Metatranscriptomics-based metabolic modeling of patient-specific urinary microbiome during infection

Urinary tract infections (UTIs) are a major health concern which incurs significant socioeconomic costs in addition to substantial antibiotic prescriptions, thereby accelerating the emergence of antibiotic resistance. In addressing the challenge of antibiotic-resistant UTIs, our approach harnesses patient-specific metabolic insights to hypothesize treatment strategies. By leveraging the distinct metabolic traits of pathogens, we aim to identify metabolic dependencies of pathogens and pave the way for more targeted therapeutic interventions. Combining patient-specific metatranscriptomic data with genome-scale metabolic modeling, we explored the metabolic aspects of UTIs from a systems biology perspective. We created tailored microbial community models, each mirroring the metabolic profiles of individual UTI patients' urinary microbiomes. Delving into patient-specific bacterial gene expressions and microbial interactions, we identify metabolic signatures and propose mechanisms for UTI pathology. We identified several patient-specific metabolites linked to infection that could inform therapeutic approaches. Our research underscores the potential of integrating metatranscriptomic data using systems biological approaches, offering insights into disease metabolic mechanisms and potential phenotypic manifestations. This contribution introduces a new method that could guide treatment options for antibiotic-resistant UTIs, aiming to lessen antibiotic use by combining the pathogens' unique metabolic traits. ### Competing Interest Statement The authors have declared no competing interest.