Eduardo G. Gusmao

Analysis of computational footprinting methods for DNase sequencing experiments

DNase-seq allows nucleotide-level identification of transcription factor binding sites on the basis of a computational search of footprint-like DNase I cleavage patterns on the DNA. Frequently in high-throughput methods, experimental artifacts such as DNase I cleavage bias affect the computational analysis of DNase-seq experiments. Here we performed a comprehensive and systematic study on the performance of computational footprinting methods. We evaluated ten footprinting methods in a panel of DNase-seq experiments for their ability to recover cell-specific transcription factor binding sites. We show that three methods—HINT, DNase2TF and PIQ—consistently outperformed the other evaluated methods and that correcting the DNase-seq signal for experimental artifacts significantly improved the accuracy of computational footprints. We also propose a score that can be used to detect footprints arising from transcription factors with potentially short residence times.

Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites

BackgroundPrimary cells enter replicative senescence after a limited number of cell divisions. This process needs to be considered in cell culture experiments, and it is particularly important for regenerative medicine. Replicative senescence is associated with reproducible changes in DNA methylation (DNAm) at specific sites in the genome. The mechanism that drives senescence-associated DNAm changes remains unknown - it may involve stochastic DNAm drift due to imperfect maintenance of epigenetic marks or it is directly regulated at specific sites in the genome.ResultsIn this study, we analyzed the reorganization of nuclear architecture and DNAm changes during long-term culture of human fibroblasts and mesenchymal stromal cells (MSCs). We demonstrate that telomeres shorten and shift towards the nuclear center at later passages. In addition, DNAm profiles, either analyzed by MethylCap-seq or by 450k IlluminaBeadChip technology, revealed consistent senescence-associated hypermethylation in regions associated with H3K27me3, H3K4me3, and H3K4me1 histone marks, whereas hypomethylation was associated with chromatin containing H3K9me3 and lamina-associated domains (LADs). DNA hypermethylation was significantly enriched in the vicinity of genes that are either up- or downregulated at later passages. Furthermore, specific transcription factor binding motifs (e.g. EGR1, TFAP2A, and ETS1) were significantly enriched in differentially methylated regions and in the promoters of differentially expressed genes.ConclusionsSenescence-associated DNA hypermethylation occurs at specific sites in the genome and reflects functional changes in the course of replicative senescence. These results indicate that tightly regulated epigenetic modifications during long-term culture contribute to changes in nuclear organization and gene expression.

Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications

MOTIVATION The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction. RESULTS We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding. AVAILABILITY AND IMPLEMENTATION Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm. CONTACT ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

The interaction of MYC with the trithorax protein ASH2L promotes gene transcription by regulating H3K27 modification

The appropriate expression of the roughly 30,000 human genes requires multiple layers of control. The oncoprotein MYC, a transcriptional regulator, contributes to many of the identified control mechanisms, including the regulation of chromatin, RNA polymerases, and RNA processing. Moreover, MYC recruits core histone-modifying enzymes to DNA. We identified an additional transcriptional cofactor complex that interacts with MYC and that is important for gene transcription. We found that the trithorax protein ASH2L and MYC interact directly in vitro and co-localize in cells and on chromatin. ASH2L is a core subunit of KMT2 methyltransferase complexes that target histone H3 lysine 4 (H3K4), a mark associated with open chromatin. Indeed, MYC associates with H3K4 methyltransferase activity, dependent on the presence of ASH2L. MYC does not regulate this methyltransferase activity but stimulates demethylation and subsequently acetylation of H3K27. KMT2 complexes have been reported to associate with histone H3K27-specific demethylases, while CBP/p300, which interact with MYC, acetylate H3K27. Finally WDR5, another core subunit of KMT2 complexes, also binds directly to MYC and in genome-wide analyses MYC and WDR5 are associated with transcribed promoters. Thus, our findings suggest that MYC and ASH2L–KMT2 complexes cooperate in gene transcription by controlling H3K27 modifications and thereby regulate bivalent chromatin.

Epigenetic program and transcription factor circuitry of dendritic cell development

Dendritic cells (DC) are professional antigen presenting cells that develop from hematopoietic stem cells through successive steps of lineage commitment and differentiation. Multipotent progenitors (MPP) are committed to DC restricted common DC progenitors (CDP), which differentiate into specific DC subsets, classical DC (cDC) and plasmacytoid DC (pDC). To determine epigenetic states and regulatory circuitries during DC differentiation, we measured consecutive changes of genome-wide gene expression, histone modification and transcription factor occupancy during the sequel MPP-CDP-cDC/pDC. Specific histone marks in CDP reveal a DC-primed epigenetic signature, which is maintained and reinforced during DC differentiation. Epigenetic marks and transcription factor PU.1 occupancy increasingly coincide upon DC differentiation. By integrating PU.1 occupancy and gene expression we devised a transcription factor regulatory circuitry for DC commitment and subset specification. The circuitry provides the transcription factor hierarchy that drives the sequel MPP-CDP-cDC/pDC, including Irf4, Irf8, Tcf4, Spib and Stat factors. The circuitry also includes feedback loops inferred for individual or multiple factors, which stabilize distinct stages of DC development and DC subsets. In summary, here we describe the basic regulatory circuitry of transcription factors that drives DC development.

Sox5 regulates beta-cell phenotype and is reduced in type 2 diabetes

Type 2 diabetes (T2D) is characterized by insulin resistance and impaired insulin secretion, but the mechanisms underlying insulin secretion failure are not completely understood. Here, we show that a set of co-expressed genes, which is enriched for genes with islet-selective open chromatin, is associated with T2D. These genes are perturbed in T2D and have a similar expression pattern to that of dedifferentiated islets. We identify Sox5 as a regulator of the module. Sox5 knockdown induces gene expression changes similar to those observed in T2D and diabetic animals and has profound effects on insulin secretion, including reduced depolarization-evoked Ca2+-influx and β-cell exocytosis. SOX5 overexpression reverses the expression perturbations observed in a mouse model of T2D, increases the expression of key β-cell genes and improves glucose-stimulated insulin secretion in human islets from donors with T2D. We suggest that human islets in T2D display changes reminiscent of dedifferentiation and highlight SOX5 as a regulator of β-cell phenotype and function.

Topological Demarcation By HMGB2 Is Disrupted Early Upon Senescence Entry Across Cell Types And Induces CTCF Clustering

Ageing-relevant processes, like cellular senescence, are characterized by complex, often stochastic, events giving rise to heterogeneous cell populations. We hypothesized that entry into senescence of different primary human cells can be triggered by one early molecular event affecting the spatial organization of chromosomes. To test this, we combined whole-genome chromosome conformation capture, population and single-cell transcriptomics, super-resolution imaging, and functional analyses applied on proliferating and replicatively-senescent populations from three distinct human cell types. We found a number of genes involved in DNA conformation maintenance being suppressed upon senescence across cell types. Of these, the abundant high mobility group (HMG) B1 and B2 nuclear factors are quantitatively removed from cell nuclei before typical senescence markers appear, and mark a subset of topologically-associating domain (TAD) boundaries. Their loss coincides with obvious reorganization of chromatin interactions via the dramatic spatial clustering of CTCF foci. HMGB2 knock-down recapitulates this senescence-induced CTCF clustering, while also affecting insulation at TAD boundaries. We accordingly propose that HMGB-mediated deregulation of chromosome conformation constitutes a primer for the ensuing senescent program across cell types.

Computational footprinting methods for next-generation sequencing experiments

English) Transcriptional regulation orchestrates the proper temporal and spatial expression of genes. The identification of transcriptional regulatory elements, such as transcription factor binding sites (TFBSs), is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. The standard computational approach is to use sequence-based methods, which search over the genome’s DNA for sequences representing the DNA binding affinity sequence of transcription factors (TFs). However, this approach is not able to predict active binding sites, i.e. binding sites that are being currently bound by TFs at a particular cell state. This happens as the sequence-based methods do not account for the fact that the chromatin dynamically changes its state between an open form (and accessible to TF binding) and closed (not accessible by TFs). Advances in next-generation sequencing techniques have enabled the measurement of such open chromatin regions in a genome-wide manner with assays such as the chromatin immunoprecipitation followed by massive sequencing (ChIP-seq) and DNase I digestion followed by massive sequencing (DNase-seq). Current research has proven that such open chromatin genome-wide assays improve sequence-based detection of active TFBSs. The rationale is to restrict the sequence-based search of binding sites to genomic regions where these assays indicate the chromatin is open and accessible for TF binding, in a cell-specific manner. We propose the first computational framework which integrates both DNase-seq and ChIP-seq data to perform predictions of active TFBSs. We have previously observed that there is a distinctive pattern at active TFBSs regarding both DNase-seq and ChIP-seq data. Our framework treats these data using signal normalization strategies and searches for these distinctive patterns, the so-called “footprints”, by segmenting the genome using hidden Markov models (HMMs). Given that, our framework – termed HINT (HMM-based identification of TF footprints) – is categorized as a “computational footprinting method”. We evaluate our computational footprinting method by comparing the footprint predictions to experimentally verified active TFBSs. Our evaluation approach creates statistics which enables the comparison between our method and competing computational footprinting methods. Our comparative experiment is the most complete so far, with a total of 14 computational footprinting methods and 233 TFs evaluated. Furthermore, we successfully applied our computational footprinting method HINT in two different biological studies to identify regulatory elements involved in specific biological conditions. HINT has proven to be a useful computational framework in biological studies involving regulatory genomics.