Sequence-Based Analysis of Secondary-Metabolite Biosynthesis in Marine Actinobacteria

A diverse collection of 60 marine-sediment-derived Actinobacteria representing 52 operational taxonomic units was screened by PCR for genes associated with secondary-metabolite biosynthesis. Three primer sets were employed to specifically target adenylation domains associated with nonribosomal peptide synthetases (NRPSs) and ketosynthase (KS) domains associated with type I modular, iterative, hybrid, and enediyne polyketide synthases (PKSs). In total, two-thirds of the strains yielded a sequence-verified PCR product for at least one of these biosynthetic types. Genes associated with enediyne biosynthesis were detected in only two genera, while 88% of the ketosynthase sequences shared greatest homology with modular PKSs. Positive strains included representatives of families not traditionally associated with secondary-metabolite production, including the Corynebacteriaceae, Gordoniaceae, Intrasporangiaceae, and Micrococcaceae. In four of five cases where phylogenetic analyses of KS sequences revealed close evolutionary relationships to genes associated with experimentally characterized biosynthetic pathways, secondary-metabolite production was accurately predicted. Sequence clustering patterns were used to provide an estimate of PKS pathway diversity and to assess the biosynthetic richness of individual strains. The detection of highly similar KS sequences in distantly related strains provided evidence of horizontal gene transfer, while control experiments designed to amplify KS sequences from Salinispora arenicola strain CNS-205, for which a genome sequence is available, led to the detection of 70% of the targeted PKS pathways. The results provide a bioinformatic assessment of secondary-metabolite biosynthetic potential that can be applied in the absence of fully assembled pathways or genome sequences. The rapid identification of strains that possess the greatest potential to produce new secondary metabolites along with those that produce known compounds can be used to improve the process of natural-product discovery by providing a method to prioritize strains for fermentation studies and chemical analysis.