Microbial strain prioritization using metabolomics tools for the discovery of natural products

Abstract

Natural products profoundly impact many research areas, including medicine, organic chemistry, and cell biology. However, discovery of new natural products suffers from a lack of high throughput analytical techniques capable of identifying structural novelty in the face of a high degree of chemical redundancy. Methods to select bacterial strains for drug discovery have historically been based on phenotypic qualities or genetic differences and have not been based on laboratory production of secondary metabolites. Therefore, untargeted LC/MS-based secondary metabolomics was evaluated to rapidly and efficiently analyze marine-derived bacterial natural products using LC/MS-principal component analysis (PCA). A major goal of this work was to demonstrate that LC/MS-PCA was effective for strain prioritization in a drug discovery program. As proof of concept, we evaluated LC/MS-PCA for strain selection to support drug discovery, for the discovery of unique natural products, and for rapid assessment of regulation of natural product production.

Figure 1

Flowchart for UHPLC/HRMS-based secondary metabolomics.

Figure 2

Strain selection by secondary metabolomics. (a) PCA scores plot (PC1 vs PC2) of 47 strains (Table S2 in Supporting Information). Strains, designated by color and shape, were clustered on the basis of natural products detected by MS. G1–G7 represent group 1–group 7, respectively, and are displayed in colored circles. (b) A heat map to display distinct metabolic profiles among the seven groups. (c) Phylogenetic tree of the 47 strains. A comparison with the groups formed in the PCA scores plot is shown. Each group of strains was designated by a colored circle in the PCA scores plot; strains within each circle were matched to the same color in the phylogenetic tree. WMMB-328 is a Streptomyces sp. and was used as an outgroup.

Figure 3

Discovery of natural products unique to the group 7. (a) PCA scores plot (PC1 vs PC2) of the 47 stains (Table S1 in Supporting Information). (b) PCA loadings plot. The PCA loadings plot is geometrically related to the scores plot and can be used to identify the compounds responsible for group patterns in the PCA scores plot. Compound 1 (RT 5.5 min m/z 600.7804, [M + 2H]²⁺) is one of the major contributors to group 7. (c) The bucket statistic plot for 1 showing normalized intensities of 1 in the 47 strains. Compound 1 was only detected in group 7.

Figure 4

Discovery of natural products unique to strain WMMB-247. (a) PCA scores plot (PC1 vs PC4). The PC planes were adjusted to separate WMMB-247. (b) PCA loadings plot. The loadings plot shows compounds (2–3) that were unique to WMMB-247. (c) Structures of 2 and 3. (d) The bucket statistic plot of 2 (RT 7.4 min m/z 679.4026, [M + H]⁺). As shown, compound 2 was only detected in WMMB-247.

Figure 5

Discovery of polyenepyrones from a marine-derived Streptomyces sp. (WMMB-272). (a) PCA scores plot indicated separation of strain WMMB-272 from a collection of 53 marine-derived Streptomyces spp. (b) PCA loadings plot displayed compounds responsible for the separation. Compounds (4–6) were isolated after scale-up fermentation. (c) Structures of compounds (4–6) were determined by NMR and MS. Compounds 4–6 belong to a rare class of polyenepyrones.