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Proc Natl Acad Sci U S A. 2008 Sep 23;105(38):14482-7. doi: 10.1073/pnas.0806162105. Epub 2008 Sep 11.

Large-scale reconstruction and phylogenetic analysis of metabolic environments.

Author information

  • 1Department of Biological Sciences, Stanford University, Stanford, CA 94305, USA. ebo@stanford.edu

Abstract

The topology of metabolic networks may provide important insights not only into the metabolic capacity of species, but also into the habitats in which they evolved. Here we introduce the concept of a metabolic network's "seed set"--the set of compounds that, based on the network topology, are exogenously acquired--and provide a methodological framework to computationally infer the seed set of a given network. Such seed sets form ecological "interfaces" between metabolic networks and their surroundings, approximating the effective biochemical environment of each species. Analyzing the metabolic networks of 478 species and identifying the seed set of each species, we present a comprehensive large-scale reconstruction of such predicted metabolic environments. The seed sets' composition significantly correlates with several basic properties characterizing the species' environments and agrees with biological observations concerning major adaptations. Species whose environments are highly predictable (e.g., obligate parasites) tend to have smaller seed sets than species living in variable environments. Phylogenetic analysis of the seed sets reveals the complex dynamics governing gain and loss of seeds across the phylogenetic tree and the process of transition between seed and non-seed compounds. Our findings suggest that the seed state is transient and that seeds tend either to be dropped completely from the network or to become non-seed compounds relatively fast. The seed sets also permit a successful reconstruction of a phylogenetic tree of life. The "reverse ecology" approach presented lays the foundations for studying the evolutionary interplay between organisms and their habitats on a large scale.

PMID:
18787117
[PubMed - indexed for MEDLINE]
PMCID:
PMC2567166
Free PMC Article

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