The role of histone modifications in transcription remains incompletely understood. Here we used experimental perturbations combined with sensitive machine learning tools that infer the distribution of histone marks using maps of nascent transcription. Transcription predicted the variation in active histone marks and complex chromatin states, like bivalent promoters, down to single-nucleosome resolution and at an accuracy that rivaled the correspondence between independent ChIP-seq experiments. Blocking transcription rapidly removed two punctate marks, H3K4me3 and H3K27ac, from chromatin indicating that transcription is required for active histone modifications. Transcription was also required for maintenance of H3K27me3 consistent with a role for RNA in recruiting PRC2. A subset of DNase-I hypersensitive sites were refractory to prediction, precluding models where transcription initiates pervasively at any open chromatin. Our results, in combination with past literature, support a model in which active histone modifications serve a supportive, rather than a regulatory, role in transcription.

Background The concentrations of distinct types of RNA in cells result from a dynamic equilibrium between RNA synthesis and decay. Despite the critical importance of RNA decay rates, current approaches for measuring them are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here, we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On sequencing (PRO-seq) and RNA sequencing (RNA-seq). Results Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding and DNA methylation are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1 binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Conclusion We introduce an approach for estimating the relative half-lives of individual RNAs. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

How enhancers control target gene expression over long genomic distances remains an important unsolved problem. Here we studied enhancer-promoter contact architecture and communication by integrating data from nucleosome-resolution genomic contact maps, nascent transcription, and perturbations to transcription-associated proteins and thousands of candidate enhancers. Contact frequency between functionally validated enhancer-promoter pairs was most enriched near the +1 and +2 nucleosomes at enhancers and target promoters, indicating that functional enhancer-promoter pairs spend time in close physical proximity. Blocking RNA polymerase II (Pol II) caused major disruptions to enhancer-promoter contacts. Paused Pol II occupancy and the enzymatic activity of poly (ADP-ribose) polymerase 1 (PARP1) stabilized enhancer-promoter contacts. Based on our ndings, we propose an updated model that couples transcriptional dynamics and enhancer-promoter communication.

Innate immune responses rely on inducible gene expression programmes which, in contrast to steady-state transcription, are highly dependent on cohesin. Here we address transcriptional parameters underlying this cohesin-dependence by single-molecule RNA-FISH and single-cell RNA-sequencing. We show that inducible innate immune genes are regulated predominantly by an increase in the probability of active transcription, and that probabilities of enhancer and promoter transcription are coordinated. Cohesin has no major impact on the fraction of transcribed inducible enhancers, or the number of mature mRNAs produced per transcribing cell. Cohesin is, however, required for coupling the probabilities of enhancer and promoter transcription. Enhancer-promoter coupling may not be explained by spatial proximity alone, and at the model locus Il12b can be disrupted by selective inhibition of the cohesinopathy-associated BET bromodomain BD2. Our data identify discrete steps in enhancer-mediated inducible gene expression that differ in cohesin-dependence, and suggest that cohesin and BD2 may act on shared pathways.