Ross C. Hardison | bioRxiv Science

bioRxiv2016

A hyperactive transcriptional state marks genome reactivation at the mitosis-G1 transition

During mitosis, RNA polymerase II (Pol II) and many transcription factors dissociate from chromatin, and transcription ceases globally. Transcription is known to restart in bulk by telophase, but whether de novo transcription at the mitosis-G1 transition is in any way distinct from later in interphase remains unknown. We tracked Pol II occupancy genome-wide in mammalian cells progressing from mitosis through late G1. Unexpectedly, during the earliest rounds of transcription at the mitosis-G1 transition, ~50% of active genes and distal enhancers exhibit a spike in transcription, exceeding levels observed later in G1 phase. Enhancer-promoter chromatin contacts are depleted during mitosis and restored rapidly upon G1 entry, but do not spike. Of the chromatin-associated features examined, histone H3 lysine 27 acetylation levels at individual loci in mitosis best predict the mitosis-G1 transcriptional spike. Single-molecule RNA imaging supports that the mitosis-G1 transcriptional spike can constitute the maximum transcriptional activity per DNA copy throughout the cell division cycle. The transcriptional spike occurs heterogeneously and propagates to cell-to-cell differences in mature mRNA expression. Our results raise the possibility that passage through the mitosis-G1 transition might predispose cells to diverge in gene expression states.

Molecular Biology↗

bioRxiv2015

Bayesian Modeling of Epigenetic Variation in Multiple Human Cell Types

With massive amount of sequencing data generated for many epigenetic features in a variety of cell and tissue types, the chief challenges are to build effective and quantitative models explaining how the dynamics in multiple epigenomes lead to differential gene expression and diverse phenotypes. We developed a unified Bayesian framework for jointly annotating multiple epigenomes and detecting differential regulation among multiple tissues and cell types over regions of varying sizes. Our method, called IDEAS (integrative and discriminative epigenome annotation system), achieves superior power and accuracy over existing methods by modeling both position and cell type specific regulatory activities. Using 84 ENCODE epigenetic data sets in 6 cell types, we identified epigenetic variation of different sizes that are strongly associated with differential gene expression. The detected regions are significantly enriched in genetic variants associated with complex phenotypes. Our results yielded much stronger enrichment scores than achievable by existing approaches, and the enriched phenotypes are highly relevant to the corresponding cell types. IDEAS is a powerful statistical tool for integrative annotation of regulatory elements and detection of multivariate epigenetic variation in many tissues and cell types, which could be of important utility in elucidating the interplay between genetic variants, gene regulation and diseases.\n\nThe states generated by IDEAS can be visualized or downloaded from the \"Regulation\" section of http://main.genome-browser.bx.psu.edu/

Bioinformatics↗