoxia-inducedexpression of serum response factor (SRF) and SRF-dependent genes inpulmonary smooth muscle cell.',
'authors' => 'Kitagawa A. et al.',
'description' => '<p>Although hypoxia induces aberrant gene expression and dedifferentiation of smooth muscle cells (SMCs), mechanisms that alter dedifferentiation gene expression by hypoxia remain unclear. Therefore, we aimed to gain insight into the hypoxia-controlled gene expression in SMCs. We conducted studies using SMCs cultured in 3\% oxygen (hypoxia) and the lungs of mice exposed to 10\% oxygen (hypoxia). Our results suggest hypoxia upregulated expression of transcription factor CP2-like protein1, krüppel-like factor 4, and E2f transcription factor 1 enriched genes including basonuclin 2 (Bcn2), serum response factor (Srf), polycomb 3 (Cbx8), homeobox D9 (Hoxd9), lysine demethylase 1A (Kdm1a), etc. Additionally, we found that silencing glucose-6-phosphate dehydrogenase (G6PD) expression and inhibiting G6PD activity downregulated Srf transcript and hypomethylation of SMC genes (Myocd, Myh11, and Cnn1) and concomitantly increased their expression in the lungs of hypoxic mice. Furthermore, G6PD inhibition hypomethylated MEG3, a long non-coding RNA, gene and upregulated MEG3 expression in the lungs of hypoxic mice and in hypoxic SMCs. Silencing MEG3 expression in SMC mitigated the hypoxia-induced transcription of SRF. These findings collectively demonstrate that MEG3 and G6PD codependently regulate Srf expression in hypoxic SMCs. Moreover, G6PD inhibition upregulated SRF-MYOCD-driven gene expression, determinant of a differentiated SMC phenotype.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35491127',
'doi' => '10.1540/jsmr.58.34',
'modified' => '2022-08-04 16:21:02',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 25 => array(
'id' => '4114',
'name' => 'The ETS transcription factor ERF controls the exit from the naïve pluripotent state in a MAPK-dependent manner',
'authors' => 'Maria Vega-Sendino et. al.',
'description' => '<p><span>The naïve epiblast transitions to a pluripotent primed state during embryo implantation. Despite the relevance of the FGF pathway during this period, little is known about the downstream effectors regulating this signaling. Here, we examined the molecular mechanisms coordinating the naïve to primed transition by using inducible ESC to genetically eliminate all RAS proteins. We show that differentiated RAS</span><sup>KO</sup><span><span> </span>ESC remain trapped in an intermediate state of pluripotency with naïve-associated features. Elimination of the transcription factor ERF overcomes the developmental blockage of RAS-deficient cells by naïve enhancer decommissioning. Mechanistically, ERF regulates NANOG expression and ensures naïve pluripotency by strengthening naïve transcription factor binding at ESC enhancers. Moreover, ERF negatively regulates the expression of the methyltransferase DNMT3B, which participates in the extinction of the naïve transcriptional program. Collectively, we demonstrated an essential role for ERF controlling the exit from naïve pluripotency in a MAPK-dependent manner during the progression to primed pluripotency.</span></p>',
'date' => '2021-10-01',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34597136/',
'doi' => '10.1126/sciadv.abg8306',
'modified' => '2022-05-19 16:05:11',
'created' => '2021-10-06 08:45:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 26 => array(
'id' => '4230',
'name' => 'Adaptive Convergence of Methylomes Reveals Epigenetic Driversand Boosters of Repeated Relapses in Patient-matched ChildhoodEpendymomas and Identifies Targets for Anti-RecurrenceTherapies',
'authors' => 'Zhao S. et al.',
'description' => '<p>Ependymoma (EPN) is the third most common brain tumor in children and frequently recurs. Here, we report an integrated longitudinal analysis of epigenetic, genetic and tumorigenic changes in 30 patient-matched repeated relapses obtained from 10 pediatric patients to understand the mechanism of recurrences. Genome-wide DNA methylation analysis revealed stable molecular subtypes and convergent epigenetic reprogramming during serial relapses of the 5 RELA and 5 PFA EPNs that paralleled with elevated patient-derived orthotopic xenograft (PDOX) (13/27) formation in the late relapses. Differentially methylated CpGs (DMCs) preexisted in the primary tumors and persisted in the relapses (driver DMCs) were detected, ranging from 51 hypo-methylated in RELA to 148 hyper-methylated DMCs in PFA tumors; while newly acquired DMCs sustained in all the relapses but was absent in the primary tumors (booster DMCs) ranged from 38- 323 hyper-methylated DMCs in RELA and PFA EPNs, respectively. Integrated analysis of these DMC associated DNA methylation regions (DMRs) and RNAseq in both patient and PDOX tumors identified a small fraction of the differentially expressed genes (4.6±4.4\% in RELA and 4.5±1.1\% in PFA) as regulated by driver DMRs (e.g., up-regulated CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and booster DMRs (including the sole upregulated PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). Most these genes were novel to EPN relapses. Seven DMCs in RELA and 22 in PFA tumors were also identified as potential relapse predictors. Finally, integrating DNA methylation with histone modification identified LSD1 as a relapse driver gene. Combined treatment of a novel inhibitor SYC-836 with radiation significantly prolonged survival times in two PDOX models of recurrent PFA. This high-resolution epigenetic and genetic roadmap of EPN relapse and our 13 new PDOX models should significantly facilitate biological and preclinical studies of pediatric EPN recurrences.</p>',
'date' => '2021-10-01',
'pmid' => 'https://www.researchsquare.com/article/rs-908607/v1',
'doi' => '10.21203/rs.3.rs-908607/v1',
'modified' => '2022-05-19 16:48:13',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 27 => array(
'id' => '4115',
'name' => 'Genome-Wide Epigenomic Analyses in Patients With Nociceptive and Neuropathic Chronic Pain Subtypes Reveals Alterations in Methylation of Genes Involved in the Neuro-Musculoskeletal System',
'authors' => 'Stenz et al',
'description' => '<p><span>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs were seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</span></p>',
'date' => '2021-09-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34547430/',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-19 16:05:36',
'created' => '2021-10-22 19:01:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 28 => array(
'id' => '4299',
'name' => 'Genome-wide epigenomic analyses in patients with nociceptive andneuropathic chronic pain subtypes reveals alterations in methylation ofgenes involved in the neuro-musculoskeletal system.',
'authors' => 'Stenz Ludwig et al.',
'description' => '<p>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</p>',
'date' => '2021-09-01',
'pmid' => 'https://doi.org/10.1016%2Fj.jpain.2021.09.001',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-30 09:41:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 29 => array(
'id' => '4383',
'name' => 'Biobehavioral organization shapes the immune epigenome in infant rhesusMacaques (Macaca mulatta).',
'authors' => 'Baxter A. et al.',
'description' => '<p>How individuals respond to and cope with stress is linked with their health and well-being. It is presumed that early stress responsiveness helps shape the health of the developing organism, but the relationship between stress responsiveness and early immune function during development is not well-known. We hypothesized that stress responsiveness may shape epigenetic regulation of immune genes in infancy. We investigated whether aspects of behavioral responsiveness and hypothalamic-pituitary adrenal stress-response were associated with epigenome-wide immune cell DNA methylation patterns in 154 infant rhesus monkeys (3-4 months old). Infants' behavioral and physiological responses were collected during a standardized biobehavioral assessment, which included temporary relocation and separation from their mother and social group. Genome-wide DNA methylation was quantified using restricted representation bisulfite sequencing (RRBS) from blood DNA collected 2-hours post-separation. Epigenome-wide analyses were conducted using simple regression, multiple regression controlling for immune cell counts, and permutation regression, all corrected for false discovery rate. Across the variables analyzed, there were 20,368 unique sites (in 9,040 genes) at which methylation was significantly associated with at least one behavioral responsiveness or cortisol measure across the three analyses. There were significant associations in 442 genes in the Immune System Process ontology category, and 94 genes in the Inflammation mediated by chemokine and cytokine signaling gene pathway. Out of 35 candidate genes that were selected for further investigation, there were 13 genes with at least one site at which methylation was significantly associated with behavioral responsiveness or cortisol, including two intron sites in the glucocorticoid receptor gene, at which methylation was negatively correlated with emotional behavior the day following the social separation (Day 2 Emotionality; β = -0.39, q < 0.001) and cortisol response following a relocation stressor (Sample 1; β = -0.33, q < 0.001). We conclude that biobehavioral stress responsiveness may correlate with the developing epigenome, and that DNA methylation of immune cells may be a mechanism by which patterns of stress response affect health and immune functioning.</p>',
'date' => '2021-08-01',
'pmid' => 'https://doi.org/10.1016%2Fj.bbi.2021.06.006',
'doi' => '10.1016/j.bbi.2021.06.006',
'modified' => '2022-08-04 15:54:12',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 30 => array(
'id' => '4112',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus',
'authors' => 'Sara Zocher, Rupert W. Overall, Mathias Lesche, Andreas Dahl & Gerd Kempermann',
'description' => '<p><span>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</span></p>',
'date' => '2021-06-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34162876/',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-05-19 16:06:20',
'created' => '2021-09-06 08:02:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 31 => array(
'id' => '4111',
'name' => 'Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes',
'authors' => 'Luca Pagliaroli, Abel Fothi, Ester Nespoli, Istvan Liko, Borbala Veto, Piroska Devay, Flora Szeri, Bastian Hengerer, Csaba Barta, Tamas Aranyi',
'description' => '<p>Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.</p>',
'date' => '2021-06-09',
'pmid' => 'https://www.mdpi.com/2073-4409/10/6/1442',
'doi' => '10.3390/cells10061442',
'modified' => '2022-05-19 16:06:47',
'created' => '2021-08-27 11:27:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 32 => array(
'id' => '4324',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape inthe aged mouse hippocampus',
'authors' => 'Zocher S. et al. ',
'description' => '<p>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</p>',
'date' => '2021-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34162876',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-08-03 15:56:05',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 33 => array(
'id' => '4419',
'name' => 'Pathophysiological adaptations of resistance arteries in rat offspringexposed in utero to maternal obesity is associated with sex-specificepigenetic alterations.',
'authors' => 'Payen Cyrielle et al.',
'description' => '<p>BACKGROUND/OBJECTIVES: Maternal obesity impacts vascular functions linked to metabolic disorders in offspring, leading to cardiovascular diseases during adulthood. Even if the relation between prenatal conditioning of cardiovascular diseases by maternal obesity and vascular function begins to be documented, little is known about resistance arteries. They are of particular interest because of their specific role in the regulation of local blood flow. Then our study aims to determine if maternal obesity can directly program fetal vascular dysfunction of resistance arteries, independently of metabolic disorders. METHODS: With a model of rats exposed in utero to mild maternal diet-induced obesity (OMO), we investigated third-order mesenteric arteries of 4-month old rats in absence of metabolic disorders. The methylation profile of these vessels was determined by reduced representation bisulfite sequencing (RRBS). Vascular structure and reactivity were investigated using histomorphometry analysis and wire-myography. The metabolic function was evaluated by insulin and glucose tolerance tests, plasma lipid profile, and adipose tissue analysis. RESULTS: At 4 months of age, small mesenteric arteries of OMO presented specific epigenetic modulations of matrix metalloproteinases (MMPs), collagens, and potassium channels genes in association with an outward remodeling and perturbations in the endothelium-dependent vasodilation pathways (greater contribution of EDHFs pathway in OMO males compared to control rats, and greater implication of PGI in OMO females compared to control rats). These vascular modifications were detected in absence of metabolic disorders. CONCLUSIONS: Our study reports a specific methylation profile of resistance arteries associated with vascular remodeling and vasodilation balance perturbations in offspring exposed in utero to maternal obesity, in absence of metabolic dysfunctions.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33637953',
'doi' => '10.1038/s41366-021-00777-7',
'modified' => '2022-09-28 08:51:40',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 34 => array(
'id' => '4175',
'name' => 'Multi-omics phenotyping of the gut-liver axis reveals metabolicperturbations from a low-dose pesticide mixture in rats.',
'authors' => 'Mesnage, Robin et al.',
'description' => '<p>Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33854195',
'doi' => '10.1038/s42003-021-01990-w',
'modified' => '2021-12-21 16:12:25',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 35 => array(
'id' => '4356',
'name' => 'Muscle allele-specific expression QTLs may affect meat quality traitsin Bos indicus.',
'authors' => 'Bruscadin J.J. et al.',
'description' => '<p>Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33795794',
'doi' => '10.1038/s41598-021-86782-2',
'modified' => '2022-08-03 16:44:51',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 36 => array(
'id' => '4173',
'name' => 'The insecticide permethrin induces transgenerational behavioral changeslinked to transcriptomic and epigenetic alterations in zebrafish (Daniorerio).',
'authors' => 'Blanc, Mélanie et al.',
'description' => '<p>The pyrethroid insecticide permethrin is widely used for agricultural and domestic purposes. Previous data indicated that it acts as a developmental neurotoxicant and can induce transgenerational effects in non-target organisms. However, associated underlying mechanisms remain unclear. The aim of this study was to investigate permethrin-related transgenerational effects in the zebrafish model, and to identify possible molecular mechanisms underlying inheritance. Zebrafish (F0) were exposed to permethrin during early-life (2 h post-fertilization up to 28 days). The F1 and F2 offspring generations were obtained by pairing exposed F0 males and females, and were bred unexposed. Locomotor and anxiety behavior were investigated, together with transcriptomic and epigenomic (DNA methylation) changes in brains. Permethrin exposed F0 fish were hypoactive at adulthood, while males from the F1 and F2 generations showed a specific decrease in anxiety-like behavior. In F0, transcriptomic data showed enrichment in pathways related to glutamatergic synapse activity, which may partly underlie the behavioral effects. In F1 and F2 males, dysregulation of similar pathways was observed, including a subset of differentially methylated regions that were inherited from the F0 to the F2 generation and indicated stable dysregulation of glutamatergic signaling. Altogether, the present results provide novel evidence on the transgenerational neurotoxic effects of permethrin, as well as mechanistic insight: a transient exposure induces persistent transcriptional and DNA methylation changes that may translate into transgenerational alteration of glutamatergic signaling and, thus, into behavioral alterations.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33752003',
'doi' => '10.1016/j.scitotenv.2021.146404',
'modified' => '2021-12-21 16:02:21',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 37 => array(
'id' => '4142',
'name' => 'The aging DNA methylome reveals environment-by-aging interactions in amodel teleost',
'authors' => 'Bertucci, E. M. et al.',
'description' => '<p>The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4\% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.</p>',
'date' => '2021-03-01',
'pmid' => 'https://doi.org/10.1101%2F2021.03.01.433371',
'doi' => '10.1101/2021.03.01.433371',
'modified' => '2022-05-19 16:07:18',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 38 => array(
'id' => '4155',
'name' => 'Perturbed DNA methylation by sustained overexpression of Gadd45b induces chromatin disorganization, DNA strand breaks and dopaminergic neurondeath in mice',
'authors' => 'Ravel-Godreuil, C. et al.',
'description' => '<p>Heterochromatin disorganization is a key hallmark of aging and DNA methylation state is currently the main molecular predictor of chronological age. The most frequent neurodegenerative diseases like Parkinson disease and Alzheimer’s disease are age-related but how the aging process and chromatin alterations are linked to neurodegeneration is unknown. Here, we investigated the consequences of viral overexpression of Gadd45b, a multifactorial protein involved in active DNA demethylation, in the midbrain of wild-type mice. Gadd45b overexpression induces global and stable changes in DNA methylation, particularly on gene bodies of genes related to neuronal functions. DNA methylation changes were accompanied by perturbed H3K9me3-marked heterochromatin and increased DNA damage. Prolonged Gadd45b expression resulted in dopaminergic neuron degeneration accompanied by altered expression of candidate genes related to heterochromatin maintenance, DNA methylation or Parkinson disease. Gadd45b overexpression rendered midbrain dopaminergic neurons more vulnerable to acute oxidative stress. Heterochromatin disorganization and DNA demethylation resulted in derepression of mostly young LINE-1 transposable elements, a potential source of DNA damage, prior to Gadd45b-induced neurodegeneration. Our data implicate that alterations in DNA methylation and heterochromatin organization, LINE-1 derepression and DNA damage can represent important contributors in the pathogenic mechanisms of dopaminergic neuron degeneration with potential implications for Parkinson disease.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2020.06.23.158014',
'doi' => '10.1101/2020.06.23.158014',
'modified' => '2022-05-19 16:07:48',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 39 => array(
'id' => '4189',
'name' => 'The Identification of a Novel Fucosidosis-Associated Mutation: A Case of a5-Year-Old Polish Girl with Two Additional Rare Chromosomal Aberrations andAffected DNA Methylation Patterns.',
'authors' => 'Domin A. et al. ',
'description' => '<p>Fucosidosis is a rare neurodegenerative autosomal recessive disorder, which manifests as progressive neurological and psychomotor deterioration, growth retardation, skin and skeletal abnormalities, intellectual disability and coarsening of facial features. It is caused by biallelic mutations in encoding the α-L-fucosidase enzyme, which in turn is responsible for degradation of fucose-containing glycoproteins and glycolipids. mutations lead to severe reduction or even loss of α-L-fucosidase enzyme activity. This results in incomplete breakdown of fucose-containing compounds leading to their deposition in different tissues and, consequently, disease progression. To date, 36 pathogenic variants in associated with fucosidosis have been documented. Among these are three splice site variants. Here, we report a novel fucosidosis-related 9-base-pair deletion (NG_013346.1:g.10233_10241delACAGGTAAG) affecting the exon 3/intron 3 junction within a sequence. This novel pathogenic variant was identified in a five-year-old Polish girl with a well-defined pattern of fucosidosis symptoms. Since it is postulated that other genetic, nongenetic or environmental factors can also contribute to fucosidosis pathogenesis, we performed further analysis and found two rare de novo chromosomal aberrations in the girl's genome involving a 15q11.1-11.2 microdeletion and an Xq22.2 gain. These abnormalities were associated with genome-wide changes in DNA methylation status in the epigenome of blood cells.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33435586',
'doi' => '10.3390/genes12010074',
'modified' => '2022-05-19 16:08:10',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 40 => array(
'id' => '4357',
'name' => 'Developmental cannabidiol exposure increases anxiety and modifiesgenome-wide brain DNA methylation in adult female mice.',
'authors' => 'Wanner N. M. et al. ',
'description' => '<p>BACKGROUND: Use of cannabidiol (CBD), the primary non-psychoactive compound found in cannabis, has recently risen dramatically, while relatively little is known about the underlying molecular mechanisms of its effects. Previous work indicates that direct CBD exposure strongly impacts the brain, with anxiolytic, antidepressant, antipsychotic, and other effects being observed in animal and human studies. The epigenome, particularly DNA methylation, is responsive to environmental input and can direct persistent patterns of gene regulation impacting phenotype. Epigenetic perturbation is particularly impactful during embryogenesis, when exogenous exposures can disrupt critical resetting of epigenetic marks and impart phenotypic effects lasting into adulthood. The impact of prenatal CBD exposure has not been evaluated; however, studies using the psychomimetic cannabinoid Δ9-tetrahydrocannabinol (THC) have identified detrimental effects on psychological outcomes in developmentally exposed adult offspring. We hypothesized that developmental CBD exposure would have similar negative effects on behavior mediated in part by the epigenome. Nulliparous female wild-type Agouti viable yellow (A) mice were exposed to 20 mg/kg CBD or vehicle daily from two weeks prior to mating through gestation and lactation. Coat color shifts, a readout of DNA methylation at the Agouti locus in this strain, were measured in F1 A/a offspring. Young adult F1 a/a offspring were then subjected to tests of working spatial memory and anxiety/compulsive behavior. Reduced-representation bisulfite sequencing was performed on both F0 and F1 cerebral cortex and F1 hippocampus to identify genome-wide changes in DNA methylation for direct and developmental exposure, respectively. RESULTS: F1 offspring exposed to CBD during development exhibited increased anxiety and improved memory behavior in a sex-specific manner. Further, while no significant coat color shift was observed in A/a offspring, thousands of differentially methylated loci (DMLs) were identified in both brain regions with functional enrichment for neurogenesis, substance use phenotypes, and other psychologically relevant terms. CONCLUSIONS: These findings demonstrate for the first time that despite positive effects of direct exposure, developmental CBD is associated with mixed behavioral outcomes and perturbation of the brain epigenome.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33407853',
'doi' => '10.1186/s13148-020-00993-4',
'modified' => '2022-08-03 17:04:44',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 41 => array(
'id' => '4208',
'name' => 'Hepatic transcriptome and DNA methylation patterns following perinataland chronic BPS exposure in male mice.',
'authors' => 'Brulport A. et al. ',
'description' => '<p>BACKGROUND: Bisphenol S (BPS) is a common bisphenol A (BPA) substitute, since BPA is virtually banned worldwide. However, BPS and BPA have both endocrine disrupting properties. Their effects appear mostly in adulthood following perinatal exposures. The objective of the present study was to investigate the impact of perinatal and chronic exposure to BPS at the low dose of 1.5 μg/kg body weight/day on the transcriptome and methylome of the liver in 23 weeks-old C57BL6/J male mice. RESULTS: This multi-omic study highlights a major impact of BPS on gene expression (374 significant deregulated genes) and Gene Set Enrichment Analysis show an enrichment focused on several biological pathways related to metabolic liver regulation. BPS exposure also induces a hypomethylation in 58.5\% of the differentially methylated regions (DMR). Systematic connections were not found between gene expression and methylation profile excepted for 18 genes, including 4 genes involved in lipid metabolism pathways (Fasn, Hmgcr, Elovl6, Lpin1), which were downregulated and featured differentially methylated CpGs in their exons or introns. CONCLUSIONS: This descriptive study shows an impact of BPS on biological pathways mainly related to an integrative disruption of metabolism (energy metabolism, detoxification, protein and steroid metabolism) and, like most high-throughput studies, contributes to the identification of potential exposure biomarkers.</p>',
'date' => '2020-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33297965',
'doi' => '10.1186/s12864-020-07294-3',
'modified' => '2022-01-13 14:57:00',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 42 => array(
'id' => '4033',
'name' => 'Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans',
'authors' => 'Neeraj K. Sharma, Mary E. Comeau, Dennis Montoya, Matteo Pellegrini, Timothy D. Howard, Carl D. Langefeld, Swapan K. Das',
'description' => '<p><span>Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in Type 2 Diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans from AAGMEx cohort, we performed<span> </span></span><em>cis</em><span>-expression quantitative trait methylation (</span><em>cis</em><span>-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated CpG-regions for 82 transcripts (FDR-P<0.05). The strongest eQTM locus was observed for the proopiomelanocortin (</span><em>POMC</em><span>; ρ= -0.632, P= 4.70X10</span><sup>-27</sup><span>) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 CpG regions associated (FDR-P <0.05) with Matsuda index, S</span><sub>I</sub><span><span> </span>and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, S</span><sub>I</sub><span><span> </span>and/or BMI in EWAS. These associated genes include<span> </span></span><em>FERMT3</em><span>,<span> </span></span><em>ITGAM</em><span>,<span> </span></span><em>ITGAX</em><span>, and<span> </span></span><em>POMC</em><span>. In summary, applying an integrative multi-omics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.</span></p>',
'date' => '2020-09-20',
'pmid' => 'https://diabetes.diabetesjournals.org/content/early/2020/09/03/db20-0117',
'doi' => '10.2337/db20-0117',
'modified' => '2022-05-19 16:08:46',
'created' => '2020-10-22 10:55:58',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 43 => array(
'id' => '4020',
'name' => 'DNA CpG methylation in sequential glioblastoma specimens.',
'authors' => 'Kraboth, Z and Galik, B and Tompa, M and Kajtar, B and Urban, P andGyenesei, A and Miseta, A and Kalman, B',
'description' => '<p>PURPOSE: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples. METHODS: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters. RESULTS: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport. CONCLUSION: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.</p>',
'date' => '2020-08-10',
'pmid' => 'http://www.pubmed.gov/32779022',
'doi' => '10.1007/s00432-020-03349-w',
'modified' => '2022-05-19 16:09:06',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 44 => array(
'id' => '3989',
'name' => 'Early Life Exposure to Environmentally Relevant Levels of Endocrine Disruptors Drive Multigenerational and Transgenerational Epigenetic Changes in a Fish Model',
'authors' => 'Major Kaley M., DeCourten Bethany M., Li Jie, Britton Monica, Settles Matthew L., Mehinto Alvine C., Connon Richard E., Brander Susanne M.',
'description' => '<p>The inland silverside, Menidia beryllina, is a euryhaline fish and a model organism in ecotoxicology. We previously showed that exposure to picomolar (ng/L) levels of endocrine disrupting chemicals (EDCs) can cause a variety of effects in M. beryllina, from changes in gene expression to phenotypic alterations. Here we explore the potential for early life exposure to EDCs to modify the epigenome in silversides, with a focus on multi- and transgenerational effects. EDCs included contaminants of emerging concern (the pyrethroid insecticide bifenthrin and the synthetic progestin levonorgestrel), as well as a commonly detected synthetic estrogen (ethinylestradiol), and a synthetic androgen (trenbolone) at exposure levels ranging from 3 to 10 ng/L. In a multigenerational experiment, we exposed parental silversides to EDCs from fertilization until 21 days post hatch (dph). Then we assessed DNA methylation patterns for three generations (F0, F1, and F2) in whole body larval fish using reduced representation bisulfite sequencing (RRBS). We found significant (α = 0.05) differences in promoter and/or gene body methylation in treatment fish relative to controls for all EDCs and all generations indicating that both multigenerational (F1) and transgenerational (F2) effects that were caused by strict inheritance of DNA methylation alterations and the dysregulation of epigenetic control mechanisms. Using gene ontology and pathway analyses, we found enrichment in biological processes and pathways representative of growth and development, immune function, reproduction, pigmentation, epigenetic regulation, stress response and repair (including pathways important in carcinogenesis). Further, we found that a subset of potentially EDC responsive genes (EDCRGs) were differentially methylated across all treatments and generations and included hormone receptors, genes involved in steroidogenesis, prostaglandin synthesis, sexual development, DNA methylation, protein metabolism and synthesis, cell signaling, and neurodevelopment. The analysis of EDCRGs provided additional evidence that differential methylation is inherited by the offspring of EDC-treated animals, sometimes in the F2 generation that was never exposed. These findings show that low, environmentally relevant levels of EDCs can cause altered methylation in genes that are functionally relevant to impaired phenotypes documented in EDC-exposed animals and that EDC exposure has the potential to affect epigenetic regulation in future generations of fish that have never been exposed.</p>',
'date' => '2020-06-24',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fmars.2020.00471/full',
'doi' => '10.3389/fmars.2020.00471',
'modified' => '2022-05-19 16:09:23',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 45 => array(
'id' => '3983',
'name' => 'Chronic cannabidiol alters genome-wide DNA methylation in adult mouse hippocampus: epigenetic implications for psychiatric disease.',
'authors' => 'Wanner NM, Colwell M, Drown C, Faulk C',
'description' => '<p>Cannabidiol (CBD) is the primary non-psychoactive compound found in cannabis (Cannabis sativa) and an increasingly popular dietary supplement as a result of widespread availability of CBD-containing products. CBD is FDA-approved for the treatment of epilepsy and exhibits anxiolytic, antipsychotic, prosocial, and other behavioral effects in animal and human studies, however, the underlying mechanisms governing these phenotypes are still being elucidated. The epigenome, particularly DNA methylation, is responsive to environmental input and can govern persistent patterns of gene regulation affecting phenotype across the life course. In order to understand the epigenomic activity of chronic cannabidiol exposure in the adult brain, 12-week-old male C57BL/6 mice were exposed to either 20 mg/kg CBD or vehicle daily by oral administration for fourteen days. Hippocampal tissue was collected and reduced-representation bisulfite sequencing (RRBS) was performed. Analyses revealed 3,323 differentially methylated loci (DMLs) in CBD-exposed animals with a small skew toward global hypomethylation. Genes for cell adhesion and migration, dendritic spine development, and excitatory postsynaptic potential were found to be enriched in a gene ontology term analysis of DML-containing genes, and disease ontology enrichment revealed an overrepresentation of DMLs in gene sets associated with autism spectrum disorder, schizophrenia, and other phenotypes. These results suggest that the epigenome may be a key substrate for CBD's behavioral effects and provides a wealth of gene regulatory information for further study. This article is protected by copyright. All rights reserved.</p>',
'date' => '2020-06-24',
'pmid' => 'http://www.pubmed.gov/32579259',
'doi' => '10.1002/em.22396',
'modified' => '2022-05-19 16:09:42',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 46 => array(
'id' => '3885',
'name' => 'Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease',
'authors' => 'Guan Y, Liu H, Ma Z, Li SY, Park J, Sheng X, Susztak K',
'description' => '<p>BACKGROUND: Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing methylation. METHODS: We generated mice with genetic deletion of and in nephron progenitor cells () and kidney tubule cells (). We characterized mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. RESULTS: mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that and play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of and resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by and were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with and deletion. CONCLUSIONS: Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.</p>',
'date' => '2020-03-03',
'pmid' => 'http://www.pubmed.gov/32127410',
'doi' => '10.1681/ASN.2019080797',
'modified' => '2022-05-19 16:10:07',
'created' => '2020-03-13 13:45:54',
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[maximum depth reached]
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(int) 47 => array(
'id' => '3877',
'name' => 'Rheumatoid Arthritis Patients, Both Newly Diagnosed and Methotrexate Treated, Show More DNA Methylation Differences in CD4+ Memory Than in CD4+ Naïve T Cells',
'authors' => 'Guderud Kari, Sunde Line H., Flåm Siri T., Mæhlen Marthe T., Mjaavatten Maria D., Lillegraven Siri, Aga Anna-Birgitte, Evenrød Ida M., Norli Ellen S., Andreassen Bettina K., Franzenburg Sören, Franke Andre, Haavardsholm Espen A., Rayner Simon, Gervin Kris',
'description' => '<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes pain and swelling of multiple joints in the body. The underlying disease mechanisms are believed to involve a complex interplay between common genetic and environmental factors. The heritability of RA has been estimated to be ~50% for anti-citrullinated protein antibody (ACPA) positive RA and ~20% for ACPA negative RA in a large familial aggregation study (1). Genome-wide association studies (GWAS) have identified more than 100 RA risk loci, mostly conferring risk to ACPA positive RA, marked by lead single nucleotide polymorphisms (SNPs) across various populations (2). The risk SNPs have small effect sizes, and only explain parts of heritability in RA. Environmental and epigenetic factors are also thought to be involved in the RA disease pathogenesis (3) of which smoking is the only established environmental risk factor (4, 5). Epigenetic modifications are important for regulation and maintenance of cell type specific biological functions, and alterations in the epigenome have been found to be associated with RA (6). The most studied epigenetic modification in humans is DNA methylation of cytosine followed by a guanine at so-called CpG sites (CpGs). CpGs are often clustered in regions called CpG islands (CGIs), which frequently overlap gene promoters (7). DNA methylation in promotor regions is usually negatively correlated with transcription of the nearby gene (8). A wide range of immune cells has been implicated in the pathogenesis of RA. One of the most widely used drugs for treatment of RA, methotrexate (MTX) (9), acts as an immunosuppressant in proliferating cells (10), and of these, the most relevant cell population for RA is CD4+ T cells (11). Interestingly, the RA risk loci are enriched in accessible chromatin regions (H3K4me3 peaks) in T cells, including both CD4+ naïve and CD4+ memory T cells (2). Studies have identified cell type specific DNA methylation differences in B (CD19+) and T (CD3+) lymphocytes (12, 13), as well as CD4+ T cells subsets (14, 15) isolated from RA patients compared to healthy controls. However, memory and naïve CD4+ T cells also display distinct genome-wide and gene-specific DNA methylation patterns as a result of normal differentiation (16); hence analyses of bulk T cells may be confounded by different proportions of naïve and memory T cells. Given the recent observations that CD4+ T cell subset distributions are abnormal both in treatment naïve RA patients and in RA patients who has undergone MTX treatment (17) methylation profiles for distinct CD4+ T cell subpopulations should be investigated separately. Methylation levels have so far only been assessed by array-based methods in RA, however reduced representation bisulfite sequencing (RRBS) using next generation sequencers allows for an interrogation of even more CpG sites. RRBS enriches for CpG dinucleotides by utilizes the restriction enzyme MspI (C∧CGG) to digest the DNA sample before bisulfite conversion and sequencing. In this study, we aimed to investigate whether we could detect DNA methylation differences in primary naïve and memory CD4+ T cells from RA patients. To do this, we conducted an epigenome-wide association study using RRBS on isolated T cell populations from two different RA cohorts; (1) disease modifying anti-rheumatic drug (DMARD) naïve RA patients with active disease and (2) MTX-treated RA patients who had been in remission for >12 months. The two cohorts were compared to matched healthy controls.</p>',
'date' => '2020-02-14',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fimmu.2020.00194/full',
'doi' => '10.3389/fimmu.2020.00194',
'modified' => '2022-05-19 16:10:24',
'created' => '2020-03-13 13:45:54',
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(int) 48 => array(
'id' => '3794',
'name' => 'Obesogen effect of bisphenol S alters mRNA expression and DNA methylation profiling in male mouse liver',
'authors' => 'Brulport Axelle, Vaiman Daniel, Chagnon Marie-Christine, Le Corre Ludovic',
'description' => '<p>Environmental pollution is increasingly considered an important factor involved in the obesity incidence. Endocrine disruptors (EDs) are important actors in the concept of DOHaD (Developmental Origins of Health and Disease), where epigenetic mechanisms play crucial roles. Bisphenol A (BPA), a monomer used in the manufacture of plastics and resins is one of the most studied obesogenic endocrine disruptor. Bisphenol S (BPS), a BPA substitute, has the same obesogenic properties, acting at low doses with a sex-specific effect following perinatal exposure. Since the liver is a major organ in regulating body lipid homeostasis, we investigated gene expression and DNA methylation under low-dose BPS exposure. The BPS obesogenic effect was associated with an increase of hepatic triglyceride content. These physiological disturbances were accompanied by genome-wide changes in gene expression (1366 genes significantly modified more than 1.5-fold). Gene ontology analysis revealed alteration of gene cascades involved in protein translation and complement regulation. It was associated with hepatic DNA hypomethylation in autosomes and hypermethylation in sex chromosomes. Although no systematic correlation has been found between gene repression and hypermethylation, several genes related to liver metabolism were either hypermethylated (Acsl4, Gpr40, Cel, Pparδ, Abca6, Ces3a, Sgms2) or hypomethylated (Soga1, Gpihbp1, Nr1d2, Mlxipl, Rps6kb2, Esrrb, Thra, Cidec). In specific cases (Hapln4, ApoA4, Cidec, genes involved in lipid metabolism and liver fibrosis) mRNA upregulation was associated with hypomethylation. In conclusion, we show for the first time wide disruptive physiological effects of low-dose of BPS, which raises the question of its harmlessness as an industrial substitute for BPA.</p>',
'date' => '2019-10-15',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/31683443',
'doi' => '10.1016/j.chemosphere.2019.125092',
'modified' => '2022-05-19 16:10:42',
'created' => '2019-12-02 15:25:44',
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(int) 49 => array(
'id' => '3674',
'name' => 'Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA.',
'authors' => 'Mayorga L, Salassa BN, Marzese DM, Loos MA, Eiroa HD, Lubieniecki F, García Samartino C, Romano PS, Roqué M',
'description' => '<p>Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a "pro-survival" adaptive state that seems to be triggered by the differential methylation of nuclear genes.</p>',
'date' => '2019-04-01',
'pmid' => 'http://www.pubmed.gov/30673822',
'doi' => '10.1007/s00018-019-03008-5',
'modified' => '2022-05-19 16:10:59',
'created' => '2019-06-21 14:55:31',
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(int) 50 => array(
'id' => '3416',
'name' => 'Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination.',
'authors' => 'Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG',
'description' => '<p>Bacillus Calmette-Guérin (BCG) is the only licensed vaccine for tuberculosis (TB) and induces highly variable protection against pulmonary disease in different countries. We hypothesised that DNA methylation is one of the molecular mechanisms driving variability in BCG-induced immune responses. DNA methylation in peripheral blood mononuclear cells (PBMC) from BCG vaccinated infants was measured and comparisons made between low and high BCG-specific cytokine responders. We found 318 genes and 67 pathways with distinct patterns of DNA methylation, including immune pathways, e.g. for T cell activation, that are known to directly affect immune responses. We also highlight signalling pathways that could indirectly affect the BCG-induced immune response: potassium and calcium channel, muscarinic acetylcholine receptor, G Protein coupled receptor (GPCR), glutamate signalling and WNT pathways. This study suggests that in addition to immune pathways, cellular processes drive vaccine-induced immune responses. Our results highlight mechanisms that require consideration when designing new TB vaccines.</p>',
'date' => '2018-08-30',
'pmid' => 'http://www.pubmed.gov/30166570',
'doi' => '10.1038/s41598-018-31537-9',
'modified' => '2022-05-19 16:11:19',
'created' => '2018-12-04 09:51:07',
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(int) 51 => array(
'id' => '3322',
'name' => 'In Situ Fixation Redefines Quiescence and Early Activation of Skeletal Muscle Stem Cells',
'authors' => 'Machado L. et al.',
'description' => '<div class="abstract">
<h2 class="sectionTitle" tabindex="0">Summary</h2>
<div class="content">
<p>State of the art techniques have been developed to isolate and analyze cells from various tissues, aiming to capture their <em>in vivo</em> state. However, the majority of cell isolation protocols involve lengthy mechanical and enzymatic dissociation steps followed by flow cytometry, exposing cells to stress and disrupting their physiological niche. Focusing on adult skeletal muscle stem cells, we have developed a protocol that circumvents the impact of isolation procedures and captures cells in their native quiescent state. We show that current isolation protocols induce major transcriptional changes accompanied by specific histone modifications while having negligible effects on DNA methylation. In addition to proposing a protocol to avoid isolation-induced artifacts, our study reveals previously undetected quiescence and early activation genes of potential biological interest.</p>
</div>
</div>',
'date' => '2017-11-14',
'pmid' => 'http://www.cell.com/cell-reports/abstract/S2211-1247(17)31543-7',
'doi' => '',
'modified' => '2022-05-19 16:11:43',
'created' => '2018-02-02 16:36:37',
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(int) 52 => array(
'id' => '3286',
'name' => 'DNMT3B overexpression contributes to aberrant DNA methylation and MYC-driven tumor maintenance in T-ALL and Burkitt’s lymphoma',
'authors' => 'Poole et al.',
'description' => '<p>Aberrant DNA methylation is a hallmark of cancer. However, our understanding of how tumor cell-specific DNA methylation patterns are established and maintained is limited. Here, we report that in T-cell acute lymphoblastic leukemia (T-ALL) and Burkitt’s lymphoma the <em>MYC </em>oncogene causes overexpression of DNA methyltransferase (DNMT) 1 and 3B, which contributes to tumor maintenance. By utilizing a tetracycline-regulated <em>MYC </em>transgene in a mouse T-ALL (EμSRα-tTA;tet-o- MYC) and human Burkitt’s lymphoma (P493-6) model, we demonstrated that DNMT1 and DNMT3B expression depend on high MYC levels, and that their transcription decreased upon MYC-inactivation. Chromatin immunoprecipitation indicated that MYC binds to the <em>DNMT1 </em>and <em>DNMT3B </em>promoters, implicating a direct transcriptional regulation. Hence, shRNA-mediated knock-down of endogenous MYC in human T-ALL and Burkitt’s lymphoma cell lines, downregulated DNMT3B expression. Knock-down and pharmacologic inhibition of DNMT3B in T-ALL reduced cell proliferation associated with genome-wide changes in DNA methylation, indicating a tumor promoter function during tumor maintenance. We provide novel evidence that MYC directly deregulates the expression of both <em>de novo </em>and maintenance DNMTs, showing that MYC controls DNA methylation in a genome-wide fashion. Our finding that a coordinated interplay between the components of the DNA methylating machinery contributes to MYC-driven tumor maintenance highlights the potential of specific DNMTs for targeted therapies.</p>',
'date' => '2017-08-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/29100357',
'doi' => '10.18632/oncotarget.20176',
'modified' => '2022-05-19 16:12:01',
'created' => '2017-11-10 11:44:30',
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(int) 53 => array(
'id' => '3063',
'name' => 'DNA methylation and alcohol use disorders: Progress and challenges',
'authors' => 'Zhang H. and Gelernter J.',
'description' => '<section class="article-section article-body-section" id="ajad12465-sec-0001">
<h3>Background and Objectives</h3>
<p>Risk for alcohol use disorders (AUDs) is influenced by gene–environment interactions. Environmental factors can affect gene expression through epigenetic mechanisms such as DNA methylation. This review outlines the findings regarding the association of DNA methylation and AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0002">
<h3>Methods</h3>
<p>We searched PubMed (by April 2016) and identified 29 studies that examined the association of DNA methylation and AUDs. We also evaluated the methods used in these studies.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0003">
<h3>Results</h3>
<p>Two studies demonstrated elevated global (repetitive element) DNA methylation levels in AUD subjects. Fifteen candidate gene studies showed hypermethylation of promoter regions of six genes (<em>AVP</em>, <em>DNMT3B</em>, <em>HERP</em>, <em>HTR3A</em>, <em>OPRM1</em>, and <em>SNCA</em>) or hypomethylation of the <em>GDAP1</em> promoter region in AUD subjects. Five genome-wide DNA methylation studies demonstrated widespread DNA methylation changes across the genome in AUD subjects. Six studies showed significant correlations of DNA methylation with gene expression in AUD subjects. Three studies revealed interactive effects of genetic variation and DNA methylation on susceptibility to AUDs. Most studies analyzed AUD-associated DNA methylation changes in the peripheral blood; a few studies examined DNA methylation changes in postmortem brains of AUD subjects.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0004">
<h3>Discussion and Conclusions</h3>
<p>Chronic alcohol consumption may result in DNA methylation changes, leading to neuroadaptations that may underlie some of the mechanisms of AUD risk and persistence. Future studies are needed to confirm the few existing results, and then to elucidate whether DNA methylation changes are the cause or consequence of AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0005">
<h3>Scientific Significance</h3>
<p>DNA methylation profiles may be used to assess AUD status or monitor AUD treatment response. (Am J Addict 2016;XX:1–14)</p>
</section>',
'date' => '2016-10-19',
'pmid' => 'http://onlinelibrary.wiley.com/doi/10.1111/ajad.12465/abstract?campaign=wolsavedsearch',
'doi' => '',
'modified' => '2022-05-19 16:12:16',
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'name' => 'Diagenode® Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage',
'authors' => 'Anne-Clémence Veillard, Paul Datlinger, Miklos Laczik, Sharon Squazzo & Christoph Bock',
'description' => '<p>Reduced representation bisulfite sequencing (RRBS) enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of whole-genome sequencing. The Diagenode Premium RRBS kit makes this technology widely available and provides high coverage (up to 4 million CpGs in human samples). Multiplexing prior to bisulfite conversion allows processing of 96 samples per experiment, enabling studies of large cohorts.</p>',
'date' => '2016-01-28',
'pmid' => 'http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html',
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
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<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
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<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
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<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
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<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<h6 style="height:60px">Premium Methyl UMI-UDI Adapters - Set B</h6>
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<h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>our epigenomics services</strong>. Fill out the form below and we will be in touch with you very soon.</p><p><small>All <span style="font-size:16px;color:red;">*</span> fields are mandatory</small></p>
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<h2>Service Information</h2>
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<h4>Which services are you interested in?</h4>
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<div class="small-12 large-12 columns">
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<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="MeDIP-seq" id="QuoteEpigenomicsServiceMeDIPSeq" /><label for="QuoteEpigenomicsServiceMeDIPSeq">MeDIP-seq</label></div>
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<div class="row collapse">
<div class="small-12 medium-12 large-3 columns">
<span class="prefix">Sample species</span>
</div>
<div class="small-12 medium-12 large-9 columns">
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<div class="small-12 medium-12 large-6 columns">
<span class="prefix">Total number of samples (including replicates)</span>
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<div class="small-12 medium-12 large-6 columns">
<input name="data[Quote][number_samples]" maxlength="255" type="text" id="QuoteNumberSamples"/> </div>
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<h2>Contact Information</h2>
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<span class="prefix">First name <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][first_name]" placeholder="john" maxlength="255" type="text" id="QuoteFirstName" required="required"/> </div>
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<span class="prefix">Last name <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][last_name]" placeholder="doe" maxlength="255" type="text" id="QuoteLastName" required="required"/> </div>
</div>
<div class="row collapse">
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<span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][company]" placeholder="Organisation / Institute" maxlength="255" type="text" id="QuoteCompany" required="required"/> </div>
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<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Phone number</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][phone_number]" placeholder="+1 862 209-4680" maxlength="255" type="text" id="QuotePhoneNumber"/> </div>
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<div class="small-3 large-2 columns">
<span class="prefix">City</span>
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<div class="small-3 large-2 columns">
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<option value="">-- select a country --</option>
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<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
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<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
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<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d'Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
<option value="DK">Denmark</option>
<option value="DJ">Djibouti</option>
<option value="DM">Dominica</option>
<option value="DO">Dominican Republic</option>
<option value="EC">Ecuador</option>
<option value="EG">Egypt</option>
<option value="SV">El Salvador</option>
<option value="GQ">Equatorial Guinea</option>
<option value="ER">Eritrea</option>
<option value="EE">Estonia</option>
<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
<option value="FO">Faroe Islands</option>
<option value="FJ">Fiji</option>
<option value="FI">Finland</option>
<option value="FR">France</option>
<option value="GF">French Guiana</option>
<option value="PF">French Polynesia</option>
<option value="TF">French Southern Territories</option>
<option value="GA">Gabon</option>
<option value="GM">Gambia</option>
<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
<option value="GI">Gibraltar</option>
<option value="GR">Greece</option>
<option value="GL">Greenland</option>
<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
<option value="GG">Guernsey</option>
<option value="GN">Guinea</option>
<option value="GW">Guinea-Bissau</option>
<option value="GY">Guyana</option>
<option value="HT">Haiti</option>
<option value="HM">Heard Island and McDonald Islands</option>
<option value="VA">Holy See (Vatican City State)</option>
<option value="HN">Honduras</option>
<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
<option value="IS">Iceland</option>
<option value="IN">India</option>
<option value="ID">Indonesia</option>
<option value="IR">Iran, Islamic Republic of</option>
<option value="IQ">Iraq</option>
<option value="IE">Ireland</option>
<option value="IM">Isle of Man</option>
<option value="IL">Israel</option>
<option value="IT">Italy</option>
<option value="JM">Jamaica</option>
<option value="JP">Japan</option>
<option value="JE">Jersey</option>
<option value="JO">Jordan</option>
<option value="KZ">Kazakhstan</option>
<option value="KE">Kenya</option>
<option value="KI">Kiribati</option>
<option value="KP">Korea, Democratic People's Republic of</option>
<option value="KR">Korea, Republic of</option>
<option value="KW">Kuwait</option>
<option value="KG">Kyrgyzstan</option>
<option value="LA">Lao People's Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
<option value="LS">Lesotho</option>
<option value="LR">Liberia</option>
<option value="LY">Libya</option>
<option value="LI">Liechtenstein</option>
<option value="LT">Lithuania</option>
<option value="LU">Luxembourg</option>
<option value="MO">Macao</option>
<option value="MK">Macedonia, Republic of</option>
<option value="MG">Madagascar</option>
<option value="MW">Malawi</option>
<option value="MY">Malaysia</option>
<option value="MV">Maldives</option>
<option value="ML">Mali</option>
<option value="MT">Malta</option>
<option value="MH">Marshall Islands</option>
<option value="MQ">Martinique</option>
<option value="MR">Mauritania</option>
<option value="MU">Mauritius</option>
<option value="YT">Mayotte</option>
<option value="MX">Mexico</option>
<option value="FM">Micronesia, Federated States of</option>
<option value="MD">Moldova</option>
<option value="MC">Monaco</option>
<option value="MN">Mongolia</option>
<option value="ME">Montenegro</option>
<option value="MS">Montserrat</option>
<option value="MA">Morocco</option>
<option value="MZ">Mozambique</option>
<option value="MM">Myanmar</option>
<option value="NA">Namibia</option>
<option value="NR">Nauru</option>
<option value="NP">Nepal</option>
<option value="NL">Netherlands</option>
<option value="NC">New Caledonia</option>
<option value="NZ">New Zealand</option>
<option value="NI">Nicaragua</option>
<option value="NE">Niger</option>
<option value="NG">Nigeria</option>
<option value="NU">Niue</option>
<option value="NF">Norfolk Island</option>
<option value="MP">Northern Mariana Islands</option>
<option value="NO">Norway</option>
<option value="OM">Oman</option>
<option value="PK">Pakistan</option>
<option value="PW">Palau</option>
<option value="PS">Palestine, State of</option>
<option value="PA">Panama</option>
<option value="PG">Papua New Guinea</option>
<option value="PY">Paraguay</option>
<option value="PE">Peru</option>
<option value="PH">Philippines</option>
<option value="PN">Pitcairn</option>
<option value="PL">Poland</option>
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</div>
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<div class="small-12 columns" >
<h6 style="height:60px">RRBS service (Reduced Representation Bisulfite ...</h6>
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<p>将 <input name="data[Cart][quantity]" placeholder="1" value="1" min="1" style="width:60px;display:inline" type="number" id="CartQuantity" required="required"/> <strong> Premium RRBS kit V2 <br /> RRBS for low DNA amounts and accurate analysis</strong> 添加至我的购物车。</p>
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<p>将 <input name="data[Cart][quantity]" placeholder="1" value="1" min="1" style="width:60px;display:inline" type="number" id="CartQuantity" required="required"/> <strong> Premium RRBS kit V2 x96<br /> RRBS for low DNA amounts and accurate analysis</strong> 添加至我的购物车。</p>
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'
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'id' => '3224',
'antibody_id' => null,
'name' => 'Premium RRBS kit V2 x96<br /> RRBS for low DNA amounts and accurate analysis',
'description' => '<p><a href="https://www.diagenode.com/files/products/kits/RRBS-KIT-V2_manual.pdf"><img src="https://www.diagenode.com/img/buttons/bt-manual.png" /></a></p>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<script src="chrome-extension://hhojmcideegachlhfgfdhailpfhgknjm/web_accessible_resources/index.js"></script>',
'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>RRBS workflow allow you to:</p>
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<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
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<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
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<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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'description' => '<h4>(Coming soon)</h4>
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, targeted solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p></p>
<p>RRBS workflow allow you to:</p>
<ul style="list-style-type: disc;">
<li>Perform cost-effective and reliable genome-scale DNA methylation analysis</li>
<li>Detect- up to 4 million CpGs (with coverage > 10) in human samples</li>
<li>Use DNA inputs as low as 25ng</li>
<li>Obtain higher accuracy with UMI integration -- included in kit</li>
<li>Identify methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Benefit from increased accuracy with the use of Unique Molecular Identifiers (UMIs)</li>
<li>Get better insight of your epigenetics research</li>
</ul>
<p><span>Diagenode's <strong>Premium RRBS kit</strong></span><strong> V2</strong><span><span> </span>has been further optimized to generate RRBS libraries from the<span> </span></span><strong>lowest DNA amounts<span> (down to 25 ng)</span></strong><strong> </strong><span>and secure<span> </span></span><strong>high-quality NGS data<span> </span></strong><span>for DNA methylation analysis. Combined with <strong>unique molecular identifiers (UMI)</strong>, this complete solution ensures an</span><span><span><span> improved quantification accuracy during the DNA methyaltion analysis</span></span></span></p>
<h2>How Do UMIs Work?</h2>
<p><span>UMIs incorporate a unique barcode onto each molecule within a given sample library. By incorporating individual barcodes on each original DNA fragment, variant alleles present in the original sample (true variants) can be distinguished from errors introduced during library preparation, target enrichment, or sequencing.</span></p>
<p><span><< Image.jpg>></span></p>
<h2><span>Accurate Coverage of CpGs</span></h2>
<p><span><<Image.jpg>></span></p>',
'label1' => 'Validation: Sequencing quality',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><b>Figure 1</b>. <b>Premium RRBS V2 construct</b>. Integrated UMIs enable removal of PCR duplicates during the data analysis.</p>
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<p>Successful ChIP-seq results generated on 50,000 of K562 cells using True MicroChIP technology. ChIP has been performed accordingly to True MicroChIP protocol (Diagenode, Cat. No. C01010130), including DNA purification using the MicroChIP DiaPure columns. For the library preparation the MicroPlex Library Preparation Kit (Diagenode, Cat. No. C05010001) has been used. The below figure shows the peaks from ChIP-seq experiments using the following Diagenode antibodies: H3K4me1 (C15410194), H3K9/14ac (C15410200), H3K27ac (C15410196) and H3K36me3 (C15410192).</p>
<p><img src="https://www.diagenode.com/img/product/kits/figure-igv-microchip.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 1:</strong> Integrative genomics viewer (IGV) visualization of ChIP-seq experiments using 50,000 of K562 cells.</p>
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<h2 style="text-align: center;"></h2>
<h2 style="text-align: center;">MicroChIP DiaPure columns after CUT&Tag</h2>
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<p><img src="https://www.diagenode.com/img/product/kits/figure-diapure-igv.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<p><strong>Figure 2:</strong> Integrative genomics viewer (IGV) visualization of CUT&Tag experiments: MicroChIP DiaPure columns vs phenol-chloroform purification using the H3K27me3 antibody.</p>',
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<p><strong>Reduced representation bisulfite sequencing (RRBS) </strong> <span>enables </span><span>genome-s</span><span>cale </span>DNA methylation<span> analysis</span> at the single nucleotide level <span>in any vertebrate species. </span><span>The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of</span> whole genome sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent<span> biologically relevant target</span> CpG-rich regions including <span>promoters and </span>CpG islands.<span> Our RRBS service makes this technology widely available and provides high coverage (up to 7 million CpGs</span><span> detected </span><span>in human samples).</span></p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
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<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
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<h2>Complete end-to-end service</h2>
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<li><span><span class="ui-provider ee cfi bsv cfj cfk cfl cfm cfn cfo cfp cfq cfr cfs cft cfu cfv cfw cfx cfy cfz cga cgb cgc cgd cge cgf cgg cgh cgi cgj cgk cgl cgm cgn cgo" dir="ltr">From DNA QC to sequencing raw data</span></span></li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2</a> widely adopted</li>
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<div class="extra-spaced">
<h2></h2>
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<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>',
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<h4>RRBS Service includes:</h4>
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<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
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<li>Assessment of DNA quality</li>
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<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
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<td style="width: 264px;"><strong>Deep sequencing</strong></td>
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<h4><strong>Analysis</strong></h4>
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<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="width: 262px;"><strong>Standard</strong></td>
<td style="width: 624px;">
<ul>
<li>FASTQ raw data</li>
<li>FASTQC quality control insights</li>
<li>Alignment of bisulfite sequencing data against reference genome</li>
<li>Methylation calling and extraction</li>
<li>Summary statistics</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;"><strong>Differential methylation analysis<br /></strong></td>
<td style="width: 624px;">
<ul>
<li>Methylation level analysis</li>
<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, …)</li>
<li>Clustering analysis</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
</ul>
</td>
</tr>
<tr>
<td style="width: 262px;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 624px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
</ul>
</td>
</tr>
</tbody>
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<div class="small-12 medium-8 large-8 columns"><br />
<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
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<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div class="large-12 columns">
<div style="text-align: justify;" class="small-12 medium-8 large-8 columns">
<h2>Complete solutions for DNA methylation studies</h2>
<p>Whether you are experienced or new to the field of DNA methylation, Diagenode has everything you need to make your assay as easy and convenient as possible while ensuring consistent data between samples and experiments. Diagenode offers sonication instruments, reagent kits, high quality antibodies, and high-throughput automation capability to address all of your specific DNA methylation analysis requirements.</p>
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<p>DNA methylation was the first discovered epigenetic mark and is the most widely studied topic in epigenetics. <em>In vivo</em>, DNA is methylated following DNA replication and is involved in a number of biological processes including the regulation of imprinted genes, X chromosome inactivation. and tumor suppressor gene silencing in cancer cells. Methylation often occurs in cytosine-guanine rich regions of DNA (CpG islands), which are commonly upstream of promoter regions.</p>
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<div class="small-12 medium-12 large-12 columns"><br /><br />
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#dnamethyl"><i class="fa fa-caret-right"></i> Learn more</a>
<div id="dnamethyl" class="content">5-methylcytosine (5-mC) has been known for a long time as the only modification of DNA for epigenetic regulation. In 2009, however, Kriaucionis discovered a second methylated cytosine, 5-hydroxymethylcytosine (5-hmC). The so-called 6th base, is generated by enzymatic conversion of 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine by the TET family of oxygenases. Early reports suggested that 5-hmC may represent an intermediate of active demethylation in a new pathway which demethylates DNA, converting 5-mC to cytosine. Recent evidence fuel this hypothesis suggesting that further oxidation of the hydroxymethyl group leads to a formyl or carboxyl group followed by either deformylation or decarboxylation. The formyl and carboxyl groups of 5-formylcytosine (5-fC) and 5-carboxylcytosine (5-caC) could be enzymatically removed without excision of the base.
<p class="text-center"><img src="https://www.diagenode.com/img/categories/kits_dna/dna_methylation_variants.jpg" /></p>
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</li>
</ul>
<br />
<h2>Main DNA methylation technologies</h2>
<p style="text-align: justify;">Overview of the <span style="font-weight: 400;">three main approaches for studying DNA methylation.</span></p>
<div class="row">
<ol>
<li style="font-weight: 400;"><span style="font-weight: 400;">Chemical modification with bisulfite – Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Enrichment of methylated DNA (including MeDIP and MBD)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Treatment with methylation-sensitive or dependent restriction enzymes</span></li>
</ol>
<p><span style="font-weight: 400;"> </span></p>
<div class="row">
<table>
<thead>
<tr>
<th></th>
<th>Description</th>
<th width="350">Features</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td><span style="font-weight: 400;">Chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at single nucleotide resolution.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Single nucleotide resolution</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Quantitative analysis - methylation rate (%)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Gold standard and well studied</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><b>Methylated DNA enrichment</b></td>
<td><span style="font-weight: 400;">(Hydroxy-)Methylated DNA is enriched by using specific antibodies (hMeDIP or MeDIP) or proteins (MBD) that specifically bind methylated CpG sites in fragmented genomic DNA.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Resolution depends on the fragment size of the enriched methylated DNA (300 bp)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Qualitative analysis</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><strong>Restriction enzyme-based digestion</strong></td>
<td><span style="font-weight: 400;">Use of (hydroxy)methylation-sensitive or (hydroxy)methylation-dependent restriction enzymes for DNA methylation analysis at specific sites.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Determination of methylation status is limited by the enzyme recognition site</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Easy to use</span></li>
</ul>
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<div style="text-align: justify;" class="large-12 columns">Bisulfite modification of DNA is the most commonly used, "<strong>gold standard</strong>" method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. T<span style="font-weight: 400;">his technology is based on the chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at the singe nucleotide level.</span></div>
<div style="text-align: justify;" class="large-12 columns"></div>
<div style="text-align: justify;" class="large-12 columns">Various analyses can be performed on the altered sequence to retrieve this information: bisulfite sequencing, pyrosequencing, methylation-specific PCR, high resolution melting curve analysis, microarray-based approaches, and next-generation sequencing.
<h3>How it works</h3>
Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected (see Figure 1).
<p class="text-center"><img src="https://www.diagenode.com/img/applications/bisulfite.png" /><br />Figure 1: Overview of bisulfite conversion of DNA</p>
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<p>Sodium bisulfite conversion of genomic DNA is the most commonly used method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. It enables <span>to differentiate and detect unmethylated versus methylated cytosines. This procedure can then be followed either by <strong>PCR amplification</strong> or <strong>next generation sequencing</strong> to reveal the methylation status of every cytosine in gene specific amplification or whole genome amplification.</span></p>
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<h2>How it works</h2>
<p style="text-align: left;">Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, while methylated cytosines remain unchanged. <span>The DNA is then amplified by PCR where the uracils are converted to thymines. </span></p>
<p style="text-align: center;"><span></span></p>
<p><img src="https://www.diagenode.com/img/categories/bisulfite-conversion/bisulfite-conversion-acgautac.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<h2>Advantages</h2>
<ul class="nobullet" style="font-size: 19px;">
<li><i class="fa fa-arrow-circle-right"></i><strong> </strong><strong>Single nucleotide</strong> resolution</li>
<li><i class="fa fa-arrow-circle-right"></i><strong> Gene-specific </strong>and <strong>genome-wide</strong><span> analyses</span></li>
<li><i class="fa fa-arrow-circle-right"></i><strong> NGS</strong><span> </span>compatible</li>
</ul>
<h2>Downstream analysis techniques</h2>
<ul class="square">
<li>Reduced Representation Bisulfite Sequencing (RRBS) with our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS Kit V2</a></li>
<li>Bisulfite conversion with our <a href="https://www.diagenode.com/en/p/premium-bisulfite-kit-50-rxns">Premium Bisulfite Kit</a> followed by qPCR, Sanger, Pyrosequencing</li>
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<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
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'slug' => 'rrbs-v2-methylated-control-fa',
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'modified' => '2022-06-22 15:15:36',
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(int) 4 => array(
'id' => '1157',
'name' => 'Sequence of the unmethylated spike-in control in FASTA format – Premium RRBS kit v2',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit v2 in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/RRBS-KIT-V2_manual.pdf">Premium RRBS Kit v2 Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-V2-x24">Premium RRBS Kit v2 page</a><span> </span>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_v2_unmethylated_control.fa',
'slug' => 'rrbs-v2-unmethylated-control-fa',
'meta_keywords' => '',
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'modified' => '2022-06-22 15:16:45',
'created' => '2022-06-22 15:16:45',
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(int) 5 => array(
'id' => '1158',
'name' => 'Positions of the unmethylated cytosines in the sequence of the methylated control in BED format – Premium RRBS kit v2',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit v2 in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/RRBS-KIT-V2_manual.pdf">Premium RRBS Kit v2 Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-V2-x24">Premium RRBS Kit v2 page</a><span> </span>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_v2_methylated_spike_in_unmeth_positions.bed',
'slug' => 'rrbs-v2-methylated-spike-in-unmeth-positions',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2022-06-22 15:18:20',
'created' => '2022-06-22 15:18:20',
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[maximum depth reached]
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(int) 6 => array(
'id' => '1159',
'name' => 'Premium RRBS kit V2 - Spike-in controls analysis to estimate bisulfite conversion efficiency',
'description' => '<p>This document describes how to process demultiplexed RRBS V2 sequencing data to extract the information from the spike-in controls included in the Premium RRBS Kit v2 in order to estimate the bisulfite conversion efficiency.</p>
<p>Sequences of the methylated and unmethylated spike-in controls, as well as the positions of the unmethylated cytosines present in the methylated spike-in control, can be downloaded from the ‘Documents’ section of the Premium RRBS Kit v2 page <a href="../p/premium-rrbs-kit-V2-x24">https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24</a>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => 'Manual',
'url' => 'files/products/kits/Premium RRBS-spikein-controls-manual.pdf',
'slug' => 'premium-rrbs-spike-in-controls-manual',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2022-06-29 14:34:07',
'created' => '2022-06-29 14:34:07',
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'Feature' => array(),
'Image' => array(
(int) 0 => array(
'id' => '1778',
'name' => 'product/kits/methyl-kit-icon.png',
'alt' => 'Methylation kit icon',
'modified' => '2019-04-23 15:17:01',
'created' => '2018-03-15 15:52:12',
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'Protocol' => array(),
'Publication' => array(
(int) 0 => array(
'id' => '4986',
'name' => 'Multi-omics characterization of chronic social defeat stress recall-activated engram nuclei in Arc-GFP mice',
'authors' => 'Monika Chanu Chongtham et al.',
'description' => '<p><span>Susceptibility to chronic social stressors often results in the development of mental health disorders including major depressive and anxiety disorders. In contrast, some individuals remain resilient even after repeated stress exposure. Understanding the molecular drivers behind these divergent phenotypic outcomes is crucial. However, previous studies using the chronic social defeat (CSD) stress model have been limited by the use of bulk tissues investigating single omics domains. To overcome these limitations, here, we applied the CSD mouse model to Arc-GFP mice for investigating the mechanistic divergence between susceptibility and resilience, specifically in stress recall-activated engram nuclei. By conducting an in-depth analysis of the less-known differential methylome landscape in the ventral hippocampal engrams, we noted unique phenotype-specific alterations in multiple biological processes with an overrepresentation of GTPase-related mechanisms. Interestingly, the differentially methylated regions were enriched in ETS transcription factor binding sites (TFBSs), important targets of the Ras-ETS signaling pathway. This differential methylation in the ETS TFBSs could form the basis of persisting stress effects long after stressor exposure. Furthermore, by integrating the methylome modifications with transcriptomic alterations, we resolved the GTPase-related mechanisms differentially activated in the resilient and susceptible phenotypes with alterations in endocytosis overrepresented in the susceptible phenotype. Overall, our findings implicate critical avenues for future therapeutic applications.</span></p>',
'date' => '2024-10-09',
'pmid' => 'https://www.researchsquare.com/article/rs-4643912/v1',
'doi' => 'https://doi.org/10.21203/rs.3.rs-4643912/v1',
'modified' => '2024-10-14 09:08:24',
'created' => '2024-10-14 09:08:24',
'ProductsPublication' => array(
[maximum depth reached]
)
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(int) 1 => array(
'id' => '4959',
'name' => 'Pesticide-induced transgenerational alterations of genome-wide DNA methylation patterns in the pancreas of Xenopus tropicalis correlate with metabolic phenotypes',
'authors' => 'Roza M. et al. ',
'description' => '<p><span>The unsustainable use of manmade chemicals poses significant threats to biodiversity and human health. Emerging evidence highlights the potential of certain chemicals to cause transgenerational impacts on metabolic health. Here, we investigate male transmitted epigenetic transgenerational effects of the anti-androgenic herbicide linuron in the pancreas of </span><em>Xenopus tropicalis</em><span><span> </span>frogs, and their association with metabolic phenotypes. Reduced representation bisulfite sequencing (RRBS) was used to assess genome-wide DNA methylation patterns in the pancreas of adult male F2 generation ancestrally exposed to environmentally relevant linuron levels (44 ± 4.7 μg/L). We identified 1117 differentially methylated regions (DMRs) distributed across the<span> </span></span><em>X. tropicalis</em><span><span> </span>genome, revealing potential regulatory mechanisms underlying metabolic disturbances. DMRs were identified in genes crucial for pancreatic function, including calcium signalling (</span><em>clstn2, cacna1d</em><span><span> </span>and<span> </span></span><em>cadps2</em><span>), genes associated with type 2 diabetes (</span><em>tcf7l2</em><span><span> </span>and<span> </span></span><em>adcy5</em><span>) and a biomarker for pancreatic ductal adenocarcinoma (</span><em>plec</em><span>). Correlation analysis revealed associations between DNA methylation levels in these genes and metabolic phenotypes, indicating epigenetic regulation of glucose metabolism. Moreover, differential methylation in genes related to histone modifications suggests alterations in the epigenetic machinery. These findings underscore the long-term consequences of environmental contamination on pancreatic function and raise concerns about the health risks associated with transgenerational effects of pesticides.</span></p>',
'date' => '2024-10-05',
'pmid' => 'https://www.sciencedirect.com/science/article/pii/S030438942402034X',
'doi' => 'https://doi.org/10.1016/j.jhazmat.2024.135455',
'modified' => '2024-09-02 10:08:08',
'created' => '2024-09-02 10:08:08',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 2 => array(
'id' => '4962',
'name' => 'Triphenyl Phosphate Alters Methyltransferase Expression and Induces Genome-Wide Aberrant DNA Methylation in Zebrafish Larvae',
'authors' => 'Negi C.K. et al.',
'description' => '<p><span>Emerging environmental contaminants, organophosphate flame retardants (OPFRs), pose significant threats to ecosystems and human health. Despite numerous studies reporting the toxic effects of OPFRs, research on their epigenetic alterations remains limited. In this study, we investigated the effects of exposure to 2-ethylhexyl diphenyl phosphate (EHDPP), tricresyl phosphate (TMPP), and triphenyl phosphate (TPHP) on DNA methylation patterns during zebrafish embryonic development. We assessed general toxicity and morphological changes, measured global DNA methylation and hydroxymethylation levels, and evaluated DNA methyltransferase (DNMT) enzyme activity, as well as mRNA expression of DNMTs and ten-eleven translocation (TET) methylcytosine dioxygenase genes. Additionally, we analyzed genome-wide methylation patterns in zebrafish larvae using reduced-representation bisulfite sequencing. Our morphological assessment revealed no general toxicity, but a statistically significant yet subtle decrease in body length following exposure to TMPP and EHDPP, along with a reduction in head height after TPHP exposure, was observed. Eye diameter and head width were unaffected by any of the OPFRs. There were no significant changes in global DNA methylation levels in any exposure group, and TMPP showed no clear effect on DNMT expression. However, EHDPP significantly decreased only DNMT1 expression, while TPHP exposure reduced the expression of several DNMT orthologues and TETs in zebrafish larvae, leading to genome-wide aberrant DNA methylation. Differential methylation occurred primarily in introns (43%) and intergenic regions (37%), with 9% and 10% occurring in exons and promoter regions, respectively. Pathway enrichment analysis of differentially methylated region-associated genes indicated that TPHP exposure enhanced several biological and molecular functions corresponding to metabolism and neurological development. KEGG enrichment analysis further revealed TPHP-mediated potential effects on several signaling pathways including TGFβ, cytokine, and insulin signaling. This study identifies specific changes in DNA methylation in zebrafish larvae after TPHP exposure and brings novel insights into the epigenetic mode of action of TPHP.</span></p>',
'date' => '2024-08-29',
'pmid' => 'https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.4c00223',
'doi' => 'https://doi.org/10.1021/acs.chemrestox.4c00223',
'modified' => '2024-09-02 10:16:01',
'created' => '2024-09-02 10:16:01',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 3 => array(
'id' => '4934',
'name' => 'Differential DNA methylation in iPSC-derived dopaminergic neurons: a step forward on the role of SNORD116 microdeletion in the pathophysiology of addictive behavior in Prader-Willi syndrome',
'authors' => 'Salles J. et al.',
'description' => '<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Introduction</h3>
<p>A microdeletion including the<span> </span><i>SNORD116</i><span> </span>gene (<i>SNORD116</i><span> </span>MD) has been shown to drive the Prader-Willi syndrome (PWS) features. PWS is a neurodevelopmental disorder clinically characterized by endocrine impairment, intellectual disability and psychiatric symptoms such as a lack of emotional regulation, impulsivity, and intense temper tantrums with outbursts. In addition, this syndrome is associated with a nutritional trajectory characterized by addiction-like behavior around food in adulthood. PWS is related to the genetic loss of expression of a minimal region that plays a potential role in epigenetic regulation. Nevertheless, the role of the<span> </span><i>SNORD116</i><span> </span>MD in DNA methylation, as well as the impact of the oxytocin (OXT) on it, have never been investigated in human neurons.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Methods</h3>
<p>We studied the methylation marks in induced pluripotent stem-derived dopaminergic neurons carrying a<span> </span><i>SNORD116</i><span> </span>MD in comparison with those from an age-matched adult healthy control. We also performed identical neuron differentiation in the presence of OXT. We performed a genome-wide DNA methylation analysis from the iPSC-derived dopaminergic neurons by reduced-representation bisulfite sequencing. In addition, we performed RNA sequencing analysis in these iPSC-derived dopaminergic neurons differentiated with or without OXT.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Results</h3>
<p>The analysis revealed that 153,826 cytosines were differentially methylated between<span> </span><i>SNORD116</i><span> </span>MD neurons and control neurons. Among the differentially methylated genes, we determined a list of genes also differentially expressed. Enrichment analysis of this list encompassed the dopaminergic system with<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i>.<span> </span><i>COMT</i><span> </span>displayed hypermethylation and under-expression in<span> </span><i>SNORD116</i><span> </span>MD, and<span> </span><i>SLC6A3</i><span> </span>displayed hypomethylation and over-expression in<span> </span><i>SNORD116</i><span> </span>MD. RT-qPCR confirmed significant over-expression of<span> </span><i>SLC6A3</i><span> </span>in<span> </span><i>SNORD116 MD</i><span> </span>neurons. Moreover, the expression of this gene was significantly decreased in the case of OXT adjunction during the differentiation.</p>
<h3 class="c-article__sub-heading" data-test="abstract-sub-heading">Conclusion</h3>
<p><i>SNORD116</i><span> </span>MD dopaminergic neurons displayed differential methylation and expression in the<span> </span><i>COMT</i><span> </span>and<span> </span><i>SLC6A3</i><span> </span>genes, which are related to dopaminergic clearance.</p>',
'date' => '2024-04-02',
'pmid' => 'https://www.nature.com/articles/s41380-024-02542-4',
'doi' => 'https://doi.org/10.1038/s41380-024-02542-4',
'modified' => '2024-04-09 00:46:18',
'created' => '2024-04-09 00:46:18',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 4 => array(
'id' => '4913',
'name' => 'Long-term effects of myo-inositol on traumatic brain injury: Epigenomic and transcriptomic studies',
'authors' => 'Oganezovi N. et al.',
'description' => '<h6>Background and purpose</h6>
<div class="section-paragraph">Traumatic brain injury (TBI) and its consequences remain great challenges for neurology. Consequences of TBI are associated with various alterations in the brain but little is known about long-term changes of epigenetic DNA methylation patterns. Moreover, nothing is known about potential treatments that can alter these epigenetic changes in beneficial ways. Therefore, we have examined myo-inositol (MI), which has positive effects on several pathological conditions.</div>
<h6></h6>
<h6>Methods</h6>
<div class="section-paragraph">TBI was induced in mice by controlled cortical impact (CCI). One group of CCI animals received saline injections for two months (TBI+SAL), another CCI group received MI treatment (TBI+MI) for the same period and one group served as a sham-operated control. Mice were sacrificed 4 months after CCI and changes in DNA methylome and transcriptomes were examined.</div>
<h6></h6>
<h6>Results</h6>
<div class="section-paragraph">For the first time we: (i) provide comprehensive map of long-term DNA methylation changes after CCI in the hippocampus; (ii) identify differences by methylation sites between the groups; (iii) characterize transcriptome changes; (iv) provide association between DNA methylation sites and gene expression. MI treatment is linked with upregulation of genes covering 33 biological processes, involved in immune response and inflammation. In support of these findings, we have shown that expression of BATF2, a transcription factor involved in immune-regulatory networks, is upregulated in the hippocampus of the TBI+MI group where the BATF2 gene is demethylated.</div>
<h6></h6>
<h6>Conclusion</h6>
<div class="section-paragraph">TBI is followed by long-term epigenetic and transcriptomic changes in hippocampus. MI treatment has a significant effect on these processes by modulation of immune response and biological pathways of inflammation.</div>',
'date' => '2024-01-30',
'pmid' => 'https://www.ibroneuroreports.org/article/S2667-2421(24)00013-7/fulltext',
'doi' => 'https://doi.org/10.1016/j.ibneur.2024.01.009',
'modified' => '2024-03-28 11:30:49',
'created' => '2024-02-22 12:28:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4898',
'name' => 'DNA methylome, R-loop and clinical exome profiling of patients with sporadic amyotrophic lateral sclerosis.',
'authors' => 'Feró, O. et al.',
'description' => '<p><span>Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the death of motor neurons, the aetiology of which is essentially unknown. Here, we present an integrative epigenomic study in blood samples from seven clinically characterised sporadic ALS patients to elucidate molecular factors associated with the disease. We used clinical exome sequencing (CES) to study DNA variants, DNA-RNA hybrid immunoprecipitation sequencing (DRIP-seq) to assess R-loop distribution, and reduced representation bisulfite sequencing (RRBS) to examine DNA methylation changes. The above datasets were combined to create a comprehensive repository of genetic and epigenetic changes associated with the ALS cases studied. This repository is well-suited to unveil new correlations within individual patients and across the entire patient cohort. The molecular attributes described here are expected to guide further mechanistic studies on ALS, shedding light on the underlying genetic causes and facilitating the development of new epigenetic therapies to combat this life-threatening disease.</span></p>',
'date' => '2024-01-24',
'pmid' => 'https://www.nature.com/articles/s41597-024-02985-y',
'doi' => 'https://doi.org/10.1038/s41597-024-02985-y',
'modified' => '2024-01-29 10:11:43',
'created' => '2024-01-29 10:11:43',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4896',
'name' => 'Gestational Caloric Restriction Alters Adipose Tissue Methylome and Offspring’s Metabolic Profile in a Swine Model',
'authors' => 'Mas-Pares B. et al.',
'description' => '<p><span>Limited nutrient supply to the fetus results in physiologic and metabolic adaptations that have unfavorable consequences in the offspring. In a swine animal model, we aimed to study the effects of gestational caloric restriction and early postnatal metformin administration on offspring’s adipose tissue epigenetics and their association with morphometric and metabolic variables. Sows were either underfed (30% restriction of total food) or kept under standard diet during gestation, and piglets were randomly assigned at birth to receive metformin (n = 16 per group) or vehicle treatment (n = 16 per group) throughout lactation. DNA methylation and gene expression were assessed in the retroperitoneal adipose tissue of piglets at weaning. Results showed that gestational caloric restriction had a negative effect on the metabolic profile of the piglets, increased the expression of inflammatory markers in the adipose tissue, and changed the methylation of several genes related to metabolism. Metformin treatment resulted in positive changes in the adipocyte morphology and regulated the methylation of several genes related to atherosclerosis, insulin, and fatty acids signaling pathways. The methylation and gene expression of the differentially methylated </span><span class="html-italic">FASN</span><span>,<span> </span></span><span class="html-italic">SLC5A10</span><span>,<span> </span></span><span class="html-italic">COL5A1</span><span>, and<span> </span></span><span class="html-italic">PRKCZ</span><span><span> </span>genes in adipose tissue associated with the metabolic profile in the piglets born to underfed sows. In conclusion, our swine model showed that caloric restriction during pregnancy was associated with impaired inflammatory and DNA methylation markers in the offspring’s adipose tissue that could predispose the offspring to later metabolic abnormalities. Early metformin administration could modulate the size of adipocytes and the DNA methylation changes.</span></p>',
'date' => '2024-01-17',
'pmid' => 'https://www.mdpi.com/1422-0067/25/2/1128',
'doi' => 'https://doi.org/10.3390/ijms25021128',
'modified' => '2024-01-22 13:45:24',
'created' => '2024-01-22 13:45:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => array(
'id' => '4890',
'name' => 'Diagnostic Algorithm to Subclassify Atypical Spitzoid Tumors in Low and High Risk According to Their Methylation Status',
'authors' => 'Gonzales-Munoz J.F. et al.',
'description' => '<p><span>Current diagnostic algorithms are insufficient for the optimal clinical and therapeutic management of cutaneous spitzoid tumors, particularly atypical spitzoid tumors (AST). Therefore, it is crucial to identify new markers that allow for reliable and reproducible diagnostic assessment and can also be used as a predictive tool to anticipate the individual malignant potential of each patient, leading to tailored individual therapy. Using Reduced Representation Bisulfite Sequencing (RRBS), we studied genome–wide methylation profiles of a series of Spitz nevi (SN), spitzoid melanoma (SM), and AST. We established a diagnostic algorithm based on the methylation status of seven cg sites located in </span><span class="html-italic">TETK4P2</span><span><span> </span>(Tektin 4 Pseudogene 2),<span> </span></span><span class="html-italic">MYO1D</span><span><span> </span>(Myosin ID), and<span> </span></span><span class="html-italic">PMF1-BGLAP</span><span><span> </span>(PMF1-BGLAP Readthrough), which allows the distinction between SN and SM but is also capable of subclassifying AST according to their similarity to the methylation levels of Spitz nevi or spitzoid melanoma. Thus, our epigenetic algorithm can predict the risk level of AST and predict its potential clinical outcomes.</span></p>',
'date' => '2023-12-25',
'pmid' => 'https://www.mdpi.com/1422-0067/25/1/318',
'doi' => 'https://doi.org/10.3390/ijms25010318',
'modified' => '2024-01-02 11:11:57',
'created' => '2024-01-02 11:11:57',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 8 => array(
'id' => '4808',
'name' => 'Knockout of TRDMT1 methyltransferase affects DNA methylome inglioblastoma cells.',
'authors' => 'Zabek T. et al.',
'description' => '<p><strong class="sub-title">Purpose:<span> </span></strong>We have previously shown that TRDMT1 methyltransferase is a regulator of chemotherapy-associated responses in glioblastoma cells. Despite the fact that glioblastoma, a common and malignant brain tumor, is widely characterized in terms of genetic and epigenetic markers, there are no data on TRDMT1-related changes in 5-methylcytosine pools in the genome. In the present study, the effect of TRDMT1 gene knockout (KO) on DNA methylome was analyzed.</p>
<p><strong class="sub-title">Methods:<span> </span></strong>CRISPR-based approach was used to obtain TRDMT1 KO glioblastoma cells. Total 5-methylcytosine levels in DNA, DNMT1 pools and DNMT activity were studied using ELISA. Reduced representation bisulfite sequencing (RRBS) was considered to comprehensively evaluate DNA methylome in glioblastoma cells with TRDMT1 KO.</p>
<p><strong class="sub-title">Results:<span> </span></strong>TRDMT1 KO cells were characterized by decreased levels of total 5-methylcytosine in DNA and DNMT1, and DNMT activity. RRBS-based methylome analysis revealed statistically significant differences in methylation-relevant DMS-linked genes in control cells compared to TRDMT1 KO cells. TRDMT1 KO-associated changes in DNA methylome may affect the activity of several processes and pathways such as telomere maintenance, cell cycle and longevity regulating pathway, proteostasis, DNA and RNA biology.</p>
<p><strong class="sub-title">Conclusions:<span> </span></strong>TRDMT1 may be suggested as a novel modulator of gene expression by changes in DNA methylome that may affect cancer cell fates during chemotherapy. We postulate that the levels and mutation status of TRDMT1 should be considered as a prognostic marker and carefully monitored during glioblastoma progression.</p>',
'date' => '2023-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37169948',
'doi' => '10.1007/s11060-023-04304-8',
'modified' => '2023-06-15 08:50:24',
'created' => '2023-06-13 21:11:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => array(
'id' => '4786',
'name' => 'Sperm DNA methylation is predominantly stable in mice offspring bornafter transplantation of long-term cultured spermatogonial stem cells.',
'authors' => 'Serrano J. B.et al.',
'description' => '<p>BACKGROUND: Spermatogonial stem cell transplantation (SSCT) is proposed as a fertility therapy for childhood cancer survivors. SSCT starts with cryopreserving a testicular biopsy prior to gonadotoxic treatments such as cancer treatments. When the childhood cancer survivor reaches adulthood and desires biological children, the biopsy is thawed and SSCs are propagated in vitro and subsequently auto-transplanted back into their testis. However, culturing stress during long-term propagation can result in epigenetic changes in the SSCs, such as DNA methylation alterations, and might be inherited by future generations born after SSCT. Therefore, SSCT requires a detailed preclinical epigenetic assessment of the derived offspring before this novel cell therapy is clinically implemented. With this aim, the DNA methylation status of sperm from SSCT-derived offspring, with in vitro propagated SSCs, was investigated in a multi-generational mouse model using reduced-representation bisulfite sequencing. RESULTS: Although there were some methylation differences, they represent less than 0.5\% of the total CpGs and methylated regions, in all generations. Unsupervised clustering of all samples showed no distinct grouping based on their pattern of methylation differences. After selecting the few single genes that are significantly altered in multiple generations of SSCT offspring compared to control, we validated the results with quantitative Bisulfite Sanger sequencing and RT-qPCRin various organs. Differential methylation was confirmed only for Tal2, being hypomethylated in sperm of SSCT offspring and presenting higher gene expression in ovaries of SSCT F1 offspring compared to control F1. CONCLUSIONS: We found no major differences in DNA methylation between SSCT-derived offspring and control, both in F1 and F2 sperm. The reassuring outcomes from our study are a prerequisite for promising translation of SSCT to the human situation.</p>',
'date' => '2023-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37029425',
'doi' => '10.1186/s13148-023-01469-x',
'modified' => '2023-06-12 08:55:47',
'created' => '2023-05-05 12:34:24',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 10 => array(
'id' => '4760',
'name' => 'DNA methylation changes from primary cultures through senescence-bypassin Syrian hamster fetal cells initially exposed to benzo[a]pyrene.',
'authors' => 'Desaulniers D. et al.',
'description' => '<p>Current chemical testing strategies are limited in their ability to detect non-genotoxic carcinogens (NGTxC). Epigenetic anomalies develop during carcinogenesis regardless of whether the molecular initiating event is associated with genotoxic (GTxC) or NGTxC events; therefore, epigenetic markers may be harnessed to develop new approach methodologies that improve the detection of both types of carcinogens. This study used Syrian hamster fetal cells to establish the chronology of carcinogen-induced DNA methylation changes from primary cells until senescence-bypass as an essential carcinogenic step. Cells exposed to solvent control for 7 days were compared to naïve primary cultures, to cells exposed for 7 days to benzo[a]pyrene, and to cells at the subsequent transformation stages: normal colonies, morphologically transformed colonies, senescence, senescence-bypass, and sustained proliferation in vitro. DNA methylation changes identified by reduced representation bisulphite sequencing were minimal at day-7. Profound DNA methylation changes arose during cellular senescence and some of these early differentially methylated regions (DMRs) were preserved through the final sustained proliferation stage. A set of these DMRs (e.g., Pou4f1, Aifm3, B3galnt2, Bhlhe22, Gja8, Klf17, and L1l) were validated by pyrosequencing and their reproducibility was confirmed across multiple clones obtained from a different laboratory. These DNA methylation changes could serve as biomarkers to enhance objectivity and mechanistic understanding of cell transformation and could be used to predict senescence-bypass and chemical carcinogenicity.</p>',
'date' => '2023-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36754249',
'doi' => '10.1016/j.tox.2023.153451',
'modified' => '2023-04-17 09:08:16',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 11 => array(
'id' => '4616',
'name' => 'Myelodysplastic Syndrome associated TET2 mutations affect NK cellfunction and genome methylation.',
'authors' => 'Boy M. et al.',
'description' => '<p>Myelodysplastic syndromes (MDS) are clonal hematopoietic disorders, representing high risk of progression to acute myeloid leukaemia, and frequently associated to somatic mutations, notably in the epigenetic regulator TET2. Natural Killer (NK) cells play a role in the anti-leukemic immune response via their cytolytic activity. Here we show that patients with MDS clones harbouring mutations in the TET2 gene are characterised by phenotypic defects in their circulating NK cells. Remarkably, NK cells and MDS clones from the same patient share the TET2 genotype, and the NK cells are characterised by increased methylation of genomic DNA and reduced expression of Killer Immunoglobulin-like receptors (KIR), perforin, and TNF-α. In vitro inhibition of TET2 in NK cells of healthy donors reduces their cytotoxicity, supporting its critical role in NK cell function. Conversely, NK cells from patients treated with azacytidine (#NCT02985190; https://clinicaltrials.gov/ ) show increased KIR and cytolytic protein expression, and IFN-γ production. Altogether, our findings show that, in addition to their oncogenic consequences in the myeloid cell subsets, TET2 mutations contribute to repressing NK-cell function in MDS patients.</p>',
'date' => '2023-02-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36737440',
'doi' => '10.1038/s41467-023-36193-w',
'modified' => '2023-04-04 08:43:27',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '4588',
'name' => 'Epigenetics and stroke: role of DNA methylation and effect of aging onblood-brain barrier recovery.',
'authors' => 'Phillips C. et al.',
'description' => '<p>Incomplete recovery of blood-brain barrier (BBB) function contributes to stroke outcomes. How the BBB recovers after stroke remains largely unknown. Emerging evidence suggests that epigenetic factors play a significant role in regulating post-stroke BBB recovery. This study aimed to evaluate the epigenetic and transcriptional profile of cerebral microvessels after thromboembolic (TE) stroke to define potential causes of limited BBB recovery. RNA-sequencing and reduced representation bisulfite sequencing (RRBS) analyses were performed using microvessels isolated from young (6 months) and old (18 months) mice seven days poststroke compared to age-matched sham controls. DNA methylation profiling of poststroke brain microvessels revealed 11287 differentially methylated regions (DMR) in old and 9818 DMR in young mice, corresponding to annotated genes. These DMR were enriched in genes encoding cell structural proteins (e.g., cell junction, and cell polarity, actin cytoskeleton, extracellular matrix), transporters and channels (e.g., potassium transmembrane transporter, organic anion and inorganic cation transporters, calcium ion transport), and proteins involved in endothelial cell processes (e.g., angiogenesis/vasculogenesis, cell signaling and transcription regulation). Integrated analysis of methylation and RNA sequencing identified changes in cell junctions (occludin), actin remodeling (ezrin) as well as signaling pathways like Rho GTPase (RhoA and Cdc42ep4). Aging as a hub of aberrant methylation affected BBB recovery processes by profound alterations (hypermethylation and repression) in structural protein expression (e.g., claudin-5) as well as activation of a set of genes involved in endothelial to mesenchymal transformation (e.g., , ), repression of angiogenesis and epigenetic regulation. These findings revealed that DNA methylation plays an important role in regulating BBB repair after stroke, through regulating processes associated with BBB restoration and prevalently with processes enhancing BBB injury.</p>',
'date' => '2023-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36711725',
'doi' => '10.21203/rs.3.rs-2444060/v1',
'modified' => '2023-04-11 10:01:44',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => array(
'id' => '4761',
'name' => 'Development of DNA methylation-based epigenetic age predictors inloblolly pine (Pinus taeda).',
'authors' => 'Gardner S. T. et al.',
'description' => '<p>Biological ageing is connected to life history variation across ecological scales and informs a basic understanding of age-related declines in organismal function. Altered DNA methylation dynamics are a conserved aspect of biological ageing and have recently been modelled to predict chronological age among vertebrate species. In addition to their utility in estimating individual age, differences between chronological and predicted ages arise due to acceleration or deceleration of epigenetic ageing, and these discrepancies are linked to disease risk and multiple life history traits. Although evidence suggests that patterns of DNA methylation can describe ageing in plants, predictions with epigenetic clocks have yet to be performed. Here, we resolve the DNA methylome across CpG, CHG, and CHH-methylation contexts in the loblolly pine tree (Pinus taeda) and construct epigenetic clocks capable of predicting ages in this species within 6\% of its maximum lifespan. Although patterns of CHH-methylation showed little association with age, both CpG and CHG-methylation contexts were strongly associated with ageing, largely becoming hypomethylated with age. Among age-associated loci were those in close proximity to malate dehydrogenase, NADH dehydrogenase, and 18S and 26S ribosomal RNA genes. This study reports one of the first epigenetic clocks in plants and demonstrates the universality of age-associated DNA methylation dynamics which can inform conservation and management practices, as well as our ecological and evolutionary understanding of biological ageing in plants.</p>',
'date' => '2023-01-01',
'pmid' => 'https://doi.org/10.1101%2F2022.01.27.477887',
'doi' => '10.1111/1755-0998.13698',
'modified' => '2023-04-17 09:09:49',
'created' => '2023-04-14 13:41:22',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 14 => array(
'id' => '4729',
'name' => 'Molecular toxicity study on glyphosate, Roundup MON 52276 and alow-dose pesticide mixture administered to adult Female rats for 90 days',
'authors' => 'Mesnage Robin and Antoniou Michael N.',
'description' => '<p>We describe a comprehensive repository describing a collection of data from a range of studies investigating the molecular mechanisms of toxicity of glyphosate, the glyphosate-based herbicide commercial formulation Roundup, and a mixture of glyphosate and 5 other most frequently used pesticides (azoxystrobin, boscalid, chlorpyrifos, imidacloprid and thiabendazole) present as residues in food products in Europe. The data were obtained by analysing tissues from rats exposed to the pesticides for 90 days via drinking water. The administration of the mixture of six pesticides was chosen to mimic a possible human exposure scenario. We compared conventional methods used in regulatory toxicity studies to evaluate the safety of pesticide exposure (gross pathology, serum biochemistry) to new molecular profiling methods encompassing the analysis of the caecum and blood metabolome, liver transcriptome, liver DNA methylation, liver small RNA profiles, and caecum metagenome of the exposed animals. Altogether, these investigations provided in-depth molecular profiling in laboratory animals exposed to pesticides revealing metabolic perturbations that would remain undetected by standard regulatory biochemical measures. Our results highlight how multi-omics phenotyping can be used to improve the predictability of health risk assessment from exposure to toxic chemicals to better protect public health.</p>',
'date' => '2022-12-01',
'pmid' => 'https://doi.org/10.1080%2F26895293.2022.2156626',
'doi' => '10.1080/26895293.2022.2156626',
'modified' => '2023-03-07 09:09:33',
'created' => '2023-02-28 12:19:11',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '4652',
'name' => 'Differential methylation patterns in lean and obese non-alcoholicsteatohepatitis-associated hepatocellular carcinoma.',
'authors' => 'Hymel Emma et al.',
'description' => '<p>BACKGROUND: Nonalcoholic fatty liver disease affects about 24\% of the world's population and may progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). While more common in those that are obese, NASH-HCC can develop in lean individuals. The mechanisms by which HCC develops and the role of epigenetic changes in the context of obesity and normal weight are not well understood. METHODS: In this study, we used previously generated mouse models of lean and obese HCC using a choline deficient/high trans-fat/fructose/cholesterol diet and a choline supplemented/high trans-fat/fructose/cholesterol diet, respectively, to evaluate methylation differences in HCC progression in lean versus obese mice. Differentially methylated regions were determined using reduced representation bisulfite sequencing. RESULTS: A larger number of differentially methylated regions (DMRs) were seen in NASH-HCC progression in the obese mice compared to the non-obese mice. No overlap existed in the DMRs with the largest methylation differences between the two models. In lean NASH-HCC, methylation differences were seen in genes involved with cancer progression and prognosis (including HCC), such as CHCHD2, FSCN1, and ZDHHC12, and lipid metabolism, including PNPLA6 and LDLRAP1. In obese NASH- HCC, methylation differences were seen in genes known to be associated with HCC, including RNF217, GJA8, PTPRE, PSAPL1, and LRRC8D. Genes involved in Wnt-signaling pathways were enriched in hypomethylated DMRs in the obese NASH-HCC. CONCLUSIONS: These data suggest that differential methylation may play a role in hepatocarcinogenesis in lean versus obese NASH. Hypomethylation of Wnt signaling pathway-related genes in obese mice may drive progression of HCC, while progression of HCC in lean mice may be driven through other signaling pathways, including lipid metabolism.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36474183',
'doi' => '10.1186/s12885-022-10389-7',
'modified' => '2023-03-13 08:50:33',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => array(
'id' => '4628',
'name' => 'Altered DNA methylation in estrogen-responsive repetitive sequences ofspermatozoa of infertile men with shortened anogenital distance.',
'authors' => 'Stenz L. et al.',
'description' => '<p>BACKGROUND: It has been suggested that antenatal exposure to environmental endocrine disruptors is responsible for adverse trends in male reproductive health, including male infertility, impaired semen quality, cryptorchidism and testicular cancer, a condition known as testicular dysgenesis syndrome. Anogenital distance (AGD) is an anthropomorphic measure of antenatal exposure to endocrine disruptors, with higher exposure levels leading to shortened AGD. We hypothesized that exposure to endocrine disruptors could lead to changes in DNA methylation during early embryonic development, which could then persist in the sperm of infertile men with shortened AGD. RESULTS: Using fluorescence activated cell sorting based on staining with either YO-PRO-1 (YOPRO) or chromomycin-3 (CMA3), we isolated four sperm fractions from eleven infertile men with short AGD and ten healthy semen donors. We examined DNA methylation in these sorted spermatozoa using reduced representation bisulfite sequencing. We found that fractions of spermatozoa from infertile men stained with CMA3 or YOPRO were more likely to contain transposable elements harboring an estrogen receptor response element (ERE). Abnormal sperm (as judged by high CMA3 or YOPRO staining) from infertile men shows substantial hypomethylation in estrogenic Alu sequences. Conversely, normal sperm fractions (as judged by low CMA3 or YO-PRO-1 staining) of either healthy donors or infertile patients were more likely to contain hypermethylated Alu sequences with ERE. CONCLUSIONS: Shortened AGD, as related to previous exposure to endocrine disruptors, and male infertility are accompanied by increased presence of hormonal response elements in the differentially methylated regulatory sequences of the genome of sperm fractions characterized by chromatin decondensation and apoptosis.</p>',
'date' => '2022-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36572941',
'doi' => '10.1186/s13148-022-01409-1',
'modified' => '2023-03-28 09:09:22',
'created' => '2023-02-21 09:59:46',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 17 => array(
'id' => '4537',
'name' => 'Epigenetic Alterations of Repeated Relapses in Patient-matchedChildhood Ependymomas.',
'authors' => 'Zhao Sibo et al.',
'description' => '<p>Recurrence is frequent in pediatric ependymoma (EPN). Our longitudinal integrated analysis of 30 patient-matched repeated relapses (3.67 ± 1.76 times) over 13 years (5.8 ± 3.8) reveals stable molecular subtypes (RELA and PFA) and convergent DNA methylation reprogramming during serial relapses accompanied by increased orthotopic patient derived xenograft (PDX) (13/27) formation in the late recurrences. A set of differentially methylated CpGs (DMCs) and DNA methylation regions (DMRs) are found to persist in primary and relapse tumors (potential driver DMCs) and are acquired exclusively in the relapses (potential booster DMCs). Integrating with RNAseq reveals differentially expressed genes regulated by potential driver DMRs (CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and potential booster DMRs (PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). DMCs predicators of relapse are also identified in the primary tumors. This study provides a high-resolution epigenetic roadmap of serial EPN relapses and 13 orthotopic PDX models to facilitate biological and preclinical studies.</p>',
'date' => '2022-11-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36335125',
'doi' => '10.1038/s41467-022-34514-z',
'modified' => '2022-11-25 08:55:12',
'created' => '2022-11-24 08:49:52',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '4441',
'name' => 'Epigenetic Suppression of the IL-7 Pathway in ProgressiveGlioblastoma.',
'authors' => 'Tompa M. et al.',
'description' => '<p>BACKGROUND: Immune evasion in glioblastoma (GBM) shields cancer cells from cytotoxic immune response. METHODS: We investigated CpG methylation in promoters, genes, and pathways in 22 pairs of formalin-fixed paraffin-embedded sequential (FFPE) GBM using restricted resolution bisulfite sequencing (RRBS) and bioinformatic analyses. RESULTS: Gene ontology revealed hypermethylation in elements of the innate and adaptive immune system when recurrent GBM samples (GBM) were compared to control (CG) and primary GBM samples (GBM). Higher methylation levels of the IL-7 signaling pathway and response to IL-7 were found in GBM suggesting a progressive blockade of the IL-7 driven T cell response in sequential GBM. Analyses of the Cancer Genome Atlas array-based data confirmed hypermethylation of the IL-7 pathway in recurrent compared with primary GBM. We also quantified DNA CpG methylation in promoter and gene regions of the IL-7 ligand and IL-7 α-receptor subunit in individual samples of a large RRBS-based sequential cohort of GBM in a Viennese database and found significantly higher methylation levels in the IL-7 receptor α-subunit in GBM compared with GBM. CONCLUSIONS: This study revealed the progressive suppression of the IL-7 receptor-mediated pathway as a means of immune evasion by GBM and thereby highlighted it as a new treatment target.</p>',
'date' => '2022-09-01',
'pmid' => 'https://doi.org/10.3390%2Fbiomedicines10092174',
'doi' => '10.3390/biomedicines10092174',
'modified' => '2022-10-14 16:32:44',
'created' => '2022-09-28 09:53:13',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '4371',
'name' => 'DNA methylation may affect beef tenderness through signal transduction inBos indicus.',
'authors' => 'de Souza M. M. et al.',
'description' => '<p>BACKGROUND: Beef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus. RESULTS: Comparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity. CONCLUSIONS: We present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.</p>',
'date' => '2022-05-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1415533%2Fv1',
'doi' => '10.1186/s13072-022-00449-4',
'modified' => '2022-08-04 16:05:03',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => array(
'id' => '4401',
'name' => 'Folic Acid Treatment Directly Influences the Genetic andEpigenetic Regulation along with the Associated CellularMaintenance Processes of HT-29 and SW480 Colorectal CancerCell Lines.',
'authors' => 'Zsigrai S. et al.',
'description' => '<p>Folic acid (FA) is a synthetic form of vitamin B9, generally used as a nutritional supplement and an adjunctive medication in cancer therapy. FA is involved in genetic and epigenetic regulation; therefore, it has a dual modulatory role in established neoplasms. We aimed to investigate the effect of short-term (72 h) FA supplementation on colorectal cancer; hence, HT-29 and SW480 cells were exposed to different FA concentrations (0, 100, 10,000 ng/mL). HT-29 cell proliferation and viability levels elevated after 100 ng/mL but decreased for 10,000 ng/mL FA. Additionally, a significant ( ≤ 0.05) improvement of genomic stability was detected in HT-29 cells with micronucleus scoring and comet assay. Conversely, the FA treatment did not alter these parameters in SW480 samples. RRBS results highlighted that DNA methylation changes were bidirectional in both cells, mainly affecting carcinogenesis-related pathways. Based on the microarray analysis, promoter methylation status was in accordance with FA-induced expression alterations of 27 genes. Our study demonstrates that the FA effect was highly dependent on the cell type, which can be attributed to the distinct molecular background and the different expression of proliferation- and DNA-repair-associated genes (, , , ). Moreover, new aspects of FA-regulated DNA methylation and consecutive gene expression were revealed.</p>',
'date' => '2022-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35406592',
'doi' => '10.3390/cancers14071820',
'modified' => '2022-08-11 14:41:59',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => array(
'id' => '4405',
'name' => 'Complex regulatory role of DNA methylation in caste- and age-specificexpression of a termite',
'authors' => 'Harrison Mark C. et al. ',
'description' => '<p>The reproductive castes of eusocial insects are often characterised by extreme lifespans and reproductive output, indicating an absence of the fecundity/longevity trade-off. The role of DNA methylation in the regulation of caste- and age-specific gene expression in eusocial insects is controversial. While some studies find a clear link to caste formation in honeybees and ants, others find no correlation when replication is increased across independent colonies. Although recent studies have identified transcription patterns involved in the maintenance of high reproduction throughout the long lives of queens, the role of DNA methylation in the regulation of these genes is unknown. We carried out a comparative analysis of DNA methylation in the regulation of caste-specific transcription and its importance for the regulation of fertility and longevity in queens of the higher termite, Macrotermes natalensis. We found evidence for significant, well-regulated changes in DNA methylation in mature compared to young queens, especially in several genes related to ageing and fecundity in mature queens. We also found a strong link between methylation and caste-specific alternative splicing. This study reveals a complex regulatory role of fat body DNA methylation both in the division of labour in termites, and during the reproductive maturation of queens.</p>',
'date' => '2022-03-01',
'pmid' => 'https://doi.org/10.1101%2F2022.03.08.483442',
'doi' => '10.1101/2022.03.08.483442',
'modified' => '2022-08-11 15:01:34',
'created' => '2022-08-11 12:14:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => array(
'id' => '4229',
'name' => 'When left does not seem right: epigenetic and bioelectric differencesbetween left- and right-sided breast cancer.',
'authors' => 'Sofía, Masuelli and Sebastián, Real and Emanuel, Campoy andBranham, María Teresita and Marzese, Diego Matías andMatthew, Salomon and De Blas, Gerardo and Rodolfo, Arias andMichael, Levin and María, Roqué',
'description' => '<p>BACKGROUND: During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms. METHODS: We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts. RESULTS: We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count. CONCLUSIONS: Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-1020823%2Fv1',
'doi' => '10.1186/s10020-022-00440-5',
'modified' => '2022-05-19 16:03:56',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '4248',
'name' => 'Comparative Toxicogenomics of Glyphosate and Roundup Herbicidesby Mammalian Stem Cell-Based Genotoxicity Assays andMolecular Profiling in Sprague-Dawley Rats.',
'authors' => 'Mesnage R. et al.',
'description' => '<p>Whether glyphosate-based herbicides (GBHs) are more potent than glyphosate alone at activating cellular mechanisms, which drive carcinogenesis remain controversial. As GBHs are more cytotoxic than glyphosate, we reasoned they may also be more capable of activating carcinogenic pathways. We tested this hypothesis by comparing the effects of glyphosate with Roundup GBHs both in vitro and in vivo. First, glyphosate was compared with representative GBHs, namely MON 52276 (European Union), MON 76473 (United Kingdom), and MON 76207 (United States) using the mammalian stem cell-based ToxTracker system. Here, MON 52276 and MON 76473, but not glyphosate and MON 76207, activated oxidative stress and unfolded protein responses. Second, molecular profiling of liver was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (at 0.5, 50, and 175 mg/kg bw/day glyphosate) for 90 days. MON 52276 but not glyphosate increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and circadian rhythm regulation. Genes most affected in liver were similarly altered in kidneys. Small RNA profiling in liver showed decreased amounts of miR-22 and miR-17 from MON 52276 ingestion. Glyphosate decreased miR-30, whereas miR-10 levels were increased. DNA methylation profiling of liver revealed 5727 and 4496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed animals, respectively. Apurinic/apyrimidinic DNA damage formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup formulations cause more biological changes linked with carcinogenesis than glyphosate.</p>',
'date' => '2022-02-01',
'pmid' => 'https://doi.org/10.1093%2Ftoxsci%2Fkfab143',
'doi' => '10.1093/toxsci/kfab143',
'modified' => '2022-05-20 09:32:37',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 24 => array(
'id' => '4368',
'name' => 'Glucose-6-phosphate dehydrogenase and MEG3 controls hypoxia-inducedexpression of serum response factor (SRF) and SRF-dependent genes inpulmonary smooth muscle cell.',
'authors' => 'Kitagawa A. et al.',
'description' => '<p>Although hypoxia induces aberrant gene expression and dedifferentiation of smooth muscle cells (SMCs), mechanisms that alter dedifferentiation gene expression by hypoxia remain unclear. Therefore, we aimed to gain insight into the hypoxia-controlled gene expression in SMCs. We conducted studies using SMCs cultured in 3\% oxygen (hypoxia) and the lungs of mice exposed to 10\% oxygen (hypoxia). Our results suggest hypoxia upregulated expression of transcription factor CP2-like protein1, krüppel-like factor 4, and E2f transcription factor 1 enriched genes including basonuclin 2 (Bcn2), serum response factor (Srf), polycomb 3 (Cbx8), homeobox D9 (Hoxd9), lysine demethylase 1A (Kdm1a), etc. Additionally, we found that silencing glucose-6-phosphate dehydrogenase (G6PD) expression and inhibiting G6PD activity downregulated Srf transcript and hypomethylation of SMC genes (Myocd, Myh11, and Cnn1) and concomitantly increased their expression in the lungs of hypoxic mice. Furthermore, G6PD inhibition hypomethylated MEG3, a long non-coding RNA, gene and upregulated MEG3 expression in the lungs of hypoxic mice and in hypoxic SMCs. Silencing MEG3 expression in SMC mitigated the hypoxia-induced transcription of SRF. These findings collectively demonstrate that MEG3 and G6PD codependently regulate Srf expression in hypoxic SMCs. Moreover, G6PD inhibition upregulated SRF-MYOCD-driven gene expression, determinant of a differentiated SMC phenotype.</p>',
'date' => '2022-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35491127',
'doi' => '10.1540/jsmr.58.34',
'modified' => '2022-08-04 16:21:02',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 25 => array(
'id' => '4114',
'name' => 'The ETS transcription factor ERF controls the exit from the naïve pluripotent state in a MAPK-dependent manner',
'authors' => 'Maria Vega-Sendino et. al.',
'description' => '<p><span>The naïve epiblast transitions to a pluripotent primed state during embryo implantation. Despite the relevance of the FGF pathway during this period, little is known about the downstream effectors regulating this signaling. Here, we examined the molecular mechanisms coordinating the naïve to primed transition by using inducible ESC to genetically eliminate all RAS proteins. We show that differentiated RAS</span><sup>KO</sup><span><span> </span>ESC remain trapped in an intermediate state of pluripotency with naïve-associated features. Elimination of the transcription factor ERF overcomes the developmental blockage of RAS-deficient cells by naïve enhancer decommissioning. Mechanistically, ERF regulates NANOG expression and ensures naïve pluripotency by strengthening naïve transcription factor binding at ESC enhancers. Moreover, ERF negatively regulates the expression of the methyltransferase DNMT3B, which participates in the extinction of the naïve transcriptional program. Collectively, we demonstrated an essential role for ERF controlling the exit from naïve pluripotency in a MAPK-dependent manner during the progression to primed pluripotency.</span></p>',
'date' => '2021-10-01',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34597136/',
'doi' => '10.1126/sciadv.abg8306',
'modified' => '2022-05-19 16:05:11',
'created' => '2021-10-06 08:45:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 26 => array(
'id' => '4230',
'name' => 'Adaptive Convergence of Methylomes Reveals Epigenetic Driversand Boosters of Repeated Relapses in Patient-matched ChildhoodEpendymomas and Identifies Targets for Anti-RecurrenceTherapies',
'authors' => 'Zhao S. et al.',
'description' => '<p>Ependymoma (EPN) is the third most common brain tumor in children and frequently recurs. Here, we report an integrated longitudinal analysis of epigenetic, genetic and tumorigenic changes in 30 patient-matched repeated relapses obtained from 10 pediatric patients to understand the mechanism of recurrences. Genome-wide DNA methylation analysis revealed stable molecular subtypes and convergent epigenetic reprogramming during serial relapses of the 5 RELA and 5 PFA EPNs that paralleled with elevated patient-derived orthotopic xenograft (PDOX) (13/27) formation in the late relapses. Differentially methylated CpGs (DMCs) preexisted in the primary tumors and persisted in the relapses (driver DMCs) were detected, ranging from 51 hypo-methylated in RELA to 148 hyper-methylated DMCs in PFA tumors; while newly acquired DMCs sustained in all the relapses but was absent in the primary tumors (booster DMCs) ranged from 38- 323 hyper-methylated DMCs in RELA and PFA EPNs, respectively. Integrated analysis of these DMC associated DNA methylation regions (DMRs) and RNAseq in both patient and PDOX tumors identified a small fraction of the differentially expressed genes (4.6±4.4\% in RELA and 4.5±1.1\% in PFA) as regulated by driver DMRs (e.g., up-regulated CACNA1H, SLC12A7, RARA in RELA and HSPB8, GMPR, ITGB4 in PFA) and booster DMRs (including the sole upregulated PLEKHG1 in RELA and NOTCH, EPHA2, SUFU, FOXJ1 in PFA tumors). Most these genes were novel to EPN relapses. Seven DMCs in RELA and 22 in PFA tumors were also identified as potential relapse predictors. Finally, integrating DNA methylation with histone modification identified LSD1 as a relapse driver gene. Combined treatment of a novel inhibitor SYC-836 with radiation significantly prolonged survival times in two PDOX models of recurrent PFA. This high-resolution epigenetic and genetic roadmap of EPN relapse and our 13 new PDOX models should significantly facilitate biological and preclinical studies of pediatric EPN recurrences.</p>',
'date' => '2021-10-01',
'pmid' => 'https://www.researchsquare.com/article/rs-908607/v1',
'doi' => '10.21203/rs.3.rs-908607/v1',
'modified' => '2022-05-19 16:48:13',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 27 => array(
'id' => '4115',
'name' => 'Genome-Wide Epigenomic Analyses in Patients With Nociceptive and Neuropathic Chronic Pain Subtypes Reveals Alterations in Methylation of Genes Involved in the Neuro-Musculoskeletal System',
'authors' => 'Stenz et al',
'description' => '<p><span>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs were seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</span></p>',
'date' => '2021-09-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34547430/',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-19 16:05:36',
'created' => '2021-10-22 19:01:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 28 => array(
'id' => '4299',
'name' => 'Genome-wide epigenomic analyses in patients with nociceptive andneuropathic chronic pain subtypes reveals alterations in methylation ofgenes involved in the neuro-musculoskeletal system.',
'authors' => 'Stenz Ludwig et al.',
'description' => '<p>Nociceptive pain involves the activation of nociceptors without damage to the nervous system, whereas neuropathic pain is related to an alteration in the central or peripheral nervous system. Chronic pain itself and the transition from acute to chronic pain may be epigenetically controlled. In this cross-sectional study, a genome-wide DNA methylation analysis was performed using the blood DNA reduced representation bisulfite sequencing (RRBS) technique. Three prospective cohorts including 20 healthy controls (CTL), 18 patients with chronic nociceptive pain (NOCI), and 19 patients with chronic neuropathic pain (NEURO) were compared at both the single CpG and differentially methylated region (DMR) levels. Genes with DMRs seen in the NOCI and NEURO groups belonged to the neuro-musculoskeletal system and differed between NOCI and NEURO patients. Our results demonstrate that the epigenetic disturbances accompanying nociceptive pain are very different from those accompanying neuropathic pain. In the former, among others, the epigenetic disturbance observed would affect the function of the opioid analgesic system, whereas in the latter it would affect that of the GABAergic reward system. This study presents biological findings that help to characterize NOCI- and NEURO-affected pathways and opens the possibility of developing epigenetic diagnostic assays.</p>',
'date' => '2021-09-01',
'pmid' => 'https://doi.org/10.1016%2Fj.jpain.2021.09.001',
'doi' => '10.1016/j.jpain.2021.09.001',
'modified' => '2022-05-30 09:41:23',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 29 => array(
'id' => '4383',
'name' => 'Biobehavioral organization shapes the immune epigenome in infant rhesusMacaques (Macaca mulatta).',
'authors' => 'Baxter A. et al.',
'description' => '<p>How individuals respond to and cope with stress is linked with their health and well-being. It is presumed that early stress responsiveness helps shape the health of the developing organism, but the relationship between stress responsiveness and early immune function during development is not well-known. We hypothesized that stress responsiveness may shape epigenetic regulation of immune genes in infancy. We investigated whether aspects of behavioral responsiveness and hypothalamic-pituitary adrenal stress-response were associated with epigenome-wide immune cell DNA methylation patterns in 154 infant rhesus monkeys (3-4 months old). Infants' behavioral and physiological responses were collected during a standardized biobehavioral assessment, which included temporary relocation and separation from their mother and social group. Genome-wide DNA methylation was quantified using restricted representation bisulfite sequencing (RRBS) from blood DNA collected 2-hours post-separation. Epigenome-wide analyses were conducted using simple regression, multiple regression controlling for immune cell counts, and permutation regression, all corrected for false discovery rate. Across the variables analyzed, there were 20,368 unique sites (in 9,040 genes) at which methylation was significantly associated with at least one behavioral responsiveness or cortisol measure across the three analyses. There were significant associations in 442 genes in the Immune System Process ontology category, and 94 genes in the Inflammation mediated by chemokine and cytokine signaling gene pathway. Out of 35 candidate genes that were selected for further investigation, there were 13 genes with at least one site at which methylation was significantly associated with behavioral responsiveness or cortisol, including two intron sites in the glucocorticoid receptor gene, at which methylation was negatively correlated with emotional behavior the day following the social separation (Day 2 Emotionality; β = -0.39, q < 0.001) and cortisol response following a relocation stressor (Sample 1; β = -0.33, q < 0.001). We conclude that biobehavioral stress responsiveness may correlate with the developing epigenome, and that DNA methylation of immune cells may be a mechanism by which patterns of stress response affect health and immune functioning.</p>',
'date' => '2021-08-01',
'pmid' => 'https://doi.org/10.1016%2Fj.bbi.2021.06.006',
'doi' => '10.1016/j.bbi.2021.06.006',
'modified' => '2022-08-04 15:54:12',
'created' => '2022-08-04 14:55:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 30 => array(
'id' => '4112',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape in the aged mouse hippocampus',
'authors' => 'Sara Zocher, Rupert W. Overall, Mathias Lesche, Andreas Dahl & Gerd Kempermann',
'description' => '<p><span>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</span></p>',
'date' => '2021-06-21',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34162876/',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-05-19 16:06:20',
'created' => '2021-09-06 08:02:36',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 31 => array(
'id' => '4111',
'name' => 'Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes',
'authors' => 'Luca Pagliaroli, Abel Fothi, Ester Nespoli, Istvan Liko, Borbala Veto, Piroska Devay, Flora Szeri, Bastian Hengerer, Csaba Barta, Tamas Aranyi',
'description' => '<p>Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.</p>',
'date' => '2021-06-09',
'pmid' => 'https://www.mdpi.com/2073-4409/10/6/1442',
'doi' => '10.3390/cells10061442',
'modified' => '2022-05-19 16:06:47',
'created' => '2021-08-27 11:27:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 32 => array(
'id' => '4324',
'name' => 'Environmental enrichment preserves a young DNA methylation landscape inthe aged mouse hippocampus',
'authors' => 'Zocher S. et al. ',
'description' => '<p>The decline of brain function during aging is associated with epigenetic changes, including DNA methylation. Lifestyle interventions can improve brain function during aging, but their influence on age-related epigenetic changes is unknown. Using genome-wide DNA methylation sequencing, we here show that experiencing a stimulus-rich environment counteracts age-related DNA methylation changes in the hippocampal dentate gyrus of mice. Specifically, environmental enrichment prevented the aging-induced CpG hypomethylation at target sites of the methyl-CpG-binding protein Mecp2, which is critical to neuronal function. The genes at which environmental enrichment counteracted aging effects have described roles in neuronal plasticity, neuronal cell communication and adult hippocampal neurogenesis and are dysregulated with age-related cognitive decline in the human brain. Our results highlight the stimulating effects of environmental enrichment on hippocampal plasticity at the level of DNA methylation and give molecular insights into the specific aspects of brain aging that can be counteracted by lifestyle interventions.</p>',
'date' => '2021-06-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/34162876',
'doi' => '10.1038/s41467-021-23993-1',
'modified' => '2022-08-03 15:56:05',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 33 => array(
'id' => '4419',
'name' => 'Pathophysiological adaptations of resistance arteries in rat offspringexposed in utero to maternal obesity is associated with sex-specificepigenetic alterations.',
'authors' => 'Payen Cyrielle et al.',
'description' => '<p>BACKGROUND/OBJECTIVES: Maternal obesity impacts vascular functions linked to metabolic disorders in offspring, leading to cardiovascular diseases during adulthood. Even if the relation between prenatal conditioning of cardiovascular diseases by maternal obesity and vascular function begins to be documented, little is known about resistance arteries. They are of particular interest because of their specific role in the regulation of local blood flow. Then our study aims to determine if maternal obesity can directly program fetal vascular dysfunction of resistance arteries, independently of metabolic disorders. METHODS: With a model of rats exposed in utero to mild maternal diet-induced obesity (OMO), we investigated third-order mesenteric arteries of 4-month old rats in absence of metabolic disorders. The methylation profile of these vessels was determined by reduced representation bisulfite sequencing (RRBS). Vascular structure and reactivity were investigated using histomorphometry analysis and wire-myography. The metabolic function was evaluated by insulin and glucose tolerance tests, plasma lipid profile, and adipose tissue analysis. RESULTS: At 4 months of age, small mesenteric arteries of OMO presented specific epigenetic modulations of matrix metalloproteinases (MMPs), collagens, and potassium channels genes in association with an outward remodeling and perturbations in the endothelium-dependent vasodilation pathways (greater contribution of EDHFs pathway in OMO males compared to control rats, and greater implication of PGI in OMO females compared to control rats). These vascular modifications were detected in absence of metabolic disorders. CONCLUSIONS: Our study reports a specific methylation profile of resistance arteries associated with vascular remodeling and vasodilation balance perturbations in offspring exposed in utero to maternal obesity, in absence of metabolic dysfunctions.</p>',
'date' => '2021-05-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33637953',
'doi' => '10.1038/s41366-021-00777-7',
'modified' => '2022-09-28 08:51:40',
'created' => '2022-09-08 16:32:20',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 34 => array(
'id' => '4175',
'name' => 'Multi-omics phenotyping of the gut-liver axis reveals metabolicperturbations from a low-dose pesticide mixture in rats.',
'authors' => 'Mesnage, Robin et al.',
'description' => '<p>Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33854195',
'doi' => '10.1038/s42003-021-01990-w',
'modified' => '2021-12-21 16:12:25',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 35 => array(
'id' => '4356',
'name' => 'Muscle allele-specific expression QTLs may affect meat quality traitsin Bos indicus.',
'authors' => 'Bruscadin J.J. et al.',
'description' => '<p>Single nucleotide polymorphisms (SNPs) located in transcript sequences showing allele-specific expression (ASE SNPs) were previously identified in the Longissimus thoracis muscle of a Nelore (Bos indicus) population consisting of 190 steers. Given that the allele-specific expression pattern may result from cis-regulatory SNPs, called allele-specific expression quantitative trait loci (aseQTLs), in this study, we searched for aseQTLs in a window of 1 Mb upstream and downstream from each ASE SNP. After this initial analysis, aiming to investigate variants with a potential regulatory role, we further screened our aseQTL data for sequence similarity with transcription factor binding sites and microRNA (miRNA) binding sites. These aseQTLs were overlapped with methylation data from reduced representation bisulfite sequencing (RRBS) obtained from 12 animals of the same population. We identified 1134 aseQTLs associated with 126 different ASE SNPs. For 215 aseQTLs, one allele potentially affected the affinity of a muscle-expressed transcription factor to its binding site. 162 aseQTLs were predicted to affect 149 miRNA binding sites, from which 114 miRNAs were expressed in muscle. Also, 16 aseQTLs were methylated in our population. Integration of aseQTL with GWAS data revealed enrichment for traits such as meat tenderness, ribeye area, and intramuscular fat . To our knowledge, this is the first report of aseQTLs identification in bovine muscle. Our findings indicate that various cis-regulatory and epigenetic mechanisms can affect multiple variants to modulate the allelic expression. Some of the potential regulatory variants described here were associated with the expression pattern of genes related to interesting phenotypes for livestock. Thus, these variants might be useful for the comprehension of the genetic control of these phenotypes.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33795794',
'doi' => '10.1038/s41598-021-86782-2',
'modified' => '2022-08-03 16:44:51',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 36 => array(
'id' => '4173',
'name' => 'The insecticide permethrin induces transgenerational behavioral changeslinked to transcriptomic and epigenetic alterations in zebrafish (Daniorerio).',
'authors' => 'Blanc, Mélanie et al.',
'description' => '<p>The pyrethroid insecticide permethrin is widely used for agricultural and domestic purposes. Previous data indicated that it acts as a developmental neurotoxicant and can induce transgenerational effects in non-target organisms. However, associated underlying mechanisms remain unclear. The aim of this study was to investigate permethrin-related transgenerational effects in the zebrafish model, and to identify possible molecular mechanisms underlying inheritance. Zebrafish (F0) were exposed to permethrin during early-life (2 h post-fertilization up to 28 days). The F1 and F2 offspring generations were obtained by pairing exposed F0 males and females, and were bred unexposed. Locomotor and anxiety behavior were investigated, together with transcriptomic and epigenomic (DNA methylation) changes in brains. Permethrin exposed F0 fish were hypoactive at adulthood, while males from the F1 and F2 generations showed a specific decrease in anxiety-like behavior. In F0, transcriptomic data showed enrichment in pathways related to glutamatergic synapse activity, which may partly underlie the behavioral effects. In F1 and F2 males, dysregulation of similar pathways was observed, including a subset of differentially methylated regions that were inherited from the F0 to the F2 generation and indicated stable dysregulation of glutamatergic signaling. Altogether, the present results provide novel evidence on the transgenerational neurotoxic effects of permethrin, as well as mechanistic insight: a transient exposure induces persistent transcriptional and DNA methylation changes that may translate into transgenerational alteration of glutamatergic signaling and, thus, into behavioral alterations.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33752003',
'doi' => '10.1016/j.scitotenv.2021.146404',
'modified' => '2021-12-21 16:02:21',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 37 => array(
'id' => '4142',
'name' => 'The aging DNA methylome reveals environment-by-aging interactions in amodel teleost',
'authors' => 'Bertucci, E. M. et al.',
'description' => '<p>The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4\% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.</p>',
'date' => '2021-03-01',
'pmid' => 'https://doi.org/10.1101%2F2021.03.01.433371',
'doi' => '10.1101/2021.03.01.433371',
'modified' => '2022-05-19 16:07:18',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 38 => array(
'id' => '4155',
'name' => 'Perturbed DNA methylation by sustained overexpression of Gadd45b induces chromatin disorganization, DNA strand breaks and dopaminergic neurondeath in mice',
'authors' => 'Ravel-Godreuil, C. et al.',
'description' => '<p>Heterochromatin disorganization is a key hallmark of aging and DNA methylation state is currently the main molecular predictor of chronological age. The most frequent neurodegenerative diseases like Parkinson disease and Alzheimer’s disease are age-related but how the aging process and chromatin alterations are linked to neurodegeneration is unknown. Here, we investigated the consequences of viral overexpression of Gadd45b, a multifactorial protein involved in active DNA demethylation, in the midbrain of wild-type mice. Gadd45b overexpression induces global and stable changes in DNA methylation, particularly on gene bodies of genes related to neuronal functions. DNA methylation changes were accompanied by perturbed H3K9me3-marked heterochromatin and increased DNA damage. Prolonged Gadd45b expression resulted in dopaminergic neuron degeneration accompanied by altered expression of candidate genes related to heterochromatin maintenance, DNA methylation or Parkinson disease. Gadd45b overexpression rendered midbrain dopaminergic neurons more vulnerable to acute oxidative stress. Heterochromatin disorganization and DNA demethylation resulted in derepression of mostly young LINE-1 transposable elements, a potential source of DNA damage, prior to Gadd45b-induced neurodegeneration. Our data implicate that alterations in DNA methylation and heterochromatin organization, LINE-1 derepression and DNA damage can represent important contributors in the pathogenic mechanisms of dopaminergic neuron degeneration with potential implications for Parkinson disease.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2020.06.23.158014',
'doi' => '10.1101/2020.06.23.158014',
'modified' => '2022-05-19 16:07:48',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 39 => array(
'id' => '4189',
'name' => 'The Identification of a Novel Fucosidosis-Associated Mutation: A Case of a5-Year-Old Polish Girl with Two Additional Rare Chromosomal Aberrations andAffected DNA Methylation Patterns.',
'authors' => 'Domin A. et al. ',
'description' => '<p>Fucosidosis is a rare neurodegenerative autosomal recessive disorder, which manifests as progressive neurological and psychomotor deterioration, growth retardation, skin and skeletal abnormalities, intellectual disability and coarsening of facial features. It is caused by biallelic mutations in encoding the α-L-fucosidase enzyme, which in turn is responsible for degradation of fucose-containing glycoproteins and glycolipids. mutations lead to severe reduction or even loss of α-L-fucosidase enzyme activity. This results in incomplete breakdown of fucose-containing compounds leading to their deposition in different tissues and, consequently, disease progression. To date, 36 pathogenic variants in associated with fucosidosis have been documented. Among these are three splice site variants. Here, we report a novel fucosidosis-related 9-base-pair deletion (NG_013346.1:g.10233_10241delACAGGTAAG) affecting the exon 3/intron 3 junction within a sequence. This novel pathogenic variant was identified in a five-year-old Polish girl with a well-defined pattern of fucosidosis symptoms. Since it is postulated that other genetic, nongenetic or environmental factors can also contribute to fucosidosis pathogenesis, we performed further analysis and found two rare de novo chromosomal aberrations in the girl's genome involving a 15q11.1-11.2 microdeletion and an Xq22.2 gain. These abnormalities were associated with genome-wide changes in DNA methylation status in the epigenome of blood cells.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33435586',
'doi' => '10.3390/genes12010074',
'modified' => '2022-05-19 16:08:10',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 40 => array(
'id' => '4357',
'name' => 'Developmental cannabidiol exposure increases anxiety and modifiesgenome-wide brain DNA methylation in adult female mice.',
'authors' => 'Wanner N. M. et al. ',
'description' => '<p>BACKGROUND: Use of cannabidiol (CBD), the primary non-psychoactive compound found in cannabis, has recently risen dramatically, while relatively little is known about the underlying molecular mechanisms of its effects. Previous work indicates that direct CBD exposure strongly impacts the brain, with anxiolytic, antidepressant, antipsychotic, and other effects being observed in animal and human studies. The epigenome, particularly DNA methylation, is responsive to environmental input and can direct persistent patterns of gene regulation impacting phenotype. Epigenetic perturbation is particularly impactful during embryogenesis, when exogenous exposures can disrupt critical resetting of epigenetic marks and impart phenotypic effects lasting into adulthood. The impact of prenatal CBD exposure has not been evaluated; however, studies using the psychomimetic cannabinoid Δ9-tetrahydrocannabinol (THC) have identified detrimental effects on psychological outcomes in developmentally exposed adult offspring. We hypothesized that developmental CBD exposure would have similar negative effects on behavior mediated in part by the epigenome. Nulliparous female wild-type Agouti viable yellow (A) mice were exposed to 20 mg/kg CBD or vehicle daily from two weeks prior to mating through gestation and lactation. Coat color shifts, a readout of DNA methylation at the Agouti locus in this strain, were measured in F1 A/a offspring. Young adult F1 a/a offspring were then subjected to tests of working spatial memory and anxiety/compulsive behavior. Reduced-representation bisulfite sequencing was performed on both F0 and F1 cerebral cortex and F1 hippocampus to identify genome-wide changes in DNA methylation for direct and developmental exposure, respectively. RESULTS: F1 offspring exposed to CBD during development exhibited increased anxiety and improved memory behavior in a sex-specific manner. Further, while no significant coat color shift was observed in A/a offspring, thousands of differentially methylated loci (DMLs) were identified in both brain regions with functional enrichment for neurogenesis, substance use phenotypes, and other psychologically relevant terms. CONCLUSIONS: These findings demonstrate for the first time that despite positive effects of direct exposure, developmental CBD is associated with mixed behavioral outcomes and perturbation of the brain epigenome.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33407853',
'doi' => '10.1186/s13148-020-00993-4',
'modified' => '2022-08-03 17:04:44',
'created' => '2022-05-19 10:41:50',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 41 => array(
'id' => '4208',
'name' => 'Hepatic transcriptome and DNA methylation patterns following perinataland chronic BPS exposure in male mice.',
'authors' => 'Brulport A. et al. ',
'description' => '<p>BACKGROUND: Bisphenol S (BPS) is a common bisphenol A (BPA) substitute, since BPA is virtually banned worldwide. However, BPS and BPA have both endocrine disrupting properties. Their effects appear mostly in adulthood following perinatal exposures. The objective of the present study was to investigate the impact of perinatal and chronic exposure to BPS at the low dose of 1.5 μg/kg body weight/day on the transcriptome and methylome of the liver in 23 weeks-old C57BL6/J male mice. RESULTS: This multi-omic study highlights a major impact of BPS on gene expression (374 significant deregulated genes) and Gene Set Enrichment Analysis show an enrichment focused on several biological pathways related to metabolic liver regulation. BPS exposure also induces a hypomethylation in 58.5\% of the differentially methylated regions (DMR). Systematic connections were not found between gene expression and methylation profile excepted for 18 genes, including 4 genes involved in lipid metabolism pathways (Fasn, Hmgcr, Elovl6, Lpin1), which were downregulated and featured differentially methylated CpGs in their exons or introns. CONCLUSIONS: This descriptive study shows an impact of BPS on biological pathways mainly related to an integrative disruption of metabolism (energy metabolism, detoxification, protein and steroid metabolism) and, like most high-throughput studies, contributes to the identification of potential exposure biomarkers.</p>',
'date' => '2020-12-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33297965',
'doi' => '10.1186/s12864-020-07294-3',
'modified' => '2022-01-13 14:57:00',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 42 => array(
'id' => '4033',
'name' => 'Integrative Analysis of Glucometabolic Traits, Adipose Tissue DNA Methylation and Gene Expression Identifies Epigenetic Regulatory Mechanisms of Insulin Resistance and Obesity in African Americans',
'authors' => 'Neeraj K. Sharma, Mary E. Comeau, Dennis Montoya, Matteo Pellegrini, Timothy D. Howard, Carl D. Langefeld, Swapan K. Das',
'description' => '<p><span>Decline in insulin sensitivity due to dysfunction of adipose tissue (AT) is one of the earliest pathogenic events in Type 2 Diabetes. We hypothesize that differential DNA methylation (DNAm) controls insulin sensitivity and obesity by modulating transcript expression in AT. Integrating AT DNAm profiles with transcript profile data measured in a cohort of 230 African Americans from AAGMEx cohort, we performed<span> </span></span><em>cis</em><span>-expression quantitative trait methylation (</span><em>cis</em><span>-eQTM) analysis to identify epigenetic regulatory loci for glucometabolic trait-associated transcripts. We identified significantly associated CpG-regions for 82 transcripts (FDR-P<0.05). The strongest eQTM locus was observed for the proopiomelanocortin (</span><em>POMC</em><span>; ρ= -0.632, P= 4.70X10</span><sup>-27</sup><span>) gene. Epigenome-wide association studies (EWAS) further identified 155, 46, and 168 CpG regions associated (FDR-P <0.05) with Matsuda index, S</span><sub>I</sub><span><span> </span>and BMI, respectively. Intersection of EWAS, transcript level to trait association, and eQTM results, followed by causal inference test identified significant eQTM loci for 23 genes that were also associated with Matsuda index, S</span><sub>I</sub><span><span> </span>and/or BMI in EWAS. These associated genes include<span> </span></span><em>FERMT3</em><span>,<span> </span></span><em>ITGAM</em><span>,<span> </span></span><em>ITGAX</em><span>, and<span> </span></span><em>POMC</em><span>. In summary, applying an integrative multi-omics approach, our study provides evidence for DNAm-mediated regulation of gene expression at both previously identified and novel loci for many key AT transcripts influencing insulin resistance and obesity.</span></p>',
'date' => '2020-09-20',
'pmid' => 'https://diabetes.diabetesjournals.org/content/early/2020/09/03/db20-0117',
'doi' => '10.2337/db20-0117',
'modified' => '2022-05-19 16:08:46',
'created' => '2020-10-22 10:55:58',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 43 => array(
'id' => '4020',
'name' => 'DNA CpG methylation in sequential glioblastoma specimens.',
'authors' => 'Kraboth, Z and Galik, B and Tompa, M and Kajtar, B and Urban, P andGyenesei, A and Miseta, A and Kalman, B',
'description' => '<p>PURPOSE: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples. METHODS: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters. RESULTS: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport. CONCLUSION: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.</p>',
'date' => '2020-08-10',
'pmid' => 'http://www.pubmed.gov/32779022',
'doi' => '10.1007/s00432-020-03349-w',
'modified' => '2022-05-19 16:09:06',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 44 => array(
'id' => '3989',
'name' => 'Early Life Exposure to Environmentally Relevant Levels of Endocrine Disruptors Drive Multigenerational and Transgenerational Epigenetic Changes in a Fish Model',
'authors' => 'Major Kaley M., DeCourten Bethany M., Li Jie, Britton Monica, Settles Matthew L., Mehinto Alvine C., Connon Richard E., Brander Susanne M.',
'description' => '<p>The inland silverside, Menidia beryllina, is a euryhaline fish and a model organism in ecotoxicology. We previously showed that exposure to picomolar (ng/L) levels of endocrine disrupting chemicals (EDCs) can cause a variety of effects in M. beryllina, from changes in gene expression to phenotypic alterations. Here we explore the potential for early life exposure to EDCs to modify the epigenome in silversides, with a focus on multi- and transgenerational effects. EDCs included contaminants of emerging concern (the pyrethroid insecticide bifenthrin and the synthetic progestin levonorgestrel), as well as a commonly detected synthetic estrogen (ethinylestradiol), and a synthetic androgen (trenbolone) at exposure levels ranging from 3 to 10 ng/L. In a multigenerational experiment, we exposed parental silversides to EDCs from fertilization until 21 days post hatch (dph). Then we assessed DNA methylation patterns for three generations (F0, F1, and F2) in whole body larval fish using reduced representation bisulfite sequencing (RRBS). We found significant (α = 0.05) differences in promoter and/or gene body methylation in treatment fish relative to controls for all EDCs and all generations indicating that both multigenerational (F1) and transgenerational (F2) effects that were caused by strict inheritance of DNA methylation alterations and the dysregulation of epigenetic control mechanisms. Using gene ontology and pathway analyses, we found enrichment in biological processes and pathways representative of growth and development, immune function, reproduction, pigmentation, epigenetic regulation, stress response and repair (including pathways important in carcinogenesis). Further, we found that a subset of potentially EDC responsive genes (EDCRGs) were differentially methylated across all treatments and generations and included hormone receptors, genes involved in steroidogenesis, prostaglandin synthesis, sexual development, DNA methylation, protein metabolism and synthesis, cell signaling, and neurodevelopment. The analysis of EDCRGs provided additional evidence that differential methylation is inherited by the offspring of EDC-treated animals, sometimes in the F2 generation that was never exposed. These findings show that low, environmentally relevant levels of EDCs can cause altered methylation in genes that are functionally relevant to impaired phenotypes documented in EDC-exposed animals and that EDC exposure has the potential to affect epigenetic regulation in future generations of fish that have never been exposed.</p>',
'date' => '2020-06-24',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fmars.2020.00471/full',
'doi' => '10.3389/fmars.2020.00471',
'modified' => '2022-05-19 16:09:23',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 45 => array(
'id' => '3983',
'name' => 'Chronic cannabidiol alters genome-wide DNA methylation in adult mouse hippocampus: epigenetic implications for psychiatric disease.',
'authors' => 'Wanner NM, Colwell M, Drown C, Faulk C',
'description' => '<p>Cannabidiol (CBD) is the primary non-psychoactive compound found in cannabis (Cannabis sativa) and an increasingly popular dietary supplement as a result of widespread availability of CBD-containing products. CBD is FDA-approved for the treatment of epilepsy and exhibits anxiolytic, antipsychotic, prosocial, and other behavioral effects in animal and human studies, however, the underlying mechanisms governing these phenotypes are still being elucidated. The epigenome, particularly DNA methylation, is responsive to environmental input and can govern persistent patterns of gene regulation affecting phenotype across the life course. In order to understand the epigenomic activity of chronic cannabidiol exposure in the adult brain, 12-week-old male C57BL/6 mice were exposed to either 20 mg/kg CBD or vehicle daily by oral administration for fourteen days. Hippocampal tissue was collected and reduced-representation bisulfite sequencing (RRBS) was performed. Analyses revealed 3,323 differentially methylated loci (DMLs) in CBD-exposed animals with a small skew toward global hypomethylation. Genes for cell adhesion and migration, dendritic spine development, and excitatory postsynaptic potential were found to be enriched in a gene ontology term analysis of DML-containing genes, and disease ontology enrichment revealed an overrepresentation of DMLs in gene sets associated with autism spectrum disorder, schizophrenia, and other phenotypes. These results suggest that the epigenome may be a key substrate for CBD's behavioral effects and provides a wealth of gene regulatory information for further study. This article is protected by copyright. All rights reserved.</p>',
'date' => '2020-06-24',
'pmid' => 'http://www.pubmed.gov/32579259',
'doi' => '10.1002/em.22396',
'modified' => '2022-05-19 16:09:42',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 46 => array(
'id' => '3885',
'name' => 'Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease',
'authors' => 'Guan Y, Liu H, Ma Z, Li SY, Park J, Sheng X, Susztak K',
'description' => '<p>BACKGROUND: Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing methylation. METHODS: We generated mice with genetic deletion of and in nephron progenitor cells () and kidney tubule cells (). We characterized mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. RESULTS: mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that and play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of and resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by and were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with and deletion. CONCLUSIONS: Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.</p>',
'date' => '2020-03-03',
'pmid' => 'http://www.pubmed.gov/32127410',
'doi' => '10.1681/ASN.2019080797',
'modified' => '2022-05-19 16:10:07',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 47 => array(
'id' => '3877',
'name' => 'Rheumatoid Arthritis Patients, Both Newly Diagnosed and Methotrexate Treated, Show More DNA Methylation Differences in CD4+ Memory Than in CD4+ Naïve T Cells',
'authors' => 'Guderud Kari, Sunde Line H., Flåm Siri T., Mæhlen Marthe T., Mjaavatten Maria D., Lillegraven Siri, Aga Anna-Birgitte, Evenrød Ida M., Norli Ellen S., Andreassen Bettina K., Franzenburg Sören, Franke Andre, Haavardsholm Espen A., Rayner Simon, Gervin Kris',
'description' => '<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes pain and swelling of multiple joints in the body. The underlying disease mechanisms are believed to involve a complex interplay between common genetic and environmental factors. The heritability of RA has been estimated to be ~50% for anti-citrullinated protein antibody (ACPA) positive RA and ~20% for ACPA negative RA in a large familial aggregation study (1). Genome-wide association studies (GWAS) have identified more than 100 RA risk loci, mostly conferring risk to ACPA positive RA, marked by lead single nucleotide polymorphisms (SNPs) across various populations (2). The risk SNPs have small effect sizes, and only explain parts of heritability in RA. Environmental and epigenetic factors are also thought to be involved in the RA disease pathogenesis (3) of which smoking is the only established environmental risk factor (4, 5). Epigenetic modifications are important for regulation and maintenance of cell type specific biological functions, and alterations in the epigenome have been found to be associated with RA (6). The most studied epigenetic modification in humans is DNA methylation of cytosine followed by a guanine at so-called CpG sites (CpGs). CpGs are often clustered in regions called CpG islands (CGIs), which frequently overlap gene promoters (7). DNA methylation in promotor regions is usually negatively correlated with transcription of the nearby gene (8). A wide range of immune cells has been implicated in the pathogenesis of RA. One of the most widely used drugs for treatment of RA, methotrexate (MTX) (9), acts as an immunosuppressant in proliferating cells (10), and of these, the most relevant cell population for RA is CD4+ T cells (11). Interestingly, the RA risk loci are enriched in accessible chromatin regions (H3K4me3 peaks) in T cells, including both CD4+ naïve and CD4+ memory T cells (2). Studies have identified cell type specific DNA methylation differences in B (CD19+) and T (CD3+) lymphocytes (12, 13), as well as CD4+ T cells subsets (14, 15) isolated from RA patients compared to healthy controls. However, memory and naïve CD4+ T cells also display distinct genome-wide and gene-specific DNA methylation patterns as a result of normal differentiation (16); hence analyses of bulk T cells may be confounded by different proportions of naïve and memory T cells. Given the recent observations that CD4+ T cell subset distributions are abnormal both in treatment naïve RA patients and in RA patients who has undergone MTX treatment (17) methylation profiles for distinct CD4+ T cell subpopulations should be investigated separately. Methylation levels have so far only been assessed by array-based methods in RA, however reduced representation bisulfite sequencing (RRBS) using next generation sequencers allows for an interrogation of even more CpG sites. RRBS enriches for CpG dinucleotides by utilizes the restriction enzyme MspI (C∧CGG) to digest the DNA sample before bisulfite conversion and sequencing. In this study, we aimed to investigate whether we could detect DNA methylation differences in primary naïve and memory CD4+ T cells from RA patients. To do this, we conducted an epigenome-wide association study using RRBS on isolated T cell populations from two different RA cohorts; (1) disease modifying anti-rheumatic drug (DMARD) naïve RA patients with active disease and (2) MTX-treated RA patients who had been in remission for >12 months. The two cohorts were compared to matched healthy controls.</p>',
'date' => '2020-02-14',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fimmu.2020.00194/full',
'doi' => '10.3389/fimmu.2020.00194',
'modified' => '2022-05-19 16:10:24',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 48 => array(
'id' => '3794',
'name' => 'Obesogen effect of bisphenol S alters mRNA expression and DNA methylation profiling in male mouse liver',
'authors' => 'Brulport Axelle, Vaiman Daniel, Chagnon Marie-Christine, Le Corre Ludovic',
'description' => '<p>Environmental pollution is increasingly considered an important factor involved in the obesity incidence. Endocrine disruptors (EDs) are important actors in the concept of DOHaD (Developmental Origins of Health and Disease), where epigenetic mechanisms play crucial roles. Bisphenol A (BPA), a monomer used in the manufacture of plastics and resins is one of the most studied obesogenic endocrine disruptor. Bisphenol S (BPS), a BPA substitute, has the same obesogenic properties, acting at low doses with a sex-specific effect following perinatal exposure. Since the liver is a major organ in regulating body lipid homeostasis, we investigated gene expression and DNA methylation under low-dose BPS exposure. The BPS obesogenic effect was associated with an increase of hepatic triglyceride content. These physiological disturbances were accompanied by genome-wide changes in gene expression (1366 genes significantly modified more than 1.5-fold). Gene ontology analysis revealed alteration of gene cascades involved in protein translation and complement regulation. It was associated with hepatic DNA hypomethylation in autosomes and hypermethylation in sex chromosomes. Although no systematic correlation has been found between gene repression and hypermethylation, several genes related to liver metabolism were either hypermethylated (Acsl4, Gpr40, Cel, Pparδ, Abca6, Ces3a, Sgms2) or hypomethylated (Soga1, Gpihbp1, Nr1d2, Mlxipl, Rps6kb2, Esrrb, Thra, Cidec). In specific cases (Hapln4, ApoA4, Cidec, genes involved in lipid metabolism and liver fibrosis) mRNA upregulation was associated with hypomethylation. In conclusion, we show for the first time wide disruptive physiological effects of low-dose of BPS, which raises the question of its harmlessness as an industrial substitute for BPA.</p>',
'date' => '2019-10-15',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/31683443',
'doi' => '10.1016/j.chemosphere.2019.125092',
'modified' => '2022-05-19 16:10:42',
'created' => '2019-12-02 15:25:44',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 49 => array(
'id' => '3674',
'name' => 'Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA.',
'authors' => 'Mayorga L, Salassa BN, Marzese DM, Loos MA, Eiroa HD, Lubieniecki F, García Samartino C, Romano PS, Roqué M',
'description' => '<p>Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a "pro-survival" adaptive state that seems to be triggered by the differential methylation of nuclear genes.</p>',
'date' => '2019-04-01',
'pmid' => 'http://www.pubmed.gov/30673822',
'doi' => '10.1007/s00018-019-03008-5',
'modified' => '2022-05-19 16:10:59',
'created' => '2019-06-21 14:55:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 50 => array(
'id' => '3416',
'name' => 'Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination.',
'authors' => 'Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG',
'description' => '<p>Bacillus Calmette-Guérin (BCG) is the only licensed vaccine for tuberculosis (TB) and induces highly variable protection against pulmonary disease in different countries. We hypothesised that DNA methylation is one of the molecular mechanisms driving variability in BCG-induced immune responses. DNA methylation in peripheral blood mononuclear cells (PBMC) from BCG vaccinated infants was measured and comparisons made between low and high BCG-specific cytokine responders. We found 318 genes and 67 pathways with distinct patterns of DNA methylation, including immune pathways, e.g. for T cell activation, that are known to directly affect immune responses. We also highlight signalling pathways that could indirectly affect the BCG-induced immune response: potassium and calcium channel, muscarinic acetylcholine receptor, G Protein coupled receptor (GPCR), glutamate signalling and WNT pathways. This study suggests that in addition to immune pathways, cellular processes drive vaccine-induced immune responses. Our results highlight mechanisms that require consideration when designing new TB vaccines.</p>',
'date' => '2018-08-30',
'pmid' => 'http://www.pubmed.gov/30166570',
'doi' => '10.1038/s41598-018-31537-9',
'modified' => '2022-05-19 16:11:19',
'created' => '2018-12-04 09:51:07',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 51 => array(
'id' => '3322',
'name' => 'In Situ Fixation Redefines Quiescence and Early Activation of Skeletal Muscle Stem Cells',
'authors' => 'Machado L. et al.',
'description' => '<div class="abstract">
<h2 class="sectionTitle" tabindex="0">Summary</h2>
<div class="content">
<p>State of the art techniques have been developed to isolate and analyze cells from various tissues, aiming to capture their <em>in vivo</em> state. However, the majority of cell isolation protocols involve lengthy mechanical and enzymatic dissociation steps followed by flow cytometry, exposing cells to stress and disrupting their physiological niche. Focusing on adult skeletal muscle stem cells, we have developed a protocol that circumvents the impact of isolation procedures and captures cells in their native quiescent state. We show that current isolation protocols induce major transcriptional changes accompanied by specific histone modifications while having negligible effects on DNA methylation. In addition to proposing a protocol to avoid isolation-induced artifacts, our study reveals previously undetected quiescence and early activation genes of potential biological interest.</p>
</div>
</div>',
'date' => '2017-11-14',
'pmid' => 'http://www.cell.com/cell-reports/abstract/S2211-1247(17)31543-7',
'doi' => '',
'modified' => '2022-05-19 16:11:43',
'created' => '2018-02-02 16:36:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 52 => array(
'id' => '3286',
'name' => 'DNMT3B overexpression contributes to aberrant DNA methylation and MYC-driven tumor maintenance in T-ALL and Burkitt’s lymphoma',
'authors' => 'Poole et al.',
'description' => '<p>Aberrant DNA methylation is a hallmark of cancer. However, our understanding of how tumor cell-specific DNA methylation patterns are established and maintained is limited. Here, we report that in T-cell acute lymphoblastic leukemia (T-ALL) and Burkitt’s lymphoma the <em>MYC </em>oncogene causes overexpression of DNA methyltransferase (DNMT) 1 and 3B, which contributes to tumor maintenance. By utilizing a tetracycline-regulated <em>MYC </em>transgene in a mouse T-ALL (EμSRα-tTA;tet-o- MYC) and human Burkitt’s lymphoma (P493-6) model, we demonstrated that DNMT1 and DNMT3B expression depend on high MYC levels, and that their transcription decreased upon MYC-inactivation. Chromatin immunoprecipitation indicated that MYC binds to the <em>DNMT1 </em>and <em>DNMT3B </em>promoters, implicating a direct transcriptional regulation. Hence, shRNA-mediated knock-down of endogenous MYC in human T-ALL and Burkitt’s lymphoma cell lines, downregulated DNMT3B expression. Knock-down and pharmacologic inhibition of DNMT3B in T-ALL reduced cell proliferation associated with genome-wide changes in DNA methylation, indicating a tumor promoter function during tumor maintenance. We provide novel evidence that MYC directly deregulates the expression of both <em>de novo </em>and maintenance DNMTs, showing that MYC controls DNA methylation in a genome-wide fashion. Our finding that a coordinated interplay between the components of the DNA methylating machinery contributes to MYC-driven tumor maintenance highlights the potential of specific DNMTs for targeted therapies.</p>',
'date' => '2017-08-10',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/29100357',
'doi' => '10.18632/oncotarget.20176',
'modified' => '2022-05-19 16:12:01',
'created' => '2017-11-10 11:44:30',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 53 => array(
'id' => '3063',
'name' => 'DNA methylation and alcohol use disorders: Progress and challenges',
'authors' => 'Zhang H. and Gelernter J.',
'description' => '<section class="article-section article-body-section" id="ajad12465-sec-0001">
<h3>Background and Objectives</h3>
<p>Risk for alcohol use disorders (AUDs) is influenced by gene–environment interactions. Environmental factors can affect gene expression through epigenetic mechanisms such as DNA methylation. This review outlines the findings regarding the association of DNA methylation and AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0002">
<h3>Methods</h3>
<p>We searched PubMed (by April 2016) and identified 29 studies that examined the association of DNA methylation and AUDs. We also evaluated the methods used in these studies.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0003">
<h3>Results</h3>
<p>Two studies demonstrated elevated global (repetitive element) DNA methylation levels in AUD subjects. Fifteen candidate gene studies showed hypermethylation of promoter regions of six genes (<em>AVP</em>, <em>DNMT3B</em>, <em>HERP</em>, <em>HTR3A</em>, <em>OPRM1</em>, and <em>SNCA</em>) or hypomethylation of the <em>GDAP1</em> promoter region in AUD subjects. Five genome-wide DNA methylation studies demonstrated widespread DNA methylation changes across the genome in AUD subjects. Six studies showed significant correlations of DNA methylation with gene expression in AUD subjects. Three studies revealed interactive effects of genetic variation and DNA methylation on susceptibility to AUDs. Most studies analyzed AUD-associated DNA methylation changes in the peripheral blood; a few studies examined DNA methylation changes in postmortem brains of AUD subjects.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0004">
<h3>Discussion and Conclusions</h3>
<p>Chronic alcohol consumption may result in DNA methylation changes, leading to neuroadaptations that may underlie some of the mechanisms of AUD risk and persistence. Future studies are needed to confirm the few existing results, and then to elucidate whether DNA methylation changes are the cause or consequence of AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0005">
<h3>Scientific Significance</h3>
<p>DNA methylation profiles may be used to assess AUD status or monitor AUD treatment response. (Am J Addict 2016;XX:1–14)</p>
</section>',
'date' => '2016-10-19',
'pmid' => 'http://onlinelibrary.wiley.com/doi/10.1111/ajad.12465/abstract?campaign=wolsavedsearch',
'doi' => '',
'modified' => '2022-05-19 16:12:16',
'created' => '2016-11-03 11:11:49',
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[maximum depth reached]
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(int) 54 => array(
'id' => '2812',
'name' => 'Diagenode® Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage',
'authors' => 'Anne-Clémence Veillard, Paul Datlinger, Miklos Laczik, Sharon Squazzo & Christoph Bock',
'description' => '<p>Reduced representation bisulfite sequencing (RRBS) enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of bisulfite sequencing while avoiding the cost of whole-genome sequencing. The Diagenode Premium RRBS kit makes this technology widely available and provides high coverage (up to 4 million CpGs in human samples). Multiplexing prior to bisulfite conversion allows processing of 96 samples per experiment, enabling studies of large cohorts.</p>',
'date' => '2016-01-28',
'pmid' => 'http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html',
'doi' => '',
'modified' => '2022-05-19 16:12:34',
'created' => '2016-01-29 09:36:39',
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'countries' => 'US',
'modified' => '2022-08-29 16:35:27',
'created' => '2022-08-29 16:35:27',
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
</div>
</li>
</ul>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<div class="small-3 large-2 columns">
<span class="prefix">City</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][city]" placeholder="Denville" maxlength="255" type="text" id="QuoteCity"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Country <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<select name="data[Quote][country]" required="required" class="triggers" id="country_selector_quote-2836">
<option value="">-- select a country --</option>
<option value="AF">Afghanistan</option>
<option value="AX">Åland Islands</option>
<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
<option value="AR">Argentina</option>
<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
<option value="AZ">Azerbaijan</option>
<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d'Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
<option value="DK">Denmark</option>
<option value="DJ">Djibouti</option>
<option value="DM">Dominica</option>
<option value="DO">Dominican Republic</option>
<option value="EC">Ecuador</option>
<option value="EG">Egypt</option>
<option value="SV">El Salvador</option>
<option value="GQ">Equatorial Guinea</option>
<option value="ER">Eritrea</option>
<option value="EE">Estonia</option>
<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
<option value="FO">Faroe Islands</option>
<option value="FJ">Fiji</option>
<option value="FI">Finland</option>
<option value="FR">France</option>
<option value="GF">French Guiana</option>
<option value="PF">French Polynesia</option>
<option value="TF">French Southern Territories</option>
<option value="GA">Gabon</option>
<option value="GM">Gambia</option>
<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
<option value="GI">Gibraltar</option>
<option value="GR">Greece</option>
<option value="GL">Greenland</option>
<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
<option value="GG">Guernsey</option>
<option value="GN">Guinea</option>
<option value="GW">Guinea-Bissau</option>
<option value="GY">Guyana</option>
<option value="HT">Haiti</option>
<option value="HM">Heard Island and McDonald Islands</option>
<option value="VA">Holy See (Vatican City State)</option>
<option value="HN">Honduras</option>
<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
<option value="IS">Iceland</option>
<option value="IN">India</option>
<option value="ID">Indonesia</option>
<option value="IR">Iran, Islamic Republic of</option>
<option value="IQ">Iraq</option>
<option value="IE">Ireland</option>
<option value="IM">Isle of Man</option>
<option value="IL">Israel</option>
<option value="IT">Italy</option>
<option value="JM">Jamaica</option>
<option value="JP">Japan</option>
<option value="JE">Jersey</option>
<option value="JO">Jordan</option>
<option value="KZ">Kazakhstan</option>
<option value="KE">Kenya</option>
<option value="KI">Kiribati</option>
<option value="KP">Korea, Democratic People's Republic of</option>
<option value="KR">Korea, Republic of</option>
<option value="KW">Kuwait</option>
<option value="KG">Kyrgyzstan</option>
<option value="LA">Lao People's Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
<option value="LS">Lesotho</option>
<option value="LR">Liberia</option>
<option value="LY">Libya</option>
<option value="LI">Liechtenstein</option>
<option value="LT">Lithuania</option>
<option value="LU">Luxembourg</option>
<option value="MO">Macao</option>
<option value="MK">Macedonia, Republic of</option>
<option value="MG">Madagascar</option>
<option value="MW">Malawi</option>
<option value="MY">Malaysia</option>
<option value="MV">Maldives</option>
<option value="ML">Mali</option>
<option value="MT">Malta</option>
<option value="MH">Marshall Islands</option>
<option value="MQ">Martinique</option>
<option value="MR">Mauritania</option>
<option value="MU">Mauritius</option>
<option value="YT">Mayotte</option>
<option value="MX">Mexico</option>
<option value="FM">Micronesia, Federated States of</option>
<option value="MD">Moldova</option>
<option value="MC">Monaco</option>
<option value="MN">Mongolia</option>
<option value="ME">Montenegro</option>
<option value="MS">Montserrat</option>
<option value="MA">Morocco</option>
<option value="MZ">Mozambique</option>
<option value="MM">Myanmar</option>
<option value="NA">Namibia</option>
<option value="NR">Nauru</option>
<option value="NP">Nepal</option>
<option value="NL">Netherlands</option>
<option value="NC">New Caledonia</option>
<option value="NZ">New Zealand</option>
<option value="NI">Nicaragua</option>
<option value="NE">Niger</option>
<option value="NG">Nigeria</option>
<option value="NU">Niue</option>
<option value="NF">Norfolk Island</option>
<option value="MP">Northern Mariana Islands</option>
<option value="NO">Norway</option>
<option value="OM">Oman</option>
<option value="PK">Pakistan</option>
<option value="PW">Palau</option>
<option value="PS">Palestine, State of</option>
<option value="PA">Panama</option>
<option value="PG">Papua New Guinea</option>
<option value="PY">Paraguay</option>
<option value="PE">Peru</option>
<option value="PH">Philippines</option>
<option value="PN">Pitcairn</option>
<option value="PL">Poland</option>
<option value="PT">Portugal</option>
<option value="PR">Puerto Rico</option>
<option value="QA">Qatar</option>
<option value="RE">Réunion</option>
<option value="RO">Romania</option>
<option value="RU">Russian Federation</option>
<option value="RW">Rwanda</option>
<option value="BL">Saint Barthélemy</option>
<option value="SH">Saint Helena, Ascension and Tristan da Cunha</option>
<option value="KN">Saint Kitts and Nevis</option>
<option value="LC">Saint Lucia</option>
<option value="MF">Saint Martin (French part)</option>
<option value="PM">Saint Pierre and Miquelon</option>
<option value="VC">Saint Vincent and the Grenadines</option>
<option value="WS">Samoa</option>
<option value="SM">San Marino</option>
<option value="ST">Sao Tome and Principe</option>
<option value="SA">Saudi Arabia</option>
<option value="SN">Senegal</option>
<option value="RS">Serbia</option>
<option value="SC">Seychelles</option>
<option value="SL">Sierra Leone</option>
<option value="SG">Singapore</option>
<option value="SX">Sint Maarten (Dutch part)</option>
<option value="SK">Slovakia</option>
<option value="SI">Slovenia</option>
<option value="SB">Solomon Islands</option>
<option value="SO">Somalia</option>
<option value="ZA">South Africa</option>
<option value="GS">South Georgia and the South Sandwich Islands</option>
<option value="ES">Spain</option>
<option value="LK">Sri Lanka</option>
<option value="SD">Sudan</option>
<option value="SR">Suriname</option>
<option value="SS">South Sudan</option>
<option value="SJ">Svalbard and Jan Mayen</option>
<option value="SZ">Swaziland</option>
<option value="SE">Sweden</option>
<option value="CH">Switzerland</option>
<option value="SY">Syrian Arab Republic</option>
<option value="TW">Taiwan</option>
<option value="TJ">Tajikistan</option>
<option value="TZ">Tanzania</option>
<option value="TH">Thailand</option>
<option value="TL">Timor-Leste</option>
<option value="TG">Togo</option>
<option value="TK">Tokelau</option>
<option value="TO">Tonga</option>
<option value="TT">Trinidad and Tobago</option>
<option value="TN">Tunisia</option>
<option value="TR">Turkey</option>
<option value="TM">Turkmenistan</option>
<option value="TC">Turks and Caicos Islands</option>
<option value="TV">Tuvalu</option>
<option value="UG">Uganda</option>
<option value="UA">Ukraine</option>
<option value="AE">United Arab Emirates</option>
<option value="GB">United Kingdom</option>
<option value="US" selected="selected">United States</option>
<option value="UM">United States Minor Outlying Islands</option>
<option value="UY">Uruguay</option>
<option value="UZ">Uzbekistan</option>
<option value="VU">Vanuatu</option>
<option value="VE">Venezuela</option>
<option value="VN">Viet Nam</option>
<option value="VG">Virgin Islands, British</option>
<option value="VI">Virgin Islands, U.S.</option>
<option value="WF">Wallis and Futuna</option>
<option value="EH">Western Sahara</option>
<option value="YE">Yemen</option>
<option value="ZM">Zambia</option>
<option value="ZW">Zimbabwe</option>
</select><script>
$('#country_selector_quote-2836').selectize();
</script><br />
</div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">State</span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][state]" id="state-2836" maxlength="3" type="text"/><br />
</div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Email <sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][email]" placeholder="email@address.com" maxlength="255" type="email" id="QuoteEmail" required="required"/> </div>
</div>
<div class="row collapse" id="email_v">
<div class="small-3 large-2 columns">
<span class="prefix">Email verification<sup style="font-size:16px;color:red;">*</sup></span>
</div>
<div class="small-9 large-10 columns">
<input name="data[Quote][email_v]" autocomplete="nope" type="text" id="QuoteEmailV"/> </div>
</div>
<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">Project</span>
</div>
<div class="small-9 large-10 columns">
<textarea name="data[Quote][comment]" placeholder="Describe your project" cols="30" rows="6" id="QuoteComment"></textarea> </div>
</div>
<!------------SERVICES PARTICULAR FORM START---------------->
<!------------DATA TO POPULATE REGARDING SPECIFIC SERVICES----->
<div class="row collapse">
<div class="small-3 large-2 columns">
</div>
<div class="small-9 large-10 columns">
<div class="recaptcha"><div id="recaptcha67418bcba615d"></div></div> </div>
</div>
<br />
<div class="row collapse">
<div class="small-3 large-2 columns">
</div>
<div class="small-9 large-10 columns">
<button id="submit_btn-2836" class="alert button expand" form="Quote-2836" type="submit">Contact me</button> </div>
</div>
</form><script>
var pardotFormHandlerURL = 'https://go.diagenode.com/l/928883/2022-10-10/36b1c';
function postToPardot(formAction, id) {
$('#pardot-form-handler').load(function(){
$('#Quote-' + id).attr('action', formAction);
$('#Quote-' + id).submit();
});
$('#pardot-form-handler').attr('src', pardotFormHandlerURL + '?' + $('#Quote-' + id).serialize());
}
$(document).ready(function() {
$('body').append('<iframe id="pardot-form-handler" height="0" width="0" style="position:absolute; left:-100000px; top:-100000px" src="javascript:false;"></iframe>');
$('#Quote-2836').attr('action','javascript:postToPardot(\'' + $('#Quote-2836').attr('action') + '\', 2836)');
});
$("#Quote-2836 #submit_btn-2836").click(function (e) {
if($(this).closest('form')[0].checkValidity()){
e.preventDefault();
//disable the submit button
$("#Quote-2836 #submit_btn-2836").attr("disabled", true);
$("#Quote-2836 #submit_btn-2836").html("Processing...");
//submit the form
$("#Quote-2836").submit();
}
})
</script>
<script>
if ($("#Quote-2836 #country_selector_quote-2836.selectized").val() == 'US') {
var val = $("#state-2836").val();
$("#Quote-2836 #state-2836").replaceWith('<select name="data[Quote][state]" id="state-2836" required><option disabled selected value> -- select a state -- </option><option value="AL">Alabama (AL)</option><option value="AK">Alaska (AK)</option><option value="AZ">Arizona (AZ)</option><option value="AR">Arkansas (AR)</option><option value="CA">California (CA)</option><option value="CO">Colorado (CO)</option><option value="CT">Connecticut (CT)</option><option value="DE">Delaware (DE)</option><option value="FL">Florida (FL)</option><option value="GA">Georgia (GA)</option><option value="HI">Hawaii (HI)</option><option value="ID">Idaho (ID)</option><option value="IL">Illinois (IL)</option><option value="IN">Indiana (IN)</option><option value="IA">Iowa (IA)</option><option value="KS">Kansas (KS)</option><option value="KY">Kentucky (KY)</option><option value="LA">Louisiana (LA)</option><option value="ME">Maine (ME)</option><option value="MD">Maryland (MD)</option><option value="MA">Massachusetts (MA)</option><option value="MI">Michigan (MI)</option><option value="MN">Minnesota (MN)</option><option value="MS">Mississippi (MS)</option><option value="MO">Missouri (MO)</option><option value="MT">Montana (MT)</option><option value="NE">Nebraska (NE)</option><option value="NV">Nevada (NV)</option><option value="NH">New Hampshire (NH)</option><option value="NJ">New Jersey (NJ)</option><option value="NM">New Mexico (NM)</option><option value="NY">New York (NY)</option><option value="NC">North Carolina (NC)</option><option value="ND">North Dakota (ND)</option><option value="OH">Ohio (OH)</option><option value="OK">Oklahoma (OK)</option><option value="OR">Oregon (OR)</option><option value="PA">Pennsylvania (PA)</option><option value="PR">Puerto Rico (PR)</option><option value="RI">Rhode Island (RI)</option><option value="SC">South Carolina (SC)</option><option value="SD">South Dakota (SD)</option><option value="TN">Tennessee (TN)</option><option value="TX">Texas (TX)</option><option value="UT">Utah (UT)</option><option value="VT">Vermont (VT)</option><option value="VA">Virginia (VA)</option><option value="WA">Washington (WA)</option><option value="WV">West Virginia (WV)</option><option value="WI">Wisconsin (WI)</option><option value="WY">Wyoming (WY)</option><option value="DC">District of Columbia (DC)</option><option value="AS">American Samoa (AS)</option><option value="GU">Guam (GU)</option><option value="MP">Northern Mariana Islands (MP)</option><option value="PR">Puerto Rico (PR)</option><option value="UM">United States Minor Outlying Islands (UM)</option><option value="VI">Virgin Islands (VI)</option></select>');
if (val.length == 2) {
$("#state-2836").val(val);
}
$("#state-2836").parent().parent().show();
} else if ($("#Quote-2836 #country_selector_quote-2836.selectized").val() == 'CA') {
var val = $("#state-2836").val();
$("#Quote-2836 #state-2836").replaceWith('<select name="data[Quote][state]" id="state-2836" required><option disabled selected value> -- select a state -- </option><option value="AB">Alberta (AB)</option><option value="BC">British Columbia (BC)</option><option value="MB">Manitoba (MB)</option><option value="NB">New Brunswick (NB)</option><option value="NL">Newfoundland and Labrador (NL)</option><option value="NS">Nova Scotia (NS)</option><option value="ON">Ontario (ON)</option><option value="PE">Prince Edward Island (PE)</option><option value="QC">Quebec (QC)</option><option value="SK">Saskatchewan (SK)</option></select>');
if (val.length == 2) {
$("#state-2836").val(val);
}
$("#state-2836").parent().parent().show();
} else if ($("#Quote-2836 #country_selector_quote-2836.selectized").val() == 'DE') {
var val = $("#state-2836").val();
$("#Quote-2836 #state-2836").replaceWith('<select name="data[Quote][state]" id="state-2836" required><option disabled selected value> -- select a state -- </option><option value="BW">Baden-Württemberg (BW)</option><option value="BY">Bayern (BY)</option><option value="BE">Berlin (BE)</option><option value="BB">Brandeburg (BB)</option><option value="HB">Bremen (HB)</option><option value="HH">Hamburg (HH)</option><option value="HE">Hessen (HE)</option><option value="MV">Mecklenburg-Vorpommern (MV)</option><option value="NI">Niedersachsen (NI)</option><option value="NW">Nordrhein-Westfalen (NW)</option><option value="RP">Rheinland-Pfalz (RP)</option><option value="SL">Saarland (SL)</option><option value="SN">Sachsen (SN)</option><option value ="SA">Sachsen-Anhalt (SA)</option><option value="SH">Schleswig-Holstein (SH)</option><option value="TH">Thüringen</option></select>');
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$("#state-2836").val(val);
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$("#Quote-2836 #state-2836").replaceWith('<input name="data[Quote][state]" maxlength="255" type="text" id="state-2836" value="">');
}
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$("#Quote-2836 #state-2836").parent().parent().hide();
$("#Quote-2836 #state-2836").replaceWith('<input name="data[Quote][state]" maxlength="255" type="text" id="state-2836" value="">');
}
});
</script>
<a class="close-reveal-modal" aria-label="Close">×</a></div><!-- END: QUOTE MODAL --><a href="#" id="rrbs-service" data-reveal-id="quoteModal-2836" class="quote_btn" style="color:#B21329"><i class="fa fa-info-circle"></i></a>
</div>
</div>
<div class="small-12 columns" >
<h6 style="height:60px">RRBS service (Reduced Representation Bisulfite ...</h6>
</div>
</div>
</li>
<li>
<div class="row">
<div class="small-12 columns">
<a href="/cn/p/premium-rrbs-kit-V2-x24"><img src="/img/product/kits/methyl-kit-icon.png" alt="Methylation kit icon" class="th"/></a> </div>
<div class="small-12 columns">
<div class="small-6 columns" style="padding-left:0px;padding-right:0px;margin-top:-6px;margin-left:-1px">
<span class="success label" style="">C02030036</span>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
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</li>
</ul>
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'label1' => 'Characteristics',
'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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<p><strong>Reduced Representation Bisulfite Sequencing (RRBS)</strong> offers a <strong>cost-effective, focused solution</strong> to perform genome-scale DNA methylation analysis at the <strong>single nucleotide level</strong> in any vertebrate species. The fundamental idea of RRBS is to get a “reduced representation” of the genome. <span>By cutting the genome using the <strong>restriction MspI enzyme</strong> (CCGG target sites) followed by size selection, the DNA sample is enriched with</span><span><span> </span>biologically relevant </span><span><strong>CpG-rich regions</strong> (including<span> </span></span><span>promoters and<span> </span></span><span>CpG islands) in which DNA methylation marks are typically found.</span><span><span> </span></span></p>
<p>With Diagenode’s Premium RRBS Kit V2 perform cost-effective and reliable genome-scale DNA methylation analysis. Detect around <strong>4 million CpGs</strong> (with coverage > 10) in human samples and identify methylation patterns in CpG rich regions including promoters and CpG islands.</p>
<p>Secure high-quality NGS data for DNA methylation analysis at single base resolution and enjoy our specific and upgraded features:</p>
<ul>
<li>Work with <strong>the lowest DNA amounts</strong> from <strong>25 -100ng gDNA</strong></li>
<li>Process <strong>up to 96 sample</strong> in parallel while saving handling-time and cost per sample with early sample pooling strategy</li>
<li>Utilize our <strong>Software for Intelligent Pooling</strong> (SIP) for better pooling strategies</li>
<li>Get <strong>optimal results</strong> from your sequencer with <strong>Unique Dual Indexing</strong> (UDI)</li>
<li><strong>Identify</strong> and <strong>remove PCR duplicates</strong> from your data with <strong>Unique Molecular Identifiers</strong> (UMIs)</li>
</ul>
<p>The Premium RRBS kit V2 includes all reagents necessary for the library preparation. Specific adapters were designed and validated to fit the technology and are available separately as described below.</p>
<p><strong>For RRBS V2 with UDI and UMI library construction:</strong></p>
<ul>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-A-x24">C02030040 - Premium Methyl UMI-UDI Adapters - Set A</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-set-B-x24">C02030041 - Premium Methyl UMI-UDI Adapters - Set B</a></li>
<li><a href="https://www.diagenode.com/en/p/premium-methyl-UDI-UMI-adapters-96-rxns">C02030042 - Premium Methyl UMI-UDI Adapters - 96 rxns</a></li>
</ul>
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#v5" style="color: #13b29c;"><i class="fa fa-caret-right"></i> Software for Intelligent Pooling (SIP)</a>
<div id="v5" class="content">
<p>Diagenode's new online<strong> intelligent pooling aid</strong> (RRBS SIP) provides the <strong>optimal pool design</strong> for RRBS to meet your specific sample and analysis needs:</p>
<ul style="list-style-type: disc;">
<li><strong>Time-saving: </strong>avoid<strong> </strong>complex caculations</li>
<li><strong>Highest pooling efficiency</strong> based on qPCR quantification - elevate your pooling effectiveness</li>
<li><strong>Powerful:</strong> incorporates advanced aspects such as number of samples per pool required and the separation between projects</li>
<li><strong>Accurate:</strong> identifies outliers</li>
</ul>
<p></p>
<p>Get access to Diagenode's <a href="https://diagenode.shinyapps.io/RRBS_SIP/">RRBS Software for Intelligent Pooling (SIP</a>)</p>
<p>Get your RRBS template file - <a href="https://www.diagenode.com/files/products/kits/Template_SIP_RRBS.xlsx">Here</a></p>
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</li>
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'info1' => '<h4>Sequencing quality</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig_RRBS_50ng_1_R1_trimmed_per_base_quality.png" width="850px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 1. Excellent sequencing quality. </b>RRBS libraries were prepared from different starting amounts of human gDNA using Diagenode’s Premium RRBS V2 kit and sequenced in paired-end 50 bp on Illumina NovaSeq instrument generating 30-40 million read pairs per sample. Sequencing statistics reveal that all samples performed well with mean Phred scores above 30 along the entire reads 1 (A) and 2 (B) (data shown for 50 ng gDNA input after trimming).</p>
<p><br /><br /></p>
<h4>Accurate Coverage of CpGs</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig2_table_rrbs.png" width="100%" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Table 1. Examples of Premium RRBS V2 sequencing data.</b> UMI data processing enables accurate estimation of CpG counts.</p>
<p><br /><br /></p>
<h4>Focus on CpG-rich regions</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3_50ng_10X_coverage_annotation_cpg.png" width="400px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure </b><b>2. </b><b>Coverage</b> <b>of </b><b>CpGs</b><b> and genomic regions by Premium RRBS V2</b><b>. </b>Diagenode’s Premium RRBS V2 allows a wide interrogation of CpGs (with a sequencing depth >10) of the human genome with a focus on CpG rich regions, especially CpG islands (data shown for 50 ng gDNA input).</p>
<p><br /><br /></p>
<h4>Compatible with all vertebrates</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table2-rrbs.png" width="100%" alt="CpG-dense regions" caption="false" /></p>
<p><b>Table 2.</b> The kit RRBS v2 is compatible with all vertebrates. The Table 2 shows some examples of results obtained for different species.</p>
<p><br /><br /></p>
<h4>Superior performance</h4>
<ul>
<li>Lower duplicate rate</li>
<li>More CpGs detected</li>
<li>Higher % of uniquely aligned reads containing CpGs</li>
<li>Higher genome coverage (3,3%)</li>
</ul>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/table3-rrbs.png" width="100%" alt="RRBS Kit V2 x96" caption="false" /></p>
<p><b>Table 3.</b> RRBS was performed using Diagenode RRBS v2 kit on HeLa cells and human blood samples (total 10 samples) as well as using competitor kit on HeLa cells, human blood and brain samples (total 10 samples). The Table 3 shows sequencing parameters for 10 samples processed with each kit.</p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/fig3-rrbs.png" width="500px" alt="Reduced Representation Bisulfite Sequencing" caption="false" /></p>
<p><b>Figure 3.</b> Comparison of CpG coverage between competing technologies.</p>
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'label2' => 'How it works?',
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<p>By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</p>
<p><span class="label-green" style="margin-bottom: 16px; margin-left: -22px;">NATURE METHODS</span></p>
<p></p>
<h4 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h4>
<p><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a>,<sup href="#affil-auth"> </sup><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><span style="font-size: 13.3333px;">, </span><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a></p>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p><a href="https://www.nature.com/articles/nmeth.f.391" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
<p><iframe width="320" height="180" src="https://www.youtube.com/embed/CJn3XEAznu0?rel=0" frameborder="0" allowfullscreen="allowfullscreen"></iframe> <iframe width="320" height="180" src="https://www.youtube.com/embed/4xgRG9qVT5E"></iframe></p>
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'label3' => 'Examples of data analysis ',
'info3' => '<h4>Comparative analysis using DNA methylation data from RRBS V2:</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/cluster-dendrogram-rrbs.png" width="500px" alt="DNA methylation data from RRBS V2" caption="false" /></p>
<p><b>Figure 1. Example of cluster dendrogram</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>After performing alignment to the reference genome, the methylation calling step provides the methylation percentages for every detected CpG. This methylation rate profile, available now for each sample, can be used to visually explore sample distance by hierarchical clustering. The main branches in this type of dendograms show how the clusters are distinct from each other, and how the objects within each cluster are broadly similar to each other.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/clustering-heatmap-rrbs.png" width="850px" alt="DNA methylation data from Premium RRBS kit V2" caption="false" /></p>
<p><b>Figure 2. Example of results: Clustering heatmap of significant DMRs (left) and Volcano plot of DMR (right)</b><br />Samples: WT – Wild type; Single KO – Single knock-out; Double KO – Double knock-out</p>
<p>If the groups of interest contain two or more replicates, a differential methylation analysis can be performed. This type of analysis will identify individual CpGs or regions in the genome that are hyper or hypomethylated in the study group with respect to the control group. A heatmap (left) can be used to visualize the overall distribution of differential methylation between the groups. A volcano plot (right) will show more precisely, the mehtylation difference (x axis) and level of significance of that difference (y axis) allowing the researcher to identify CpGs or regions with large and statistically signifcant differences.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/percentage-hypo-rrbs.png" width="500px" alt="CpG-dense regions" caption="false" /></p>
<p><b>Figure 3. Example of results: </b><strong>Percentage of hypo and hypermethylated regions in the samples per chromosome</strong></p>
<p>Horizontal bar plots show the number of hyper and hypomethylated events per chromosome, as a percentage of the sites with 10X coverage and a 25% difference of methylation status.</p>
<p></p>
<h4>QC of the samples</h4>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/per_base_sequence_content-rrbs.png" width="500px" alt="single nucleotide resolution" caption="false" /></p>
<p><b>Figure 4. Example of samples QC:</b> <strong>Per base sequence content</strong></p>
<p>A sequencing quality control software such as FastQC will show the per base sequence content of the sequenced reads. With Diagenode’s RRBS Premium kit, read 2 should show a specific profile for the first three bases that correspond to the MspI restrictive site CGG. The first base being a cytosine followed by a guanine can be either methylated or not; 5mC will remain as C after bisulfite conversion whereas unmethylated C will be converted to T after PCR amplification. The first base in the FastQC per base content plot will therefore show a percentage of reads that vary at that position. In this example, 70% of the reads are methylated at position 1 (C) and 30% are unmethylated. Positions 2 and 3 should show 100% G content.</p>
<p></p>
<p><img src="https://www.diagenode.com/img/product/kits/rrbs_v2/histogram-cpg-rrbs.png" width="500px" alt="DNA methylation" caption="false" /></p>
<p><b>Figure 5. Example of samples QC: </b><strong>Histogram of % CpG methylation</strong></p>
<p>Another quality control step consists in looking at the frequency of methylation percentages for all CpGs detected. This step can only be performed after alignment and methylation call. The histogram shows a bimodal distribution, where most of the cytosines are either fully methylated or unmethylated. Some cytosines have intermediate methylation levels. A bump in the middle of the plot or a non-bimodal distribution would suggest an inefficiency of the bisulfite treatment. With Diagenode’s RRBS Premium kit, the exact conversion rate can be calculated thanks to the spike-in control sequences that are included in the kit.</p>
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