Literature DB >> 18787117

Large-scale reconstruction and phylogenetic analysis of metabolic environments.

Elhanan Borenstein1, Martin Kupiec, Marcus W Feldman, Eytan Ruppin.   

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.

Mesh:

Year:  2008        PMID: 18787117      PMCID: PMC2567166          DOI: 10.1073/pnas.0806162105

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  38 in total

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10.  The outcomes of pathway database computations depend on pathway ontology.

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  100 in total

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Review 6.  Evolution of biomolecular networks: lessons from metabolic and protein interactions.

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Review 7.  Systems-biology approaches for predicting genomic evolution.

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Review 9.  'Unknown' proteins and 'orphan' enzymes: the missing half of the engineering parts list--and how to find it.

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10.  Evolutionary constraints permeate large metabolic networks.

Authors:  Andreas Wagner
Journal:  BMC Evol Biol       Date:  2009-09-11       Impact factor: 3.260

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