CpG content-dependent associations between transcription factors and histone modifications

Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between tran...

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Published inPloS one Vol. 16; no. 4; p. e0249985
Main Authors Fischer, Jonas, Ardakani, Fatemeh Behjati, Kattler, Kathrin, Walter, Jörn, Schulz, Marcel H.
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 15.04.2021
Public Library of Science (PLoS)
Subjects
Abundance
Antibodies
Binding sites
Biology
Biology and life sciences
Deoxyribonucleic acid
DNA
Enzymes
Epigenetics
Gene expression
Genetic aspects
Genomes
Genomics
Histones
Informatics
Model testing
Noise measurement
Optimization
Outliers (statistics)
Regression models
Signal strength
Transcription factors
Germany
Online AccessGet full text
ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0249985

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Abstract Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.
AbstractList Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.
Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several experimental and computational studies examined the relationship between different factors involved. Here we investigate the relationship between transcription factors (TFs) and histone modifications (HMs), based on ChIP-seq data in cell lines. As it was shown that gene regulation by TFs differs depending on the CpG class of a promoter, we study the impact of the CpG content in promoters on the associations between TFs and HMs. We suggest an approach based on sparse linear regression models to infer associations between TFs and HMs with respect to CpG content. A study of the partial correlation of HMs for the two classes of high and low CpG content reveals possible CpG dependence and potential candidates for confounding factors in our models. We show that the models are accurate, inferred associations reflect known biological relationships, and we give new insight into associations with respect to CpG content. Moreover, analysis of a ChIP-seq dataset in HepG2 cells of the HM H3K122ac, an HM about little is known, reveals novel TF associations and supports a previously established link to active transcription.
[...]we speculate that TF–HM associations may differ in these two promoter classes. Materials and methods Basic model HM presence has been associated with the presence of several TFs [20, 31, 34]. [...]it is suggestive to predict the presence of a given HM from a set of TFs that could recruit other histone modifying enzymes or directly act as readers, writers, or erasers of that HM. A straightforward way of predicting HMs based on TFs is to train a regression model that takes the abundance of m TFs as input (features) and the abundance of an HM as output (response). [...]we can formulate the prediction of the abundance abd(HMY) of a Histone Modification HMY as(1)where β0 is the model bias term and βi, i = 1‥n are model coefficients subject to optimization. To prevent a loss of prediction power due to outliers and differing signal strength, caused by e.g. measurement errors, noise, and distinct antibody affinity, we normalize the logarithm of each signal to [0, 1] (abdnorm) instead of using the raw read counts. [...]we modify Eq 1 to obtain the following model:considering k bins for each of the m TFs.
[...]we speculate that TF–HM associations may differ in these two promoter classes. Materials and methods Basic model HM presence has been associated with the presence of several TFs [20, 31, 34]. [...]it is suggestive to predict the presence of a given HM from a set of TFs that could recruit other histone modifying enzymes or directly act as readers, writers, or erasers of that HM. A straightforward way of predicting HMs based on TFs is to train a regression model that takes the abundance of m TFs as input (features) and the abundance of an HM as output (response). [...]we can formulate the prediction of the abundance abd(HMY) of a Histone Modification HMY as(1)where β0 is the model bias term and βi, i = 1‥n are model coefficients subject to optimization. To prevent a loss of prediction power due to outliers and differing signal strength, caused by e.g. measurement errors, noise, and distinct antibody affinity, we normalize the logarithm of each signal to [0, 1] (abdnorm) instead of using the raw read counts. [...]we modify Eq 1 to obtain the following model:considering k bins for each of the m TFs.
Audience Academic
Author Kattler, Kathrin
Fischer, Jonas
Walter, Jörn
Schulz, Marcel H.
Ardakani, Fatemeh Behjati
AuthorAffiliation 2 Max Planck Institute for Informatics, Computational Biology and Applied Algorithmics, Saarbrücken, Germany
Università degli Studi di Milano, ITALY
1 Max Planck Institute for Informatics, Databases and Information Systems, Saarbrücken, Germany
5 Institute of Cardiovascular Regeneration, Goethe University, Frankfurt, Germany
3 Cluster of Excellence for Multimodal Computing and Interaction, High Throughput Genomics and Systems Biology, Saarbrücken, Germany
4 Department of Genetics, University of Saarland, Saarbrücken, Germany
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SSID ssj0053866
Score 2.3559616
Snippet Understanding the factors that underlie the epigenetic regulation of genes is crucial to understand the gene regulatory machinery as a whole. Several...
[...]we speculate that TF–HM associations may differ in these two promoter classes. Materials and methods Basic model HM presence has been associated with the...
[...]we speculate that TF–HM associations may differ in these two promoter classes. Materials and methods Basic model HM presence has been associated with the...
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SourceType Open Website
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StartPage e0249985
SubjectTerms Abundance
Antibodies
Binding sites
Biology
Biology and life sciences
Deoxyribonucleic acid
DNA
Enzymes
Epigenetics
Gene expression
Genetic aspects
Genomes
Genomics
Histones
Informatics
Model testing
Noise measurement
Optimization
Outliers (statistics)
Regression models
Signal strength
Transcription factors
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Title CpG content-dependent associations between transcription factors and histone modifications
URI https://www.ncbi.nlm.nih.gov/pubmed/33857234
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