Characterization and comparative analysis of urinary bacterial microbiome profiling in healthy cats and cats with feline idiopathic cystitis

Over the past decade, the make up of urinary microbiota and its association with disease has garnered substantial attention. However, the urinary microbiota patterns in healthy cats remain unclear, as do the potential dysbiosis conditions in cats with feline idiopathic cystitis (FIC). We hypothesized that urinary microbiota differ by age and sex in healthy cats, and vary between healthy cats and those with FIC. Using 16S rRNA gene sequencing, we analyzed the urinary microbiota of 25 healthy cats and 15 cats with FIC. Our results indicate that the feline urinary microbiota is primarily composed of Proteobacteria. While no significant differences in bacterial abundance were found between sexes in healthy cats, some taxa showed variations by age. Cats with FIC demonstrated microbiota disruption, characterized by an increase in Firmicutes and Bacilli, along with a decrease in Proteobacteria, particularly a reduction in the Pseudomonas genus, compared to healthy cats. These results demonstrate the presence of a bacterial microbiome in the urine of cats and suggest a potential association between altered microbiota and FIC. Multi-omics approaches could provide further insights into the core urobiome and its role in feline urinary tract disease.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-01-06
DOI
https://doi.org/10.1038/s41598-025-34196-9
Citations
1
Primary Topic
Urinary Tract Infections Management
Type
article
Field-Weighted Citation Impact
31.52

Funders

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

Characterization and comparative analysis of urinary bacterial microbiome profiling in healthy cats and cats with feline idiopathic cystitis

P. Phuektes, P. Sukon, Patchaya Thassakorn, Numfa Fungbun
1 citations
Scientific Reports
Urinary Tract Infections Management
31.52
article

Characterization and comparative analysis of urinary bacterial microbiome profiling in healthy cats and cats with feline idiopathic cystitis

P. Phuektes, P. Sukon, Patchaya Thassakorn, Numfa Fungbun
article en
1 citations

Abstract

Over the past decade, the make up of urinary microbiota and its association with disease has garnered substantial attention. However, the urinary microbiota patterns in healthy cats remain unclear, as do the potential dysbiosis conditions in cats with feline idiopathic cystitis (FIC). We hypothesized that urinary microbiota differ by age and sex in healthy cats, and vary between healthy cats and those with FIC. Using 16S rRNA gene sequencing, we analyzed the urinary microbiota of 25 healthy cats and 15 cats with FIC. Our results indicate that the feline urinary microbiota is primarily composed of Proteobacteria. While no significant differences in bacterial abundance were found between sexes in healthy cats, some taxa showed variations by age. Cats with FIC demonstrated microbiota disruption, characterized by an increase in Firmicutes and Bacilli, along with a decrease in Proteobacteria, particularly a reduction in the Pseudomonas genus, compared to healthy cats. These results demonstrate the presence of a bacterial microbiome in the urine of cats and suggest a potential association between altered microbiota and FIC. Multi-omics approaches could provide further insights into the core urobiome and its role in feline urinary tract disease.

Scientific ReportsVol. 16(1)
Khon Kaen University (TH)
Khon Kaen University
Openalex Percentile: Top 2%
Urinary Tract Infections Management
31.52
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.