| Autor: |
Gianoulis TA; Program in Computational Biology and Bioinformatics, Departments of Molecular Biophysics and Biochemistry, Computer Science, and Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520, USA., Raes J, Patel PV, Bjornson R, Korbel JO, Letunic I, Yamada T, Paccanaro A, Jensen LJ, Snyder M, Bork P, Gerstein MB |
| Jazyk: |
angličtina |
| Zdroj: |
Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2009 Feb 03; Vol. 106 (5), pp. 1374-9. Date of Electronic Publication: 2009 Jan 22. |
| DOI: |
10.1073/pnas.0808022106 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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