Our conventional understanding of antibiotic resistance is based almost entirely on the notion of a bacterial population?s ability to maintain growth under steady-state drug conditions. Yet, it is becoming increasingly apparent that the outcome of drug treatment depends on highly-dynamic responses that require complex regulation. Despite a growing body of knowledge on the regulatory circuits governing the behavior of different classes of antibiotic-resistance mechanisms, a quantitative understanding of how these architectures evolved and diversified to optimize expression in different environments is still lacking. A comprehensive understanding of the design principles of gene regulation is essential to explain how control mechanisms can mitigate the costs of antibiotic resistance and allow fixation throughout bacterial populations. Recent findings from this research group show that the tetracycline resistance, tet, operon in E. coli, when suddenly exposed to tetracycline, optimizes gene expression by rapidly expressing the repressor (TetR) of the efflux pump (TetA). Moreover, variations in the dynamics of gene expression reveal a diversity of cell fates at the single-cell level. Recognizing that the time-dependent component of cell responses makes an important contribution to the fitness of an organism, the goal of this study is to investigate the process by which evolution optimizes antibiotic responses when addressing environmental pressures that require fast action (?dynamical efficacy?). Focusing on the tet operon, this project will test the concept that gene regulation of a resistance mechanism is optimized for the dynamics of gene expression. Through the following specific aims, this study will combine bioinformatics, mathematical modeling, and experimental approaches to determine what kinds of optimized regulatory architectures emerge in response to given environmental constrains, and to explain how gene regulation can be diversified in response to ecological challenges. The proposed aims are:
Aim 1. Explore the dynamics of antibiotic response in natural circuits: design, optimality, and variability.
This aim will analyze whole-genome databases to investigate the idea that natural variation will identify key regulatory strategies for effective resistance.
Aim 2. Develop synthetic circuits optimized for specific dynamical regimes. Work in this aim will develop quantitative models of antibiotic resistance to design and implement optimal regulatory architectures and investigate the hypothesis that gene regulation found in nature is optimized to specific environments.
Aim 3. Perform experimental evolution of resistance mechanisms in different drug regimes.
This aim will experimentally evolve a resistance mechanism under different dynamical settings to explore how gene regulation changes in response to new environmental challenges. The understanding of how changes in gene regulation define the dynamics of cellular processes will directly inform the development of new antimicrobial therapies and explain how misregulation may be the cause of human disease, such as cancer. !