Integrative Analysis of the Genomics of the Human Glucocorticoid Response

Abstract

Glucocorticoids (GCs) are potent steroid hormones that regulate immunity and metabolism, and do so primarily by activating the transcription factor glucocorticoid receptor (GR). Once activated, GR binds to thousands of promoter-distal sites and regulates gene expression. Chromatin accessibility is believed to predetermine GR binding; however, several observations suggest preprogramming is more complex. For example, accessible sites far outnumber GR sites, suggesting additional discriminating influences. To investigate the relative importance of the component factors and marks in determining GR binding and the effects of GR binding on those factors and marks, I integrated hundreds of genome-wide measurements of transcription factor binding, epigenetic state, and gene expression across a 12-hour time course of GC exposure and reanalyzed complementary data in diverse cellular contexts. I found that GC treatment induces GR to bind preferentially to enhancers, which initiates a cascade of highly coordinated changes in occupancy of transcription factors and histone modifications. While GR recruits to most enhancers, the strength and persistence of binding—which ultimately determines enhancer dynamics—depends on motif content and spatial interactions between enhancers.Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, I present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models cluster number with a Dirichlet process and temporal dependencies with Gaussian processes. I demonstrate the accuracy of DPGP in comparison with state-of-the-art approaches using hundreds of simulated data sets. To further test our method, I apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the GC dexamethasone. I validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal novel regulatory mechanisms.