TY - JOUR
T1 - Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data
AU - Tobalina, Luis
AU - Bargiela, Rafael
AU - Pey, Jon
AU - Herbst, Florian-Alexander
AU - Lores, Iván
AU - Rojo, David
AU - Barbas, Coral
AU - Peláez, Ana I
AU - Sánchez, Jesús
AU - von Bergen, Martin
AU - Seifert, Jana
AU - Ferrer, Manuel
AU - Planes, Francisco J
N1 - © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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 hypothesise different pathway organisations 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.CONTACT: fplanes@ceit.es; mferrer@icp.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - 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 hypothesise different pathway organisations 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.CONTACT: fplanes@ceit.es; mferrer@icp.csic.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
U2 - 10.1093/bioinformatics/btv036
DO - 10.1093/bioinformatics/btv036
M3 - Journal article
C2 - 25618865
SN - 1367-4803
VL - 31
SP - 1771
EP - 1779
JO - Bioinformatics
JF - Bioinformatics
IS - 11
ER -