Molecular networking and pattern-based genome mining improves discovery of biosynthetic gene clusters and their products from Salinispora species

Katherine R. Duncan, Max Crüsemann, Anna Lechner, Anindita Sarkar, Jie Li, Nadine Ziemert, Mingxun Wang, Nuno Bandeira, Bradley S. Moore, Pieter C. Dorrestein, Paul R. Jensen

Research output: Contribution to journalArticle

82 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)460-471
Number of pages12
JournalChemistry and Biology
Volume22
Issue number4
DOIs
Publication statusPublished - 23 Apr 2015

Keywords

  • chromatography, high pressure liquid
  • depsipeptides
  • genome, bacterial
  • genomics
  • metabolic networks and pathways
  • metabolome
  • micromonosporaceae
  • multigene Family
  • peptide synthases
  • tandem mass spectrometry

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