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Multi-omics analysis identifies a microbiota–bile acid–TLR signaling axis driving bladder injury in interstitial cystitis

GSE310557 Homo sapiens Expression profiling by high throughput sequencing 17 samples 2025/11/21 GPL34284
Summary
Hunner-type interstitial cystitis/bladder pain syndrome (HIC) is a complex and clinically challenging chronic disorder characterized by bladder-centered symptoms, including persistent pelvic pain and urinary urgency frequency, with elusive pathogenesis involving poorly defined multi-system interactions and systemic implications. To investigate upstream drivers of urothelial injury, we employed an integrative multi-omics approach, incorporating metagenomic sequencing, targeted metabolomics of urine and serum, and single-cell RNA sequencing. Our analysis identified Enterococcus avium, Megasphaera micronuciformis, and Fusobacterium nucleatum as the significantly enriched species in HIC patients, with bile acid metabolism appearing as a prominently altered pathway. Metabolomic profiling revealed the elevated levels of taurochenodeoxycholic acid (TCDCA) and tauroursodeoxycholic acid, while single-cell transcriptomic data pointed to TLR3 signaling as a key mediator of urothelial injury. Cross-omics correlation proposed a mechanistic axis involving E. avium, TCDCA, and TLR3 activation. Functional validation using fecal microbiota transplantation, E. avium transplantation, and TCDCA administration demonstrated that this axis contributes to the urothelial barrier disruption and inflammatory responses via TLR3. Intervention with a TLR3 inhibitor demonstrated therapeutic potential. These findings shed light on the pathogenesis of HIC, highlighting the potential role of the gut-bladder axis in disease development. This may contribute not only to the discovery of novel biomarkers but also to the development of personalized treatment strategies targeting specific molecular and microbial pathways.
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NCBI GEO page ↗ Paper (PMID 41457077) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human RNA-seq datasets →
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