Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression

Abstract

Background In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a complex network that regulate the state of chromatin and processes that depend on it including transcription and DNA replication. Currently, a large number of these epigenetic modifications are being mapped in a variety of cell lines at different stages of development using high throughput sequencing by members of the ENCODE consortium, the NIH Roadmap Epigenomics Program and the Human Epigenome Project. An extremely promising and underexplored area of research is the application of machine learning methods, which are designed to construct predictive network models, to these large-scale epigenomic data sets. Results Using a ChIP-Seq data set of 20 histone lysine and arginine methylations and histone variant H2A.Z in human CD4 + T-cells, we built predictive models of gene expression as a function of histone modification/variant levels using Multilinear (ML) Regression and Multivariate Adaptive Regression Splines (MARS). Along with extensive crosstalk among the 20 histone methylations, we found H4R3me2 was the most and second most globally repressive histone methylation among the 20 studied in the ML and MARS models, respectively. In support of our finding, a number of experimental studies show that PRMT5-catalyzed symmetric dimethylation of H4R3 is associated with repression of gene expression. This includes a recent study, which demonstrated that H4R3me2 is required for DNMT3A-mediated DNA methylation--a known global repressor of gene expression. Conclusion In stark contrast to univariate analysis of the relationship between H4R3me2 and gene expression levels, our study showed that the regulatory role of some modifications like H4R3me2 is masked by confounding variables, but can be elucidated by multivariate/systems-level approaches.

Histone arginine methylation

“…Unexpectedly, they did not find any correlation between this mark and transcriptionally repressed loci. However, reevaluation of this data, by a different group, indicated that the H4R3me2s mark is strongly associated with repressed gene expression [103].…”

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

“…A simple approach for integrating epigenomic and transcriptomic data is to replace the genetic with epigenetic measurements in the regression model [15][16][17]. Stepwise regression procedures were proposed to study interaction effects between different histone modifications that may not act independently on gene transcription [18]. Moreover, various machine learning approaches were applied to predict gene transcription levels based on transcription factor binding, histone modification or other epigenomic data [19,20].…”

Protein arginine methyltransferase 5 is a potential oncoprotein that upregulatesG1 cyclins/cyclin‐dependent kinases and the phosphoinositide 3‐kinase/AKTsignaling cascade

“…PRMT5 acts as a protein arginine methyltransferase and catalyzes the transfer of two methyl groups symmetrically to the arginine residue of protein substrates. PRMT5 is involved in a variety of biological processes, such as the regulation of gene expression through various mechanisms that include histone modification, chromatin remodeling, mRNA splicing, and protein biosynthesis . PRMT5 also functions in DNA replication and repair, formation of the Golgi apparatus and ribosomes, cell cycle checkpoints, cell migration, cell reprogramming, development, liver metabolism, and the circadian clock …”