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Research Article

Functional characterization of bacterial sRNAs using a network biology approach

Sheetal R. Modi, Diogo M. Camacho, Michael A. Kohanski, Graham C. Walker, and James J. Collins
  1. aThe Howard Hughes Medical Institute, Department of Biomedical Engineering, and Center for BioDynamics, Boston University, Boston, MA 02215;
  2. bBoston University School of Medicine, Boston, MA 02118;
  3. cDepartment of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139; and
  4. dThe Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115

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PNAS first published August 29, 2011; https://doi.org/10.1073/pnas.1104318108
Sheetal R. Modi
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Diogo M. Camacho
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Michael A. Kohanski
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Graham C. Walker
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James J. Collins
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  • For correspondence: jcollins@bu.edu
  1. Edited* by Susan Gottesman, National Cancer Institute, Bethesda, MD, and approved August 3, 2011 (received for review March 19, 2011)

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Abstract

Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

  • microbiology
  • network inference
  • sRNA regulation

Footnotes

  • ↵1S.R.M. and D.M.C. contributed equally to this work.

  • ↵2To whom correspondence should be addressed. E-mail: jcollins{at}bu.edu.
  • Author contributions: S.R.M., D.M.C., M.A.K., G.C.W., and J.J.C. designed research; S.R.M., D.M.C., and M.A.K. performed research; S.R.M. and D.M.C. analyzed data; and S.R.M., D.M.C., M.A.K., G.C.W., and J.J.C. wrote the paper.

  • The authors declare no conflict of interest.

  • Data deposition: The microarray data reported in this paper have been deposited at www.bu.edu/abl.

  • ↵*This Direct Submission article had a prearranged editor.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1104318108/-/DCSupplemental.

Freely available online through the PNAS open access option.

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Functional characterization of bacterial sRNAs using a network biology approach
Sheetal R. Modi, Diogo M. Camacho, Michael A. Kohanski, Graham C. Walker, James J. Collins
Proceedings of the National Academy of Sciences Aug 2011, 201104318; DOI: 10.1073/pnas.1104318108

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Functional characterization of bacterial sRNAs using a network biology approach
Sheetal R. Modi, Diogo M. Camacho, Michael A. Kohanski, Graham C. Walker, James J. Collins
Proceedings of the National Academy of Sciences Aug 2011, 201104318; DOI: 10.1073/pnas.1104318108
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