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微生物群落的人工选择:我们学到了什么以及如何改进?

Artificial selection of microbial communities: what have we learnt and how can we improve?

作者信息

Thomas Joshua L, Rowland-Chandler Jamila, Shou Wenying

机构信息

Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, United Kingdom.

Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, United Kingdom.

出版信息

Curr Opin Microbiol. 2024 Feb;77:102400. doi: 10.1016/j.mib.2023.102400. Epub 2023 Dec 12.

Abstract

Microbial communities are capable of performing diverse functions with important bioindustrial and medical applications. One approach to improving community function is to breed new communities by artificially selecting for those displaying high community function ('community selection'). Importantly, community selection can improve the function of interest without needing to understand how the function arises, just like in classical artificial selection of individuals. However, experimental studies of community selection have had varied and largely limited success. Here, we review a conceptual framework to help foster an understanding of community selection and its associated challenges, and provide broad insights for designing effective selection strategies.

摘要

微生物群落能够执行多种具有重要生物工业和医学应用的功能。一种改善群落功能的方法是通过人工选择那些表现出高群落功能的群落(“群落选择”)来培育新的群落。重要的是,群落选择可以在无需了解功能如何产生的情况下提高目标功能,这与经典的个体人工选择类似。然而,群落选择的实验研究取得的成果各不相同,且在很大程度上有限。在此,我们回顾一个概念框架,以帮助增进对群落选择及其相关挑战的理解,并为设计有效的选择策略提供广泛的见解。

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