Author ORCID Identifier

https://orcid.org/0000-0003-4803-5424

Date of Award

Spring 2026

Document Type

Thesis (Ph.D.)

Department or Program

Microbiology and Immunology

First Advisor

Dr. Daniel Schultz

Abstract

Typical antibiotic susceptibility testing (AST) of microbial samples is performed in homogeneous cultures in batch environments, which does not account for the highly heterogeneous and dynamic nature of antibiotic responses. The most common mutation found in P. aeruginosa lineages evolved during chronic cystic fibrosis infections in the human lung, a loss of function of repressor MexZ, increases basal levels of multidrug efflux MexXY, but does not increase resistance by traditional minimal inhibitory concentration (MIC) assays. Here, we use single cell microfluidics to show that P. aeruginosa’s response to aminoglycosides is highly heterogeneous, with only a subpopulation of cells surviving exposure. In contrast, strains carrying mexZ mutations bypass the lengthy process of MexXY activation, increasing survival to sudden drug exposures and conferring a fitness advantage in fluctuating environments. Building on the data we present here, we propose a simple “Response Dynamics” assay to quantify the rate of population-level recovery to drug exposures across strains. We used this assay to profile a representative panel of 49 P. aeruginosa strains from diverse environments, showing that the presence of mexZ mutations correlates with faster population recovery from exposures to aminoglycosides, and thus confers an advantage to cells exposed to a sudden, large dose of antibiotic. Apart from mexXYZ, several other efflux pumps are also targeted by evolution in lung infections. We analyzed 450 isolates from a single human lung, finding strong genetic diversity and evidence of frequent migration between lobes. We also found many lineages with several mutations in efflux pump mechanisms, resulting in diverse drug resistance profiles. These results provide an example of within-host evolution leading to heterogeneity that will inform future studies of infection population dynamics over the course of chronic infections.

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