Literature DB >> 33877964

Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments.

Sylvie Estrela1, Alicia Sanchez-Gorostiaga1,2, Jean Cc Vila1, Alvaro Sanchez1.   

Abstract

A major open question in microbial community ecology is whether we can predict how the components of a diet collectively determine the taxonomic composition of microbial communities. Motivated by this challenge, we investigate whether communities assembled in pairs of nutrients can be predicted from those an class="Chemical">ssembled in every single nutrient alone. We find that although the null, naturally additive model generally predicts well the family-level community composition, there exist systematic deviations from the additive predictions that reflect generic patterns of nutrient dominance at the family level. Pairs of more-similar nutrients (e.g. two sugars) are on average more additive than pairs of more dissimilar nutrients (one sugar-one organic acid). Furthermore, sugar-acid communities are generally more similar to the sugar than the acid community, which may be explained by family-level asymmetries in nutrient benefits. Overall, our results suggest that regularities in how nutrients interact may help predict community responses to dietary changes.
© 2021, Estrela et al.

Entities:  

Keywords:  Microbial community assembly; computational biology; consumer-resource models; ecology; environmental complexity; nutrient interactions; resource specialization; systems biology

Year:  2021        PMID: 33877964     DOI: 10.7554/eLife.65948

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  11 in total

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7.  McComedy: A user-friendly tool for next-generation individual-based modeling of microbial consumer-resource systems.

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8.  Stability criteria for the consumption and exchange of essential resources.

Authors:  Theo Gibbs; Yifan Zhang; Zachary R Miller; James P O'Dwyer
Journal:  PLoS Comput Biol       Date:  2022-09-08       Impact factor: 4.779

9.  An evolutionary algorithm for designing microbial communities via environmental modification.

Authors:  Alan R Pacheco; Daniel Segrè
Journal:  J R Soc Interface       Date:  2021-06-23       Impact factor: 4.118

10.  Top-down and bottom-up cohesiveness in microbial community coalescence.

Authors:  Juan Diaz-Colunga; Nanxi Lu; Alicia Sanchez-Gorostiaga; Chang-Yu Chang; Helen S Cai; Joshua E Goldford; Mikhail Tikhonov; Álvaro Sánchez
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-08       Impact factor: 12.779

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