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<li>Latest technology in bisulfite-seq using Illumina sequencers: > 10x coverage per CpG</li>
<li>More than 4 million CpGs detected in human samples</li>
<li>As little as 100 ng in mammals, birds, fish ,and more</li>
<li><strong>Complete end-to-end service</strong>: wet lab to bioinformatics for only <strong>550 per sample</strong>!</li>
</ul>
</div>
<div class="extra-spaced">
<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
<center><img src="https://www.diagenode.com/img/product/services/RRBS_figure.jpg" alt="DNA Methylation Profiling Service" title="DNA Methylation RRBS Service" caption="false" width="900" height="278" /></center>
<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
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<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
<table>
<thead>
<tr>
<th>Species</th>
<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
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<th style="text-align: center;">Mean sequencing depth per covered CpG (×)</th>
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<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
</tr>
<tr style="height: 61px;">
<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
</tr>
<tr style="height: 61px;">
<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
</tr>
<tr style="height: 61px;">
<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
</tr>
<tr style="height: 61px;">
<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
</tr>
</tbody>
</table>
<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<li>Assessment of DNA quality</li>
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<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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<li>Differentially Methylated CpGs (DMCs) analysis</li>
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<li>Latest technology in bisulfite-seq using Illumina sequencers: > 10x coverage per CpG</li>
<li>More than 4 million CpGs detected in human samples</li>
<li>As little as 100 ng in mammals, birds, fish ,and more</li>
<li><strong>Complete end-to-end service</strong>: wet lab to bioinformatics for only <strong>550 per sample</strong>!</li>
</ul>
</div>
<div class="extra-spaced">
<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
<center><img src="https://www.diagenode.com/img/product/services/RRBS_figure.jpg" alt="DNA Methylation Profiling Service" title="DNA Methylation RRBS Service" caption="false" width="900" height="278" /></center>
<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
</div>
<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
<table>
<thead>
<tr>
<th>Species</th>
<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
<th style="text-align: center;">Bisulfite conversion rate (%)</th>
<th style="text-align: center;">Mean sequencing depth per covered CpG (×)</th>
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<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
</tr>
<tr style="height: 61px;">
<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
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<tr style="height: 61px;">
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<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
</tr>
<tr style="height: 61px;">
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<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
</tr>
<tr style="height: 61px;">
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<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
</tr>
</tbody>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<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>
</ul>
</td>
</tr>
<tr>
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<td style="width: 636px;">
<ul style="list-style-type: circle;">
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<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>
</ul>
</td>
</tr>
<tr>
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<td style="width: 636px;">
<ul style="list-style-type: circle;">
<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>
</ul>
</td>
</tr>
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<th style="width: 624px;">
<h4><strong>Features</strong></h4>
</th>
</tr>
</thead>
<tbody>
<tr>
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<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>
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<td style="width: 624px;">
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<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>
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<li>Get functional insights</li>
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</td>
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'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
<p></p>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
<div class="portal-inner"><nav class="portal-nav" style="text-align: left;">
<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
</div>
<div class="tabs-content">
<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially “finding a needle in a haystack.”</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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'description' => '<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">Whole-genome bisulfite sequencing（</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">WGBS</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">）は、</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">DNA</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">メチル化プロファイリングの中でも最も包括的な次世代シーケンシングであり、ゲノム全体で</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">5-mC</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">の単一塩基分解能を可能にします。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">同じ場所での変換されていないシトシンと変換されたシトシンの割合を比較することにより、メチル化レベルが決定されます。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;"></span></p>
<pre class="tw-data-text tw-text-large tw-ta" data-placeholder="Translation" id="tw-target-text" dir="ltr"><span class="Y2IQFc" lang="ja"></span><span lang="EN" style="font-family: 'Arial Unicode MS'; color: #b21329;">DNA</span><span lang="FR" style="font-family: 'Arial Unicode MS'; color: #b21329;">メチル化研究において最も高いカバレッジ</span></pre>
<ul>
<li class="normal"><span>ゲノム全体のDNAメチル化と単一塩基分解能スクリーニング</span></li>
<li class="normal">低密度と反復領域における5mCの高カバレッジ</li>
<li class="normal">専任の科学者がハイタッチコミュニケーションで各プロジェクトをサポート</li>
<li class="normal">包括的なサービス - バイサルファイト処理、ライブラリーの調製、シーケンシングと解析</li>
</ul>
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<p><strong></strong></p>
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<p style="text-align: left;"><strong></strong></p>
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<p style="text-align: center;"><strong>WGBS</strong></p>
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<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
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<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
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<td style="width: 94px; text-align: center; height: 107px; background-color: #f9f9f9;"><strong>EM-seq</strong></td>
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<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
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<span style="font-weight: 400;"></span></td>
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<tr style="height: 129px;">
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<p><strong>Deep sequencing</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 129px; background-color: white;">
<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150) </span> </span></li>
<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage >30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of >50 million CpGs with 6-9X average CpG coverage for human samples </span></li>
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<li>Clustering analysis</li>
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<li>Get functional insights</li>
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<td style="width: 262px; height: 86px;">
<p><strong>Pathway analysis</strong></p>
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<td style="width: 262px; height: 86px;">
<p><strong>Data mining</strong></p>
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<td style="width: 624px; height: 86px;">
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<li><span>Biomarker discovery</span></li>
<li><span>Determination of methylation pattern/signature that can be predictive and discriminate between different groups/conditions</span></li>
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'description' => '<div class="extra-spaced">
<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
</div>
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<p>Let us do your epigenetics sample preparation. Save time and utilize our epigenetics expertise. Discover modifications and how changes on chromatin and DNA influence expression.</p>
<p class="text-center"><img src="https://www.diagenode.com/img/categories/services/workflow_services.jpg" /></p>
<div class="row">
<div class="small-12 medium-6 large-6 columns text-center"><img src="https://www.diagenode.com/img/categories/services/services_chipseq.png" alt="services chip-seq" height="105" width="105" /> <br /><br /> <a href="../categories/chip-seq-service" class="details radius button">ChIP-seq service</a></div>
<div class="small-12 medium-6 large-6 columns text-center"><img src="https://www.diagenode.com/img/categories/services/services_methylation.png" alt="services methylation" height="105" width="105" /> <br /><br /> <a href="../categories/rrbs-service" class="details radius button">RRBS service</a></div>
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<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation – hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
</div>
<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns">
<p>Our Epigenomics Profiling Services helps you study DNA methylation (5mC) and methylation variants (5hmC). Our experienced scientists will guide you to the best solution according to your needs, depending on whether you want to analyze DNA methylation at genome-wide or gene specific scales. They will process the arrays or generate meaningful libraries for DNA sequencing and deliver high quality data. Our bioinformatic experts will closely work with you to provide standard and customized analysis with comprehensive publication-ready figures.</p>
<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis <span>using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<!--<h3><a href="https://www.diagenode.com/en/p/infinium-mouse-methylation-array-service">Infinium Mouse Methylation Array Service</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 285,000 markers detected in mouse samples at single nucleotide resolution</li>
<li>Suitable for common laboratory mouse strains</li>
<li><span>Differential methylation analysis using our<span> </span><a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<h3><a href="https://www.diagenode.com/en/p/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS) Service</a></h3>
<ul>
<li>Robust and cost-effective solution with reliable results</li>
<li>Unique technology enables low DNA inputs down to 25ng</li>
<li>Accurate analyses with UDIs and UMIs</li>
<li>Up to 7 million CpGs detected in human samples at single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG-rich regions across the genome including promoters and CpG islands</li>
<li><span>Differential methylation analysis <span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
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<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
<ul>
<li>Very powerful solution for genome-wide biomarker discovery using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome </span>at single nucleotide resolution</li>
<li>Detection of global methylation patterns including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation levels between groups using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
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<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Custom Targeted Methyl-seq</a></h3>
<ul>
<li>Focus on DNA methylation analysis of pre-defined regions with single nucleotide resolution</li>
<li>Custom hybridization capture approach with enzymatic conversion</li>
<li>Enhanced accuracy, sensitivity and specificity with high coverage while reducing overall cost</li>
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<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
<ul>
<li>Immunoprecipitation assay (antibody-based approach) to study 5-mC genome-wide</li>
<li>Resolution of 100-500 bp</li>
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<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
<p></p>
<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
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'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized “Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
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'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',
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'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',
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'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',
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'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',
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'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',
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'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',
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'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) 7 => 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) 8 => 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) 9 => 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) 10 => 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) 11 => 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) 12 => 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) 13 => 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) 14 => array(
'id' => '4154',
'name' => 'IGFBP2 protects against pulmonary fibrosis through inhibiting P21-mediated senescence',
'authors' => 'Chiahsuan, C. et al.',
'description' => '<p>Accumulation of senescent cells contributes to age related diseases including idiopathic pulmonary fibrosis (IPF). Insulin-like growth factor binding proteins (IGFBPs) are evolutionarily conserved proteins that play a vital role in many biological processes. Overall, little is known about the functions of IGFBP2 in the epigenetic regulation of cellular senescence and pulmonary fibrosis. Here, we show that Igfbp2 expression was significantly downregulated at both mRNA and protein levels in a low-dose bleomycin-induced pulmonary fibrosis model of aged mice. Using the reduced representation of bisulfite sequencing technique, we demonstrated Igfbp2 downregulation is attributed to DNA methylation of CpG islands in fibrotic lungs of aged mice. Furthermore, Igfbp2 siRNA knockdown increased both P53 and P21 protein levels in mouse lung epithelial cells exposed to hypoxia treatment. Lentiviral mediated expression of Igfb2 decreased P21 protein levels and significantly reduced beta galactosidase activity in mouse lung epithelial cells challenged with a senescent drug (atazanavir) and hypoxia treatments. Using the RT2 Profiler PCR Array, we found that P21, PAI-1, IRF-5 and IRF-7, important regulators of senescence pathway, were significantly downregulated specifically in type-II alveolar epithelial cells (AECs) of aged human-Igfbp2 transgenic mice after bleomycin challenge. Finally, transgenic expression of human-Igfbp2 in type-II AECs from aged bleomycin challenged mice significantly decreased senescent associated secretory phenotype factors and also reduced extracellular matrix markers compared to aged wild-type mice challenged with bleomycin injury. Collectively, these findings reveal that epigenetic repression of Igfbp2 promotes pulmonary fibrosis and that restoring IGFBP2 in fibrotic lungs could prove effective in IPF treatment.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2021.01.21.427684',
'doi' => '10.1101/2021.01.21.427684',
'modified' => '2021-12-16 10:38:38',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => 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) 16 => 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) 17 => 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) 18 => 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) 19 => 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) 20 => array(
'id' => '3919',
'name' => 'LY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/β-Catenin Pathway.',
'authors' => 'Mehdi S, Bachvarova M, Scott-Boyer MP, Droit A, Bachvarov D',
'description' => '<p>Growing evidence demonstrates that epithelial-mesenchymal transition (EMT) plays an important role in epithelial ovarian cancer (EOC) progression and spreading; however, its molecular mechanisms remain poorly defined. We have previously shown that the antigen receptor LY75 can modulate EOC cell phenotype and metastatic potential, as LY75 depletion directed mesenchymal-epithelial transition (MET) in EOC cell lines with mesenchymal phenotype. We used the LY75-mediated modulation of EMT as a model to investigate for DNA methylation changes during EMT in EOC cells, by applying the reduced representation bisulfite sequencing (RRBS) methodology. Numerous genes have displayed EMT-related DNA methylation patterns alterations in their promoter/exon regions. Ten selected genes, whose DNA methylation alterations were further confirmed by alternative methods, were further identified, some of which could represent new EOC biomarkers/therapeutic targets. Moreover, our methylation data were strongly indicative for the predominant implication of the Wnt/β-catenin pathway in the EMT-induced DNA methylation variations in EOC cells. Consecutive experiments, including alterations in the Wnt/β-catenin pathway activity in EOC cells with a specific inhibitor and the identification of LY75-interacting partners by a proteomic approach, were strongly indicative for the direct implication of the LY75 receptor in modulating the Wnt/β-catenin signaling in EOC cells.</p>',
'date' => '2020-03-07',
'pmid' => 'http://www.pubmed.gov/32156068',
'doi' => '10.3390/ijms21051848',
'modified' => '2020-08-17 11:00:08',
'created' => '2020-08-10 12:12:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => 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',
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'name' => 'Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model.',
'authors' => 'Logan NJ, Camman M, Williams G, Higgins CA',
'description' => '<p>Trauma-induced heterotopic ossification is an intriguing phenomenon involving the inappropriate ossification of soft tissues within the body such as the muscle and ligaments. This inappropriate formation of bone is highly prevalent in those affected by blast injuries. Here, we developed a simplified cell culture model to evaluate the molecular events involved in heterotopic ossification onset that arise from the shock wave component of the disease. We exposed three subtypes of human mesenchymal cells in vitro to a single, high-energy shock wave and observed increased transcription in the osteogenic master regulators, Runx2 and Dlx5, and significantly accelerated cell mineralisation. Reduced representation bisulfite sequencing revealed that the shock wave altered methylation of gene promoters, leading to opposing changes in gene expression. Using a drug to target ITGAV, whose expression was perturbed by the shock wave, we found that we could abrogate the deposition of mineral in our model. These findings show how new therapeutics for the treatment of heterotopic ossification can be identified using cell culture models.</p>',
'date' => '2018-10-11',
'pmid' => 'http://www.pubmed.gov/30219480',
'doi' => '10.1016/j.bone.2018.09.008',
'modified' => '2018-12-31 11:21:44',
'created' => '2018-12-04 09:51:07',
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[maximum depth reached]
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(int) 23 => array(
'id' => '3378',
'name' => 'Paternal sepsis induces alterations of the sperm methylome and dampens offspring immune responses—an animal study',
'authors' => 'Katharina Bomans, Judith Schenz, Sandra Tamulyte, Dominik Schaack, Markus Alexander Weigand and Florian Uhle',
'description' => '<section xmlns="" xmlns:fn="http://www.w3.org/2005/xpath-functions" xmlns:meta="http://www.springer.com/app/meta" class="Abstract Section1 RenderAsSection1" id="Abs1" lang="en">
<h2 class="Heading js-ToggleCollapseSection is-shown" data-component="collapse-fulltext" data-sticky-update="true">Abstract</h2>
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<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec1">
<h3 xmlns="" class="Heading">Background</h3>
<p id="Par1" class="Para">Sepsis represents the utmost severe consequence of infection, involving a dysregulated and self-damaging immune response of the host. While different environmental exposures like chronic stress or malnutrition have been well described to reprogram the germline and subsequently offspring attributes, the intergenerational impact of sepsis as a tremendous immunological stressor has not been examined yet.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec2">
<h3 xmlns="" class="Heading">Methods</h3>
<p id="Par2" class="Para">Polymicrobial sepsis in 12-week-old male C57BL/6 mice was induced by cecal ligation and puncture (CLP), followed by a mating of the male survivors (or appropriate sham control animals) 6 weeks later with healthy females. Alveolar macrophages of offspring animals were isolated and stimulated with either LPS or Zymosan, and supernatant levels of TNF-α were quantified by ELISA. Furthermore, systemic cytokine response to intraperitoneally injected LPS was assessed after 24 h. Also, morphology, motility, and global DNA methylation of the sepsis survivors’ sperm was examined.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec3">
<h3 xmlns="" class="Heading">Results</h3>
<p id="Par3" class="Para">Comparative reduced reduction bisulfite sequencing (RRBS) of sperm revealed changes of DNA methylation (<em xmlns="" class="EmphasisTypeItalic">n</em> = 381), most pronounced in the intergenic genome as well as within introns of developmentally relevant genes. Offspring of sepsis fathers exhibited a slight decrease in body weight, with a more pronounced weight difference in male animals (CLP vs. sham). Male descendants of sepsis fathers, but not female descendants, exhibited lower plasma concentrations of IL-6, TNF-alpha, and IL-10 24 h after injection of LPS. In line, only alveolar macrophages of male descendants of sepsis fathers produced less TNF-alpha upon Zymosan stimulation compared to sham descendants, while LPS responses kept unchanged.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec4">
<h3 xmlns="" class="Heading">Conclusion</h3>
<p id="Par4" class="Para">We can prove that male—but surprisingly not female—descendants of post-sepsis fathers show a dampened systemic as well as pulmonary immune response. Based on this observation of an immune hypo-responsivity, we propose that male descendants of sepsis fathers are at risk to develop fungal and bacterial infections and might benefit from therapeutic immune modulation.</p>
</div>
</div>
</section>',
'date' => '2018-06-18',
'pmid' => 'https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-018-0522-z',
'doi' => '10.1186/s13148-018-0522-z',
'modified' => '2018-06-28 17:08:31',
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'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',
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'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' => '',
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'author' => 'Prof. Lucia Altucci, MD, PhD, Seconda Università degli Studi di Napoli, Dipartimento di Biochimica, Biofisica e Patologia generale .',
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'description' => '<p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p>',
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'author' => 'Satu Mäki-Nevala, PhD, University of Helsinki',
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'author' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
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<blockquote><p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p><cite>Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </cite></blockquote>
<blockquote><p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p><cite>Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina</cite></blockquote>
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<h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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<h4>Which services are you interested in?</h4>
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<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-2989">
<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-2989').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-2989" 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="recaptcha673fa71e95be0"></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-2989" class="alert button expand" form="Quote-2989" 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-2989').attr('action','javascript:postToPardot(\'' + $('#Quote-2989').attr('action') + '\', 2989)');
});
$("#Quote-2989 #submit_btn-2989").click(function (e) {
if($(this).closest('form')[0].checkValidity()){
e.preventDefault();
//disable the submit button
$("#Quote-2989 #submit_btn-2989").attr("disabled", true);
$("#Quote-2989 #submit_btn-2989").html("Processing...");
//submit the form
$("#Quote-2989").submit();
}
})
</script>
<script>
if ($("#Quote-2989 #country_selector_quote-2989.selectized").val() == 'US') {
var val = $("#state-2989").val();
$("#Quote-2989 #state-2989").replaceWith('<select name="data[Quote][state]" id="state-2989" 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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$("#Quote-2989 #state-2989").replaceWith('<input name="data[Quote][state]" maxlength="255" type="text" id="state-2989" value="">');
}
});
</script>
<a class="close-reveal-modal" aria-label="Close">×</a></div><!-- END: QUOTE MODAL --><a href="#" id="wgbs-service" data-reveal-id="quoteModal-2989" 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">WGBS service (Whole Genome Bisulfite Sequencing)</h6>
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<h6 style="height:60px">Methylation Data Analysis</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
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<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<li>Single-base resolution CpG detection with our popular Premium RRBS kit (internal bisulfite conversion controls included)</li>
<li>Latest technology in bisulfite-seq using Illumina sequencers: > 10x coverage per CpG</li>
<li>More than 4 million CpGs detected in human samples</li>
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<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
<center><img src="https://www.diagenode.com/img/product/services/RRBS_figure.jpg" alt="DNA Methylation Profiling Service" title="DNA Methylation RRBS Service" caption="false" width="900" height="278" /></center>
<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
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<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
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<thead>
<tr>
<th>Species</th>
<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
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<th style="text-align: center;">Mean sequencing depth per covered CpG (×)</th>
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<td><strong>Human</strong></td>
<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
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<tr style="height: 61px;">
<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
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<tr style="height: 61px;">
<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
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<tr style="height: 61px;">
<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
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<tr style="height: 61px;">
<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<li>Single-base resolution CpG detection with our popular Premium RRBS kit (internal bisulfite conversion controls included)</li>
<li>Latest technology in bisulfite-seq using Illumina sequencers: > 10x coverage per CpG</li>
<li>More than 4 million CpGs detected in human samples</li>
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<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
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<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
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<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
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<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
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<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
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<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
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<tr style="height: 61px;">
<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
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<tr style="height: 61px;">
<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
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<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
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<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
</div>
<div class="tabs-content">
<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially “finding a needle in a haystack.”</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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'description' => '<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">Whole-genome bisulfite sequencing（</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">WGBS</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">）は、</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">DNA</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">メチル化プロファイリングの中でも最も包括的な次世代シーケンシングであり、ゲノム全体で</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">5-mC</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">の単一塩基分解能を可能にします。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">同じ場所での変換されていないシトシンと変換されたシトシンの割合を比較することにより、メチル化レベルが決定されます。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;"></span></p>
<pre class="tw-data-text tw-text-large tw-ta" data-placeholder="Translation" id="tw-target-text" dir="ltr"><span class="Y2IQFc" lang="ja"></span><span lang="EN" style="font-family: 'Arial Unicode MS'; color: #b21329;">DNA</span><span lang="FR" style="font-family: 'Arial Unicode MS'; color: #b21329;">メチル化研究において最も高いカバレッジ</span></pre>
<ul>
<li class="normal"><span>ゲノム全体のDNAメチル化と単一塩基分解能スクリーニング</span></li>
<li class="normal">低密度と反復領域における5mCの高カバレッジ</li>
<li class="normal">専任の科学者がハイタッチコミュニケーションで各プロジェクトをサポート</li>
<li class="normal">包括的なサービス - バイサルファイト処理、ライブラリーの調製、シーケンシングと解析</li>
</ul>
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<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
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<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
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<p>Let us do your epigenetics sample preparation. Save time and utilize our epigenetics expertise. Discover modifications and how changes on chromatin and DNA influence expression.</p>
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<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation – hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
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<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
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<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis <span>using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<li><span>Differential methylation analysis <span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
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<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
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<li>Very powerful solution for genome-wide biomarker discovery using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome </span>at single nucleotide resolution</li>
<li>Detection of global methylation patterns including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation levels between groups using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
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<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Custom Targeted Methyl-seq</a></h3>
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<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
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<li>Resolution of 100-500 bp</li>
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<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
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<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
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'id' => '900',
'name' => 'The Diagenode Epigenetics custom service',
'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized “Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
'image_id' => null,
'type' => 'Poster',
'url' => 'files/posters/diagenode-epigenetics-custom-service.pdf',
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'modified' => '2021-04-29 09:26:52',
'created' => '2016-04-27 15:27:04',
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(int) 2 => array(
'id' => '1052',
'name' => 'Epigenomics Profiling Services',
'description' => '<ul>
<li>Chromatin analysis</li>
<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
</ul>',
'image_id' => null,
'type' => 'Flyer',
'url' => 'files/flyers/epigenomics_profiling_services_flyer.pdf',
'slug' => 'services-flyer',
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'meta_description' => '',
'modified' => '2020-04-23 16:22:55',
'created' => '2019-06-13 11:36:20',
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(int) 0 => 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',
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(int) 1 => 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',
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[maximum depth reached]
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(int) 2 => 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',
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[maximum depth reached]
)
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(int) 3 => 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',
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[maximum depth reached]
)
),
(int) 4 => 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) 5 => 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) 6 => 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',
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[maximum depth reached]
)
),
(int) 7 => 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) 8 => 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) 9 => 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) 10 => 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) 11 => 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) 12 => 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) 13 => 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) 14 => array(
'id' => '4154',
'name' => 'IGFBP2 protects against pulmonary fibrosis through inhibiting P21-mediated senescence',
'authors' => 'Chiahsuan, C. et al.',
'description' => '<p>Accumulation of senescent cells contributes to age related diseases including idiopathic pulmonary fibrosis (IPF). Insulin-like growth factor binding proteins (IGFBPs) are evolutionarily conserved proteins that play a vital role in many biological processes. Overall, little is known about the functions of IGFBP2 in the epigenetic regulation of cellular senescence and pulmonary fibrosis. Here, we show that Igfbp2 expression was significantly downregulated at both mRNA and protein levels in a low-dose bleomycin-induced pulmonary fibrosis model of aged mice. Using the reduced representation of bisulfite sequencing technique, we demonstrated Igfbp2 downregulation is attributed to DNA methylation of CpG islands in fibrotic lungs of aged mice. Furthermore, Igfbp2 siRNA knockdown increased both P53 and P21 protein levels in mouse lung epithelial cells exposed to hypoxia treatment. Lentiviral mediated expression of Igfb2 decreased P21 protein levels and significantly reduced beta galactosidase activity in mouse lung epithelial cells challenged with a senescent drug (atazanavir) and hypoxia treatments. Using the RT2 Profiler PCR Array, we found that P21, PAI-1, IRF-5 and IRF-7, important regulators of senescence pathway, were significantly downregulated specifically in type-II alveolar epithelial cells (AECs) of aged human-Igfbp2 transgenic mice after bleomycin challenge. Finally, transgenic expression of human-Igfbp2 in type-II AECs from aged bleomycin challenged mice significantly decreased senescent associated secretory phenotype factors and also reduced extracellular matrix markers compared to aged wild-type mice challenged with bleomycin injury. Collectively, these findings reveal that epigenetic repression of Igfbp2 promotes pulmonary fibrosis and that restoring IGFBP2 in fibrotic lungs could prove effective in IPF treatment.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2021.01.21.427684',
'doi' => '10.1101/2021.01.21.427684',
'modified' => '2021-12-16 10:38:38',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => 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) 16 => 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) 17 => 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) 18 => 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) 19 => 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) 20 => array(
'id' => '3919',
'name' => 'LY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/β-Catenin Pathway.',
'authors' => 'Mehdi S, Bachvarova M, Scott-Boyer MP, Droit A, Bachvarov D',
'description' => '<p>Growing evidence demonstrates that epithelial-mesenchymal transition (EMT) plays an important role in epithelial ovarian cancer (EOC) progression and spreading; however, its molecular mechanisms remain poorly defined. We have previously shown that the antigen receptor LY75 can modulate EOC cell phenotype and metastatic potential, as LY75 depletion directed mesenchymal-epithelial transition (MET) in EOC cell lines with mesenchymal phenotype. We used the LY75-mediated modulation of EMT as a model to investigate for DNA methylation changes during EMT in EOC cells, by applying the reduced representation bisulfite sequencing (RRBS) methodology. Numerous genes have displayed EMT-related DNA methylation patterns alterations in their promoter/exon regions. Ten selected genes, whose DNA methylation alterations were further confirmed by alternative methods, were further identified, some of which could represent new EOC biomarkers/therapeutic targets. Moreover, our methylation data were strongly indicative for the predominant implication of the Wnt/β-catenin pathway in the EMT-induced DNA methylation variations in EOC cells. Consecutive experiments, including alterations in the Wnt/β-catenin pathway activity in EOC cells with a specific inhibitor and the identification of LY75-interacting partners by a proteomic approach, were strongly indicative for the direct implication of the LY75 receptor in modulating the Wnt/β-catenin signaling in EOC cells.</p>',
'date' => '2020-03-07',
'pmid' => 'http://www.pubmed.gov/32156068',
'doi' => '10.3390/ijms21051848',
'modified' => '2020-08-17 11:00:08',
'created' => '2020-08-10 12:12:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => 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) 22 => array(
'id' => '3420',
'name' => 'Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model.',
'authors' => 'Logan NJ, Camman M, Williams G, Higgins CA',
'description' => '<p>Trauma-induced heterotopic ossification is an intriguing phenomenon involving the inappropriate ossification of soft tissues within the body such as the muscle and ligaments. This inappropriate formation of bone is highly prevalent in those affected by blast injuries. Here, we developed a simplified cell culture model to evaluate the molecular events involved in heterotopic ossification onset that arise from the shock wave component of the disease. We exposed three subtypes of human mesenchymal cells in vitro to a single, high-energy shock wave and observed increased transcription in the osteogenic master regulators, Runx2 and Dlx5, and significantly accelerated cell mineralisation. Reduced representation bisulfite sequencing revealed that the shock wave altered methylation of gene promoters, leading to opposing changes in gene expression. Using a drug to target ITGAV, whose expression was perturbed by the shock wave, we found that we could abrogate the deposition of mineral in our model. These findings show how new therapeutics for the treatment of heterotopic ossification can be identified using cell culture models.</p>',
'date' => '2018-10-11',
'pmid' => 'http://www.pubmed.gov/30219480',
'doi' => '10.1016/j.bone.2018.09.008',
'modified' => '2018-12-31 11:21:44',
'created' => '2018-12-04 09:51:07',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 23 => array(
'id' => '3378',
'name' => 'Paternal sepsis induces alterations of the sperm methylome and dampens offspring immune responses—an animal study',
'authors' => 'Katharina Bomans, Judith Schenz, Sandra Tamulyte, Dominik Schaack, Markus Alexander Weigand and Florian Uhle',
'description' => '<section xmlns="" xmlns:fn="http://www.w3.org/2005/xpath-functions" xmlns:meta="http://www.springer.com/app/meta" class="Abstract Section1 RenderAsSection1" id="Abs1" lang="en">
<h2 class="Heading js-ToggleCollapseSection is-shown" data-component="collapse-fulltext" data-sticky-update="true">Abstract</h2>
<div class="js-CollapseSection CollapseSection is-shown" data-test="collapse-section">
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec1">
<h3 xmlns="" class="Heading">Background</h3>
<p id="Par1" class="Para">Sepsis represents the utmost severe consequence of infection, involving a dysregulated and self-damaging immune response of the host. While different environmental exposures like chronic stress or malnutrition have been well described to reprogram the germline and subsequently offspring attributes, the intergenerational impact of sepsis as a tremendous immunological stressor has not been examined yet.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec2">
<h3 xmlns="" class="Heading">Methods</h3>
<p id="Par2" class="Para">Polymicrobial sepsis in 12-week-old male C57BL/6 mice was induced by cecal ligation and puncture (CLP), followed by a mating of the male survivors (or appropriate sham control animals) 6 weeks later with healthy females. Alveolar macrophages of offspring animals were isolated and stimulated with either LPS or Zymosan, and supernatant levels of TNF-α were quantified by ELISA. Furthermore, systemic cytokine response to intraperitoneally injected LPS was assessed after 24 h. Also, morphology, motility, and global DNA methylation of the sepsis survivors’ sperm was examined.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec3">
<h3 xmlns="" class="Heading">Results</h3>
<p id="Par3" class="Para">Comparative reduced reduction bisulfite sequencing (RRBS) of sperm revealed changes of DNA methylation (<em xmlns="" class="EmphasisTypeItalic">n</em> = 381), most pronounced in the intergenic genome as well as within introns of developmentally relevant genes. Offspring of sepsis fathers exhibited a slight decrease in body weight, with a more pronounced weight difference in male animals (CLP vs. sham). Male descendants of sepsis fathers, but not female descendants, exhibited lower plasma concentrations of IL-6, TNF-alpha, and IL-10 24 h after injection of LPS. In line, only alveolar macrophages of male descendants of sepsis fathers produced less TNF-alpha upon Zymosan stimulation compared to sham descendants, while LPS responses kept unchanged.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec4">
<h3 xmlns="" class="Heading">Conclusion</h3>
<p id="Par4" class="Para">We can prove that male—but surprisingly not female—descendants of post-sepsis fathers show a dampened systemic as well as pulmonary immune response. Based on this observation of an immune hypo-responsivity, we propose that male descendants of sepsis fathers are at risk to develop fungal and bacterial infections and might benefit from therapeutic immune modulation.</p>
</div>
</div>
</section>',
'date' => '2018-06-18',
'pmid' => 'https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-018-0522-z',
'doi' => '10.1186/s13148-018-0522-z',
'modified' => '2018-06-28 17:08:31',
'created' => '2018-06-28 17:08:31',
'ProductsPublication' => array(
[maximum depth reached]
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),
(int) 24 => 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) 25 => 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',
'ProductsPublication' => array(
[maximum depth reached]
)
)
),
'Testimonial' => array(
(int) 0 => array(
'id' => '59',
'name' => 'RRBS service - Prof. Lucia Altucci',
'description' => '<p><span>Our laboratory has used <a href="../products/view/2836">RRBS sevice</a> of Diagenode on murine and human samples. The service was impeccable in each phase, from the sample preparation to bionformatic analysis because it was always customer-oriented. I highly recommend my colleagues to use the RRBS service from Diagenode.</span></p>',
'author' => 'Prof. Lucia Altucci, MD, PhD, Seconda Università degli Studi di Napoli, Dipartimento di Biochimica, Biofisica e Patologia generale .',
'featured' => false,
'slug' => '',
'meta_keywords' => '',
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'modified' => '2018-01-16 09:53:47',
'created' => '2016-11-18 15:34:13',
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[maximum depth reached]
)
),
(int) 1 => array(
'id' => '61',
'name' => 'Premium RRBS',
'description' => '<p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p>',
'author' => 'Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria ',
'featured' => false,
'slug' => '',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2018-01-16 09:53:18',
'created' => '2016-11-24 11:26:22',
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[maximum depth reached]
)
),
(int) 2 => array(
'id' => '69',
'name' => 'Satu Mäki-Nevala - University of Helsinki',
'description' => '<p>We are very happy with the services provided by Diagenode. In our project, <a href="../categories/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS)</a> was used and it worked well for both high-quality DNA and DNA extracted from formalin-fixed, paraffin-embedded (FFPE) material. Bioinformatic analyses were comprehensive and high-quality results were obtained. Diagenode’s services can be recommended.</p>',
'author' => 'Satu Mäki-Nevala, PhD, University of Helsinki',
'featured' => true,
'slug' => '',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2018-01-16 09:51:15',
'created' => '2017-09-15 10:16:34',
'ProductsTestimonial' => array(
[maximum depth reached]
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),
(int) 3 => array(
'id' => '70',
'name' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
'description' => '<p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p>',
'author' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
'featured' => false,
'slug' => '',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2018-01-16 09:50:52',
'created' => '2017-09-25 14:40:38',
'ProductsTestimonial' => array(
[maximum depth reached]
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$testimonials = '<blockquote><p><span>Our laboratory has used <a href="../products/view/2836">RRBS sevice</a> of Diagenode on murine and human samples. The service was impeccable in each phase, from the sample preparation to bionformatic analysis because it was always customer-oriented. I highly recommend my colleagues to use the RRBS service from Diagenode.</span></p><cite>Prof. Lucia Altucci, MD, PhD, Seconda Università degli Studi di Napoli, Dipartimento di Biochimica, Biofisica e Patologia generale .</cite></blockquote>
<blockquote><p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p><cite>Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </cite></blockquote>
<blockquote><p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p><cite>Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina</cite></blockquote>
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'name' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
'description' => '<p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p>',
'author' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
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<h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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<a href="/cn/p/wgbs-service"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a> </div>
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<span class="success label" style="">G02040000</span>
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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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<form action="/cn/quotes/quote?id=2989" id="Quote-2989" class="quote" method="post" accept-charset="utf-8"><div style="display:none;"><input type="hidden" name="_method" value="POST"/></div><input type="hidden" name="data[Quote][product_id]" value="2989" id="QuoteProductId"/><input type="hidden" name="data[Quote][formLoaded6tY4bPYk]" value="bG9Tem83aDhYczdoWlpVL2o4SVpGUT09" id="QuoteFormLoaded6tY4bPYk"/><input type="hidden" name="data[Quote][product_rfq_tag]" value="wgbs-service" id="QuoteProductRfqTag"/><input type="hidden" name="data[Quote][source_quote]" value="modal quote" id="QuoteSourceQuote"/>
<div class="row collapse">
<h2>Service Information</h2>
</div>
<div class="small-12 large-12 columns">
<h4>Which services are you interested in?</h4>
</div>
<div class="small-12 large-12 columns">
<input type="hidden" name="data[Quote][epigenomics_service]" value="" id="QuoteEpigenomicsService"/>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ATAC-seq" id="QuoteEpigenomicsServiceATACSeq" /><label for="QuoteEpigenomicsServiceATACSeq">ATAC-seq</label></div>
<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>
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<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>
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<option value="AM">Armenia</option>
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<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
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<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>
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<option value="IQ">Iraq</option>
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<option value="KP">Korea, Democratic People's Republic of</option>
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<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>
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</div>
</div>
<div class="small-12 columns" >
<h6 style="height:60px">WGBS service (Whole Genome Bisulfite Sequencing)</h6>
</div>
</div>
</li>
<li>
<div class="row">
<div class="small-12 columns">
<a href="/cn/p/methylation-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" 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="">G02020107</span>
</div>
<div class="small-6 columns text-right" style="padding-left:0px;padding-right:0px;margin-top:-6px">
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<div class="small-12 medium-12 large-12 columns">
<h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Methylation Data Analysis</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>
</div>
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<h2>Contact Information</h2>
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<h6 style="height:60px">Methylation Data Analysis</h6>
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<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
</div>
<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
<table>
<thead>
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<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
<th style="text-align: center;">Bisulfite conversion rate (%)</th>
<th style="text-align: center;">Mean sequencing depth per covered CpG (×)</th>
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<td><strong>Human</strong></td>
<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
</tr>
<tr style="height: 61px;">
<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
</tr>
<tr style="height: 61px;">
<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
</tr>
<tr style="height: 61px;">
<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
</tr>
<tr style="height: 61px;">
<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
</tr>
</tbody>
</table>
<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<li>Latest technology in bisulfite-seq using Illumina sequencers: > 10x coverage per CpG</li>
<li>More than 4 million CpGs detected in human samples</li>
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<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
<center><img src="https://www.diagenode.com/img/product/services/RRBS_figure.jpg" alt="DNA Methylation Profiling Service" title="DNA Methylation RRBS Service" caption="false" width="900" height="278" /></center>
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<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
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<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
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<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
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<td style="text-align: center;">38</td>
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<td style="text-align: center;">9</td>
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<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration </li>
<li>Assessment of DNA quality</li>
</ul>
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<p></p>
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<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
<p></p>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially “finding a needle in a haystack.”</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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'description' => '<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">Whole-genome bisulfite sequencing（</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">WGBS</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">）は、</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">DNA</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">メチル化プロファイリングの中でも最も包括的な次世代シーケンシングであり、ゲノム全体で</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">5-mC</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">の単一塩基分解能を可能にします。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">同じ場所での変換されていないシトシンと変換されたシトシンの割合を比較することにより、メチル化レベルが決定されます。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;"></span></p>
<pre class="tw-data-text tw-text-large tw-ta" data-placeholder="Translation" id="tw-target-text" dir="ltr"><span class="Y2IQFc" lang="ja"></span><span lang="EN" style="font-family: 'Arial Unicode MS'; color: #b21329;">DNA</span><span lang="FR" style="font-family: 'Arial Unicode MS'; color: #b21329;">メチル化研究において最も高いカバレッジ</span></pre>
<ul>
<li class="normal"><span>ゲノム全体のDNAメチル化と単一塩基分解能スクリーニング</span></li>
<li class="normal">低密度と反復領域における5mCの高カバレッジ</li>
<li class="normal">専任の科学者がハイタッチコミュニケーションで各プロジェクトをサポート</li>
<li class="normal">包括的なサービス - バイサルファイト処理、ライブラリーの調製、シーケンシングと解析</li>
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<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage >30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of >50 million CpGs with 6-9X average CpG coverage for human samples </span></li>
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'description' => '<div class="extra-spaced">
<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
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<h2>What do we provide with the analysis?</h2>
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<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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'description' => '<h2>Epigenetic services</h2>
<p>Let us do your epigenetics sample preparation. Save time and utilize our epigenetics expertise. Discover modifications and how changes on chromatin and DNA influence expression.</p>
<p class="text-center"><img src="https://www.diagenode.com/img/categories/services/workflow_services.jpg" /></p>
<div class="row">
<div class="small-12 medium-6 large-6 columns text-center"><img src="https://www.diagenode.com/img/categories/services/services_chipseq.png" alt="services chip-seq" height="105" width="105" /> <br /><br /> <a href="../categories/chip-seq-service" class="details radius button">ChIP-seq service</a></div>
<div class="small-12 medium-6 large-6 columns text-center"><img src="https://www.diagenode.com/img/categories/services/services_methylation.png" alt="services methylation" height="105" width="105" /> <br /><br /> <a href="../categories/rrbs-service" class="details radius button">RRBS service</a></div>
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'meta_description' => 'Diagenode offers solutions for epigenetic services such as ChIP-seq, RRBS (Bisulfite Sequencing) services for your genome-wide applications',
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'description' => '<div class="row">
<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation – hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
</div>
<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns">
<p>Our Epigenomics Profiling Services helps you study DNA methylation (5mC) and methylation variants (5hmC). Our experienced scientists will guide you to the best solution according to your needs, depending on whether you want to analyze DNA methylation at genome-wide or gene specific scales. They will process the arrays or generate meaningful libraries for DNA sequencing and deliver high quality data. Our bioinformatic experts will closely work with you to provide standard and customized analysis with comprehensive publication-ready figures.</p>
<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis <span>using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>
<!--<h3><a href="https://www.diagenode.com/en/p/infinium-mouse-methylation-array-service">Infinium Mouse Methylation Array Service</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 285,000 markers detected in mouse samples at single nucleotide resolution</li>
<li>Suitable for common laboratory mouse strains</li>
<li><span>Differential methylation analysis using our<span> </span><a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
</ul>-->
<h3><a href="https://www.diagenode.com/en/p/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS) Service</a></h3>
<ul>
<li>Robust and cost-effective solution with reliable results</li>
<li>Unique technology enables low DNA inputs down to 25ng</li>
<li>Accurate analyses with UDIs and UMIs</li>
<li>Up to 7 million CpGs detected in human samples at single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG-rich regions across the genome including promoters and CpG islands</li>
<li><span>Differential methylation analysis <span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
</ul>
<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
<ul>
<li>Very powerful solution for genome-wide biomarker discovery using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome </span>at single nucleotide resolution</li>
<li>Detection of global methylation patterns including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation levels between groups using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
</ul>
<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Custom Targeted Methyl-seq</a></h3>
<ul>
<li>Focus on DNA methylation analysis of pre-defined regions with single nucleotide resolution</li>
<li>Custom hybridization capture approach with enzymatic conversion</li>
<li>Enhanced accuracy, sensitivity and specificity with high coverage while reducing overall cost</li>
</ul>
<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
<ul>
<li>Immunoprecipitation assay (antibody-based approach) to study 5-mC genome-wide</li>
<li>Resolution of 100-500 bp</li>
</ul>-->
<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
<p></p>
<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
</ul>
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'meta_title' => 'DNA Methylation Profiling Services EPIC array, RRBS, WGBS, MeDIP/hMeDIP-seq and Targeted DNA methylation analysis | Diagenode',
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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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'description' => '<h2><a href="https://www.diagenode.com/en/categories/Services">Complete workflows</a> for genome-scale DNA methylation and histone marks analysis</h2>
<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized “Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Learn more</a></p>',
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<li>Chromatin analysis</li>
<li>DNA methylation services</li>
<li>RNA-seq analysis</li>
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'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',
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'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',
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'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',
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'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',
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'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',
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'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',
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(int) 6 => 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',
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'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) 8 => 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) 9 => 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) 10 => 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) 11 => 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) 12 => 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) 13 => 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) 14 => array(
'id' => '4154',
'name' => 'IGFBP2 protects against pulmonary fibrosis through inhibiting P21-mediated senescence',
'authors' => 'Chiahsuan, C. et al.',
'description' => '<p>Accumulation of senescent cells contributes to age related diseases including idiopathic pulmonary fibrosis (IPF). Insulin-like growth factor binding proteins (IGFBPs) are evolutionarily conserved proteins that play a vital role in many biological processes. Overall, little is known about the functions of IGFBP2 in the epigenetic regulation of cellular senescence and pulmonary fibrosis. Here, we show that Igfbp2 expression was significantly downregulated at both mRNA and protein levels in a low-dose bleomycin-induced pulmonary fibrosis model of aged mice. Using the reduced representation of bisulfite sequencing technique, we demonstrated Igfbp2 downregulation is attributed to DNA methylation of CpG islands in fibrotic lungs of aged mice. Furthermore, Igfbp2 siRNA knockdown increased both P53 and P21 protein levels in mouse lung epithelial cells exposed to hypoxia treatment. Lentiviral mediated expression of Igfb2 decreased P21 protein levels and significantly reduced beta galactosidase activity in mouse lung epithelial cells challenged with a senescent drug (atazanavir) and hypoxia treatments. Using the RT2 Profiler PCR Array, we found that P21, PAI-1, IRF-5 and IRF-7, important regulators of senescence pathway, were significantly downregulated specifically in type-II alveolar epithelial cells (AECs) of aged human-Igfbp2 transgenic mice after bleomycin challenge. Finally, transgenic expression of human-Igfbp2 in type-II AECs from aged bleomycin challenged mice significantly decreased senescent associated secretory phenotype factors and also reduced extracellular matrix markers compared to aged wild-type mice challenged with bleomycin injury. Collectively, these findings reveal that epigenetic repression of Igfbp2 promotes pulmonary fibrosis and that restoring IGFBP2 in fibrotic lungs could prove effective in IPF treatment.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2021.01.21.427684',
'doi' => '10.1101/2021.01.21.427684',
'modified' => '2021-12-16 10:38:38',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => 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) 16 => 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) 17 => 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) 18 => 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) 19 => 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) 20 => array(
'id' => '3919',
'name' => 'LY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/β-Catenin Pathway.',
'authors' => 'Mehdi S, Bachvarova M, Scott-Boyer MP, Droit A, Bachvarov D',
'description' => '<p>Growing evidence demonstrates that epithelial-mesenchymal transition (EMT) plays an important role in epithelial ovarian cancer (EOC) progression and spreading; however, its molecular mechanisms remain poorly defined. We have previously shown that the antigen receptor LY75 can modulate EOC cell phenotype and metastatic potential, as LY75 depletion directed mesenchymal-epithelial transition (MET) in EOC cell lines with mesenchymal phenotype. We used the LY75-mediated modulation of EMT as a model to investigate for DNA methylation changes during EMT in EOC cells, by applying the reduced representation bisulfite sequencing (RRBS) methodology. Numerous genes have displayed EMT-related DNA methylation patterns alterations in their promoter/exon regions. Ten selected genes, whose DNA methylation alterations were further confirmed by alternative methods, were further identified, some of which could represent new EOC biomarkers/therapeutic targets. Moreover, our methylation data were strongly indicative for the predominant implication of the Wnt/β-catenin pathway in the EMT-induced DNA methylation variations in EOC cells. Consecutive experiments, including alterations in the Wnt/β-catenin pathway activity in EOC cells with a specific inhibitor and the identification of LY75-interacting partners by a proteomic approach, were strongly indicative for the direct implication of the LY75 receptor in modulating the Wnt/β-catenin signaling in EOC cells.</p>',
'date' => '2020-03-07',
'pmid' => 'http://www.pubmed.gov/32156068',
'doi' => '10.3390/ijms21051848',
'modified' => '2020-08-17 11:00:08',
'created' => '2020-08-10 12:12:25',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 21 => 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) 22 => array(
'id' => '3420',
'name' => 'Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model.',
'authors' => 'Logan NJ, Camman M, Williams G, Higgins CA',
'description' => '<p>Trauma-induced heterotopic ossification is an intriguing phenomenon involving the inappropriate ossification of soft tissues within the body such as the muscle and ligaments. This inappropriate formation of bone is highly prevalent in those affected by blast injuries. Here, we developed a simplified cell culture model to evaluate the molecular events involved in heterotopic ossification onset that arise from the shock wave component of the disease. We exposed three subtypes of human mesenchymal cells in vitro to a single, high-energy shock wave and observed increased transcription in the osteogenic master regulators, Runx2 and Dlx5, and significantly accelerated cell mineralisation. Reduced representation bisulfite sequencing revealed that the shock wave altered methylation of gene promoters, leading to opposing changes in gene expression. Using a drug to target ITGAV, whose expression was perturbed by the shock wave, we found that we could abrogate the deposition of mineral in our model. These findings show how new therapeutics for the treatment of heterotopic ossification can be identified using cell culture models.</p>',
'date' => '2018-10-11',
'pmid' => 'http://www.pubmed.gov/30219480',
'doi' => '10.1016/j.bone.2018.09.008',
'modified' => '2018-12-31 11:21:44',
'created' => '2018-12-04 09:51:07',
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(int) 23 => array(
'id' => '3378',
'name' => 'Paternal sepsis induces alterations of the sperm methylome and dampens offspring immune responses—an animal study',
'authors' => 'Katharina Bomans, Judith Schenz, Sandra Tamulyte, Dominik Schaack, Markus Alexander Weigand and Florian Uhle',
'description' => '<section xmlns="" xmlns:fn="http://www.w3.org/2005/xpath-functions" xmlns:meta="http://www.springer.com/app/meta" class="Abstract Section1 RenderAsSection1" id="Abs1" lang="en">
<h2 class="Heading js-ToggleCollapseSection is-shown" data-component="collapse-fulltext" data-sticky-update="true">Abstract</h2>
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<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec1">
<h3 xmlns="" class="Heading">Background</h3>
<p id="Par1" class="Para">Sepsis represents the utmost severe consequence of infection, involving a dysregulated and self-damaging immune response of the host. While different environmental exposures like chronic stress or malnutrition have been well described to reprogram the germline and subsequently offspring attributes, the intergenerational impact of sepsis as a tremendous immunological stressor has not been examined yet.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec2">
<h3 xmlns="" class="Heading">Methods</h3>
<p id="Par2" class="Para">Polymicrobial sepsis in 12-week-old male C57BL/6 mice was induced by cecal ligation and puncture (CLP), followed by a mating of the male survivors (or appropriate sham control animals) 6 weeks later with healthy females. Alveolar macrophages of offspring animals were isolated and stimulated with either LPS or Zymosan, and supernatant levels of TNF-α were quantified by ELISA. Furthermore, systemic cytokine response to intraperitoneally injected LPS was assessed after 24 h. Also, morphology, motility, and global DNA methylation of the sepsis survivors’ sperm was examined.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec3">
<h3 xmlns="" class="Heading">Results</h3>
<p id="Par3" class="Para">Comparative reduced reduction bisulfite sequencing (RRBS) of sperm revealed changes of DNA methylation (<em xmlns="" class="EmphasisTypeItalic">n</em> = 381), most pronounced in the intergenic genome as well as within introns of developmentally relevant genes. Offspring of sepsis fathers exhibited a slight decrease in body weight, with a more pronounced weight difference in male animals (CLP vs. sham). Male descendants of sepsis fathers, but not female descendants, exhibited lower plasma concentrations of IL-6, TNF-alpha, and IL-10 24 h after injection of LPS. In line, only alveolar macrophages of male descendants of sepsis fathers produced less TNF-alpha upon Zymosan stimulation compared to sham descendants, while LPS responses kept unchanged.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec4">
<h3 xmlns="" class="Heading">Conclusion</h3>
<p id="Par4" class="Para">We can prove that male—but surprisingly not female—descendants of post-sepsis fathers show a dampened systemic as well as pulmonary immune response. Based on this observation of an immune hypo-responsivity, we propose that male descendants of sepsis fathers are at risk to develop fungal and bacterial infections and might benefit from therapeutic immune modulation.</p>
</div>
</div>
</section>',
'date' => '2018-06-18',
'pmid' => 'https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-018-0522-z',
'doi' => '10.1186/s13148-018-0522-z',
'modified' => '2018-06-28 17:08:31',
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'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',
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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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$testimonials = '<blockquote><p><span>Our laboratory has used <a href="../products/view/2836">RRBS sevice</a> of Diagenode on murine and human samples. The service was impeccable in each phase, from the sample preparation to bionformatic analysis because it was always customer-oriented. I highly recommend my colleagues to use the RRBS service from Diagenode.</span></p><cite>Prof. Lucia Altucci, MD, PhD, Seconda Università degli Studi di Napoli, Dipartimento di Biochimica, Biofisica e Patologia generale .</cite></blockquote>
<blockquote><p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p><cite>Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </cite></blockquote>
<blockquote><p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p><cite>Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina</cite></blockquote>
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<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>
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<h6 style="height:60px">Bioinformatics Data Mining Service</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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<div class="row collapse">
<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="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-seq</label></div>
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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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<span class="prefix">Sample species</span>
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<span class="prefix">Total number of samples (including replicates)</span>
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<h6 style="height:60px">WGBS service (Whole Genome Bisulfite Sequencing)</h6>
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</div>
<form action="/cn/quotes/quote?id=3061" id="Quote-3061" class="quote" method="post" accept-charset="utf-8"><div style="display:none;"><input type="hidden" name="_method" value="POST"/></div><input type="hidden" name="data[Quote][product_id]" value="3061" id="QuoteProductId"/><input type="hidden" name="data[Quote][formLoaded6tY4bPYk]" value="bG9Tem83aDhYczdoWlpVL2o4SVpGUT09" id="QuoteFormLoaded6tY4bPYk"/><input type="hidden" name="data[Quote][product_rfq_tag]" value="methylation-data-analysis" id="QuoteProductRfqTag"/><input type="hidden" name="data[Quote][source_quote]" value="modal quote" id="QuoteSourceQuote"/>
<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-3061">
<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-3061').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-3061" 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">Comment</span>
</div>
<div class="small-9 large-10 columns">
<textarea name="data[Quote][comment]" placeholder="Additional comments" 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="recaptcha673fa71ea1452"></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-3061" class="alert button expand" form="Quote-3061" 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-3061').attr('action','javascript:postToPardot(\'' + $('#Quote-3061').attr('action') + '\', 3061)');
});
$("#Quote-3061 #submit_btn-3061").click(function (e) {
if($(this).closest('form')[0].checkValidity()){
e.preventDefault();
//disable the submit button
$("#Quote-3061 #submit_btn-3061").attr("disabled", true);
$("#Quote-3061 #submit_btn-3061").html("Processing...");
//submit the form
$("#Quote-3061").submit();
}
})
</script>
<script>
if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'US') {
var val = $("#state-3061").val();
$("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" 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-3061").val(val);
}
$("#state-3061").parent().parent().show();
} else if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'CA') {
var val = $("#state-3061").val();
$("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" 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-3061").val(val);
}
$("#state-3061").parent().parent().show();
} else if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'DE') {
var val = $("#state-3061").val();
$("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" 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>');
if (val.length == 2) {
$("#state-3061").val(val);
}
$("#state-3061").parent().parent().show();
} else {
$("#Quote-3061 #state-3061").parent().parent().hide();
$("#Quote-3061 #state-3061").replaceWith('<input name="data[Quote][state]" maxlength="255" type="text" id="state-3061" value="">');
}
$("#Quote-3061 #country_selector_quote-3061.selectized").change(function() {
if (this.value == 'US') {
$("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" 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>');
$("#Quote-3061 #state-3061").parent().parent().show();
} else if (this.value == 'CA') {
$("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" 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>');
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<h6 style="height:60px">Methylation Data Analysis</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
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<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
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<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
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<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
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<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
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<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
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<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
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<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
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<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
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<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
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<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
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<td style="text-align: center;">48</td>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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<li>More than 4 million CpGs detected in human samples</li>
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<li><strong>Complete end-to-end service</strong>: wet lab to bioinformatics for only <strong>550 per sample</strong>!</li>
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<h4>Diagenode’s RRBS gives excellent coverage at a great price</h4>
<center><img src="https://www.diagenode.com/img/product/services/RRBS_figure.jpg" alt="DNA Methylation Profiling Service" title="DNA Methylation RRBS Service" caption="false" width="900" height="278" /></center>
<p>This figure shows the perfect match obtained between the expected versus the observed efficiency of the MspI enzyme used in the Premium RRBS kit. The four upper tracks represent two samples (control and treatment). The first and second tracks show respectively, the read coverage and the detected methylation for each sample. The height of the methylation bars is equivalent to the percentage methylation. The MspI track shows the in-silico digestion of gDNA.</p>
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<p><strong>RRBS data generated with the Diagenode RRBS service using samples from four different species</strong></p>
<table>
<thead>
<tr>
<th>Species</th>
<th style="text-align: center;">No. of reads aligned (alignment rate)</th>
<th style="text-align: center;">No. of unique CpGs covered (alignment rate)</th>
<th style="text-align: center;">Bisulfite conversion rate (%)</th>
<th style="text-align: center;">Mean sequencing depth per covered CpG (×)</th>
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<td><strong>Human</strong></td>
<td style="text-align: center;">21,787,346 (74%)</td>
<td style="text-align: center;">3,913,287</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">14</td>
</tr>
<tr style="height: 61px;">
<td><strong>Human FFPE</strong></td>
<td style="text-align: center;">27,290,117 (79%)</td>
<td style="text-align: center;">2,525,053</td>
<td style="text-align: center;">99.7</td>
<td style="text-align: center;">28</td>
</tr>
<tr style="height: 61px;">
<td><strong>Rat</strong></td>
<td style="text-align: center;">27,499,313 (86%)</td>
<td style="text-align: center;">1,663,104</td>
<td style="text-align: center;">99.0</td>
<td style="text-align: center;">38</td>
</tr>
<tr style="height: 61px;">
<td><strong>Dog</strong></td>
<td style="text-align: center;">4,053,009 (84%)</td>
<td style="text-align: center;">3,572,384</td>
<td style="text-align: center;">99.4</td>
<td style="text-align: center;">9</td>
</tr>
<tr style="height: 61px;">
<td><strong>Zebrafish</strong></td>
<td style="text-align: center;">29,035,877 (83%)</td>
<td style="text-align: center;">1,685,466</td>
<td style="text-align: center;">99.3</td>
<td style="text-align: center;">48</td>
</tr>
</tbody>
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<p><strong>Reduced representation bisulfite sequencing (RRBS)</strong> enables genome-scale DNA methylation analysis in any vertebrate species. The assay benefits from the practical advantages of <strong>bisulfite sequencing</strong> while avoiding the cost of whole-genome sequencing. The Diagenode RRBS service 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>
<p><i class="fa fa-arrow-circle-right"></i> <a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA methylation analysis service options for reduced, whole genome, and targeted analysis</a></p>
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'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
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</blockquote>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially “finding a needle in a haystack.”</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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'description' => '<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">Whole-genome bisulfite sequencing（</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">WGBS</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">）は、</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">DNA</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">メチル化プロファイリングの中でも最も包括的な次世代シーケンシングであり、ゲノム全体で</span><span lang="EN" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">5-mC</span><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">の単一塩基分解能を可能にします。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;">同じ場所での変換されていないシトシンと変換されたシトシンの割合を比較することにより、メチル化レベルが決定されます。</span></p>
<p class="normal" style="text-align: justify; line-height: 150%; background: white;"><span lang="FR" style="font-size: 12.0pt; line-height: 150%; font-family: 'Arial Unicode MS'; color: #222222;"></span></p>
<pre class="tw-data-text tw-text-large tw-ta" data-placeholder="Translation" id="tw-target-text" dir="ltr"><span class="Y2IQFc" lang="ja"></span><span lang="EN" style="font-family: 'Arial Unicode MS'; color: #b21329;">DNA</span><span lang="FR" style="font-family: 'Arial Unicode MS'; color: #b21329;">メチル化研究において最も高いカバレッジ</span></pre>
<ul>
<li class="normal"><span>ゲノム全体のDNAメチル化と単一塩基分解能スクリーニング</span></li>
<li class="normal">低密度と反復領域における5mCの高カバレッジ</li>
<li class="normal">専任の科学者がハイタッチコミュニケーションで各プロジェクトをサポート</li>
<li class="normal">包括的なサービス - バイサルファイト処理、ライブラリーの調製、シーケンシングと解析</li>
</ul>
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<p style="text-align: left;"><strong>Library Preparation</strong></p>
<p style="text-align: left;"><strong></strong></p>
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<td style="width: 94px; height: 129px;">
<p style="text-align: center;"><strong>WGBS</strong></p>
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<ul>
<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Library preparation </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">QC of the WGBS libraries (DNA concentration, analysis of the profile)</span></li>
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<td style="width: 94px; text-align: center; height: 107px; background-color: #f9f9f9;"><strong>EM-seq</strong></td>
<td style="width: 766px; height: 107px; background-color: #f9f9f9;">
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<li style="font-weight: 400;"><span style="font-weight: 400;">gDNA shearing on Bioruptor Pico (not necessary for cfDNA or FFPE)</span></li>
<li style="font-weight: 400;">Library preparation with Enzymatic conversion</li>
<li style="font-weight: 400;">QC of EM-seq libraries (DNA concentration, analysis of the profile)</li>
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<span style="font-weight: 400;"></span></td>
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<tr style="height: 129px;">
<td style="width: 469px; height: 129px; background-color: white;" colspan="2">
<p><strong>Deep sequencing</strong></p>
<p><strong></strong></p>
</td>
<td style="width: 766px; height: 129px; background-color: white;">
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<li style="font-weight: 400;"><span style="font-weight: 400;"><span>Samples are sequenced on Illumina platform, paired-end reads of 150bp length (PE150) </span> </span></li>
<li style="font-weight: 400;">400M raw reads on average per samples (when pooling 6 samples/lane)</li>
<li style="font-weight: 400;">Theoretical Coverage >30X for human, mouse and rat samples</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Detection of >50 million CpGs with 6-9X average CpG coverage for human samples </span></li>
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<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<p>Let us do your epigenetics sample preparation. Save time and utilize our epigenetics expertise. Discover modifications and how changes on chromatin and DNA influence expression.</p>
<p class="text-center"><img src="https://www.diagenode.com/img/categories/services/workflow_services.jpg" /></p>
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<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/dna-workflow.png" alt="DNA-methylation -Diagenode" /></center>
<p>The pattern of DNA methylation and histone modification(s) plays an essential role in maintaining cellular function. Abnormal DNA methylation – hypermethylation and hypomethylation - can result in adverse outcomes such as cancer or other disease. The quantification of 5-mC through genome-wide DNA methylation analysis can provide information for detection and prognosis of disease. Our DNA methylation analysis services include numerous bisulfite sequencing options for targeted or whole genome DNA methylation analysis across any species.</p>
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<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
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<p>Our Epigenomics Profiling Services helps you study DNA methylation (5mC) and methylation variants (5hmC). Our experienced scientists will guide you to the best solution according to your needs, depending on whether you want to analyze DNA methylation at genome-wide or gene specific scales. They will process the arrays or generate meaningful libraries for DNA sequencing and deliver high quality data. Our bioinformatic experts will closely work with you to provide standard and customized analysis with comprehensive publication-ready figures.</p>
<h3><a href="https://www.diagenode.com/en/p/infinium-methylation-epic-array-v2-service">Infinium MethylationEPIC Array Service V2</a></h3>
<ul>
<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 930,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differential methylation analysis <span>using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<!--<h3><a href="https://www.diagenode.com/en/p/infinium-mouse-methylation-array-service">Infinium Mouse Methylation Array Service</a></h3>
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<li>Cost-effective solution with rapid turnaround time</li>
<li>Over 285,000 markers detected in mouse samples at single nucleotide resolution</li>
<li>Suitable for common laboratory mouse strains</li>
<li><span>Differential methylation analysis using our<span> </span><a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
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<h3><a href="https://www.diagenode.com/en/p/rrbs-service">Reduced Representation Bisulfite Sequencing (RRBS) Service</a></h3>
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<li>Robust and cost-effective solution with reliable results</li>
<li>Unique technology enables low DNA inputs down to 25ng</li>
<li>Accurate analyses with UDIs and UMIs</li>
<li>Up to 7 million CpGs detected in human samples at single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG-rich regions across the genome including promoters and CpG islands</li>
<li><span>Differential methylation analysis <span>using our</span> <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></span></li>
<li>Suitable for epigenetic biomarker discovery</li>
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<h3><a href="https://www.diagenode.com/en/p/wgbs-service">Whole Genome Bisulfite Sequencing (WGBS) or Enzymatic Methylation(EM-seq) Service</a></h3>
<ul>
<li>Very powerful solution for genome-wide biomarker discovery using bisulfite or enzymatic conversion</li>
<li><span>Evaluation of methylation status of nearly every CpG sites of the entire genome </span>at single nucleotide resolution</li>
<li>Detection of global methylation patterns including in low CpG-density regions and outside of CpG islands</li>
<li>Identification of regions or even loci with differential methylation levels between groups using our <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatics tools</a></li>
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<h3><a href="https://www.diagenode.com/en/p/targeted-dna-methylation-service">Custom Targeted Methyl-seq</a></h3>
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<li>Focus on DNA methylation analysis of pre-defined regions with single nucleotide resolution</li>
<li>Custom hybridization capture approach with enzymatic conversion</li>
<li>Enhanced accuracy, sensitivity and specificity with high coverage while reducing overall cost</li>
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<!--<h3><a href="../p/medip-seq-service">Methylated DNA IP Sequencing</a></h3>
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<li>Immunoprecipitation assay (antibody-based approach) to study 5-mC genome-wide</li>
<li>Resolution of 100-500 bp</li>
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<h3><a href="../p/human-methylome-service">Human Methylome</a></h3>
<h3>Watch our webinar:</h3>
<p>Cost-Effective Genome Wide DNA Methylation Analysis using Twist Hybrid-Capture Methylome Panel</p>
<a class="popup-youtube" href="#webinar"><img src="https://www.diagenode.com/img/webinar/eshg-miniature.png" class="webinar" alt="Webinar series: Genome Wide DNA Methylation using Twist Hybrid-Capture Methylome Panel" /></a>
<p></p>
<ul>
<li>NGS service assay for comprehensive DNA methylation profiling in human samples from solid or liquid biopsy</li>
<li>Optimal biomarker discovery tool for cancer, neurodegenerative, cardiovascular and metabolic diseases</li>
<li>High coverage for more than 3.89 million methylation sites (~9 million CpGs at single strand level)</li>
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<p>Epigenetics is crucial for the regulation of gene expression and has broad relevance in biological processes like development, disease and response to the environment. For more than 10 years Diagenode has been developing innovative tools to study epigenetic marks such as post-translational modi cations of histones and DNA methylation. We are now utilizing our expertise by offering custom services. Our <a href="https://www.diagenode.com/en/categories/Services">services</a> include full work ows for ChIP-sequencing as well as reduced representation bisul te sequencing (RRBS) with our new optimized “Premium RRBSTM technology. In addition, we also offer bioinformatic analysis of your results, both standard and customized. The <a href="https://www.diagenode.com/en/categories/Services">Diagenode Epigenetics Custom Services</a> helps you to complete your epigenetics work ow from your starting biological material to your nal results.</p>
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'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) 1 => 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) 2 => 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) 3 => 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) 4 => 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) 5 => 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) 6 => 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) 7 => 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) 8 => 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) 9 => 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) 10 => 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) 11 => 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) 12 => 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) 13 => 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) 14 => array(
'id' => '4154',
'name' => 'IGFBP2 protects against pulmonary fibrosis through inhibiting P21-mediated senescence',
'authors' => 'Chiahsuan, C. et al.',
'description' => '<p>Accumulation of senescent cells contributes to age related diseases including idiopathic pulmonary fibrosis (IPF). Insulin-like growth factor binding proteins (IGFBPs) are evolutionarily conserved proteins that play a vital role in many biological processes. Overall, little is known about the functions of IGFBP2 in the epigenetic regulation of cellular senescence and pulmonary fibrosis. Here, we show that Igfbp2 expression was significantly downregulated at both mRNA and protein levels in a low-dose bleomycin-induced pulmonary fibrosis model of aged mice. Using the reduced representation of bisulfite sequencing technique, we demonstrated Igfbp2 downregulation is attributed to DNA methylation of CpG islands in fibrotic lungs of aged mice. Furthermore, Igfbp2 siRNA knockdown increased both P53 and P21 protein levels in mouse lung epithelial cells exposed to hypoxia treatment. Lentiviral mediated expression of Igfb2 decreased P21 protein levels and significantly reduced beta galactosidase activity in mouse lung epithelial cells challenged with a senescent drug (atazanavir) and hypoxia treatments. Using the RT2 Profiler PCR Array, we found that P21, PAI-1, IRF-5 and IRF-7, important regulators of senescence pathway, were significantly downregulated specifically in type-II alveolar epithelial cells (AECs) of aged human-Igfbp2 transgenic mice after bleomycin challenge. Finally, transgenic expression of human-Igfbp2 in type-II AECs from aged bleomycin challenged mice significantly decreased senescent associated secretory phenotype factors and also reduced extracellular matrix markers compared to aged wild-type mice challenged with bleomycin injury. Collectively, these findings reveal that epigenetic repression of Igfbp2 promotes pulmonary fibrosis and that restoring IGFBP2 in fibrotic lungs could prove effective in IPF treatment.</p>',
'date' => '2021-01-01',
'pmid' => 'https://doi.org/10.1101%2F2021.01.21.427684',
'doi' => '10.1101/2021.01.21.427684',
'modified' => '2021-12-16 10:38:38',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => 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) 16 => 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',
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(int) 17 => 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',
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(int) 18 => 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',
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(int) 19 => 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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(int) 20 => array(
'id' => '3919',
'name' => 'LY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/β-Catenin Pathway.',
'authors' => 'Mehdi S, Bachvarova M, Scott-Boyer MP, Droit A, Bachvarov D',
'description' => '<p>Growing evidence demonstrates that epithelial-mesenchymal transition (EMT) plays an important role in epithelial ovarian cancer (EOC) progression and spreading; however, its molecular mechanisms remain poorly defined. We have previously shown that the antigen receptor LY75 can modulate EOC cell phenotype and metastatic potential, as LY75 depletion directed mesenchymal-epithelial transition (MET) in EOC cell lines with mesenchymal phenotype. We used the LY75-mediated modulation of EMT as a model to investigate for DNA methylation changes during EMT in EOC cells, by applying the reduced representation bisulfite sequencing (RRBS) methodology. Numerous genes have displayed EMT-related DNA methylation patterns alterations in their promoter/exon regions. Ten selected genes, whose DNA methylation alterations were further confirmed by alternative methods, were further identified, some of which could represent new EOC biomarkers/therapeutic targets. Moreover, our methylation data were strongly indicative for the predominant implication of the Wnt/β-catenin pathway in the EMT-induced DNA methylation variations in EOC cells. Consecutive experiments, including alterations in the Wnt/β-catenin pathway activity in EOC cells with a specific inhibitor and the identification of LY75-interacting partners by a proteomic approach, were strongly indicative for the direct implication of the LY75 receptor in modulating the Wnt/β-catenin signaling in EOC cells.</p>',
'date' => '2020-03-07',
'pmid' => 'http://www.pubmed.gov/32156068',
'doi' => '10.3390/ijms21051848',
'modified' => '2020-08-17 11:00:08',
'created' => '2020-08-10 12:12:25',
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[maximum depth reached]
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(int) 21 => 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) 22 => array(
'id' => '3420',
'name' => 'Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model.',
'authors' => 'Logan NJ, Camman M, Williams G, Higgins CA',
'description' => '<p>Trauma-induced heterotopic ossification is an intriguing phenomenon involving the inappropriate ossification of soft tissues within the body such as the muscle and ligaments. This inappropriate formation of bone is highly prevalent in those affected by blast injuries. Here, we developed a simplified cell culture model to evaluate the molecular events involved in heterotopic ossification onset that arise from the shock wave component of the disease. We exposed three subtypes of human mesenchymal cells in vitro to a single, high-energy shock wave and observed increased transcription in the osteogenic master regulators, Runx2 and Dlx5, and significantly accelerated cell mineralisation. Reduced representation bisulfite sequencing revealed that the shock wave altered methylation of gene promoters, leading to opposing changes in gene expression. Using a drug to target ITGAV, whose expression was perturbed by the shock wave, we found that we could abrogate the deposition of mineral in our model. These findings show how new therapeutics for the treatment of heterotopic ossification can be identified using cell culture models.</p>',
'date' => '2018-10-11',
'pmid' => 'http://www.pubmed.gov/30219480',
'doi' => '10.1016/j.bone.2018.09.008',
'modified' => '2018-12-31 11:21:44',
'created' => '2018-12-04 09:51:07',
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[maximum depth reached]
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(int) 23 => array(
'id' => '3378',
'name' => 'Paternal sepsis induces alterations of the sperm methylome and dampens offspring immune responses—an animal study',
'authors' => 'Katharina Bomans, Judith Schenz, Sandra Tamulyte, Dominik Schaack, Markus Alexander Weigand and Florian Uhle',
'description' => '<section xmlns="" xmlns:fn="http://www.w3.org/2005/xpath-functions" xmlns:meta="http://www.springer.com/app/meta" class="Abstract Section1 RenderAsSection1" id="Abs1" lang="en">
<h2 class="Heading js-ToggleCollapseSection is-shown" data-component="collapse-fulltext" data-sticky-update="true">Abstract</h2>
<div class="js-CollapseSection CollapseSection is-shown" data-test="collapse-section">
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec1">
<h3 xmlns="" class="Heading">Background</h3>
<p id="Par1" class="Para">Sepsis represents the utmost severe consequence of infection, involving a dysregulated and self-damaging immune response of the host. While different environmental exposures like chronic stress or malnutrition have been well described to reprogram the germline and subsequently offspring attributes, the intergenerational impact of sepsis as a tremendous immunological stressor has not been examined yet.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec2">
<h3 xmlns="" class="Heading">Methods</h3>
<p id="Par2" class="Para">Polymicrobial sepsis in 12-week-old male C57BL/6 mice was induced by cecal ligation and puncture (CLP), followed by a mating of the male survivors (or appropriate sham control animals) 6 weeks later with healthy females. Alveolar macrophages of offspring animals were isolated and stimulated with either LPS or Zymosan, and supernatant levels of TNF-α were quantified by ELISA. Furthermore, systemic cytokine response to intraperitoneally injected LPS was assessed after 24 h. Also, morphology, motility, and global DNA methylation of the sepsis survivors’ sperm was examined.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec3">
<h3 xmlns="" class="Heading">Results</h3>
<p id="Par3" class="Para">Comparative reduced reduction bisulfite sequencing (RRBS) of sperm revealed changes of DNA methylation (<em xmlns="" class="EmphasisTypeItalic">n</em> = 381), most pronounced in the intergenic genome as well as within introns of developmentally relevant genes. Offspring of sepsis fathers exhibited a slight decrease in body weight, with a more pronounced weight difference in male animals (CLP vs. sham). Male descendants of sepsis fathers, but not female descendants, exhibited lower plasma concentrations of IL-6, TNF-alpha, and IL-10 24 h after injection of LPS. In line, only alveolar macrophages of male descendants of sepsis fathers produced less TNF-alpha upon Zymosan stimulation compared to sham descendants, while LPS responses kept unchanged.</p>
</div>
<div xmlns:func="http://oscar.fig.bmc.com" xmlns="http://www.w3.org/1999/xhtml" class="AbstractSection" id="ASec4">
<h3 xmlns="" class="Heading">Conclusion</h3>
<p id="Par4" class="Para">We can prove that male—but surprisingly not female—descendants of post-sepsis fathers show a dampened systemic as well as pulmonary immune response. Based on this observation of an immune hypo-responsivity, we propose that male descendants of sepsis fathers are at risk to develop fungal and bacterial infections and might benefit from therapeutic immune modulation.</p>
</div>
</div>
</section>',
'date' => '2018-06-18',
'pmid' => 'https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-018-0522-z',
'doi' => '10.1186/s13148-018-0522-z',
'modified' => '2018-06-28 17:08:31',
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'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',
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'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',
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'author' => 'Prof. Lucia Altucci, MD, PhD, Seconda Università degli Studi di Napoli, Dipartimento di Biochimica, Biofisica e Patologia generale .',
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'author' => 'Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria ',
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'author' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
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<blockquote><p>The new Diagenode <a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit</a> makes it easy to use RRBS cost-effectively and with high throughput, using early sample pooling and multiplex sequencing. Most importantly, the method provides an improved coverage of up to 4 million CpGs for the human genome. We successfully used this protocol on more than 1,000 samples comprising of six different species, various cancers, FFPE and lowinput samples.</p><cite>Paul Datlinger and Christoph Bock, CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </cite></blockquote>
<blockquote><p>We have had an excellent experience with the <a href="../categories/rrbs-service">RRBS service</a> provided by Diagenode. Our project was based on DNA extracted from human fresh and paraffin-embedded skeletal muscle, and the RRBS and Bioinformatic results were reliable and consistent with what we expécted. We highly recommend their service</p><cite>Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina</cite></blockquote>
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'author' => 'Mario Roque - Instituto de Histología y Embriología de Mendoza (IHEM), Mendoza, Argentina',
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<a href="/cn/p/data-mining-service"><img src="https://www.diagenode.com/img/product/data-mining.jpg" alt="Bioinformatics Data Mining Service" class="th"/></a> </div>
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<span class="success label" style="">G02100000</span>
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<h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Bioinformatics Data Mining Service</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>Contact Information</h2>
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<span class="prefix">Phone number</span>
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$("#Quote-3022 #state-3022").replaceWith('<select name="data[Quote][state]" id="state-3022" 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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</script>
<a class="close-reveal-modal" aria-label="Close">×</a></div><!-- END: QUOTE MODAL --><a href="#" id="data-mining-service" data-reveal-id="quoteModal-3022" 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">Bioinformatics Data Mining Service</h6>
</div>
</div>
</li>
<li>
<div class="row">
<div class="small-12 columns">
<a href="/cn/p/wgbs-service"><img src="/img/grey-logo.jpg" alt="default alt" 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="">G02040000</span>
</div>
<div class="small-6 columns text-right" style="padding-left:0px;padding-right:0px;margin-top:-6px">
<!--a href="#" style="color:#B21329"><i class="fa fa-info-circle"></i></a-->
<!-- BEGIN: QUOTE MODAL --><div id="quoteModal-2989" class="reveal-modal small" data-reveal aria-labelledby="modalTitle" aria-hidden="true" role="dialog">
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<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>
</div>
</div>
<form action="/cn/quotes/quote?id=2989" id="Quote-2989" class="quote" method="post" accept-charset="utf-8"><div style="display:none;"><input type="hidden" name="_method" value="POST"/></div><input type="hidden" name="data[Quote][product_id]" value="2989" id="QuoteProductId"/><input type="hidden" name="data[Quote][formLoaded6tY4bPYk]" value="bG9Tem83aDhYczdoWlpVL2o4SVpGUT09" id="QuoteFormLoaded6tY4bPYk"/><input type="hidden" name="data[Quote][product_rfq_tag]" value="wgbs-service" id="QuoteProductRfqTag"/><input type="hidden" name="data[Quote][source_quote]" value="modal quote" id="QuoteSourceQuote"/>
<div class="row collapse">
<h2>Service Information</h2>
</div>
<div class="small-12 large-12 columns">
<h4>Which services are you interested in?</h4>
</div>
<div class="small-12 large-12 columns">
<input type="hidden" name="data[Quote][epigenomics_service]" value="" id="QuoteEpigenomicsService"/>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ChIP-seq" id="QuoteEpigenomicsServiceChIPSeq" /><label for="QuoteEpigenomicsServiceChIPSeq">ChIP-seq</label></div>
<div class="checkbox"><input type="checkbox" name="data[Quote][epigenomics_service][]" value="ATAC-seq" id="QuoteEpigenomicsServiceATACSeq" /><label for="QuoteEpigenomicsServiceATACSeq">ATAC-seq</label></div>
<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-2989">
<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>
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<option value="UA">Ukraine</option>
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<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>
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$('#country_selector_quote-2989').selectize();
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<div class="row collapse">
<div class="small-3 large-2 columns">
<span class="prefix">State</span>
</div>
<div class="small-9 large-10 columns">
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</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----->
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<div class="small-3 large-2 columns">
</div>
<div class="small-9 large-10 columns">
<div class="recaptcha"><div id="recaptcha673fa71e95be0"></div></div> </div>
</div>
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<div class="small-12 columns" >
<h6 style="height:60px">WGBS service (Whole Genome Bisulfite Sequencing)</h6>
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</li>
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<div class="small-12 columns">
<a href="/cn/p/methylation-data-analysis"><img src="/img/grey-logo.jpg" alt="default alt" class="th"/></a> </div>
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<div class="small-6 columns" style="padding-left:0px;padding-right:0px;margin-top:-6px;margin-left:-1px">
<span class="success label" style="">G02020107</span>
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<h3>Get a quote</h3><p class="lead">You are about to request a quote for <strong>Methylation Data Analysis</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>Contact Information</h2>
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<h6 style="height:60px">Methylation Data Analysis</h6>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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