Mining Cryptic Biosynthetic Gene Clusters for Novel Bioactive Compounds

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PROJECT SUMMARY/ABSTRACT Natural products from the soil-dwelling bacteria Streptomyces have been a rich source of medicines, including antimicrobials and anticancer agents. Unfortunately, discovery of novel bioactive natural products from Streptomyces using traditional techniques is often unsuccessful due to the re-discovery of known molecules. Genome sequencing suggests that Streptomyces are capable of making many more (likely hundreds-of thousands more) molecules than those typically observed in the laboratory. However, the biosynthetic machinery responsible for producing these novel natural products is often cryptic (i.e. transcriptionally inactive). Co-culture of Streptomyces with other microorganisms induces production of natural products not observed in monocultures. However, the signals that control this induction are poorly understood. A significant gap remains in the strategies available to discover new bioactive natural products from cryptic biosynthetic gene clusters. Our long-term goal is to develop strategies to overcome this gap, thus maximizing the natural product potential from Streptomyces. Over the next five years, we aim to identify small molecules capable of inducing natural product production (i.e. chemical elicitors, Project 1) as well as utilizing state-of-the-art bioinformatics to predict and directly chemically synthesize natural products (Project 2). Low levels of certain antibiotics have been found to induce production of a few natural products. The generality of this effect remains unknown, as does the mechanism by which these compounds induce production of natural products. The objectives for the first project are to 1) Study the ability of mechanistically distinct antibiotics to act as chemical elicitors in a variety of distantly related Streptomyces strains and 2) Determine the mechanisms of the chemical elicitors. This work will provide a greater understanding of antibiotic regulation of natural product production, which will allow both our laboratory and others to activate production of natural products in a more targeted manner and ultimately increase the number of bioactive natural products that we as a community can discover. In the second project, we are directly chemically synthesizing cyclic peptide natural products that are bioinformatically predicted from non-ribosomal peptide synthetase biosynthetic gene clusters. Cyclic peptides are an important family of natural products, including many FDA-approved drugs. Their large size and rigidity allows them to target challenging-to-hit targets (e.g. protein-protein interactions). The objectives for the second project are to 1) Develop a bioinformatics method to identify cryptic non-ribosomal peptide synthetase genes that encode production of diverse cyclic peptides, 2) Chemically synthesize a library of several hundred of the predicted cyclic peptides, 3) Use the library to study the rules that regulate peptide cell-membrane permeability and 4) Screen the library for antibiotic and anticancer activity. This work will provide access to hundreds of previously inaccessible natural products that will be useful tools for the study of cyclic peptide membrane permeability and will likely have interesting bioactivities.