Antimicrobial drug resistance is a major clinical problem, with resistant strains of bacteria emerging at a rate that dramatically outpaces development of new drugs. Traditionally, studies on antibiotic resistance have focused on genetic changes that confer resistance, such as those encoding mechanisms that block the drug target or modify the drug itself. However, bacteria can also evade antibiotics through expression of transient resistance mechanisms, such as multi-drug efflux pumps that turn on either stochastically or in response to antibiotic stress. Studies have implicated these transient resistance mechanisms in chronic, recalcitrant infections, however, recent research has revealed examples where they also play a critical role in increasing mutation propensity. It is unclear how heterogeneity and temporal variability in expression of resistance genes leads to mutations and what the ultimate implications are for the evolution of drug resistance at the population- level. This proposal addresses this gap directly by measuring expression of resistance genes over time alongside reporters for mutation. These single-cell level studies are joined by population-level experiments that modulate expression of the transient resistance genes while measuring growth under antibiotic stress. A complementary modeling approach uses stochastic models to describe heterogeneity in gene expression, mutation rate, and growth. Our central hypothesis is that heterogeneity in expression of transient resistance genes can lead to single-cell-level differences in mutation rate, both via inducing spontaneous mutations due to elevated endogenous stress in the absence of antibiotics and by extending survival times in the presence of antibiotics. We will test this hypothesis using a quantitative approach that integrates single-cell time-lapse microscopy, stochastic modeling, whole genome sequencing, parallelized continuous culture methods, and optogenetic control. The project is organized around three Aims: (1) Measure expression history of transient resistance genes in cells prior to spontaneous mutation. (2) Quantify time to death of single cells and the evolution of resistance under antibiotic treatment. (3) Control temporal variation of AcrAB efflux pump expression to determine frequency-dependent resistance levels and mutation rate. This research is significant because it links dynamic, single-cell-level effects due to heterogeneity in expression of transient resistance genes to the emergence of population-level increases in resistance. Identifying and eliminating nucleation points for the emergence of drug resistance can inform assessment and treatment approaches.

Public Health Relevance

This proposal focuses on the initial steps in the emergence of antibiotic resistance, looking at single-cell-level mechanisms that predispose bacteria to mutation. It will provide quantitative data on how single-cell differences in expression of genes involved in transient resistance and stress response can lead to differences in survival under antibiotic treatment. The resulting data will be relevant to understanding pathways towards drug resistance and has the potential to inform novel treatment approaches.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Ernst, Nancy L
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Boston University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Langevin, Ariel M; Dunlop, Mary J (2018) Stress Introduction Rate Alters the Benefit of AcrAB-TolC Efflux Pumps. J Bacteriol 200:
Wen, Xi; Langevin, Ariel M; Dunlop, Mary J (2018) Antibiotic export by efflux pumps affects growth of neighboring bacteria. Sci Rep 8:15120
El Meouche, Imane; Dunlop, Mary J (2018) Heterogeneity in efflux pump expression predisposes antibiotic-resistant cells to mutation. Science 362:686-690
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
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 (2016) Phenotypic Diversity Using Bimodal and Unimodal Expression of Stress Response Proteins. Biophys J 111:675
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 (2015) Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms. Biophys J 108:184-93