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Genomic structure predicts metabolite dynamics in microbial communities.
Gowda, Karna; Ping, Derek; Mani, Madhav; Kuehn, Seppe.
Afiliación
  • Gowda K; Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA; Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA.
  • Ping D; Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
  • Mani M; Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL 60208, USA; Department of Molecular Biosciences, Northwestern University, Evanston, IL 60208, USA; NSF-Simons Center for Quantitative Biology, Northwestern University, Northwestern University, Evanston,
  • Kuehn S; Department of Ecology and Evolution, University of Chicago, Chicago, IL 60637, USA; Center for the Physics of Evolving Systems, University of Chicago, Chicago, IL 60637, USA. Electronic address: seppe.kuehn@gmail.com.
Cell ; 185(3): 530-546.e25, 2022 02 03.
Article en En | MEDLINE | ID: mdl-35085485
The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Genómica / Metabolómica / Microbiota Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cell Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Genómica / Metabolómica / Microbiota Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cell Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos