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Positional specificity of different transcription factor classes within enhancers.

ABSTRACT: Gene expression is controlled by sequence-specific transcription factors (TFs), which bind to regulatory sequences in DNA. TF binding occurs in nucleosome-depleted regions of DNA (NDRs), which generally encompass regions with lengths similar to those protected by nucleosomes. However, less is known about where within these regions specific TFs tend to be found. Here, we characterize the positional bias of inferred binding sites for 103 TFs within ?500,000 NDRs across 47 cell types. We find that distinct classes of TFs display different binding preferences: Some tend to have binding sites toward the edges, some toward the center, and some at other positions within the NDR. These patterns are highly consistent across cell types, suggesting that they may reflect TF-specific intrinsic structural or functional characteristics. In particular, TF classes with binding sites at NDR edges are enriched for those known to interact with histones and chromatin remodelers, whereas TFs with central enrichment interact with other TFs and cofactors such as p300. Our results suggest distinct regiospecific binding patterns and functions of TF classes within enhancers.

SUBMITTER: Grossman SR

PROVIDER: S-EPMC6065035 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Positional specificity of different transcription factor classes within enhancers.

Grossman Sharon R SR Engreitz Jesse J Ray John P JP Nguyen Tung H TH Hacohen Nir N Lander Eric S ES

Proceedings of the National Academy of Sciences of the United States of America 20180709 30

Gene expression is controlled by sequence-specific transcription factors (TFs), which bind to regulatory sequences in DNA. TF binding occurs in nucleosome-depleted regions of DNA (NDRs), which generally encompass regions with lengths similar to those protected by nucleosomes. However, less is known about where within these regions specific TFs tend to be found. Here, we characterize the positional bias of inferred binding sites for 103 TFs within ∼500,000 NDRs across 47 cell types. We find that ...[more]

PMID: 29987030

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Project description:BackgroundIn eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types.ResultsWe propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes.ConclusionsTFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers. The R code for learning a TFcoop model and for reproducing the main experiments described in the paper is available in an R Markdown file at address https://gite.lirmm.fr/brehelin/TFcoop .

Dataset Information

Positional specificity of different transcription factor classes within enhancers.

Publications

Positional specificity of different transcription factor classes within enhancers.

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