PROJECT SUMMARY Urinary tract infections (UTIs) are common, drive extensive antibiotic use, and are becoming increasingly resistant to treatment. There is general agreement that urinary chemical composition influences UTI pathogenesis, but this has been difficult to translate to clinical practice. Clinical urinary pathogens possess numerous genetic adaptations to the urinary environment, suggesting multiple selective pressures related to urinary composition. Recently, we identified individual differences in urine's ability to support antibacterial iron chelation by the innate immune protein siderocalin (SCN; also known as Lipocalin-2 or NGAL). Using mass spectrometry-based metabolomics and biochemical hypothesis-testing, we have mechanistically linked these differences to a specific chemical class of human urinary metabolites. Here we will identify human urinary metabolomic influences on SCN activity and other aspects of UTI pathogenesis. Because human urine is chemically complex, we will combine recent bioanalytical advances with contemporary data science approaches to identify metabolomic networks that influence bacterial growth and behavior. Our objective is to identify networks associated with SCN activity and bacterial growth, explore their mechanism(s) of action, and learn their physiologic origins as a basis for new prophylactic and therapeutic strategies. The proposed analyses and experiments are a rigorous initial evaluation of the hypothesis that urinary composition influences infection susceptibility and can be modified as a non-antibiotic therapy.