Gene Regulation and Memory in Bacterial Metabolism and Antibiotic Resistance

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Project Summary: Bacterial cells have a repertoire of responses that can be used to survive under different types of environmental stress. Changes in carbon sources cause cells to turn on specific metabolic genes, which are later repressed when those sources are depleted. Antibiotic exposure can trigger the expression of molecular pumps that remove the antibiotic from the cell, or the production of enzymes that specifically inactivate or degrade it. In a continually fluctuating environment, the process of turning genes on and off can be inefficient and cause growth lags. Our work shows that bacteria combine their responses with phenotypic memory – the passage of stable proteins from mother to daughter cells – which allows cells to avoid growth lags in fluctuating environments. Using a combination of quantitative microbiology, microfluidics, microscopy, sequencing, and modeling, we will study the costs and benefits of gene regulation in fluctuating environments. We will measure and model the fitness landscape of phenotypic memory using a library of strains with perturbed memory levels. Competition experiments in fluctuating environments will be used to test biophysical and population dynamics models. We present an innovative modular system that enables direct comparison of different gene regulatory strategies – including responsive, bistable, and constitutive regulation – for any gene of interest. We apply the system to study different classes of antibiotic resistance mechanisms. The proposed experiments make use of a custom-built microfluidic ‘chemoflux’ system that we developed, in which bacterial populations grow in monolayers, tracked at single cell resolution under the microscope, while the growth media can be arbitrarily fluctuated in time. Using the chemoflux and our image analysis algorithms, we are able to simultaneously track hundreds of independent bacterial populations, and thereby measure population dynamics in fluctuating environments. We combine experiments with biophysical modeling to gain insights into the costs and benefits of gene regulation and memory. Models are parameterized using experimental data in a wide range of conditions, and rigorously tested by their predictions on competition experiments in fluctuating environments. The range of experiments and modeling employed address different aspects of gene regulation and memory, and allows us to bridge from detailed laboratory measurements to the general biological principles that underlie bacterial survival.