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PMID: 20195496 Published · epublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species.

PLoS computational biology ·Vol. 6 ·No. 2 ·2010-02-26 ·Pages e1000690

Freilich S, Kreimer A, Borenstein E, Gophna U, Sharan R, Ruppin E

Abstract

The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle (specialized/generalist); the second, environmental-independent, is associated (correlation = approximately 0.6) with the intrinsic metabolic capacities of a species-higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets.

MeSH Terms
Bacteria/genetics Bacterial Physiological Phenomena Computational Biology/methods Computer Simulation Environment Genetic Variation Metabolic Networks and Pathways Models, Genetic Selection, Genetic Systems Biology/methods
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Freilich Shiri
The Blavatnik School of Computer Sciences, Faculty of Life Sciences, Ramat Aviv, Israel. [email protected]
Kreimer Anat
Borenstein Elhanan
Gophna Uri
Sharan Roded
Ruppin Eytan
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-7358
Published
2010-02-26
Epub
2010-00-26
Pages
e1000690
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC2829043
Subset
IM
Grants
NIGMS NIH HHS · R01 GM028016 · United States
NIGMS NIH HHS · GM28016 · United States
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