Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data

Tobalina L, Bargiela R, Pey J, Herbst F-A, Lores I, Rojo D, Barbas C, Peláez AI, Sánchez J, von Bergen M, Seifert J, Ferrer M, Planes FJ (2015) Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data. Bioinformatics. 2015 Jun 1;31(11):1771-9. doi: 10.1093/bioinformatics/btv036. Epub 2015 Jan 23.
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Abstract

Motivation: With the advent of meta-‘omics’ data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited.
Results: Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.

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