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From a Natural Product to Its Biosynthetic Gene Cluster: A Demonstration Using Polyketomycin from Streptomyces diastatochromogenes Tü6028
Published on: January 13, 2017
Harnessing regulatory networks in Actinobacteria for natural product discovery
Hannah E Augustijn1,2, Anna M Roseboom2, Marnix H Medema1,2
1Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
Abstract:
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.
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