Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of...

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Main Authors:	Chen, Chieh-Chun, Xiao, Shu, Xie, Dan, Cao, Xiaoyi, Song, Chun-Xiao, Wang, Ting, He, Chuan, Zhong, Sheng
Format:	Online
Language:	English
Published:	Public Library of Science 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3854512/

id	pubmed-3854512
recordtype	oai_dc
spelling	pubmed-38545122013-12-11 Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions Chen, Chieh-Chun Xiao, Shu Xie, Dan Cao, Xiaoyi Song, Chun-Xiao Wang, Ting He, Chuan Zhong, Sheng Research Article Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg). Public Library of Science 2013-12-05 /pmc/articles/PMC3854512/ /pubmed/24339764 http://dx.doi.org/10.1371/journal.pcbi.1003367 Text en © 2013 Chen et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Chen, Chieh-Chun Xiao, Shu Xie, Dan Cao, Xiaoyi Song, Chun-Xiao Wang, Ting He, Chuan Zhong, Sheng
spellingShingle	Chen, Chieh-Chun Xiao, Shu Xie, Dan Cao, Xiaoyi Song, Chun-Xiao Wang, Ting He, Chuan Zhong, Sheng Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
author_facet	Chen, Chieh-Chun Xiao, Shu Xie, Dan Cao, Xiaoyi Song, Chun-Xiao Wang, Ting He, Chuan Zhong, Sheng
author_sort	Chen, Chieh-Chun
title	Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
title_short	Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
title_full	Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
title_fullStr	Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
title_full_unstemmed	Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions
title_sort	understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions
description	Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).
publisher	Public Library of Science
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3854512/
_version_	1612035939358474240

Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions

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