Context-specific cellular networks in bacterial pathogens

About This Grant

Project Summary/Abstract The microbiome revolution created a combinatorial explosion in microbiology. There are thousands of species of bacteria that impact our health, and each microbe responds differently to an exponential number of environ- ments and stressors. Human scientists alone cannot study the response of every bacterium in all possible environments. Instead, the Jensen Lab develops AI-driven robotic scientists that plan, execute, and interpret thousands of scientific experiments each day. Our robot scientists have performed over one million automated experiments to map the phenotypic landscape of human-associated bacteria and identify novel quorum sensing pathways that mediate intercellular communication. Our lab’s previous research made two key discoveries about the streptococci, a genus replete with species that impact human health. First, we learned that each species has a unique and complex pattern of auxotrophies. These different nutrient preferences are surprising since many streptococci live exclusively in the same niche and share an evolutionary history of co-adaptation. Our second discovery is that streptococci possess multiple quorum sensing pathways that interact within and across species. Our genome mining has uncovered several novel classes of quorum sensing systems that streptococci use to communicate, interact, and colonize humans. Our future research will integrate metabolism and quorum sensing into a comprehensive, quantitative view of a pathogen’s global context. We will transition from studying metabolism and quorum sensing in isolation to a systems-level view that explains how environmental and genetic factors rewire individual pathways. This ambi- tious goal will require us to train our robot scientist to perform genetic perturbations on demand and incorporate our newly developed expertise in automated fluorescence microscopy. Bringing together imaging, combinatorial phenotyping, and high-throughput genetics will automate the dis- covery of interactions between genes, cells, and environmental stressors. Our approach to automated science can be transferred to other cellular networks and organisms, accelerating scientific discovery and advancing our understanding of the complex bacteria that shape our health.