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真菌次生代谢产物的调控与诱导:综述

Regulation and induction of fungal secondary metabolites: a comprehensive review.

作者信息

Prakash Shaurya, Kumari Hemlata, Sinha Minakshi, Kumar Antresh

机构信息

Department of Biochemistry, Central University of Haryana, Mahendragarh, Haryana, 123031, India.

出版信息

Arch Microbiol. 2025 Jul 1;207(8):189. doi: 10.1007/s00203-025-04386-0.

Abstract

Fungal secondary metabolites (SMs) represent a vast reservoir of bioactive compounds with immense therapeutic, agricultural, and industrial potential. These small molecules, including antibiotics, immunosuppressants, and anticancer agents, are synthesized through dedicated biosynthetic gene clusters (BGCs) regulated by various epigenetic, transcriptional, and environmental mechanisms. However, their cryptic biosynthesis and low natural yields pose significant challenges for large-scale production. This review comprehensively analyzes the regulatory landscape governing fungal SMs biosynthesis, advanced OMICS-driven approaches for identification of cryptic BGCs, and significantly emphasizes strategies to enhance SMs production. Furthermore, the integration of statistical and computational models (e.g., response surface methodology, artificial neural networks) is discussed for optimizing fermentation processes. The review underscores the diverse applications of fungal SMs in pharmaceuticals, agriculture, and cosmetics, while advocating for interdisciplinary innovations in synthetic biology and AI-driven metabolic engineering to sustainably harness fungal biodiversity.

摘要

真菌次级代谢产物(SMs)是具有巨大治疗、农业和工业潜力的生物活性化合物的巨大宝库。这些小分子,包括抗生素、免疫抑制剂和抗癌药物,是通过由各种表观遗传、转录和环境机制调控的专用生物合成基因簇(BGCs)合成的。然而,它们隐秘的生物合成和低天然产量给大规模生产带来了重大挑战。本综述全面分析了控制真菌SMs生物合成的调控格局、用于鉴定隐秘BGCs的先进组学驱动方法,并着重强调了提高SMs产量的策略。此外,还讨论了统计和计算模型(如响应面法、人工神经网络)的整合,以优化发酵过程。该综述强调了真菌SMs在制药、农业和化妆品中的多样应用,同时倡导合成生物学和人工智能驱动的代谢工程中的跨学科创新,以可持续地利用真菌生物多样性。

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