Microbial chassis engineering drives heterologous production of complex secondary metabolites

底盘 合成生物学 代谢工程 异源的 计算生物学 生化工程 次生代谢物 工业微生物学 生物 微生物 生物技术 酿酒酵母 酵母 基因 生物化学 细菌 遗传学 工程类 结构工程
作者
Jiaqi Liu,Xue Wang,Guangzhi Dai,Youming Zhang,Xiaoying Bian
出处
期刊:Biotechnology Advances [Elsevier]
卷期号:59: 107966-107966 被引量:37
标识
DOI:10.1016/j.biotechadv.2022.107966
摘要

The cryptic secondary metabolite biosynthetic gene clusters (BGCs) far outnumber currently known secondary metabolites. Heterologous production of secondary metabolite BGCs in suitable chassis facilitates yield improvement and discovery of new-to-nature compounds. The two juxtaposed conventional model microorganisms, Escherichia coli, Saccharomyces cerevisiae, have been harnessed as microbial chassis to produce a bounty of secondary metabolites with the help of certain host engineering. In last decade, engineering non-model microbes to efficiently biosynthesize secondary metabolites has received increasing attention due to their peculiar advantages in metabolic networks and/or biosynthesis. The state-of-the-art synthetic biology tools lead the way in operating genetic manipulation in non-model microorganisms for phenotypic optimization or yields improvement of desired secondary metabolites. In this review, we firstly discuss the pros and cons of several model and non-model microbial chassis, as well as the importance of developing broader non-model microorganisms as alternative programmable heterologous hosts to satisfy the desperate needs of biosynthesis study and industrial production. Then we highlight the lately advances in the synthetic biology tools and engineering strategies for optimization of non-model microbial chassis, in particular, the successful applications for efficient heterologous production of multifarious complex secondary metabolites, e.g., polyketides, nonribosomal peptides, as well as ribosomally synthesized and post-translationally modified peptides. Lastly, emphasis is on the perspectives of chassis cells development to access the ideal cell factory in the artificial intelligence-driven genome era.
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