High-resolution sequencing and modeling identifies distinct dynamic RNA regulatory strategies.

Cell
Authors
Keywords
Abstract

Cells control dynamic transitions in transcript levels by regulating transcription, processing, and/or degradation through an integrated regulatory strategy. Here, we combine RNA metabolic labeling, rRNA-depleted RNA-seq, and DRiLL, a novel computational framework, to quantify the level; editing sites; and transcription, processing, and degradation rates of each transcript at a splice junction resolution during the LPS response of mouse dendritic cells. Four key regulatory strategies, dominated by RNA transcription changes, generate most temporal gene expression patterns. Noncanonical strategies that also employ dynamic posttranscriptional regulation control only a minority of genes, but provide unique signal processing features. We validate Tristetraprolin (TTP) as a major regulator of RNA degradation in one noncanonical strategy. Applying DRiLL to the regulation of noncoding RNAs and to zebrafish embryogenesis demonstrates its broad utility. Our study provides a new quantitative approach to discover transcriptional and posttranscriptional events that control dynamic changes in transcript levels using RNA sequencing data.

Year of Publication
2014
Journal
Cell
Volume
159
Issue
7
Pages
1698-710
Date Published
2014 Dec 18
ISSN
1097-4172
URL
DOI
10.1016/j.cell.2014.11.015
PubMed ID
25497548
PubMed Central ID
PMC4272607
Links
Grant list
DP1 CA174427 / CA / NCI NIH HHS / United States
Intramural NIH HHS / United States
Howard Hughes Medical Institute / United States
P50 HG006193 / HG / NHGRI NIH HHS / United States
R01 GM056211 / GM / NIGMS NIH HHS / United States
R01GM056211 / GM / NIGMS NIH HHS / United States
1P50HG006193-01 / HG / NHGRI NIH HHS / United States
DP1OD003958-01, / OD / NIH HHS / United States
K99-HD076935 / HD / NICHD NIH HHS / United States
DP1 OD003958 / OD / NIH HHS / United States