Abstract Background Alcohol-induced intestinal dysbiosis disrupts and inflammatory response are essential in the development of alcoholic fatty liver disease (AFLD). Here, we investigate the effects of Fmo5 on changes in enteric microbiome composition in a model of AFLD and dissect the pathogenic role of Fmo5 in AFLD-induced liver pathology. Materials and methods The expression profile data of GSE8006 and GSE40334 datasets were downloaded from the GEO database. Weighted gene co-expression network analysis (WGCNA) approach allowed us to investigate the AFLD-correlated module. DEGs were used to perform Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Four PPI networks were constructed using the STRING database and visualized by Cytoscape software. Cytohubba plug-in was used to identify the hub genes. Western blot and immunohistochemistry assays were used to detect protein expressions. ELISA assay was used to detect the level of serum inflammatory cytokines. Lipid droplets in cytoplasm were observed using Oil Red O staining. Cell apoptosis was detected using TUNEL assay and flow cytometry analysis. ROS level were detected using flow cytometry analysis. The nuclear translocation of NF-κB p65 was observed using immunofluorescence staining. Co-immunoprecipitation were used to detect the co-expression of PPARα and Fmo5 in L02 cells. 16S rDNA sequencing defined the bacterial communities in mice with AFLD. Results Fmo5 was a key DEG and closely associated with gut microbiota and PPAR signaling pathway. Gut microbiome function in AFLD was significantly related to PPAR signaling pathway. AFLD induced shifts in various bacterial phyla in the cecum, including a reduction in Bacteroidetes and increased Firmicutes. Fmo5 and PPARα co-expression in cell and animal model with AFLD, which decreased significantly. Silencing of Fmo5 and PPARα aggravated the functions of AFLD inducing apoptosis and inflammatory response, promoting liver injury, and activating the NF-κB signaling pathway in vivo and in vitro. NF-κB inhibitor abolished the functions of silencing of Fmo5 and PPARα promoting AFLD-induced apoptosis, inflammatory response, and liver injury. Conclusions Our data indicated that the co-expression of Fmo5 and PPARα was involved in AFLD-related gut microbiota composition, alleviated AFLD-induced liver injury, apoptosis and inflammatory response via inhibiting the nuclear translocation of NF-κB p65 to inhibit NF-κB signaling pathway.

Objectives: The study evaluated the bioelectrical impedance analysis (BIA) device against the body composition parameters measured by anthropometry and quantitative computer tomography (QCT) to assess its reliability and accuracy among Chinese adults.Methods: Body composition parameters (waist circumstance [WC], body weight, body mass index [BMI] and visceral fat area [VFA]) were measured in 1,379 subjects (20-81 years old), both manually and by BIA, and in 1,317 of 1,379 subjects by QCT. The correlation coefficients were calculated between these measurements. Linear regression models were used to estimate each parameter based on the BIA measurements. Multivariate linear regression models were applied to calculate the correlation among VFA, WC and BMI. The concordance correlation coefficient from the Bland-Altman plots were calculated for VFA between QCT and BIA. Results: High correlation was observed for WC, weight and BMI (adjusted R2=0.78, 0.99 and 0.99) between BIA and anthropometry, and for VFA between BIA and QCT in both sex (adjusted R2=0.549 and 0.462). The multivariate regression models were established for the accurate prediction of QCT-VFA using WC and BMI (adjusted R2=0.603). In addition, a strong consistency of VFA measurement was found between BIA and QCT.Conclusion: Body composition parameters could be accurately determined in clinic using simple measurements of BIA. WC is more reliable as a predictor of visceral fat in the metabolic syndrome. Being non-invasive, accurate and free of radiation, BIA can be used as a safe and convenient tool in scientific research and clinical practice for the quick measurement of anthropometric parameters.

Background and Aim: We aimed to explore risk factors and construct a model of MAFLD with fibrosis fibrosis and compare the performance of the panels of APRI, FIB-4 and BARD.Methods: We retrieved clinical information and enrolled 3671 patients. Subjects were assigned to four groups: MAFLD, MAFLD with fibrosis, and MAFLD with advanced fibrosis. Multivariate regression analysis and randomforest model were to construct the model of MAFLD with fibrosis, the receiver operating characteristics (ROC) were used to compare the diagnostic efficacy of predictive model, APRI, BARD, FIB-4. Results: ①The proportion of fibrosis in MAFLD was higher than that of non-MAFLD group; Compared with the control group, advanced fibrosis (≥F3) has no statistical significance. ②Four variables were selected to build the model of MAFLD. The diagnostic accuracy (AUROC=0.730) was superior to that of APRI and random forest model. ③Multivariate logistic analysis showed that increased BMI and AST was risk factors for advanced fibrosis and elevated PLT was a protective factor for advanced fibrosis; The diagnostic power of the model (AUROC=0.714) was superior to that of the FIB-4 and BARD.Conclusion: The model of fibrosis stage with MAFLD is superior to other non-invasive markers of fibrosis of MAFLD, which can assist clinicians in screening and warning for potential risks of MAFLD with fibrosis.

BackgroundAlcohol-induced intestinal dysbiosis disrupts and inflammatory responses are essential in the development of alcoholic fatty liver disease (AFLD). Here, we investigated the effects of Fmo5 on changes in enteric microbiome composition in a model of AFLD and dissected the pathogenic role of Fmo5 in AFLD-induced liver pathology.MethodsThe expression profile data of GSE8006 and GSE40334 datasets were downloaded from the GEO database. The WGCNA approach allowed us to investigate the AFLD-correlated module. DEGs were used to perform KEGG pathway enrichment analyses. Four PPI networks were constructed using the STRING database and visualized using Cytoscape software. The Cytohubba plug-in was used to identify the hub genes. Western blot and immunohistochemistry assays were used to detect protein expression. ELISA assay was used to detect the levels of serum inflammatory cytokines. Lipid droplets in the cytoplasm were observed using Oil Red O staining. Apoptosis was detected using a TUNEL assay and flow cytometry analysis. ROS levels were detected using flow cytometry analysis. Nuclear translocation of NF-κB p65 was observed using immunofluorescence staining. Co-immunoprecipitation was used to detect the co-expression of PPARα and Fmo5 in L02 cells. 16S rDNA sequencing defined the bacterial communities in mice with AFLD.ResultsFmo5 is a key DEG and is closely associated with the gut microbiota and PPAR signaling pathway. Gut microbiome function in AFLD was significantly related to the PPAR signaling pathway. AFLD induced shifts in various bacterial phyla in the cecum, including a reduction in Bacteroidetes and increased Firmicutes. Fmo5 and PPARα co-expression in cell and animal models with AFLD, which decreased significantly. Silencing of Fmo5 and PPARα aggravated the functions of AFLD inducing apoptosis and inflammatory response, promoting liver injury, and activating the NF-κB signaling pathway in vivo and in vitro. The NF-κB inhibitor abolished the functions of silencing of Fmo5 and PPARα promoting AFLD-induced apoptosis, inflammatory response, and liver injury.ConclusionOur data indicated that the co-expression of Fmo5 and PPARα was involved in AFLD-related gut microbiota composition and alleviated AFLD-induced liver injury, apoptosis, and inflammatory response by inhibiting the nuclear translocation of NF-κB p65 to inhibit the NF-κB signaling pathway.