The Role of Genome Accessibility in Transcription Factor Binding in Bacteria.
ABSTRACT: ChIP-seq enables genome-scale identification of regulatory regions that govern gene expression. However, the biological insights generated from ChIP-seq analysis have been limited to predictions of binding sites and cooperative interactions. Furthermore, ChIP-seq data often poorly correlate with in vitro measurements or predicted motifs, highlighting that binding affinity alone is insufficient to explain transcription factor (TF)-binding in vivo. One possibility is that binding sites are not equally accessible across the genome. A more comprehensive biophysical representation of TF-binding is required to improve our ability to understand, predict, and alter gene expression. Here, we show that genome accessibility is a key parameter that impacts TF-binding in bacteria. We developed a thermodynamic model that parameterizes ChIP-seq coverage in terms of genome accessibility and binding affinity. The role of genome accessibility is validated using a large-scale ChIP-seq dataset of the M. tuberculosis regulatory network. We find that accounting for genome accessibility led to a model that explains 63% of the ChIP-seq profile variance, while a model based in motif score alone explains only 35% of the variance. Moreover, our framework enables de novo ChIP-seq peak prediction and is useful for inferring TF-binding peaks in new experimental conditions by reducing the need for additional experiments. We observe that the genome is more accessible in intergenic regions, and that increased accessibility is positively correlated with gene expression and anti-correlated with distance to the origin of replication. Our biophysically motivated model provides a more comprehensive description of TF-binding in vivo from first principles towards a better representation of gene regulation in silico, with promising applications in systems biology.
Project description:Differential binding of transcription factors (TFs) at cis-regulatory loci drives the differentiation and function of diverse cellular lineages. Understanding the regulatory interactions that underlie cell fate decisions requires characterizing TF binding sites (TFBS) across multiple cell types and conditions. Techniques, e.g. ChIP-Seq can reveal genome-wide patterns of TF binding, but typically requires laborious and costly experiments for each TF-cell-type (TFCT) condition of interest. Chromosomal accessibility assays can connect accessible chromatin in one cell type to many TFs through sequence motif mapping. Such methods, however, rarely take into account that the genomic context preferred by each factor differs from TF to TF, and from cell type to cell type. To address the differences in TF behaviors, we developed Mocap, a method that integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. We show that integration of genomic features, such as CpG islands improves TFBS prediction in some TFCT. Further, we describe a method for mapping new TFCT, for which no ChIP-seq data exists, onto our ensemble of classifiers and show that our cross-sample TFBS prediction method outperforms several previously described methods.
Project description:Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genome-wide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNase-seq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATAC-seq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.
Project description:ChIP-seq reveals genomic regions where proteins, e.g. transcription factors (TFs) interact with DNA. A substantial fraction of these regions, however, do not contain the cognate binding site for the TF of interest. This phenomenon might be explained by protein-protein interactions and co-precipitation of interacting gene regulatory elements. We uniformly processed 3727 human ChIP-seq data sets and determined the cistrome of 292 TFs, as well as the distances between the TF binding motif centers and the ChIP-seq peak summits. ChIPSummitDB enables the analysis of ChIP-seq data using multiple approaches. The 292 cistromes and corresponding ChIP-seq peak sets can be browsed in GenomeView. Overlapping SNPs can be inspected in dbSNPView. Most importantly, the MotifView and PairShiftView pages show the average distance between motif centers and overlapping ChIP-seq peak summits and distance distributions thereof, respectively. In addition to providing a comprehensive human TF binding site collection, the ChIPSummitDB database and web interface allows for the examination of the topological arrangement of TF complexes genome-wide. ChIPSummitDB is freely accessible at http://summit.med.unideb.hu/summitdb/. The database will be regularly updated and extended with the newly available human and mouse ChIP-seq data sets.
Project description:Chromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS).We introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks' local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset's peaks are bound by the ChIP'd TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11.Topological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding.
Project description:Activation of regulatory elements is thought to be inversely correlated with DNA methylation levels. However, it is difficult to determine whether DNA methylation is compatible with chromatin accessibility or transcription factor (TF) binding if assays are performed separately. We developed a fast, low-input, low sequencing depth method, EpiMethylTag, that combines ATAC-seq or ChIP-seq (M-ATAC or M-ChIP) with bisulfite conversion, to simultaneously examine accessibility/TF binding and methylation on the same DNA. Here we demonstrate that EpiMethylTag can be used to study the functional interplay between chromatin accessibility and TF binding (CTCF and KLF4) at methylated sites.
Project description:Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data.In this study, we build regression-based models that relate gene expression to the binding of 12 different TFs, 7 histone modifications and chromatin accessibility (DNase I hypersensitivity) in two different tissues. We find that expression models based on computationally predicted TF binding can achieve similar accuracy to those using in vivo TF binding data and that including binding at weak sites is critical for accurate prediction of gene expression. We also find that incorporating histone modification and chromatin accessibility data results in additional accuracy. Surprisingly, we find that models that use no TF binding data at all, but only histone modification and chromatin accessibility data, can be as (or more) accurate than those based on in vivo TF binding data.All scripts, motifs and data presented in this article are available online at http://research.imb.uq.edu.au/t.bailey/supplementary_data/McLeay2011a.
Project description:The Encyclopedia of DNA Elements (ENCODE) consortium aims to identify all functional elements in the human genome including transcripts, transcriptional regulatory regions, along with their chromatin states and DNA methylation patterns. The ENCODE project generates data utilizing a variety of techniques that can enrich for regulatory regions, such as chromatin immunoprecipitation (ChIP), micrococcal nuclease (MNase) digestion and DNase I digestion, followed by deeply sequencing the resulting DNA. As part of the ENCODE project, we have developed a Web-accessible repository accessible at http://factorbook.org. In Wiki format, factorbook is a transcription factor (TF)-centric repository of all ENCODE ChIP-seq datasets on TF-binding regions, as well as the rich analysis results of these data. In the first release, factorbook contains 457 ChIP-seq datasets on 119 TFs in a number of human cell lines, the average profiles of histone modifications and nucleosome positioning around the TF-binding regions, sequence motifs enriched in the regions and the distance and orientation preferences between motif sites.
Project description:In vertebrates, multiple transcription factors (TFs) bind to gene regulatory elements (promoters, enhancers, and silencers) to execute developmental expression changes. ChIP experiments are often used to identify where TFs bind to regulatory elements in the genome, but the requirement of TF-specific antibodies hampers analyses of tens of TFs at multiple loci. Here we tested whether TF binding predictions using ATAC-seq can be used to infer the identity of TFs that bind to functionally validated enhancers of the Cd4, Cd8, and Gata3 genes in thymocytes. We performed ATAC-seq at four distinct stages of development in mouse thymus, probing the chromatin accessibility landscape in double negative (DN), double positive (DP), CD4 single positive (SP4) and CD8 SP (SP8) thymocytes. Integration of chromatin accessibility with TF motifs genome-wide allowed us to infer stage-specific occupied TF binding sites within known and potentially novel regulatory elements. Our results provide genome-wide stage-specific T cell open chromatin profiles, and allow the identification of candidate TFs that drive thymocyte differentiation at each developmental stage.
Project description:Single-nucleotide variants that underlie phenotypic variation can affect chromatin occupancy of transcription factors (TFs). To delineate determinants of in vivo TF binding and chromatin accessibility, we introduce an approach that compares ChIP-seq and DNase-seq data sets from genetically divergent murine erythroid cell lines. The impact of discriminatory single-nucleotide variants on TF ChIP signal enables definition at single base resolution of in vivo binding characteristics of nuclear factors GATA1, TAL1, and CTCF. We further develop a facile complementary approach to more deeply test the requirements of critical nucleotide positions for TF binding by combining CRISPR-Cas9-mediated mutagenesis with ChIP and targeted deep sequencing. Finally, we extend our analytical pipeline to identify nearby contextual DNA elements that modulate chromatin binding by these three TFs, and to define sequences that impact kb-scale chromatin accessibility. Combined, our approaches reveal insights into the genetic basis of TF occupancy and their interplay with chromatin features.
Project description:The concept of tissue-specific gene expression posits that lineage-determining transcription factors (LDTFs) determine the open chromatin profile of a cell via collaborative binding, providing molecular beacons to signal-dependent transcription factors (SDTFs). However, the guiding principles of LDTF binding, chromatin accessibility and enhancer activity have not yet been systematically evaluated. We sought to study these features of the macrophage genome by the combination of experimental (ChIP-seq, ATAC-seq and GRO-seq) and computational approaches. We show that Random Forest and Support Vector Regression machine learning methods can accurately predict chromatin accessibility using the binding patterns of the LDTF PU.1 and four other key TFs of macrophages (IRF8, JUNB, CEBPA and RUNX1). Any of these TFs alone were not sufficient to predict open chromatin, indicating that TF binding is widespread at closed or weakly opened chromatin regions. Analysis of the PU.1 cistrome revealed that two-thirds of PU.1 binding occurs at low accessible chromatin. We termed these sites labelled regulatory elements (LREs), which may represent a dormant state of a future enhancer and contribute to macrophage cellular plasticity. Collectively, our work demonstrates the existence of LREs occupied by various key TFs, regulating specific gene expression programs triggered by divergent macrophage polarizing stimuli.