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Multiscale analyses of cellular signaling and regulation in response to multiple stress conditions
McElfresh, GW
McElfresh, GW
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Abstract
Understanding the relationship between signaling and its corresponding cellular response is critical to combating stress responses, especially responses related antibiotic resistance and non-genetic phenotypic transitions to antibiotic tolerance. However, bacterial signal responses are notoriously noisy and difficult to predict. This work first develops a multiscale cell cycle-aware signal modeling framework to explore the energetics and dynamics of the phosphate starvation stress response two-component system, PhoBR, to better understand the relationship between stress response proteins and the bounds of cellular memory in stress response. I found that the transcription factor responsible for stress response remains nominally “active” for 2-4 generations after the stress response is relieved due to sequestration effects, with differential memory in offspring cells dictated by stochastic protein inheritance. Next, I studied a novel antibiotic persister phenotype that arises in non-canonical conditions. This phenotype exhibited a previously unknown stress response that resulted in growth arrest, granting it antibiotic tolerance. The tolerance seems to be imparted by a global stress response arising from toxic excessive lactose import, seemingly opposite of the starvation response that induces canonical persister cell formation. Finally, I improved the PhoBR stress response model to measure stochastic fluctuations of proteins within the two-component system to identify the principles of signal fluctuations and how they drive variability in the bacterial cell cycle (i.e., growth rate). The downstream regulon of the PhoB response regulator is the main driver of the growth rate, but the transcriptionally active dimerized PhoB acts as the link between fast molecular fluctuations and slower gene expression fluctuations within the system. Finally, I present a vision for future developments of this style of modeling to include spatial information.
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Date
2020-08-31
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University of Kansas
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Keywords
Bioinformatics, Microbiology, cell modeling, e coli, multiscale, persistence, stress response, Two-component systems