This study aimed to evaluate the aggravating effect of overdosed Longdan Xiegan Decoction (LXD) on D-galactosamine-induced liver injury and to investigate the underlying mechanisms. Histopathological examination, serum biochemical analysis (ALT, AST), serum LPS levels, inflammatory cytokine expression, 16S rRNA gene sequencing of gut microbiota, and untargeted fecal metabolomics were performed to assess the effects of LXD at doses of 150, 300, and 600 mg/kg. LC-MS was employed to analyze gut bacterial biotransformation products of LXD. Network pharmacology and molecular docking were used to predict potential genipin-interacting proteins. An LPS-induced LX-2 cell model was established to evaluate the effects of excessive genipin on cell viability and ROS production. Flow cytometry, Western blot, and immunofluorescence were applied to investigate the activation of the TLR4/NF-κB pathway. 600 mg/kg LXD significantly aggravated liver injury and enhanced the gut biotransformation of geniposide to genipin by altering gut microbiota composition. TLR4 was identified as a potential mediator of genipin effects. Moreover, excessive genipin reduced cell viability, increased ROS levels, and elevated expression of TLR4, MyD88, and NF-κB p65, along with promoting nuclear translocation of NF-κB p65 in LX-2 cells. Overdosed LXD aggravates D-galactosamine-induced liver injury through gut microbiota-mediated biotransformation of geniposide to genipin, which subsequently activates the TLR4/NF-κB inflammatory pathway.
Ranran Zhang, Ying Ling, Jiaxin Sun et al.· Fitoterapia· 0 citations
Background Alterations in the gut microbiota have been associated with a variety of psychiatric disorders, including major depressive disorder (MDD). However, the relationship between MDD and gut microbial communities remains incompletely understood. Most previous studies have primarily focused on gut bacteria, with relatively limited attention to other microbial components. Methods In this study, we analyzed gut microbial profiles from 36 patients with MDD and 36 healthy controls using metagenomic sequencing data. The MaAsLin2 algorithm was applied to identify potential microbial biomarkers associated with MDD. Results A total of 6 bacterial biomarkers and 7 viral biomarkers were identified. The models based on these features demonstrated strong predictive performance, with area under the curve (AUC) values of 0.891 for bacteria and 0.878 for viruses. Notably, the combined bacterial-viral model achieved an AUC of 0.946. These findings were further evaluated through external testing in two unrelated research cohorts. In the Shanxi cohort, the AUC values were 0.825 (bacteria), 0.803 (viruses), and 0.972 (combined model). In the Wuhan cohort, the AUC values were 0.683 (bacteria), 0.693 (viruses), and 0.784 (combined model). Conclusion In summary, our results highlight the potential of gut bacterial and viral biomarkers as candidate biomarkers and potential auxiliary tools for MDD assessment and suggest that integrating multi-domain microbial features may improve prediction accuracy.
Xuan Wang, Wei Chen, Hanlin Zhang et al.· Frontiers in Cellular and In...· 0 citations