Substrate availability and toxicity shape the structure of microbial communities engaged in metabolic division of labor

Metabolic division of labor (MDOL) represents a widespread natural phenomenon, whereby a complex metabolic pathway is shared between different strains within a community in a mutually beneficial manner. However, little is known about how the composition of such a microbial community is regulated. We...

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Bibliographic Details
Published in:mLife 2022-06, Vol.1 (2), p.131-145
Main Authors: Wang, Miaoxiao, Chen, Xiaoli, Tang, Yue‐Qin, Nie, Yong, Wu, Xiao‐Lei
Format: Article
Language:English
Subjects:
community structure
Hypotheses
mathematical model
metabolic division of labor
Metabolism
Metabolites
Microorganisms
Ordinary differential equations
Original Research
Pollutants
Privatization
Simulation
substrate
synthetic microbial consortium
Toxicity
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Summary:Metabolic division of labor (MDOL) represents a widespread natural phenomenon, whereby a complex metabolic pathway is shared between different strains within a community in a mutually beneficial manner. However, little is known about how the composition of such a microbial community is regulated. We hypothesized that when degradation of an organic compound is carried out via MDOL, the concentration and toxicity of the substrate modulate the benefit allocation between the two microbial populations, thus affecting the structure of this community. We tested this hypothesis by combining modeling with experiments using a synthetic consortium. Our modeling analysis suggests that the proportion of the population executing the first metabolic step can be simply estimated by Monod‐like formulas governed by substrate concentration and toxicity. Our model and the proposed formula were able to quantitatively predict the structure of our synthetic consortium. Further analysis demonstrates that our rule is also applicable in estimating community structures in spatially structured environments. Together, our work clearly demonstrates that the structure of MDOL communities can be quantitatively predicted using available information on environmental factors, thus providing novel insights into how to manage artificial microbial systems for the wide application of the bioindustry.
ISSN:2770-100X
2097-1699
2770-100X
DOI:10.1002/mlf2.12025