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

Abstract

AbstractUrinary 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. To address 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 to provide suggestions for targeted 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 to mirror 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. 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.Graphical AbstractThis study investigates the functional uromicrobiome across a female cohort. Initially, total RNA was extracted from patients’ urine and sequenced to assess the metatranscriptome, providing insights into the structure and function of the uromicrobiome. Metatranscriptomic data was further utilized to construct context-specific uromicrobiome models, enabling an understanding of each patient’s unique microbiome. Using metatranscriptomics and systems biology, we aimed to identify patient-specific dynamics and suggest various metabolic features that can be utilized in future studies for individualized intervention strategies.