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<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>',
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<li><strong>High efficiency and minimal bias</strong> - Low DNA degradation and reduced amplification</li>
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<li class="accordion-navigation" style="list-style-type: circle; display: list-item;"><strong>Already tested on various species </strong>– human, mouse, rat, pig, cow, dog, zebrafish, Daphnia <a href="#species" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;">and more</a> <span class="content" id="species">cichlid fish (Astatotilapia calliptera), mossy frog, yellow-bellied slider, dice snake, zebra finch, humboldt penguin, leaf bird, buzzard, vulturine guinea fowe, parma kangaroo, cheetah, mouflon</span></li>
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<td>One bisulfite reaction per sample</td>
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<h1 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h1>
<ul class="citation dates">
<li style="text-align: left;" class="vcard last-author"><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a><sup><a href="#a2">2</a></sup><sup href="#affil-auth">, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><sup><a href="#a3">3</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><sup><a href="#a2">2</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard c1"><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
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<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><strong></strong><strong>F</strong><strong>igure 1.</strong> Using the MBD approach, two methylated regions were detected in different elution fractions according to their methylated CpG density (A). Low, Medium and High refer to the sequenced DNA from different elution fractions with increasing salt concentration. Methylated patterns of these two different methylated regions were validated by bisulfite conversion assay (B).</p>',
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<p style="text-align: center;"><strong>Make your Bisulfite conversion now in only 60 minutes !</strong></p>
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<li><strong>Cost-efficient</strong> – Multiplex up to 6 samples/sequencing lane</li>
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<li><strong>High efficiency and minimal bias</strong> - Low DNA degradation and reduced amplification</li>
<li><strong>Complete kit</strong> – Bisulfite conversion reagents, MspI enzyme, library preparation reagents including barcodes, and spike-in controls</li>
<li class="accordion-navigation" style="list-style-type: circle; display: list-item;"><strong>Already tested on various species </strong>– human, mouse, rat, pig, cow, dog, zebrafish, Daphnia <a href="#species" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;">and more</a> <span class="content" id="species">cichlid fish (Astatotilapia calliptera), mossy frog, yellow-bellied slider, dice snake, zebra finch, humboldt penguin, leaf bird, buzzard, vulturine guinea fowe, parma kangaroo, cheetah, mouflon</span></li>
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<p> </p>
<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/product/kits/rrbs-figure-1.png" alt="Chr Shearing" style="display: block; margin-left: auto; margin-right: auto;" /></p>
</div>
<div class="small-6 columns"><br />
<p><b>Superior coverage</b></p>
<p>Comparison of CpG coverage between competing technologies.</p>
<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
<p><em> </em></p>
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<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/product/kits/rrbs-figure-2.jpg" alt="dna methylation" style="display: block; margin-left: auto; margin-right: auto;" /></p>
</div>
<div class="small-6 columns">
<p><b>Accurate determination of DNA methylation level</b></p>
Excellent results were obtained using Diagenode’s Premium RRBS kit: almost 90% alignment rate, 4.1 million CpGs covered and bisulfite conversion rates around 99.5% for all samples. DNA methylation percentages in the region of IGF2 obtained with the Diagenode’s Premium RRBS Kit. Two human cell lines were analyzed: Gm12878 and MCF7. The MCF7 cell line was studied in duplicates. Each peak represents the DNA methylation percentage at one CpG. The methylated CpGs are shown in red and the unmethylated CpGs in grey.
<p><br /><br /></p>
</div>
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<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/landing-pages/rrbs_how_it_works.jpg" alt="rrbs how it works" style="display: block; margin-left: auto; margin-right: auto;" /><br /><br /></p>
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<div class="small-6 columns">
<p><b>How it works</b></p>
By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</div>
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<table width="1039">
<tbody>
<tr></tr>
<tr>
<td width="281"> </td>
<td width="379">
<h4>Premium RRBS Kit</h4>
</td>
<td width="379">
<h5>Illumina® RRBS protocol</h5>
</td>
</tr>
<tr>
<td><strong>DNA input</strong></td>
<td>100 ng</td>
<td>2-5 µg</td>
</tr>
<tr>
<td><strong>Multiplexing</strong></td>
<td>Pooling of 6 samples (one HiSeq lane)</td>
<td>no guidelines</td>
</tr>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td>One bisulfite reaction per pool (6 samples)</td>
<td>One bisulfite reaction per sample</td>
</tr>
<tr>
<td><strong># purification steps</strong></td>
<td>2</td>
<td>7</td>
</tr>
<tr>
<td><strong>Key features</strong></td>
<td width="379">low input - cost-effective - optimized for high-throughput - complete kit</td>
<td width="379">high input - not optimized for high-throughput - reagents from different providers</td>
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<p></p>
<h1 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h1>
<ul class="citation dates">
<li style="text-align: left;" class="vcard last-author"><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a><sup><a href="#a2">2</a></sup><sup href="#affil-auth">, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><sup><a href="#a3">3</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><sup><a href="#a2">2</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard c1"><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
</ul>
<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><em>Looking for hMeDIP-seq protocol? <a href="https://go.diagenode.com/l/928883/2022-01-07/2m1ht" target="_blank" title="Contact us">Contact us</a></em></p>
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<div class="extra-spaced">
<p>メチル化アレイとWGBS技術の長所を有するReduced representation bisulfite sequencing (RRBS) 技術は、あらゆる脊椎動物種で柔軟に使用できるため、大規模コホートの研究においても正確で費用対効果の高いアッセイを行えます。制限MspI酵素（CCGGターゲットサイト）を使用してゲノムを切断し、次にサイズを選択することで、濃縮されたDNAはプロモーターやCpGアイランドを含む関連するターゲットCpGリッチ領域を表します。またRRBS技術はゲノムの一部分を濃縮してメチル化解析を行うことによってシーケンス量を減らす手法でカバレッジが非常に高く、CpGメチル化解析に適しています。（ヒトサンプルで最大700万CpGが検出）。</p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with reliable results</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
</ul>
<h2>Complete end-to-end service</h2>
<ul class="square">
<li>From DNA QC to standard bioinformatics </li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2 </a>widely adopted</li>
<li>As little as 25 ng for any vertebrate species</li>
<li>Differentially methylated site analysis </li>
</ul>
</div>
<div class="extra-spaced">
<h2></h2>
</div>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>
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<td style="width: 264px;"><strong>QC of the genomic DNA</strong></td>
<td style="width: 636px;">
<ul style="list-style-type: circle;">
<li>Measurement of DNA concentration </li>
<li>Assessment of DNA quality</li>
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<td style="width: 264px;"><strong>Preparation of RRBS libraries</strong></td>
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<li>MspI digestion</li>
<li>Library preparation (ends preparation, adaptor ligation)</li>
<li>Size selection</li>
<li>Sample pooling</li>
<li>Bisulfite conversion</li>
<li>Library amplification and clean-up</li>
<li>QC of the RRBS library pool (DNA concentration, analysis of the pool profile)</li>
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<td style="width: 264px;"><strong>Deep sequencing</strong></td>
<td style="width: 636px;">
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<li>Samples are sequenced on an Illumina platform, paired-end reads, read length 50 bp (PE50)</li>
<li>40 million raw reads (on average) per sample when pooling 10 samples/lane</li>
<li>7 million CpGs (on average) for human samples</li>
<li>7-11x CpG coverage (on average) for human samples</li>
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<h4><strong>Analysis</strong></h4>
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<td style="width: 262px;"><strong>Standard</strong></td>
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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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<td style="width: 262px;"><strong>Differential methylation analysis<br /></strong></td>
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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>
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<p><strong>Gene ontology terms analysis</strong></p>
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<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
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<p><strong>Pathway analysis</strong></p>
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<td style="width: 624px;">
<ul>
<li>Identification of biological pathways in which genes associated with DMCs and DMRs may be over-represented (or under-represented)</li>
<li>Get mechanistic insights</li>
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<p style="text-align: justify;"><span>Reduced Representation Bisulfite Sequencing provides a powerful method to efficiently analyze DNA methylation at the </span><strong>single nucleotide level</strong><span> without the higher costs associated with whole genome bisulfite sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent CpG-rich regions (including CpG islands), in which DNA methylation marks are typically found. Thus, RRBS provides a </span><strong>cost-effective</strong><span> method for analyzing DNA methylation by reducing the part of the genome that actually needs to be sequenced and focusing on relevant CpG islands.</span></p>',
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<div style="text-align: justify;" class="large-12 columns">Bisulfite modification of DNA is the most commonly used, "<strong>gold standard</strong>" method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. T<span style="font-weight: 400;">his technology is based on the chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at the singe nucleotide level.</span></div>
<div style="text-align: justify;" class="large-12 columns"></div>
<div style="text-align: justify;" class="large-12 columns">Various analyses can be performed on the altered sequence to retrieve this information: bisulfite sequencing, pyrosequencing, methylation-specific PCR, high resolution melting curve analysis, microarray-based approaches, and next-generation sequencing.
<h3>How it works</h3>
Treatment of DNA with bisulfite converts cytosine residues to uracil, but leaves 5-methylcytosine residues unaffected (see Figure 1).
<p class="text-center"><img src="https://www.diagenode.com/img/applications/bisulfite.png" /><br />Figure 1: Overview of bisulfite conversion of DNA</p>
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<h2>Complete solutions for DNA methylation studies</h2>
<p>Whether you are experienced or new to the field of DNA methylation, Diagenode has everything you need to make your assay as easy and convenient as possible while ensuring consistent data between samples and experiments. Diagenode offers sonication instruments, reagent kits, high quality antibodies, and high-throughput automation capability to address all of your specific DNA methylation analysis requirements.</p>
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<p>DNA methylation was the first discovered epigenetic mark and is the most widely studied topic in epigenetics. <em>In vivo</em>, DNA is methylated following DNA replication and is involved in a number of biological processes including the regulation of imprinted genes, X chromosome inactivation. and tumor suppressor gene silencing in cancer cells. Methylation often occurs in cytosine-guanine rich regions of DNA (CpG islands), which are commonly upstream of promoter regions.</p>
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<div class="small-12 medium-12 large-12 columns"><br /><br />
<ul class="accordion" data-accordion="">
<li class="accordion-navigation"><a href="#dnamethyl"><i class="fa fa-caret-right"></i> Learn more</a>
<div id="dnamethyl" class="content">5-methylcytosine (5-mC) has been known for a long time as the only modification of DNA for epigenetic regulation. In 2009, however, Kriaucionis discovered a second methylated cytosine, 5-hydroxymethylcytosine (5-hmC). The so-called 6th base, is generated by enzymatic conversion of 5-methylcytosine (5-mC) into 5-hydroxymethylcytosine by the TET family of oxygenases. Early reports suggested that 5-hmC may represent an intermediate of active demethylation in a new pathway which demethylates DNA, converting 5-mC to cytosine. Recent evidence fuel this hypothesis suggesting that further oxidation of the hydroxymethyl group leads to a formyl or carboxyl group followed by either deformylation or decarboxylation. The formyl and carboxyl groups of 5-formylcytosine (5-fC) and 5-carboxylcytosine (5-caC) could be enzymatically removed without excision of the base.
<p class="text-center"><img src="https://www.diagenode.com/img/categories/kits_dna/dna_methylation_variants.jpg" /></p>
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</li>
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<br />
<h2>Main DNA methylation technologies</h2>
<p style="text-align: justify;">Overview of the <span style="font-weight: 400;">three main approaches for studying DNA methylation.</span></p>
<div class="row">
<ol>
<li style="font-weight: 400;"><span style="font-weight: 400;">Chemical modification with bisulfite – Bisulfite conversion</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Enrichment of methylated DNA (including MeDIP and MBD)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Treatment with methylation-sensitive or dependent restriction enzymes</span></li>
</ol>
<p><span style="font-weight: 400;"> </span></p>
<div class="row">
<table>
<thead>
<tr>
<th></th>
<th>Description</th>
<th width="350">Features</th>
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</thead>
<tbody>
<tr>
<td><strong>Bisulfite conversion</strong></td>
<td><span style="font-weight: 400;">Chemical conversion of unmethylated cytosine to uracil. Methylated cytosines are protected from this conversion allowing to determine DNA methylation at single nucleotide resolution.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Single nucleotide resolution</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Quantitative analysis - methylation rate (%)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Gold standard and well studied</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><b>Methylated DNA enrichment</b></td>
<td><span style="font-weight: 400;">(Hydroxy-)Methylated DNA is enriched by using specific antibodies (hMeDIP or MeDIP) or proteins (MBD) that specifically bind methylated CpG sites in fragmented genomic DNA.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Resolution depends on the fragment size of the enriched methylated DNA (300 bp)</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Qualitative analysis</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Compatible with automation</span></li>
</ul>
</td>
</tr>
<tr>
<td><strong>Restriction enzyme-based digestion</strong></td>
<td><span style="font-weight: 400;">Use of (hydroxy)methylation-sensitive or (hydroxy)methylation-dependent restriction enzymes for DNA methylation analysis at specific sites.</span></td>
<td>
<ul style="list-style-type: circle;">
<li style="font-weight: 400;"><span style="font-weight: 400;">Determination of methylation status is limited by the enzyme recognition site</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Easy to use</span></li>
</ul>
</td>
</tr>
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<div class="large-12 columns">MBD方法は、メチル化DNAに対するH6-GST-MBD融合タンパク質の非常に高い親和性に基づいています。 このタンパク質は、N末端His6タグを含むグルタチオン-S-トランスフェラーゼ（GST）とのC末端融合物として、ヒトMeCP2のメチル結合ドメイン（MBD）を含有します。 このH6-GST-MBD融合タンパク質を用いて、メチル化CpGを含むDNAを特異的に単離する事が可能です。<br /><br />DiagenodeのMethylCap®キットは、二本鎖DNAの高濃縮と、メチル化CpG密度の関数における微分分画を可能にします。 分画はサンプルの複雑さを軽減し、次世代のシーケンシングを容易にします。 MethylCapアッセイに先立ち、DNAを最初に抽出し、Picoruptorソニケーターを用いて断片化します。<br />
<h3>概要</h3>
<p class="text-center"><br /><img src="https://www.diagenode.com/img/applications/methyl_binding_domain_overview.jpg" /></p>
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<p>Sodium bisulfite conversion of genomic DNA is the most commonly used method for DNA methylation studies providing <strong>single nucleotide resolution</strong>. It enables <span>to differentiate and detect unmethylated versus methylated cytosines. This procedure can then be followed either by <strong>PCR amplification</strong> or <strong>next generation sequencing</strong> to reveal the methylation status of every cytosine in gene specific amplification or whole genome amplification.</span></p>
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<h2>How it works</h2>
<p style="text-align: left;">Treatment of DNA with sodium bisulfite converts unmethylated cytosine to uracil, while methylated cytosines remain unchanged. <span>The DNA is then amplified by PCR where the uracils are converted to thymines. </span></p>
<p style="text-align: center;"><span></span></p>
<p><img src="https://www.diagenode.com/img/categories/bisulfite-conversion/bisulfite-conversion-acgautac.png" style="display: block; margin-left: auto; margin-right: auto;" /></p>
<h2>Advantages</h2>
<ul class="nobullet" style="font-size: 19px;">
<li><i class="fa fa-arrow-circle-right"></i><strong> </strong><strong>Single nucleotide</strong> resolution</li>
<li><i class="fa fa-arrow-circle-right"></i><strong> Gene-specific </strong>and <strong>genome-wide</strong><span> analyses</span></li>
<li><i class="fa fa-arrow-circle-right"></i><strong> NGS</strong><span> </span>compatible</li>
</ul>
<h2>Downstream analysis techniques</h2>
<ul class="square">
<li>Reduced Representation Bisulfite Sequencing (RRBS) with our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS Kit V2</a></li>
<li>Bisulfite conversion with our <a href="https://www.diagenode.com/en/p/premium-bisulfite-kit-50-rxns">Premium Bisulfite Kit</a> followed by qPCR, Sanger, Pyrosequencing</li>
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'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>',
'image_id' => null,
'type' => 'Application note',
'url' => 'files/application_notes/application-note_RRBS-10_15.pdf',
'slug' => 'application-note-rrbs',
'meta_keywords' => '',
'meta_description' => '',
'modified' => '2016-05-19 13:52:49',
'created' => '2015-12-09 16:36:19',
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'id' => '887',
'name' => 'Premium RRBS kit',
'description' => '<div class="page" title="Page 4">
<div class="layoutArea">
<div class="column">
<p><span>Diagenode’s Premium RRBS technology </span></p>
<ul>
<li>
<p><span>Positive and negative spike-in controls are included for the monitoring of bisulfite conversion efficiency. </span></p>
</li>
<li>
<p><span>Size selection has been optimized to keep small fragments of interest and to remove adaptor dimers, resulting in a better coverag. </span></p>
</li>
<li>
<p><span>The pooling protocol includes a quantification of the samples and a pooling application, available on our website, to help you to choose the groups, depending on the DNA amount and adaptor barcode of each sample. </span></p>
</li>
<li>
<p><span>The bisulfite conversion protocol has been improved to decrease DNA degradation while keeping a highly efficient conversion of unmethylated cytosine. </span></p>
</li>
<li>
<p><span>The minimum number of amplification cycles needed for each pool is determined to avoid amplification biases. Our MethylTaq Plus enzyme was developed to amplify bisulfite converted DNA with high efficiency, and reduces the number of PCR cycles required. </span></p>
</li>
</ul>
</div>
</div>
</div>',
'image_id' => null,
'type' => 'Manual',
'url' => 'files/products/kits/Premium_RRBS_kit_manual.pdf',
'slug' => 'premium-rrbs-kit-manual-24rxns',
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'created' => '2016-03-15 17:01:11',
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(int) 3 => array(
'id' => '1029',
'name' => 'Premium RRBS spike-in controls - data processing',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS Kit in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the <a href="https://www.diagenode.com/files/products/kits/Premium_RRBS_kit_manual.pdf">Premium RRBS Kit manual</a>. </p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-x24-24-rxns#">Premium RRBS Kit page</a>: </p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => 'Manual',
'url' => 'files/products/kits/premium-RRBS-spike-in controls-08_18.pdf',
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'modified' => '2022-06-22 11:10:41',
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(int) 4 => array(
'id' => '1030',
'name' => 'Sequence of the methylated spike-in control in FASTA format',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS kit in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/Premium_RRBS_kit_manual.pdf">Premium RRBS Kit Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit page</a> :</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_methylated_control.fa',
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'name' => 'Sequence of the unmethylated spike-in control in FASTA format',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS kit in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/Premium_RRBS_kit_manual.pdf">Premium RRBS Kit Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit page</a> :</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_unmethylated_control.fa',
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'id' => '1032',
'name' => 'Positions of the unmethylated cytosines in the sequence of the methylated control in BED format',
'description' => '<p>This document and files are to process the sequencing data from the spike-in controls included in the RRBS kit in order to estimate the bisulfite conversion efficiency. You can find the sequences of the controls and the methylation status of each cytosine in the<span> </span><a href="../../files/products/kits/Premium_RRBS_kit_manual.pdf">Premium RRBS Kit Manual</a>.</p>
<p>However, to ease the data processing, we provide three files that can be downloaded from the<span> </span><a href="../p/premium-rrbs-kit-x24-24-rxns">Premium RRBS Kit page</a> :</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => '',
'url' => 'files/products/kits/RRBS_control_unmC.bed',
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'modified' => '2018-09-18 17:26:24',
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(int) 7 => array(
'id' => '1159',
'name' => 'Premium RRBS kit V2 - Spike-in controls analysis to estimate bisulfite conversion efficiency',
'description' => '<p>This document describes how to process demultiplexed RRBS V2 sequencing data to extract the information from the spike-in controls included in the Premium RRBS Kit v2 in order to estimate the bisulfite conversion efficiency.</p>
<p>Sequences of the methylated and unmethylated spike-in controls, as well as the positions of the unmethylated cytosines present in the methylated spike-in control, can be downloaded from the ‘Documents’ section of the Premium RRBS Kit v2 page <a href="../p/premium-rrbs-kit-V2-x24">https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24</a>:</p>
<ul>
<li>RRBS_methylated_control.fa: the sequence of the methylated spike-in control in FASTA format</li>
<li>RRBS_unmethylated_control.fa: the sequence of the unmethylated spike-in control in FASTA format</li>
<li>RRBS_control_unmC.bed: the positions of the unmethylated cytosines in the sequence of the methylated control in BED format</li>
</ul>',
'image_id' => null,
'type' => 'Manual',
'url' => 'files/products/kits/Premium RRBS-spikein-controls-manual.pdf',
'slug' => 'premium-rrbs-spike-in-controls-manual',
'meta_keywords' => '',
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'modified' => '2022-06-29 14:34:07',
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(int) 0 => array(
'id' => '1778',
'name' => 'product/kits/methyl-kit-icon.png',
'alt' => 'Methylation kit icon',
'modified' => '2019-04-23 15:17:01',
'created' => '2018-03-15 15:52:12',
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'Publication' => array(
(int) 0 => array(
'id' => '4114',
'name' => 'The ETS transcription factor ERF controls the exit from the naïve pluripotent state in a MAPK-dependent manner',
'authors' => 'Maria Vega-Sendino et. al.',
'description' => '<p><span>The naïve epiblast transitions to a pluripotent primed state during embryo implantation. Despite the relevance of the FGF pathway during this period, little is known about the downstream effectors regulating this signaling. Here, we examined the molecular mechanisms coordinating the naïve to primed transition by using inducible ESC to genetically eliminate all RAS proteins. We show that differentiated RAS</span><sup>KO</sup><span><span> </span>ESC remain trapped in an intermediate state of pluripotency with naïve-associated features. Elimination of the transcription factor ERF overcomes the developmental blockage of RAS-deficient cells by naïve enhancer decommissioning. Mechanistically, ERF regulates NANOG expression and ensures naïve pluripotency by strengthening naïve transcription factor binding at ESC enhancers. Moreover, ERF negatively regulates the expression of the methyltransferase DNMT3B, which participates in the extinction of the naïve transcriptional program. Collectively, we demonstrated an essential role for ERF controlling the exit from naïve pluripotency in a MAPK-dependent manner during the progression to primed pluripotency.</span></p>',
'date' => '2021-10-01',
'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/34597136/',
'doi' => '10.1126/sciadv.abg8306',
'modified' => '2022-05-19 16:05:11',
'created' => '2021-10-06 08:45:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 1 => 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) 2 => 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]
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),
(int) 3 => array(
'id' => '4111',
'name' => 'Riluzole Administration to Rats with Levodopa-Induced Dyskinesia Leads to Loss of DNA Methylation in Neuronal Genes',
'authors' => 'Luca Pagliaroli, Abel Fothi, Ester Nespoli, Istvan Liko, Borbala Veto, Piroska Devay, Flora Szeri, Bastian Hengerer, Csaba Barta, Tamas Aranyi',
'description' => '<p>Dyskinesias are characterized by abnormal repetitive involuntary movements due to dysfunctional neuronal activity. Although levodopa-induced dyskinesia, characterized by tic-like abnormal involuntary movements, has no clinical treatment for Parkinson’s disease patients, animal studies indicate that Riluzole, which interferes with glutamatergic neurotransmission, can improve the phenotype. The rat model of Levodopa-Induced Dyskinesia is a unilateral lesion with 6-hydroxydopamine in the medial forebrain bundle, followed by the repeated administration of levodopa. The molecular pathomechanism of Levodopa-Induced Dyskinesia is still not deciphered; however, the implication of epigenetic mechanisms was suggested. In this study, we investigated the striatum for DNA methylation alterations under chronic levodopa treatment with or without co-treatment with Riluzole. Our data show that the lesioned and contralateral striata have nearly identical DNA methylation profiles. Chronic levodopa and levodopa + Riluzole treatments led to DNA methylation loss, particularly outside of promoters, in gene bodies and CpG poor regions. We observed that several genes involved in the Levodopa-Induced Dyskinesia underwent methylation changes. Furthermore, the Riluzole co-treatment, which improved the phenotype, pinpointed specific methylation targets, with a more than 20% methylation difference relative to levodopa treatment alone. These findings indicate potential new druggable targets for Levodopa-Induced Dyskinesia.</p>',
'date' => '2021-06-09',
'pmid' => 'https://www.mdpi.com/2073-4409/10/6/1442',
'doi' => '10.3390/cells10061442',
'modified' => '2022-05-19 16:06:47',
'created' => '2021-08-27 11:27:35',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 4 => array(
'id' => '4175',
'name' => 'Multi-omics phenotyping of the gut-liver axis reveals metabolicperturbations from a low-dose pesticide mixture in rats.',
'authors' => 'Mesnage, Robin et al.',
'description' => '<p>Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.</p>',
'date' => '2021-04-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33854195',
'doi' => '10.1038/s42003-021-01990-w',
'modified' => '2021-12-21 16:12:25',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 5 => array(
'id' => '4142',
'name' => 'The aging DNA methylome reveals environment-by-aging interactions in amodel teleost',
'authors' => 'Bertucci, E. M. et al.',
'description' => '<p>The rate at which individuals age underlies variation in life history and attendant health and disease trajectories. Age specific patterning of the DNA methylome (“epigenetic aging”) is strongly correlated with chronological age in humans and can be modeled to produce epigenetic age predictors. However, epigenetic age estimates vary among individuals of the same age, and this mismatch is correlated to the onset of age-related disease and all-cause mortality. Yet, the origins of epigenetic-to-chronological age discordance are not resolved. In an effort to develop a tractable model in which environmental drivers of epigenetic aging can be assessed, we investigate the relationship between aging and DNA methylation in a small teleost, medaka (Oryzias latipes). We find that age-associated DNA methylation patterning occurs broadly across the genome, with the majority of age-related changes occurring during early life. By modeling the stereotypical nature of age-associated DNA methylation dynamics, we built an epigenetic clock, which predicts chronological age with a mean error of 29.1 days (~4\% of average lifespan). Characterization of clock loci suggests that aspects of epigenetic aging are functionally similar across vertebrates. To understand how environmental factors interact with epigenetic aging, we exposed medaka to four doses of ionizing radiation for seven weeks, hypothesizing that exposure to such an environmental stressor would accelerate epigenetic aging. While the epigenetic clock was not significantly affected, radiation exposure accelerated and decelerated patterns of normal epigenetic aging, with radiation-induced epigenetic alterations enriched at loci that become hypermethylated with age. Together, our findings advance ongoing research attempting to elucidate the functional role of DNA methylation in integrating environmental factors into the rate of biological aging.</p>',
'date' => '2021-03-01',
'pmid' => 'https://doi.org/10.1101%2F2021.03.01.433371',
'doi' => '10.1101/2021.03.01.433371',
'modified' => '2022-05-19 16:07:18',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 6 => array(
'id' => '4173',
'name' => 'The insecticide permethrin induces transgenerational behavioral changeslinked to transcriptomic and epigenetic alterations in zebrafish (Daniorerio).',
'authors' => 'Blanc, Mélanie et al.',
'description' => '<p>The pyrethroid insecticide permethrin is widely used for agricultural and domestic purposes. Previous data indicated that it acts as a developmental neurotoxicant and can induce transgenerational effects in non-target organisms. However, associated underlying mechanisms remain unclear. The aim of this study was to investigate permethrin-related transgenerational effects in the zebrafish model, and to identify possible molecular mechanisms underlying inheritance. Zebrafish (F0) were exposed to permethrin during early-life (2 h post-fertilization up to 28 days). The F1 and F2 offspring generations were obtained by pairing exposed F0 males and females, and were bred unexposed. Locomotor and anxiety behavior were investigated, together with transcriptomic and epigenomic (DNA methylation) changes in brains. Permethrin exposed F0 fish were hypoactive at adulthood, while males from the F1 and F2 generations showed a specific decrease in anxiety-like behavior. In F0, transcriptomic data showed enrichment in pathways related to glutamatergic synapse activity, which may partly underlie the behavioral effects. In F1 and F2 males, dysregulation of similar pathways was observed, including a subset of differentially methylated regions that were inherited from the F0 to the F2 generation and indicated stable dysregulation of glutamatergic signaling. Altogether, the present results provide novel evidence on the transgenerational neurotoxic effects of permethrin, as well as mechanistic insight: a transient exposure induces persistent transcriptional and DNA methylation changes that may translate into transgenerational alteration of glutamatergic signaling and, thus, into behavioral alterations.</p>',
'date' => '2021-03-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33752003',
'doi' => '10.1016/j.scitotenv.2021.146404',
'modified' => '2021-12-21 16:02:21',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 7 => 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) 8 => array(
'id' => '4189',
'name' => 'The Identification of a Novel Fucosidosis-Associated Mutation: A Case of a5-Year-Old Polish Girl with Two Additional Rare Chromosomal Aberrations andAffected DNA Methylation Patterns.',
'authors' => 'Domin A. et al. ',
'description' => '<p>Fucosidosis is a rare neurodegenerative autosomal recessive disorder, which manifests as progressive neurological and psychomotor deterioration, growth retardation, skin and skeletal abnormalities, intellectual disability and coarsening of facial features. It is caused by biallelic mutations in encoding the α-L-fucosidase enzyme, which in turn is responsible for degradation of fucose-containing glycoproteins and glycolipids. mutations lead to severe reduction or even loss of α-L-fucosidase enzyme activity. This results in incomplete breakdown of fucose-containing compounds leading to their deposition in different tissues and, consequently, disease progression. To date, 36 pathogenic variants in associated with fucosidosis have been documented. Among these are three splice site variants. Here, we report a novel fucosidosis-related 9-base-pair deletion (NG_013346.1:g.10233_10241delACAGGTAAG) affecting the exon 3/intron 3 junction within a sequence. This novel pathogenic variant was identified in a five-year-old Polish girl with a well-defined pattern of fucosidosis symptoms. Since it is postulated that other genetic, nongenetic or environmental factors can also contribute to fucosidosis pathogenesis, we performed further analysis and found two rare de novo chromosomal aberrations in the girl's genome involving a 15q11.1-11.2 microdeletion and an Xq22.2 gain. These abnormalities were associated with genome-wide changes in DNA methylation status in the epigenome of blood cells.</p>',
'date' => '2021-01-01',
'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33435586',
'doi' => '10.3390/genes12010074',
'modified' => '2022-05-19 16:08:10',
'created' => '2021-12-06 15:53:19',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 9 => 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) 10 => 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) 11 => array(
'id' => '4020',
'name' => 'DNA CpG methylation in sequential glioblastoma specimens.',
'authors' => 'Kraboth, Z and Galik, B and Tompa, M and Kajtar, B and Urban, P andGyenesei, A and Miseta, A and Kalman, B',
'description' => '<p>PURPOSE: Glioblastoma is the most aggressive form of brain tumors. A better understanding of the molecular mechanisms leading to its evolution is essential for the development of treatments more effective than the available modalities. Here, we aim to identify molecular drivers of glioblastoma development and recurrence by analyzing DNA CpG methylation patterns in sequential samples. METHODS: DNA was isolated from 22 pairs of primary and recurrent formalin-fixed, paraffin-embedded glioblastoma specimens, and subjected to reduced representation bisulfite sequencing. Bioinformatic analyses were conducted to identify differentially methylated sites and pathways, and biostatistics was used to test correlations among clinical and pathological parameters. RESULTS: Differentially methylated pathways likely involved in primary tumor development included those of neuronal differentiation, myelination, metabolic processes, synapse organization and endothelial cell proliferation, while pathways differentially active during glioblastoma recurrence involved those associated with cell processes and differentiation, immune response, Wnt regulation and catecholamine secretion and transport. CONCLUSION: DNA CpG methylation analyses in sequential clinical specimens revealed hypomethylation in certain pathways such as neuronal tissue development and angiogenesis likely involved in early tumor development and growth, while suggested altered regulation in catecholamine secretion and transport, Wnt expression and immune response contributing to glioblastoma recurrence. These pathways merit further investigations and may represent novel therapeutic targets.</p>',
'date' => '2020-08-10',
'pmid' => 'http://www.pubmed.gov/32779022',
'doi' => '10.1007/s00432-020-03349-w',
'modified' => '2022-05-19 16:09:06',
'created' => '2020-10-12 14:54:59',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 12 => array(
'id' => '3989',
'name' => 'Early Life Exposure to Environmentally Relevant Levels of Endocrine Disruptors Drive Multigenerational and Transgenerational Epigenetic Changes in a Fish Model',
'authors' => 'Major Kaley M., DeCourten Bethany M., Li Jie, Britton Monica, Settles Matthew L., Mehinto Alvine C., Connon Richard E., Brander Susanne M.',
'description' => '<p>The inland silverside, Menidia beryllina, is a euryhaline fish and a model organism in ecotoxicology. We previously showed that exposure to picomolar (ng/L) levels of endocrine disrupting chemicals (EDCs) can cause a variety of effects in M. beryllina, from changes in gene expression to phenotypic alterations. Here we explore the potential for early life exposure to EDCs to modify the epigenome in silversides, with a focus on multi- and transgenerational effects. EDCs included contaminants of emerging concern (the pyrethroid insecticide bifenthrin and the synthetic progestin levonorgestrel), as well as a commonly detected synthetic estrogen (ethinylestradiol), and a synthetic androgen (trenbolone) at exposure levels ranging from 3 to 10 ng/L. In a multigenerational experiment, we exposed parental silversides to EDCs from fertilization until 21 days post hatch (dph). Then we assessed DNA methylation patterns for three generations (F0, F1, and F2) in whole body larval fish using reduced representation bisulfite sequencing (RRBS). We found significant (α = 0.05) differences in promoter and/or gene body methylation in treatment fish relative to controls for all EDCs and all generations indicating that both multigenerational (F1) and transgenerational (F2) effects that were caused by strict inheritance of DNA methylation alterations and the dysregulation of epigenetic control mechanisms. Using gene ontology and pathway analyses, we found enrichment in biological processes and pathways representative of growth and development, immune function, reproduction, pigmentation, epigenetic regulation, stress response and repair (including pathways important in carcinogenesis). Further, we found that a subset of potentially EDC responsive genes (EDCRGs) were differentially methylated across all treatments and generations and included hormone receptors, genes involved in steroidogenesis, prostaglandin synthesis, sexual development, DNA methylation, protein metabolism and synthesis, cell signaling, and neurodevelopment. The analysis of EDCRGs provided additional evidence that differential methylation is inherited by the offspring of EDC-treated animals, sometimes in the F2 generation that was never exposed. These findings show that low, environmentally relevant levels of EDCs can cause altered methylation in genes that are functionally relevant to impaired phenotypes documented in EDC-exposed animals and that EDC exposure has the potential to affect epigenetic regulation in future generations of fish that have never been exposed.</p>',
'date' => '2020-06-24',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fmars.2020.00471/full',
'doi' => '10.3389/fmars.2020.00471',
'modified' => '2022-05-19 16:09:23',
'created' => '2020-08-21 16:41:39',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 13 => 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) 14 => array(
'id' => '3885',
'name' => 'Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease',
'authors' => 'Guan Y, Liu H, Ma Z, Li SY, Park J, Sheng X, Susztak K',
'description' => '<p>BACKGROUND: Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing methylation. METHODS: We generated mice with genetic deletion of and in nephron progenitor cells () and kidney tubule cells (). We characterized mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells. RESULTS: mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that and play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of and resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by and were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with and deletion. CONCLUSIONS: Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.</p>',
'date' => '2020-03-03',
'pmid' => 'http://www.pubmed.gov/32127410',
'doi' => '10.1681/ASN.2019080797',
'modified' => '2022-05-19 16:10:07',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 15 => array(
'id' => '3877',
'name' => 'Rheumatoid Arthritis Patients, Both Newly Diagnosed and Methotrexate Treated, Show More DNA Methylation Differences in CD4+ Memory Than in CD4+ Naïve T Cells',
'authors' => 'Guderud Kari, Sunde Line H., Flåm Siri T., Mæhlen Marthe T., Mjaavatten Maria D., Lillegraven Siri, Aga Anna-Birgitte, Evenrød Ida M., Norli Ellen S., Andreassen Bettina K., Franzenburg Sören, Franke Andre, Haavardsholm Espen A., Rayner Simon, Gervin Kris',
'description' => '<p>Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes pain and swelling of multiple joints in the body. The underlying disease mechanisms are believed to involve a complex interplay between common genetic and environmental factors. The heritability of RA has been estimated to be ~50% for anti-citrullinated protein antibody (ACPA) positive RA and ~20% for ACPA negative RA in a large familial aggregation study (1). Genome-wide association studies (GWAS) have identified more than 100 RA risk loci, mostly conferring risk to ACPA positive RA, marked by lead single nucleotide polymorphisms (SNPs) across various populations (2). The risk SNPs have small effect sizes, and only explain parts of heritability in RA. Environmental and epigenetic factors are also thought to be involved in the RA disease pathogenesis (3) of which smoking is the only established environmental risk factor (4, 5). Epigenetic modifications are important for regulation and maintenance of cell type specific biological functions, and alterations in the epigenome have been found to be associated with RA (6). The most studied epigenetic modification in humans is DNA methylation of cytosine followed by a guanine at so-called CpG sites (CpGs). CpGs are often clustered in regions called CpG islands (CGIs), which frequently overlap gene promoters (7). DNA methylation in promotor regions is usually negatively correlated with transcription of the nearby gene (8). A wide range of immune cells has been implicated in the pathogenesis of RA. One of the most widely used drugs for treatment of RA, methotrexate (MTX) (9), acts as an immunosuppressant in proliferating cells (10), and of these, the most relevant cell population for RA is CD4+ T cells (11). Interestingly, the RA risk loci are enriched in accessible chromatin regions (H3K4me3 peaks) in T cells, including both CD4+ naïve and CD4+ memory T cells (2). Studies have identified cell type specific DNA methylation differences in B (CD19+) and T (CD3+) lymphocytes (12, 13), as well as CD4+ T cells subsets (14, 15) isolated from RA patients compared to healthy controls. However, memory and naïve CD4+ T cells also display distinct genome-wide and gene-specific DNA methylation patterns as a result of normal differentiation (16); hence analyses of bulk T cells may be confounded by different proportions of naïve and memory T cells. Given the recent observations that CD4+ T cell subset distributions are abnormal both in treatment naïve RA patients and in RA patients who has undergone MTX treatment (17) methylation profiles for distinct CD4+ T cell subpopulations should be investigated separately. Methylation levels have so far only been assessed by array-based methods in RA, however reduced representation bisulfite sequencing (RRBS) using next generation sequencers allows for an interrogation of even more CpG sites. RRBS enriches for CpG dinucleotides by utilizes the restriction enzyme MspI (C∧CGG) to digest the DNA sample before bisulfite conversion and sequencing. In this study, we aimed to investigate whether we could detect DNA methylation differences in primary naïve and memory CD4+ T cells from RA patients. To do this, we conducted an epigenome-wide association study using RRBS on isolated T cell populations from two different RA cohorts; (1) disease modifying anti-rheumatic drug (DMARD) naïve RA patients with active disease and (2) MTX-treated RA patients who had been in remission for >12 months. The two cohorts were compared to matched healthy controls.</p>',
'date' => '2020-02-14',
'pmid' => 'https://www.frontiersin.org/articles/10.3389/fimmu.2020.00194/full',
'doi' => '10.3389/fimmu.2020.00194',
'modified' => '2022-05-19 16:10:24',
'created' => '2020-03-13 13:45:54',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 16 => 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) 17 => array(
'id' => '3674',
'name' => 'Mitochondrial stress triggers a pro-survival response through epigenetic modifications of nuclear DNA.',
'authors' => 'Mayorga L, Salassa BN, Marzese DM, Loos MA, Eiroa HD, Lubieniecki F, García Samartino C, Romano PS, Roqué M',
'description' => '<p>Mitochondrial dysfunction represents an important cellular stressor and when intense and persistent cells must unleash an adaptive response to prevent their extinction. Furthermore, mitochondria can induce nuclear transcriptional changes and DNA methylation can modulate cellular responses to stress. We hypothesized that mitochondrial dysfunction could trigger an epigenetically mediated adaptive response through a distinct DNA methylation patterning. We studied cellular stress responses (i.e., apoptosis and autophagy) in mitochondrial dysfunction models. In addition, we explored nuclear DNA methylation in response to this stressor and its relevance in cell survival. Experiments in cultured human myoblasts revealed that intense mitochondrial dysfunction triggered a methylation-dependent pro-survival response. Assays done on mitochondrial disease patient tissues showed increased autophagy and enhanced DNA methylation of tumor suppressor genes and pathways involved in cell survival regulation. In conclusion, mitochondrial dysfunction leads to a "pro-survival" adaptive state that seems to be triggered by the differential methylation of nuclear genes.</p>',
'date' => '2019-04-01',
'pmid' => 'http://www.pubmed.gov/30673822',
'doi' => '10.1007/s00018-019-03008-5',
'modified' => '2022-05-19 16:10:59',
'created' => '2019-06-21 14:55:31',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 18 => array(
'id' => '3416',
'name' => 'Differential DNA methylation of potassium channel KCa3.1 and immune signalling pathways is associated with infant immune responses following BCG vaccination.',
'authors' => 'Hasso-Agopsowicz M, Scriba TJ, Hanekom WA, Dockrell HM, Smith SG',
'description' => '<p>Bacillus Calmette-Guérin (BCG) is the only licensed vaccine for tuberculosis (TB) and induces highly variable protection against pulmonary disease in different countries. We hypothesised that DNA methylation is one of the molecular mechanisms driving variability in BCG-induced immune responses. DNA methylation in peripheral blood mononuclear cells (PBMC) from BCG vaccinated infants was measured and comparisons made between low and high BCG-specific cytokine responders. We found 318 genes and 67 pathways with distinct patterns of DNA methylation, including immune pathways, e.g. for T cell activation, that are known to directly affect immune responses. We also highlight signalling pathways that could indirectly affect the BCG-induced immune response: potassium and calcium channel, muscarinic acetylcholine receptor, G Protein coupled receptor (GPCR), glutamate signalling and WNT pathways. This study suggests that in addition to immune pathways, cellular processes drive vaccine-induced immune responses. Our results highlight mechanisms that require consideration when designing new TB vaccines.</p>',
'date' => '2018-08-30',
'pmid' => 'http://www.pubmed.gov/30166570',
'doi' => '10.1038/s41598-018-31537-9',
'modified' => '2022-05-19 16:11:19',
'created' => '2018-12-04 09:51:07',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 19 => array(
'id' => '3322',
'name' => 'In Situ Fixation Redefines Quiescence and Early Activation of Skeletal Muscle Stem Cells',
'authors' => 'Machado L. et al.',
'description' => '<div class="abstract">
<h2 class="sectionTitle" tabindex="0">Summary</h2>
<div class="content">
<p>State of the art techniques have been developed to isolate and analyze cells from various tissues, aiming to capture their <em>in vivo</em> state. However, the majority of cell isolation protocols involve lengthy mechanical and enzymatic dissociation steps followed by flow cytometry, exposing cells to stress and disrupting their physiological niche. Focusing on adult skeletal muscle stem cells, we have developed a protocol that circumvents the impact of isolation procedures and captures cells in their native quiescent state. We show that current isolation protocols induce major transcriptional changes accompanied by specific histone modifications while having negligible effects on DNA methylation. In addition to proposing a protocol to avoid isolation-induced artifacts, our study reveals previously undetected quiescence and early activation genes of potential biological interest.</p>
</div>
</div>',
'date' => '2017-11-14',
'pmid' => 'http://www.cell.com/cell-reports/abstract/S2211-1247(17)31543-7',
'doi' => '',
'modified' => '2022-05-19 16:11:43',
'created' => '2018-02-02 16:36:37',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 20 => 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) 21 => array(
'id' => '3063',
'name' => 'DNA methylation and alcohol use disorders: Progress and challenges',
'authors' => 'Zhang H. and Gelernter J.',
'description' => '<section class="article-section article-body-section" id="ajad12465-sec-0001">
<h3>Background and Objectives</h3>
<p>Risk for alcohol use disorders (AUDs) is influenced by gene–environment interactions. Environmental factors can affect gene expression through epigenetic mechanisms such as DNA methylation. This review outlines the findings regarding the association of DNA methylation and AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0002">
<h3>Methods</h3>
<p>We searched PubMed (by April 2016) and identified 29 studies that examined the association of DNA methylation and AUDs. We also evaluated the methods used in these studies.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0003">
<h3>Results</h3>
<p>Two studies demonstrated elevated global (repetitive element) DNA methylation levels in AUD subjects. Fifteen candidate gene studies showed hypermethylation of promoter regions of six genes (<em>AVP</em>, <em>DNMT3B</em>, <em>HERP</em>, <em>HTR3A</em>, <em>OPRM1</em>, and <em>SNCA</em>) or hypomethylation of the <em>GDAP1</em> promoter region in AUD subjects. Five genome-wide DNA methylation studies demonstrated widespread DNA methylation changes across the genome in AUD subjects. Six studies showed significant correlations of DNA methylation with gene expression in AUD subjects. Three studies revealed interactive effects of genetic variation and DNA methylation on susceptibility to AUDs. Most studies analyzed AUD-associated DNA methylation changes in the peripheral blood; a few studies examined DNA methylation changes in postmortem brains of AUD subjects.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0004">
<h3>Discussion and Conclusions</h3>
<p>Chronic alcohol consumption may result in DNA methylation changes, leading to neuroadaptations that may underlie some of the mechanisms of AUD risk and persistence. Future studies are needed to confirm the few existing results, and then to elucidate whether DNA methylation changes are the cause or consequence of AUDs.</p>
</section>
<section class="article-section article-body-section" id="ajad12465-sec-0005">
<h3>Scientific Significance</h3>
<p>DNA methylation profiles may be used to assess AUD status or monitor AUD treatment response. (Am J Addict 2016;XX:1–14)</p>
</section>',
'date' => '2016-10-19',
'pmid' => 'http://onlinelibrary.wiley.com/doi/10.1111/ajad.12465/abstract?campaign=wolsavedsearch',
'doi' => '',
'modified' => '2022-05-19 16:12:16',
'created' => '2016-11-03 11:11:49',
'ProductsPublication' => array(
[maximum depth reached]
)
),
(int) 22 => 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' => '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 ',
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<p><b>Superior coverage</b></p>
<p>Comparison of CpG coverage between competing technologies.</p>
<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
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<h4>Premium RRBS Kit</h4>
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<td width="379">
<h5>Illumina® RRBS protocol</h5>
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<td>no guidelines</td>
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<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>',
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'author' => 'Borbála Vető, Institute of Enzymology, Budapest, Hungary',
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'modified' => '2016-07-12 11:21:32',
'created' => '2016-01-20 09:53:36',
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<button class="alert small button expand" onclick="$(this).addToCart('MethylCap kit',
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<p>Add <input name="data[Cart][quantity]" placeholder="1" value="1" min="1" style="width:60px;display:inline" type="number" id="CartQuantity" required="required"/> <strong> Premium Bisulfite kit</strong> to my shopping cart.</p>
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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>our epigenomics services</strong>. Fill out the form below and we will be in touch with you very soon.</p><p><small>All <span style="font-size:16px;color:red;">*</span> fields are mandatory</small></p>
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<h2>Service Information</h2>
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<div class="small-12 large-12 columns">
<h4>Which services are you interested in?</h4>
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<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>
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<div class="row collapse">
<div class="small-12 medium-12 large-3 columns">
<span class="prefix">Sample species</span>
</div>
<div class="small-12 medium-12 large-9 columns">
<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>
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<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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<div class="small-9 large-10 columns">
<select name="data[Quote][country]" required="required" class="triggers" id="country_selector_quote-2836">
<option value="">-- select a country --</option>
<option value="AF">Afghanistan</option>
<option value="AX">Åland Islands</option>
<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
<option value="AR">Argentina</option>
<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
<option value="AZ">Azerbaijan</option>
<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d'Ivoire</option>
<option value="HR">Croatia</option>
<option value="CU">Cuba</option>
<option value="CW">Curaçao</option>
<option value="CY">Cyprus</option>
<option value="CZ">Czech Republic</option>
<option value="DK">Denmark</option>
<option value="DJ">Djibouti</option>
<option value="DM">Dominica</option>
<option value="DO">Dominican Republic</option>
<option value="EC">Ecuador</option>
<option value="EG">Egypt</option>
<option value="SV">El Salvador</option>
<option value="GQ">Equatorial Guinea</option>
<option value="ER">Eritrea</option>
<option value="EE">Estonia</option>
<option value="ET">Ethiopia</option>
<option value="FK">Falkland Islands (Malvinas)</option>
<option value="FO">Faroe Islands</option>
<option value="FJ">Fiji</option>
<option value="FI">Finland</option>
<option value="FR">France</option>
<option value="GF">French Guiana</option>
<option value="PF">French Polynesia</option>
<option value="TF">French Southern Territories</option>
<option value="GA">Gabon</option>
<option value="GM">Gambia</option>
<option value="GE">Georgia</option>
<option value="DE">Germany</option>
<option value="GH">Ghana</option>
<option value="GI">Gibraltar</option>
<option value="GR">Greece</option>
<option value="GL">Greenland</option>
<option value="GD">Grenada</option>
<option value="GP">Guadeloupe</option>
<option value="GU">Guam</option>
<option value="GT">Guatemala</option>
<option value="GG">Guernsey</option>
<option value="GN">Guinea</option>
<option value="GW">Guinea-Bissau</option>
<option value="GY">Guyana</option>
<option value="HT">Haiti</option>
<option value="HM">Heard Island and McDonald Islands</option>
<option value="VA">Holy See (Vatican City State)</option>
<option value="HN">Honduras</option>
<option value="HK">Hong Kong</option>
<option value="HU">Hungary</option>
<option value="IS">Iceland</option>
<option value="IN">India</option>
<option value="ID">Indonesia</option>
<option value="IR">Iran, Islamic Republic of</option>
<option value="IQ">Iraq</option>
<option value="IE">Ireland</option>
<option value="IM">Isle of Man</option>
<option value="IL">Israel</option>
<option value="IT">Italy</option>
<option value="JM">Jamaica</option>
<option value="JP">Japan</option>
<option value="JE">Jersey</option>
<option value="JO">Jordan</option>
<option value="KZ">Kazakhstan</option>
<option value="KE">Kenya</option>
<option value="KI">Kiribati</option>
<option value="KP">Korea, Democratic People's Republic of</option>
<option value="KR">Korea, Republic of</option>
<option value="KW">Kuwait</option>
<option value="KG">Kyrgyzstan</option>
<option value="LA">Lao People's Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
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<h6 style="height:60px">RRBS Service (Reduced Representation Bisulfite ...</h6>
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<h6 style="height:60px">Bisulfite conversion reagent for RRBS</h6>
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<p style="text-align: justify;"><span>Reduced Representation Bisulfite Sequencing provides a powerful method to efficiently analyze DNA methylation at the </span><strong>single nucleotide level</strong><span> without the higher costs associated with whole genome bisulfite sequencing. By cutting the genome using the restriction MspI enzyme (CCGG target sites) followed by size selection, DNA is enriched to represent CpG-rich regions (including CpG islands), in which DNA methylation marks are typically found. Thus, RRBS provides a </span><strong>cost-effective</strong><span> method for analyzing DNA methylation by reducing the part of the genome that actually needs to be sequenced and focusing on relevant CpG islands.</span></p>',
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<li><strong>Superior coverage</strong> – 4 million CpGs</li>
<li><strong>High throughput</strong> - 96 samples processed in one experiment</li>
<li><strong>Cost-efficient</strong> – Multiplex up to 6 samples/sequencing lane</li>
<li><strong>Validated</strong> with FFPE, cancer, and low-input samples</li>
<li><strong>High efficiency and minimal bias</strong> - Low DNA degradation and reduced amplification</li>
<li><strong>Complete kit</strong> – Bisulfite conversion reagents, MspI enzyme, library preparation reagents including barcodes, and spike-in controls</li>
<li class="accordion-navigation" style="list-style-type: circle; display: list-item;"><strong>Already tested on various species </strong>– human, mouse, rat, pig, cow, dog, zebrafish, Daphnia <a href="#species" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;">and more</a> <span class="content" id="species">cichlid fish (Astatotilapia calliptera), mossy frog, yellow-bellied slider, dice snake, zebra finch, humboldt penguin, leaf bird, buzzard, vulturine guinea fowe, parma kangaroo, cheetah, mouflon</span></li>
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<p><b>Superior coverage</b></p>
<p>Comparison of CpG coverage between competing technologies.</p>
<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
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Excellent results were obtained using Diagenode’s Premium RRBS kit: almost 90% alignment rate, 4.1 million CpGs covered and bisulfite conversion rates around 99.5% for all samples. DNA methylation percentages in the region of IGF2 obtained with the Diagenode’s Premium RRBS Kit. Two human cell lines were analyzed: Gm12878 and MCF7. The MCF7 cell line was studied in duplicates. Each peak represents the DNA methylation percentage at one CpG. The methylated CpGs are shown in red and the unmethylated CpGs in grey.
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By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</div>
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<h4>Premium RRBS Kit</h4>
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<h5>Illumina® RRBS protocol</h5>
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<td>100 ng</td>
<td>2-5 µg</td>
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<td>Pooling of 6 samples (one HiSeq lane)</td>
<td>no guidelines</td>
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<td>One bisulfite reaction per pool (6 samples)</td>
<td>One bisulfite reaction per sample</td>
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<td><strong># purification steps</strong></td>
<td>2</td>
<td>7</td>
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<td><strong>Key features</strong></td>
<td width="379">low input - cost-effective - optimized for high-throughput - complete kit</td>
<td width="379">high input - not optimized for high-throughput - reagents from different providers</td>
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<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
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Excellent results were obtained using Diagenode’s Premium RRBS kit: almost 90% alignment rate, 4.1 million CpGs covered and bisulfite conversion rates around 99.5% for all samples. DNA methylation percentages in the region of IGF2 obtained with the Diagenode’s Premium RRBS Kit. Two human cell lines were analyzed: Gm12878 and MCF7. The MCF7 cell line was studied in duplicates. Each peak represents the DNA methylation percentage at one CpG. The methylated CpGs are shown in red and the unmethylated CpGs in grey.
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<h1 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h1>
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<li style="text-align: left;" class="vcard last-author"><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a><sup><a href="#a2">2</a></sup><sup href="#affil-auth">, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><sup><a href="#a3">3</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><sup><a href="#a2">2</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard c1"><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
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<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>',
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<p><b>Superior coverage</b></p>
<p>Comparison of CpG coverage between competing technologies.</p>
<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
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Excellent results were obtained using Diagenode’s Premium RRBS kit: almost 90% alignment rate, 4.1 million CpGs covered and bisulfite conversion rates around 99.5% for all samples. DNA methylation percentages in the region of IGF2 obtained with the Diagenode’s Premium RRBS Kit. Two human cell lines were analyzed: Gm12878 and MCF7. The MCF7 cell line was studied in duplicates. Each peak represents the DNA methylation percentage at one CpG. The methylated CpGs are shown in red and the unmethylated CpGs in grey.
<p><br /><br /></p>
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</div>
<div class="small-6 columns">
<p><b>How it works</b></p>
By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</div>
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<td>2-5 µg</td>
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<tr>
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<p></p>
<h1 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h1>
<ul class="citation dates">
<li style="text-align: left;" class="vcard last-author"><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a><sup><a href="#a2">2</a></sup><sup href="#affil-auth">, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><sup><a href="#a3">3</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
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<li style="text-align: left;" class="vcard c1"><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
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<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<li><strong>Validated</strong> with FFPE, cancer, and low-input samples</li>
<li><strong>High efficiency and minimal bias</strong> - Low DNA degradation and reduced amplification</li>
<li><strong>Complete kit</strong> – Bisulfite conversion reagents, MspI enzyme, library preparation reagents including barcodes, and spike-in controls</li>
<li class="accordion-navigation" style="list-style-type: circle; display: list-item;"><strong>Already tested on various species </strong>– human, mouse, rat, pig, cow, dog, zebrafish, Daphnia <a href="#species" style="color: #13b29c; background-color: transparent; display: inline; padding: 0;">and more</a> <span class="content" id="species">cichlid fish (Astatotilapia calliptera), mossy frog, yellow-bellied slider, dice snake, zebra finch, humboldt penguin, leaf bird, buzzard, vulturine guinea fowe, parma kangaroo, cheetah, mouflon</span></li>
</ul>
<p> </p>
<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/product/kits/rrbs-figure-1.png" alt="Chr Shearing" style="display: block; margin-left: auto; margin-right: auto;" /></p>
</div>
<div class="small-6 columns"><br />
<p><b>Superior coverage</b></p>
<p>Comparison of CpG coverage between competing technologies.</p>
<p><strong><em><small>They love it! </small></em></strong><br /><em><small>The new Diagenode Premium RRBS Kit 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. <br /><strong>Paul Datlinger and Christoph Bock, </strong><strong>CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria </strong></small></em></p>
<p><em> </em></p>
</div>
</div>
<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/product/kits/rrbs-figure-2.jpg" alt="dna methylation" style="display: block; margin-left: auto; margin-right: auto;" /></p>
</div>
<div class="small-6 columns">
<p><b>Accurate determination of DNA methylation level</b></p>
Excellent results were obtained using Diagenode’s Premium RRBS kit: almost 90% alignment rate, 4.1 million CpGs covered and bisulfite conversion rates around 99.5% for all samples. DNA methylation percentages in the region of IGF2 obtained with the Diagenode’s Premium RRBS Kit. Two human cell lines were analyzed: Gm12878 and MCF7. The MCF7 cell line was studied in duplicates. Each peak represents the DNA methylation percentage at one CpG. The methylated CpGs are shown in red and the unmethylated CpGs in grey.
<p><br /><br /></p>
</div>
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<div class="row">
<div class="small-6 columns">
<p><img src="https://www.diagenode.com/img/landing-pages/rrbs_how_it_works.jpg" alt="rrbs how it works" style="display: block; margin-left: auto; margin-right: auto;" /><br /><br /></p>
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<div class="small-6 columns">
<p><b>How it works</b></p>
By cutting the genome using the <strong>restriction enzyme MspI</strong> (CCGG target sites) followed by size selection, DNA is enriched to represent <strong>CpG-rich genomic regions</strong> (including CpG islands, CpG island shores, enhancers, and other gene-regulatory elements), which are particularly relevant for epigenetic regulation. Similar to exome-sequencing for mutation discovery, the RRBS protocol enriches for some of the most interesting target regions and thereby achieves a reduction in sequencing cost of a factor of 10-20 compared to whole genome bisulfite sequencing.</div>
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<table width="1039">
<tbody>
<tr></tr>
<tr>
<td width="281"> </td>
<td width="379">
<h4>Premium RRBS Kit</h4>
</td>
<td width="379">
<h5>Illumina® RRBS protocol</h5>
</td>
</tr>
<tr>
<td><strong>DNA input</strong></td>
<td>100 ng</td>
<td>2-5 µg</td>
</tr>
<tr>
<td><strong>Multiplexing</strong></td>
<td>Pooling of 6 samples (one HiSeq lane)</td>
<td>no guidelines</td>
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<tr>
<td><strong>Bisulfite conversion</strong></td>
<td>One bisulfite reaction per pool (6 samples)</td>
<td>One bisulfite reaction per sample</td>
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<tr>
<td><strong># purification steps</strong></td>
<td>2</td>
<td>7</td>
</tr>
<tr>
<td><strong>Key features</strong></td>
<td width="379">low input - cost-effective - optimized for high-throughput - complete kit</td>
<td width="379">high input - not optimized for high-throughput - reagents from different providers</td>
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<p></p>
<h1 class="advertising-feature"><strong>Premium RRBS technology: cost-effective DNA methylation mapping with superior coverage</strong></h1>
<ul class="citation dates">
<li style="text-align: left;" class="vcard last-author"><a href="#auth-5" class="name"><span class="fn">Christoph Bock</span></a><sup><a href="#a2">2</a></sup><sup href="#affil-auth">, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-4" class="name"><span class="fn">Sharon Squazzo</span></a><sup><a href="#a3">3</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-3" class="name"><span class="fn">Miklos Laczik</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard"><a href="#auth-2" class="name"><span class="fn">Paul Datlinger</span></a><sup><a href="#a2">2</a></sup><sup>, </sup></li>
<li style="text-align: left;" class="vcard c1"><a href="#auth-1" class="name"><span class="fn">Anne-Clémence Veillard</span></a><sup><a href="#a1">1</a></sup><sup>, </sup></li>
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<p>Nature Methods 13 (2016)</p>
<p>Published online <time datetime="2016-01-28">28 January 2016 </time></p>
<p></p>
<p><a href="http://www.nature.com/nmeth/journal/v13/n2/full/nmeth.f.391.html" class="alert button"><span style="font-weight: 400;">CHECK OUT THE NATURE METHODS PAPER</span></a></p>
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<p>メチル化アレイとWGBS技術の長所を有するReduced representation bisulfite sequencing (RRBS) 技術は、あらゆる脊椎動物種で柔軟に使用できるため、大規模コホートの研究においても正確で費用対効果の高いアッセイを行えます。制限MspI酵素（CCGGターゲットサイト）を使用してゲノムを切断し、次にサイズを選択することで、濃縮されたDNAはプロモーターやCpGアイランドを含む関連するターゲットCpGリッチ領域を表します。またRRBS技術はゲノムの一部分を濃縮してメチル化解析を行うことによってシーケンス量を減らす手法でカバレッジが非常に高く、CpGメチル化解析に適しています。（ヒトサンプルで最大700万CpGが検出）。</p>
<h2>Genome-scale DNA methylation analysis</h2>
<ul class="square">
<li>Robust and cost-effective solution with reliable results</li>
<li>High coverage - up to 7 million CpGs detected in human samples</li>
<li>Single nucleotide resolution</li>
<li>Detection of methylation patterns in CpG rich regions including promoters and CpG islands</li>
<li>Suitable for any vertebrate species</li>
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<h2>Complete end-to-end service</h2>
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<li>From DNA QC to standard bioinformatics </li>
<li>Leveraging our <a href="https://www.diagenode.com/en/p/premium-rrbs-kit-V2-x24">Premium RRBS kit V2 </a>widely adopted</li>
<li>As little as 25 ng for any vertebrate species</li>
<li>Differentially methylated site analysis </li>
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<div class="extra-spaced">
<h2></h2>
</div>
<p><span><i class="fa fa-arrow-circle-right"></i> </span><a href="https://www.diagenode.com/en/categories/dna-methylation-profiling-services">See our other DNA Methylation Profiling Services</a></p>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<li>Summary statistics</li>
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<li>Differentially Methylated CpGs (DMCs) analysis</li>
<li>Differentially Methylated Regions (DMRs) analysis</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, …)</li>
<li>Clustering analysis</li>
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<li>Enrichment analysis on gene associated with DMCs and DMRs</li>
<li>Get functional insights</li>
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<p><strong>Pathway analysis</strong></p>
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