Grant: Project Grant

We are surrounded by bacteria that could potentially cause harmful infections. However, we have been able to keep them at bay because of antibiotics. Indeed, advances in medicine, such as chemotherapy, surgery, and treatment of chronic diseases, crucially depend on our ability to control infections with antibiotics. But this has been compromised by the evolution of bacterial strains with mutations that make them resistant to antibiotics. The classic approach to antibiotic resistance is to discover new drugs. But this may not be enough. We also need to conserve the efficacy and maximize the duration of the usefulness of both current and future antibiotics. Antibiotic resistance will always arise, but if we are able to predict how they evolve, we could potentially delay their emergence. To predict bacterial evolution, we need to know what types of mutations can arise in the bacteria and how much resistance level they confer. Moreover, since multiple co-existing mutations can compete amongst themselves for evolutionary dominance, we also need to track the history of the bacterial population. This project proposes an interdisciplinary approach that brings together concepts from biochemistry, microbiology, and evolutionary biology. We will use state-of-the-art experimental techniques in protein engineering to determine the fitness effects of several thousands of mutations in 3 proteins that are crucial antibiotic targets. Then, by performing lab evolution using robotics and microfluidics, we will determine the survival probability of these mutations. We also developed a novel genetic tool that uniquely barcodes each cell in an evolving bacterial community. This enables us to track at high resolution the lineage and history of the bacterial population as resistance emerges. Ultimately, uncovering how bacteria evolve resistance could lead to novel regimens for optimal drug use that may delay antibiotic resistance and thus help preserve our ability to fight infections.