Mangrove actinomycetia are considered one of the promising sources for discovering novel biologically active compounds. Traditional bioactivity- and/or taxonomy-based methods are inefficient and usually result in the re-discovery of known metabolites. Thus, improving selection efficiency among strain candidates is of interest especially in the early stage of the antibiotic discovery program. In this study, an integrated strategy of combining phylogenetic data and bioactivity tests with a metabolomics-based dereplication approach was applied to fast track the selection process. A total of 521 actinomycetial strains affiliated to 40 genera in 23 families were isolated from 13 different mangrove soil samples by the culture-dependent method. A total of 179 strains affiliated to 40 different genera with a unique colony morphology were selected to evaluate antibacterial activity against 12 indicator bacteria. Of the 179 tested isolates, 47 showed activities against at least one of the tested pathogens. Analysis of 23 out of 47 active isolates using UPLC-HRMS-PCA revealed six outliers. Further analysis using the OPLS-DA model identified five compounds from two outliers contributing to the bioactivity against drug-sensitive A. baumannii. Molecular networking was used to determine the relationship of significant metabolites in six outliers and to find their potentially new congeners. Finally, two Streptomyces strains (M22, H37) producing potentially new compounds were rapidly prioritized on the basis of their distinct chemistry profiles, dereplication results, and antibacterial activities, as well as taxonomical information. Two new trioxacarcins with keto-reduced trioxacarcinose B, gutingimycin B (16) and trioxacarcin G (20), together with known gutingimycin (12), were isolated from the scale-up fermentation broth of Streptomyces sp. M22. Our study demonstrated that metabolomics tools could greatly assist classic antibiotic discovery methods in strain prioritization to improve efficiency in discovering novel antibiotics from those highly productive and rich diversity ecosystems.

Glycosylation inactivation is one of the important macrolide resistance mechanisms. The accumulated evidences attributed glycosylation inactivation to a glucosylation modification at the inactivation sites of macrolides. Whether other glycosylation modifications lead to macrolides inactivation is unclear. Herein, we demonstrated that varied glycosylation modifications could cause inactivation of midecamycin, a 16-membered macrolide antibiotic used clinically and agriculturally. Specifically, an actinomycetic glycosyltransferase (GT) OleD was selected for its glycodiversification capacity towards midecamycin. OleD was demonstrated to recognize UDP-D-glucose, UDP-D-xylose, UDP-galactose, UDP-rhamnose and UDP-N-acetylglucosamine to yield corresponding midecamycin 2′-O-glycosides, most of which displayed low yields. Protein engineering of OleD was thus performed to improve its conversions towards sugar donors. Q327F was the most favorable variant with seven times the conversion enhancement towards UDP-N-acetylglucosamine. Likewise, Q327A exhibited 30% conversion enhancement towards UDP-D-xylose. Potent biocatalysts for midecamycin glycosylation were thus obtained through protein engineering. Wild OleD, Q327F and Q327A were used as biocatalysts for scale-up preparation of midecamycin 2′-O-glucopyranoside, midecamycin 2′-O-GlcNAc and midecamycin 2′-O-xylopyranoside. In contrast to midecamycin, these midecamycin 2′-O-glycosides displayed no antimicrobial activities. These evidences suggested that besides glucosylation, other glycosylation patterns also could inactivate midecamycin, providing a new inactivation mechanism for midecamycin resistance. Cumulatively, glycosylation inactivation of midecamycin was independent of the type of attached sugar moieties at its inactivation site.