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

• Published in: Applied and environmental microbiology Vol. 90; no. 6; p. e0060024
• Main Authors: Harper, Christopher P., Day, Anna, Tsingos, Maya, Ding, Edward, Zeng, Elizabeth, Stumpf, Spencer D., Qi, Yunci, Robinson, Adam, Greif, Jennifer, Blodgett, Joshua A. V.
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 18.06.2024
• Subjects: Bioinformatics; Biosynthesis; Biosynthetic Pathways - genetics; Biotechnology; Complexity; Computational Biology; Gene clusters; Genetics and Molecular Biology; Lactams - metabolism; Lysobacter - classification; Lysobacter - genetics; Lysobacter - metabolism; Metabolomics; Multigene Family; Natural products; Phylogeny; Streptomyces - classification; Streptomyces - genetics; Streptomyces - metabolism; biosynthetic gene clusters; phylogeny; comparative metabologenomics; polycyclic tetramate macrolactams
• ISSN: 0099-2240; 1098-5336; 1098-5336
• DOI: 10.1128/aem.00600-24

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 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.