Discovery of a lagriamide polyketide by integrated genome mining, isotopic labeling, and untargeted metabolomics.

Microorganisms from the order Burkholderiales have been the source of a number of important classes of natural products in recent years. For example, study of the beetle-associated symbiont Burkholderia gladioli led to the discovery of the antifungal polyketide lagriamide; an important molecule from the perspectives of both biotechnology and chemical ecology. As part of a wider project to sequence Burkholderiales genomes from our in-house Burkholderiales library we identified a strain containing a biosynthetic gene cluster (BGC) similar to the original lagriamide BGC. Structure prediction failed to identify any candidate masses for the products of this BGC from untargeted metabolomics mass spectrometry data. However, genome mining from publicly available databases identified fragments of this BGC from a culture collection strain of Paraburkholderia. Whole genome sequencing of this strain revealed the presence of a homologue of this BGC with very high sequence identity. Stable isotope feeding of the two strains in parallel using our newly developed IsoAnalyst platform identified the product of this lagriamide-like BGC directly from the crude fermentation extracts, affording a culturable supply of this interesting compound class. Using a combination of bioinformatic, computational and spectroscopic methods we defined the absolute configurations for all 11 chiral centers in this new metabolite, which we named lagriamide B. Biological testing of lagriamide B against a panel of 21 bacterial and fungal pathogens revealed antifungal activity against the opportunistic human pathogen Aspergillus niger, while image-based Cell Painting analysis indicated that lagriamide B also causes actin filament disruption in U2-OS osteosarcoma cells.

Identification of the lipodepsipeptide selethramide encoded in a giant nonribosomal peptide synthetase from a Burkholderia bacterium.

Bacterial natural products have found many important industrial applications. Yet traditional discovery pipelines often prioritize individual natural product families despite the presence of multiple natural product biosynthetic gene clusters in each bacterial genome. Systematic characterization of talented strains is a means to expand the known natural product space. Here, we report genomics, epigenomics, and metabolomics studies of Burkholderia sp. FERM BP-3421, a soil isolate and known producer of antitumor spliceostatins. Its genome is composed of two chromosomes and two plasmids encoding at least 29 natural product families. Metabolomics studies showed that FERM BP-3421 also produces antifungal aminopyrrolnitrin and approved anticancer romidepsin. From the orphan metabolome features, we connected a lipopeptide of 1,928 Da to an 18-module nonribosomal peptide synthetase encoded as a single gene in chromosome 1. Isolation and structure elucidation led to the identification of selethramide which contains a repeating pattern of serine and leucine and is cyclized at the side chain oxygen of the one threonine residue at position 13. A (R)-3-hydroxybutyric acid moiety decorates the N-terminal serine. Initial attempts to obtain deletion mutants to probe the role of selethramide failed. After acquiring epigenome (methylome) data for FERM BP-3421, we employed a mimicry by methylation strategy that improved DNA transfer efficiency. Mutants defective in selethramide biosynthesis showed reduced surfactant activity and impaired swarming motility that could be chemically complemented with selethramide. This work unveils a lipopeptide that promotes surface motility, establishes improved DNA transfer efficiency, and sets the stage for continued natural product identification from a prolific strain.

Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products.

The chemical space covered by natural products is immense and widely unrecognized. Therefore, convenient methodologies to perform wide-ranging evaluation of their functions in nature and potential human benefits (e.g., for drug discovery applications) are desired. This protocol describes the combination of genome mining (GM) and molecular networking (MN), two contemporary approaches that match gene cluster-encoded annotations in whole genome sequencing with chemical structure signatures from crude metabolic extracts. This is the first step towards the discovery of new natural entities. These concepts, when applied together, are defined here as MS-guided genome mining. In this method, the main components are previously designated (using MN), and structurally related new candidates are associated with genome sequence annotations (using GM). Combining GM and MN is a profitable strategy to target new molecule backbones or harvest metabolic profiles in order to identify analogues from already known compounds.