Multi-omics analysis reveals the host-microbe interactions on the dysbiosis of tissue microbiota in male genital lichen sclerosus-induced urethral strictures

Our study combined full-length 16S rDNA sequencing, transcriptome data, and clinical information from MGLSc patients to explore the relationships between host-microbe interactions and the development of tissue dysbiosis in MGLSc. Importantly, through staining for lipopolysaccharide and lipoteichoic acid, as well as full-length 16S rDNA sequencing, we identified, for the first time, the presence of microorganisms distribution pattern in lichen sclerosus prepuce tissue. Significant differences in the abundance of unclassified Muribaculaceae, <i>Escherichia coli</i>, <i>Finegoldia magna</i>, and other taxa were observed between the prepuce of MGLSc patients and controls. These differences were associated with altered gene expression in MGLSc patients, while the differential microbiota, in turn, influenced host gene expression. Although patterns of host-microbe interactions varied across populations, dysbiosis was linked to key clinical indicators in MGLSc patients. These findings provide valuable insights into the role of dysbiosis in MGLSc pathogenesis, laying a foundation for understanding disease progression and identifying potential biomarkers.

Authors

Institutions

Publication Details

Journal
Microbiology Spectrum
Published
2025-08-18
DOI
https://doi.org/10.1128/spectrum.00074-25
Citations
2
Primary Topic
Urological Disorders and Treatments
Type
article
Field-Weighted Citation Impact
3.67

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi-omics analysis reveals the host-microbe interactions on the dysbiosis of tissue microbiota in male genital lichen sclerosus-induced urethral strictures

Xianjie Xiu, Lujie Song, Juan Tang, Guangyu Mao et al.
2 citations
Microbiology Spectrum
Urological Disorders and Treatments
3.67
article

Multi-omics analysis reveals the host-microbe interactions on the dysbiosis of tissue microbiota in male genital lichen sclerosus-induced urethral strictures

Xianjie Xiu, Lujie Song, Juan Tang, Guangyu Mao, Ruochen Zhang, Zeyu Wang, Zhenwei Yu
article en
2 citations

Abstract

Our study combined full-length 16S rDNA sequencing, transcriptome data, and clinical information from MGLSc patients to explore the relationships between host-microbe interactions and the development of tissue dysbiosis in MGLSc. Importantly, through staining for lipopolysaccharide and lipoteichoic acid, as well as full-length 16S rDNA sequencing, we identified, for the first time, the presence of microorganisms distribution pattern in lichen sclerosus prepuce tissue. Significant differences in the abundance of unclassified Muribaculaceae, <i>Escherichia coli</i>, <i>Finegoldia magna</i>, and other taxa were observed between the prepuce of MGLSc patients and controls. These differences were associated with altered gene expression in MGLSc patients, while the differential microbiota, in turn, influenced host gene expression. Although patterns of host-microbe interactions varied across populations, dysbiosis was linked to key clinical indicators in MGLSc patients. These findings provide valuable insights into the role of dysbiosis in MGLSc pathogenesis, laying a foundation for understanding disease progression and identifying potential biomarkers.

Microbiology SpectrumVol. 13(10)
Shanghai Jiao Tong University (CN), Institute for Advanced Reconstruction (US), Shanghai Sixth People's Hospital (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 7%
Urological Disorders and Treatments
3.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.