Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.

The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that u...

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Autores principales:	Liliana Angeles-Martinez, Vassily Hatzimanikatis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/30ecdf439cf542fba4a4682184a197ed
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spelling	oai:doaj.org-article:30ecdf439cf542fba4a4682184a197ed2021-12-02T19:57:31ZSpatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.1553-734X1553-735810.1371/journal.pcbi.1009140https://doaj.org/article/30ecdf439cf542fba4a4682184a197ed2021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009140https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i) Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.Liliana Angeles-MartinezVassily HatzimanikatisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009140 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Liliana Angeles-Martinez Vassily Hatzimanikatis Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
description	The metabolic capabilities of the species and the local environment shape the microbial interactions in a community either through the exchange of metabolic products or the competition for the resources. Cells are often arranged in close proximity to each other, creating a crowded environment that unevenly reduce the diffusion of nutrients. Herein, we investigated how the crowding conditions and metabolic variability among cells shape the dynamics of microbial communities. For this, we developed CROMICS, a spatio-temporal framework that combines techniques such as individual-based modeling, scaled particle theory, and thermodynamic flux analysis to explicitly incorporate the cell metabolism and the impact of the presence of macromolecular components on the nutrients diffusion. This framework was used to study two archetypical microbial communities (i) Escherichia coli and Salmonella enterica that cooperate with each other by exchanging metabolites, and (ii) two E. coli with different production level of extracellular polymeric substances (EPS) that compete for the same nutrients. In the mutualistic community, our results demonstrate that crowding enhanced the fitness of cooperative mutants by reducing the leakage of metabolites from the region where they are produced, avoiding the resource competition with non-cooperative cells. Moreover, we also show that E. coli EPS-secreting mutants won the competition against the non-secreting cells by creating less dense structures (i.e. increasing the spacing among the cells) that allow mutants to expand and reach regions closer to the nutrient supply point. A modest enhancement of the relative fitness of EPS-secreting cells over the non-secreting ones were found when the crowding effect was taken into account in the simulations. The emergence of cell-cell interactions and the intracellular conflicts arising from the trade-off between growth and the secretion of metabolites or EPS could provide a local competitive advantage to one species, either by supplying more cross-feeding metabolites or by creating a less dense neighborhood.
format	article
author	Liliana Angeles-Martinez Vassily Hatzimanikatis
author_facet	Liliana Angeles-Martinez Vassily Hatzimanikatis
author_sort	Liliana Angeles-Martinez
title	Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
title_short	Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
title_full	Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
title_fullStr	Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
title_full_unstemmed	Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
title_sort	spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/30ecdf439cf542fba4a4682184a197ed
work_keys_str_mv	AT lilianaangelesmartinez spatiotemporalmodelingofthecrowdingconditionsandmetabolicvariabilityinmicrobialcommunities AT vassilyhatzimanikatis spatiotemporalmodelingofthecrowdingconditionsandmetabolicvariabilityinmicrobialcommunities
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Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities.

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