Biology subjects

Leighton J Core

Publications and source records attributed to Leighton J Core.

2 recordsLinked to original sources

bioRxiv2016

Transcription factors GAF and HSF act at distinct regulatory steps to modulate stress-induced gene activation

The coordinated regulation of gene expression at the transcriptional level is fundamental to organismal development and homeostasis. Inducible systems are invaluable when studying transcription because the regulatory process can be triggered instantaneously, allowing the tracking of ordered mechanistic events. Here, we use Precision Run-On sequencing (PRO-seq) to examine the genome-wide Heat Shock (HS) response in Drosophila and the function of two key transcription factors on the immediate transcription activation or repression of all genes regulated by HS. We identify the primary HS response genes and the rate-limiting steps in the transcription cycle that GAGA-Associated Factor (GAF) and HS Factor (HSF) regulate. We demonstrate that GAF acts upstream of promoter-proximally paused RNA Polymerase II (Pol II) formation, likely at the step of chromatin opening, and that GAF-facilitated Pol II pausing is critical for HS activation. In contrast, HSF is dispensable for establishing or maintaining Pol II pausing, but is critical for the release of paused Pol II into the gene body at a subset of highly-activated genes. Additionally, HSF has no detectable role in the rapid HS-repression of thousands of genes.

Genomics↗

bioRxiv2014

Accurate Identification of Active Transcriptional Regulatory Elements from Global Run-On and Sequencing Data

Identification of the genomic regions that regulate transcription remains an important open problem. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5'-capped RNAs reveals patterns of divergent transcription that accurately mark active transcriptional regulatory elements (TREs), including enhancers and promoters. Here, we demonstrate that active TREs can be identified with comparable accuracy by applying sensitive machine-learning methods to standard GRO-seq and PRO-seq data, allowing TREs to be assayed together with transcription levels, elongation rates, and other transcriptional features, in a single experiment. Our method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to predict active TREs. The predicted TREs are strongly enriched for marks associated with functional elements, including H3K27ac, transcription factor binding sites, eQTLs, and GWAS-associated SNPs. Using dREG, we survey TREs in eight cell types and provide new insights into global patterns of TRE assembly and function.

Genomics↗