Triptolide (TP) is the principal bioactive compound of Tripterygium wilfordii with significant anti-tumor, anti-inflammatory and immunosuppressive activities. However, its severe hepatotoxicity greatly limits its clinical use. The underlying mechanism of TP-induced liver damage is still poorly understood. Here, we estimate the role of the gut microbiota in TP hepatotoxicity and investigate the bile acid metabolism mechanisms involved. The results of the antibiotic cocktail (ABX) and fecal microbiota transplantation (FMT) experiment demonstrate the involvement of intestinal flora in TP hepatotoxicity. Moreover, TP treatment significantly perturbed gut microbial composition and reduced the relative abundances of Lactobacillus rhamnosus GG (LGG). Supplementation with LGG reversed TP-induced hepatotoxicity by increasing bile salt hydrolase (BSH) activity and reducing the increased conjugated bile acids (BA). LGG supplementation upregulates hepatic FXR expression and inhibits NLRP3 inflammasome activation in TP-treated mice. In summary, this study found that gut microbiota is involved in TP hepatotoxicity. LGG supplementation protects mice against TP-induced liver damage. The underlying mechanism was associated with the gut microbiota-BA-FXR axis. Therefore, LGG holds the potential to prevent and treat TP hepatotoxicity in the clinic.

CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.