AI-guided discovery of antimicrobial peptides for urinary tract infections leveraging a new catalogue of the human urinary microbiome

Abstract

Urinary tract infections (UTIs) are common infections that pose a critical burden on healthcare and society. Despite growing recognition that the human urinary tract harbors its own microbiome, its composition, functional potential, and alterations in UTI remain limited. Here, we leveraged the publicly available whole-metagenome shotgun sequencing data from 450 urinary microbiome samples collected in four independent cohorts together with genome assembly and metagenomic binning to construct an extensive human urinary microbiome catalog consisting of [~]1.3 million non-redundant microbial genes and 705 non-redundant metagenome-assembled genomes (nrMAGs). We found that microbiomes from patients with UTI carry significantly more genes linked to antibiotic resistance and virulence vs controls. There was an enrichment of multiple Escherichia strains in patients with UTI from two independent case-control cohorts. UTIs are becoming multidrug-resistant, and we used machine learning models to identify potential antimicrobial peptides (AMPs) in 705 nrMAGs. Furthermore, we experimentally demonstrated that two of these AMPs exhibited strong inhibitory activity against uropathogenic Escherichia coli strains. Our study provides a valuable resource for studying the human urinary microbiome and suggests urinary microbiome-derived AMPs represent a source of new therapeutics for UTIs.