Engineering complex communities by directed evolution.

Autor: Chang CY; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Vila JCC; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Bender M; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Li R; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA., Mankowski MC; Department of Immunobiology and Department of Laboratory Medicine, Yale University, New Haven, CT, USA., Bassette M; Biomedical Sciences Graduate Program, University of California San Francisco, San Francisco, CA, USA., Borden J; Department of Molecular & Cellular Biology, University of California Berkeley, Berkeley, CA, USA., Golfier S; Max Planck Institute of Molecular Cell Biology and Genetics, and Max Planck Institute for the Physics of Complex Systems, Dresden, Germany., Sanchez PGL; European Molecular Biology Laboratory (EMBL), Developmental Biology Unit, Heidelberg, Germany., Waymack R; Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, USA., Zhu X; Department of Biomedical Engineering and the Biological Design Center, Boston University, Boston, MA, USA., Diaz-Colunga J; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Estrela S; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Rebolleda-Gomez M; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA.; Microbial Sciences Institute, Yale University, New Haven, CT, USA., Sanchez A; Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, USA. alvaro.sanchez@yale.edu.; Microbial Sciences Institute, Yale University, New Haven, CT, USA. alvaro.sanchez@yale.edu.
Jazyk: angličtina
Zdroj: Nature ecology & evolution [Nat Ecol Evol] 2021 Jul; Vol. 5 (7), pp. 1011-1023. Date of Electronic Publication: 2021 May 13.
DOI: 10.1038/s41559-021-01457-5
Abstrakt: Directed evolution has been used for decades to engineer biological systems at or below the organismal level. Above the organismal level, a small number of studies have attempted to artificially select microbial ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. Here, we have developed a flexible modelling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions. By artificially selecting hundreds of in silico microbial metacommunities under identical conditions, we first show that the main breeding methods used to date, which do not necessarily let communities reach their ecological equilibrium, are outperformed by a simple screen of sufficiently mature communities. We then identify a range of alternative directed evolution strategies that, particularly when applied in combination, are well suited for the top-down engineering of large, diverse and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search of dynamically stable and ecologically resilient communities with desired quantitative attributes.
Databáze: MEDLINE
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