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基于基因组规模代谢模型和分析评估益生菌潜力。

Genome scale metabolic models and analysis for evaluating probiotic potentials.

机构信息

School of Chemical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, Republic of Korea.

Departments of Biological Systems Engineering and Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE, U.S.A.

出版信息

Biochem Soc Trans. 2020 Aug 28;48(4):1309-1321. doi: 10.1042/BST20190668.

Abstract

Probiotics are live beneficial microorganisms that can be consumed in the form of dairy and food products as well as dietary supplements to promote a healthy balance of gut bacteria in humans. Practically, the main challenge is to identify and select promising strains and formulate multi-strain probiotic blends with consistent efficacy which is highly dependent on individual dietary regimes, gut environments, and health conditions. Limitations of current in vivo and in vitro methods for testing probiotic strains can be overcome by in silico model guided systems biology approaches where genome scale metabolic models (GEMs) can be used to describe their cellular behaviors and metabolic states of probiotic strains under various gut environments. Here, we summarize currently available GEMs of microbial strains with probiotic potentials and propose a knowledge-based framework to evaluate metabolic capabilities on the basis of six probiotic criteria. They include metabolic characteristics, stability, safety, colonization, postbiotics, and interaction with the gut microbiome which can be assessed by in silico approaches. As such, the most suitable strains can be identified to design personalized multi-strain probiotics in the future.

摘要

益生菌是有益的活菌微生物,可以通过乳制品和食品形式以及膳食补充剂来消费,以促进人类肠道细菌的健康平衡。实际上,主要的挑战是识别和选择有前途的菌株,并制定具有一致功效的多菌株益生菌混合物,这高度依赖于个体饮食制度、肠道环境和健康状况。通过基于计算模型的系统生物学方法,可以克服当前用于测试益生菌菌株的体内和体外方法的局限性,在这些方法中,可以使用基因组规模代谢模型 (GEM) 来描述益生菌菌株在各种肠道环境下的细胞行为和代谢状态。在这里,我们总结了目前具有益生菌潜力的微生物菌株的可用 GEM,并提出了一个基于知识的框架,根据六个益生菌标准来评估代谢能力。它们包括代谢特征、稳定性、安全性、定植、后生元和与肠道微生物组的相互作用,可以通过计算方法进行评估。因此,可以识别出最合适的菌株,以便在未来设计个性化的多菌株益生菌。

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