Genome-wide nucleosome and transcription factor responses to genetic perturbations reveal chromatin-mediated mechanisms of transcriptional regulation.

Epigenetic mechanisms contribute to gene regulation by altering chromatin accessibility through changes in transcription factor (TF) and nucleosome occupancy throughout the genome. Despite numerous studies focusing on changes in gene expression, the intricate chromatin-mediated regulatory code remains largely unexplored on a comprehensive scale. We address this by employing a factor-agnostic, reverse-genetics approach that uses MNase-seq to capture genome-wide TF and nucleosome occupancies in response to the individual deletion of 201 transcriptional regulators in Saccharomyces cerevisiae, thereby assaying nearly one million mutant-gene interactions. We develop a principled approach to identify and quantify chromatin changes genome-wide, observing differences in TF and nucleosome occupancy that recapitulate well-established pathways identified by gene expression data. We also discover distinct chromatin signatures associated with the up- and downregulation of genes, and use these signatures to reveal regulatory mechanisms previously unexplored in expression-based studies. Finally, we demonstrate that chromatin features are predictive of transcriptional activity and leverage these features to reconstruct chromatin-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes genome-wide, providing a deeper, more mechanistic understanding of the complex relationship between chromatin organization and transcription.

GPCRs show widespread differential mRNA expression and frequent mutation and copy number variation in solid tumors.

G protein-coupled receptors (GPCRs) are the most widely targeted gene family for Food and Drug Administration (FDA)-approved drugs. To assess possible roles for GPCRs in cancer, we analyzed The Cancer Genome Atlas (TCGA) data for mRNA expression, mutations, and copy number variation (CNV) in 20 categories and 45 subtypes of solid tumors and quantified differential expression (DE) of GPCRs by comparing tumors against normal tissue from the Gene Tissue Expression Project (GTEx) database. GPCRs are overrepresented among coding genes with elevated expression in solid tumors. This analysis reveals that most tumor types differentially express >50 GPCRs, including many targets for approved drugs, hitherto largely unrecognized as targets of interest in cancer. GPCR mRNA signatures characterize specific tumor types and correlate with expression of cancer-related pathways. Tumor GPCR mRNA signatures have prognostic relevance for survival and correlate with expression of numerous cancer-related genes and pathways. GPCR expression in tumors is largely independent of staging, grading, metastasis, and/or driver mutations. GPCRs expressed in cancer cell lines largely parallel GPCR expression in tumors. Certain GPCRs are frequently mutated and appear to be hotspots, serving as bellwethers of accumulated genomic damage. CNV of GPCRs is common but does not generally correlate with mRNA expression. Our results suggest a previously underappreciated role for GPCRs in cancer, perhaps as functional oncogenes, biomarkers, surface antigens, and pharmacological targets.

Detection and Quantification of GPCR mRNA: An Assessment and Implications of Data from High-Content Methods.

G protein-coupled receptors (GPCRs) are the largest family of membrane receptors and targets for approved drugs. The analysis of GPCR expression is, thus, important for drug discovery and typically involves messenger RNA (mRNA)-based methods. We compared transcriptomic complementary DNA (cDNA) (Affymetrix) microarrays, RNA sequencing (RNA-seq), and quantitative polymerase chain reaction (qPCR)-based TaqMan arrays for their ability to detect and quantify expression of endoGPCRs (nonchemosensory GPCRs with endogenous agonists). In human pancreatic cancer-associated fibroblasts, RNA-seq and TaqMan arrays yielded closely correlated values for GPCR number (∼100) and expression levels, as validated by independent qPCR. By contrast, the microarrays failed to identify ∼30 such GPCRs and generated data poorly correlated with results from those methods. RNA-seq and TaqMan arrays also yielded comparable results for GPCRs in human cardiac fibroblasts, pancreatic stellate cells, cancer cell lines, and pulmonary arterial smooth muscle cells. The magnitude of mRNA expression for several Gq/11-coupled GPCRs predicted cytosolic calcium increase and cell migration by cognate agonists. RNA-seq also revealed splice variants for endoGPCRs. Thus, RNA-seq and qPCR-based arrays are much better suited than transcriptomic cDNA microarrays for assessing GPCR expression and can yield results predictive of functional responses, findings that have implications for GPCR biology and drug discovery.