利用基因组尺度建模,探索合成二元微生物组内物种间的代谢相互作用。

Microbiome research reports Pub Date : 2024-05-27 eCollection Date: 2024-01-01 DOI:10.20517/mrr.2023.70
Kiumars Badr, Q Peter He, Jin Wang
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引用次数: 0

摘要

目的:微生物群落内的代谢作用在决定群落的结构、功能和组成方面起着关键作用。然而,由于天然微生物群落的复杂性和难处理性,对群落内物种间相互作用的了解十分有限。在这项工作中,我们使用二元合成微生物群落--甲烷养分-光能自养菌(M-P)共培养作为模型系统,研究了不同的基因组尺度代谢建模(GEM)方法,以更好地了解共培养内的代谢相互作用、它们如何促进共培养中观察到的生长增强以及它们如何随时间演变。研究方法利用 M-P 模型共培养的批次生长数据,我们比较了微生物群落的三种 GEM 方法。其中两种方法是现有的方法:SteadyCom 是一种稳态 GEM,动态通量平衡分析(DFBA)实验室是一种动态 GEM。我们还为 M-P 协同培养提出了一种改进的动态 GEM 方法 DynamiCom。结果SteadyCom 可以预测细胞培养物内部的代谢相互作用,但不能预测其动态演变;DFBA Lab 可以预测细胞培养物的动态,但不能确定种间相互作用。DynamiCom 能够识别共培养物中的交叉喂养代谢物,并预测种间相互作用随时间的演变。结论针对 M-P 模型共培养,开发了一种新的动态 GEM 方法 DynamiCom。在经过验证的动力学模型预测的限制下,DynamiCom 能够持续预测 M-P 协同培养中交换的主要代谢物,以及甲烷菌和蓝藻之间建立的相互氮交换关系。DynamiCom预测的种间相互作用及其动态演化得到了有关甲烷菌、蓝藻和其他蓝藻-异养菌共培养的大量文献证据的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probing interspecies metabolic interactions within a synthetic binary microbiome using genome-scale modeling.

Aim: Metabolic interactions within a microbial community play a key role in determining the structure, function, and composition of the community. However, due to the complexity and intractability of natural microbiomes, limited knowledge is available on interspecies interactions within a community. In this work, using a binary synthetic microbiome, a methanotroph-photoautotroph (M-P) coculture, as the model system, we examined different genome-scale metabolic modeling (GEM) approaches to gain a better understanding of the metabolic interactions within the coculture, how they contribute to the enhanced growth observed in the coculture, and how they evolve over time. Methods: Using batch growth data of the model M-P coculture, we compared three GEM approaches for microbial communities. Two of the methods are existing approaches: SteadyCom, a steady state GEM, and dynamic flux balance analysis (DFBA) Lab, a dynamic GEM. We also proposed an improved dynamic GEM approach, DynamiCom, for the M-P coculture. Results: SteadyCom can predict the metabolic interactions within the coculture but not their dynamic evolutions; DFBA Lab can predict the dynamics of the coculture but cannot identify interspecies interactions. DynamiCom was able to identify the cross-fed metabolite within the coculture, as well as predict the evolution of the interspecies interactions over time. Conclusion: A new dynamic GEM approach, DynamiCom, was developed for a model M-P coculture. Constrained by the predictions from a validated kinetic model, DynamiCom consistently predicted the top metabolites being exchanged in the M-P coculture, as well as the establishment of the mutualistic N-exchange between the methanotroph and cyanobacteria. The interspecies interactions and their dynamic evolution predicted by DynamiCom are supported by ample evidence in the literature on methanotroph, cyanobacteria, and other cyanobacteria-heterotroph cocultures.

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