Quantifying environmental adaptation of metabolic pathways in metagenomics

Research output: Contribution to journalJournal article

  • Tara A Gianoulis
  • Jeroen Raes
  • Prianka V Patel
  • Robert Bjornson
  • Jan O Korbel
  • Ivica Letunic
  • Takuji Yamada
  • Alberto Paccanaro
  • Michael Snyder
  • Peer Bork
  • Mark B Gerstein
  • Jensen, Lars Juhl
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.
Original languageEnglish
JournalProceedings of the National Academy of Science of the United States of America
Volume106
Issue number5
Pages (from-to)1374-9
Number of pages5
ISSN0027-8424
DOIs
Publication statusPublished - 2009

Bibliographical note

Keywords: Amino Acids; Biosensing Techniques; Genomics; Lipid Metabolism; Microbiology; Polysaccharides

ID: 20417512