© 2014 Medema et al. Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties a

A Systematic Computational Analysis of Biosynthetic Gene Cluster Evolution: Lessons for Engineering Biosynthesis - eScholarship

Bacterial specialized metabolites are a proven source of antibiotics and cancer therapies, but whether we have sampled all the secondary metabolite chemical diversity of cultivated bacteria is not known. We analysed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes (MAGs) using a modified BiG-SLiCE and the new clust-o-matic algorithm. We estimate that only 3% of the natural products potentially encoded in bacterial genomes have been experimentally characterized. We show that the variation in secondary metabolite biosynthetic diversity drops significantly at the genus level, identifying it as an appropriate taxonomic rank for comparison. Equal comparison of genera based on relative evolutionary distance revealed that Streptomyces bacteria encode the largest biosynthetic diversity by far, with Amycolatopsis, Kutzneria and Micromonospora also encoding substantial diversity. Finally, we find that several less-well-studied taxa, such as Weeksellaceae (Bacteroidota), Myxococcaceae (Myxococcota), Pleurocapsa and Nostocaceae (Cyanobacteria), have potential to produce highly diverse sets of secondary metabolites that warrant further investigation. 

Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes

Significance Microbial communities have been implicated in human and plant disease and are essential to global biogeochemical cycles. However, our ability to reliably alter these communities is limited by insufficient understanding of the networks that drive community processes. The goal of this study was to understand how community membership alters secondary metabolism in a model microbial community. We found that community species composition affects expression of biosynthetic genes and abundance of metabolites. Dramatic changes were observed when the biosynthetic gene cluster of one metabolite, koreenceine, was deleted, suggesting that interspecies interaction networks may be driven by secondary metabolites. This work offers an approach to dissecting the flow of information through communities, which could lead to strategies for manipulating community function.

Microbiome composition modulates secondary metabolism in a multispecies bacterial community

Large-scale genome-mining analyses have identified an enormous number of cryptic biosynthetic gene clusters (BGCs) as a great source of novel bioactive natural products. Given the sheer number of natural product (NP) candidates, effective strategies and computational methods are keys to choosing appropriate BGCs for further NP characterization and production. This review discusses genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities. We group these studies based on their BGC-prioritization logics: detecting presence of resistance genes, use of phylogenomics analysis as a guide, and targeting for specific chemical structures. We also briefly comment on the different bioinformatics tools used in the field and examine practical considerations when employing a large-scale genome mining study.

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Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.2c02245

Athina Gavrilidou

Papers

The confluence of big data and evolutionary genome mining for the discovery of natural products.