Genome composition and phylogeny of microbes predict their co-occurrence in the environment.

The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise m...

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Main Author: Olga K Kamneva
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2017-02-01
Series:PLoS Computational Biology
Online Access:http://europepmc.org/articles/PMC5313232?pdf=render
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spelling doaj-6a933a210e554c2e9ffa3d38915354f22020-11-25T01:18:26ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-02-01132e100536610.1371/journal.pcbi.1005366Genome composition and phylogeny of microbes predict their co-occurrence in the environment.Olga K KamnevaThe genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.http://europepmc.org/articles/PMC5313232?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Olga K Kamneva
spellingShingle Olga K Kamneva
Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
PLoS Computational Biology
author_facet Olga K Kamneva
author_sort Olga K Kamneva
title Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
title_short Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
title_full Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
title_fullStr Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
title_full_unstemmed Genome composition and phylogeny of microbes predict their co-occurrence in the environment.
title_sort genome composition and phylogeny of microbes predict their co-occurrence in the environment.
publisher Public Library of Science (PLoS)
series PLoS Computational Biology
issn 1553-734X
1553-7358
publishDate 2017-02-01
description The genomic information of microbes is a major determinant of their phenotypic properties, yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition. To bridge this gap, this study introduces two new genome-wide pairwise measures of microbe-microbe interaction. The first (genome content similarity index) quantifies similarity in genome composition between two microbes, while the second (microbe-microbe functional association index) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries. These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets, accounting for phylogenetic relatedness of the taxa. Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data, but genome composition was found to be a strong predictor as well, it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination, even after accounting for evolutionary relationships between the species. On their own, the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons. In summary, results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology, and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions.
url http://europepmc.org/articles/PMC5313232?pdf=render
work_keys_str_mv AT olgakkamneva genomecompositionandphylogenyofmicrobespredicttheircooccurrenceintheenvironment
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