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

ABSTRACT 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 suggests their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. Towards addressing the origins of this complexity and understanding 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. IMPORTANCE Polycyclic 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 suggests new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize practical limitations to genomics-based discovery by exposing hidden complexity.