Molecular Networking and Pattern-Based Genome Mining Improves Discovery of Biosynthetic Gene Clusters and their Products from Salinispora Species

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.

Antibiotic drug discovery

“…Methods such as peptide and glycogenomics (Kersten et al ., 2011, 2013) connect computational predictions with state of the art mass spectrometric methods ( alternative methods for drug discovery ), whereas pattern‐based genome mining, for example, uses the huge amount of DNA sequence data available in a more comparative genomic approach combined with MS analysis (Duncan et al ., 2015). …”

Deciphering ocean carbon in a changing world

“…Correspondingly, nontargeted workflows are helping us to discover new molecules we didn't know to look for (33,39,70,105,135). Newly developed informatics approaches are supporting data mining across multiple studies and systems (136,137) (see Box 1). 'Omics data are allowing us to use microbes as biosensors for the compounds being synthesized, assimilated, and metabolized in the ocean microbiome (27,28,(138)(139)(140).…”

Dissecting Bottromycin Biosynthesis Using Comparative Untargeted Metabolomics

“…Mature bottromycins ( 1 – 5 , Figure 2) were clearly absent in every mutant, but the complexity of the data hampered the detailed characterization of metabolites. Therefore, this was followed by mass spectral network analysis,18 which is a powerful tool that identifies similarities in MS 2 fragmentation data and builds a network of species with related MS 2 spectra, thus identifying structurally‐related molecules within a complex mixture 18, 19, 20, 21, 22, 23, 24. This has been used to assess the global metabolic profiles of a single organism, either in isolation19 or when interacting with neighboring species,18 to compare the metabolomes of related organisms,20, 21, 22 to assess the metabolic potential of a new bacterial taxon,23 and to identify metabolites related to the colibactin pathway 24…”