ChIP-seq Profiling Service

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	'meta_keywords' => 'ChIP-seq services,Chromatin shearing, Immunoprecipitation,Library Preparation ',
	'meta_description' => 'Diagenode offers end-to-end or customized ChIP-seq service including wet lab and bioinformatic services. Collaborative and customized project design to meet your needs. Dedicated in-house expert.',
	'meta_title' => 'Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Services | Diagenode',
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			'name' => 'ChIP-seq Profiling Service',
			'description' => '<p><strong>Chromatin immunoprecipitation followed by sequencing</strong> (<strong>ChIP-seq</strong>) is a powerful method allowing the genome-wide identification of DNA binding sites for proteins such as modified histones, transcription factors or chromatin remodelers. Determining how proteins interact with DNA to regulate gene expression is essential to fully understand many biological processes such as cell differentiation, organ development, and disease progression. The experts from our Epigenomics Profiling Services can help you to design your ChIP-seq project, optimize the workflow for your specific sample type/target, and provide you with high quality ChIP-seq data.</p>
<p>We use in house optimized reagents:</p>
<ul>
<li>Validated ChIP-seq Kits</li>
<li>Validated ChIP-seq grade antibodies</li>
</ul>
<p>And in-house developed equipment:</p>
<ul>
<li><strong>Bioruptor </strong>&ndash; for efficient and reproducible chromatin shearing</li>
</ul>
<p>We have expertise with many different types of samples as well as with a broad range of chromatin marks and can provide you with quality data even on very low input samples. Our bioinformatic experts will closely work with you to provide you with standard or customized analysis and will generate comprehensive publication-ready figures.</p>
<ul>
<li>Collaborative and customized project design to meet your needs</li>
<li>Dedicated in-house expert</li>
<li>End-to-end or customized service including wet lab and bioinformatic services</li>
</ul>
<p class="text-center"><img alt="ChIP Sequencing Services" src="https://www.diagenode.com/img/categories/chip_seq/ChIP-seq-success-experiment-WEB.png" /></p>
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			'label1' => 'ChIP-seq service description',
			'info1' => '<p>In order to provide you with the highest quality data, our ChIP-seq service is composed of two major steps:</p>
<ol>
<li><strong>ChIP validation</strong> - during this step we optimize the ChIP conditions that depend on your sample type, target and amount of cells.</li>
<li><strong>ChIP on sample of interest</strong> - once the best ChIP conditions are validated, the samples of interest can be processed.</li>
</ol>
<p></p>
<p>ChIP-seq service includes:</p>
<table style="width: 918px;">
<tbody>
<tr style="height: 62px;">
<td style="width: 218px; height: 220px; background-color: #f9f9f9;" rowspan="3"><strong>1. ChIP validation</strong></td>
<td style="width: 417px; height: 62px;"><strong>Chromatin Shearing validation</strong></td>
<td style="width: 568px; height: 62px;">
<ul>
<li>Testing 2 shearing times for each cell type/tissue type</li>
</ul>
</td>
</tr>
<tr style="height: 27px;">
<td style="width: 417px; height: 27px; background-color: white;"><strong>ChIP/Ab validation</strong></td>
<td style="width: 417px; height: 27px; background-color: white;">
<ul>
<li>Testing 2 Ab amounts and/or 2 Ab references per target of interest</li>
<li>qPCR analysis if positive and negative control regions can be provided</li>
<li>Library preparation on IPs and INPUTs material</li>
<li>Illumina sequencing run : Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 131px;">
<td style="width: 417px; height: 131px;"><strong>Primers design and validation</strong></td>
<td style="width: 568px; height: 131px;">
<ul>
<li>Performed on gDNA based on the data obtained during the ChIP/Ab validation. Those primers will be used for further qPCR validation of the ChIP on the samples of interest</li>
</ul>
</td>
</tr>
<tr style="height: 38px;">
<td style="width: 218px; height: 38px; background-color: white;"></td>
<td style="width: 218px; height: 38px;"></td>
<td style="width: 218px; height: 38px;"></td>
</tr>
<tr style="height: 59px;">
<td style="width: 218px; height: 216px; background-color: #f9f9f9; text-align: center;" rowspan="3"><strong>2. ChIP on sample of interest</strong></td>
<td style="width: 417px; height: 59px;"><strong>Chromatin IP</strong></td>
<td style="width: 568px; height: 59px;">
<ul>
<li>Chromatin shearing</li>
<li>IPs</li>
<li>qPCR analysis using primers that have been validated during the ChIP validation</li>
</ul>
</td>
</tr>
<tr style="height: 69px;">
<td style="width: 568px; height: 69px; background-color: white;"><strong>Library Preparation</strong></td>
<td style="width: 568px; height: 69px; background-color: white;">
<ul>
<li>Library preparation and purification on IPs and INPUTs material</li>
</ul>
</td>
</tr>
<tr style="height: 88px;">
<td style="width: 417px; height: 88px;"><strong>Sequencing</strong></td>
<td style="width: 568px; height: 88px;">
<ul>
<li>Illumina sequencing run: Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
</ul>
</td>
</tr>
</tbody>
</table>
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<table style="width: 972px;">
<tbody>
<tr style="height: 107px; border-color: 1px;">
<td style="width: 137px; height: 107px; background-color: #f9f9f9; text-align: center;"><strong>Standard bioinformatics</strong></td>
<td style="width: 358px; height: 107px;">
<p style="text-align: center;"></p>
</td>
<td style="width: 466px; height: 107px;">
<ul>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 24px;">
<td style="width: 137px; height: 24px; background-color: white; text-align: center;"><strong></strong></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
</tr>
<tr style="height: 41px;">
<td style="width: 137px; height: 170px; background-color: #f9f9f9; text-align: center;" rowspan="5"><strong>Advanced bioinformatics</strong></td>
<td style="width: 466px; height: 41px; background-color: white;">
<p style="text-align: center;"><strong>Differential binding analysis</strong></p>
</td>
<td style="width: 466px; height: 41px; background-color: white;">
<ul>
<li>Identification and annotation of differential binding sites between samples based on previously identified ChIP-seq peaks</li>
</ul>
</td>
</tr>
<tr style="height: 33px;">
<td style="width: 466px; height: 33px; background-color: white; text-align: center;"><strong>Annotation in genomic regions</strong></td>
<td style="width: 466px; height: 33px; background-color: white;">
<ul>
<li>Annotation of ChIP-seq peaks with genomic regions: introns, exons, promoters, 1-to-5 kb upstream-TSS and intergenic regions</li>
</ul>
</td>
</tr>
<tr style="height: 26px;">
<td style="width: 466px; height: 26px; background-color: white; text-align: center;"><strong>Gene ontology termes analysis</strong></td>
<td style="width: 466px; height: 26px; background-color: white;">
<ul>
<li>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in bound regions or differentially bound regions may indicate the underlying biological processes involved</li>
</ul>
</td>
</tr>
<tr style="height: 56px;">
<td style="width: 466px; height: 56px; background-color: white; text-align: center;"><strong>Pathway analysis</strong></td>
<td style="width: 466px; height: 56px; background-color: white;">
<ul>
<li>Identify biochemical pathways in which genes associated with bound regions or differentially bound regions may be overrepresented</li>
</ul>
</td>
</tr>
<tr style="height: 14px;">
<td style="width: 466px; height: 14px; background-color: white; text-align: center;"><strong>Visualization of specific genomic regions</strong></td>
<td style="width: 466px; height: 14px; background-color: white;">
<ul>
<li>Visualization of results (i.e. sequencing data, peaks) at specific genomic regions (e.g. genes, promoters) in publication-ready images</li>
</ul>
</td>
</tr>
</tbody>
</table>
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$meta_title = 'Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Services | Diagenode'
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		'name' => 'ChIP-seq Profiling Service',
		'description' => '<p><strong>Chromatin immunoprecipitation followed by sequencing</strong> (<strong>ChIP-seq</strong>) is a powerful method allowing the genome-wide identification of DNA binding sites for proteins such as modified histones, transcription factors or chromatin remodelers. Determining how proteins interact with DNA to regulate gene expression is essential to fully understand many biological processes such as cell differentiation, organ development, and disease progression. The experts from our Epigenomics Profiling Services can help you to design your ChIP-seq project, optimize the workflow for your specific sample type/target, and provide you with high quality ChIP-seq data.</p>
<p>We use in house optimized reagents:</p>
<ul>
<li>Validated ChIP-seq Kits</li>
<li>Validated ChIP-seq grade antibodies</li>
</ul>
<p>And in-house developed equipment:</p>
<ul>
<li><strong>Bioruptor </strong>&ndash; for efficient and reproducible chromatin shearing</li>
</ul>
<p>We have expertise with many different types of samples as well as with a broad range of chromatin marks and can provide you with quality data even on very low input samples. Our bioinformatic experts will closely work with you to provide you with standard or customized analysis and will generate comprehensive publication-ready figures.</p>
<ul>
<li>Collaborative and customized project design to meet your needs</li>
<li>Dedicated in-house expert</li>
<li>End-to-end or customized service including wet lab and bioinformatic services</li>
</ul>
<p class="text-center"><img alt="ChIP Sequencing Services" src="https://www.diagenode.com/img/categories/chip_seq/ChIP-seq-success-experiment-WEB.png" /></p>
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		'label1' => 'ChIP-seq service description',
		'info1' => '<p>In order to provide you with the highest quality data, our ChIP-seq service is composed of two major steps:</p>
<ol>
<li><strong>ChIP validation</strong> - during this step we optimize the ChIP conditions that depend on your sample type, target and amount of cells.</li>
<li><strong>ChIP on sample of interest</strong> - once the best ChIP conditions are validated, the samples of interest can be processed.</li>
</ol>
<p></p>
<p>ChIP-seq service includes:</p>
<table style="width: 918px;">
<tbody>
<tr style="height: 62px;">
<td style="width: 218px; height: 220px; background-color: #f9f9f9;" rowspan="3"><strong>1. ChIP validation</strong></td>
<td style="width: 417px; height: 62px;"><strong>Chromatin Shearing validation</strong></td>
<td style="width: 568px; height: 62px;">
<ul>
<li>Testing 2 shearing times for each cell type/tissue type</li>
</ul>
</td>
</tr>
<tr style="height: 27px;">
<td style="width: 417px; height: 27px; background-color: white;"><strong>ChIP/Ab validation</strong></td>
<td style="width: 417px; height: 27px; background-color: white;">
<ul>
<li>Testing 2 Ab amounts and/or 2 Ab references per target of interest</li>
<li>qPCR analysis if positive and negative control regions can be provided</li>
<li>Library preparation on IPs and INPUTs material</li>
<li>Illumina sequencing run : Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 131px;">
<td style="width: 417px; height: 131px;"><strong>Primers design and validation</strong></td>
<td style="width: 568px; height: 131px;">
<ul>
<li>Performed on gDNA based on the data obtained during the ChIP/Ab validation. Those primers will be used for further qPCR validation of the ChIP on the samples of interest</li>
</ul>
</td>
</tr>
<tr style="height: 38px;">
<td style="width: 218px; height: 38px; background-color: white;"></td>
<td style="width: 218px; height: 38px;"></td>
<td style="width: 218px; height: 38px;"></td>
</tr>
<tr style="height: 59px;">
<td style="width: 218px; height: 216px; background-color: #f9f9f9; text-align: center;" rowspan="3"><strong>2. ChIP on sample of interest</strong></td>
<td style="width: 417px; height: 59px;"><strong>Chromatin IP</strong></td>
<td style="width: 568px; height: 59px;">
<ul>
<li>Chromatin shearing</li>
<li>IPs</li>
<li>qPCR analysis using primers that have been validated during the ChIP validation</li>
</ul>
</td>
</tr>
<tr style="height: 69px;">
<td style="width: 568px; height: 69px; background-color: white;"><strong>Library Preparation</strong></td>
<td style="width: 568px; height: 69px; background-color: white;">
<ul>
<li>Library preparation and purification on IPs and INPUTs material</li>
</ul>
</td>
</tr>
<tr style="height: 88px;">
<td style="width: 417px; height: 88px;"><strong>Sequencing</strong></td>
<td style="width: 568px; height: 88px;">
<ul>
<li>Illumina sequencing run: Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
</ul>
</td>
</tr>
</tbody>
</table>
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		'label2' => 'Bioinformatic analysis',
		'info2' => '<h3>Bioinformatic analyses on samples of interest</h3>
<table style="width: 972px;">
<tbody>
<tr style="height: 107px; border-color: 1px;">
<td style="width: 137px; height: 107px; background-color: #f9f9f9; text-align: center;"><strong>Standard bioinformatics</strong></td>
<td style="width: 358px; height: 107px;">
<p style="text-align: center;"></p>
</td>
<td style="width: 466px; height: 107px;">
<ul>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 24px;">
<td style="width: 137px; height: 24px; background-color: white; text-align: center;"><strong></strong></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
</tr>
<tr style="height: 41px;">
<td style="width: 137px; height: 170px; background-color: #f9f9f9; text-align: center;" rowspan="5"><strong>Advanced bioinformatics</strong></td>
<td style="width: 466px; height: 41px; background-color: white;">
<p style="text-align: center;"><strong>Differential binding analysis</strong></p>
</td>
<td style="width: 466px; height: 41px; background-color: white;">
<ul>
<li>Identification and annotation of differential binding sites between samples based on previously identified ChIP-seq peaks</li>
</ul>
</td>
</tr>
<tr style="height: 33px;">
<td style="width: 466px; height: 33px; background-color: white; text-align: center;"><strong>Annotation in genomic regions</strong></td>
<td style="width: 466px; height: 33px; background-color: white;">
<ul>
<li>Annotation of ChIP-seq peaks with genomic regions: introns, exons, promoters, 1-to-5 kb upstream-TSS and intergenic regions</li>
</ul>
</td>
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<tr style="height: 26px;">
<td style="width: 466px; height: 26px; background-color: white; text-align: center;"><strong>Gene ontology termes analysis</strong></td>
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<tr style="height: 56px;">
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
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<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
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</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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<p>ヒストンの翻訳後修飾は、遺伝子発現を調節します。 Diagenodeのエピゲノムプロファイリングサービスでは、活性化プロモーターとエンハンサー解析を用いて、調節エレメントとその相互作用タンパク質を調べて、 転写調節を理解するためにChIP-seq解析によるゲノム全体のヒストン修飾をプロファイルします。</p>
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<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/chip-workflow.png" alt="Chromatin-Immunoprecipitation-Diagenode" /></center>
<p>Chromatin consists of DNA, histones and non-histone proteins. Understanding the roles of histones and transcription factors is critical in understanding the regulation of gene expression.</p>
<p>Using ChIP-seq analysis, it is possible to profile histone modifications and transcription factor binding genome-wide to&nbsp;elucidate&nbsp;control of gene expression in disease or in response to a drug treatment. Diagenode&rsquo;s Epigenomic Profiling Services offer a wide variety of chromatin analysis options through ChIP-seq and ATAC-seq.</p>
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<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns"><center><img src="http://www.diagenode.com/img/applications/histone-marks-helice.png" alt="Histone-Antibodies-Diagenode" /></center><br href="../p/service-chip-seq" />
<h3><a href="../p/atac-seq-service">ATAC-seq: open chromatin regions</a></h3>
<p>Study genome-wide chromatin accessibility. Identify open chromatin sites and active regulatory elements such as promoter, enhancers, and insulators.</p>
<ul>
<ul>
<li>Nucleosome positioning</li>
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<h3><a href="../p/service-chip-seq">ChIP-seq histone modifications</a></h3>
<p>Post-translational modification of histones is implicated in the regulation of gene expression, necessitating the study of regulatory elements and their interacting proteins like active promoter and enhancer analysis. Profile genome-wide histone modifications by ChIP-seq analysis to understand transcriptional regulation</p>
<ul>
<li><strong>Active promoter profiling</strong>: H3K4me3 enrichment</li>
<li><strong>Inactive promoter profiling</strong>: H3K27me3 enrichment</li>
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<p>Explore the effects of transcription factor binding through ChIP-seq analysis of a multitude of TFs including:</p>
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<li>p300/CBP: histone acetyltransferase</li>
<li>Pol II, p53, and more</li>
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<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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<h3><span class="green">Chromatin</span>&nbsp;analysis</h3>
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<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>
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<div class="small-12 medium-4 large-4 columns text-left">
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<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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<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>
</ul>
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			'id' => '4577',
			'name' => 'Impact of Fetal Exposure to Endocrine Disrupting ChemicalMixtures on FOXA3 Gene and Protein Expression in Adult RatTestes.',
			'authors' => 'Walker  C. et al.',
			'description' => '<p>Perinatal exposure to endocrine disrupting chemicals (EDCs) has been shown to affect male reproductive functions. However, the effects on male reproduction of exposure to EDC mixtures at doses relevant to humans have not been fully characterized. In previous studies, we found that in utero exposure to mixtures of the plasticizer di(2-ethylhexyl) phthalate (DEHP) and the soy-based phytoestrogen genistein (Gen) induced abnormal testis development in rats. In the present study, we investigated the molecular basis of these effects in adult testes from the offspring of pregnant SD rats gavaged with corn oil or Gen + DEHP mixtures at 0.1 or 10 mg/kg/day. Testicular transcriptomes were determined by microarray and RNA-seq analyses. A protein analysis was performed on paraffin and frozen testis sections, mainly by immunofluorescence. The transcription factor forkhead box protein 3 (FOXA3), a key regulator of Leydig cell function, was identified as the most significantly downregulated gene in testes from rats exposed in utero to Gen + DEHP mixtures. FOXA3 protein levels were decreased in testicular interstitium at a dose previously found to reduce testosterone levels, suggesting a primary effect of fetal exposure to Gen + DEHP on adult Leydig cells, rather than on spermatids and Sertoli cells, also expressing FOXA3. Thus, FOXA3 downregulation in adult testes following fetal exposure to Gen + DEHP may contribute to adverse male reproductive outcomes.</p>',
			'date' => '2023-01-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36674726',
			'doi' => '10.3390/ijms24021211',
			'modified' => '2023-04-11 10:18:58',
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			'id' => '4545',
			'name' => 'Histone Deacetylases 1 and 2 target gene regulatory networks of nephronprogenitors to control nephrogenesis.',
			'authors' => 'Liu  Hongbing et al.',
			'description' => '<p>Our studies demonstrated the critical role of Histone deacetylases (HDACs) in the regulation of nephrogenesis. To better understand the key pathways regulated by HDAC1/2 in early nephrogenesis, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) of Hdac1/2 on isolated nephron progenitor cells (NPCs) from mouse E16.5 kidneys. Our analysis revealed that 11802 (40.4\%) of Hdac1 peaks overlap with Hdac2 peaks, further demonstrates the redundant role of Hdac1 and Hdac2 during nephrogenesis. Common Hdac1/2 peaks are densely concentrated close to the transcriptional start site (TSS). GREAT Gene Ontology analysis of overlapping Hdac1/2 peaks reveals that Hdac1/2 are associated with metanephric nephron morphogenesis, chromatin assembly or disassembly, as well as other DNA checkpoints. Pathway analysis shows that negative regulation of Wnt signaling pathway is one of Hdac1/2's most significant function in NPCs. Known motif analysis indicated that Hdac1 is enriched in motifs for Six2, Hox family, and Tcf family members, which are essential for self-renewal and differentiation of nephron progenitors. Interestingly, we found the enrichment of HDAC1/2 at the enhancer and promoter regions of actively transcribed genes, especially those concerned with NPC self-renewal. HDAC1/2 simultaneously activate or repress the expression of different genes to maintain the cellular state of nephron progenitors. We used the Integrative Genomics Viewer to visualize these target genes associated with each function and found that Hdac1/2 co-bound to the enhancers or/and promoters of genes associated with nephron morphogenesis, differentiation, and cell cycle control. Taken together, our ChIP-Seq analysis demonstrates that Hdac1/2 directly regulate the molecular cascades essential for nephrogenesis.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36356658',
			'doi' => '10.1016/j.bcp.2022.115341',
			'modified' => '2022-11-24 10:24:07',
			'created' => '2022-11-24 08:49:52',
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		(int) 2 => array(
			'id' => '4535',
			'name' => 'Identification of genomic binding sites and direct target genes for thetranscription factor DDIT3/CHOP.',
			'authors' => 'Osman  A. et al.',
			'description' => '<p>DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 is believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36402425',
			'doi' => '10.1016/j.yexcr.2022.113418',
			'modified' => '2022-11-25 08:47:49',
			'created' => '2022-11-24 08:49:52',
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			'id' => '4217',
			'name' => 'CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells.',
			'authors' => 'Bommi-Reddy A. et al.',
			'description' => '<p><span>Therapeutic targeting of the estrogen receptor (ER) is a clinically validated approach for estrogen receptor positive breast cancer (ER+ BC), but sustained response is limited by acquired resistance. Targeting the transcriptional coactivators required for estrogen receptor activity represents an alternative approach that is not subject to the same limitations as targeting estrogen receptor itself. In this report we demonstrate that the acetyltransferase activity of coactivator paralogs CREBBP/EP300 represents a promising therapeutic target in ER+ BC. Using the potent and selective inhibitor CPI-1612, we show that CREBBP/EP300 acetyltransferase inhibition potently suppresses in vitro and in vivo growth of breast cancer cell line models and acts in a manner orthogonal to directly targeting ER. CREBBP/EP300 acetyltransferase inhibition suppresses ER-dependent transcription by targeting lineage-specific enhancers defined by the pioneer transcription factor FOXA1. These results validate CREBBP/EP300 acetyltransferase activity as a viable target for clinical development in ER+ breast cancer.</span></p>',
			'date' => '2022-03-30',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/35353838/',
			'doi' => '10.1371/journal.pone.0262378',
			'modified' => '2022-04-12 10:56:54',
			'created' => '2022-04-12 10:56:54',
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		(int) 4 => array(
			'id' => '4091',
			'name' => 'Epigenetic regulation of the lineage specificity of primary human dermallymphatic and blood vascular endothelial cells.',
			'authors' => 'Tacconi, Carlotta and He, Yuliang and Ducoli, Luca and Detmar, Michael',
			'description' => '<p>Lymphatic and blood vascular endothelial cells (ECs) share several molecular and developmental features. However, these two cell types possess distinct phenotypic signatures, reflecting their different biological functions. Despite significant advances in elucidating how the specification of lymphatic and blood vascular ECs is regulated at the transcriptional level during development, the key molecular mechanisms governing their lineage identity under physiological or pathological conditions remain poorly understood. To explore the epigenomic signatures in the maintenance of EC lineage specificity, we compared the transcriptomic landscapes, histone composition (H3K4me3 and H3K27me3) and DNA methylomes of cultured matched human primary dermal lymphatic and blood vascular ECs. Our findings reveal that blood vascular lineage genes manifest a more 'repressed' histone composition in lymphatic ECs, whereas DNA methylation at promoters is less linked to the differential transcriptomes of lymphatic versus blood vascular ECs. Meta-analyses identified two transcriptional regulators, BCL6 and MEF2C, which potentially govern endothelial lineage specificity. Notably, the blood vascular endothelial lineage markers CD34, ESAM and FLT1 and the lymphatic endothelial lineage markers PROX1, PDPN and FLT4 exhibited highly differential epigenetic profiles and responded in distinct manners to epigenetic drug treatments. The perturbation of histone and DNA methylation selectively promoted the expression of blood vascular endothelial markers in lymphatic endothelial cells, but not vice versa. Overall, our study reveals that the fine regulation of lymphatic and blood vascular endothelial transcriptomes is maintained via several epigenetic mechanisms, which are crucial to the maintenance of endothelial cell identity.</p>',
			'date' => '2020-09-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/32918672',
			'doi' => '10.1007/s10456-020-09743-9',
			'modified' => '2021-03-17 17:09:36',
			'created' => '2021-02-18 10:21:53',
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			'id' => '4031',
			'name' => 'Battle of the sex chromosomes: competition between X- and Y-chromosomeencoded proteins for partner interaction and chromatin occupancy drivesmulti-copy gene expression and evolution in muroid rodents.',
			'authors' => 'Moretti, C and Blanco, M and Ialy-Radio, C and Serrentino, ME and Gobé,C and Friedman, R and Battail, C and Leduc, M and Ward, MA and Vaiman, Dand Tores, F and Cocquet, J',
			'description' => '<p>Transmission distorters (TDs) are genetic elements that favor their own transmission to the detriments of others. Slx/Slxl1 (Sycp3-like-X-linked and Slx-like1) and Sly (Sycp3-like-Y-linked) are TDs which have been co-amplified on the X and Y chromosomes of Mus species. They are involved in an intragenomic conflict in which each favors its own transmission, resulting in sex ratio distortion of the progeny when Slx/Slxl1 vs. Sly copy number is unbalanced. They are specifically expressed in male postmeiotic gametes (spermatids) and have opposite effects on gene expression: Sly knockdown leads to the upregulation of hundreds of spermatid-expressed genes, while Slx/Slxl1-deficiency downregulates them. When both Slx/Slxl1 and Sly are knocked-down, sex ratio distortion and gene deregulation are corrected. Slx/Slxl1 and Sly are, therefore, in competition but the molecular mechanism remains unknown. By comparing their chromatin binding profiles and protein partners, we show that SLX/SLXL1 and SLY proteins compete for interaction with H3K4me3-reader SSTY1 (Spermiogenesis-specific-transcript-on-the-Y1) at the promoter of thousands of genes to drive their expression, and that the opposite effect they have on gene expression is mediated by different abilities to recruit SMRT/N-Cor transcriptional complex. Their target genes are predominantly spermatid-specific multicopy genes encoded by the sex chromosomes and the autosomal Speer/Takusan. Many of them have co-amplified with Slx/Slxl1/Sly but also Ssty during muroid rodent evolution. Overall, we identify Ssty as a key element of the X vs. Y intragenomic conflict, which may have influenced gene content and hybrid sterility beyond Mus lineage since Ssty amplification on the Y pre-dated that of Slx/Slxl1/Sly.</p>',
			'date' => '2020-07-13',
			'pmid' => 'http://www.pubmed.gov/32658962',
			'doi' => '10.1093/molbev/msaa175/5870835',
			'modified' => '2020-12-18 13:27:51',
			'created' => '2020-10-12 14:54:59',
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		),
		(int) 6 => array(
			'id' => '3922',
			'name' => 'Multi-omic analysis of gametogenesis reveals a novel signature at the promoters and distal enhancers of active genes.',
			'authors' => 'Crespo M, Damont A, Blanco M, Lastrucci E, Kennani SE, Ialy-Radio C, Khattabi LE, Terrier S, Louwagie M, Kieffer-Jaquinod S, Hesse AM, Bruley C, Chantalat S, Govin J, Fenaille F, Battail C, Cocquet J, Pflieger D',
			'description' => '<p>Epigenetic regulation of gene expression is tightly controlled by the dynamic modification of histones by chemical groups, the diversity of which has largely expanded over the past decade with the discovery of lysine acylations, catalyzed from acyl-coenzymes A. We investigated the dynamics of lysine acetylation and crotonylation on histones H3 and H4 during mouse spermatogenesis. Lysine crotonylation appeared to be of significant abundance compared to acetylation, particularly on Lys27 of histone H3 (H3K27cr) that accumulates in sperm in a cleaved form of H3. We identified the genomic localization of H3K27cr and studied its effects on transcription compared to the classical active mark H3K27ac at promoters and distal enhancers. The presence of both marks was strongly associated with highest gene expression. Assessment of their co-localization with transcription regulators (SLY, SOX30) and chromatin-binding proteins (BRD4, BRDT, BORIS and CTCF) indicated systematic highest binding when both active marks were present and different selective binding when present alone at chromatin. H3K27cr and H3K27ac finally mark the building of some sperm super-enhancers. This integrated analysis of omics data provides an unprecedented level of understanding of gene expression regulation by H3K27cr in comparison to H3K27ac, and reveals both synergistic and specific actions of each histone modification.</p>',
			'date' => '2020-03-17',
			'pmid' => 'http://www.pubmed.gov/32182340',
			'doi' => '10.1093/nar/gkaa163',
			'modified' => '2020-08-17 10:56:19',
			'created' => '2020-08-10 12:12:25',
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		),
		(int) 7 => array(
			'id' => '3848',
			'name' => 'A comprehensive epigenomic analysis of phenotypically distinguishable, genetically identical female and male Daphnia pulex.',
			'authors' => 'Kvist J, Athanàsio CG, Pfrender ME, Brown JB, Colbourne JK, Mirbahai L',
			'description' => '<p>BACKGROUND: Daphnia species reproduce by cyclic parthenogenesis involving both sexual and asexual reproduction. The sex of the offspring is environmentally determined and mediated via endocrine signalling by the mother. Interestingly, male and female Daphnia can be genetically identical, yet display large differences in behaviour, morphology, lifespan and metabolic activity. Our goal was to integrate multiple omics datasets, including gene expression, splicing, histone modification and DNA methylation data generated from genetically identical female and male Daphnia pulex under controlled laboratory settings with the aim of achieving a better understanding of the underlying epigenetic factors that may contribute to the phenotypic differences observed between the two genders. RESULTS: In this study we demonstrate that gene expression level is positively correlated with increased DNA methylation, and histone H3 trimethylation at lysine&thinsp;4 (H3K4me3) at predicted promoter regions. Conversely, elevated histone H3 trimethylation at lysine&thinsp;27 (H3K27me3), distributed across the entire transcript length, is negatively correlated with gene expression level. Interestingly, male Daphnia are dominated with epigenetic modifications that globally promote elevated gene expression, while female Daphnia are dominated with epigenetic modifications that reduce gene expression globally. For examples, CpG methylation (positively correlated with gene expression level) is significantly higher in almost all differentially methylated sites in male compared to female Daphnia. Furthermore, H3K4me3 modifications are higher in male compared to female Daphnia in more than 3/4 of the differentially regulated promoters. On the other hand, H3K27me3 is higher in female compared to male Daphnia in more than 5/6 of differentially modified sites. However, both sexes demonstrate roughly equal number of genes that are up-regulated in one gender compared to the other sex. Since, gene expression analyses typically assume that most genes are expressed at equal level among samples and different conditions, and thus cannot detect global changes affecting most genes. CONCLUSIONS: The epigenetic differences between male and female in Daphnia pulex are vast and dominated by changes that promote elevated gene expression in male Daphnia. Furthermore, the differences observed in both gene expression changes and epigenetic modifications between the genders relate to pathways that are physiologically relevant to the observed phenotypic differences.</p>',
			'date' => '2020-01-06',
			'pmid' => 'http://www.pubmed.gov/31906859',
			'doi' => '10.1186/s12864-019-6415-5',
			'modified' => '2020-02-20 11:34:47',
			'created' => '2020-02-13 10:02:44',
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		),
		(int) 8 => array(
			'id' => '3839',
			'name' => 'Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq.',
			'authors' => 'Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR',
			'description' => '<p>One of the primary aims of the Functional Annotation of ANimal Genomes (FAANG) initiative is to characterize tissue-specific regulation within animal genomes. To this end, we used chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in eight prioritized tissues collected as part of the FAANG equine biobank from two thoroughbred mares. Data were generated according to optimized experimental parameters developed during quality control testing. To ensure that we obtained sufficient ChIP and successful peak-calling, data and peak-calls were assessed using six quality metrics, replicate comparisons, and site-specific evaluations. Tissue specificity was explored by identifying binding motifs within unique active regions, and motifs were further characterized by gene ontology (GO) and protein-protein interaction analyses. The histone marks identified in this study represent some of the first resources for tissue-specific regulation within the equine genome. As such, these publicly available annotation data can be used to advance equine studies investigating health, performance, reproduction, and other traits of economic interest in the horse.</p>',
			'date' => '2019-12-18',
			'pmid' => 'http://www.pubmed.gov/31861495',
			'doi' => '10.3390/genes11010003',
			'modified' => '2020-02-20 11:20:25',
			'created' => '2020-02-13 10:02:44',
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		),
		(int) 9 => array(
			'id' => '3742',
			'name' => 'Development and epigenetic plasticity of murine Müller glia.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'date' => '2019-07-02',
			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
			'modified' => '2019-08-13 10:50:24',
			'created' => '2019-07-31 13:35:50',
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		(int) 10 => array(
			'id' => '3569',
			'name' => 'The epigenetic basis for the impaired ability of adult murine retinal pigment epithelium cells to regenerate retinal tissue.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
			'date' => '2019-03-07',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
			'created' => '2019-03-21 14:12:08',
			'ProductsPublication' => array(
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		),
		(int) 11 => array(
			'id' => '3463',
			'name' => 'Epigenetic modifiers promote mitochondrial biogenesis and oxidative metabolism leading to enhanced differentiation of neuroprogenitor cells.',
			'authors' => 'Martine Uittenbogaard, Christine A. Brantner, Anne Chiaramello1',
			'description' => '<p>During neural development, epigenetic modulation of chromatin acetylation is part of a dynamic, sequential and critical process to steer the fate of multipotent neural progenitors toward a specific lineage. Pan-HDAC inhibitors (HDCis) trigger neuronal differentiation by generating an "acetylation" signature and promoting the expression of neurogenic bHLH transcription factors. Our studies and others have revealed a link between neuronal differentiation and increase of mitochondrial mass. However, the neuronal regulation of mitochondrial biogenesis has remained largely unexplored. Here, we show that the HDACi, sodium butyrate (NaBt), promotes mitochondrial biogenesis via the NRF-1/Tfam axis in embryonic hippocampal progenitor cells and neuroprogenitor-like PC12-NeuroD6 cells, thereby enhancing their neuronal differentiation competency. Increased mitochondrial DNA replication by several pan-HDACis indicates a common mechanism by which they regulate mitochondrial biogenesis. NaBt also induces coordinates mitochondrial ultrastructural changes and enhanced OXPHOS metabolism, thereby increasing key mitochondrial bioenergetics parameters in neural progenitor cells. NaBt also endows the neuronal cells with increased mitochondrial spare capacity to confer resistance to oxidative stress associated with neuronal differentiation. We demonstrate that mitochondrial biogenesis is under HDAC-mediated epigenetic regulation, the timing of which is consistent with its integrative role during neuronal differentiation. Thus, our findings add a new facet to our mechanistic understanding of how pan-HDACis induce differentiation of neuronal progenitor cells. Our results reveal the concept that epigenetic modulation of the mitochondrial pool prior to neurotrophic signaling dictates the efficiency of initiation of neuronal differentiation during the transition from progenitor to differentiating neuronal cells. The histone acetyltransferase CREB-binding protein plays a key role in regulating the mitochondrial biomass. By ChIP-seq analysis, we show that NaBt confers an H3K27ac epigenetic signature in several interconnected nodes of nuclear genes vital for neuronal differentiation and mitochondrial reprogramming. Collectively, our study reports a novel developmental epigenetic layer that couples mitochondrial biogenesis to neuronal differentiation.</p>',
			'date' => '2018-03-02',
			'pmid' => 'http://www.pubmed.gov/29500414',
			'doi' => '10.1038/s41419-018-0396-1',
			'modified' => '2019-02-15 21:21:45',
			'created' => '2019-02-14 15:01:22',
			'ProductsPublication' => array(
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		(int) 12 => array(
			'id' => '3212',
			'name' => 'The epigenetic architecture at gene promoters determines cell type-specific LPS tolerance',
			'authors' => 'Kerstin Klein , Mojca Frank-Bertoncelj , Emmanuel Karouzakis , Renate E. Gay , Christoph Kolling , Adrian Ciurea , Nagihan Bostanci , Georgios N. Belibasakis , Lih-Ling Lin , Oliver Distler , Steffen Gay , Caroline Ospelt',
			'description' => '<p>Synovial fibroblasts (SF) drive inflammation and joint destruction in chronic arthritis. Here we show that SF possess a distinct type of LPS tolerance compared to macrophages and other types of fibroblasts. In SF and dermal fibroblasts, genes that were non-tolerizable after repeated LPS stimulation included proinflammatory cytokines, chemokines and matrix metalloproteinases, whereas anti-viral genes were tolerizable. In macrophages, all measured genes were tolerizable, whereas in gingival and foreskin fibroblasts<br />these genes were non-tolerizable. Repeated stimulation of SF with LPS resulted in loss of activating histone marks only in promoters of tolerizable genes. The epigenetic landscape at promoters of tolerizable genes was similar in unstimulated SF and monocytes, whereas the basal configuration of histone marks profoundly differed in genes that were non-tolerizable in SF only. Our data suggest that the epigenetic configuration at gene promoters regulates cell-specific LPS-induced responses and primes SF to sustain their inflammatory response in chronic arthritis.</p>',
			'date' => '2017-07-09',
			'pmid' => 'http://www.sciencedirect.com/science/article/pii/S0896841117303001',
			'doi' => '10.1016/j.jaut.2017.07.001 0',
			'modified' => '2017-07-18 08:20:47',
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		(int) 13 => array(
			'id' => '3169',
			'name' => 'PPARγ Links BMP2 and TGFβ1 Pathways in Vascular Smooth Muscle Cells, Regulating Cell Proliferation and Glucose Metabolism',
			'authors' => 'Laurent Calvier, Philippe Chouvarine, Ekaterina Legchenko, Nadine Hoffmann, Jonas Geldner, Paul Borchert, Danny Jonigk, Miklos M. Mozes, Georg Hansmann',
			'description' => '<p><span>BMP2 and TGF&beta;1 are functional antagonists of pathological remodeling in the arteries, heart, and lung; however, the mechanisms in VSMCs, and their&nbsp;disturbance in pulmonary arterial hypertension (PAH), are unclear. We found a pro-proliferative TGF&beta;1-Stat3-FoxO1 axis in VSMCs, and PPAR&gamma; as&nbsp;inhibitory regulator of TGF&beta;1-Stat3-FoxO1 and TGF&beta;1-Smad3/4, by physically interacting with Stat3 and Smad3. TGF&beta;1 induces fibrosis-related genes and miR-130a/301b, suppressing PPAR&gamma;. Conversely, PPAR&gamma; inhibits TGF&beta;1-induced mitochondrial activation and VSMC proliferation, and regulates two glucose metabolism-related enzymes, platelet isoform of phosphofructokinase (PFKP, a PPAR&gamma; target, via miR-331-5p) and protein phosphatase 1 regulatory subunit 3G (PPP1R3G, a Smad3 target). PPAR&gamma; knockdown/deletion in VSMCs activates TGF&beta;1 signaling. The PPAR&gamma; agonist pioglitazone reverses PAH and inhibits the TGF&beta;1-Stat3-FoxO1 axis in TGF&beta;1-overexpressing mice. We identified PPAR&gamma; as a missing link between BMP2 and TGF&beta;1 pathways in VSMCs. PPAR&gamma; activation can be beneficial in TGF&beta;1-associated diseases, such as PAH, parenchymal lung diseases, and Marfan&rsquo;s syndrome.</span></p>',
			'date' => '2017-05-02',
			'pmid' => 'http://www.cell.com/cell-metabolism/abstract/S1550-4131(17)30163-8',
			'doi' => 'http://dx.doi.org/10.1016/j.cmet.2017.03.011',
			'modified' => '2017-05-11 11:30:23',
			'created' => '2017-05-09 19:10:49',
			'ProductsPublication' => array(
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		(int) 14 => array(
			'id' => '3188',
			'name' => 'Epigenetically-driven anatomical diversity of synovial fibroblasts guides joint-specific fibroblast functions',
			'authors' => 'Mojca Frank-Bertoncelj, Michelle Trenkmann, Kerstin Klein, Emmanuel Karouzakis, Hubert Rehrauer, Anna Bratus, Christoph Kolling, Maria Armaka, Andrew Filer, Beat Michel, Renate E. Gay, Christopher D. Buckley, George Kollias, Steffen Gay & Caroline Ospelt',
			'description' => '<p><span>A number of human diseases, such as arthritis and atherosclerosis, include characteristic pathology in specific anatomical locations. Here we show transcriptomic differences in synovial fibroblasts from different joint locations and that HOX gene signatures reflect the joint-specific origins of mouse and human synovial fibroblasts and synovial tissues. Alongside DNA methylation and histone modifications, bromodomain and extra-terminal reader proteins regulate joint-specific HOX gene expression. Anatomical transcriptional diversity translates into joint-specific synovial fibroblast phenotypes with distinct adhesive, proliferative, chemotactic and matrix-degrading characteristics and differential responsiveness to TNF, creating a unique microenvironment in each joint. These findings indicate that local stroma might control positional disease patterns not only in arthritis but in any disease with a prominent stromal component.</span></p>',
			'date' => '2017-03-23',
			'pmid' => 'https://www.nature.com/articles/ncomms14852',
			'doi' => '10.1038/ncomms14852',
			'modified' => '2017-06-21 14:44:43',
			'created' => '2017-06-08 05:21:07',
			'ProductsPublication' => array(
				[maximum depth reached]
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
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<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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	'name' => 'Epigenetically-driven anatomical diversity of synovial fibroblasts guides joint-specific fibroblast functions',
	'authors' => 'Mojca Frank-Bertoncelj, Michelle Trenkmann, Kerstin Klein, Emmanuel Karouzakis, Hubert Rehrauer, Anna Bratus, Christoph Kolling, Maria Armaka, Andrew Filer, Beat Michel, Renate E. Gay, Christopher D. Buckley, George Kollias, Steffen Gay & Caroline Ospelt',
	'description' => '<p><span>A number of human diseases, such as arthritis and atherosclerosis, include characteristic pathology in specific anatomical locations. Here we show transcriptomic differences in synovial fibroblasts from different joint locations and that HOX gene signatures reflect the joint-specific origins of mouse and human synovial fibroblasts and synovial tissues. Alongside DNA methylation and histone modifications, bromodomain and extra-terminal reader proteins regulate joint-specific HOX gene expression. Anatomical transcriptional diversity translates into joint-specific synovial fibroblast phenotypes with distinct adhesive, proliferative, chemotactic and matrix-degrading characteristics and differential responsiveness to TNF, creating a unique microenvironment in each joint. These findings indicate that local stroma might control positional disease patterns not only in arthritis but in any disease with a prominent stromal component.</span></p>',
	'date' => '2017-03-23',
	'pmid' => 'https://www.nature.com/articles/ncomms14852',
	'doi' => '10.1038/ncomms14852',
	'modified' => '2017-06-21 14:44:43',
	'created' => '2017-06-08 05:21:07',
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		'id' => '2128',
		'product_id' => '2681',
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$externalLink = ' <a href="https://www.nature.com/articles/ncomms14852" target="_blank"><i class="fa fa-external-link"></i></a>'
include - APP/View/Products/view.ctp, line 755
View::_evaluate() - CORE/Cake/View/View.php, line 971
View::_render() - CORE/Cake/View/View.php, line 933
View::render() - CORE/Cake/View/View.php, line 473
Controller::render() - CORE/Cake/Controller/Controller.php, line 963
ProductsController::slug() - APP/Controller/ProductsController.php, line 1052
ReflectionMethod::invokeArgs() - [internal], line ??
Controller::invokeAction() - CORE/Cake/Controller/Controller.php, line 491
Dispatcher::_invoke() - CORE/Cake/Routing/Dispatcher.php, line 193
Dispatcher::dispatch() - CORE/Cake/Routing/Dispatcher.php, line 167
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Notice (8): Undefined variable: header [APP/View/Products/view.ctp, line 755]
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<li>Validated ChIP-seq grade antibodies</li>
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<ul>
<li><strong>Bioruptor </strong>&ndash; for efficient and reproducible chromatin shearing</li>
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<li>Testing 2 Ab amounts and/or 2 Ab references per target of interest</li>
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<li>Illumina sequencing run : Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
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<td style="width: 137px; height: 107px; background-color: #f9f9f9; text-align: center;"><strong>Standard bioinformatics</strong></td>
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<p style="text-align: center;"></p>
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<ul>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
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<td style="width: 137px; height: 24px; background-color: white; text-align: center;"><strong></strong></td>
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<td style="width: 137px; height: 170px; background-color: #f9f9f9; text-align: center;" rowspan="5"><strong>Advanced bioinformatics</strong></td>
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<p style="text-align: center;"><strong>Differential binding analysis</strong></p>
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
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<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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			'name' => 'ヒストン ChIP-seq受託サービス',
			'description' => '<h3>DiagenodeのヒストンChIP サービス： 10,000細胞に至るまで調べるChIP-seqヒストン修飾&nbsp;</h3>
<p>ヒストンの翻訳後修飾は、遺伝子発現を調節します。 Diagenodeのエピゲノムプロファイリングサービスでは、活性化プロモーターとエンハンサー解析を用いて、調節エレメントとその相互作用タンパク質を調べて、 転写調節を理解するためにChIP-seq解析によるゲノム全体のヒストン修飾をプロファイルします。</p>
<p>更に詳しく</p>
<ul>
<li>活性なプロモーター：H3K4me3</li>
<li>不活性なプロモーター：H3K27me3</li>
<li>エンハンサー：調節領域におけるH3K4me1およびH3K27acの</li>
<li>活性遺伝子体：H3K36me3</li>
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			'description' => '<div class="row">
<div class="small-12 medium-12 large-12 columns"><center></center></div>
</div>
<div class="row">
<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/chip-workflow.png" alt="Chromatin-Immunoprecipitation-Diagenode" /></center>
<p>Chromatin consists of DNA, histones and non-histone proteins. Understanding the roles of histones and transcription factors is critical in understanding the regulation of gene expression.</p>
<p>Using ChIP-seq analysis, it is possible to profile histone modifications and transcription factor binding genome-wide to&nbsp;elucidate&nbsp;control of gene expression in disease or in response to a drug treatment. Diagenode&rsquo;s Epigenomic Profiling Services offer a wide variety of chromatin analysis options through ChIP-seq and ATAC-seq.</p>
</div>
<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns"><center><img src="http://www.diagenode.com/img/applications/histone-marks-helice.png" alt="Histone-Antibodies-Diagenode" /></center><br href="../p/service-chip-seq" />
<h3><a href="../p/atac-seq-service">ATAC-seq: open chromatin regions</a></h3>
<p>Study genome-wide chromatin accessibility. Identify open chromatin sites and active regulatory elements such as promoter, enhancers, and insulators.</p>
<ul>
<ul>
<li>Nucleosome positioning</li>
</ul>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq histone modifications</a></h3>
<p>Post-translational modification of histones is implicated in the regulation of gene expression, necessitating the study of regulatory elements and their interacting proteins like active promoter and enhancer analysis. Profile genome-wide histone modifications by ChIP-seq analysis to understand transcriptional regulation</p>
<ul>
<li><strong>Active promoter profiling</strong>: H3K4me3 enrichment</li>
<li><strong>Inactive promoter profiling</strong>: H3K27me3 enrichment</li>
<li><strong>Enhancer profiling</strong>: H3K4me1 and H3K27ac enrichment in regulatory regions</li>
<li><strong>Active gene body</strong>: H3K36me3</li>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq Transcription Factors</a></h3>
<p>Explore the effects of transcription factor binding through ChIP-seq analysis of a multitude of TFs including:</p>
<ul>
<li>CTCF: transcriptional repressor and insulator activity</li>
<li>p300/CBP: histone acetyltransferase</li>
<li>Pol II, p53, and more</li>
<li>Epigenetic writers, readers, erasers</li>
<li>Nuclear receptors</li>
<li>Tumor suppressor genes</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>
<div class="row">
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<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>
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<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>
<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>
</ul>
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			'id' => '4577',
			'name' => 'Impact of Fetal Exposure to Endocrine Disrupting ChemicalMixtures on FOXA3 Gene and Protein Expression in Adult RatTestes.',
			'authors' => 'Walker  C. et al.',
			'description' => '<p>Perinatal exposure to endocrine disrupting chemicals (EDCs) has been shown to affect male reproductive functions. However, the effects on male reproduction of exposure to EDC mixtures at doses relevant to humans have not been fully characterized. In previous studies, we found that in utero exposure to mixtures of the plasticizer di(2-ethylhexyl) phthalate (DEHP) and the soy-based phytoestrogen genistein (Gen) induced abnormal testis development in rats. In the present study, we investigated the molecular basis of these effects in adult testes from the offspring of pregnant SD rats gavaged with corn oil or Gen + DEHP mixtures at 0.1 or 10 mg/kg/day. Testicular transcriptomes were determined by microarray and RNA-seq analyses. A protein analysis was performed on paraffin and frozen testis sections, mainly by immunofluorescence. The transcription factor forkhead box protein 3 (FOXA3), a key regulator of Leydig cell function, was identified as the most significantly downregulated gene in testes from rats exposed in utero to Gen + DEHP mixtures. FOXA3 protein levels were decreased in testicular interstitium at a dose previously found to reduce testosterone levels, suggesting a primary effect of fetal exposure to Gen + DEHP on adult Leydig cells, rather than on spermatids and Sertoli cells, also expressing FOXA3. Thus, FOXA3 downregulation in adult testes following fetal exposure to Gen + DEHP may contribute to adverse male reproductive outcomes.</p>',
			'date' => '2023-01-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36674726',
			'doi' => '10.3390/ijms24021211',
			'modified' => '2023-04-11 10:18:58',
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			'id' => '4545',
			'name' => 'Histone Deacetylases 1 and 2 target gene regulatory networks of nephronprogenitors to control nephrogenesis.',
			'authors' => 'Liu  Hongbing et al.',
			'description' => '<p>Our studies demonstrated the critical role of Histone deacetylases (HDACs) in the regulation of nephrogenesis. To better understand the key pathways regulated by HDAC1/2 in early nephrogenesis, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) of Hdac1/2 on isolated nephron progenitor cells (NPCs) from mouse E16.5 kidneys. Our analysis revealed that 11802 (40.4\%) of Hdac1 peaks overlap with Hdac2 peaks, further demonstrates the redundant role of Hdac1 and Hdac2 during nephrogenesis. Common Hdac1/2 peaks are densely concentrated close to the transcriptional start site (TSS). GREAT Gene Ontology analysis of overlapping Hdac1/2 peaks reveals that Hdac1/2 are associated with metanephric nephron morphogenesis, chromatin assembly or disassembly, as well as other DNA checkpoints. Pathway analysis shows that negative regulation of Wnt signaling pathway is one of Hdac1/2's most significant function in NPCs. Known motif analysis indicated that Hdac1 is enriched in motifs for Six2, Hox family, and Tcf family members, which are essential for self-renewal and differentiation of nephron progenitors. Interestingly, we found the enrichment of HDAC1/2 at the enhancer and promoter regions of actively transcribed genes, especially those concerned with NPC self-renewal. HDAC1/2 simultaneously activate or repress the expression of different genes to maintain the cellular state of nephron progenitors. We used the Integrative Genomics Viewer to visualize these target genes associated with each function and found that Hdac1/2 co-bound to the enhancers or/and promoters of genes associated with nephron morphogenesis, differentiation, and cell cycle control. Taken together, our ChIP-Seq analysis demonstrates that Hdac1/2 directly regulate the molecular cascades essential for nephrogenesis.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36356658',
			'doi' => '10.1016/j.bcp.2022.115341',
			'modified' => '2022-11-24 10:24:07',
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			'id' => '4535',
			'name' => 'Identification of genomic binding sites and direct target genes for thetranscription factor DDIT3/CHOP.',
			'authors' => 'Osman  A. et al.',
			'description' => '<p>DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 is believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36402425',
			'doi' => '10.1016/j.yexcr.2022.113418',
			'modified' => '2022-11-25 08:47:49',
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			'id' => '4217',
			'name' => 'CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells.',
			'authors' => 'Bommi-Reddy A. et al.',
			'description' => '<p><span>Therapeutic targeting of the estrogen receptor (ER) is a clinically validated approach for estrogen receptor positive breast cancer (ER+ BC), but sustained response is limited by acquired resistance. Targeting the transcriptional coactivators required for estrogen receptor activity represents an alternative approach that is not subject to the same limitations as targeting estrogen receptor itself. In this report we demonstrate that the acetyltransferase activity of coactivator paralogs CREBBP/EP300 represents a promising therapeutic target in ER+ BC. Using the potent and selective inhibitor CPI-1612, we show that CREBBP/EP300 acetyltransferase inhibition potently suppresses in vitro and in vivo growth of breast cancer cell line models and acts in a manner orthogonal to directly targeting ER. CREBBP/EP300 acetyltransferase inhibition suppresses ER-dependent transcription by targeting lineage-specific enhancers defined by the pioneer transcription factor FOXA1. These results validate CREBBP/EP300 acetyltransferase activity as a viable target for clinical development in ER+ breast cancer.</span></p>',
			'date' => '2022-03-30',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/35353838/',
			'doi' => '10.1371/journal.pone.0262378',
			'modified' => '2022-04-12 10:56:54',
			'created' => '2022-04-12 10:56:54',
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		(int) 4 => array(
			'id' => '4091',
			'name' => 'Epigenetic regulation of the lineage specificity of primary human dermallymphatic and blood vascular endothelial cells.',
			'authors' => 'Tacconi, Carlotta and He, Yuliang and Ducoli, Luca and Detmar, Michael',
			'description' => '<p>Lymphatic and blood vascular endothelial cells (ECs) share several molecular and developmental features. However, these two cell types possess distinct phenotypic signatures, reflecting their different biological functions. Despite significant advances in elucidating how the specification of lymphatic and blood vascular ECs is regulated at the transcriptional level during development, the key molecular mechanisms governing their lineage identity under physiological or pathological conditions remain poorly understood. To explore the epigenomic signatures in the maintenance of EC lineage specificity, we compared the transcriptomic landscapes, histone composition (H3K4me3 and H3K27me3) and DNA methylomes of cultured matched human primary dermal lymphatic and blood vascular ECs. Our findings reveal that blood vascular lineage genes manifest a more 'repressed' histone composition in lymphatic ECs, whereas DNA methylation at promoters is less linked to the differential transcriptomes of lymphatic versus blood vascular ECs. Meta-analyses identified two transcriptional regulators, BCL6 and MEF2C, which potentially govern endothelial lineage specificity. Notably, the blood vascular endothelial lineage markers CD34, ESAM and FLT1 and the lymphatic endothelial lineage markers PROX1, PDPN and FLT4 exhibited highly differential epigenetic profiles and responded in distinct manners to epigenetic drug treatments. The perturbation of histone and DNA methylation selectively promoted the expression of blood vascular endothelial markers in lymphatic endothelial cells, but not vice versa. Overall, our study reveals that the fine regulation of lymphatic and blood vascular endothelial transcriptomes is maintained via several epigenetic mechanisms, which are crucial to the maintenance of endothelial cell identity.</p>',
			'date' => '2020-09-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/32918672',
			'doi' => '10.1007/s10456-020-09743-9',
			'modified' => '2021-03-17 17:09:36',
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			'id' => '4031',
			'name' => 'Battle of the sex chromosomes: competition between X- and Y-chromosomeencoded proteins for partner interaction and chromatin occupancy drivesmulti-copy gene expression and evolution in muroid rodents.',
			'authors' => 'Moretti, C and Blanco, M and Ialy-Radio, C and Serrentino, ME and Gobé,C and Friedman, R and Battail, C and Leduc, M and Ward, MA and Vaiman, Dand Tores, F and Cocquet, J',
			'description' => '<p>Transmission distorters (TDs) are genetic elements that favor their own transmission to the detriments of others. Slx/Slxl1 (Sycp3-like-X-linked and Slx-like1) and Sly (Sycp3-like-Y-linked) are TDs which have been co-amplified on the X and Y chromosomes of Mus species. They are involved in an intragenomic conflict in which each favors its own transmission, resulting in sex ratio distortion of the progeny when Slx/Slxl1 vs. Sly copy number is unbalanced. They are specifically expressed in male postmeiotic gametes (spermatids) and have opposite effects on gene expression: Sly knockdown leads to the upregulation of hundreds of spermatid-expressed genes, while Slx/Slxl1-deficiency downregulates them. When both Slx/Slxl1 and Sly are knocked-down, sex ratio distortion and gene deregulation are corrected. Slx/Slxl1 and Sly are, therefore, in competition but the molecular mechanism remains unknown. By comparing their chromatin binding profiles and protein partners, we show that SLX/SLXL1 and SLY proteins compete for interaction with H3K4me3-reader SSTY1 (Spermiogenesis-specific-transcript-on-the-Y1) at the promoter of thousands of genes to drive their expression, and that the opposite effect they have on gene expression is mediated by different abilities to recruit SMRT/N-Cor transcriptional complex. Their target genes are predominantly spermatid-specific multicopy genes encoded by the sex chromosomes and the autosomal Speer/Takusan. Many of them have co-amplified with Slx/Slxl1/Sly but also Ssty during muroid rodent evolution. Overall, we identify Ssty as a key element of the X vs. Y intragenomic conflict, which may have influenced gene content and hybrid sterility beyond Mus lineage since Ssty amplification on the Y pre-dated that of Slx/Slxl1/Sly.</p>',
			'date' => '2020-07-13',
			'pmid' => 'http://www.pubmed.gov/32658962',
			'doi' => '10.1093/molbev/msaa175/5870835',
			'modified' => '2020-12-18 13:27:51',
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			'id' => '3922',
			'name' => 'Multi-omic analysis of gametogenesis reveals a novel signature at the promoters and distal enhancers of active genes.',
			'authors' => 'Crespo M, Damont A, Blanco M, Lastrucci E, Kennani SE, Ialy-Radio C, Khattabi LE, Terrier S, Louwagie M, Kieffer-Jaquinod S, Hesse AM, Bruley C, Chantalat S, Govin J, Fenaille F, Battail C, Cocquet J, Pflieger D',
			'description' => '<p>Epigenetic regulation of gene expression is tightly controlled by the dynamic modification of histones by chemical groups, the diversity of which has largely expanded over the past decade with the discovery of lysine acylations, catalyzed from acyl-coenzymes A. We investigated the dynamics of lysine acetylation and crotonylation on histones H3 and H4 during mouse spermatogenesis. Lysine crotonylation appeared to be of significant abundance compared to acetylation, particularly on Lys27 of histone H3 (H3K27cr) that accumulates in sperm in a cleaved form of H3. We identified the genomic localization of H3K27cr and studied its effects on transcription compared to the classical active mark H3K27ac at promoters and distal enhancers. The presence of both marks was strongly associated with highest gene expression. Assessment of their co-localization with transcription regulators (SLY, SOX30) and chromatin-binding proteins (BRD4, BRDT, BORIS and CTCF) indicated systematic highest binding when both active marks were present and different selective binding when present alone at chromatin. H3K27cr and H3K27ac finally mark the building of some sperm super-enhancers. This integrated analysis of omics data provides an unprecedented level of understanding of gene expression regulation by H3K27cr in comparison to H3K27ac, and reveals both synergistic and specific actions of each histone modification.</p>',
			'date' => '2020-03-17',
			'pmid' => 'http://www.pubmed.gov/32182340',
			'doi' => '10.1093/nar/gkaa163',
			'modified' => '2020-08-17 10:56:19',
			'created' => '2020-08-10 12:12:25',
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		(int) 7 => array(
			'id' => '3848',
			'name' => 'A comprehensive epigenomic analysis of phenotypically distinguishable, genetically identical female and male Daphnia pulex.',
			'authors' => 'Kvist J, Athanàsio CG, Pfrender ME, Brown JB, Colbourne JK, Mirbahai L',
			'description' => '<p>BACKGROUND: Daphnia species reproduce by cyclic parthenogenesis involving both sexual and asexual reproduction. The sex of the offspring is environmentally determined and mediated via endocrine signalling by the mother. Interestingly, male and female Daphnia can be genetically identical, yet display large differences in behaviour, morphology, lifespan and metabolic activity. Our goal was to integrate multiple omics datasets, including gene expression, splicing, histone modification and DNA methylation data generated from genetically identical female and male Daphnia pulex under controlled laboratory settings with the aim of achieving a better understanding of the underlying epigenetic factors that may contribute to the phenotypic differences observed between the two genders. RESULTS: In this study we demonstrate that gene expression level is positively correlated with increased DNA methylation, and histone H3 trimethylation at lysine&thinsp;4 (H3K4me3) at predicted promoter regions. Conversely, elevated histone H3 trimethylation at lysine&thinsp;27 (H3K27me3), distributed across the entire transcript length, is negatively correlated with gene expression level. Interestingly, male Daphnia are dominated with epigenetic modifications that globally promote elevated gene expression, while female Daphnia are dominated with epigenetic modifications that reduce gene expression globally. For examples, CpG methylation (positively correlated with gene expression level) is significantly higher in almost all differentially methylated sites in male compared to female Daphnia. Furthermore, H3K4me3 modifications are higher in male compared to female Daphnia in more than 3/4 of the differentially regulated promoters. On the other hand, H3K27me3 is higher in female compared to male Daphnia in more than 5/6 of differentially modified sites. However, both sexes demonstrate roughly equal number of genes that are up-regulated in one gender compared to the other sex. Since, gene expression analyses typically assume that most genes are expressed at equal level among samples and different conditions, and thus cannot detect global changes affecting most genes. CONCLUSIONS: The epigenetic differences between male and female in Daphnia pulex are vast and dominated by changes that promote elevated gene expression in male Daphnia. Furthermore, the differences observed in both gene expression changes and epigenetic modifications between the genders relate to pathways that are physiologically relevant to the observed phenotypic differences.</p>',
			'date' => '2020-01-06',
			'pmid' => 'http://www.pubmed.gov/31906859',
			'doi' => '10.1186/s12864-019-6415-5',
			'modified' => '2020-02-20 11:34:47',
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		(int) 8 => array(
			'id' => '3839',
			'name' => 'Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq.',
			'authors' => 'Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR',
			'description' => '<p>One of the primary aims of the Functional Annotation of ANimal Genomes (FAANG) initiative is to characterize tissue-specific regulation within animal genomes. To this end, we used chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in eight prioritized tissues collected as part of the FAANG equine biobank from two thoroughbred mares. Data were generated according to optimized experimental parameters developed during quality control testing. To ensure that we obtained sufficient ChIP and successful peak-calling, data and peak-calls were assessed using six quality metrics, replicate comparisons, and site-specific evaluations. Tissue specificity was explored by identifying binding motifs within unique active regions, and motifs were further characterized by gene ontology (GO) and protein-protein interaction analyses. The histone marks identified in this study represent some of the first resources for tissue-specific regulation within the equine genome. As such, these publicly available annotation data can be used to advance equine studies investigating health, performance, reproduction, and other traits of economic interest in the horse.</p>',
			'date' => '2019-12-18',
			'pmid' => 'http://www.pubmed.gov/31861495',
			'doi' => '10.3390/genes11010003',
			'modified' => '2020-02-20 11:20:25',
			'created' => '2020-02-13 10:02:44',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 9 => array(
			'id' => '3742',
			'name' => 'Development and epigenetic plasticity of murine Müller glia.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'date' => '2019-07-02',
			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
			'modified' => '2019-08-13 10:50:24',
			'created' => '2019-07-31 13:35:50',
			'ProductsPublication' => array(
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		),
		(int) 10 => array(
			'id' => '3569',
			'name' => 'The epigenetic basis for the impaired ability of adult murine retinal pigment epithelium cells to regenerate retinal tissue.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
			'date' => '2019-03-07',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
			'created' => '2019-03-21 14:12:08',
			'ProductsPublication' => array(
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		),
		(int) 11 => array(
			'id' => '3463',
			'name' => 'Epigenetic modifiers promote mitochondrial biogenesis and oxidative metabolism leading to enhanced differentiation of neuroprogenitor cells.',
			'authors' => 'Martine Uittenbogaard, Christine A. Brantner, Anne Chiaramello1',
			'description' => '<p>During neural development, epigenetic modulation of chromatin acetylation is part of a dynamic, sequential and critical process to steer the fate of multipotent neural progenitors toward a specific lineage. Pan-HDAC inhibitors (HDCis) trigger neuronal differentiation by generating an "acetylation" signature and promoting the expression of neurogenic bHLH transcription factors. Our studies and others have revealed a link between neuronal differentiation and increase of mitochondrial mass. However, the neuronal regulation of mitochondrial biogenesis has remained largely unexplored. Here, we show that the HDACi, sodium butyrate (NaBt), promotes mitochondrial biogenesis via the NRF-1/Tfam axis in embryonic hippocampal progenitor cells and neuroprogenitor-like PC12-NeuroD6 cells, thereby enhancing their neuronal differentiation competency. Increased mitochondrial DNA replication by several pan-HDACis indicates a common mechanism by which they regulate mitochondrial biogenesis. NaBt also induces coordinates mitochondrial ultrastructural changes and enhanced OXPHOS metabolism, thereby increasing key mitochondrial bioenergetics parameters in neural progenitor cells. NaBt also endows the neuronal cells with increased mitochondrial spare capacity to confer resistance to oxidative stress associated with neuronal differentiation. We demonstrate that mitochondrial biogenesis is under HDAC-mediated epigenetic regulation, the timing of which is consistent with its integrative role during neuronal differentiation. Thus, our findings add a new facet to our mechanistic understanding of how pan-HDACis induce differentiation of neuronal progenitor cells. Our results reveal the concept that epigenetic modulation of the mitochondrial pool prior to neurotrophic signaling dictates the efficiency of initiation of neuronal differentiation during the transition from progenitor to differentiating neuronal cells. The histone acetyltransferase CREB-binding protein plays a key role in regulating the mitochondrial biomass. By ChIP-seq analysis, we show that NaBt confers an H3K27ac epigenetic signature in several interconnected nodes of nuclear genes vital for neuronal differentiation and mitochondrial reprogramming. Collectively, our study reports a novel developmental epigenetic layer that couples mitochondrial biogenesis to neuronal differentiation.</p>',
			'date' => '2018-03-02',
			'pmid' => 'http://www.pubmed.gov/29500414',
			'doi' => '10.1038/s41419-018-0396-1',
			'modified' => '2019-02-15 21:21:45',
			'created' => '2019-02-14 15:01:22',
			'ProductsPublication' => array(
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		(int) 12 => array(
			'id' => '3212',
			'name' => 'The epigenetic architecture at gene promoters determines cell type-specific LPS tolerance',
			'authors' => 'Kerstin Klein , Mojca Frank-Bertoncelj , Emmanuel Karouzakis , Renate E. Gay , Christoph Kolling , Adrian Ciurea , Nagihan Bostanci , Georgios N. Belibasakis , Lih-Ling Lin , Oliver Distler , Steffen Gay , Caroline Ospelt',
			'description' => '<p>Synovial fibroblasts (SF) drive inflammation and joint destruction in chronic arthritis. Here we show that SF possess a distinct type of LPS tolerance compared to macrophages and other types of fibroblasts. In SF and dermal fibroblasts, genes that were non-tolerizable after repeated LPS stimulation included proinflammatory cytokines, chemokines and matrix metalloproteinases, whereas anti-viral genes were tolerizable. In macrophages, all measured genes were tolerizable, whereas in gingival and foreskin fibroblasts<br />these genes were non-tolerizable. Repeated stimulation of SF with LPS resulted in loss of activating histone marks only in promoters of tolerizable genes. The epigenetic landscape at promoters of tolerizable genes was similar in unstimulated SF and monocytes, whereas the basal configuration of histone marks profoundly differed in genes that were non-tolerizable in SF only. Our data suggest that the epigenetic configuration at gene promoters regulates cell-specific LPS-induced responses and primes SF to sustain their inflammatory response in chronic arthritis.</p>',
			'date' => '2017-07-09',
			'pmid' => 'http://www.sciencedirect.com/science/article/pii/S0896841117303001',
			'doi' => '10.1016/j.jaut.2017.07.001 0',
			'modified' => '2017-07-18 08:20:47',
			'created' => '2017-07-18 08:18:41',
			'ProductsPublication' => array(
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		(int) 13 => array(
			'id' => '3169',
			'name' => 'PPARγ Links BMP2 and TGFβ1 Pathways in Vascular Smooth Muscle Cells, Regulating Cell Proliferation and Glucose Metabolism',
			'authors' => 'Laurent Calvier, Philippe Chouvarine, Ekaterina Legchenko, Nadine Hoffmann, Jonas Geldner, Paul Borchert, Danny Jonigk, Miklos M. Mozes, Georg Hansmann',
			'description' => '<p><span>BMP2 and TGF&beta;1 are functional antagonists of pathological remodeling in the arteries, heart, and lung; however, the mechanisms in VSMCs, and their&nbsp;disturbance in pulmonary arterial hypertension (PAH), are unclear. We found a pro-proliferative TGF&beta;1-Stat3-FoxO1 axis in VSMCs, and PPAR&gamma; as&nbsp;inhibitory regulator of TGF&beta;1-Stat3-FoxO1 and TGF&beta;1-Smad3/4, by physically interacting with Stat3 and Smad3. TGF&beta;1 induces fibrosis-related genes and miR-130a/301b, suppressing PPAR&gamma;. Conversely, PPAR&gamma; inhibits TGF&beta;1-induced mitochondrial activation and VSMC proliferation, and regulates two glucose metabolism-related enzymes, platelet isoform of phosphofructokinase (PFKP, a PPAR&gamma; target, via miR-331-5p) and protein phosphatase 1 regulatory subunit 3G (PPP1R3G, a Smad3 target). PPAR&gamma; knockdown/deletion in VSMCs activates TGF&beta;1 signaling. The PPAR&gamma; agonist pioglitazone reverses PAH and inhibits the TGF&beta;1-Stat3-FoxO1 axis in TGF&beta;1-overexpressing mice. We identified PPAR&gamma; as a missing link between BMP2 and TGF&beta;1 pathways in VSMCs. PPAR&gamma; activation can be beneficial in TGF&beta;1-associated diseases, such as PAH, parenchymal lung diseases, and Marfan&rsquo;s syndrome.</span></p>',
			'date' => '2017-05-02',
			'pmid' => 'http://www.cell.com/cell-metabolism/abstract/S1550-4131(17)30163-8',
			'doi' => 'http://dx.doi.org/10.1016/j.cmet.2017.03.011',
			'modified' => '2017-05-11 11:30:23',
			'created' => '2017-05-09 19:10:49',
			'ProductsPublication' => array(
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		(int) 14 => array(
			'id' => '3188',
			'name' => 'Epigenetically-driven anatomical diversity of synovial fibroblasts guides joint-specific fibroblast functions',
			'authors' => 'Mojca Frank-Bertoncelj, Michelle Trenkmann, Kerstin Klein, Emmanuel Karouzakis, Hubert Rehrauer, Anna Bratus, Christoph Kolling, Maria Armaka, Andrew Filer, Beat Michel, Renate E. Gay, Christopher D. Buckley, George Kollias, Steffen Gay & Caroline Ospelt',
			'description' => '<p><span>A number of human diseases, such as arthritis and atherosclerosis, include characteristic pathology in specific anatomical locations. Here we show transcriptomic differences in synovial fibroblasts from different joint locations and that HOX gene signatures reflect the joint-specific origins of mouse and human synovial fibroblasts and synovial tissues. Alongside DNA methylation and histone modifications, bromodomain and extra-terminal reader proteins regulate joint-specific HOX gene expression. Anatomical transcriptional diversity translates into joint-specific synovial fibroblast phenotypes with distinct adhesive, proliferative, chemotactic and matrix-degrading characteristics and differential responsiveness to TNF, creating a unique microenvironment in each joint. These findings indicate that local stroma might control positional disease patterns not only in arthritis but in any disease with a prominent stromal component.</span></p>',
			'date' => '2017-03-23',
			'pmid' => 'https://www.nature.com/articles/ncomms14852',
			'doi' => '10.1038/ncomms14852',
			'modified' => '2017-06-21 14:44:43',
			'created' => '2017-06-08 05:21:07',
			'ProductsPublication' => array(
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<option value="MN">Mongolia</option>
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<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>
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<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>
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
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<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
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<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
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<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
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<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
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</div>
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<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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<p>ヒストンの翻訳後修飾は、遺伝子発現を調節します。 Diagenodeのエピゲノムプロファイリングサービスでは、活性化プロモーターとエンハンサー解析を用いて、調節エレメントとその相互作用タンパク質を調べて、 転写調節を理解するためにChIP-seq解析によるゲノム全体のヒストン修飾をプロファイルします。</p>
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			'name' => 'Chromatin Profiling Services',
			'description' => '<div class="row">
<div class="small-12 medium-12 large-12 columns"><center></center></div>
</div>
<div class="row">
<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/chip-workflow.png" alt="Chromatin-Immunoprecipitation-Diagenode" /></center>
<p>Chromatin consists of DNA, histones and non-histone proteins. Understanding the roles of histones and transcription factors is critical in understanding the regulation of gene expression.</p>
<p>Using ChIP-seq analysis, it is possible to profile histone modifications and transcription factor binding genome-wide to&nbsp;elucidate&nbsp;control of gene expression in disease or in response to a drug treatment. Diagenode&rsquo;s Epigenomic Profiling Services offer a wide variety of chromatin analysis options through ChIP-seq and ATAC-seq.</p>
</div>
<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns"><center><img src="http://www.diagenode.com/img/applications/histone-marks-helice.png" alt="Histone-Antibodies-Diagenode" /></center><br href="../p/service-chip-seq" />
<h3><a href="../p/atac-seq-service">ATAC-seq: open chromatin regions</a></h3>
<p>Study genome-wide chromatin accessibility. Identify open chromatin sites and active regulatory elements such as promoter, enhancers, and insulators.</p>
<ul>
<ul>
<li>Nucleosome positioning</li>
</ul>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq histone modifications</a></h3>
<p>Post-translational modification of histones is implicated in the regulation of gene expression, necessitating the study of regulatory elements and their interacting proteins like active promoter and enhancer analysis. Profile genome-wide histone modifications by ChIP-seq analysis to understand transcriptional regulation</p>
<ul>
<li><strong>Active promoter profiling</strong>: H3K4me3 enrichment</li>
<li><strong>Inactive promoter profiling</strong>: H3K27me3 enrichment</li>
<li><strong>Enhancer profiling</strong>: H3K4me1 and H3K27ac enrichment in regulatory regions</li>
<li><strong>Active gene body</strong>: H3K36me3</li>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq Transcription Factors</a></h3>
<p>Explore the effects of transcription factor binding through ChIP-seq analysis of a multitude of TFs including:</p>
<ul>
<li>CTCF: transcriptional repressor and insulator activity</li>
<li>p300/CBP: histone acetyltransferase</li>
<li>Pol II, p53, and more</li>
<li>Epigenetic writers, readers, erasers</li>
<li>Nuclear receptors</li>
<li>Tumor suppressor genes</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>
</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>
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<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">
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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>
</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>
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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>
</li>
</ul>
</div>
<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>
</ul>
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			'id' => '4577',
			'name' => 'Impact of Fetal Exposure to Endocrine Disrupting ChemicalMixtures on FOXA3 Gene and Protein Expression in Adult RatTestes.',
			'authors' => 'Walker  C. et al.',
			'description' => '<p>Perinatal exposure to endocrine disrupting chemicals (EDCs) has been shown to affect male reproductive functions. However, the effects on male reproduction of exposure to EDC mixtures at doses relevant to humans have not been fully characterized. In previous studies, we found that in utero exposure to mixtures of the plasticizer di(2-ethylhexyl) phthalate (DEHP) and the soy-based phytoestrogen genistein (Gen) induced abnormal testis development in rats. In the present study, we investigated the molecular basis of these effects in adult testes from the offspring of pregnant SD rats gavaged with corn oil or Gen + DEHP mixtures at 0.1 or 10 mg/kg/day. Testicular transcriptomes were determined by microarray and RNA-seq analyses. A protein analysis was performed on paraffin and frozen testis sections, mainly by immunofluorescence. The transcription factor forkhead box protein 3 (FOXA3), a key regulator of Leydig cell function, was identified as the most significantly downregulated gene in testes from rats exposed in utero to Gen + DEHP mixtures. FOXA3 protein levels were decreased in testicular interstitium at a dose previously found to reduce testosterone levels, suggesting a primary effect of fetal exposure to Gen + DEHP on adult Leydig cells, rather than on spermatids and Sertoli cells, also expressing FOXA3. Thus, FOXA3 downregulation in adult testes following fetal exposure to Gen + DEHP may contribute to adverse male reproductive outcomes.</p>',
			'date' => '2023-01-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36674726',
			'doi' => '10.3390/ijms24021211',
			'modified' => '2023-04-11 10:18:58',
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			'name' => 'Histone Deacetylases 1 and 2 target gene regulatory networks of nephronprogenitors to control nephrogenesis.',
			'authors' => 'Liu  Hongbing et al.',
			'description' => '<p>Our studies demonstrated the critical role of Histone deacetylases (HDACs) in the regulation of nephrogenesis. To better understand the key pathways regulated by HDAC1/2 in early nephrogenesis, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) of Hdac1/2 on isolated nephron progenitor cells (NPCs) from mouse E16.5 kidneys. Our analysis revealed that 11802 (40.4\%) of Hdac1 peaks overlap with Hdac2 peaks, further demonstrates the redundant role of Hdac1 and Hdac2 during nephrogenesis. Common Hdac1/2 peaks are densely concentrated close to the transcriptional start site (TSS). GREAT Gene Ontology analysis of overlapping Hdac1/2 peaks reveals that Hdac1/2 are associated with metanephric nephron morphogenesis, chromatin assembly or disassembly, as well as other DNA checkpoints. Pathway analysis shows that negative regulation of Wnt signaling pathway is one of Hdac1/2's most significant function in NPCs. Known motif analysis indicated that Hdac1 is enriched in motifs for Six2, Hox family, and Tcf family members, which are essential for self-renewal and differentiation of nephron progenitors. Interestingly, we found the enrichment of HDAC1/2 at the enhancer and promoter regions of actively transcribed genes, especially those concerned with NPC self-renewal. HDAC1/2 simultaneously activate or repress the expression of different genes to maintain the cellular state of nephron progenitors. We used the Integrative Genomics Viewer to visualize these target genes associated with each function and found that Hdac1/2 co-bound to the enhancers or/and promoters of genes associated with nephron morphogenesis, differentiation, and cell cycle control. Taken together, our ChIP-Seq analysis demonstrates that Hdac1/2 directly regulate the molecular cascades essential for nephrogenesis.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36356658',
			'doi' => '10.1016/j.bcp.2022.115341',
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			'id' => '4535',
			'name' => 'Identification of genomic binding sites and direct target genes for thetranscription factor DDIT3/CHOP.',
			'authors' => 'Osman  A. et al.',
			'description' => '<p>DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 is believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.</p>',
			'date' => '2022-11-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/36402425',
			'doi' => '10.1016/j.yexcr.2022.113418',
			'modified' => '2022-11-25 08:47:49',
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			'id' => '4217',
			'name' => 'CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells.',
			'authors' => 'Bommi-Reddy A. et al.',
			'description' => '<p><span>Therapeutic targeting of the estrogen receptor (ER) is a clinically validated approach for estrogen receptor positive breast cancer (ER+ BC), but sustained response is limited by acquired resistance. Targeting the transcriptional coactivators required for estrogen receptor activity represents an alternative approach that is not subject to the same limitations as targeting estrogen receptor itself. In this report we demonstrate that the acetyltransferase activity of coactivator paralogs CREBBP/EP300 represents a promising therapeutic target in ER+ BC. Using the potent and selective inhibitor CPI-1612, we show that CREBBP/EP300 acetyltransferase inhibition potently suppresses in vitro and in vivo growth of breast cancer cell line models and acts in a manner orthogonal to directly targeting ER. CREBBP/EP300 acetyltransferase inhibition suppresses ER-dependent transcription by targeting lineage-specific enhancers defined by the pioneer transcription factor FOXA1. These results validate CREBBP/EP300 acetyltransferase activity as a viable target for clinical development in ER+ breast cancer.</span></p>',
			'date' => '2022-03-30',
			'pmid' => 'https://pubmed.ncbi.nlm.nih.gov/35353838/',
			'doi' => '10.1371/journal.pone.0262378',
			'modified' => '2022-04-12 10:56:54',
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		(int) 4 => array(
			'id' => '4091',
			'name' => 'Epigenetic regulation of the lineage specificity of primary human dermallymphatic and blood vascular endothelial cells.',
			'authors' => 'Tacconi, Carlotta and He, Yuliang and Ducoli, Luca and Detmar, Michael',
			'description' => '<p>Lymphatic and blood vascular endothelial cells (ECs) share several molecular and developmental features. However, these two cell types possess distinct phenotypic signatures, reflecting their different biological functions. Despite significant advances in elucidating how the specification of lymphatic and blood vascular ECs is regulated at the transcriptional level during development, the key molecular mechanisms governing their lineage identity under physiological or pathological conditions remain poorly understood. To explore the epigenomic signatures in the maintenance of EC lineage specificity, we compared the transcriptomic landscapes, histone composition (H3K4me3 and H3K27me3) and DNA methylomes of cultured matched human primary dermal lymphatic and blood vascular ECs. Our findings reveal that blood vascular lineage genes manifest a more 'repressed' histone composition in lymphatic ECs, whereas DNA methylation at promoters is less linked to the differential transcriptomes of lymphatic versus blood vascular ECs. Meta-analyses identified two transcriptional regulators, BCL6 and MEF2C, which potentially govern endothelial lineage specificity. Notably, the blood vascular endothelial lineage markers CD34, ESAM and FLT1 and the lymphatic endothelial lineage markers PROX1, PDPN and FLT4 exhibited highly differential epigenetic profiles and responded in distinct manners to epigenetic drug treatments. The perturbation of histone and DNA methylation selectively promoted the expression of blood vascular endothelial markers in lymphatic endothelial cells, but not vice versa. Overall, our study reveals that the fine regulation of lymphatic and blood vascular endothelial transcriptomes is maintained via several epigenetic mechanisms, which are crucial to the maintenance of endothelial cell identity.</p>',
			'date' => '2020-09-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/32918672',
			'doi' => '10.1007/s10456-020-09743-9',
			'modified' => '2021-03-17 17:09:36',
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			'id' => '4031',
			'name' => 'Battle of the sex chromosomes: competition between X- and Y-chromosomeencoded proteins for partner interaction and chromatin occupancy drivesmulti-copy gene expression and evolution in muroid rodents.',
			'authors' => 'Moretti, C and Blanco, M and Ialy-Radio, C and Serrentino, ME and Gobé,C and Friedman, R and Battail, C and Leduc, M and Ward, MA and Vaiman, Dand Tores, F and Cocquet, J',
			'description' => '<p>Transmission distorters (TDs) are genetic elements that favor their own transmission to the detriments of others. Slx/Slxl1 (Sycp3-like-X-linked and Slx-like1) and Sly (Sycp3-like-Y-linked) are TDs which have been co-amplified on the X and Y chromosomes of Mus species. They are involved in an intragenomic conflict in which each favors its own transmission, resulting in sex ratio distortion of the progeny when Slx/Slxl1 vs. Sly copy number is unbalanced. They are specifically expressed in male postmeiotic gametes (spermatids) and have opposite effects on gene expression: Sly knockdown leads to the upregulation of hundreds of spermatid-expressed genes, while Slx/Slxl1-deficiency downregulates them. When both Slx/Slxl1 and Sly are knocked-down, sex ratio distortion and gene deregulation are corrected. Slx/Slxl1 and Sly are, therefore, in competition but the molecular mechanism remains unknown. By comparing their chromatin binding profiles and protein partners, we show that SLX/SLXL1 and SLY proteins compete for interaction with H3K4me3-reader SSTY1 (Spermiogenesis-specific-transcript-on-the-Y1) at the promoter of thousands of genes to drive their expression, and that the opposite effect they have on gene expression is mediated by different abilities to recruit SMRT/N-Cor transcriptional complex. Their target genes are predominantly spermatid-specific multicopy genes encoded by the sex chromosomes and the autosomal Speer/Takusan. Many of them have co-amplified with Slx/Slxl1/Sly but also Ssty during muroid rodent evolution. Overall, we identify Ssty as a key element of the X vs. Y intragenomic conflict, which may have influenced gene content and hybrid sterility beyond Mus lineage since Ssty amplification on the Y pre-dated that of Slx/Slxl1/Sly.</p>',
			'date' => '2020-07-13',
			'pmid' => 'http://www.pubmed.gov/32658962',
			'doi' => '10.1093/molbev/msaa175/5870835',
			'modified' => '2020-12-18 13:27:51',
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			'id' => '3922',
			'name' => 'Multi-omic analysis of gametogenesis reveals a novel signature at the promoters and distal enhancers of active genes.',
			'authors' => 'Crespo M, Damont A, Blanco M, Lastrucci E, Kennani SE, Ialy-Radio C, Khattabi LE, Terrier S, Louwagie M, Kieffer-Jaquinod S, Hesse AM, Bruley C, Chantalat S, Govin J, Fenaille F, Battail C, Cocquet J, Pflieger D',
			'description' => '<p>Epigenetic regulation of gene expression is tightly controlled by the dynamic modification of histones by chemical groups, the diversity of which has largely expanded over the past decade with the discovery of lysine acylations, catalyzed from acyl-coenzymes A. We investigated the dynamics of lysine acetylation and crotonylation on histones H3 and H4 during mouse spermatogenesis. Lysine crotonylation appeared to be of significant abundance compared to acetylation, particularly on Lys27 of histone H3 (H3K27cr) that accumulates in sperm in a cleaved form of H3. We identified the genomic localization of H3K27cr and studied its effects on transcription compared to the classical active mark H3K27ac at promoters and distal enhancers. The presence of both marks was strongly associated with highest gene expression. Assessment of their co-localization with transcription regulators (SLY, SOX30) and chromatin-binding proteins (BRD4, BRDT, BORIS and CTCF) indicated systematic highest binding when both active marks were present and different selective binding when present alone at chromatin. H3K27cr and H3K27ac finally mark the building of some sperm super-enhancers. This integrated analysis of omics data provides an unprecedented level of understanding of gene expression regulation by H3K27cr in comparison to H3K27ac, and reveals both synergistic and specific actions of each histone modification.</p>',
			'date' => '2020-03-17',
			'pmid' => 'http://www.pubmed.gov/32182340',
			'doi' => '10.1093/nar/gkaa163',
			'modified' => '2020-08-17 10:56:19',
			'created' => '2020-08-10 12:12:25',
			'ProductsPublication' => array(
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		),
		(int) 7 => array(
			'id' => '3848',
			'name' => 'A comprehensive epigenomic analysis of phenotypically distinguishable, genetically identical female and male Daphnia pulex.',
			'authors' => 'Kvist J, Athanàsio CG, Pfrender ME, Brown JB, Colbourne JK, Mirbahai L',
			'description' => '<p>BACKGROUND: Daphnia species reproduce by cyclic parthenogenesis involving both sexual and asexual reproduction. The sex of the offspring is environmentally determined and mediated via endocrine signalling by the mother. Interestingly, male and female Daphnia can be genetically identical, yet display large differences in behaviour, morphology, lifespan and metabolic activity. Our goal was to integrate multiple omics datasets, including gene expression, splicing, histone modification and DNA methylation data generated from genetically identical female and male Daphnia pulex under controlled laboratory settings with the aim of achieving a better understanding of the underlying epigenetic factors that may contribute to the phenotypic differences observed between the two genders. RESULTS: In this study we demonstrate that gene expression level is positively correlated with increased DNA methylation, and histone H3 trimethylation at lysine&thinsp;4 (H3K4me3) at predicted promoter regions. Conversely, elevated histone H3 trimethylation at lysine&thinsp;27 (H3K27me3), distributed across the entire transcript length, is negatively correlated with gene expression level. Interestingly, male Daphnia are dominated with epigenetic modifications that globally promote elevated gene expression, while female Daphnia are dominated with epigenetic modifications that reduce gene expression globally. For examples, CpG methylation (positively correlated with gene expression level) is significantly higher in almost all differentially methylated sites in male compared to female Daphnia. Furthermore, H3K4me3 modifications are higher in male compared to female Daphnia in more than 3/4 of the differentially regulated promoters. On the other hand, H3K27me3 is higher in female compared to male Daphnia in more than 5/6 of differentially modified sites. However, both sexes demonstrate roughly equal number of genes that are up-regulated in one gender compared to the other sex. Since, gene expression analyses typically assume that most genes are expressed at equal level among samples and different conditions, and thus cannot detect global changes affecting most genes. CONCLUSIONS: The epigenetic differences between male and female in Daphnia pulex are vast and dominated by changes that promote elevated gene expression in male Daphnia. Furthermore, the differences observed in both gene expression changes and epigenetic modifications between the genders relate to pathways that are physiologically relevant to the observed phenotypic differences.</p>',
			'date' => '2020-01-06',
			'pmid' => 'http://www.pubmed.gov/31906859',
			'doi' => '10.1186/s12864-019-6415-5',
			'modified' => '2020-02-20 11:34:47',
			'created' => '2020-02-13 10:02:44',
			'ProductsPublication' => array(
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		),
		(int) 8 => array(
			'id' => '3839',
			'name' => 'Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq.',
			'authors' => 'Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR',
			'description' => '<p>One of the primary aims of the Functional Annotation of ANimal Genomes (FAANG) initiative is to characterize tissue-specific regulation within animal genomes. To this end, we used chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in eight prioritized tissues collected as part of the FAANG equine biobank from two thoroughbred mares. Data were generated according to optimized experimental parameters developed during quality control testing. To ensure that we obtained sufficient ChIP and successful peak-calling, data and peak-calls were assessed using six quality metrics, replicate comparisons, and site-specific evaluations. Tissue specificity was explored by identifying binding motifs within unique active regions, and motifs were further characterized by gene ontology (GO) and protein-protein interaction analyses. The histone marks identified in this study represent some of the first resources for tissue-specific regulation within the equine genome. As such, these publicly available annotation data can be used to advance equine studies investigating health, performance, reproduction, and other traits of economic interest in the horse.</p>',
			'date' => '2019-12-18',
			'pmid' => 'http://www.pubmed.gov/31861495',
			'doi' => '10.3390/genes11010003',
			'modified' => '2020-02-20 11:20:25',
			'created' => '2020-02-13 10:02:44',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '3742',
			'name' => 'Development and epigenetic plasticity of murine Müller glia.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'date' => '2019-07-02',
			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
			'modified' => '2019-08-13 10:50:24',
			'created' => '2019-07-31 13:35:50',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 10 => array(
			'id' => '3569',
			'name' => 'The epigenetic basis for the impaired ability of adult murine retinal pigment epithelium cells to regenerate retinal tissue.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
			'date' => '2019-03-07',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
			'created' => '2019-03-21 14:12:08',
			'ProductsPublication' => array(
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		),
		(int) 11 => array(
			'id' => '3463',
			'name' => 'Epigenetic modifiers promote mitochondrial biogenesis and oxidative metabolism leading to enhanced differentiation of neuroprogenitor cells.',
			'authors' => 'Martine Uittenbogaard, Christine A. Brantner, Anne Chiaramello1',
			'description' => '<p>During neural development, epigenetic modulation of chromatin acetylation is part of a dynamic, sequential and critical process to steer the fate of multipotent neural progenitors toward a specific lineage. Pan-HDAC inhibitors (HDCis) trigger neuronal differentiation by generating an "acetylation" signature and promoting the expression of neurogenic bHLH transcription factors. Our studies and others have revealed a link between neuronal differentiation and increase of mitochondrial mass. However, the neuronal regulation of mitochondrial biogenesis has remained largely unexplored. Here, we show that the HDACi, sodium butyrate (NaBt), promotes mitochondrial biogenesis via the NRF-1/Tfam axis in embryonic hippocampal progenitor cells and neuroprogenitor-like PC12-NeuroD6 cells, thereby enhancing their neuronal differentiation competency. Increased mitochondrial DNA replication by several pan-HDACis indicates a common mechanism by which they regulate mitochondrial biogenesis. NaBt also induces coordinates mitochondrial ultrastructural changes and enhanced OXPHOS metabolism, thereby increasing key mitochondrial bioenergetics parameters in neural progenitor cells. NaBt also endows the neuronal cells with increased mitochondrial spare capacity to confer resistance to oxidative stress associated with neuronal differentiation. We demonstrate that mitochondrial biogenesis is under HDAC-mediated epigenetic regulation, the timing of which is consistent with its integrative role during neuronal differentiation. Thus, our findings add a new facet to our mechanistic understanding of how pan-HDACis induce differentiation of neuronal progenitor cells. Our results reveal the concept that epigenetic modulation of the mitochondrial pool prior to neurotrophic signaling dictates the efficiency of initiation of neuronal differentiation during the transition from progenitor to differentiating neuronal cells. The histone acetyltransferase CREB-binding protein plays a key role in regulating the mitochondrial biomass. By ChIP-seq analysis, we show that NaBt confers an H3K27ac epigenetic signature in several interconnected nodes of nuclear genes vital for neuronal differentiation and mitochondrial reprogramming. Collectively, our study reports a novel developmental epigenetic layer that couples mitochondrial biogenesis to neuronal differentiation.</p>',
			'date' => '2018-03-02',
			'pmid' => 'http://www.pubmed.gov/29500414',
			'doi' => '10.1038/s41419-018-0396-1',
			'modified' => '2019-02-15 21:21:45',
			'created' => '2019-02-14 15:01:22',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 12 => array(
			'id' => '3212',
			'name' => 'The epigenetic architecture at gene promoters determines cell type-specific LPS tolerance',
			'authors' => 'Kerstin Klein , Mojca Frank-Bertoncelj , Emmanuel Karouzakis , Renate E. Gay , Christoph Kolling , Adrian Ciurea , Nagihan Bostanci , Georgios N. Belibasakis , Lih-Ling Lin , Oliver Distler , Steffen Gay , Caroline Ospelt',
			'description' => '<p>Synovial fibroblasts (SF) drive inflammation and joint destruction in chronic arthritis. Here we show that SF possess a distinct type of LPS tolerance compared to macrophages and other types of fibroblasts. In SF and dermal fibroblasts, genes that were non-tolerizable after repeated LPS stimulation included proinflammatory cytokines, chemokines and matrix metalloproteinases, whereas anti-viral genes were tolerizable. In macrophages, all measured genes were tolerizable, whereas in gingival and foreskin fibroblasts<br />these genes were non-tolerizable. Repeated stimulation of SF with LPS resulted in loss of activating histone marks only in promoters of tolerizable genes. The epigenetic landscape at promoters of tolerizable genes was similar in unstimulated SF and monocytes, whereas the basal configuration of histone marks profoundly differed in genes that were non-tolerizable in SF only. Our data suggest that the epigenetic configuration at gene promoters regulates cell-specific LPS-induced responses and primes SF to sustain their inflammatory response in chronic arthritis.</p>',
			'date' => '2017-07-09',
			'pmid' => 'http://www.sciencedirect.com/science/article/pii/S0896841117303001',
			'doi' => '10.1016/j.jaut.2017.07.001 0',
			'modified' => '2017-07-18 08:20:47',
			'created' => '2017-07-18 08:18:41',
			'ProductsPublication' => array(
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		(int) 13 => array(
			'id' => '3169',
			'name' => 'PPARγ Links BMP2 and TGFβ1 Pathways in Vascular Smooth Muscle Cells, Regulating Cell Proliferation and Glucose Metabolism',
			'authors' => 'Laurent Calvier, Philippe Chouvarine, Ekaterina Legchenko, Nadine Hoffmann, Jonas Geldner, Paul Borchert, Danny Jonigk, Miklos M. Mozes, Georg Hansmann',
			'description' => '<p><span>BMP2 and TGF&beta;1 are functional antagonists of pathological remodeling in the arteries, heart, and lung; however, the mechanisms in VSMCs, and their&nbsp;disturbance in pulmonary arterial hypertension (PAH), are unclear. We found a pro-proliferative TGF&beta;1-Stat3-FoxO1 axis in VSMCs, and PPAR&gamma; as&nbsp;inhibitory regulator of TGF&beta;1-Stat3-FoxO1 and TGF&beta;1-Smad3/4, by physically interacting with Stat3 and Smad3. TGF&beta;1 induces fibrosis-related genes and miR-130a/301b, suppressing PPAR&gamma;. Conversely, PPAR&gamma; inhibits TGF&beta;1-induced mitochondrial activation and VSMC proliferation, and regulates two glucose metabolism-related enzymes, platelet isoform of phosphofructokinase (PFKP, a PPAR&gamma; target, via miR-331-5p) and protein phosphatase 1 regulatory subunit 3G (PPP1R3G, a Smad3 target). PPAR&gamma; knockdown/deletion in VSMCs activates TGF&beta;1 signaling. The PPAR&gamma; agonist pioglitazone reverses PAH and inhibits the TGF&beta;1-Stat3-FoxO1 axis in TGF&beta;1-overexpressing mice. We identified PPAR&gamma; as a missing link between BMP2 and TGF&beta;1 pathways in VSMCs. PPAR&gamma; activation can be beneficial in TGF&beta;1-associated diseases, such as PAH, parenchymal lung diseases, and Marfan&rsquo;s syndrome.</span></p>',
			'date' => '2017-05-02',
			'pmid' => 'http://www.cell.com/cell-metabolism/abstract/S1550-4131(17)30163-8',
			'doi' => 'http://dx.doi.org/10.1016/j.cmet.2017.03.011',
			'modified' => '2017-05-11 11:30:23',
			'created' => '2017-05-09 19:10:49',
			'ProductsPublication' => array(
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		(int) 14 => array(
			'id' => '3188',
			'name' => 'Epigenetically-driven anatomical diversity of synovial fibroblasts guides joint-specific fibroblast functions',
			'authors' => 'Mojca Frank-Bertoncelj, Michelle Trenkmann, Kerstin Klein, Emmanuel Karouzakis, Hubert Rehrauer, Anna Bratus, Christoph Kolling, Maria Armaka, Andrew Filer, Beat Michel, Renate E. Gay, Christopher D. Buckley, George Kollias, Steffen Gay & Caroline Ospelt',
			'description' => '<p><span>A number of human diseases, such as arthritis and atherosclerosis, include characteristic pathology in specific anatomical locations. Here we show transcriptomic differences in synovial fibroblasts from different joint locations and that HOX gene signatures reflect the joint-specific origins of mouse and human synovial fibroblasts and synovial tissues. Alongside DNA methylation and histone modifications, bromodomain and extra-terminal reader proteins regulate joint-specific HOX gene expression. Anatomical transcriptional diversity translates into joint-specific synovial fibroblast phenotypes with distinct adhesive, proliferative, chemotactic and matrix-degrading characteristics and differential responsiveness to TNF, creating a unique microenvironment in each joint. These findings indicate that local stroma might control positional disease patterns not only in arthritis but in any disease with a prominent stromal component.</span></p>',
			'date' => '2017-03-23',
			'pmid' => 'https://www.nature.com/articles/ncomms14852',
			'doi' => '10.1038/ncomms14852',
			'modified' => '2017-06-21 14:44:43',
			'created' => '2017-06-08 05:21:07',
			'ProductsPublication' => array(
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
<div id="portal" class="main-portal">
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<ul data-tab="" class="tips-menu">
<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
<li><a href="#panel2" class="tips portal button">Breast cancer</a></li>
</ul>
</nav></div>
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<div class="tabs-content">
<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
<div class="content" id="panel2">
<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
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<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
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<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
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<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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		'description' => '<p><strong>Chromatin immunoprecipitation followed by sequencing</strong> (<strong>ChIP-seq</strong>) is a powerful method allowing the genome-wide identification of DNA binding sites for proteins such as modified histones, transcription factors or chromatin remodelers. Determining how proteins interact with DNA to regulate gene expression is essential to fully understand many biological processes such as cell differentiation, organ development, and disease progression. The experts from our Epigenomics Profiling Services can help you to design your ChIP-seq project, optimize the workflow for your specific sample type/target, and provide you with high quality ChIP-seq data.</p>
<p>We use in house optimized reagents:</p>
<ul>
<li>Validated ChIP-seq Kits</li>
<li>Validated ChIP-seq grade antibodies</li>
</ul>
<p>And in-house developed equipment:</p>
<ul>
<li><strong>Bioruptor </strong>&ndash; for efficient and reproducible chromatin shearing</li>
</ul>
<p>We have expertise with many different types of samples as well as with a broad range of chromatin marks and can provide you with quality data even on very low input samples. Our bioinformatic experts will closely work with you to provide you with standard or customized analysis and will generate comprehensive publication-ready figures.</p>
<ul>
<li>Collaborative and customized project design to meet your needs</li>
<li>Dedicated in-house expert</li>
<li>End-to-end or customized service including wet lab and bioinformatic services</li>
</ul>
<p class="text-center"><img alt="ChIP Sequencing Services" src="https://www.diagenode.com/img/categories/chip_seq/ChIP-seq-success-experiment-WEB.png" /></p>
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		'label1' => 'ChIP-seq service description',
		'info1' => '<p>In order to provide you with the highest quality data, our ChIP-seq service is composed of two major steps:</p>
<ol>
<li><strong>ChIP validation</strong> - during this step we optimize the ChIP conditions that depend on your sample type, target and amount of cells.</li>
<li><strong>ChIP on sample of interest</strong> - once the best ChIP conditions are validated, the samples of interest can be processed.</li>
</ol>
<p></p>
<p>ChIP-seq service includes:</p>
<table style="width: 918px;">
<tbody>
<tr style="height: 62px;">
<td style="width: 218px; height: 220px; background-color: #f9f9f9;" rowspan="3"><strong>1. ChIP validation</strong></td>
<td style="width: 417px; height: 62px;"><strong>Chromatin Shearing validation</strong></td>
<td style="width: 568px; height: 62px;">
<ul>
<li>Testing 2 shearing times for each cell type/tissue type</li>
</ul>
</td>
</tr>
<tr style="height: 27px;">
<td style="width: 417px; height: 27px; background-color: white;"><strong>ChIP/Ab validation</strong></td>
<td style="width: 417px; height: 27px; background-color: white;">
<ul>
<li>Testing 2 Ab amounts and/or 2 Ab references per target of interest</li>
<li>qPCR analysis if positive and negative control regions can be provided</li>
<li>Library preparation on IPs and INPUTs material</li>
<li>Illumina sequencing run : Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 131px;">
<td style="width: 417px; height: 131px;"><strong>Primers design and validation</strong></td>
<td style="width: 568px; height: 131px;">
<ul>
<li>Performed on gDNA based on the data obtained during the ChIP/Ab validation. Those primers will be used for further qPCR validation of the ChIP on the samples of interest</li>
</ul>
</td>
</tr>
<tr style="height: 38px;">
<td style="width: 218px; height: 38px; background-color: white;"></td>
<td style="width: 218px; height: 38px;"></td>
<td style="width: 218px; height: 38px;"></td>
</tr>
<tr style="height: 59px;">
<td style="width: 218px; height: 216px; background-color: #f9f9f9; text-align: center;" rowspan="3"><strong>2. ChIP on sample of interest</strong></td>
<td style="width: 417px; height: 59px;"><strong>Chromatin IP</strong></td>
<td style="width: 568px; height: 59px;">
<ul>
<li>Chromatin shearing</li>
<li>IPs</li>
<li>qPCR analysis using primers that have been validated during the ChIP validation</li>
</ul>
</td>
</tr>
<tr style="height: 69px;">
<td style="width: 568px; height: 69px; background-color: white;"><strong>Library Preparation</strong></td>
<td style="width: 568px; height: 69px; background-color: white;">
<ul>
<li>Library preparation and purification on IPs and INPUTs material</li>
</ul>
</td>
</tr>
<tr style="height: 88px;">
<td style="width: 417px; height: 88px;"><strong>Sequencing</strong></td>
<td style="width: 568px; height: 88px;">
<ul>
<li>Illumina sequencing run: Paired-end reads, 2x 50 bp, sequencing depth will be adjusted depending on the mark/specie studied</li>
</ul>
</td>
</tr>
</tbody>
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		'info2' => '<h3>Bioinformatic analyses on samples of interest</h3>
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<td style="width: 137px; height: 107px; background-color: #f9f9f9; text-align: center;"><strong>Standard bioinformatics</strong></td>
<td style="width: 358px; height: 107px;">
<p style="text-align: center;"></p>
</td>
<td style="width: 466px; height: 107px;">
<ul>
<li>Quality check, alignment to reference genome, identification of enriched regions (peak calling)</li>
</ul>
</td>
</tr>
<tr style="height: 24px;">
<td style="width: 137px; height: 24px; background-color: white; text-align: center;"><strong></strong></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
<td style="width: 137px; height: 24px; background-color: #f9f9f9; text-align: center;"></td>
</tr>
<tr style="height: 41px;">
<td style="width: 137px; height: 170px; background-color: #f9f9f9; text-align: center;" rowspan="5"><strong>Advanced bioinformatics</strong></td>
<td style="width: 466px; height: 41px; background-color: white;">
<p style="text-align: center;"><strong>Differential binding analysis</strong></p>
</td>
<td style="width: 466px; height: 41px; background-color: white;">
<ul>
<li>Identification and annotation of differential binding sites between samples based on previously identified ChIP-seq peaks</li>
</ul>
</td>
</tr>
<tr style="height: 33px;">
<td style="width: 466px; height: 33px; background-color: white; text-align: center;"><strong>Annotation in genomic regions</strong></td>
<td style="width: 466px; height: 33px; background-color: white;">
<ul>
<li>Annotation of ChIP-seq peaks with genomic regions: introns, exons, promoters, 1-to-5 kb upstream-TSS and intergenic regions</li>
</ul>
</td>
</tr>
<tr style="height: 26px;">
<td style="width: 466px; height: 26px; background-color: white; text-align: center;"><strong>Gene ontology termes analysis</strong></td>
<td style="width: 466px; height: 26px; background-color: white;">
<ul>
<li>Enrichment analysis on gene sets. Gene Ontology terms that are overrepresented in bound regions or differentially bound regions may indicate the underlying biological processes involved</li>
</ul>
</td>
</tr>
<tr style="height: 56px;">
<td style="width: 466px; height: 56px; background-color: white; text-align: center;"><strong>Pathway analysis</strong></td>
<td style="width: 466px; height: 56px; background-color: white;">
<ul>
<li>Identify biochemical pathways in which genes associated with bound regions or differentially bound regions may be overrepresented</li>
</ul>
</td>
</tr>
<tr style="height: 14px;">
<td style="width: 466px; height: 14px; background-color: white; text-align: center;"><strong>Visualization of specific genomic regions</strong></td>
<td style="width: 466px; height: 14px; background-color: white;">
<ul>
<li>Visualization of results (i.e. sequencing data, peaks) at specific genomic regions (e.g. genes, promoters) in publication-ready images</li>
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</td>
</tr>
</tbody>
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		'meta_description' => 'Diagenode offers end-to-end or customized ChIP-seq service including wet lab and bioinformatic services. Collaborative and customized project design to meet your needs. Dedicated in-house expert.',
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			'name' => 'Bioinformatics Data Mining Service',
			'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
<p></p>
<div class="row">
<div class="small-12 medium-12 large-12 columns">
<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
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<li><a href="#panel1" class="tips portal button">Smokers vs non-smokers </a></li>
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</ul>
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</div>
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<div class="content active" id="panel1">
<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo" class="alert small button" target="_blank">Read more</a></div>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
</div>
</blockquote>
</div>
</div>
<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
</td>
<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
</td>
</tr>
</tbody>
</table>
<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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			'label1' => 'Data mining modules ',
			'info1' => '<ol>
<li><strong>Feasibility study</strong></li>
</ol>
<ul>
<li>Assessment of data characteristics and <span></span>applicability of different machine learning (ML)</li>
<li>Prototypic analyses:</li>
<li>Initial feasibility report</li>
</ul>
<p></p>
<ol start="2">
<li><strong>Data Mining</strong></li>
</ol>
<ul>
<li>Machine learning on data</li>
<li>Data evaluation and validation</li>
<li>Report generation</li>
</ul>
<p>&nbsp;</p>
<ol start="3">
<li><strong>Data Interpretation</strong></li>
</ol>
<ul>
<li>Integration of background knowledge</li>
<li>Functional interpretation / pathway mining</li>
<li>Scientific reporting</li>
</ul>
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			'label2' => 'Integration with wet lab services and bioinformatics',
			'info2' => '<p>A clear advantage of <strong>Diagenode&rsquo;s data mining services</strong> is the close connection with<span>&nbsp; </span>other service offerings like <strong>wet lab analysis services</strong> and <strong>bioinformatics services</strong>. You can retrieve a full service package from a single source.</p>
<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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			'id' => '48',
			'position' => '0',
			'parent_id' => '47',
			'name' => 'ヒストン ChIP-seq受託サービス',
			'description' => '<h3>DiagenodeのヒストンChIP サービス： 10,000細胞に至るまで調べるChIP-seqヒストン修飾&nbsp;</h3>
<p>ヒストンの翻訳後修飾は、遺伝子発現を調節します。 Diagenodeのエピゲノムプロファイリングサービスでは、活性化プロモーターとエンハンサー解析を用いて、調節エレメントとその相互作用タンパク質を調べて、 転写調節を理解するためにChIP-seq解析によるゲノム全体のヒストン修飾をプロファイルします。</p>
<p>更に詳しく</p>
<ul>
<li>活性なプロモーター：H3K4me3</li>
<li>不活性なプロモーター：H3K27me3</li>
<li>エンハンサー：調節領域におけるH3K4me1およびH3K27acの</li>
<li>活性遺伝子体：H3K36me3</li>
</ul>
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<ul>
<li>CTCF: 転写リプレッサーおよびインシュレーター活性</li>
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<li>Pol II, p53など</li>
<li>エピジェネティック writers, readers, erasers</li>
<li>核内受容体&nbsp;</li>
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<div class="small-12 medium-12 large-12 columns"><center></center></div>
</div>
<div class="row">
<div class="small-12 medium-4 large-4 columns">
<div class="panel"><center><img src="http://www.diagenode.com/img/categories/services/chip-workflow.png" alt="Chromatin-Immunoprecipitation-Diagenode" /></center>
<p>Chromatin consists of DNA, histones and non-histone proteins. Understanding the roles of histones and transcription factors is critical in understanding the regulation of gene expression.</p>
<p>Using ChIP-seq analysis, it is possible to profile histone modifications and transcription factor binding genome-wide to&nbsp;elucidate&nbsp;control of gene expression in disease or in response to a drug treatment. Diagenode&rsquo;s Epigenomic Profiling Services offer a wide variety of chromatin analysis options through ChIP-seq and ATAC-seq.</p>
</div>
<center><img src="https://www.diagenode.com/img/logo-scientist-registered-supplier.png" /></center></div>
<div class="small-12 medium-8 large-8 columns"><center><img src="http://www.diagenode.com/img/applications/histone-marks-helice.png" alt="Histone-Antibodies-Diagenode" /></center><br href="../p/service-chip-seq" />
<h3><a href="../p/atac-seq-service">ATAC-seq: open chromatin regions</a></h3>
<p>Study genome-wide chromatin accessibility. Identify open chromatin sites and active regulatory elements such as promoter, enhancers, and insulators.</p>
<ul>
<ul>
<li>Nucleosome positioning</li>
</ul>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq histone modifications</a></h3>
<p>Post-translational modification of histones is implicated in the regulation of gene expression, necessitating the study of regulatory elements and their interacting proteins like active promoter and enhancer analysis. Profile genome-wide histone modifications by ChIP-seq analysis to understand transcriptional regulation</p>
<ul>
<li><strong>Active promoter profiling</strong>: H3K4me3 enrichment</li>
<li><strong>Inactive promoter profiling</strong>: H3K27me3 enrichment</li>
<li><strong>Enhancer profiling</strong>: H3K4me1 and H3K27ac enrichment in regulatory regions</li>
<li><strong>Active gene body</strong>: H3K36me3</li>
</ul>
<h3><a href="../p/service-chip-seq">ChIP-seq Transcription Factors</a></h3>
<p>Explore the effects of transcription factor binding through ChIP-seq analysis of a multitude of TFs including:</p>
<ul>
<li>CTCF: transcriptional repressor and insulator activity</li>
<li>p300/CBP: histone acetyltransferase</li>
<li>Pol II, p53, and more</li>
<li>Epigenetic writers, readers, erasers</li>
<li>Nuclear receptors</li>
<li>Tumor suppressor genes</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>
<div class="row">
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<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>
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<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>
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<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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<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>
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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>
</li>
</ul>
</div>
<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>
</ul>
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			'name' => 'Impact of Fetal Exposure to Endocrine Disrupting ChemicalMixtures on FOXA3 Gene and Protein Expression in Adult RatTestes.',
			'authors' => 'Walker  C. et al.',
			'description' => '<p>Perinatal exposure to endocrine disrupting chemicals (EDCs) has been shown to affect male reproductive functions. However, the effects on male reproduction of exposure to EDC mixtures at doses relevant to humans have not been fully characterized. In previous studies, we found that in utero exposure to mixtures of the plasticizer di(2-ethylhexyl) phthalate (DEHP) and the soy-based phytoestrogen genistein (Gen) induced abnormal testis development in rats. In the present study, we investigated the molecular basis of these effects in adult testes from the offspring of pregnant SD rats gavaged with corn oil or Gen + DEHP mixtures at 0.1 or 10 mg/kg/day. Testicular transcriptomes were determined by microarray and RNA-seq analyses. A protein analysis was performed on paraffin and frozen testis sections, mainly by immunofluorescence. The transcription factor forkhead box protein 3 (FOXA3), a key regulator of Leydig cell function, was identified as the most significantly downregulated gene in testes from rats exposed in utero to Gen + DEHP mixtures. FOXA3 protein levels were decreased in testicular interstitium at a dose previously found to reduce testosterone levels, suggesting a primary effect of fetal exposure to Gen + DEHP on adult Leydig cells, rather than on spermatids and Sertoli cells, also expressing FOXA3. Thus, FOXA3 downregulation in adult testes following fetal exposure to Gen + DEHP may contribute to adverse male reproductive outcomes.</p>',
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			'name' => 'Histone Deacetylases 1 and 2 target gene regulatory networks of nephronprogenitors to control nephrogenesis.',
			'authors' => 'Liu  Hongbing et al.',
			'description' => '<p>Our studies demonstrated the critical role of Histone deacetylases (HDACs) in the regulation of nephrogenesis. To better understand the key pathways regulated by HDAC1/2 in early nephrogenesis, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) of Hdac1/2 on isolated nephron progenitor cells (NPCs) from mouse E16.5 kidneys. Our analysis revealed that 11802 (40.4\%) of Hdac1 peaks overlap with Hdac2 peaks, further demonstrates the redundant role of Hdac1 and Hdac2 during nephrogenesis. Common Hdac1/2 peaks are densely concentrated close to the transcriptional start site (TSS). GREAT Gene Ontology analysis of overlapping Hdac1/2 peaks reveals that Hdac1/2 are associated with metanephric nephron morphogenesis, chromatin assembly or disassembly, as well as other DNA checkpoints. Pathway analysis shows that negative regulation of Wnt signaling pathway is one of Hdac1/2's most significant function in NPCs. Known motif analysis indicated that Hdac1 is enriched in motifs for Six2, Hox family, and Tcf family members, which are essential for self-renewal and differentiation of nephron progenitors. Interestingly, we found the enrichment of HDAC1/2 at the enhancer and promoter regions of actively transcribed genes, especially those concerned with NPC self-renewal. HDAC1/2 simultaneously activate or repress the expression of different genes to maintain the cellular state of nephron progenitors. We used the Integrative Genomics Viewer to visualize these target genes associated with each function and found that Hdac1/2 co-bound to the enhancers or/and promoters of genes associated with nephron morphogenesis, differentiation, and cell cycle control. Taken together, our ChIP-Seq analysis demonstrates that Hdac1/2 directly regulate the molecular cascades essential for nephrogenesis.</p>',
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			'name' => 'Identification of genomic binding sites and direct target genes for thetranscription factor DDIT3/CHOP.',
			'authors' => 'Osman  A. et al.',
			'description' => '<p>DDIT3 is a tightly regulated basic leucine zipper (bZIP) transcription factor and key regulator in cellular stress responses. It is involved in a variety of pathological conditions and may cause cell cycle block and apoptosis. It is also implicated in differentiation of some specialized cell types and as an oncogene in several types of cancer. DDIT3 is believed to act as a dominant-negative inhibitor by forming heterodimers with other bZIP transcription factors, preventing their DNA binding and transactivating functions. DDIT3 has, however, been reported to bind DNA and regulate target genes. Here, we employed ChIP sequencing combined with microarray-based expression analysis to identify direct binding motifs and target genes of DDIT3. The results reveal DDIT3 binding to motifs similar to other bZIP transcription factors, known to form heterodimers with DDIT3. Binding to a class III satellite DNA repeat sequence was also detected. DDIT3 acted as a DNA-binding transcription factor and bound mainly to the promotor region of regulated genes. ChIP sequencing analysis of histone H3K27 methylation and acetylation showed a strong overlap between H3K27-acetylated marks and DDIT3 binding. These results support a role for DDIT3 as a transcriptional regulator of H3K27ac-marked genes in transcriptionally active chromatin.</p>',
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			'name' => 'CREBBP/EP300 acetyltransferase inhibition disrupts FOXA1-bound enhancers to inhibit the proliferation of ER+ breast cancer cells.',
			'authors' => 'Bommi-Reddy A. et al.',
			'description' => '<p><span>Therapeutic targeting of the estrogen receptor (ER) is a clinically validated approach for estrogen receptor positive breast cancer (ER+ BC), but sustained response is limited by acquired resistance. Targeting the transcriptional coactivators required for estrogen receptor activity represents an alternative approach that is not subject to the same limitations as targeting estrogen receptor itself. In this report we demonstrate that the acetyltransferase activity of coactivator paralogs CREBBP/EP300 represents a promising therapeutic target in ER+ BC. Using the potent and selective inhibitor CPI-1612, we show that CREBBP/EP300 acetyltransferase inhibition potently suppresses in vitro and in vivo growth of breast cancer cell line models and acts in a manner orthogonal to directly targeting ER. CREBBP/EP300 acetyltransferase inhibition suppresses ER-dependent transcription by targeting lineage-specific enhancers defined by the pioneer transcription factor FOXA1. These results validate CREBBP/EP300 acetyltransferase activity as a viable target for clinical development in ER+ breast cancer.</span></p>',
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			'doi' => '10.1371/journal.pone.0262378',
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			'authors' => 'Tacconi, Carlotta and He, Yuliang and Ducoli, Luca and Detmar, Michael',
			'description' => '<p>Lymphatic and blood vascular endothelial cells (ECs) share several molecular and developmental features. However, these two cell types possess distinct phenotypic signatures, reflecting their different biological functions. Despite significant advances in elucidating how the specification of lymphatic and blood vascular ECs is regulated at the transcriptional level during development, the key molecular mechanisms governing their lineage identity under physiological or pathological conditions remain poorly understood. To explore the epigenomic signatures in the maintenance of EC lineage specificity, we compared the transcriptomic landscapes, histone composition (H3K4me3 and H3K27me3) and DNA methylomes of cultured matched human primary dermal lymphatic and blood vascular ECs. Our findings reveal that blood vascular lineage genes manifest a more 'repressed' histone composition in lymphatic ECs, whereas DNA methylation at promoters is less linked to the differential transcriptomes of lymphatic versus blood vascular ECs. Meta-analyses identified two transcriptional regulators, BCL6 and MEF2C, which potentially govern endothelial lineage specificity. Notably, the blood vascular endothelial lineage markers CD34, ESAM and FLT1 and the lymphatic endothelial lineage markers PROX1, PDPN and FLT4 exhibited highly differential epigenetic profiles and responded in distinct manners to epigenetic drug treatments. The perturbation of histone and DNA methylation selectively promoted the expression of blood vascular endothelial markers in lymphatic endothelial cells, but not vice versa. Overall, our study reveals that the fine regulation of lymphatic and blood vascular endothelial transcriptomes is maintained via several epigenetic mechanisms, which are crucial to the maintenance of endothelial cell identity.</p>',
			'date' => '2020-09-01',
			'pmid' => 'https://www.ncbi.nlm.nih.gov/pubmed/32918672',
			'doi' => '10.1007/s10456-020-09743-9',
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			'name' => 'Battle of the sex chromosomes: competition between X- and Y-chromosomeencoded proteins for partner interaction and chromatin occupancy drivesmulti-copy gene expression and evolution in muroid rodents.',
			'authors' => 'Moretti, C and Blanco, M and Ialy-Radio, C and Serrentino, ME and Gobé,C and Friedman, R and Battail, C and Leduc, M and Ward, MA and Vaiman, Dand Tores, F and Cocquet, J',
			'description' => '<p>Transmission distorters (TDs) are genetic elements that favor their own transmission to the detriments of others. Slx/Slxl1 (Sycp3-like-X-linked and Slx-like1) and Sly (Sycp3-like-Y-linked) are TDs which have been co-amplified on the X and Y chromosomes of Mus species. They are involved in an intragenomic conflict in which each favors its own transmission, resulting in sex ratio distortion of the progeny when Slx/Slxl1 vs. Sly copy number is unbalanced. They are specifically expressed in male postmeiotic gametes (spermatids) and have opposite effects on gene expression: Sly knockdown leads to the upregulation of hundreds of spermatid-expressed genes, while Slx/Slxl1-deficiency downregulates them. When both Slx/Slxl1 and Sly are knocked-down, sex ratio distortion and gene deregulation are corrected. Slx/Slxl1 and Sly are, therefore, in competition but the molecular mechanism remains unknown. By comparing their chromatin binding profiles and protein partners, we show that SLX/SLXL1 and SLY proteins compete for interaction with H3K4me3-reader SSTY1 (Spermiogenesis-specific-transcript-on-the-Y1) at the promoter of thousands of genes to drive their expression, and that the opposite effect they have on gene expression is mediated by different abilities to recruit SMRT/N-Cor transcriptional complex. Their target genes are predominantly spermatid-specific multicopy genes encoded by the sex chromosomes and the autosomal Speer/Takusan. Many of them have co-amplified with Slx/Slxl1/Sly but also Ssty during muroid rodent evolution. Overall, we identify Ssty as a key element of the X vs. Y intragenomic conflict, which may have influenced gene content and hybrid sterility beyond Mus lineage since Ssty amplification on the Y pre-dated that of Slx/Slxl1/Sly.</p>',
			'date' => '2020-07-13',
			'pmid' => 'http://www.pubmed.gov/32658962',
			'doi' => '10.1093/molbev/msaa175/5870835',
			'modified' => '2020-12-18 13:27:51',
			'created' => '2020-10-12 14:54:59',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 6 => array(
			'id' => '3922',
			'name' => 'Multi-omic analysis of gametogenesis reveals a novel signature at the promoters and distal enhancers of active genes.',
			'authors' => 'Crespo M, Damont A, Blanco M, Lastrucci E, Kennani SE, Ialy-Radio C, Khattabi LE, Terrier S, Louwagie M, Kieffer-Jaquinod S, Hesse AM, Bruley C, Chantalat S, Govin J, Fenaille F, Battail C, Cocquet J, Pflieger D',
			'description' => '<p>Epigenetic regulation of gene expression is tightly controlled by the dynamic modification of histones by chemical groups, the diversity of which has largely expanded over the past decade with the discovery of lysine acylations, catalyzed from acyl-coenzymes A. We investigated the dynamics of lysine acetylation and crotonylation on histones H3 and H4 during mouse spermatogenesis. Lysine crotonylation appeared to be of significant abundance compared to acetylation, particularly on Lys27 of histone H3 (H3K27cr) that accumulates in sperm in a cleaved form of H3. We identified the genomic localization of H3K27cr and studied its effects on transcription compared to the classical active mark H3K27ac at promoters and distal enhancers. The presence of both marks was strongly associated with highest gene expression. Assessment of their co-localization with transcription regulators (SLY, SOX30) and chromatin-binding proteins (BRD4, BRDT, BORIS and CTCF) indicated systematic highest binding when both active marks were present and different selective binding when present alone at chromatin. H3K27cr and H3K27ac finally mark the building of some sperm super-enhancers. This integrated analysis of omics data provides an unprecedented level of understanding of gene expression regulation by H3K27cr in comparison to H3K27ac, and reveals both synergistic and specific actions of each histone modification.</p>',
			'date' => '2020-03-17',
			'pmid' => 'http://www.pubmed.gov/32182340',
			'doi' => '10.1093/nar/gkaa163',
			'modified' => '2020-08-17 10:56:19',
			'created' => '2020-08-10 12:12:25',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 7 => array(
			'id' => '3848',
			'name' => 'A comprehensive epigenomic analysis of phenotypically distinguishable, genetically identical female and male Daphnia pulex.',
			'authors' => 'Kvist J, Athanàsio CG, Pfrender ME, Brown JB, Colbourne JK, Mirbahai L',
			'description' => '<p>BACKGROUND: Daphnia species reproduce by cyclic parthenogenesis involving both sexual and asexual reproduction. The sex of the offspring is environmentally determined and mediated via endocrine signalling by the mother. Interestingly, male and female Daphnia can be genetically identical, yet display large differences in behaviour, morphology, lifespan and metabolic activity. Our goal was to integrate multiple omics datasets, including gene expression, splicing, histone modification and DNA methylation data generated from genetically identical female and male Daphnia pulex under controlled laboratory settings with the aim of achieving a better understanding of the underlying epigenetic factors that may contribute to the phenotypic differences observed between the two genders. RESULTS: In this study we demonstrate that gene expression level is positively correlated with increased DNA methylation, and histone H3 trimethylation at lysine&thinsp;4 (H3K4me3) at predicted promoter regions. Conversely, elevated histone H3 trimethylation at lysine&thinsp;27 (H3K27me3), distributed across the entire transcript length, is negatively correlated with gene expression level. Interestingly, male Daphnia are dominated with epigenetic modifications that globally promote elevated gene expression, while female Daphnia are dominated with epigenetic modifications that reduce gene expression globally. For examples, CpG methylation (positively correlated with gene expression level) is significantly higher in almost all differentially methylated sites in male compared to female Daphnia. Furthermore, H3K4me3 modifications are higher in male compared to female Daphnia in more than 3/4 of the differentially regulated promoters. On the other hand, H3K27me3 is higher in female compared to male Daphnia in more than 5/6 of differentially modified sites. However, both sexes demonstrate roughly equal number of genes that are up-regulated in one gender compared to the other sex. Since, gene expression analyses typically assume that most genes are expressed at equal level among samples and different conditions, and thus cannot detect global changes affecting most genes. CONCLUSIONS: The epigenetic differences between male and female in Daphnia pulex are vast and dominated by changes that promote elevated gene expression in male Daphnia. Furthermore, the differences observed in both gene expression changes and epigenetic modifications between the genders relate to pathways that are physiologically relevant to the observed phenotypic differences.</p>',
			'date' => '2020-01-06',
			'pmid' => 'http://www.pubmed.gov/31906859',
			'doi' => '10.1186/s12864-019-6415-5',
			'modified' => '2020-02-20 11:34:47',
			'created' => '2020-02-13 10:02:44',
			'ProductsPublication' => array(
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		(int) 8 => array(
			'id' => '3839',
			'name' => 'Functionally Annotating Regulatory Elements in the Equine Genome Using Histone Mark ChIP-Seq.',
			'authors' => 'Kingsley NB, Kern C, Creppe C, Hales EN, Zhou H, Kalbfleisch TS, MacLeod JN, Petersen JL, Finno CJ, Bellone RR',
			'description' => '<p>One of the primary aims of the Functional Annotation of ANimal Genomes (FAANG) initiative is to characterize tissue-specific regulation within animal genomes. To this end, we used chromatin immunoprecipitation followed by sequencing (ChIP-Seq) to map four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in eight prioritized tissues collected as part of the FAANG equine biobank from two thoroughbred mares. Data were generated according to optimized experimental parameters developed during quality control testing. To ensure that we obtained sufficient ChIP and successful peak-calling, data and peak-calls were assessed using six quality metrics, replicate comparisons, and site-specific evaluations. Tissue specificity was explored by identifying binding motifs within unique active regions, and motifs were further characterized by gene ontology (GO) and protein-protein interaction analyses. The histone marks identified in this study represent some of the first resources for tissue-specific regulation within the equine genome. As such, these publicly available annotation data can be used to advance equine studies investigating health, performance, reproduction, and other traits of economic interest in the horse.</p>',
			'date' => '2019-12-18',
			'pmid' => 'http://www.pubmed.gov/31861495',
			'doi' => '10.3390/genes11010003',
			'modified' => '2020-02-20 11:20:25',
			'created' => '2020-02-13 10:02:44',
			'ProductsPublication' => array(
				[maximum depth reached]
			)
		),
		(int) 9 => array(
			'id' => '3742',
			'name' => 'Development and epigenetic plasticity of murine Müller glia.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The ability to regenerate the entire retina and restore lost sight after injury is found in some species and relies mostly on the epigenetic plasticity of M&uuml;ller glia. To understand the role of mammalian M&uuml;ller glia as a source of progenitors for retinal regeneration, we investigated changes in gene expression during differentiation of retinal progenitor cells (RPCs) into M&uuml;ller glia. We also analyzed the global epigenetic profile of adult M&uuml;ller glia. We observed significant changes in gene expression during differentiation of RPCs into M&uuml;ller glia in only a small group of genes. We found a high similarity between RPCs and M&uuml;ller glia on the transcriptomic and epigenomic levels. Our findings also indicate that M&uuml;ller glia are epigenetically very close to late-born retinal neurons, but not early-born retinal neurons. Importantly, we found that key genes required for phototransduction were highly methylated. Thus, our data suggest that M&uuml;ller glia are epigenetically very similar to late RPCs. Meanwhile, obstacles for regeneration of the entire mammalian retina from M&uuml;ller glia may consist of repressive chromatin and highly methylated DNA in the promoter regions of many genes required for the development of early-born retinal neurons. In addition, DNA demethylation may be required for proper reprogramming and differentiation of M&uuml;ller glia into rod photoreceptors.</p>
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			'date' => '2019-07-02',
			'pmid' => 'http://www.pubmed.gov/31276697',
			'doi' => '10.1016/j.bbamcr.2019.06.019',
			'modified' => '2019-08-13 10:50:24',
			'created' => '2019-07-31 13:35:50',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 10 => array(
			'id' => '3569',
			'name' => 'The epigenetic basis for the impaired ability of adult murine retinal pigment epithelium cells to regenerate retinal tissue.',
			'authors' => 'Dvoriantchikova G, Seemungal RJ, Ivanov D',
			'description' => '<p>The epigenetic plasticity of amphibian retinal pigment epithelium (RPE) allows them to regenerate the entire retina, a trait known to be absent in mammals. In this study, we investigated the epigenetic plasticity of adult murine RPE to identify possible mechanisms that prevent mammalian RPE from regenerating retinal tissue. RPE were analyzed using microarray, ChIP-seq, and whole-genome bisulfite sequencing approaches. We found that the majority of key genes required for progenitor phenotypes were in a permissive chromatin state and unmethylated in RPE. We observed that the majority of non-photoreceptor genes had promoters in a repressive chromatin state, but these promoters were in unmethylated or low-methylated regions. Meanwhile, the majority of promoters for photoreceptor genes were found in a permissive chromatin state, but were highly-methylated. Methylome states of photoreceptor-related genes in adult RPE and embryonic retina (which mostly contain progenitors) were very similar. However, promoters of these genes were demethylated and activated during retinal development. Our data suggest that, epigenetically, adult murine RPE cells are a progenitor-like cell type. Most likely two mechanisms prevent adult RPE from reprogramming and differentiating into retinal neurons: 1) repressive chromatin in the promoter regions of non-photoreceptor retinal neuron genes; 2) highly-methylated promoters of photoreceptor-related genes.</p>',
			'date' => '2019-03-07',
			'pmid' => 'http://www.pubmed.gov/30846751',
			'doi' => '10.1038/s41598-019-40262-w',
			'modified' => '2019-05-09 17:33:09',
			'created' => '2019-03-21 14:12:08',
			'ProductsPublication' => array(
				[maximum depth reached]
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		),
		(int) 11 => array(
			'id' => '3463',
			'name' => 'Epigenetic modifiers promote mitochondrial biogenesis and oxidative metabolism leading to enhanced differentiation of neuroprogenitor cells.',
			'authors' => 'Martine Uittenbogaard, Christine A. Brantner, Anne Chiaramello1',
			'description' => '<p>During neural development, epigenetic modulation of chromatin acetylation is part of a dynamic, sequential and critical process to steer the fate of multipotent neural progenitors toward a specific lineage. Pan-HDAC inhibitors (HDCis) trigger neuronal differentiation by generating an "acetylation" signature and promoting the expression of neurogenic bHLH transcription factors. Our studies and others have revealed a link between neuronal differentiation and increase of mitochondrial mass. However, the neuronal regulation of mitochondrial biogenesis has remained largely unexplored. Here, we show that the HDACi, sodium butyrate (NaBt), promotes mitochondrial biogenesis via the NRF-1/Tfam axis in embryonic hippocampal progenitor cells and neuroprogenitor-like PC12-NeuroD6 cells, thereby enhancing their neuronal differentiation competency. Increased mitochondrial DNA replication by several pan-HDACis indicates a common mechanism by which they regulate mitochondrial biogenesis. NaBt also induces coordinates mitochondrial ultrastructural changes and enhanced OXPHOS metabolism, thereby increasing key mitochondrial bioenergetics parameters in neural progenitor cells. NaBt also endows the neuronal cells with increased mitochondrial spare capacity to confer resistance to oxidative stress associated with neuronal differentiation. We demonstrate that mitochondrial biogenesis is under HDAC-mediated epigenetic regulation, the timing of which is consistent with its integrative role during neuronal differentiation. Thus, our findings add a new facet to our mechanistic understanding of how pan-HDACis induce differentiation of neuronal progenitor cells. Our results reveal the concept that epigenetic modulation of the mitochondrial pool prior to neurotrophic signaling dictates the efficiency of initiation of neuronal differentiation during the transition from progenitor to differentiating neuronal cells. The histone acetyltransferase CREB-binding protein plays a key role in regulating the mitochondrial biomass. By ChIP-seq analysis, we show that NaBt confers an H3K27ac epigenetic signature in several interconnected nodes of nuclear genes vital for neuronal differentiation and mitochondrial reprogramming. Collectively, our study reports a novel developmental epigenetic layer that couples mitochondrial biogenesis to neuronal differentiation.</p>',
			'date' => '2018-03-02',
			'pmid' => 'http://www.pubmed.gov/29500414',
			'doi' => '10.1038/s41419-018-0396-1',
			'modified' => '2019-02-15 21:21:45',
			'created' => '2019-02-14 15:01:22',
			'ProductsPublication' => array(
				[maximum depth reached]
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		(int) 12 => array(
			'id' => '3212',
			'name' => 'The epigenetic architecture at gene promoters determines cell type-specific LPS tolerance',
			'authors' => 'Kerstin Klein , Mojca Frank-Bertoncelj , Emmanuel Karouzakis , Renate E. Gay , Christoph Kolling , Adrian Ciurea , Nagihan Bostanci , Georgios N. Belibasakis , Lih-Ling Lin , Oliver Distler , Steffen Gay , Caroline Ospelt',
			'description' => '<p>Synovial fibroblasts (SF) drive inflammation and joint destruction in chronic arthritis. Here we show that SF possess a distinct type of LPS tolerance compared to macrophages and other types of fibroblasts. In SF and dermal fibroblasts, genes that were non-tolerizable after repeated LPS stimulation included proinflammatory cytokines, chemokines and matrix metalloproteinases, whereas anti-viral genes were tolerizable. In macrophages, all measured genes were tolerizable, whereas in gingival and foreskin fibroblasts<br />these genes were non-tolerizable. Repeated stimulation of SF with LPS resulted in loss of activating histone marks only in promoters of tolerizable genes. The epigenetic landscape at promoters of tolerizable genes was similar in unstimulated SF and monocytes, whereas the basal configuration of histone marks profoundly differed in genes that were non-tolerizable in SF only. Our data suggest that the epigenetic configuration at gene promoters regulates cell-specific LPS-induced responses and primes SF to sustain their inflammatory response in chronic arthritis.</p>',
			'date' => '2017-07-09',
			'pmid' => 'http://www.sciencedirect.com/science/article/pii/S0896841117303001',
			'doi' => '10.1016/j.jaut.2017.07.001 0',
			'modified' => '2017-07-18 08:20:47',
			'created' => '2017-07-18 08:18:41',
			'ProductsPublication' => array(
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		(int) 13 => array(
			'id' => '3169',
			'name' => 'PPARγ Links BMP2 and TGFβ1 Pathways in Vascular Smooth Muscle Cells, Regulating Cell Proliferation and Glucose Metabolism',
			'authors' => 'Laurent Calvier, Philippe Chouvarine, Ekaterina Legchenko, Nadine Hoffmann, Jonas Geldner, Paul Borchert, Danny Jonigk, Miklos M. Mozes, Georg Hansmann',
			'description' => '<p><span>BMP2 and TGF&beta;1 are functional antagonists of pathological remodeling in the arteries, heart, and lung; however, the mechanisms in VSMCs, and their&nbsp;disturbance in pulmonary arterial hypertension (PAH), are unclear. We found a pro-proliferative TGF&beta;1-Stat3-FoxO1 axis in VSMCs, and PPAR&gamma; as&nbsp;inhibitory regulator of TGF&beta;1-Stat3-FoxO1 and TGF&beta;1-Smad3/4, by physically interacting with Stat3 and Smad3. TGF&beta;1 induces fibrosis-related genes and miR-130a/301b, suppressing PPAR&gamma;. Conversely, PPAR&gamma; inhibits TGF&beta;1-induced mitochondrial activation and VSMC proliferation, and regulates two glucose metabolism-related enzymes, platelet isoform of phosphofructokinase (PFKP, a PPAR&gamma; target, via miR-331-5p) and protein phosphatase 1 regulatory subunit 3G (PPP1R3G, a Smad3 target). PPAR&gamma; knockdown/deletion in VSMCs activates TGF&beta;1 signaling. The PPAR&gamma; agonist pioglitazone reverses PAH and inhibits the TGF&beta;1-Stat3-FoxO1 axis in TGF&beta;1-overexpressing mice. We identified PPAR&gamma; as a missing link between BMP2 and TGF&beta;1 pathways in VSMCs. PPAR&gamma; activation can be beneficial in TGF&beta;1-associated diseases, such as PAH, parenchymal lung diseases, and Marfan&rsquo;s syndrome.</span></p>',
			'date' => '2017-05-02',
			'pmid' => 'http://www.cell.com/cell-metabolism/abstract/S1550-4131(17)30163-8',
			'doi' => 'http://dx.doi.org/10.1016/j.cmet.2017.03.011',
			'modified' => '2017-05-11 11:30:23',
			'created' => '2017-05-09 19:10:49',
			'ProductsPublication' => array(
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		(int) 14 => array(
			'id' => '3188',
			'name' => 'Epigenetically-driven anatomical diversity of synovial fibroblasts guides joint-specific fibroblast functions',
			'authors' => 'Mojca Frank-Bertoncelj, Michelle Trenkmann, Kerstin Klein, Emmanuel Karouzakis, Hubert Rehrauer, Anna Bratus, Christoph Kolling, Maria Armaka, Andrew Filer, Beat Michel, Renate E. Gay, Christopher D. Buckley, George Kollias, Steffen Gay & Caroline Ospelt',
			'description' => '<p><span>A number of human diseases, such as arthritis and atherosclerosis, include characteristic pathology in specific anatomical locations. Here we show transcriptomic differences in synovial fibroblasts from different joint locations and that HOX gene signatures reflect the joint-specific origins of mouse and human synovial fibroblasts and synovial tissues. Alongside DNA methylation and histone modifications, bromodomain and extra-terminal reader proteins regulate joint-specific HOX gene expression. Anatomical transcriptional diversity translates into joint-specific synovial fibroblast phenotypes with distinct adhesive, proliferative, chemotactic and matrix-degrading characteristics and differential responsiveness to TNF, creating a unique microenvironment in each joint. These findings indicate that local stroma might control positional disease patterns not only in arthritis but in any disease with a prominent stromal component.</span></p>',
			'date' => '2017-03-23',
			'pmid' => 'https://www.nature.com/articles/ncomms14852',
			'doi' => '10.1038/ncomms14852',
			'modified' => '2017-06-21 14:44:43',
			'created' => '2017-06-08 05:21:07',
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            <h6 style="height:60px">Bioinformatics Data Mining Service</h6>
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'
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	'antibody_id' => null,
	'name' => 'Bioinformatics Data Mining Service',
	'description' => '<h2 class="text-center"><span class="diacol">New!</span> <br />Data mining using machine learning (AI) for unique epigenetic data insights</h2>
<center><img src="https://www.diagenode.com/img/product/data-mining-long.png" /></center>
<p></p>
<div id="paper" style="text-align: center;">
<h4><a href="https://www.youtube.com/watch?v=KXjnSHz3Jk8">Watch the webinar to gain insights on how data mining can be applied for epigenetics applications</a></h4>
</div>
<center><iframe width="560" height="315" src="https://www.youtube.com/embed/KXjnSHz3Jk8" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen="allowfullscreen"></iframe></center>
<p></p>
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<blockquote style="padding-bottom: 0;"><span class="label-green" style="margin-bottom: 16px; margin-left: -22px; font-size: 22px;">WHITE PAPERS</span>
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<h3 style="margin-top: 0;">Powerful new insights with epigenetic data mining.<br /> A study to distinguish smokers from non-smokers using just one droplet of blood</h3>
<p>Next generation sequencing in combination with sophisticated bioinformatics technologies for genomic, transcriptomic and epigeneomic analyses have enormous potential to establish new biomarkers for disease diagnostics, enabling true precision medicine. Analyses of liquid biopsies to measure thousands of different data points simultaneously in easily accessible body fluids (e.g. blood, urine, and saliva) are extremely promising for such biomarker studies.<br />(...)</p>
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<h3 style="margin-top: 0;">Data mining on DNA methylation data in cancer samples<br />Distinguishing normal from breast cancer tissue</h3>
<p>Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall.</p>
<p>One important aspect of cancer tissues it that they differ from normal tissues in their epigenetic make up, especially in the DNA methylation pattern. In normal cells methylation assures the proper regulation of gene expression and stable gene silencing. DNA methylation is associated with histone modifications, and the interplay of these epigenetic modifications is crucial to regulate the functioning of the genome by changing chromatin architecture.<br />(...)</p>
<a href="https://www.diagenode.com/en/pages/form-bioinfo?app_note=23" class="alert small button" target="_blank">Read more</a></div>
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<p>Diagenode's new data mining service utilizes methods at the frontier of machine learning, statistics, and database systems. This enhanced service supports new discoveries that were previously not possible by analyzing patterns in large data sets to give informative new insights.</p>
<p>If you have data from patient cohorts, single cell analyses or any other heterogeneous scenarios, our service team provides enhanced support with optimal data analysis using our latest data mining capabilities. Specifically, our team applies machine learning technologies to find previously undiscovered or unobvious relationships within and across datasets. This advanced technology allows discovery of informative features from mass data, essentially &ldquo;finding a needle in a haystack.&rdquo;</p>
<p>Diagenode utilizes multiple algorithms to achieve advanced data mining and uses the most optimal combination of algorithms specific to your data. Our goal is to build strong classifiers that separate data into two or more classes or groups depending on associated data.</p>
<p class="extra-spaced">Different and multiple -omics data classes can be mined simultaneously. Integration with phenotypic and/or clinical data is also possible. We offer data mining services for several data classes including:</p>
<table class="extra-spaced">
<tbody>
<tr>
<td><strong>Epigenetic data</strong></td>
<td><strong>Transcriptomic data</strong></td>
</tr>
<tr>
<td>
<p>DNA Methylation (RRBS, WGBS, EPIC arrays)</p>
<p>ChIP-sequencing</p>
<p>ATAC-seq</p>
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<td>
<p>mRNA-sequencing</p>
<p>Small and long non coding RNA</p>
<p>Single-cell RNA-sequencing</p>
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<p></p>
<p><strong>Biological Interpretation</strong></p>
<p class="extra-spaced">Machine learning classifiers also mirror the underlying biological differences between classes and are used to uncover the molecular processes at work. In order to achieve this, we offer biological interpretation services and pathway mining analyses for your data.</p>
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<p><a href="https://www.diagenode.com/en/categories/Services">Read about our wetlab services</a></p>
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