Geographic Distribution of Secondary Metabolite Genes in the Marine Actinomycete Salinispora arenicola

Abstract

The molecular fingerprinting technique terminal-restriction fragment length polymorphism (T-RFLP) was used in combination with sequence-based approaches to evaluate the geographic distribution of secondary metabolite biosynthetic genes in strains of the marine actinomycete Salinispora arenicola. This study targeted ketosynthase (KS) domains from type I polyketide synthase (PKS) genes and revealed four distinct clusters, the largest of which was comprised of strains from all six global locations sampled. The remaining strains fell into three smaller clusters comprised of strains derived entirely from the Red Sea, the Sea of Cortez, or around the Island of Guam. These results reveal variation in the secondary metabolite gene collectives maintained by strains that are largely clonal at the 16S rRNA level. The location specificities of the three smaller clusters provide evidence that collections of secondary metabolite genes in subpopulations of S. arenicola are endemic to these locations. Cloned KS sequences support the maintenance of distinct sets of biosynthetic genes in the strains associated with each cluster and include four that had not previously been detected in S. arenicola. Two of these new sequences were observed only in strains derived from Guam or the Sea of Cortez. Transcriptional analysis of one of the new KS sequences in conjunction with the production of the polyketide arenicolide A supports a link between this sequence and the associated biosynthetic pathway. From the perspective of natural product discovery, these results suggest that screening populations from distant locations can enhance the discovery of new natural products and provides further support for the use of molecular fingerprinting techniques, such as T-RFLP, to rapidly identify strains that possess distinct sets of biosynthetic genes.The genus Salinispora is an obligate marine actinomycete taxon comprised of the formally described species S. tropica and S. arenicola (17) and a third species for which the name "S. pacifica" has been proposed (12). The genus is broadly distributed in tropical and subtropical marine sediments, with S. arenicola displaying the widest geographical range (12). While the genus is clearly resolved based on 16S rRNA phylogeny, the three species share 99% 16S sequence identity and are better resolved using less-conserved phylogenetic markers (12). To date, no intraspecific diversity has been reported for S. tropica, and only a few nucleotide changes have been observed in S. arenicola, including two polymorphisms that are restricted to populations from the Sea of Cortez (12).Bacteria that share closely related or identical 16S rRNA gene sequences can vary greatly at the genomic level (27). These genomic differences often are concentrated in islands, regions of the chromosome that are known to house genes associated with ecological adaptation (3). A comparative analysis of the genome sequences of S. tropica (strain CNB-440) and S. arenicola (strain CNS-205) revealed 21 major genomic islands, within which secon...

Phylogenetic Approaches to Natural Product Structure Prediction

“…In general, KS or C domains derived from the same pathway often share ≥90% amino acid sequence identity. In cases where a query sequence shares this level of identity with a reference sequence, it can be predicted that the pathway from which the sequence was derived has a high probability of producing compounds in the same structural class, as has been demonstrated previously (Edlund et al, 2011; Gontang et al, 2010). For domains that share <90% identity to the top NaPDoS match, an NCBI BLAST search is highly recommended as the NaPDoS database is not comprehensive.…”

“…KS domains are the most conserved and form an essential part of each PKS gene cluster. These domains have been used to fingerprint PKS genes from individual strains (Edlund, Loesgen, Fenical, & Jensen, 2011) and environmental DNA (Wawrik et al, 2007). KS phylogeny has even been used to predict secondary metabolite diversity (Foerstner, Doerks, Creevey, Doerks, & Bork, 2008; Metsa-Ketela et al, 1999), structures (Freel, Nam, Fenical, & Jensen, 2011; Gontang, Gaudencio, Fenical, & Jensen, 2010), and the evolutionary processes that generate new structural diversity (Freel et al, 2011)…”

Marine Actinobacteria from the Gulf of California: diversity, abundance and secondary metabolite biosynthetic potential

“…Biogeographical studies have revealed that S. tropica is restricted to the Caribbean, “ S. pacifica ” occurs worldwide except for the Caribbean, and S. arenicola is broadly distributed and co-occurs with both species (Jensen and Mafnas 2006; Freel et al 2012). Previous studies have also revealed relationships between Salinispora taxonomy and secondary metabolite production (Jensen et al 2007) and correlations between where the strains were derived and the biosynthetic genes they maintain (Edlund et al 2011). These results suggest that culturing new Salinsipora species or known species from new locations is a potentially productive approach to natural product discovery.…”

Comparative Analyses of Gene Clusters and Ks-alpha Genes Involved in the Biosynthesis of Chromomycin A3 and Mithramycin

“…KSs are the most conserved domains and found as essential part of each PKS gene cluster as they have been used to identify PKS genes from individual bacterial strains [28] and environmental DNA [29] . Each catalytic site is encoded on a distinct protein by type II PKSs, which also called iterative type of PKS.…”