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促进和简化微生物适应性实验室进化的高级策略和工具。

Advanced strategies and tools to facilitate and streamline microbial adaptive laboratory evolution.

机构信息

MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China.

MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.

出版信息

Trends Biotechnol. 2022 Jan;40(1):38-59. doi: 10.1016/j.tibtech.2021.04.002. Epub 2021 May 3.

Abstract

Adaptive laboratory evolution (ALE) has served as a historic microbial engineering method that mimics natural selection to obtain desired microbes. The past decade has witnessed improvements in all aspects of ALE workflow, in terms of growth coupling, genotypic diversification, phenotypic selection, and genotype-phenotype mapping. The developing growth-coupling strategies facilitate ALE to a wider range of appealing traits. In vivo mutagenesis methods and multiplexed automated culture platforms open new gates to streamline its execution. Meanwhile, the application of multi-omics analyses and multiplexed genetic engineering promote efficient knowledge mining. This article provides a comprehensive and updated review of these advances, highlights newest significant applications, and discusses future improvements, aiming to provide a practical guide for implementation of novel, effective, and efficient ALE experiments.

摘要

适应性实验室进化 (ALE) 是一种历史悠久的微生物工程方法,它模拟自然选择来获得所需的微生物。过去十年中,ALE 工作流程的各个方面都得到了改进,包括生长偶联、基因型多样化、表型选择和基因型-表型映射。不断发展的生长偶联策略使 ALE 能够应用于更广泛的吸引人的特性。体内诱变方法和多路复用自动化培养平台为简化其执行开辟了新的途径。同时,多组学分析和多路复用遗传工程的应用促进了有效的知识挖掘。本文全面而详尽地综述了这些进展,重点介绍了最新的重要应用,并讨论了未来的改进,旨在为新型、有效和高效的 ALE 实验的实施提供实用指南。

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