Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species. Issue 4 (23rd April 2015)

• Record Type: Journal Article
• Authors: Duncan, Katherine R.; Crüsemann, Max; Lechner, Anna; Sarkar, Anindita; Li, Jie; Ziemert, Nadine; Wang, Mingxun; Bandeira, Nuno; Moore, Bradley S.; Dorrestein, Pieter C.; Jensen, Paul R.
• Is Part Of: Chemistry & biology. Volume 22:Issue 4(2015)
• Page Start: 460
• Page End: 471
• Publication Date: 2015-04-23
• Subjects: Biochemistry -- Periodicals; 540
• DOI: 10.1016/j.chembiol.2015.03.010
• Languages: English
• ISSNs: 1074-5521

Abstract

Summary: Genome sequencing has revealed that bacteria contain many more biosynthetic gene clusters than predicted based on the number of secondary metabolites discovered to date. While this biosynthetic reservoir has fostered interest in new tools for natural product discovery, there remains a gap between gene cluster detection and compound discovery. Here we apply molecular networking and the new concept of pattern-based genome mining to 35 Salinispora strains, including 30 for which draft genome sequences were either available or obtained for this study. The results provide a method to simultaneously compare large numbers of complex microbial extracts, which facilitated the identification of media components, known compounds and their derivatives, and new compounds that could be prioritized for structure elucidation. These efforts revealed considerable metabolite diversity and led to several molecular family-gene cluster pairings, of which the quinomycin-type depsipeptide retimycin A was characterized and linked to gene cluster NRPS40 using pattern-based bioinformatic approaches. Graphical Abstract: Highlights: Pattern-based genome mining was applied to 35 Salinispora strains Molecular networking facilitated new compound discovery The quinomycin-type depsipeptide retimycin A was characterized Abstract : Duncan et al. use pattern-based genome mining to help bridge the gap between the detection of biosynthetic gene clusters and their products. Coupled with molecular networking, these approaches facilitated the de-replication of known compounds, the detection of new analogs, and the prioritization of compounds for structure elucidation.