Wang Miaoxiao, Chen Xiaoli, Tang Yue-Qin, Nie Yong, Wu Xiao-Lei
Department of Energy & Resources Engineering, College of Engineering Peking University Beijing China.
Department of Environmental Systems Science ETH Zürich Zürich Switzerland.
mLife. 2022 Jun 30;1(2):131-145. doi: 10.1002/mlf2.12025. eCollection 2022 Jun.
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.
代谢分工（MDOL）是一种广泛存在的自然现象，即复杂的代谢途径以互利的方式在群落内的不同菌株之间共享。然而，对于这种微生物群落的组成是如何调控的，我们却知之甚少。我们推测，当通过代谢分工进行有机化合物降解时，底物的浓度和毒性会调节两个微生物群体之间的利益分配，从而影响这个群落的结构。我们通过将建模与使用合成菌群的实验相结合来验证这一假设。我们的建模分析表明，执行第一步代谢的群体比例可以通过受底物浓度和毒性支配的类似莫诺德公式简单估算。我们的模型和提出的公式能够定量预测我们合成菌群的结构。进一步分析表明，我们的规则也适用于估计空间结构化环境中的群落结构。总之，我们的工作清楚地表明，利用关于环境因素的现有信息可以定量预测代谢分工群落的结构，从而为如何管理人工微生物系统以广泛应用于生物产业提供了新的见解。
