Revealing Stochastic Switches in Bacteria

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PROJECT SUMMARY Stochastic switches are a broad class of genetic mechanisms that enable single cells to switch certain genes on and off randomly, without responding to their environment. Such switches are prevalent in pathogenic bacteria, where they are often involved in generating diverse surface protein repertoires across the bacterial population, which enables a subset of cells to avoid detection by the immune system. In general, stochastic switches provide a strategy for survival in fluctuating environments, by maintaining subpopulations of cells in pre-adapted states that are prepared for future, possibly unpredictable, environmental stresses. In particular, these strategies are known to be important in antibiotic persistence, a bacterial phenotypic state consisting of slow growth and enhanced tolerance for antibiotics. This grant applies highly sensitive single-cell measurements combined with mathematical models to study three major facets of stochastic switching. We use synthetic stochastic switches to drive antibiotic resistance genes, and by measuring the population dynamics under antibiotic pulses over multi-day experiments, we quantify and model the emergence of resistance, a process of major clinical importance. We use stochastic switches as a model to study the evolutionary pressures that populations experience when transferred from one environment to another through population bottlenecks, a key component of disease transmission. And, we study antibiotic persistence in Escherichia coli, where a continuum of growth states across a bacterial population can confer varying degrees of antibiotic tolerance. By using novel methods for analysis of single cell population data mapped with phenotypic information, we investigate the genetic network that underlies bacterial persistence. The proposed research will substantially advance understanding of the role of stochasticity in bacterial adaptation. Through its emphasis on predictive mathematical modeling, the research will provide the ability to predict the impact of treatment protocols on the emergence of antibiotic resistance and on levels of persistence, and to identify new ways of slowing down or reversing these complex, biomedically relevant processes.