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Infinium Methylation EPIC Array受託サービス

Catalog Number

Format

Price

G02090000

DISCONTINUED

Infinium MethylationEPIC Arrayは、バイサルファイト処理とIllumina^®技術に基づくゲノムワイドのDNAメチル化解析技術です。これは、一塩基分解能を有するゲノム全体にわたる850,000を超えるヒトCpG位置のメチル化レベルの定量的な検出を可能にします。

バイサルファイトで処理すると、メチル化されたシトシン塩基は変化しませんが、メチル化されていないシトシン塩基はウラシルに変換されます。Infinium HD配列技術は、メチル化部位と非メチル化部位に対して各々に設計された部位特異的プローブを用いて、これらの分化した遺伝子座を調べます。調べられた遺伝子座のメチル化のレベルは、メチル化部位と非メチル化部位との蛍光シグナルの比を計算することによって決定されます。

包括的なゲノムワイドカバレッジのための優れた方法

高いコストパフォーマンス、結果までの時間短縮
CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析
一塩基分解能で850,000を超えるヒトCpG位置
異なるメチル化部位解析
徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス

詳細
End-to-end array

Bisulfite conversion

Whole genome amplification

Array hybridization

Single base extension

Array scanner

バイオインフォマティック解析

Analysis	Features
Standard	Standard files provided: Sample annotation Variable annotation Scanner output (IDAT files)
Differential methylation analysis	Identification of differentially methylated CpGs between sample groups. Files provided: Report with summary of differential methylation analysis and plots File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea) File containing differential methylated regions (DMRs)
Gene ontology terms analysis	Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.
Pathway analysis	Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.

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Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth
Argentato P.P. et al.
Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women f...

Making Biological Ageing Clocks Personal
Pusparum M. et al.
Background Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called &l...

Inhibition of Aurora kinase induces endogenous retroelements to induce a type I/III interferon response via RIG-I
Choy L. et al.
Type I interferon signaling is a crucial component of anti-viral immunity that has been linked to promoting the efficacy of some chemotherapeutic drugs. We developed a reporter system in HCT116 cells that detects activation of the endogenous IFI27 locus, an interferon (IFN) target gene. We screened a library of anno...

Association of DNA methylation signatures with premature ageing andcardiovascular death in patients with end-stage kidney disease: a pilotepigenome-wide association study.
Sumida K. et al.
Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assaye...

Perturbed epigenetic transcriptional regulation in AML with IDHmutations causes increased susceptibility to NK cells.
Palau A. et al.
Isocitrate dehydrogenase (IDH) mutations are found in 20\% of acute myeloid leukemia (AML) patients. However, only 30-40\% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcription...

In skeletal muscle and neural crest cells, SMCHD1 regulates biologicalpathways relevant for Bosma syndrome and facioscapulohumeral dystrophyphenotype.
Laberthonnière C. et al.
Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In hu...

Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.
Garau J. et al.
Aicardi-Goutières Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with ident...

DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma
Soltysova A. et al.
Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliab...

Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.
Ma Ruichong et al.
BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, ...

Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling
Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen
DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ naïve and memory cells, natural kil...

Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders
Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel
DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects...

Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle
Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon
Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex d...

ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.
Hernández H.G. et al.
OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes ...

From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions
Tiane A. et al.
Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this ...

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  メチル化データ解析

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<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
<li style="font-weight: 400;">一塩基分解能で850,000を超えるヒトCpG位置</li>
<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
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<p><strong>Standard</strong></p>
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<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
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<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
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<p>バイサルファイトで処理すると、メチル化されたシトシン塩基は変化しませんが、メチル化されていないシトシン塩基はウラシルに変換されます。Infinium HD配列技術は、メチル化部位と非メチル化部位に対して各々に設計された部位特異的プローブを用いて、これらの分化した遺伝子座を調べます。調べられた遺伝子座のメチル化のレベルは、メチル化部位と非メチル化部位との蛍光シグナルの比を計算することによって決定されます。</p>
<p></p>
<h4><span style="font-weight: 400;">包括的なゲノムワイドカバレッジのための優れた方法</span></h4>
<ul>
<li style="font-weight: 400;">高いコストパフォーマンス、結果までの時間短縮</li>
<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
<li style="font-weight: 400;">一塩基分解能で850,000を超えるヒトCpG位置</li>
<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
</ul>
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<table style="width: 680px;">
<tbody>
<tr style="height: 194px;">
<td style="height: 194px; width: 168px;">
<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
<ul>
<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
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</td>
<td>
<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
</td>
</tr>
</tbody>
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<p>The <strong>Infinium MethylationEPIC Array</strong> is a genome-wide DNA methylation analysis technique based on bisulfite conversion and Illumina<sup>&reg;</sup> technology. It allows to quantitatively detect the total&nbsp;methylation level of over 850,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;<span>It offers comprehensive, expert-selected coverage of CpG islands, enhancer regions, open chromatin sites and <span>other</span><span><span>&nbsp;</span></span><span>important regions of the methylome.</span></span></p>
<p>Briefly, upon bisulfite treatment, unmethylated cytosines (both 5mC and 5hmC) are converted to uracils, while methylated cytosines remain unchanged. Infinium HD array technology interrogates these differentiated loci using site-specific probes (designed for the methylated and unmethylated sites respectively). The level of total methylation for the interrogated locus can be determined by calculating the ratio of the fluorescent signals from the methylated vs. unmethylated sites.</p>
<h2><span style="font-weight: 400;">Comprehensive Genome-Wide Coverage</span></h2>
<ul class="square">
<li><span style="font-weight: 400;">Cost-effective solution with rapid turnaround time</span></li>
<li>Over 850,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differentially methylated site analysis using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic tools</a></li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, and analysis</li>
</ul>
<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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<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
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<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
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<div class="small-12 medium-10 large-10 columns text-left">
<p>Our Epigenomics Profiling Services assure the sample preparation expertise and quality data that you seek. We provide epigenome-wide analyses for understanding epigenetic mechanisms, epigenetics-related drug discovery, transgenerational epigenetics studies, epigenetic biomarker identification (including epigenomic cancer biomarkers), and functional epigenomics. Diagenode offers expert epigenomics services that you can trust for chromatin, DNA, and RNA analysis.&nbsp;</p>
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<div class="small-12 medium-2 large-2 columns text-left"><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></div>
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<center><img alt="DNA-RNA-Chromatin-Diagenode" src="https://www.diagenode.com/img/categories/services/chromatin-dna-rna.png" /></center>
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<h3><span class="green">Chromatin</span>&nbsp;analysis</h3>
<ul>
<li>ChIP-seq analysis Histone modification</li>
<li>Promoter analysis</li>
<li>Enhancer analysis</li>
<li>ChIP-seq analysis Transcription factor&nbsp;</li>
<li>(sc)ATAC-seq Chromatin accessibility</li>
<li>ChIP-seq Cas9 off-target</li>
<li>Customized NGS service</li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/chip-seq-service">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #474546;">
<h3><span class="darkgrey">DNA methylation</span> analysis</h3>
<ul>
<li>Reduced representation bisulfite sequencing (RRBS)</li>
<li>Whole genome bisulfite sequencing (WGBS)</li>
<li>Targeted methylation</li>
<li>MeDIP-seq/hMeDIP-seq</li>
<li>Infinium MethylationEPIC array&nbsp;</li>
</ul>
<p></p>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/dna-methylation-profiling-services">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #b11e33;">
<h3><span class="diacol"><g class="gr_ gr_44 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="44" data-gr-id="44">RNA-seq</g></span> analysis</h3>
<ul>
<li><span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Gene expression profiling</span></li>
<li>mRNA<span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"> analysis</span></li>
<li>Small non-coding RNA analysis</li>
<li><b></b>Whole transcriptome analysis<br /><br /><br /><br /><br /></li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/rna-seq-category">Learn more</a></p>
</div>
</div>
</div>
<h2 style="text-align: center;"><a class="details radius small button" href="https://www.diagenode.com/en/documents/services-flyer">DOWNLOAD OUR FLYER</a></h2>
<h2>Why Diagenode</h2>
<ul>
<li><strong>Expertise and trust</strong>: Recognized epigenetics leader, official partner of <a href="http://www.blueprint-epigenome.eu/"><g class="gr_ gr_45 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="45" data-gr-id="45">BLUEPRINT</g></a>, <a href="http://ihec-epigenomes.org/">IHEC</a> <g class="gr_ gr_46 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Punctuation only-ins replaceWithoutSep" id="46" data-gr-id="46">and</g> <a href="https://www.faang.org/">FAANG</a></li>
<li><strong>Innovative technology</strong>: Utilization of the signature Bioruptor<sup>&reg;</sup> sonication device for optimal chromatin and DNA shearing and the IP-Star<sup>&reg;</sup> Automation device give reproducible and reliable optimization and results</li>
<li><strong>Quality</strong>: Multiple QC steps in all workflows and validated antibodies plus reagents deliver superior data</li>
<li><strong>Flexibility</strong>: Extensive range of sample species and sample origins</li>
<li><strong>Experience</strong> in epigenomics profiling for a wide range of applications and fields of interest</li>
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<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">HUMAN</span><center><img width="260" height="259" alt="services chip-seq - human" src="https://www.diagenode.com/img/categories/services/services-humans.png" caption="false" /></center></div>
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<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">ANIMALS</span><center><img width="260" height="259" alt="services chip-seq - animals" src="https://www.diagenode.com/img/categories/services/services-animals.png" caption="false" /></center></div>
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<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">PLANTS</span><center><img width="260" height="259" alt="services methylation-plant" src="https://www.diagenode.com/img/categories/services/services-plants.png" caption="false" /></center></div>
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<h2>12 years of expertise and trust in epigenetics</h2>
<ul>
<li><strong>End-to-end</strong> epigenetic service</li>
<li><strong>Collaborative</strong> and customized project design</li>
<li>Dedicated <strong>in-house expert</strong> for your project</li>
<li><strong>Integrative</strong> data analysis</li>
<li>Presentation-<strong>quality data</strong> and graphs</li>
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			'id' => '4931',
			'name' => 'Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth',
			'authors' => 'Argentato P.P. et al.',
			'description' => '<p><span>Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.</span></p>',
			'date' => '2024-03-25',
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			'description' => '<div id="sec-1" class="subsection">
<p id="p-3"><strong>Background</strong><span>&nbsp;</span>Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called &ldquo;ageing clocks&rdquo;). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process.</p>
</div>
<div id="sec-2" class="subsection">
<p id="p-4"><strong>Methods</strong><span>&nbsp;</span>We analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks.</p>
</div>
<div id="sec-3" class="subsection">
<p id="p-5"><strong>Findings</strong><span>&nbsp;</span>Within the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles.</p>
</div>
<div id="sec-4" class="subsection">
<p id="p-6"><strong>Interpretation</strong><span>&nbsp;</span>Our analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors&rsquo; assessments.</p>
</div>',
			'date' => '2024-02-29',
			'pmid' => 'https://www.medrxiv.org/content/10.1101/2024.02.28.24303427v1',
			'doi' => 'https://doi.org/10.1101/2024.02.28.24303427',
			'modified' => '2024-03-07 10:57:18',
			'created' => '2024-03-07 10:57:18',
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		(int) 2 => array(
			'id' => '4914',
			'name' => 'Inhibition of Aurora kinase induces endogenous retroelements to induce a type I/III interferon response via RIG-I',
			'authors' => 'Choy L. et al.',
			'description' => '<p><span>Type I interferon signaling is a crucial component of anti-viral immunity that has been linked to promoting the efficacy of some chemotherapeutic drugs. We developed a reporter system in HCT116 cells that detects activation of the endogenous IFI27 locus, an interferon (IFN) target gene. We screened a library of annotated compounds in these cells and discovered aurora kinase inhibitors (AURKi) as strong hits. Type I IFN signaling was found to be the most enriched gene signature after AURKi treatment in HCT116, and this signature was also strongly enriched in other colorectal cancer (CRC) cell lines. The ability of AURKi to activate IFN in HCT116 was dependent on MAVS and RIG-I, but independent of STING, whose signaling is deficient in these cells. MAVS dependence was recapitulated in other CRC lines with STING pathway deficiency, whereas in cells with intact STING signaling, the STING pathway was required for IFN induction by AURKi. AURKi's were found to induce expression of endogenous retroviruses (ERV's). These ERVs were distinct from those induced by the DNA methyltransferase inhibitors (DNMTi's), which can induce IFN signaling via ERV induction, suggesting a novel mechanism of action. The anti-tumor effect of alisertib in mice was accompanied by an induction of IFN expression in HCT116 or CT26 tumors. CT26 tumor growth inhibition by alisertib was absent in NOD/SCID mice vs. WT mice, and tumors from WT mice with alisertib treatment showed increased in CD8+ T cell infiltration, suggesting that anti-tumor efficacy of AURKi depends, at least in part, on an intact immune response.</span></p>',
			'date' => '2024-02-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38358346/',
			'doi' => '10.1158/2767-9764.CRC-23-0432',
			'modified' => '2024-02-22 12:33:11',
			'created' => '2024-02-22 12:33:11',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 3 => array(
			'id' => '4811',
			'name' => 'Association of DNA methylation signatures with premature ageing andcardiovascular death in patients with end-stage kidney disease: a pilotepigenome-wide association study.',
			'authors' => 'Sumida  K. et al.',
			'description' => '<p><span>Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assayed genome-wide DNAm in a pilot case-control study of 60 hemodialysis patients with (n=30, cases) and without (n=30, controls) a fatal cardiovascular event. DNAm was profiled on the Illumina EPIC BeadChip. Four established DNAm clocks (i.e., Horvath-, Hannum-, Pheno-, and GrimAge) were used to estimate epigenetic age (DNAmAge). Epigenetic age acceleration (EAA) was derived as the residuals of regressing DNAmAge on chronological age (chroAge), and its association with cardiovascular death was examined using multivariable conditional logistic regression. An epigenome-wide association study (EWAS) was performed to identify differentially methylated CpGs associated with cardiovascular death. All clocks performed well at predicting chroAge (correlation between DNAmAges and chroAge of r=0.76-0.89), with GrimAge showing the largest deviation from chroAge (a mean of +21.3 years). There was no significant association of EAAs with cardiovascular death. In the EWAS, a CpG (cg22305782) in the&nbsp;</span><i>FBXL19</i><span><span>&nbsp;</span>gene had the strongest association with cardiovascular death with significantly lower DNAm in cases vs. controls (</span><i>P</i><sub>FDR</sub><span>=2.0x10</span><sup>-6</sup><span>).<span>&nbsp;</span></span><i>FBXL19</i><span><span>&nbsp;</span>is involved in cell apoptosis, inflammation, and adipogenesis. Overall, we observed more accelerated aging in patients with ESKD, although there was no significant association of EAAs with cardiovascular death. EWAS suggests a potential novel DNAm biomarker for premature cardiovascular mortality in ESKD.</span></p>',
			'date' => '2023-12-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37207321',
			'doi' => '10.1080/15592294.2023.2214394',
			'modified' => '2023-06-15 08:57:02',
			'created' => '2023-06-13 21:11:31',
			'ProductsPublication' => array(
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		(int) 4 => array(
			'id' => '4830',
			'name' => 'Perturbed epigenetic transcriptional regulation in AML with IDHmutations causes increased susceptibility to NK cells.',
			'authors' => 'Palau  A. et al.',
			'description' => '<p>Isocitrate dehydrogenase (IDH) mutations are found in 20\% of acute myeloid leukemia (AML) patients. However, only 30-40\% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.</p>',
			'date' => '2023-07-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37495775',
			'doi' => '10.1038/s41375-023-01972-3',
			'modified' => '2023-08-01 13:39:40',
			'created' => '2023-08-01 15:59:38',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 5 => array(
			'id' => '4852',
			'name' => 'In skeletal muscle and neural crest cells, SMCHD1 regulates biologicalpathways relevant for Bosma syndrome and facioscapulohumeral dystrophyphenotype.',
			'authors' => 'Laberthonnière  C. et al.',
			'description' => '<p>Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In humans, its function as well as the impact of its mutations remains poorly defined. To fill this gap, we determined the episignature associated with heterozygous SMCHD1 variants in primary cells and cell lineages derived from induced pluripotent stem cells for Bosma arhinia and microphthalmia syndrome (BAMS) and type 2 facioscapulohumeral dystrophy (FSHD2). In human tissues, SMCHD1 regulates the distribution of methylated CpGs, H3K27 trimethylation and CTCF at repressed chromatin but also at euchromatin. Based on the exploration of tissues affected either in FSHD or in BAMS, i.e. skeletal muscle fibers and neural crest stem cells, respectively, our results emphasize multiple functions for SMCHD1, in chromatin compaction, chromatin insulation and gene regulation with variable targets or phenotypical outcomes. We concluded that in rare genetic diseases, SMCHD1 variants impact gene expression in two ways: (i) by changing the chromatin context at a number of euchromatin loci or (ii) by directly regulating some loci encoding master transcription factors required for cell fate determination and tissue differentiation.</p>',
			'date' => '2023-06-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37334829',
			'doi' => '10.1093/nar/gkad523',
			'modified' => '2023-08-01 14:35:38',
			'created' => '2023-08-01 15:59:38',
			'ProductsPublication' => array(
				[maximum depth reached]
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		(int) 6 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
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		),
		(int) 7 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
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		),
		(int) 8 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
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		(int) 9 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
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		),
		(int) 10 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
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		),
		(int) 11 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
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		(int) 12 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
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		(int) 13 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
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    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][first_name]" placeholder="john" maxlength="255" type="text" id="QuoteFirstName" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Last name <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][last_name]" placeholder="doe" maxlength="255" type="text" id="QuoteLastName" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Company <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][company]" placeholder="Organisation / Institute" maxlength="255" type="text" id="QuoteCompany" required="required"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Phone number</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][phone_number]" placeholder="+1 862 209-4680" maxlength="255" type="text" id="QuotePhoneNumber"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">City</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][city]" placeholder="Denville" maxlength="255" type="text" id="QuoteCity"/>    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Country <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <select name="data[Quote][country]" required="required" class="triggers" id="country_selector_quote-3061">
<option value="">-- select a country --</option>
<option value="AF">Afghanistan</option>
<option value="AX">Åland Islands</option>
<option value="AL">Albania</option>
<option value="DZ">Algeria</option>
<option value="AS">American Samoa</option>
<option value="AD">Andorra</option>
<option value="AO">Angola</option>
<option value="AI">Anguilla</option>
<option value="AQ">Antarctica</option>
<option value="AG">Antigua and Barbuda</option>
<option value="AR">Argentina</option>
<option value="AM">Armenia</option>
<option value="AW">Aruba</option>
<option value="AU">Australia</option>
<option value="AT">Austria</option>
<option value="AZ">Azerbaijan</option>
<option value="BS">Bahamas</option>
<option value="BH">Bahrain</option>
<option value="BD">Bangladesh</option>
<option value="BB">Barbados</option>
<option value="BY">Belarus</option>
<option value="BE">Belgium</option>
<option value="BZ">Belize</option>
<option value="BJ">Benin</option>
<option value="BM">Bermuda</option>
<option value="BT">Bhutan</option>
<option value="BO">Bolivia</option>
<option value="BQ">Bonaire, Sint Eustatius and Saba</option>
<option value="BA">Bosnia and Herzegovina</option>
<option value="BW">Botswana</option>
<option value="BV">Bouvet Island</option>
<option value="BR">Brazil</option>
<option value="IO">British Indian Ocean Territory</option>
<option value="BN">Brunei Darussalam</option>
<option value="BG">Bulgaria</option>
<option value="BF">Burkina Faso</option>
<option value="BI">Burundi</option>
<option value="KH">Cambodia</option>
<option value="CM">Cameroon</option>
<option value="CA">Canada</option>
<option value="CV">Cape Verde</option>
<option value="KY">Cayman Islands</option>
<option value="CF">Central African Republic</option>
<option value="TD">Chad</option>
<option value="CL">Chile</option>
<option value="CN">China</option>
<option value="CX">Christmas Island</option>
<option value="CC">Cocos (Keeling) Islands</option>
<option value="CO">Colombia</option>
<option value="KM">Comoros</option>
<option value="CG">Congo</option>
<option value="CD">Congo, The Democratic Republic of the</option>
<option value="CK">Cook Islands</option>
<option value="CR">Costa Rica</option>
<option value="CI">Côte d&#039;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&#039;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&#039;s Democratic Republic</option>
<option value="LV">Latvia</option>
<option value="LB">Lebanon</option>
<option value="LS">Lesotho</option>
<option value="LR">Liberia</option>
<option value="LY">Libya</option>
<option value="LI">Liechtenstein</option>
<option value="LT">Lithuania</option>
<option value="LU">Luxembourg</option>
<option value="MO">Macao</option>
<option value="MK">Macedonia, Republic of</option>
<option value="MG">Madagascar</option>
<option value="MW">Malawi</option>
<option value="MY">Malaysia</option>
<option value="MV">Maldives</option>
<option value="ML">Mali</option>
<option value="MT">Malta</option>
<option value="MH">Marshall Islands</option>
<option value="MQ">Martinique</option>
<option value="MR">Mauritania</option>
<option value="MU">Mauritius</option>
<option value="YT">Mayotte</option>
<option value="MX">Mexico</option>
<option value="FM">Micronesia, Federated States of</option>
<option value="MD">Moldova</option>
<option value="MC">Monaco</option>
<option value="MN">Mongolia</option>
<option value="ME">Montenegro</option>
<option value="MS">Montserrat</option>
<option value="MA">Morocco</option>
<option value="MZ">Mozambique</option>
<option value="MM">Myanmar</option>
<option value="NA">Namibia</option>
<option value="NR">Nauru</option>
<option value="NP">Nepal</option>
<option value="NL">Netherlands</option>
<option value="NC">New Caledonia</option>
<option value="NZ">New Zealand</option>
<option value="NI">Nicaragua</option>
<option value="NE">Niger</option>
<option value="NG">Nigeria</option>
<option value="NU">Niue</option>
<option value="NF">Norfolk Island</option>
<option value="MP">Northern Mariana Islands</option>
<option value="NO">Norway</option>
<option value="OM">Oman</option>
<option value="PK">Pakistan</option>
<option value="PW">Palau</option>
<option value="PS">Palestine, State of</option>
<option value="PA">Panama</option>
<option value="PG">Papua New Guinea</option>
<option value="PY">Paraguay</option>
<option value="PE">Peru</option>
<option value="PH">Philippines</option>
<option value="PN">Pitcairn</option>
<option value="PL">Poland</option>
<option value="PT">Portugal</option>
<option value="PR">Puerto Rico</option>
<option value="QA">Qatar</option>
<option value="RE">Réunion</option>
<option value="RO">Romania</option>
<option value="RU">Russian Federation</option>
<option value="RW">Rwanda</option>
<option value="BL">Saint Barthélemy</option>
<option value="SH">Saint Helena, Ascension and Tristan da Cunha</option>
<option value="KN">Saint Kitts and Nevis</option>
<option value="LC">Saint Lucia</option>
<option value="MF">Saint Martin (French part)</option>
<option value="PM">Saint Pierre and Miquelon</option>
<option value="VC">Saint Vincent and the Grenadines</option>
<option value="WS">Samoa</option>
<option value="SM">San Marino</option>
<option value="ST">Sao Tome and Principe</option>
<option value="SA">Saudi Arabia</option>
<option value="SN">Senegal</option>
<option value="RS">Serbia</option>
<option value="SC">Seychelles</option>
<option value="SL">Sierra Leone</option>
<option value="SG">Singapore</option>
<option value="SX">Sint Maarten (Dutch part)</option>
<option value="SK">Slovakia</option>
<option value="SI">Slovenia</option>
<option value="SB">Solomon Islands</option>
<option value="SO">Somalia</option>
<option value="ZA">South Africa</option>
<option value="GS">South Georgia and the South Sandwich Islands</option>
<option value="ES">Spain</option>
<option value="LK">Sri Lanka</option>
<option value="SD">Sudan</option>
<option value="SR">Suriname</option>
<option value="SS">South Sudan</option>
<option value="SJ">Svalbard and Jan Mayen</option>
<option value="SZ">Swaziland</option>
<option value="SE">Sweden</option>
<option value="CH">Switzerland</option>
<option value="SY">Syrian Arab Republic</option>
<option value="TW">Taiwan</option>
<option value="TJ">Tajikistan</option>
<option value="TZ">Tanzania</option>
<option value="TH">Thailand</option>
<option value="TL">Timor-Leste</option>
<option value="TG">Togo</option>
<option value="TK">Tokelau</option>
<option value="TO">Tonga</option>
<option value="TT">Trinidad and Tobago</option>
<option value="TN">Tunisia</option>
<option value="TR">Turkey</option>
<option value="TM">Turkmenistan</option>
<option value="TC">Turks and Caicos Islands</option>
<option value="TV">Tuvalu</option>
<option value="UG">Uganda</option>
<option value="UA">Ukraine</option>
<option value="AE">United Arab Emirates</option>
<option value="GB">United Kingdom</option>
<option value="US" selected="selected">United States</option>
<option value="UM">United States Minor Outlying Islands</option>
<option value="UY">Uruguay</option>
<option value="UZ">Uzbekistan</option>
<option value="VU">Vanuatu</option>
<option value="VE">Venezuela</option>
<option value="VN">Viet Nam</option>
<option value="VG">Virgin Islands, British</option>
<option value="VI">Virgin Islands, U.S.</option>
<option value="WF">Wallis and Futuna</option>
<option value="EH">Western Sahara</option>
<option value="YE">Yemen</option>
<option value="ZM">Zambia</option>
<option value="ZW">Zimbabwe</option>
</select><script>
    $('#country_selector_quote-3061').selectize();
</script><br />
    </div>
</div>
<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">State</span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][state]" id="state-3061" maxlength="3" type="text"/><br />
    </div>
</div>

<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Email <sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][email]" placeholder="email@address.com" maxlength="255" type="email" id="QuoteEmail" required="required"/>    </div>
</div>
<div class="row collapse" id="email_v">
    <div class="small-3 large-2 columns">
        <span class="prefix">Email verification<sup style="font-size:16px;color:red;">*</sup></span>
    </div>
    <div class="small-9 large-10 columns">
        <input name="data[Quote][email_v]" autocomplete="nope" type="text" id="QuoteEmailV"/>    </div>
</div>


<div class="row collapse">
    <div class="small-3 large-2 columns">
        <span class="prefix">Comment</span>
    </div>
    <div class="small-9 large-10 columns">
        <textarea name="data[Quote][comment]" placeholder="Additional comments" cols="30" rows="6" id="QuoteComment"></textarea>    </div>
</div>

<!------------SERVICES PARTICULAR FORM START---------------->


<!------------DATA TO POPULATE REGARDING SPECIFIC SERVICES----->


<div class="row collapse">
    <div class="small-3 large-2 columns">
    </div>
    <div class="small-9 large-10 columns">
        <div class="recaptcha"><div id="recaptcha66236c54e5bf9"></div></div>    </div>
</div>
<br />

<div class="row collapse">
    <div class="small-3 large-2 columns">
    </div>
    <div class="small-9 large-10 columns">
        <button id="submit_btn-3061" class="alert button expand" form="Quote-3061" type="submit">Contact me</button>    </div>
</div>
</form><script>
    var pardotFormHandlerURL = 'https://go.diagenode.com/l/928883/2022-10-10/36b1c';

    function postToPardot(formAction, id) {
        $('#pardot-form-handler').load(function(){
            $('#Quote-' + id).attr('action', formAction);
            $('#Quote-' + id).submit();
        });
        $('#pardot-form-handler').attr('src', pardotFormHandlerURL + '?' + $('#Quote-' + id).serialize());
        
    }
    $(document).ready(function() {
        $('body').append('<iframe id="pardot-form-handler" height="0" width="0" style="position:absolute; left:-100000px; top:-100000px" src="javascript:false;"></iframe>');
		$('#Quote-3061').attr('action','javascript:postToPardot(\'' + $('#Quote-3061').attr('action') + '\', 3061)');
    });

    $("#Quote-3061 #submit_btn-3061").click(function (e) {
            
        if($(this).closest('form')[0].checkValidity()){
            e.preventDefault();
            
            //disable the submit button
            $("#Quote-3061 #submit_btn-3061").attr("disabled", true);
            $("#Quote-3061 #submit_btn-3061").html("Processing...");

            //submit the form
            $("#Quote-3061").submit();
        } 
    })
</script>
<script>
    if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'US') {
        var val = $("#state-3061").val();
        $("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" required><option disabled selected value> -- select a state -- </option><option value="AL">Alabama (AL)</option><option value="AK">Alaska (AK)</option><option value="AZ">Arizona (AZ)</option><option value="AR">Arkansas (AR)</option><option value="CA">California (CA)</option><option value="CO">Colorado (CO)</option><option value="CT">Connecticut (CT)</option><option value="DE">Delaware (DE)</option><option value="FL">Florida (FL)</option><option value="GA">Georgia (GA)</option><option value="HI">Hawaii (HI)</option><option value="ID">Idaho (ID)</option><option value="IL">Illinois (IL)</option><option value="IN">Indiana (IN)</option><option value="IA">Iowa (IA)</option><option value="KS">Kansas (KS)</option><option value="KY">Kentucky (KY)</option><option value="LA">Louisiana (LA)</option><option value="ME">Maine (ME)</option><option value="MD">Maryland (MD)</option><option value="MA">Massachusetts (MA)</option><option value="MI">Michigan (MI)</option><option value="MN">Minnesota (MN)</option><option value="MS">Mississippi (MS)</option><option value="MO">Missouri (MO)</option><option value="MT">Montana (MT)</option><option value="NE">Nebraska (NE)</option><option value="NV">Nevada (NV)</option><option value="NH">New Hampshire (NH)</option><option value="NJ">New Jersey (NJ)</option><option value="NM">New Mexico (NM)</option><option value="NY">New York (NY)</option><option value="NC">North Carolina (NC)</option><option value="ND">North Dakota (ND)</option><option value="OH">Ohio (OH)</option><option value="OK">Oklahoma (OK)</option><option value="OR">Oregon (OR)</option><option value="PA">Pennsylvania (PA)</option><option value="PR">Puerto Rico (PR)</option><option value="RI">Rhode Island (RI)</option><option value="SC">South Carolina (SC)</option><option value="SD">South Dakota (SD)</option><option value="TN">Tennessee (TN)</option><option value="TX">Texas (TX)</option><option value="UT">Utah (UT)</option><option value="VT">Vermont (VT)</option><option value="VA">Virginia (VA)</option><option value="WA">Washington (WA)</option><option value="WV">West Virginia (WV)</option><option value="WI">Wisconsin (WI)</option><option value="WY">Wyoming (WY)</option><option value="DC">District of Columbia (DC)</option><option value="AS">American Samoa (AS)</option><option value="GU">Guam (GU)</option><option value="MP">Northern Mariana Islands (MP)</option><option value="PR">Puerto Rico (PR)</option><option value="UM">United States Minor Outlying Islands (UM)</option><option value="VI">Virgin Islands (VI)</option></select>');
        if (val.length == 2) {
            $("#state-3061").val(val);
        }
        $("#state-3061").parent().parent().show();
    } else if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'CA') {
        var val = $("#state-3061").val();
        $("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" required><option disabled selected value> -- select a state -- </option><option value="AB">Alberta (AB)</option><option value="BC">British Columbia (BC)</option><option value="MB">Manitoba (MB)</option><option value="NB">New Brunswick (NB)</option><option value="NL">Newfoundland and Labrador (NL)</option><option value="NS">Nova Scotia (NS)</option><option value="ON">Ontario (ON)</option><option value="PE">Prince Edward Island (PE)</option><option value="QC">Quebec (QC)</option><option value="SK">Saskatchewan (SK)</option></select>');
        if (val.length == 2) {
            $("#state-3061").val(val);
        }
        $("#state-3061").parent().parent().show();
    } else if ($("#Quote-3061 #country_selector_quote-3061.selectized").val() == 'DE') {
        var val = $("#state-3061").val();
        $("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" required><option disabled selected value> -- select a state -- </option><option value="BW">Baden-Württemberg (BW)</option><option value="BY">Bayern (BY)</option><option value="BE">Berlin (BE)</option><option value="BB">Brandeburg (BB)</option><option value="HB">Bremen (HB)</option><option value="HH">Hamburg (HH)</option><option value="HE">Hessen (HE)</option><option value="MV">Mecklenburg-Vorpommern (MV)</option><option value="NI">Niedersachsen (NI)</option><option value="NW">Nordrhein-Westfalen (NW)</option><option value="RP">Rheinland-Pfalz (RP)</option><option value="SL">Saarland (SL)</option><option value="SN">Sachsen (SN)</option><option value ="SA">Sachsen-Anhalt (SA)</option><option value="SH">Schleswig-Holstein (SH)</option><option value="TH">Thüringen</option></select>');
        if (val.length == 2) {
            $("#state-3061").val(val);
        }
        $("#state-3061").parent().parent().show();
    } else {
        $("#Quote-3061 #state-3061").parent().parent().hide();
        $("#Quote-3061 #state-3061").replaceWith('<input name="data[Quote][state]" maxlength="255" type="text" id="state-3061" value="">');
    }

    $("#Quote-3061 #country_selector_quote-3061.selectized").change(function() {
        if (this.value == 'US') {
            $("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" required><option disabled selected value> -- select a state -- </option><option value="AL">Alabama (AL)</option><option value="AK">Alaska (AK)</option><option value="AZ">Arizona (AZ)</option><option value="AR">Arkansas (AR)</option><option value="CA">California (CA)</option><option value="CO">Colorado (CO)</option><option value="CT">Connecticut (CT)</option><option value="DE">Delaware (DE)</option><option value="FL">Florida (FL)</option><option value="GA">Georgia (GA)</option><option value="HI">Hawaii (HI)</option><option value="ID">Idaho (ID)</option><option value="IL">Illinois (IL)</option><option value="IN">Indiana (IN)</option><option value="IA">Iowa (IA)</option><option value="KS">Kansas (KS)</option><option value="KY">Kentucky (KY)</option><option value="LA">Louisiana (LA)</option><option value="ME">Maine (ME)</option><option value="MD">Maryland (MD)</option><option value="MA">Massachusetts (MA)</option><option value="MI">Michigan (MI)</option><option value="MN">Minnesota (MN)</option><option value="MS">Mississippi (MS)</option><option value="MO">Missouri (MO)</option><option value="MT">Montana (MT)</option><option value="NE">Nebraska (NE)</option><option value="NV">Nevada (NV)</option><option value="NH">New Hampshire (NH)</option><option value="NJ">New Jersey (NJ)</option><option value="NM">New Mexico (NM)</option><option value="NY">New York (NY)</option><option value="NC">North Carolina (NC)</option><option value="ND">North Dakota (ND)</option><option value="OH">Ohio (OH)</option><option value="OK">Oklahoma (OK)</option><option value="OR">Oregon (OR)</option><option value="PA">Pennsylvania (PA)</option><option value="PR">Puerto Rico (PR)</option><option value="RI">Rhode Island (RI)</option><option value="SC">South Carolina (SC)</option><option value="SD">South Dakota (SD)</option><option value="TN">Tennessee (TN)</option><option value="TX">Texas (TX)</option><option value="UT">Utah (UT)</option><option value="VT">Vermont (VT)</option><option value="VA">Virginia (VA)</option><option value="WA">Washington (WA)</option><option value="WV">West Virginia (WV)</option><option value="WI">Wisconsin (WI)</option><option value="WY">Wyoming (WY)</option><option value="DC">District of Columbia (DC)</option><option value="AS">American Samoa (AS)</option><option value="GU">Guam (GU)</option><option value="MP">Northern Mariana Islands (MP)</option><option value="PR">Puerto Rico (PR)</option><option value="UM">United States Minor Outlying Islands (UM)</option><option value="VI">Virgin Islands (VI)</option></select>');
            $("#Quote-3061 #state-3061").parent().parent().show();
        } else if (this.value == 'CA') {
            $("#Quote-3061 #state-3061").replaceWith('<select name="data[Quote][state]" id="state-3061" required><option disabled selected value> -- select a state -- </option><option value="AB">Alberta (AB)</option><option value="BC">British Columbia (BC)</option><option value="MB">Manitoba (MB)</option><option value="NB">New Brunswick (NB)</option><option value="NL">Newfoundland and Labrador (NL)</option><option value="NS">Nova Scotia (NS)</option><option value="ON">Ontario (ON)</option><option value="PE">Prince Edward Island (PE)</option><option value="QC">Quebec (QC)</option><option value="SK">Saskatchewan (SK)</option></select>');
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            <h6 style="height:60px">メチル化データ解析</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
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<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
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<tr>
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<p><strong>Gene ontology terms analysis</strong></p>
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<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
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<p>The <strong>Infinium MethylationEPIC Array</strong> is a genome-wide DNA methylation analysis technique based on bisulfite conversion and Illumina<sup>&reg;</sup> technology. It allows to quantitatively detect the total&nbsp;methylation level of over 850,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;<span>It offers comprehensive, expert-selected coverage of CpG islands, enhancer regions, open chromatin sites and <span>other</span><span><span>&nbsp;</span></span><span>important regions of the methylome.</span></span></p>
<p>Briefly, upon bisulfite treatment, unmethylated cytosines (both 5mC and 5hmC) are converted to uracils, while methylated cytosines remain unchanged. Infinium HD array technology interrogates these differentiated loci using site-specific probes (designed for the methylated and unmethylated sites respectively). The level of total methylation for the interrogated locus can be determined by calculating the ratio of the fluorescent signals from the methylated vs. unmethylated sites.</p>
<h2><span style="font-weight: 400;">Comprehensive Genome-Wide Coverage</span></h2>
<ul class="square">
<li><span style="font-weight: 400;">Cost-effective solution with rapid turnaround time</span></li>
<li>Over 850,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differentially methylated site analysis using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic tools</a></li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, and analysis</li>
</ul>
<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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<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<div class="extra-spaced">
<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
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<div class="small-12 medium-10 large-10 columns text-left">
<p>Our Epigenomics Profiling Services assure the sample preparation expertise and quality data that you seek. We provide epigenome-wide analyses for understanding epigenetic mechanisms, epigenetics-related drug discovery, transgenerational epigenetics studies, epigenetic biomarker identification (including epigenomic cancer biomarkers), and functional epigenomics. Diagenode offers expert epigenomics services that you can trust for chromatin, DNA, and RNA analysis.&nbsp;</p>
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<div class="small-12 medium-2 large-2 columns text-left"><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></div>
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<h3><span class="green">Chromatin</span>&nbsp;analysis</h3>
<ul>
<li>ChIP-seq analysis Histone modification</li>
<li>Promoter analysis</li>
<li>Enhancer analysis</li>
<li>ChIP-seq analysis Transcription factor&nbsp;</li>
<li>(sc)ATAC-seq Chromatin accessibility</li>
<li>ChIP-seq Cas9 off-target</li>
<li>Customized NGS service</li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/chip-seq-service">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #474546;">
<h3><span class="darkgrey">DNA methylation</span> analysis</h3>
<ul>
<li>Reduced representation bisulfite sequencing (RRBS)</li>
<li>Whole genome bisulfite sequencing (WGBS)</li>
<li>Targeted methylation</li>
<li>MeDIP-seq/hMeDIP-seq</li>
<li>Infinium MethylationEPIC array&nbsp;</li>
</ul>
<p></p>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/dna-methylation-profiling-services">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #b11e33;">
<h3><span class="diacol"><g class="gr_ gr_44 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="44" data-gr-id="44">RNA-seq</g></span> analysis</h3>
<ul>
<li><span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Gene expression profiling</span></li>
<li>mRNA<span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"> analysis</span></li>
<li>Small non-coding RNA analysis</li>
<li><b></b>Whole transcriptome analysis<br /><br /><br /><br /><br /></li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/rna-seq-category">Learn more</a></p>
</div>
</div>
</div>
<h2 style="text-align: center;"><a class="details radius small button" href="https://www.diagenode.com/en/documents/services-flyer">DOWNLOAD OUR FLYER</a></h2>
<h2>Why Diagenode</h2>
<ul>
<li><strong>Expertise and trust</strong>: Recognized epigenetics leader, official partner of <a href="http://www.blueprint-epigenome.eu/"><g class="gr_ gr_45 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="45" data-gr-id="45">BLUEPRINT</g></a>, <a href="http://ihec-epigenomes.org/">IHEC</a> <g class="gr_ gr_46 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Punctuation only-ins replaceWithoutSep" id="46" data-gr-id="46">and</g> <a href="https://www.faang.org/">FAANG</a></li>
<li><strong>Innovative technology</strong>: Utilization of the signature Bioruptor<sup>&reg;</sup> sonication device for optimal chromatin and DNA shearing and the IP-Star<sup>&reg;</sup> Automation device give reproducible and reliable optimization and results</li>
<li><strong>Quality</strong>: Multiple QC steps in all workflows and validated antibodies plus reagents deliver superior data</li>
<li><strong>Flexibility</strong>: Extensive range of sample species and sample origins</li>
<li><strong>Experience</strong> in epigenomics profiling for a wide range of applications and fields of interest</li>
</ul>
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<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">HUMAN</span><center><img width="260" height="259" alt="services chip-seq - human" src="https://www.diagenode.com/img/categories/services/services-humans.png" caption="false" /></center></div>
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</li>
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<h2>12 years of expertise and trust in epigenetics</h2>
<ul>
<li><strong>End-to-end</strong> epigenetic service</li>
<li><strong>Collaborative</strong> and customized project design</li>
<li>Dedicated <strong>in-house expert</strong> for your project</li>
<li><strong>Integrative</strong> data analysis</li>
<li>Presentation-<strong>quality data</strong> and graphs</li>
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			'id' => '4931',
			'name' => 'Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth',
			'authors' => 'Argentato P.P. et al.',
			'description' => '<p><span>Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.</span></p>',
			'date' => '2024-03-25',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38530405/',
			'doi' => '10.1590/1678-4685-GMB-2023-0203',
			'modified' => '2024-03-28 08:51:37',
			'created' => '2024-03-28 08:51:37',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 1 => array(
			'id' => '4919',
			'name' => 'Making Biological Ageing Clocks Personal',
			'authors' => 'Pusparum M. et al.',
			'description' => '<div id="sec-1" class="subsection">
<p id="p-3"><strong>Background</strong><span>&nbsp;</span>Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called &ldquo;ageing clocks&rdquo;). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process.</p>
</div>
<div id="sec-2" class="subsection">
<p id="p-4"><strong>Methods</strong><span>&nbsp;</span>We analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks.</p>
</div>
<div id="sec-3" class="subsection">
<p id="p-5"><strong>Findings</strong><span>&nbsp;</span>Within the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles.</p>
</div>
<div id="sec-4" class="subsection">
<p id="p-6"><strong>Interpretation</strong><span>&nbsp;</span>Our analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors&rsquo; assessments.</p>
</div>',
			'date' => '2024-02-29',
			'pmid' => 'https://www.medrxiv.org/content/10.1101/2024.02.28.24303427v1',
			'doi' => 'https://doi.org/10.1101/2024.02.28.24303427',
			'modified' => '2024-03-07 10:57:18',
			'created' => '2024-03-07 10:57:18',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 2 => array(
			'id' => '4914',
			'name' => 'Inhibition of Aurora kinase induces endogenous retroelements to induce a type I/III interferon response via RIG-I',
			'authors' => 'Choy L. et al.',
			'description' => '<p><span>Type I interferon signaling is a crucial component of anti-viral immunity that has been linked to promoting the efficacy of some chemotherapeutic drugs. We developed a reporter system in HCT116 cells that detects activation of the endogenous IFI27 locus, an interferon (IFN) target gene. We screened a library of annotated compounds in these cells and discovered aurora kinase inhibitors (AURKi) as strong hits. Type I IFN signaling was found to be the most enriched gene signature after AURKi treatment in HCT116, and this signature was also strongly enriched in other colorectal cancer (CRC) cell lines. The ability of AURKi to activate IFN in HCT116 was dependent on MAVS and RIG-I, but independent of STING, whose signaling is deficient in these cells. MAVS dependence was recapitulated in other CRC lines with STING pathway deficiency, whereas in cells with intact STING signaling, the STING pathway was required for IFN induction by AURKi. AURKi's were found to induce expression of endogenous retroviruses (ERV's). These ERVs were distinct from those induced by the DNA methyltransferase inhibitors (DNMTi's), which can induce IFN signaling via ERV induction, suggesting a novel mechanism of action. The anti-tumor effect of alisertib in mice was accompanied by an induction of IFN expression in HCT116 or CT26 tumors. CT26 tumor growth inhibition by alisertib was absent in NOD/SCID mice vs. WT mice, and tumors from WT mice with alisertib treatment showed increased in CD8+ T cell infiltration, suggesting that anti-tumor efficacy of AURKi depends, at least in part, on an intact immune response.</span></p>',
			'date' => '2024-02-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38358346/',
			'doi' => '10.1158/2767-9764.CRC-23-0432',
			'modified' => '2024-02-22 12:33:11',
			'created' => '2024-02-22 12:33:11',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 3 => array(
			'id' => '4811',
			'name' => 'Association of DNA methylation signatures with premature ageing andcardiovascular death in patients with end-stage kidney disease: a pilotepigenome-wide association study.',
			'authors' => 'Sumida  K. et al.',
			'description' => '<p><span>Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assayed genome-wide DNAm in a pilot case-control study of 60 hemodialysis patients with (n=30, cases) and without (n=30, controls) a fatal cardiovascular event. DNAm was profiled on the Illumina EPIC BeadChip. Four established DNAm clocks (i.e., Horvath-, Hannum-, Pheno-, and GrimAge) were used to estimate epigenetic age (DNAmAge). Epigenetic age acceleration (EAA) was derived as the residuals of regressing DNAmAge on chronological age (chroAge), and its association with cardiovascular death was examined using multivariable conditional logistic regression. An epigenome-wide association study (EWAS) was performed to identify differentially methylated CpGs associated with cardiovascular death. All clocks performed well at predicting chroAge (correlation between DNAmAges and chroAge of r=0.76-0.89), with GrimAge showing the largest deviation from chroAge (a mean of +21.3 years). There was no significant association of EAAs with cardiovascular death. In the EWAS, a CpG (cg22305782) in the&nbsp;</span><i>FBXL19</i><span><span>&nbsp;</span>gene had the strongest association with cardiovascular death with significantly lower DNAm in cases vs. controls (</span><i>P</i><sub>FDR</sub><span>=2.0x10</span><sup>-6</sup><span>).<span>&nbsp;</span></span><i>FBXL19</i><span><span>&nbsp;</span>is involved in cell apoptosis, inflammation, and adipogenesis. Overall, we observed more accelerated aging in patients with ESKD, although there was no significant association of EAAs with cardiovascular death. EWAS suggests a potential novel DNAm biomarker for premature cardiovascular mortality in ESKD.</span></p>',
			'date' => '2023-12-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37207321',
			'doi' => '10.1080/15592294.2023.2214394',
			'modified' => '2023-06-15 08:57:02',
			'created' => '2023-06-13 21:11:31',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 4 => array(
			'id' => '4830',
			'name' => 'Perturbed epigenetic transcriptional regulation in AML with IDHmutations causes increased susceptibility to NK cells.',
			'authors' => 'Palau  A. et al.',
			'description' => '<p>Isocitrate dehydrogenase (IDH) mutations are found in 20\% of acute myeloid leukemia (AML) patients. However, only 30-40\% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.</p>',
			'date' => '2023-07-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37495775',
			'doi' => '10.1038/s41375-023-01972-3',
			'modified' => '2023-08-01 13:39:40',
			'created' => '2023-08-01 15:59:38',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 5 => array(
			'id' => '4852',
			'name' => 'In skeletal muscle and neural crest cells, SMCHD1 regulates biologicalpathways relevant for Bosma syndrome and facioscapulohumeral dystrophyphenotype.',
			'authors' => 'Laberthonnière  C. et al.',
			'description' => '<p>Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In humans, its function as well as the impact of its mutations remains poorly defined. To fill this gap, we determined the episignature associated with heterozygous SMCHD1 variants in primary cells and cell lineages derived from induced pluripotent stem cells for Bosma arhinia and microphthalmia syndrome (BAMS) and type 2 facioscapulohumeral dystrophy (FSHD2). In human tissues, SMCHD1 regulates the distribution of methylated CpGs, H3K27 trimethylation and CTCF at repressed chromatin but also at euchromatin. Based on the exploration of tissues affected either in FSHD or in BAMS, i.e. skeletal muscle fibers and neural crest stem cells, respectively, our results emphasize multiple functions for SMCHD1, in chromatin compaction, chromatin insulation and gene regulation with variable targets or phenotypical outcomes. We concluded that in rare genetic diseases, SMCHD1 variants impact gene expression in two ways: (i) by changing the chromatin context at a number of euchromatin loci or (ii) by directly regulating some loci encoding master transcription factors required for cell fate determination and tissue differentiation.</p>',
			'date' => '2023-06-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37334829',
			'doi' => '10.1093/nar/gkad523',
			'modified' => '2023-08-01 14:35:38',
			'created' => '2023-08-01 15:59:38',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 7 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 8 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 10 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 11 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 12 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 13 => array(
			'id' => '4590',
			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
	'Testimonial' => array(),
	'Area' => array(),
	'SafetySheet' => array()
)
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	(int) 1 => 'US',
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	(int) 3 => 'GB',
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	(int) 6 => 'SE',
	(int) 7 => 'FI',
	(int) 8 => 'NL',
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	(int) 11 => 'FR',
	(int) 12 => 'DE',
	(int) 13 => 'CH',
	(int) 14 => 'AT',
	(int) 15 => 'ES',
	(int) 16 => 'IT',
	(int) 17 => 'PT'
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            <h6 style="height:60px">メチル化データ解析</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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<h4><span style="font-weight: 400;">包括的なゲノムワイドカバレッジのための優れた方法</span></h4>
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<li style="font-weight: 400;">高いコストパフォーマンス、結果までの時間短縮</li>
<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
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<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
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<h4><strong>Features</strong></h4>
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<p><strong>Standard</strong></p>
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<li style="font-weight: 400;">高いコストパフォーマンス、結果までの時間短縮</li>
<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
<li style="font-weight: 400;">一塩基分解能で850,000を超えるヒトCpG位置</li>
<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
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<p><strong>End-to-end array </strong></p>
</td>
<td style="height: 194px; width: 504px;">
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<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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</tr>
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<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
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<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
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</td>
</tr>
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<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
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<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
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<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
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<p>The <strong>Infinium MethylationEPIC Array</strong> is a genome-wide DNA methylation analysis technique based on bisulfite conversion and Illumina<sup>&reg;</sup> technology. It allows to quantitatively detect the total&nbsp;methylation level of over 850,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;<span>It offers comprehensive, expert-selected coverage of CpG islands, enhancer regions, open chromatin sites and <span>other</span><span><span>&nbsp;</span></span><span>important regions of the methylome.</span></span></p>
<p>Briefly, upon bisulfite treatment, unmethylated cytosines (both 5mC and 5hmC) are converted to uracils, while methylated cytosines remain unchanged. Infinium HD array technology interrogates these differentiated loci using site-specific probes (designed for the methylated and unmethylated sites respectively). The level of total methylation for the interrogated locus can be determined by calculating the ratio of the fluorescent signals from the methylated vs. unmethylated sites.</p>
<h2><span style="font-weight: 400;">Comprehensive Genome-Wide Coverage</span></h2>
<ul class="square">
<li><span style="font-weight: 400;">Cost-effective solution with rapid turnaround time</span></li>
<li>Over 850,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differentially methylated site analysis using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic tools</a></li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, and analysis</li>
</ul>
<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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<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identify biochemical pathways in which genes associated with differentially methylated regions (or individual differentially methylated CpGs) may be overrepresented.</p>
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			'description' => '<div class="extra-spaced">
<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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</div>
<div class="extra-spaced">
<p>Based on the cytosine resolution, the analysis can be made at:</p>
<ul class="square">
<li><strong>Single base scale</strong> (for each cytosine in a CpG context &ndash; WGBS, RRBS, BSAS, EPIC, etc)</li>
<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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</div>
<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
</div>
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<div class="small-12 medium-10 large-10 columns text-left">
<p>Our Epigenomics Profiling Services assure the sample preparation expertise and quality data that you seek. We provide epigenome-wide analyses for understanding epigenetic mechanisms, epigenetics-related drug discovery, transgenerational epigenetics studies, epigenetic biomarker identification (including epigenomic cancer biomarkers), and functional epigenomics. Diagenode offers expert epigenomics services that you can trust for chromatin, DNA, and RNA analysis.&nbsp;</p>
</div>
<div class="small-12 medium-2 large-2 columns text-left"><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></div>
</div>
<center><img alt="DNA-RNA-Chromatin-Diagenode" src="https://www.diagenode.com/img/categories/services/chromatin-dna-rna.png" /></center>
<div class="row">
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #099f92;">
<h3><span class="green">Chromatin</span>&nbsp;analysis</h3>
<ul>
<li>ChIP-seq analysis Histone modification</li>
<li>Promoter analysis</li>
<li>Enhancer analysis</li>
<li>ChIP-seq analysis Transcription factor&nbsp;</li>
<li>(sc)ATAC-seq Chromatin accessibility</li>
<li>ChIP-seq Cas9 off-target</li>
<li>Customized NGS service</li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/chip-seq-service">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #474546;">
<h3><span class="darkgrey">DNA methylation</span> analysis</h3>
<ul>
<li>Reduced representation bisulfite sequencing (RRBS)</li>
<li>Whole genome bisulfite sequencing (WGBS)</li>
<li>Targeted methylation</li>
<li>MeDIP-seq/hMeDIP-seq</li>
<li>Infinium MethylationEPIC array&nbsp;</li>
</ul>
<p></p>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/dna-methylation-profiling-services">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #b11e33;">
<h3><span class="diacol"><g class="gr_ gr_44 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="44" data-gr-id="44">RNA-seq</g></span> analysis</h3>
<ul>
<li><span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Gene expression profiling</span></li>
<li>mRNA<span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"> analysis</span></li>
<li>Small non-coding RNA analysis</li>
<li><b></b>Whole transcriptome analysis<br /><br /><br /><br /><br /></li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/rna-seq-category">Learn more</a></p>
</div>
</div>
</div>
<h2 style="text-align: center;"><a class="details radius small button" href="https://www.diagenode.com/en/documents/services-flyer">DOWNLOAD OUR FLYER</a></h2>
<h2>Why Diagenode</h2>
<ul>
<li><strong>Expertise and trust</strong>: Recognized epigenetics leader, official partner of <a href="http://www.blueprint-epigenome.eu/"><g class="gr_ gr_45 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="45" data-gr-id="45">BLUEPRINT</g></a>, <a href="http://ihec-epigenomes.org/">IHEC</a> <g class="gr_ gr_46 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Punctuation only-ins replaceWithoutSep" id="46" data-gr-id="46">and</g> <a href="https://www.faang.org/">FAANG</a></li>
<li><strong>Innovative technology</strong>: Utilization of the signature Bioruptor<sup>&reg;</sup> sonication device for optimal chromatin and DNA shearing and the IP-Star<sup>&reg;</sup> Automation device give reproducible and reliable optimization and results</li>
<li><strong>Quality</strong>: Multiple QC steps in all workflows and validated antibodies plus reagents deliver superior data</li>
<li><strong>Flexibility</strong>: Extensive range of sample species and sample origins</li>
<li><strong>Experience</strong> in epigenomics profiling for a wide range of applications and fields of interest</li>
</ul>
<div class="extra-spaced">
<ul class="small-block-grid-2 medium-block-grid-3 large-block-grid-3">
<li>
<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">HUMAN</span><center><img width="260" height="259" alt="services chip-seq - human" src="https://www.diagenode.com/img/categories/services/services-humans.png" caption="false" /></center></div>
</li>
<li>
<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">ANIMALS</span><center><img width="260" height="259" alt="services chip-seq - animals" src="https://www.diagenode.com/img/categories/services/services-animals.png" caption="false" /></center></div>
</li>
<li>
<div class="block-distri"><span class="label-green" style="margin-bottom: 16px; margin-left: -3px;">PLANTS</span><center><img width="260" height="259" alt="services methylation-plant" src="https://www.diagenode.com/img/categories/services/services-plants.png" caption="false" /></center></div>
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<h2>12 years of expertise and trust in epigenetics</h2>
<ul>
<li><strong>End-to-end</strong> epigenetic service</li>
<li><strong>Collaborative</strong> and customized project design</li>
<li>Dedicated <strong>in-house expert</strong> for your project</li>
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			'meta_description' => 'Diagenode offers expert epigenomics services that you can trust for chromatin, DNA, and RNA analysis.',
			'meta_title' => 'Epigenomics Profiling Services | Diagenode',
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		(int) 0 => array(
			'id' => '4931',
			'name' => 'Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth',
			'authors' => 'Argentato P.P. et al.',
			'description' => '<p><span>Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.</span></p>',
			'date' => '2024-03-25',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38530405/',
			'doi' => '10.1590/1678-4685-GMB-2023-0203',
			'modified' => '2024-03-28 08:51:37',
			'created' => '2024-03-28 08:51:37',
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		(int) 1 => array(
			'id' => '4919',
			'name' => 'Making Biological Ageing Clocks Personal',
			'authors' => 'Pusparum M. et al.',
			'description' => '<div id="sec-1" class="subsection">
<p id="p-3"><strong>Background</strong><span>&nbsp;</span>Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called &ldquo;ageing clocks&rdquo;). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process.</p>
</div>
<div id="sec-2" class="subsection">
<p id="p-4"><strong>Methods</strong><span>&nbsp;</span>We analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks.</p>
</div>
<div id="sec-3" class="subsection">
<p id="p-5"><strong>Findings</strong><span>&nbsp;</span>Within the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles.</p>
</div>
<div id="sec-4" class="subsection">
<p id="p-6"><strong>Interpretation</strong><span>&nbsp;</span>Our analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors&rsquo; assessments.</p>
</div>',
			'date' => '2024-02-29',
			'pmid' => 'https://www.medrxiv.org/content/10.1101/2024.02.28.24303427v1',
			'doi' => 'https://doi.org/10.1101/2024.02.28.24303427',
			'modified' => '2024-03-07 10:57:18',
			'created' => '2024-03-07 10:57:18',
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		(int) 2 => array(
			'id' => '4914',
			'name' => 'Inhibition of Aurora kinase induces endogenous retroelements to induce a type I/III interferon response via RIG-I',
			'authors' => 'Choy L. et al.',
			'description' => '<p><span>Type I interferon signaling is a crucial component of anti-viral immunity that has been linked to promoting the efficacy of some chemotherapeutic drugs. We developed a reporter system in HCT116 cells that detects activation of the endogenous IFI27 locus, an interferon (IFN) target gene. We screened a library of annotated compounds in these cells and discovered aurora kinase inhibitors (AURKi) as strong hits. Type I IFN signaling was found to be the most enriched gene signature after AURKi treatment in HCT116, and this signature was also strongly enriched in other colorectal cancer (CRC) cell lines. The ability of AURKi to activate IFN in HCT116 was dependent on MAVS and RIG-I, but independent of STING, whose signaling is deficient in these cells. MAVS dependence was recapitulated in other CRC lines with STING pathway deficiency, whereas in cells with intact STING signaling, the STING pathway was required for IFN induction by AURKi. AURKi's were found to induce expression of endogenous retroviruses (ERV's). These ERVs were distinct from those induced by the DNA methyltransferase inhibitors (DNMTi's), which can induce IFN signaling via ERV induction, suggesting a novel mechanism of action. The anti-tumor effect of alisertib in mice was accompanied by an induction of IFN expression in HCT116 or CT26 tumors. CT26 tumor growth inhibition by alisertib was absent in NOD/SCID mice vs. WT mice, and tumors from WT mice with alisertib treatment showed increased in CD8+ T cell infiltration, suggesting that anti-tumor efficacy of AURKi depends, at least in part, on an intact immune response.</span></p>',
			'date' => '2024-02-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38358346/',
			'doi' => '10.1158/2767-9764.CRC-23-0432',
			'modified' => '2024-02-22 12:33:11',
			'created' => '2024-02-22 12:33:11',
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			'id' => '4811',
			'name' => 'Association of DNA methylation signatures with premature ageing andcardiovascular death in patients with end-stage kidney disease: a pilotepigenome-wide association study.',
			'authors' => 'Sumida  K. et al.',
			'description' => '<p><span>Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assayed genome-wide DNAm in a pilot case-control study of 60 hemodialysis patients with (n=30, cases) and without (n=30, controls) a fatal cardiovascular event. DNAm was profiled on the Illumina EPIC BeadChip. Four established DNAm clocks (i.e., Horvath-, Hannum-, Pheno-, and GrimAge) were used to estimate epigenetic age (DNAmAge). Epigenetic age acceleration (EAA) was derived as the residuals of regressing DNAmAge on chronological age (chroAge), and its association with cardiovascular death was examined using multivariable conditional logistic regression. An epigenome-wide association study (EWAS) was performed to identify differentially methylated CpGs associated with cardiovascular death. All clocks performed well at predicting chroAge (correlation between DNAmAges and chroAge of r=0.76-0.89), with GrimAge showing the largest deviation from chroAge (a mean of +21.3 years). There was no significant association of EAAs with cardiovascular death. In the EWAS, a CpG (cg22305782) in the&nbsp;</span><i>FBXL19</i><span><span>&nbsp;</span>gene had the strongest association with cardiovascular death with significantly lower DNAm in cases vs. controls (</span><i>P</i><sub>FDR</sub><span>=2.0x10</span><sup>-6</sup><span>).<span>&nbsp;</span></span><i>FBXL19</i><span><span>&nbsp;</span>is involved in cell apoptosis, inflammation, and adipogenesis. Overall, we observed more accelerated aging in patients with ESKD, although there was no significant association of EAAs with cardiovascular death. EWAS suggests a potential novel DNAm biomarker for premature cardiovascular mortality in ESKD.</span></p>',
			'date' => '2023-12-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37207321',
			'doi' => '10.1080/15592294.2023.2214394',
			'modified' => '2023-06-15 08:57:02',
			'created' => '2023-06-13 21:11:31',
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		(int) 4 => array(
			'id' => '4830',
			'name' => 'Perturbed epigenetic transcriptional regulation in AML with IDHmutations causes increased susceptibility to NK cells.',
			'authors' => 'Palau  A. et al.',
			'description' => '<p>Isocitrate dehydrogenase (IDH) mutations are found in 20\% of acute myeloid leukemia (AML) patients. However, only 30-40\% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.</p>',
			'date' => '2023-07-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37495775',
			'doi' => '10.1038/s41375-023-01972-3',
			'modified' => '2023-08-01 13:39:40',
			'created' => '2023-08-01 15:59:38',
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		),
		(int) 5 => array(
			'id' => '4852',
			'name' => 'In skeletal muscle and neural crest cells, SMCHD1 regulates biologicalpathways relevant for Bosma syndrome and facioscapulohumeral dystrophyphenotype.',
			'authors' => 'Laberthonnière  C. et al.',
			'description' => '<p>Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In humans, its function as well as the impact of its mutations remains poorly defined. To fill this gap, we determined the episignature associated with heterozygous SMCHD1 variants in primary cells and cell lineages derived from induced pluripotent stem cells for Bosma arhinia and microphthalmia syndrome (BAMS) and type 2 facioscapulohumeral dystrophy (FSHD2). In human tissues, SMCHD1 regulates the distribution of methylated CpGs, H3K27 trimethylation and CTCF at repressed chromatin but also at euchromatin. Based on the exploration of tissues affected either in FSHD or in BAMS, i.e. skeletal muscle fibers and neural crest stem cells, respectively, our results emphasize multiple functions for SMCHD1, in chromatin compaction, chromatin insulation and gene regulation with variable targets or phenotypical outcomes. We concluded that in rare genetic diseases, SMCHD1 variants impact gene expression in two ways: (i) by changing the chromatin context at a number of euchromatin loci or (ii) by directly regulating some loci encoding master transcription factors required for cell fate determination and tissue differentiation.</p>',
			'date' => '2023-06-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37334829',
			'doi' => '10.1093/nar/gkad523',
			'modified' => '2023-08-01 14:35:38',
			'created' => '2023-08-01 15:59:38',
			'ProductsPublication' => array(
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			)
		),
		(int) 6 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
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		(int) 7 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
			'ProductsPublication' => array(
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		),
		(int) 8 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
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		(int) 9 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
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		(int) 10 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
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			)
		),
		(int) 11 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
			'ProductsPublication' => array(
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		),
		(int) 12 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
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			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
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            <h6 style="height:60px">メチル化データ解析</h6>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
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<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
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<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
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<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
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<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
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<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
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</li>
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<h4><span style="font-weight: 400;">包括的なゲノムワイドカバレッジのための優れた方法</span></h4>
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<li style="font-weight: 400;">高いコストパフォーマンス、結果までの時間短縮</li>
<li style="font-weight: 400;">CpG、非CpG、及びCHH部位におけるメチル化部位の定量解析</li>
<li style="font-weight: 400;">一塩基分解能で850,000を超えるヒトCpG位置</li>
<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
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<h4><strong>Features</strong></h4>
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<p><strong>Standard</strong></p>
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<li>Scanner output&nbsp;(IDAT files)</li>
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<p>Identification of differentially methylated CpGs between sample groups.</p>
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<p>バイサルファイトで処理すると、メチル化されたシトシン塩基は変化しませんが、メチル化されていないシトシン塩基はウラシルに変換されます。Infinium HD配列技術は、メチル化部位と非メチル化部位に対して各々に設計された部位特異的プローブを用いて、これらの分化した遺伝子座を調べます。調べられた遺伝子座のメチル化のレベルは、メチル化部位と非メチル化部位との蛍光シグナルの比を計算することによって決定されます。</p>
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<h4><span style="font-weight: 400;">包括的なゲノムワイドカバレッジのための優れた方法</span></h4>
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<li style="font-weight: 400;">高いコストパフォーマンス、結果までの時間短縮</li>
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<li style="font-weight: 400;">異なるメチル化部位解析</li>
<li style="font-weight: 400;">徹底したバイサルファイト変換、配列交配、および解析が含まれる包括的なサービス</li>
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<li style="font-weight: 400;">Bisulfite conversion</li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array scanner&nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<p><strong>Standard</strong></p>
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<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
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<p><strong>Differential methylation analysis</strong></p>
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<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
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<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
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<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
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<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
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<p>The <strong>Infinium MethylationEPIC Array</strong> is a genome-wide DNA methylation analysis technique based on bisulfite conversion and Illumina<sup>&reg;</sup> technology. It allows to quantitatively detect the total&nbsp;methylation level of over 850,000&nbsp;methylations sites throughout the human genome at single nucleotide resolution.&nbsp;<span>It offers comprehensive, expert-selected coverage of CpG islands, enhancer regions, open chromatin sites and <span>other</span><span><span>&nbsp;</span></span><span>important regions of the methylome.</span></span></p>
<p>Briefly, upon bisulfite treatment, unmethylated cytosines (both 5mC and 5hmC) are converted to uracils, while methylated cytosines remain unchanged. Infinium HD array technology interrogates these differentiated loci using site-specific probes (designed for the methylated and unmethylated sites respectively). The level of total methylation for the interrogated locus can be determined by calculating the ratio of the fluorescent signals from the methylated vs. unmethylated sites.</p>
<h2><span style="font-weight: 400;">Comprehensive Genome-Wide Coverage</span></h2>
<ul class="square">
<li><span style="font-weight: 400;">Cost-effective solution with rapid turnaround time</span></li>
<li>Over 850,000 CpGs detected in human samples at single nucleotide resolution</li>
<li>Quantitative interrogation of CpG, non-CpG, and CHH sites</li>
<li>Differentially methylated site analysis using <a href="https://www.diagenode.com/en/categories/bioinformatics-service">bioinformatic tools</a></li>
<li>Compatible with FFPE samples with additional mandatory DNA Restoration step</li>
<li>End-to-end&nbsp;services include bisulfite conversion, array hybridization, and analysis</li>
</ul>
<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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<li style="font-weight: 400;"><span style="font-weight: 400;">Whole genome amplification &nbsp;</span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Array hybridization </span></li>
<li style="font-weight: 400;"><span style="font-weight: 400;">Single base extension &nbsp;</span></li>
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<h4><strong>Analysis</strong></h4>
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<h4><strong>Features</strong></h4>
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<td style="vertical-align: top;">
<p><strong>Standard</strong></p>
</td>
<td style="width: 70%;">
<p><em>Standard files provided:</em></p>
<ul>
<li>Sample annotation</li>
<li>Variable annotation</li>
<li>Scanner output&nbsp;(IDAT files)</li>
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</tr>
<tr>
<td style="vertical-align: top; width=30%; text-align: left;">
<p><strong>Differential methylation analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Identification of differentially methylated CpGs between sample groups.</p>
<p><em>Files provided:</em></p>
<ul>
<li>Report with summary of differential methylation analysis and plots</li>
<li>File containing the differentially methylated CpGs and breakdown of those positions in regional analysis (CpG islands, shelves, shores and open sea)</li>
<li>File containing differential methylated regions (DMRs)</li>
</ul>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Gene ontology terms analysis</strong></p>
</td>
<td style="width: 70%;">
<p>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in differentially bound regions may indicate the underlying biological processes involved.</p>
</td>
</tr>
<tr>
<td style="vertical-align: top;">
<p><strong>Pathway analysis</strong></p>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<div class="extra-spaced">
<p>Based on the cytosine resolution, the analysis can be made at:</p>
<ul class="square">
<li><strong>Single base scale</strong> (for each cytosine in a CpG context &ndash; WGBS, RRBS, BSAS, EPIC, etc)</li>
<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<div class="extra-spaced">
<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
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<div class="small-12 medium-10 large-10 columns text-left">
<p>Our Epigenomics Profiling Services assure the sample preparation expertise and quality data that you seek. We provide epigenome-wide analyses for understanding epigenetic mechanisms, epigenetics-related drug discovery, transgenerational epigenetics studies, epigenetic biomarker identification (including epigenomic cancer biomarkers), and functional epigenomics. Diagenode offers expert epigenomics services that you can trust for chromatin, DNA, and RNA analysis.&nbsp;</p>
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<div class="small-12 medium-2 large-2 columns text-left"><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></div>
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<center><img alt="DNA-RNA-Chromatin-Diagenode" src="https://www.diagenode.com/img/categories/services/chromatin-dna-rna.png" /></center>
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<h3><span class="green">Chromatin</span>&nbsp;analysis</h3>
<ul>
<li>ChIP-seq analysis Histone modification</li>
<li>Promoter analysis</li>
<li>Enhancer analysis</li>
<li>ChIP-seq analysis Transcription factor&nbsp;</li>
<li>(sc)ATAC-seq Chromatin accessibility</li>
<li>ChIP-seq Cas9 off-target</li>
<li>Customized NGS service</li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/chip-seq-service">Learn more</a></p>
</div>
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<div class="panel" style="border-color: #474546;">
<h3><span class="darkgrey">DNA methylation</span> analysis</h3>
<ul>
<li>Reduced representation bisulfite sequencing (RRBS)</li>
<li>Whole genome bisulfite sequencing (WGBS)</li>
<li>Targeted methylation</li>
<li>MeDIP-seq/hMeDIP-seq</li>
<li>Infinium MethylationEPIC array&nbsp;</li>
</ul>
<p></p>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/dna-methylation-profiling-services">Learn more</a></p>
</div>
</div>
<div class="small-12 medium-4 large-4 columns text-left">
<div class="panel" style="border-color: #b11e33;">
<h3><span class="diacol"><g class="gr_ gr_44 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="44" data-gr-id="44">RNA-seq</g></span> analysis</h3>
<ul>
<li><span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;">Gene expression profiling</span></li>
<li>mRNA<span style="display: inline !important; float: none; background-color: #f8f8f8; color: #333333; font-family: Helvetica,Arial,sans-serif; font-size: 16px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; -webkit-text-stroke-width: 0px; white-space: normal; word-spacing: 0px;"> analysis</span></li>
<li>Small non-coding RNA analysis</li>
<li><b></b>Whole transcriptome analysis<br /><br /><br /><br /><br /></li>
</ul>
<p><i class="fa fa-arrow-circle-right"></i> <a href="../categories/rna-seq-category">Learn more</a></p>
</div>
</div>
</div>
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<li><strong>Expertise and trust</strong>: Recognized epigenetics leader, official partner of <a href="http://www.blueprint-epigenome.eu/"><g class="gr_ gr_45 gr-alert gr_spell gr_inline_cards gr_disable_anim_appear ContextualSpelling ins-del multiReplace" id="45" data-gr-id="45">BLUEPRINT</g></a>, <a href="http://ihec-epigenomes.org/">IHEC</a> <g class="gr_ gr_46 gr-alert gr_gramm gr_inline_cards gr_disable_anim_appear Punctuation only-ins replaceWithoutSep" id="46" data-gr-id="46">and</g> <a href="https://www.faang.org/">FAANG</a></li>
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<h2>12 years of expertise and trust in epigenetics</h2>
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<li><strong>End-to-end</strong> epigenetic service</li>
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		(int) 0 => array(
			'id' => '4931',
			'name' => 'Integrative network analysis of differentially methylated regions to study the impact of gestational weight gain on maternal metabolism and fetal-neonatal growth',
			'authors' => 'Argentato P.P. et al.',
			'description' => '<p><span>Integrative network analysis (INA) is important for identifying gene modules or epigenetically regulated molecular pathways in diseases. This study evaluated the effect of excessive gestational weight gain (EGWG) on INA of differentially methylated regions, maternal metabolism and offspring growth. Brazilian women from "The Araraquara Cohort Study" with adequate pre-pregnancy body mass index were divided into EGWG (n=30) versus adequate gestational weight gain (AGWG, n=45) groups. The methylome analysis was performed on maternal blood using the Illumina MethylationEPIC BeadChip. Fetal-neonatal growth was assessed by ultrasound and anthropometry, respectively. Maternal lipid and glycemic profiles were investigated. Maternal triglycerides-TG (p=0.030) and total cholesterol (p=0.014); fetus occipito-frontal diameter (p=0.005); neonate head circumference-HC (p=0.016) and thoracic perimeter (p=0.020) were greater in the EGWG compared to the AGWG group. Multiple linear regression analysis showed that maternal DNA methylation was associated with maternal TG and fasting insulin, fetal abdominal circumference, and fetal and neonate HC. The DMRs studied were enriched in 142 biological processes, 21 molecular functions,and 17 cellular components with terms directed for the fatty acids metabolism. Three DMGMs were identified:COL3A1, ITGA4 and KLRK1. INA targeted chronic diseases and maternal metabolism contributing to an epigenetic understanding of the involvement of GWG in maternal metabolism and fetal-neonatal growth.</span></p>',
			'date' => '2024-03-25',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38530405/',
			'doi' => '10.1590/1678-4685-GMB-2023-0203',
			'modified' => '2024-03-28 08:51:37',
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		(int) 1 => array(
			'id' => '4919',
			'name' => 'Making Biological Ageing Clocks Personal',
			'authors' => 'Pusparum M. et al.',
			'description' => '<div id="sec-1" class="subsection">
<p id="p-3"><strong>Background</strong><span>&nbsp;</span>Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called &ldquo;ageing clocks&rdquo;). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process.</p>
</div>
<div id="sec-2" class="subsection">
<p id="p-4"><strong>Methods</strong><span>&nbsp;</span>We analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks.</p>
</div>
<div id="sec-3" class="subsection">
<p id="p-5"><strong>Findings</strong><span>&nbsp;</span>Within the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles.</p>
</div>
<div id="sec-4" class="subsection">
<p id="p-6"><strong>Interpretation</strong><span>&nbsp;</span>Our analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors&rsquo; assessments.</p>
</div>',
			'date' => '2024-02-29',
			'pmid' => 'https://www.medrxiv.org/content/10.1101/2024.02.28.24303427v1',
			'doi' => 'https://doi.org/10.1101/2024.02.28.24303427',
			'modified' => '2024-03-07 10:57:18',
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			'id' => '4914',
			'name' => 'Inhibition of Aurora kinase induces endogenous retroelements to induce a type I/III interferon response via RIG-I',
			'authors' => 'Choy L. et al.',
			'description' => '<p><span>Type I interferon signaling is a crucial component of anti-viral immunity that has been linked to promoting the efficacy of some chemotherapeutic drugs. We developed a reporter system in HCT116 cells that detects activation of the endogenous IFI27 locus, an interferon (IFN) target gene. We screened a library of annotated compounds in these cells and discovered aurora kinase inhibitors (AURKi) as strong hits. Type I IFN signaling was found to be the most enriched gene signature after AURKi treatment in HCT116, and this signature was also strongly enriched in other colorectal cancer (CRC) cell lines. The ability of AURKi to activate IFN in HCT116 was dependent on MAVS and RIG-I, but independent of STING, whose signaling is deficient in these cells. MAVS dependence was recapitulated in other CRC lines with STING pathway deficiency, whereas in cells with intact STING signaling, the STING pathway was required for IFN induction by AURKi. AURKi's were found to induce expression of endogenous retroviruses (ERV's). These ERVs were distinct from those induced by the DNA methyltransferase inhibitors (DNMTi's), which can induce IFN signaling via ERV induction, suggesting a novel mechanism of action. The anti-tumor effect of alisertib in mice was accompanied by an induction of IFN expression in HCT116 or CT26 tumors. CT26 tumor growth inhibition by alisertib was absent in NOD/SCID mice vs. WT mice, and tumors from WT mice with alisertib treatment showed increased in CD8+ T cell infiltration, suggesting that anti-tumor efficacy of AURKi depends, at least in part, on an intact immune response.</span></p>',
			'date' => '2024-02-15',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/38358346/',
			'doi' => '10.1158/2767-9764.CRC-23-0432',
			'modified' => '2024-02-22 12:33:11',
			'created' => '2024-02-22 12:33:11',
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			'id' => '4811',
			'name' => 'Association of DNA methylation signatures with premature ageing andcardiovascular death in patients with end-stage kidney disease: a pilotepigenome-wide association study.',
			'authors' => 'Sumida  K. et al.',
			'description' => '<p><span>Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assayed genome-wide DNAm in a pilot case-control study of 60 hemodialysis patients with (n=30, cases) and without (n=30, controls) a fatal cardiovascular event. DNAm was profiled on the Illumina EPIC BeadChip. Four established DNAm clocks (i.e., Horvath-, Hannum-, Pheno-, and GrimAge) were used to estimate epigenetic age (DNAmAge). Epigenetic age acceleration (EAA) was derived as the residuals of regressing DNAmAge on chronological age (chroAge), and its association with cardiovascular death was examined using multivariable conditional logistic regression. An epigenome-wide association study (EWAS) was performed to identify differentially methylated CpGs associated with cardiovascular death. All clocks performed well at predicting chroAge (correlation between DNAmAges and chroAge of r=0.76-0.89), with GrimAge showing the largest deviation from chroAge (a mean of +21.3 years). There was no significant association of EAAs with cardiovascular death. In the EWAS, a CpG (cg22305782) in the&nbsp;</span><i>FBXL19</i><span><span>&nbsp;</span>gene had the strongest association with cardiovascular death with significantly lower DNAm in cases vs. controls (</span><i>P</i><sub>FDR</sub><span>=2.0x10</span><sup>-6</sup><span>).<span>&nbsp;</span></span><i>FBXL19</i><span><span>&nbsp;</span>is involved in cell apoptosis, inflammation, and adipogenesis. Overall, we observed more accelerated aging in patients with ESKD, although there was no significant association of EAAs with cardiovascular death. EWAS suggests a potential novel DNAm biomarker for premature cardiovascular mortality in ESKD.</span></p>',
			'date' => '2023-12-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37207321',
			'doi' => '10.1080/15592294.2023.2214394',
			'modified' => '2023-06-15 08:57:02',
			'created' => '2023-06-13 21:11:31',
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		(int) 4 => array(
			'id' => '4830',
			'name' => 'Perturbed epigenetic transcriptional regulation in AML with IDHmutations causes increased susceptibility to NK cells.',
			'authors' => 'Palau  A. et al.',
			'description' => '<p>Isocitrate dehydrogenase (IDH) mutations are found in 20\% of acute myeloid leukemia (AML) patients. However, only 30-40\% of the patients respond to IDH inhibitors (IDHi). We aimed to identify a molecular vulnerability to tailor novel therapies for AML patients with IDH mutations. We characterized the transcriptional and epigenetic landscape with the IDH2i AG-221, using an IDH2 mutated AML cell line model and AML patient cohorts, and discovered a perturbed transcriptional regulatory network involving myeloid transcription factors that were partly restored after AG-221 treatment. In addition, hypermethylation of the HLA cluster caused a down-regulation of HLA class I genes, triggering an enhanced natural killer (NK) cell activation and an increased susceptibility to NK cell-mediated responses. Finally, analyses of DNA methylation data from IDHi-treated patients showed that non-responders still harbored hypermethylation in HLA class I genes. In conclusion, this study provides new insights suggesting that IDH mutated AML is particularly sensitive to NK cell-based personalized immunotherapy.</p>',
			'date' => '2023-07-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37495775',
			'doi' => '10.1038/s41375-023-01972-3',
			'modified' => '2023-08-01 13:39:40',
			'created' => '2023-08-01 15:59:38',
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		),
		(int) 5 => array(
			'id' => '4852',
			'name' => 'In skeletal muscle and neural crest cells, SMCHD1 regulates biologicalpathways relevant for Bosma syndrome and facioscapulohumeral dystrophyphenotype.',
			'authors' => 'Laberthonnière  C. et al.',
			'description' => '<p>Many genetic syndromes are linked to mutations in genes encoding factors that guide chromatin organization. Among them, several distinct rare genetic diseases are linked to mutations in SMCHD1 that encodes the structural maintenance of chromosomes flexible hinge domain containing 1 chromatin-associated factor. In humans, its function as well as the impact of its mutations remains poorly defined. To fill this gap, we determined the episignature associated with heterozygous SMCHD1 variants in primary cells and cell lineages derived from induced pluripotent stem cells for Bosma arhinia and microphthalmia syndrome (BAMS) and type 2 facioscapulohumeral dystrophy (FSHD2). In human tissues, SMCHD1 regulates the distribution of methylated CpGs, H3K27 trimethylation and CTCF at repressed chromatin but also at euchromatin. Based on the exploration of tissues affected either in FSHD or in BAMS, i.e. skeletal muscle fibers and neural crest stem cells, respectively, our results emphasize multiple functions for SMCHD1, in chromatin compaction, chromatin insulation and gene regulation with variable targets or phenotypical outcomes. We concluded that in rare genetic diseases, SMCHD1 variants impact gene expression in two ways: (i) by changing the chromatin context at a number of euchromatin loci or (ii) by directly regulating some loci encoding master transcription factors required for cell fate determination and tissue differentiation.</p>',
			'date' => '2023-06-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/37334829',
			'doi' => '10.1093/nar/gkad523',
			'modified' => '2023-08-01 14:35:38',
			'created' => '2023-08-01 15:59:38',
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		),
		(int) 6 => array(
			'id' => '4767',
			'name' => 'Altered DNA methylation and gene expression predict disease severity inpatients with Aicardi-Goutières syndrome.',
			'authors' => 'Garau  J. et al.',
			'description' => '<p>Aicardi-Gouti&egrave;res Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.</p>',
			'date' => '2023-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36963449',
			'doi' => '10.1016/j.clim.2023.109299',
			'modified' => '2023-04-17 13:07:38',
			'created' => '2023-04-14 13:41:22',
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		(int) 7 => array(
			'id' => '4774',
			'name' => 'DNA methylation aberrancy is a reliable prognostic tool in uveal melanoma',
			'authors' => 'Soltysova  A. et al.',
			'description' => '<p>Despite outstanding advances in understanding the genetic background of uveal melanoma (UM) development and prognosis, the role of DNA methylation reprogramming remains elusive. This study aims to clarify the extent of DNA methylation deregulation in the context of gene expression changes and its utility as a reliable prognostic biomarker. Methods: Transcriptomic and DNA methylation landscapes in 25 high- and low-risk UMs were interrogated by Agilent SurePrint G3 Human Gene Expression 8x60K v2 Microarray and Human Infinium Methylation EPIC Bead Chip array, respectively. DNA methylation and gene expression of the nine top discriminatory genes, selected by the integrative analysis, were validated by pyrosequencing and qPCR in 58 tissues. Results: Among 2,262 differentially expressed genes discovered in UM samples differing in metastatic risk, 60 were epigenetic regulators, mostly histone modifiers and chromatin remodelers. A total of 44,398 CpGs were differentially methylated, 27,810 hypomethylated, and 16,588 hypermethylated in high-risk tumors, with delta beta values ranging between -0.78 and 0.79. By integrative analysis, 944 differentially expressed DNA methylation-regulated genes were revealed, 635 hypomethylated/upregulated, and 309 hypermethylated/downregulated. Aberrant DNA methylation in high-risk tumors was associated with the deregulation of key oncogenic pathways such as EGFR tyrosine kinase inhibitor resistance, focal adhesion, proteoglycans in cancer, PI3K-Akt signaling, or ECM-receptor interaction. Notably, the DNA methylation values of nine genes, HTR2B , AHNAK2, CALHM2, SLC25A38, EDNRB, TLR1, RNF43, IL12RB2 , and MEGF10, validated by pyrosequencing, demonstrated excellent risk group prediction accuracies (AUCs ranging between 0.870 and 0.956). Moreover, CALHM2 hypomethylation and MEGF10, TLR1 hypermethylation, as well as two three-gene methylation signatures, Signature 1 combining A HNAK2, CALHM2, and IL12RB and Signature 2 A HNAK2, CALHM2, and SLC25A38 genes, correlated with shorter overall survival (HR = 4.38, 95\% CI 1.30-16.41, HR = 5.59, 95\% CI 1.30-16.41; HR = 3.43, 95\% CI 1.30-16.41, HR = 4.61, 95\% CI 1.30-16.41 and HR = 4.95, 95\% CI 1.39-17.58, respectively). Conclusions: Our results demonstrate a significant role of DNA methylation aberrancy in UM progression. The advantages of DNA as a biological material and the excellent prediction accuracies of methylation markers open the perspective for their more extensive clinical use.</p>',
			'date' => '2023-02-01',
			'pmid' => 'https://doi.org/10.21203%2Frs.3.rs-2502537%2Fv2',
			'doi' => '10.21203/rs.3.rs-2502537/v2',
			'modified' => '2023-04-17 13:12:52',
			'created' => '2023-04-14 13:41:22',
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		),
		(int) 8 => array(
			'id' => '4565',
			'name' => 'Decitabine increases neoantigen and cancer testis antigen expression toenhance T cell-mediated toxicity against glioblastoma.',
			'authors' => 'Ma  Ruichong et al.',
			'description' => '<p>BACKGROUND: Glioblastoma (GBM) is the most common and malignant primary brain tumour in adults. Despite maximal treatment, median survival remains dismal at 14-24 months. Immunotherapies, such as checkpoint inhibition, have revolutionised management of some cancers but have little benefit for GBM patients. This is, in part, due to the low mutational and neoantigen burden in this immunogenically 'cold' tumour. METHODS: U87MG and patient derived cell lines were treated with 5-aza-2'-deoxycytidine (DAC) and underwent whole exome and transcriptome sequencing. Cell lines were then subjected to cellular assays with neoantigen and cancer testis antigen (CTA) specific T cells. RESULTS: We demonstrate that DAC increases neoantigen and CTA mRNA expression through DNA hypomethylation. This results in increased neoantigen presentation by MHC class I in tumour cells, leading to increased neoantigen- and CTA-specific T cell activation and killing of DAC-treated cancer cells. In addition, we show that patients have endogenous cancer-specific T cells in both tumour and blood, which show increased tumour-specific activation in the presence of DAC-treated cells. CONCLUSIONS: Our work shows that DAC increases GBM immunogenicity and consequent susceptibility to T cell responses in-vitro. Our results support a potential use of DAC as a sensitizing agent to immunotherapy.</p>',
			'date' => '2022-04-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/35468205',
			'doi' => '10.1093/neuonc/noac107',
			'modified' => '2022-11-24 09:12:45',
			'created' => '2022-11-24 08:49:52',
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		(int) 9 => array(
			'id' => '4107',
			'name' => 'Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling',
			'authors' => 'Lucas A Salas, Ze Zhang, Devin C Koestler, Rondi A Butler, Helen M Hansen, Annette M Molinaro, John K Wiencke, Karl T Kelsey, Brock C Christensen',
			'description' => '<p><span>DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, B cells, CD4+ and CD8+ na&iuml;ve and memory cells, natural killer, and T regulatory cells). Including derived variables, our method provides up to 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data both for current and retrospective platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures, and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of the immune system in human health and disease.</span></p>',
			'date' => '2021-04-12',
			'pmid' => 'https://doi.org/10.1101/2021.04.11.439377',
			'doi' => '10.1101/2021.04.11.439377',
			'modified' => '2021-06-29 14:17:36',
			'created' => '2021-06-29 14:17:36',
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		(int) 10 => array(
			'id' => '4105',
			'name' => 'Interplay between Histone and DNA Methylation Seen through Comparative Methylomes in Rare Mendelian Disorders',
			'authors' => ' Guillaume Velasco, Damien Ulveling,Sophie Rondeau,Pauline Marzin,Motoko Unoki,Valérie Cormier-Daire, Claire Francastel',
			'description' => '<p><span>DNA methylation (DNAme) profiling is used to establish specific biomarkers to improve the diagnosis of patients with inherited neurodevelopmental disorders and to guide mutation screening. In the specific case of mendelian disorders of the epigenetic machinery, it also provides the basis to infer mechanistic aspects with regard to DNAme determinants and interplay between histone and DNAme that apply to humans. Here, we present comparative methylomes from patients with mutations in the de novo DNA methyltransferases DNMT3A and DNMT3B, in their catalytic domain or their N-terminal parts involved in reading histone methylation, or in histone H3 lysine (K) methylases NSD1 or SETD2 (H3 K36) or KMT2D/MLL2 (H3 K4). We provide disease-specific DNAme signatures and document the distinct consequences of mutations in enzymes with very similar or intertwined functions, including at repeated sequences and imprinted loci. We found that KMT2D and SETD2 germline mutations have little impact on DNAme profiles. In contrast, the overlapping DNAme alterations downstream of NSD1 or DNMT3 mutations underlines functional links, more specifically between NSD1 and DNMT3B at heterochromatin regions or DNMT3A at regulatory elements. Together, these data indicate certain discrepancy with the mechanisms described in animal models or the existence of redundant or complementary functions unforeseen in humans.</span></p>',
			'date' => '2021-04-03',
			'pmid' => 'https://doi.org/10.3390/ijms22073735',
			'doi' => '10.3390/ijms22073735',
			'modified' => '2021-06-29 14:12:51',
			'created' => '2021-06-29 14:12:51',
			'ProductsPublication' => array(
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		),
		(int) 11 => array(
			'id' => '4106',
			'name' => 'Genome-wide DNA methylation and transcriptome integration reveal distinct sex differences in skeletal muscle',
			'authors' => 'Shanie Landen, Macsue Jacques , Danielle Hiam , Javier Alvarez, Nicholas R Harvey, Larisa M. Haupt, Lyn R, Griffiths, Kevin J Ashton, Séverine Lamon, Sarah Voisin, Nir Eynon',
			'description' => '<p><span>Nearly all human complex traits and diseases exhibit some degree of sex differences, and epigenetics contributes to these differences as DNA methylation shows sex differences in various tissues. However, skeletal muscle epigenetic sex differences remain largely unexplored, yet skeletal muscle displays distinct sex differences at the transcriptome level. We conducted a large-scale meta-analysis of autosomal DNA methylation sex differences in human skeletal muscle in three separate cohorts (Gene SMART, FUSION, and GSE38291), totalling n = 369 human muscle samples (n = 222 males, n = 147 females). We found 10,240 differentially methylated regions (DMRs) at FDR &lt; 0.005, 94% of which were hypomethylated in males, and gene set enrichment analysis revealed that differentially methylated genes were involved in muscle contraction and metabolism. We then integrated our epigenetic results with transcriptomic data from the GTEx database and the FUSION cohort. Altogether, we identified 326 autosomal genes that display sex differences at both the DNA methylation, and transcriptome levels. Importantly, sex-biased genes at the transcriptional level were overrepresented among the sex-biased genes at the epigenetic level (p-value = 4.6e-13), which suggests differential DNA methylation and gene expression between males and females in muscle are functionally linked. Finally, we validated expression of three genes with large effect sizes (FOXO3A, ALDH1A1, and GGT7) in the Gene SMART cohort with qPCR. GGT7, involved in muscle metabolism, displays male-biased expression in skeletal muscle across the three cohorts, as well as lower methylation in males. In conclusion, we uncovered thousands of genes that exhibit DNA methylation differences between the males and females in human skeletal muscle that may modulate mechanisms controlling muscle metabolism and health.</span></p>',
			'date' => '2021-03-17',
			'pmid' => 'https://doi.org/10.1101/2021.03.16.435733',
			'doi' => '10.1101/2021.03.16.435733',
			'modified' => '2021-06-29 14:15:50',
			'created' => '2021-06-29 14:15:50',
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		(int) 12 => array(
			'id' => '4358',
			'name' => 'ZNF718, HOXA4, and ZFP57 are differentially methylated inperiodontitis in comparison with periodontal health: Epigenome-wide DNAmethylation pilot study.',
			'authors' => 'Hernández H.G. et al. ',
			'description' => '<p>OBJECTIVE: To investigate the differences in the epigenomic patterns of DNA methylation in peripheral leukocytes between patients with periodontitis and gingivally healthy controls evaluating its functional meaning by functional enrichment analysis. BACKGROUND: The DNA methylation profiling of peripheral leukocytes as immune-related tissue potentially relevant as a source of biomarkers between periodontitis patients and gingivally healthy subjects has not been investigated. METHODS: A DNA methylation epigenome-wide study of peripheral leukocytes was conducted using the Illumina MethylationEPIC platform in sixteen subjects, eight diagnosed with periodontitis patients and eight age-matched and sex-matched periodontally healthy controls. A trained periodontist performed the clinical evaluation. Global DNA methylation was estimated using methylation-sensitive high-resolution melting in LINE1. Routine cell count cytometry and metabolic laboratory tests were also performed. The analysis of differentially methylated positions (DMPs) and differentially methylated regions (DMRs) was made using R/Bioconductor environment considering leukocyte populations assessed in both routine cell counts and using the FlowSorted.Blood.EPIC package. Finally, a DMP and DMR intersection analysis was performed. Functional enrichment analysis was carried out with the differentially methylated genes found in DMP. RESULTS: DMP analysis identified 81 differentially hypermethylated genes and 21 differentially hypomethylated genes. Importantly, the intersection analysis showed that zinc finger protein 718 (ZNF718) and homeobox A4 (HOXA4) were differentially hypermethylated and zinc finger protein 57 (ZFP57) was differentially hypomethylated in periodontitis. The functional enrichment analysis found clearly immune-related ontologies such as "detection of bacterium" and "antigen processing and presentation." CONCLUSION: The results of this study propose three new periodontitis-related genes: ZNF718, HOXA4, and ZFP57 but also evidence the suitability and relevance of studying leukocytes' DNA methylome for biological interpretation of systemic immune-related epigenetic patterns in periodontitis.</p>',
			'date' => '2021-03-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/33660869',
			'doi' => '10.1111/jre.12868',
			'modified' => '2022-08-03 16:48:52',
			'created' => '2022-05-19 10:41:50',
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			'name' => 'From methylation to myelination: epigenomic and transcriptomic profilingof chronic inactive demyelinated multiple sclerosis lesions',
			'authors' => 'Tiane  A. et al.',
			'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
			'date' => '0000-00-00',
			'pmid' => 'https://doi.org/10.1101%2F2023.01.12.523740',
			'doi' => '10.1101/2023.01.12.523740',
			'modified' => '2023-04-11 10:06:33',
			'created' => '2023-02-21 09:59:46',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		)
	),
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            </div>

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        <div class="small-12 columns" >
            <h6 style="height:60px">メチル化データ解析</h6>
        </div>

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</li>
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<p>There are many alternatives available to study genome methylation. Based on the width of genome coverage, we can undertake projects such as:</p>
<ul class="square">
<li><strong>Whole Genome Bisulfite Sequencing</strong> (WGBS) which covers the entire genome</li>
<li><strong>Reduced Representation Bisulfite Sequencing</strong> (RRBS), limited to CpG-rich regions in promoters</li>
<li><strong>Bisulfite Amplicon Sequencing</strong> (BSAS), limited to targeted regions of interest (few genes)</li>
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<p>Based on the cytosine resolution, the analysis can be made at:</p>
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<li><strong>Single base scale</strong> (for each cytosine in a CpG context &ndash; WGBS, RRBS, BSAS, EPIC, etc)</li>
<li><strong>Enrichment based method</strong> (MeDIP-Seq)</li>
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<h2>What do we provide with the analysis?</h2>
<ul class="accordion" data-accordion="" id="analysis">
<li class="accordion-navigation"><a href="#first"> <i class="fa fa-square-o"></i> Single-base resolution Analysis (WGBS, RRBS, BSAS, EPIC)</a>
<div id="first" class="content">
<p>This analysis provides information on each single CpG with its methylation percentage.</p>
<h3 class="diacol" style="font-weight: 100;">Standard Analysis:</h3>
<ul>
<li>Summary statistics (total sequenced reads, total mapping reads, uniquely aligned reads, PCR duplicates (WGBS), number of CpGs detected, average coverage at CpG sites, number of CpGs detected with coverage greater than 10x, etc.)</li>
<li>Trimmed and filtered reads in fastQ files after sequencing QC</li>
<li>BAM sorted files from alignment to reference genome (indexed bam files and bigwig files included)</li>
<li>BED files from methylation calling and extraction (CpG location, number of methylated cytosines, number of unmethylated cytosines and coverage at the CpG site)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Advanced Analysis</h3>
<ul>
<li>Comparative analysis (also called differential analysis) aimed at finding differentially methylated CpGs (DMCs) and differentially methylated regions (DMRs) between two groups of samples</li>
<li>Annotation of DMCs and DMRs for genomic regions (exons, introns, etc) and for CpG island location (islands, shores, shelves, etc)</li>
<li>Gene ontology enrichment analysis on genes associated with DMCs and DMRs</li>
<li>Pathway enrichment analysis on genes associated with DMCs and DMRs (KEGG or DOSE for human samples)</li>
</ul>
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p class="text-left">If you require a type of analysis that is not in the previous list, <a href="#" data-reveal-id="quoteModal-3061">please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
<li class="accordion-navigation"><a href="#second"> <i class="fa fa-square-o"></i> Methylated region resolution Analysis (MeDIP-Seq):</a>
<div id="second" class="content">
<h3 class="diacol" style="font-weight: 100;">Customized Analysis</h3>
<p><a href="#" data-reveal-id="quoteModal-3061">Please consult with our expert bioinformatics team</a>.</p>
</div>
</li>
</ul>
</div>
<div class="extra-spaced"><center><img src="https://www.diagenode.com/img/product/services/cytosine-schema.png" /></center></div>',
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	'description' => '<p>Introduction In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity.</p>',
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