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Structured community transitions explain the switching capacity of microbial systems.
Long, Chengyi; Deng, Jie; Nguyen, Jen; Liu, Yang-Yu; Alm, Eric J; Solé, Ricard; Saavedra, Serguei.
Afiliación
  • Long C; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Deng J; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Nguyen J; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Liu YY; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
  • Alm EJ; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115.
  • Solé R; Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL 61801.
  • Saavedra S; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A ; 121(6): e2312521121, 2024 Feb 06.
Article en En | MEDLINE | ID: mdl-38285940
ABSTRACT
Microbial systems appear to exhibit a relatively high switching capacity of moving back and forth among few dominant communities (taxon memberships). While this switching behavior has been mainly attributed to random environmental factors, it remains unclear the extent to which internal community dynamics affect the switching capacity of microbial systems. Here, we integrate ecological theory and empirical data to demonstrate that structured community transitions increase the dependency of future communities on the current taxon membership, enhancing the switching capacity of microbial systems. Following a structuralist approach, we propose that each community is feasible within a unique domain in environmental parameter space. Then, structured transitions between any two communities can happen with probability proportional to the size of their feasibility domains and inversely proportional to their distance in environmental parameter space-which can be treated as a special case of the gravity model. We detect two broad classes of systems with structured transitions one class where switching capacity is high across a wide range of community sizes and another class where switching capacity is high only inside a narrow size range. We corroborate our theory using temporal data of gut and oral microbiota (belonging to class 1) as well as vaginal and ocean microbiota (belonging to class 2). These results reveal that the topology of feasibility domains in environmental parameter space is a relevant property to understand the changing behavior of microbial systems. This knowledge can be potentially used to understand the relevant community size at which internal dynamics can be operating in microbial systems.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Ecología / Ambiente / Microbiota Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Ecología / Ambiente / Microbiota Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc Natl Acad Sci U S A Año: 2024 Tipo del documento: Article