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Quantifying environmental adaptation of metabolic pathways in metagenomics

Item Type:Article
Title:Quantifying environmental adaptation of metabolic pathways in metagenomics
Creators Name:Gianoulis, T.A. and Raes, J. and Patel, P.V. and Bjornson, R. and Korbel, J.O. and Letunic, I. and Yamada, T. and Paccanaro, A. and Jensen, L.J. and Snyder, M. and Bork, P. and Gerstein, M.B.
Abstract:Recently, approaches have been developed to sample the genetic content of heterogeneous environments (metagenomics). However, by what means these sequences link distinct environmental conditions with specific biological processes is not well understood. Thus, a major challenge is how the usage of particular pathways and subnetworks reflects the adaptation of microbial communities across environments and habitats-i.e., how network dynamics relates to environmental features. Previous research has treated environments as discrete, somewhat simplified classes (e.g., terrestrial vs. marine), and searched for obvious metabolic differences among them (i.e., treating the analysis as a typical classification problem). However, environmental differences result from combinations of many factors, which often vary only slightly. Therefore, we introduce an approach that employs correlation and regression to relate multiple, continuously varying factors defining an environment to the extent of particular microbial pathways present in a geographic site. Moreover, rather than looking only at individual correlations (one-to-one), we adapted canonical correlation analysis and related techniques to define an ensemble of weighted pathways that maximally covaries with a combination of environmental variables (many-to-many), which we term a metabolic footprint. Applied to available aquatic datasets, we identified footprints predictive of their environment that can potentially be used as biosensors. For example, we show a strong multivariate correlation between the energy-conversion strategies of a community and multiple environmental gradients (e.g., temperature). Moreover, we identified covariation in amino acid transport and cofactor synthesis, suggesting that limiting amounts of cofactor can (partially) explain increased import of amino acids in nutrient-limited conditions.
Keywords:Environmental Genomics, Network Dynamics, Microbiology, Canonical Correlation Analysis, Amino Acids, Biosensing Techniques, Lipid Metabolism, Polysaccharides
Source:Proceedings of the National Academy of Sciences of the United States of America
ISSN:0027-8424
Publisher:National Academy of Sciences (U.S.A.)
Volume:106
Number:5
Page Range:1374-1379
Date:3 February 2009
Official Publication:https://doi.org/10.1073/pnas.0808022106
PubMed:View item in PubMed

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