Analysis of chromatin-state plasticity identifies cell-type–specific regulators of H3K27me3 patterns

Abstract

Chromatin states are highly cell-type-specific, but the underlying mechanisms for the establishment and maintenance of their genome-wide patterns remain poorly understood. Here we present a computational approach for investigation of chromatin-state plasticity. We applied this approach to investigate an ENCODE ChIP-seq dataset profiling the genome-wide distributions of the H3K27me3 mark in 19 human cell lines. We found that the high plasticity regions (HPRs) can be divided into two functionally and mechanistically distinct subsets, which correspond to CpG island (CGI) proximal or distal regions, respectively. Although the CGI proximal HPRs are typically associated with continuous variation across different cell-types, the distal HPRs are associated with binary-like variations. We developed a computational approach to predict putative cell-type-specific modulators of H3K27me3 patterns and validated the predictions by comparing with public ChIPseq data. Furthermore, we applied this approach to investigate mechanisms for poised enhancer establishment in primary human erythroid precursors. Importantly, we predicted and experimentally validated that the principal hematopoietic regulator T-cell acute lymphocytic leukemia-1 (TAL1) is involved in regulating H3K27me3 variations in collaboration with the transcription factor growth factor independent 1B (GFI1B), providing fresh insights into the context-specific role of TAL1 in erythropoiesis. Our approach is generally applicable to investigate the regulatory mechanisms of epigenetic pathways in establishing cellular identity.polycomb | hematopoiesis | histone modifications | motifs

Recruiting polycomb to chromatin

“…When ESCs are cultured in 2i medium instead of serum, trimethylation levels of H3K27 reduce dramatically (Marks et al 2012). However, even if H3K27me2 is not generally distributed throughout the whole genome PRC2 can also counteract acetylation of H3K27 at enhancers by trimethylation (Pinello et al 2014; Abou El Hassan et al 2015). …”

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

“…Proteins that recognize specific signals in the DNA influence its accessibility and histone modifications (Voss and Hager 2014). Given training data, models parameterized by machine learning can effectively predict protein binding, DNA accessibility, histone modifications, and DNA methylation from the sequence (Das et al 2006;Arnold et al 2013;Benveniste et al 2014;Pinello et al 2014;Lee et al 2015;Setty and Leslie 2015;Whitaker et al 2015). A trained model can then annotate the influence of every nucleotide (and variant) on these regulatory attributes.…”

A New Feature Selection Methodology for K-mers Representation of DNA Sequences

“…K-mer representation have shown its effectiveness in several in-silico analysis applied to different genomics and epigenomics studies. In particular they have been used to characterize nucleosome positioning [4], to find enhancer functional regions [5], to characterize epigenetic variability [6], in sequence alignment and transcriptome assembly [7] and in gene prediction [8]. The interested reader can find the basic ideas of k-mer based methods to different biological problems in the following review [17].…”