Prioritizing Natural Product Diversity in a Collection of 146 Bacterial Strains Based on Growth and Extraction Protocols

Abstract

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, whilst minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinispora and Streptomyces strains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions.

Evaluating Nitrogen-Containing Biosynthetic Products Produced by Saltwater Culturing of Several California Littoral Zone Gram-Negative Bacteria

“…We needed a chemodiversity prioritization filter to rank this library of >150 samples, and the strategy of genome mining provided one possible pathway. Eventually, two methods were chosen to shift from a time-consuming trial- and-error analysis, and these involved (a) aggressive use of MS n data processed by the GNPS tools 9 for comparative metabolomics analysis and/or (b) in silico analysis of draft genome sequencing data using tools such as antiSMASH 11 to assess biosynthetic richness via the presence of putative secondary metabolite gene clusters. We employed the former strategy since our strains were not well represented among currently sequenced microbial genomes in public repositories and large-scale sequencing efforts could not be justified without motivating chemistry.…”

“…Second, each unique strain is grown in a panel of five liquid media (see Table S1) with the idea that diversity in primary metabolism and environmental stress may trigger unique secondary metabolite production. Finally, extracts from each strain and media condition are analyzed using the GNPS MS/MS 9 networking strategy introduced in 2012 by Dorrestein–Bandeira. 9a By tagging each extract’s mass spectra with its strain and media attributes, especially when accurate (experimental error ±0.003 amu) MS 1 and MS 2 data are obtained, we can explore dereplication–identification of metabolites produced in a strain- or media-dependent manner.…”

“…Finally, extracts from each strain and media condition are analyzed using the GNPS MS/MS 9 networking strategy introduced in 2012 by Dorrestein–Bandeira. 9a By tagging each extract’s mass spectra with its strain and media attributes, especially when accurate (experimental error ±0.003 amu) MS 1 and MS 2 data are obtained, we can explore dereplication–identification of metabolites produced in a strain- or media-dependent manner. Reported below are results from a campaign to chemically mine Gram-negative bacteria driven by the characterization (a) of known molecules rich in nitrogen atoms, (b) of compounds possessing new structural diversity, and (c) of substances whose scaffolds can potentially answer questions about the symbiotic relationships driving marine microbial heterocycle biosynthesis.…”

The Tripod for Bacterial Natural Product Discovery: Genome Mining, Silent Pathway Induction, and Mass Spectrometry-Based Molecular Networking

“…Moreover, this technique is compatible with the Global Natural Products Social Molecular Networking (GNPS), an open-access database for organization and sharing of raw, processed, or identified MS/MS spectral data (14). In this way, combining molecular networking and GNPS can bolster the identification of specific chemical classes and compounds and assist the prioritization of samples for further investigation (15). …”

Omics-based natural product discovery and the lexicon of genome mining

“…While these methods have proven useful, it’s also possible that many BGCs are active yet their small molecule products are simply missed due to the extraction methods or analytical techniques employed (Figure 1). For example, recent metabolomics analyses have shown that extraction solvents had a major impact on the metabolites detected[59,60]. As we learn more about the relationships between orphan BGCs and their products, it is likely that much of the apparent biosynthetic potential observed in microbial genomes will ultimately be linked to compounds that are in fact produced but simply missed or ignored because they do not possess the properties that allow them to be detected or make them attractive targets for discovery.…”