{"title":"Multi-omics analysis of host-microbiome interactions in a mouse model of congenital hepatic fibrosis.","authors":"Mengfan Jiao, Ye Sun, Zixing Dai, Xiaoxue Hou, Xizhi Yin, Qingling Chen, Rui Liu, Yuwen Li, Chuanlong Zhu","doi":"10.1186/s12866-025-03892-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Congenital hepatic fibrosis (CHF) caused by mutations in the polycystic kidney and hepatic disease 1 (PKHD1) gene is a rare genetic disorder with poorly understood pathogenesis. We hypothesized that integrating gut microbiome and metabolomic analyses could uncover distinct host-microbiome interactions in CHF mice compared to wild-type controls.</p><p><strong>Methods: </strong>Pkhd1<sup>del3-4/del3-4</sup> mice were generated using CRISPR/Cas9 technology. Fecal samples were collected from 11 Pkhd1<sup>del3-4/del3-4</sup> mice and 10 littermate wild-type controls. We conducted a combined study using 16 S rDNA sequencing for microbiome analysis and untargeted metabolomics. The gut microbiome and metabolome data were integrated using Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), which helped identify key microbial and metabolic features associated with CHF.</p><p><strong>Results: </strong>CHF mouse model was successfully established. Our analysis revealed that the genera Mucispirillum, Eisenbergiella, and Oscillibacter were core microbiota in CHF, exhibiting significantly higher abundance in Pkhd1<sup>del3-4/del3-4</sup> mice and strong positive correlations among them. Network analysis demonstrated robust associations between the gut microbiome and metabolome. Multi-omics dimension reduction analysis demonstrated that both the microbiome and metabolome could effectively distinguish CHF mice from controls, with area under the curve of 0.883 and 0.982, respectively. A significant positive correlation was observed between the gut microbiome and metabolome, highlighting the intricate relationship between these two components.</p><p><strong>Conclusion: </strong>This study identifies distinct metabolic and microbiome profiles in Pkhd1<sup>del3-4/del3-4</sup> mice. Multi-omics analysis effectively differentiates CHF mice from controls and identified potential biomarkers. These findings indicate that gut microbiota and metabolites are integral to the pathogenesis of CHF, offering novel insights into the disease mechanism.</p>","PeriodicalId":9233,"journal":{"name":"BMC Microbiology","volume":"25 1","pages":"176"},"PeriodicalIF":4.0000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956230/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Microbiology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12866-025-03892-x","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Congenital hepatic fibrosis (CHF) caused by mutations in the polycystic kidney and hepatic disease 1 (PKHD1) gene is a rare genetic disorder with poorly understood pathogenesis. We hypothesized that integrating gut microbiome and metabolomic analyses could uncover distinct host-microbiome interactions in CHF mice compared to wild-type controls.
Methods: Pkhd1del3-4/del3-4 mice were generated using CRISPR/Cas9 technology. Fecal samples were collected from 11 Pkhd1del3-4/del3-4 mice and 10 littermate wild-type controls. We conducted a combined study using 16 S rDNA sequencing for microbiome analysis and untargeted metabolomics. The gut microbiome and metabolome data were integrated using Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), which helped identify key microbial and metabolic features associated with CHF.
Results: CHF mouse model was successfully established. Our analysis revealed that the genera Mucispirillum, Eisenbergiella, and Oscillibacter were core microbiota in CHF, exhibiting significantly higher abundance in Pkhd1del3-4/del3-4 mice and strong positive correlations among them. Network analysis demonstrated robust associations between the gut microbiome and metabolome. Multi-omics dimension reduction analysis demonstrated that both the microbiome and metabolome could effectively distinguish CHF mice from controls, with area under the curve of 0.883 and 0.982, respectively. A significant positive correlation was observed between the gut microbiome and metabolome, highlighting the intricate relationship between these two components.
Conclusion: This study identifies distinct metabolic and microbiome profiles in Pkhd1del3-4/del3-4 mice. Multi-omics analysis effectively differentiates CHF mice from controls and identified potential biomarkers. These findings indicate that gut microbiota and metabolites are integral to the pathogenesis of CHF, offering novel insights into the disease mechanism.
期刊介绍:
BMC Microbiology is an open access, peer-reviewed journal that considers articles on analytical and functional studies of prokaryotic and eukaryotic microorganisms, viruses and small parasites, as well as host and therapeutic responses to them and their interaction with the environment.