(374b) Understanding the Connection between Amino Acid Utilization and Antibiotic Sensitivity in Uropathogenic E. coli strains

Recurrent urinary tract infections (UTIs) are high in frequency, particularly among women, with ~25% of individuals experiencing multiple UTIs within six months. A number of factors contribute to UTI recurrence, but bacterial survival due to antibiotic treatment failure continues to be a major concern for both research and medical communities. Treatment failure is often driven by antibiotic resistance, with more than 90% of UTIs being resistant to at least one antibiotic. Beyond this, treatment is further complicated by antibiotic tolerance, a phenomenon that allows bacteria to survive antibiotic treatment via reversible phenotypic changes without acquiring resistance-conferring mutations. These tolerant, surviving cells have also been shown to contribute to and accelerate resistance development following the cessation of antibiotic treatment. Thus, understanding bacterial mechanisms of tolerance is crucial, especially under physiologically-relevant conditions.

In our study, we challenged non-resistant uropathogenic E. coli (UPEC) strains with the clinical antibiotic, levofloxacin. We observed tolerance towards levofloxacin in a strain-dependent manner. Importantly, we found considerable variability in bacterial survival among our strains depending on the culture media used (pooled urine, artificially simulated urine, and chemically-defined media). Using nuclear magnetic resonance (NMR) spectroscopy of spent media samples, we demonstrate that there is a correlation between the utilization of select amino acids in each strain and the efficacy of levofloxacin in killing the population. We are currently utilizing other approaches, including transcriptomics, proteomics (using liquid chromatography – tandem mass spectrometry [LC-MS/MS]), genetics, and time-lapse microscopy to gain mechanistic insight on how metabolic reprogramming impacts levofloxacin tolerance among UPEC strains. Insights through this work have the potential to identify important metabolic targets that drive antibiotic sensitivity in uropathogens.