Natural Products from Mammalian Gut Microbiota. Issue 5 (May 2019)

• Record Type: Journal Article
• Authors: Wang, Leli; Ravichandran, Vinothkannan; Yin, Yulong; Yin, Jia; Zhang, Youming
• Is Part Of: Trends in biotechnology. Volume 37:Issue 5(2019)
• Page Start: 492
• Page End: 504
• Publication Date: 2019-05
• Subjects: gut microbiota -- natural products -- biosynthetic gene clusters -- culture-based mining approaches -- genome metagenomics -- metabolomics; Biotechnology -- Periodicals; Biochemical engineering -- Periodicals; Genetic engineering -- Periodicals; Industrial microbiology -- Periodicals; 660.605
• DOI: 10.1016/j.tibtech.2018.10.003
• Languages: English
• ISSNs: 0167-7799

Abstract

Abstract : The mammalian gut has a remarkable abundance of microbes. These microbes have strong potential to biosynthesize distinct metabolites that are promising drugs, and many more bioactive compounds have yet to be explored as potential drug candidates. These small bioactive molecules often mediate important host–microbe and microbe–microbe interactions. In this review, we provide perspectives on and challenges associated with three mining strategies – culture-based, (meta)genomics-based, and metabolomics-based mining approaches – for discovering natural products derived from biosynthetic gene clusters (BGCs) in mammalian gut microbiota. In addition, we comprehensively summarize the structures, biological functions, and BGCs of these compounds. Improving these techniques, including by using combinatorial approaches, may accelerate drug discovery from gut microbes. Highlights: The gut microbiota has strong potential to biosynthesize large amounts of structurally distinct metabolites with a variety of biological activities that are promising drugs and drug candidates. Only a fraction of the gut microbiota have been cultivated so far, which makes it even more important to investigate microbial 'dark matter', which is a promising source for drug discovery. One emerging approach is to develop metabolomics data sets and to construct metabolic network models by machine learning for both clinics and laboratories. The boundary between genome/metagenomics-based, culture-based, and metabolomics-based mining approaches is fading and it is becoming feasible to combine two or more strategies to accelerate the mining of bioactive metabolites in the future.