DNMT1-interacting RNAs block gene specific DNA methylation

Summary DNA methylation was described almost a century ago. However, the rules governing its establishment and maintenance remain elusive. Here, we present data demonstrating that active transcription regulates levels of genomic methylation. We identified a novel RNA arising from the CEBPA gene locus critical in regulating the local DNA methylation profile. This RNA binds to DNMT1 and prevents CEBPA gene locus methylation. Deep sequencing of transcripts associated with DNMT1 combined with genome-scale methylation and expression profiling extended the generality of this finding to numerous gene loci. Collectively, these results delineate the nature of DNMT1-RNA interactions and suggest strategies for gene selective demethylation of therapeutic targets in disease

CoCAS: a ChIP-on-chip analysis suite

Motivation: High-density tiling microarrays are increasingly used in combination with ChIP assays to study transcriptional regulation. To ease the analysis of the large amounts of data generated by this approach, we have developed ChIP-on-chip Analysis Suite (CoCAS), a standalone software suite which implements optimized ChIP-on-chip data normalization, improved peak detection, as well as quality control reports. Our software allows dye swap, replicate correlation and connects easily with genome browsers and other peak detection algorithms. CoCAS can readily be used on the latest generation of Agilent high-density arrays. Also, the implemented peak detection methods are suitable for other datasets, including ChIP-Seq output

Assessing the efficiency and significance of Methylated DNA Immunoprecipitation (MeDIP) assays in using in vitro methylated genomic DNA

<p>Abstract</p> <p>Background</p> <p>DNA methylation contributes to the regulation of gene expression during development and cellular differentiation. The recently developed Methylated DNA ImmunoPrecipitation (MeDIP) assay allows a comprehensive analysis of this epigenetic mark at the genomic level in normal and disease-derived cells. However, estimating the efficiency of the MeDIP technique is difficult without previous knowledge of the methylation status of a given cell population. Attempts to circumvent this problem have involved the use of <it>in vitro </it>methylated DNA in parallel to the investigated samples. Taking advantage of this stratagem, we sought to improve the sensitivity of the approach and to assess potential biases resulting from DNA amplification and hybridization procedures using MeDIP samples.</p> <p>Findings</p> <p>We performed MeDIP assays using <it>in vitro </it>methylated DNA, with or without previous DNA amplification, and hybridization to a human promoter array. We observed that CpG content at gene promoters indeed correlates strongly with the MeDIP signal obtained using <it>in vitro </it>methylated DNA, even when lowering significantly the amount of starting material. In analyzing MeDIP products that were subjected to whole genome amplification (WGA), we also revealed a strong bias against CpG-rich promoters during this amplification procedure, which may potentially affect the significance of the resulting data.</p> <p>Conclusion</p> <p>We illustrate the use of <it>in vitro </it>methylated DNA to assess the efficiency and accuracy of MeDIP procedures. We report that efficient and reproducible genome-wide data can be obtained via MeDIP experiments using relatively low amount of starting genomic DNA; and emphasize for the precaution that must be taken in data analysis when an additional DNA amplification step is required.</p

Lysine acetyltransferase Tip60 is required for hematopoietic stem cell maintenance.

Hematopoietic stem cells (HSCs) have the potential to replenish the blood system for the lifetime of the organism. Their 2 defining properties, self-renewal and differentiation, are tightly regulated by the epigenetic machineries. Using conditional gene-knockout models, we demonstrated a critical requirement of lysine acetyltransferase 5 (Kat5, also known as Tip60) for murine HSC maintenance in both the embryonic and adult stages, which depends on its acetyltransferase activity. Genome-wide chromatin and transcriptome profiling in murine hematopoietic stem and progenitor cells revealed that Tip60 colocalizes with c-Myc and that Tip60 deletion suppress the expression of Myc target genes, which are associated with critical biological processes for HSC maintenance, cell cycling, and DNA repair. Notably, acetylated H2A.Z (acH2A.Z) was enriched at the Tip60-bound active chromatin, and Tip60 deletion induced a robust reduction in the acH2A.Z/H2A.Z ratio. These results uncover a critical epigenetic regulatory layer for HSC maintenance, at least in part through Tip60-dependent H2A.Z acetylation to activate Myc target genes.Cancer Research UK, Wellcome Trust, National Institutes of Health, Singapore state fundin

Computational biology applied to the analysis of the regulatory mechanisms in early T-cell development

Les réseaux de contrôle de l'expression génique sont, pour une large part, à la base des processus cellulaires physiologiques ou pathologiques. Ces contrôles dépendent des mécanismes épigénétiques impliquant la dynamique de la chromatine et permettent la transmission de programme spécifiques d'expression génique. Lors du développement des lymphocytes T, l'expression d'une chaîne TCRß à la surface des précurseurs CD4-CD8- (ou DN) induit une signalisation intercellulaire dont les effets multiples, regroupés sous le terme de "sélection beta", se traduisent par une prolifération cellulaire et la différenciation vers un stade de maturation ultérieur, CD4+CD8+ (ou DP). Ces événements s'accompagnent de changements d'expression d'un grand nombre de gènes sous l'effet d'un programme épigénétique spécifique. De nouvelles technologies comme le ChlP-on-Chip ou le ChlPSeq permettent de caractériser les profils cellulaires épigénétiques. Les données ainsi générées nécessitent pour leur analyse des approches informatiques et statistiques. Mon travail de thèse s'est articulé sur 3 axes :1) Elaborer des outils bioinformatiques dans le but d'analyser les profils épigénétiques de régions génomiques suspectées de jouer un rôle dans la différenciation entre les stades DN et DP de la différenciation des cellules T.2) Analyser de manière in silico deux régions phares de la régulation du locus TCRß3) Concevoir une pipeline d'analyse de données issues des technologies de séquençage à haut débit permettant de caractériser les interactions facteur de transcription/ADNGene expression regulatory networks make up, for the most part, the basis of physiological cell processes. This regulation depends on epigenetic mechanisms involving chromatin dynamics and allow propagating specific gene expression programs. during T-cell development, the expression of the surface TCRß chain in CD4- CD8- (DN) toggers intracellular signaling cascades. their multiple effects, know as "beta selection", translate as increased cell proliferation and differenciation towards the CD4+ CD8+ stage (DP). These mechanisms are supplemented by changes in expression of several genes under the effect of a specific epigenetic program. New technologies, such a ChlP-chip or ChlP-Seq, allow characterizing epigenetic cell profiles. analysis of data such generated requires computational and statistical approaches. My thesis work focused on 3 goals :1)To develop computational tools to analyse epigenetic profiles of genomic regions that are presumed to play a role in DN-DP T-cell differentiation2) To analyse txo flag regions of TCRß regulation3) To design an analysis pipeline for high-throughput sequencing technologies, in order to allow characterizing transcription factor/DNA interaction

Analyse bioinformatique des mécanismes de régulation durant le développement précoce des cellules T

Les réseaux de contrôle de l'expression génique sont, pour une large part, à la base des processus cellulaires physiologiques ou pathologiques. Ces contrôles dépendent des mécanismes épigénétiques impliquant la dynamique de la chromatine et permettent la transmission de programme spécifiques d'expression génique. Lors du développement des lymphocytes T, l'expression d'une chaîne TCRß à la surface des précurseurs CD4-CD8- (ou DN) induit une signalisation intercellulaire dont les effets multiples, regroupés sous le terme de "sélection beta", se traduisent par une prolifération cellulaire et la différenciation vers un stade de maturation ultérieur, CD4+CD8+ (ou DP). Ces événements s'accompagnent de changements d'expression d'un grand nombre de gènes sous l'effet d'un programme épigénétique spécifique. De nouvelles technologies comme le ChlP-on-Chip ou le ChlPSeq permettent de caractériser les profils cellulaires épigénétiques. Les données ainsi générées nécessitent pour leur analyse des approches informatiques et statistiques. Mon travail de thèse s'est articulé sur 3 axes :1) Elaborer des outils bioinformatiques dans le but d'analyser les profils épigénétiques de régions génomiques suspectées de jouer un rôle dans la différenciation entre les stades DN et DP de la différenciation des cellules T.2) Analyser de manière in silico deux régions phares de la régulation du locus TCRß3) Concevoir une pipeline d'analyse de données issues des technologies de séquençage à haut débit permettant de caractériser les interactions facteur de transcription/ADNGene expression regulatory networks make up, for the most part, the basis of physiological cell processes. This regulation depends on epigenetic mechanisms involving chromatin dynamics and allow propagating specific gene expression programs. during T-cell development, the expression of the surface TCRß chain in CD4- CD8- (DN) toggers intracellular signaling cascades. their multiple effects, know as "beta selection", translate as increased cell proliferation and differenciation towards the CD4+ CD8+ stage (DP). These mechanisms are supplemented by changes in expression of several genes under the effect of a specific epigenetic program. New technologies, such a ChlP-chip or ChlP-Seq, allow characterizing epigenetic cell profiles. analysis of data such generated requires computational and statistical approaches. My thesis work focused on 3 goals :1)To develop computational tools to analyse epigenetic profiles of genomic regions that are presumed to play a role in DN-DP T-cell differentiation2) To analyse txo flag regions of TCRß regulation3) To design an analysis pipeline for high-throughput sequencing technologies, in order to allow characterizing transcription factor/DNA interactionsAIX-MARSEILLE2-Bib.electronique (130559901) / SudocSudocFranceF

Correspondence on NanoVar’s performance outlined by Jiang T. et al. in “Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation”

Abstract A recent paper by Jiang et al. in BMC Bioinformatics presented guidelines on long-read sequencing settings for structural variation (SV) calling, and benchmarked the performance of various SV calling tools, including NanoVar. In their simulation-based benchmarking, NanoVar was shown to perform poorly compared to other tools, mostly due to low SV recall rates. To investigate the causes for NanoVar's poor performance, we regenerated the simulation datasets (3× to 20×) as specified by Jiang et al. and performed benchmarking for NanoVar and Sniffles. Our results did not reflect the findings described by Jiang et al. In our analysis, NanoVar displayed more than three times the F1 scores and recall rates as reported in Jiang et al. across all sequencing coverages, indicating a previous underestimation of its performance. We also observed that NanoVar outperformed Sniffles in calling SVs with genotype concordance by more than 0.13 in F1 scores, which is contrary to the trend reported by Jiang et al. Besides, we identified multiple detrimental errors encountered during the analysis which were not addressed by Jiang et al. We hope that this commentary clarifies NanoVar's validity as a long-read SV caller and provides assurance to its users and the scientific community

Processing ChIP-chip data: from the scanner to the browser.

International audienceHigh-density tiling microarrays are increasingly used in combination with chromatin immunoprecipitation (ChIP) assays to delineate the regulation of gene expression. Besides the technical challenges inherent to such complex biological assays, a critical, often daunting issue is the correct interpretation of the sheer amount of raw data generated by utilizing computational methods. Here, we go through the main steps of this intricate process, including optimized chromatin immunoprecipitation on chip (ChIP-chip) data normalization, peak detection, as well as quality control reports. We also describe convenient standalone software suites, including our own, CoCAS, which works on the latest generation of Agilent high-density arrays, allows dye-swap, replicate correlation, and easy connection with genome browsers for results interpretation, or with, e.g., other peak detection algorithms. Overall, the guidelines described herein provide an effective introduction to ChIP-chip technology and analysis

ColocZStats: a z-stack signal colocalization extension tool for 3D slicer

Confocal microscopy has evolved to be a widely adopted imaging technique in molecular biology and is frequently utilized to achieve accurate subcellular localization of proteins. Applying colocalization analysis on image z-stacks obtained from confocal fluorescence microscopes is a dependable method of revealing the relationship between different molecules. In addition, despite the established advantages and growing adoption of 3D visualization software in various microscopy research domains, there have been few systems that can support colocalization analysis within a user-specified region of interest (ROI). In this context, several broadly employed biological image visualization platforms are meticulously explored in this study to understand the current landscape. It has been observed that while these applications can generate three-dimensional (3D) reconstructions for z-stacks, and in some cases transfer them into an immersive virtual reality (VR) scene, there is still little support for performing quantitative colocalization analysis on such images based on a user-defined ROI and thresholding levels. To address these issues, an extension called ColocZStats (pronounced Coloc-Zee-Stats) has been developed for 3D Slicer, a widely used free and open-source software package for image analysis and scientific visualization. With a custom-designed user-friendly interface, ColocZStats allows investigators to conduct intensity thresholding and ROI selection on imported 3D image stacks. It can deliver several essential colocalization metrics for structures of interest and produce reports in the form of diagrams and spreadsheets