Integrated metabolomic, molecular networking, and genome mining analyses uncover novel angucyclines from Streptomyces sp. RO-S4 strain isolated from Bejaia Bay, Algeria

Abstract

AbstractMulti-omic approaches have recently made big strides towards the effective exploration of microorganisms and accelerating the discovery of new bioactive compounds. We combined metabolomic, molecular networking, and genomic-based approaches to investigate the metabolic potential of the Streptomyces sp. RO-S4 strain isolated from the polluted waters of Bejaia Bay in Algeria. Antagonistic assays against methicillin-resistant Staphylococcus aureus with RO-S4 organic extracts showed an inhibition zone of 20 mm by the agar diffusion method, and its minimum inhibitory concentration was 16 μg/mL. A molecular network was created using GNPS and annotated through the comparison of MS/MS spectra against several databases. The predominant compounds in the RO-S4 extract belonged to the angucyclines family. Three compounds were annotated as known metabolites, while all the others were putatively new to Science. Notably, all compounds had fridamycin-like aglycones, and several of them had a lactonized D ring analogous to that of urdamycin L. The whole genome of Streptomyces RO-S4 was sequenced to identify the biosynthetic gene cluster (BGC) encoding for these angucyclines, which yielded a draft genome of 7,497,846 bp with 72.4% G+C content. Subsequently, a genome mining analysis revealed 19 putative biosynthetic gene clusters, including a grincamycin-like BGC with a high similarity to that of Streptomyces sp. CZN-748 previously reported to also produce mostly open fridamycin-like aglycones. As the ring-opening process leading to these compounds is still not defined, we performed comparative analysis with other angucycline BGCs and advanced some hypotheses to explain the ring-opening and lactone formation, possibly linked to the uncoupling between the activity of GcnE and GcnM homologues in the RO-S4 strain. The combination of metabolomic and genomic approaches greatly improved the interpretation of the metabolic potential of the RO-S4 strain.