Bioinformatics-guided metabolomics is a powerful means for the discovery of novel natural products. However, the application of such metabologenomics approaches on microbial polyketides, a prominent class of natural products with diverse bioactivities, remains largely hindered due to our limited understanding on the mass spectrometry behaviors of these metabolites. Here, we present a metabologenomics approach for the targeted discovery of polyketides biosynthesized by modular type I polyketide synthases. We developed the NegMDF workflow, which uses mass defect filtering (MDF) supported by bioinformatic structural prediction, to connect the biosynthetic gene clusters to corresponding metabolite ions obtained under negative ionization mode. The efficiency of the NegMDF workflow is illustrated by rapid characterization of 22 polyketides synthesized by three gene clusters from a well-characterized strain Streptomyces cattleya NRRL 8057, including cattleyatetronates, new members of polyketides containing a rare tetronate moiety. Our results showcase the effectiveness of the MDF-based metabologenomics workflow for analyzing microbial natural products, and will accelerate the genome mining of microbial polyketides.

This article is Open Access

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