(710a) Metabolic Modeling of Susceptible and Resistant Escherichia coli under Antibiotic Stress

The surge in antimicrobial resistance requires urgent development of innovative approaches to address the numerous resistant bacterial pathogen threats outlined by the CDC and WHO. Notably, a growing body of evidence suggests that the presumed fitness disadvantages of resistant pathogens conferred by expression of resistance genes is not fully accurate in infection models. Numerous omics-driven studies focused on the adaptive evolution of resistance in bacterial pathogens have suggested that a simultaneous shift in metabolism occurs to accommodate the genetic burden of resistance. Compounding this issue, the metabolic responses of pathogens to antibiotic stress remain poorly understood despite our great appreciation of specific drug-target interactions. Arising from these data is the increasingly attractive hypothesis that context-specific modification of metabolism is a key component of antibiotic resistance. Further exploration of the relationship between metabolism, antibiotic stress, and resistance is clearly needed.

To address these gaps in our fundamental understanding, we have compared the metabolic behaviors of wild type and resistant strains of Escherichia coli through a combined transcriptomic and fluxomic analysis. Specifically, we compared wildtype E. coli to isogenic strains expressing integrated copies of tetRA and dhfr resistance genes, respectively under normal and antibiotic stress conditions. Differential expression analysis identified significant shifts in activity in a diverse range of pathways when comparing the WT and resistant strains, as well as the resistant strains with and without antibiotic challenge. Furthermore, the resistant strains produced significantly more CO₂ than the wildtype strain in both the presence and absence of antibiotic challenge. Our preliminary findings suggest that the expression of resistance genes may drive resistant strains to reductively constrain their metabolism upon genomic and/or antibacterial stress. To elucidate the specific metabolic alterations underlying our observed phenotypes, we are generating comprehensive, genome-scale flux predictions through the integration of transcriptomics data with metabolic flux analysis simulations. In our presentation, we will discuss the integrated flux predictions for each condition and explore the metabolic shifts that correspond to resistance and antibiotic stress. This study represents the first application of quantitative flux analysis to study the metabolism of resistant bacteria and should provide significant insight into the role of metabolic adaptation in antibiotic resistance.