Draft Genome Sequences of Two Polycyclic Tetramate Macrolactam Producers, Streptomyces sp. Strains JV180 and SP18CM02

Critical analysis of polycyclic tetramate macrolactam biosynthetic gene cluster phylogeny and functional diversity

Polycyclic tetramate macrolactams (PTMs) are bioactive natural products commonly associated with certain actinobacterial and proteobacterial lineages. These molecules have been the subject of numerous structure-activity investigations since the 1970s. New members continue to be pursued in wild and engineered bacterial strains, and advances in PTM biosynthesis suggest their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. To address the origins of this complexity and understand its influence on PTM discovery, we engaged in a combination of bioinformatics to systematically classify PTM BGCs and PTM-targeted metabolomics to compare the products of select BGC types. By comparing groups of producers and BGC mutants, we exposed knowledge gaps that complicate bioinformatics-driven product predictions. In sum, we provide new insights into the evolution of PTM BGCs while systematically accounting for the PTMs discovered thus far. The combined computational and metabologenomic findings presented here should prove useful for guiding future discovery.IMPORTANCEPolycyclic tetramate macrolactam (PTM) pathways are frequently found within the genomes of biotechnologically important bacteria, including Streptomyces and Lysobacter spp. Their molecular products are typically bioactive, having substantial agricultural and therapeutic interest. Leveraging bacterial genomics for the discovery of new related molecules is thus desirable, but drawing accurate structural predictions from bioinformatics alone remains challenging. This difficulty stems from a combination of previously underappreciated biosynthetic complexity and remaining knowledge gaps, compounded by a stream of yet-uncharacterized PTM biosynthetic loci gleaned from recently sequenced bacterial genomes. We engaged in the following study to create a useful framework for cataloging historic PTM clusters, identifying new cluster variations, and tracing evolutionary paths for these molecules. Our data suggest new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize the practical limitations of genomics-based discovery by exposing hidden complexity.

A comparative metabologenomic approach reveals mechanistic insights into Streptomyces antibiotic crypticity

Streptomyces genomes harbor numerous, biosynthetic gene clusters (BGCs) encoding for drug-like compounds. While some of these BGCs readily yield expected products, many do not. Biosynthetic crypticity represents a significant hurdle to drug discovery, and the biological mechanisms that underpin it remain poorly understood. Polycyclic tetramate macrolactam (PTM) antibiotic production is widespread within the Streptomyces genus, and examples of active and cryptic PTM BGCs are known. To reveal further insights into the causes of biosynthetic crypticity, we employed a PTM-targeted comparative metabologenomics approach to analyze a panel of S. griseus clade strains that included both poor and robust PTM producers. By comparing the genomes and PTM production profiles of these strains, we systematically mapped the PTM promoter architecture within the group, revealed that these promoters are directly activated via the global regulator AdpA, and discovered that small promoter insertion-deletion lesions (indels) differentiate weaker PTM producers from stronger ones. We also revealed an unexpected link between robust PTM expression and griseorhodin pigment coproduction, with weaker S. griseus-clade PTM producers being unable to produce the latter compound. This study highlights promoter indels and biosynthetic interactions as important, genetically encoded factors that impact BGC outputs, providing mechanistic insights that will undoubtedly extend to other Streptomyces BGCs. We highlight comparative metabologenomics as a powerful approach to expose genomic features that differentiate strong, antibiotic producers from weaker ones. This should prove useful for rational discovery efforts and is orthogonal to current engineering and molecular signaling approaches now standard in the field.