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利用放线菌中的调控网络进行天然产物发现。

Harnessing regulatory networks in Actinobacteria for natural product discovery.

作者信息

Augustijn Hannah E, Roseboom Anna M, Medema Marnix H, van Wezel Gilles P

机构信息

Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.

Molecular Biotechnology, Institute of Biology, Leiden University, Leiden, The Netherlands.

出版信息

J Ind Microbiol Biotechnol. 2024 Jan 9;51. doi: 10.1093/jimb/kuae011.

Abstract

UNLABELLED

Microbes typically live in complex habitats where they need to rapidly adapt to continuously changing growth conditions. To do so, they produce an astonishing array of natural products with diverse structures and functions. Actinobacteria stand out for their prolific production of bioactive molecules, including antibiotics, anticancer agents, antifungals, and immunosuppressants. Attention has been directed especially towards the identification of the compounds they produce and the mining of the large diversity of biosynthetic gene clusters (BGCs) in their genomes. However, the current return on investment in random screening for bioactive compounds is low, while it is hard to predict which of the millions of BGCs should be prioritized. Moreover, many of the BGCs for yet undiscovered natural products are silent or cryptic under laboratory growth conditions. To identify ways to prioritize and activate these BGCs, knowledge regarding the way their expression is controlled is crucial. Intricate regulatory networks control global gene expression in Actinobacteria, governed by a staggering number of up to 1000 transcription factors per strain. This review highlights recent advances in experimental and computational methods for characterizing and predicting transcription factor binding sites and their applications to guide natural product discovery. We propose that regulation-guided genome mining approaches will open new avenues toward eliciting the expression of BGCs, as well as prioritizing subsets of BGCs for expression using synthetic biology approaches.

ONE-SENTENCE SUMMARY: This review provides insights into advances in experimental and computational methods aimed at predicting transcription factor binding sites and their applications to guide natural product discovery.

摘要

未标注

微生物通常生活在复杂的栖息地中,需要快速适应不断变化的生长条件。为此,它们会产生一系列结构和功能各异的天然产物。放线菌因其大量产生生物活性分子而脱颖而出,这些分子包括抗生素、抗癌剂、抗真菌剂和免疫抑制剂。人们尤其关注放线菌所产生化合物的鉴定以及其基因组中大量多样的生物合成基因簇(BGC)的挖掘。然而,目前对生物活性化合物进行随机筛选的投资回报率较低,同时很难预测数百万个BGC中哪些应被优先考虑。此外,许多尚未发现的天然产物的BGC在实验室生长条件下是沉默的或隐秘的。为了确定优先考虑和激活这些BGC的方法,了解其表达的控制方式至关重要。复杂的调控网络控制着放线菌中的全局基因表达,每个菌株高达1000个转录因子,数量惊人。本综述重点介绍了用于表征和预测转录因子结合位点的实验和计算方法的最新进展及其在指导天然产物发现中的应用。我们认为,调控引导的基因组挖掘方法将为引发BGC的表达开辟新途径,同时利用合成生物学方法对BGC的表达子集进行优先排序。

一句话总结

本综述深入探讨了旨在预测转录因子结合位点的实验和计算方法的进展及其在指导天然产物发现中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9389/10996143/64c3300ab77f/kuae011fig1g.jpg

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