Heterogeneity during induction of resistance delays Pseudomonas aeruginosa recovery from antibiotic exposures.

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 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 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. We propose that the Response Dynamics assay can be used alongside MIC assays for profiling of antibiotic sensitivity to better predict clinical outcomes from in vitro sensitivity/resistance profiles. SignificanceCommon mutations affecting the regulation of antibiotic resistance in bacterial pathogens often do not increase resistance by traditional measures. However, antibiotic resistance is typically measured in stable cultures, without accounting for fluctuations in drug concentration. Here, we show that P. aeruginosa response to aminoglycosides is highly heterogeneous, and that the most common mutation found in clinical isolates improves resistance by increasing single-cell survival to drug exposures. Therefore, the success of antibiotic treatments depends on the dynamics of drug delivery and the heterogeneous activation of microbial responses. We then develop an assay to measure resistance in a dynamic context, which captures this overlooked aspect of antibiotic resistance and can be used alongside traditional measures in the profiling of clinical isolates.