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Species-specific transcriptomic network inference of interspecies interactions.

Ryan S McClure1, Christopher C Overall1, Eric A Hill1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.

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|May 26, 2018
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Summary

This study reveals gene networks in microbial communities, showing how Thermosynechococcus elongatus supports Meiothermus ruber growth. The findings highlight metabolite exchange and are applicable to understanding microbial community functions.

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Area of Science:

  • Microbial Ecology
  • Systems Biology
  • Genomics

Background:

  • High-throughput 'omics and computational genomics enable species-resolved functional studies.
  • Microbial communities involve complex interactions between different species.
  • Understanding interspecies metabolic exchange is crucial for community functioning.

Purpose of the Study:

  • To construct a gene association network for a commensal consortium using a mutual information approach.
  • To identify novel gene interactions and metabolic exchanges between Thermosynechococcus elongatus and Meiothermus ruber.
  • To demonstrate the applicability of the approach for analyzing microbial community interactions.

Main Methods:

  • Applied the context likelihood of relatedness (CLR) algorithm to 25 transcriptomic datasets.
  • Generated an interspecies gene association network for Thermosynechococcus elongatus and Meiothermus ruber.
  • Performed topological and functional analysis of inferred gene linkages.

Main Results:

  • Constructed a gene association network revealing numerous interspecies linkages.
  • Identified known and novel interactions, including those in amino acid, carbohydrate, vitamin, and terpenoid metabolism, and cell adhesion.
  • Inferred that interactions center on the exchange of energetically costly metabolites.

Conclusions:

  • The CLR approach effectively infers gene associations and metabolic interactions in microbial communities.
  • The study provides insights into the functional basis of a phototroph-heterotroph consortium.
  • The methodology is broadly applicable to characterizing the functioning of diverse microbial communities.