文献类型：期刊文献

英文题名：Reporter systems in actinomycetes: Versatile tools for natural product discovery and production

作者：Jiang, Yue[1,2];Liu, Yuxin[1,2];Wang, Shuliu[1,2];Zeng, Xiaoqian[1,2];Lv, Dongyuan[1,2];Tong, Yaojun[3];Bai, Linquan[3];Zhang, Lixin[1,2];Tan, Gao-Yi[1,2]

机构：[1]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, Sch Biotechnol, Shanghai, Peoples R China;[3]Shanghai Jiao Tong 11 Univ, Sch Life Sci & Biotechnol, State Key Lab Microbial Metab, Shanghai 200240, Peoples R China

年份：2026

卷号：88

外文期刊名：BIOTECHNOLOGY ADVANCES

收录：;EI(收录号：20260720045812);WOS:【SCI-EXPANDED(收录号:WOS:001688419300001)】；

基金：This work was supported by the National Natural Science Foundation of China [32370064 and 32121005] ; the Shanghai Science and Technology Commission (24HC2820200) ; the 111 Project (B18022) ; the 2023 Double World-class Project-Key Program-Intelligent Biomanufacturing.

语种：英文

外文关键词：Reporter system; Actinomycetes; Natural product

摘要：Actinomycetes produce secondary metabolites that are crucial for medicine, health, and agriculture; however, their physiological and genetic characteristics present challenges for the discovery and production of these compounds. Reporter systems act as versatile molecular beacons in actinomycetes, linking genotype to phenotype by empowering the dissection of regulatory networks, the programmable design of genetic circuits, and high-throughput strain screening. This review highlights common reporter systems including resistance genes, pigment biosynthetic genes, fluorescent proteins, luciferases, and enzymatic reporter genes, along with their applications in enhancing metabolite production and activating silent gene clusters in actinomycetes. Beyond summarizing major reporter systems in actinomycetes, this review provides a comparative assessment of their strengths and limitations, offering guidance for tool selection and highlighting the need for broader, more versatile next-generation reporters integrated with AI-driven modeling to accelerate natural product discovery and production.