PLoS Computational Biology (Apr 2015)

Transcriptional dynamics reveal critical roles for non-coding RNAs in the immediate-early response.

  • Stuart 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,
  • FANTOM Consortium,
  • Levon M Khachigian,
  • Mariko Okada-Hatakeyama,
  • Colin A Semple

DOI
https://doi.org/10.1371/journal.pcbi.1004217
Journal volume & issue
Vol. 11, no. 4
p. e1004217

Abstract

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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.