MS/MS-based networking and peptidogenomics guided genome mining revealed the stenothricin gene cluster in Streptomyces roseosporus

Abstract

Most (75%) of the anti-infectives that save countless lives and enormously improve quality of life originate from microbes found in nature. Herein, we described a global visualization of the detectable molecules produced from a single microorganism, which we define as the ‘molecular network’ of that organism, followed by studies to characterize the cellular effects of antibacterial molecules. We demonstrate that Streptomyces roseosporus produces at least four non-ribosomal peptide synthetase-derived molecular families and their gene subnetworks (daptomycin, arylomycin, napsamycin and stenothricin) were identified with different modes of action. A number of previously unreported analogs involving truncation, glycosylation, hydrolysis and biosynthetic intermediates and/or shunt products were also captured and visualized by creation of a map through MS/MS networking. The diversity of antibacterial compounds produced by S. roseosporus highlights the importance of developing new approaches to characterize the molecular capacity of an organism in a more global manner. This allows one to more deeply interrogate the biosynthetic capacities of microorganisms with the goal to streamline the discovery pipeline for biotechnological applications in agriculture and medicine. This is a contribution to a special issue to honor Chris Walsh’s amazing career.

Antibiotic drug discovery

“…Among these new approaches, molecular networking (MN) is a particularly effective one to organize MS/MS fragmentation spectra. MN compares all MS/MS spectra in a given extract and groups them according to their similarity (Watrous et al ., 2012; Bandeira, 2011; Liu et al ., 2013; Fang and Dorrestein, 2014). The application of these tools leads to the identification of novel compounds by avoiding re‐isolation of known compounds.…”

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…”

Dissecting Bottromycin Biosynthesis Using Comparative Untargeted Metabolomics

“…Mature bottromycins (1-5,F igure 2) were clearly absent in every mutant, but the complexity of the data hampered the detailed characterization of metabolites.T herefore,t his was followed by mass spectral network analysis, [18] which is ap owerful tool that identifies similarities in MS 2 fragmentation data and builds anetwork of species with related MS 2 spectra, thus identifying structurally-related molecules within acomplex mixture. [18][19][20][21][22][23][24] This has been used to assess the global metabolic profiles of as ingle organism, either in isolation [19] or when interacting with neighboring species, [18] to compare the metabolomes of related organisms, [20][21][22] to assess the metabolic potential of anew bacterial taxon, [23] and to identify metabolites related to the colibactin pathway. [24] Mass spectral network analysis of WT, DbtmC, DbtmD, DbtmF, DbtmI,a nd DbtmJ strains revealed an extensive metabolic network (Supporting Information, Figure S5).…”