Cell-to-cell heterogeneity and the emergence of antibiotic resistance

Project Summary / Abstract Antibiotic resistance is a major clinical challenge, with the propagation of drug resistant strains of bacteria dramatically outpacing new drugs emerging from the development pipeline. Resistance typically arises when bacteria undergo genetic modification, either due to acquisition of resistance genes via horizontal gene transfer or chromosomal mutations. These genetic changes can occur in the presence of antibiotics, which raises the question of how bacteria can survive antibiotic exposure and go on to express the resistance genes. Recently, studies on antibiotic tolerance have begun to reveal examples where intrinsic mechanisms can provide low or temporary protection that allows some cells to survive periods of drug treatment. Expression of tolerance mechanisms can be stochastic and transient suggesting that this may occur only in a subset of cells and under specific dynamic gene expression conditions. This project seeks to address key questions related to the initial steps in the emergence of drug resistance, quantifying how long individual cells remain tolerant, how the transient dynamics of these tolerance mechanisms impacts antibiotic evasion, and how single-cell level events result in the spread of antibiotic resistance across populations of cells. Our central hypothesis is that expression of intrinsic tolerance genes can provide periods of protection, allowing for the acquisition and expression of drug resistance genes, but that this protection is transient and variable across cells. To test this, our approach is focused around three complementary Aims: (1) Quantify antibiotic tolerance in interval between acquisition and expression of resistance genes, (2) Characterize how dynamic expression of genes involved in antibiotic tolerance impacts survival, and (3) Measure transition to resistance enabled by tolerance in clinically relevant bacterial populations. This research is significant because precise understanding of the timescales of tolerance and the mechanisms involved could offer key insights to guide antibiotic dosing regimens or suggest new drug targets or adjuvant therapy approaches. Innovative elements of the research include the use of optogenetic control to precisely regulate gene expression in single cells; the integration of deep learning-based feedback via model predictive control to dramatically increase throughput and precision when controlling gene expression; and the ability to independently and precisely control resistance gene acquisition, tolerance gene dynamics, and antibiotic exposure. In addition, the project includes detailed characterization and comparisons between Escherichia coli K-12 lab strains and clinically relevant bacteria including Salmonella enterica serovar Typhimurium and E. coli isolates derived from blood and urine infections.