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

Urinary tract infections (UTIs) are among the most common bacterial infections and are increasingly complicated by multidrug resistance (MDR). While Escherichia coli is frequently implicated, the contribution of broader microbial communities remains less understood. Here, we integrate metatranscriptomic sequencing with genome-scale metabolic modeling to characterize active metabolic functions of patient-specific urinary microbiomes during acute UTI. We analyzed urine samples from 19 female patients with confirmed uropathogenic E. coli (UPEC) infections, reconstructing personalized community models constrained by gene expression and simulated in a virtual urine environment. This systems biology approach revealed marked inter-patient variability in microbial composition, transcriptional activity, and metabolic behavior. We identified distinct virulence strategies, metabolic cross-feeding, and a modulatory role for Lactobacillus species. Comparisons between transcript-constrained and unconstrained models showed that integrating gene expression narrows flux variability and enhances biological relevance. These findings highlight the metabolic heterogeneity of UTI-associated microbiota and point to microbiome-informed diagnostic and therapeutic strategies for managing MDR infections.