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Evolutionary constraints permeate large metabolic networks

  • Wagner, Andreas1, 2, 3, 4
  • 1 Dept. of Biochemistry, University of Zurich, Bldg. Y27, Winterthurerstrasse 190, Zurich, CH-8057, Switzerland , Zurich
  • 2 University of New Mexico, Department of Biology, Albuquerque, New Mexico, USA , Albuquerque
  • 3 The Santa Fe Institute, Santa Fe New Mexico, USA , Santa Fe New Mexico
  • 4 Quartier Sorge - Batiment Genopode, The Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
Published Article
BMC Evolutionary Biology
Springer (Biomed Central Ltd.)
Publication Date
Sep 11, 2009
DOI: 10.1186/1471-2148-9-231
Springer Nature


BackgroundMetabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.ResultsTo ask whether this is the case, I characterized pairwise and higher-order associations in the co-occurrence of genes encoding metabolic enzymes in more than 200 completely sequenced representatives of prokaryotic genera. The majority of reactions show constrained evolution. Specifically, genes encoding most reactions tend to co-occur with genes encoding other reaction(s). Constrained reaction pairs occur in small sets whose number is substantially greater than expected by chance alone. Most such sets are associated with single biochemical pathways. The respective genes are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association.ConclusionEven a limited number of available genomes suffices to show that metabolic network evolution is highly constrained by reaction combinations that are favored by natural selection. With increasing numbers of completely sequenced genomes, an evolutionary constraint-based approach may enable a detailed characterization of co-evolving metabolic modules.

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