Antibiotic resistance is an increasing problem in clinical settings and strains of bacteria that are resistant to multiple drugs are appearing with alarming frequency. While genetic changes have traditionally been studied as the source of drug resistance, bacteria can also evade antibiotics through transient, noisy expression of resistance mechanisms. Studies on transient resistance to date have focused on all-or-none tolerance mechanisms such as bacterial persistence where cells switch between a drug-tolerant and a drug-sensitive state. There is a fundamental gap in understanding of mechanisms that can generate a continuum of resistance levels within a population. This is a significant problem because bacteria can use such a strategy as a stepping stone to achieve higher, permanent levels of drug resistance. To address this, we will study an important regulatory protein, the multiple antibiotic resistance activator (MarA), which controls expression of many antibiotic resistance genes in clinically relevant pathogens. Our preliminary data show that expression of MarA is noisy in single cells, generating a continuum of expression levels within a population. These findings provoke the question of whether this noise leads to diversity in drug resistance, allowing populations of bacteria to hedge against the sudden appearance of an antibiotic. Our central hypothesis is that the regulatory circuit architecture controlling MarA amplifies noise, leading to variability in expression of resistance genes, and allowing a subset of cells to survive antibiotic treatment. We will test this hypothesis using an approach that integrates quantitative time-lapse microscopy and stochastic mathematical modeling to determine the mechanism and function of noise in MarA. The project is focused around three Aims: (1) Identify the genetic basis for phenotypic variability in MarA by comparing the regulatory network to alternative engineered networks. (2) Quantify how noise in MarA propagates to the diverse downstream antibiotic resistance genes it regulates. (3) Determine how variability in MarA impacts survival under time-varying antibiotic treatment. This integrative research is significant because it is expected to suggest treatment strategies for combating transient antibiotic resistance and will reveal important dynamic information about the period over which transient resistance develops and persists. Furthermore, it examines a novel mechanism for introducing diversity in antibiotic resistance gene expression, which is likely to be generally relevant to other mechanisms that generate transient antibiotic resistance.
This proposal will establish a comprehensive description of how variability in expression of a multiple antibiotic resistance activator allows bacteria to evade antibiotic treatment. The resulting data will be relevant to the development of novel treatment strategies, suggesting drug targets that reduce population diversity and treatment schedules that account for the time scales over which bacteria transiently express resistance mechanisms.
|Wang, Tiebin; El Meouche, Imane; Dunlop, Mary J (2017) Bacterial persistence induced by salicylate via reactive oxygen species. Sci Rep 7:43839|
|Rossi, Nicholas A; Dunlop, Mary J (2017) Customized Regulation of Diverse Stress Response Genes by the Multiple Antibiotic Resistance Activator MarA. PLoS Comput Biol 13:e1005310|
|El Meouche, Imane; Siu, Yik; Dunlop, Mary J (2016) Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Sci Rep 6:19538|
|Garcia-Bernardo, Javier; Dunlop, Mary J (2016) Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins. Biophys J 111:675|
|Lindle, Jessica M; Dunlop, Mary J (2016) Performing selections under dynamic conditions for synthetic biology applications. Integr Biol (Camb) 8:556-63|
|Garcia-Bernardo, Javier; Dunlop, Mary J (2015) Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms. Biophys J 108:184-93|