Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

FANTOM Consortium, James Aitken, Shigeyuki Magi, Ahmad M N Alhendi, Masayoshi Itoh, Hideya Kawaji, Timo Lassmann, Carsten O Daub, Erik Arner, Piero Carninci, Alistair R R Forrest, Yoshihide Hayashizaki, Levon M Khachigian, Mariko Okada-Hatakeyama, Colin A Semple

Research output: Contribution to journalArticlepeer-review

Abstract

The immediate-early response mediates cell fate in response to a variety of extracellular stimuli and is dysregulated in many cancers. However, the specificity of the response across stimuli and cell types, and the roles of non-coding RNAs are not well understood. Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities over time. Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. Surprisingly, these data suggest that the earliest transcriptional responses often involve promoters generating non-coding RNAs, many of which are produced in advance of canonical protein-coding IEGs. IEGs are known to be capable of induction without de novo protein synthesis. Consistent with this, we find that the response of both protein-coding and non-coding RNA IEGs can be explained by their transcriptionally poised, permissive chromatin state prior to stimulation. We also explore the function of non-coding RNAs in the attenuation of the immediate early response in a small RNA sequencing dataset matched to the CAGE data: We identify a novel set of microRNAs responsible for the attenuation of the IEG response in an estrogen receptor positive cancer cell line. Our computational statistical method is well suited to meta-analyses as there is no requirement for transcripts to pass thresholds for significant differential expression between time points, and it is agnostic to the number of time points per dataset.

Original languageEnglish
Article numbere1004217
JournalPLoS Computational Biology
Volume11
Issue number4
DOIs
Publication statusPublished - 17 Apr 2015

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