Substrate availability and toxicity shape the structure of microbial communities engaged in metabolic division of labor
作者机构：Department of Energy&Resources EngineeringCollege of EngineeringPeking UniversityBeijingChina Department of Environmental Systems ScienceETH ZurichZürichSwitzerland Department of Environmental MicrobiologyEawagDübendorfSwitzerland Department of Environmental Science and EngineeringCollege of Architecture and EnvironmentSichuan UniversityChengduChina Institute of Ocean ResearchPeking UniversityBeijingChina Institute of EcologyPeking UniversityBeijingChina
出 版 物：《mLife》 (微生物（英文）)
年 卷 期：2022年第1卷第2期
页 面：131-145页
学科分类：0710[理学-生物学] 1007[医学-药学(可授医学、理学学位)] 100705[医学-微生物与生化药学] 07[理学] 071005[理学-微生物学] 10[医学]
基 金：supported by the National Key R&D Program of China(2018YFA0902100 and 2021YFA0910300) the National Natural Science Foundation of China(32161133023,32130004,91951204,and 32170113)
主 题：community structure mathematical model metabolic division of labor substrate synthetic microbial consortium
摘 要：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 ***,little is known about how the composition of such a microbial community is *** 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 *** tested this hypothesis by combining modeling with experiments using a synthetic *** 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 *** model and the proposed formula were able to quantitatively predict the structure of our synthetic *** analysis demonstrates that our rule is also applicable in estimating community structures in spatially structured ***,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.
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