Evidence map›Paper›PMID 25885578›Full record

ArticlePLoS computational biology2015

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 and 5 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.4field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 60 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. The circadian dynamics of small nucleolar RNA in the mouse liver.Journal of the Royal Society, Interface · 2017
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors at 5 institutions in 3 countries.

Stuart AitkenMRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Shigeyuki MagiLaboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, IMS, W518, Suehiro-cho, Tsurumi-ku, Yokohama, Japan.
Ahmad M N AlhendiUNSW Centre for Vascular Research, University of New South Wales, Sydney, New South Wales, Australia.
Masayoshi ItohRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Hideya KawajiRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Timo LassmannRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Carsten O DaubRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Erik ArnerRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Piero CarninciRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Alistair R R ForrestRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Omics Science Center, Yokohama, Kanagawa, Japan.
Yoshihide HayashizakiRIKEN Center for Life Science Technologies, Division of Genomic Technologies, Yokohama, Kanagawa, Japan; RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Saitama, Japan.
FANTOM Consortium
Levon M KhachigianUNSW Centre for Vascular Research, University of New South Wales, Sydney, New South Wales, Australia.
Mariko Okada-HatakeyamaLaboratory for Integrated Cellular Systems, RIKEN Center for Integrative Medical Sciences, IMS, W518, Suehiro-cho, Tsurumi-ku, Yokohama, Japan.
Colin A SempleMRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.
RIKEN Center for Integrative Medical Sciences · JPRIKEN · JPUNSW Sydney · AUMRC Institute of Genetics and Molecular Medicine · GBUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Computational BiologyHumansImmediate-Early ProteinsKineticsMCF-7 CellsMicroRNAsModels, StatisticalRNA, UntranslatedTranscription, GeneticImmediate-Early ProteinsMicroRNAsRNA, Untranslated

Identifiers

PMID25885578
PMCPMC4401570
OpenAlexW2098556911

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.