1. Biochemistry and Chemical Biology
  2. Chromosomes and Gene Expression

Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation

  1. Ira Alexandra Iosub
  2. Rob Willem van Nues
  3. Stuart William McKellar
  4. Karen Jule Nieken
  5. Marta Marchioretto
  6. Brandon Sy
  7. Jai J Tree
  8. Gabriella Viero
  9. Sander Granneman  Is a corresponding author
  1. University of Edinburgh, United Kingdom
  2. CNR, Italy
  3. University of New South Wales, Australia
  4. CNR Unit, Italy
https://doi.org/10.7554/eLife.54655
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Cite this article
  1. Ira Alexandra Iosub
  2. Rob Willem van Nues
  3. Stuart William McKellar
  4. Karen Jule Nieken
  5. Marta Marchioretto
  6. Brandon Sy
  7. Jai J Tree
  8. Gabriella Viero
  9. Sander Granneman
(2020)
Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation
eLife 9:e54655.
https://doi.org/10.7554/eLife.54655
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Abstract

By shaping gene expression profiles, small RNAs (sRNAs) enable bacteria to efficiently adapt to changes in their environment. To better understand how Escherichia coli acclimatizes to nutrient availability, we performed UV cross-linking, ligation and sequencing of hybrids (CLASH) to uncover Hfq-associated RNA-RNA interactions at specific growth stages. We demonstrate that Hfq CLASH robustly captures bona fide RNA-RNA interactions identified hundreds of novel sRNA base-pairing interactions, including many sRNA-sRNA interactions and involving 3'UTR-derived sRNAs. We rediscovered known and identified novel sRNA seed sequences. The sRNA-mRNA interactions identified by CLASH have strong base-pairing potential and are highly enriched for complementary sequence motifs, even those supported by only a few reads. Yet, steady state levels of most mRNA targets were not significantly affected upon over-expression of the sRNA regulator. Our results reinforce the idea that the reproducibility of the interaction, not base-pairing potential, is a stronger predictor for a regulatory outcome.

Data availability

The next generation sequencing data have been deposited on the NCBI Gene Expression Omnibus (GEO) with accession number GSE123050. The python pyCRAC (Webb et al., 2014), kinetic-CRAC and GenomeBrowser software packages used for analysing the data are available from https://bitbucket.org/sgrann (pyCRAC up to version 1.4.3), https://git.ecdf.ed.ac.uk/sgrannem/ and pypi (https://pypi.org/user/g_ronimo/). The hyb pipeline for identifying chimeric reads is available from https://github.com/gkudla/hyb. The scripts for statistical analysis of hyb data is available from https://bitbucket.org/jaitree/hyb_stats/. The FLASH algorithm for merging paired reads is available from https://github.com/dstreett/FLASH2. Bedgraph and Gene Transfer Format (GTF) generated from the analysis of the Hfq CLASH, RNA-seq and TEX RNA-seq data (Thomason et al., 2015) are available from the Granneman lab DataShare repository (https://datashare.is.ed.ac.uk/handle/10283/2915).

The following data sets were generated
The following previously published data sets were used

Article and author information

Author details

  1. Ira Alexandra Iosub

    SynthSys, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2924-2471

  2. Rob Willem van Nues

    ICB, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  3. Stuart William McKellar

    IQB3, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0792-9878

  4. Karen Jule Nieken

    Wellcome Trust Centre for Cell Biology, University of Edinburgh, Edinburgh, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  5. Marta Marchioretto

    Institute of Biophysics, CNR, Trento, Italy
    Competing interests
    The authors declare that no competing interests exist.

  6. Brandon Sy

    School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.

  7. Jai J Tree

    School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.

  8. Gabriella Viero

    Institute of Biophysics, CNR Unit, Trento, Italy
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6755-285X

  9. Sander Granneman

    SynthSys, University of Edinburgh, Edinburgh, United Kingdom
    For correspondence
    sgrannem@ed.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4387-1271

Funding

Wellcome (102334)

  • Ira Alexandra Iosub

Wellcome (091549)

  • Sander Granneman

Medical Research Council (MR/R008205/1)

  • Sander Granneman

Australian National Health (GNT1067241)

  • Jai J Tree

Australian Medical Research Council (GNT1139313)

  • Jai J Tree

Axonomix (N/A)

  • Gabriella Viero

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2020, Iosub et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Ira Alexandra Iosub
  2. Rob Willem van Nues
  3. Stuart William McKellar
  4. Karen Jule Nieken
  5. Marta Marchioretto
  6. Brandon Sy
  7. Jai J Tree
  8. Gabriella Viero
  9. Sander Granneman
(2020)
Hfq CLASH uncovers sRNA-target interaction networks linked to nutrient availability adaptation
eLife 9:e54655.
https://doi.org/10.7554/eLife.54655

Share this article

https://doi.org/10.7554/eLife.54655

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