Splicing-associated chromatin signatures: a combinatorial and position-dependent role for histone marks in splicing definition

E Agirre,A Segelle,N Bellora,R F Luco,A J Oldfield

doi:10.1038/s41467-021-20979-x

Abstract

Alternative splicing relies on the combinatorial recruitment of splicing regulators to specific RNA binding sites. Chromatin has been shown to impact this recruitment. However, a limited number of histone marks have been studied at a global level. In this work, a machine learning approach, applied to extensive epigenomics datasets in human H1 embryonic stem cells and IMR90 foetal fibroblasts, has identified eleven chromatin modifications that differentially mark alternatively spliced exons depending on the level of exon inclusion. These marks act in a combinatorial and position-dependent way, creating characteristic splicing-associated chromatin signatures (SACS). In support of a functional role for SACS in coordinating splicing regulation, changes in the alternative splicing of SACS-marked exons between ten different cell lines correlate with changes in SACS enrichment levels and recruitment of the splicing regulators predicted by RNA motif search analysis. We propose the dynamic nature of chromatin modifications as a mechanism to rapidly fine-tune alternative splicing when necessary.

Highlights

Alternative splicing relies on the combinatorial recruitment of splicing regulators to specific RNA binding sites
We first selected in two cell lines in which the most extensive epigenomics data is available, which are human H1 embryonic stem cells and IMR90 foetal lung fibroblasts, splicing events in which an alternatively spliced exon was flanked by two constitutive exons
Using a supervised machine learning algorithm, we found that 34% of the alternatively spliced cassette exons expressed in human embryonic stem cells are differentially marked by specific combinations of 11 chromatin modifications, including DNA methylation

Summary

Introduction

Alternative splicing relies on the combinatorial recruitment of splicing regulators to specific RNA binding sites. A machine learning approach, applied to extensive epigenomics datasets in human H1 embryonic stem cells and IMR90 foetal fibroblasts, has identified eleven chromatin modifications that differentially mark alternatively spliced exons depending on the level of exon inclusion These marks act in a combinatorial and position-dependent way, creating characteristic splicing-associated chromatin signatures (SACS). A shift in exon inclusion levels between two different cell lines correlated with a change in histone marks enrichment levels and binding of the corresponding splicing regulator, as predicted by the SACS model, further supporting a functional link between chromatin and cell-specific alternative splicing

Methods

Results

Conclusion