Scalable discovery of saccharide natural products using high-throughput multi-omics

Project Summary. Antibiotic resistance is becoming a major public health problem worldwide, with antibiotic resistant pathogens infecting hundreds of millions and killing over a million patients worldwide each year. Infections such as pneumonia, tuberculosis, blood poisoning, gonorrhoea, and foodborne diseases are becoming harder or impossible to treat with the existing medicine. Majority of antibiotics currently in clinical use are natural products or their derivatives. However, recently discovery of natural products with novel mechanisms to kill pathogens have become more challenging due to the high rate of rediscovery of known molecules, as the traditional technologies only capture the highest abundant molecular products of microbial isolates. The introduction of modern sequencing technologies and genome mining in early 2000 has revolutionized the field of natural product discovery. While these technologies have revealed millions of biosynthetic gene clusters (BGCs, clusters of genes that encode for natural products) in microbial genomes, currently these methods cannot predict the precise action of enzymes in BGCs, and therefore fail to correctly predict the final molecular product of BGCs. Chemia Biosciences is developing technologies to predict the molecular product of these BGCs based on high- throughput mass spectrometry data collected on extracts of microbial cultures. In the past, the PI and co-PI have developed techniques for identifying known and discovering novel natural products by a computational analysis of mass spectrometry data. The main goal of this proposal is to develop methods for discovering novel antimicrobial polysaccharides and aminoglycosides, a biomedically important class of natural products that include antibiotics streptomycin, gentamicin, neomycin, kanamycin and tobramycin. The software developed in the course of this proposal will be available to partners as a cloud service.