Pregnane X receptor (PXR) is a liver-enriched xenobiotic-responsive transcription factor. Although recent studies suggest that PXR shows anti-inflammatory effects by suppressing nuclear factor kappa B (NF-κB), the detailed mechanism remains unclear. In this study, we aimed to elucidate this mechanism. Mice were treated intraperitoneally with the PXR agonist pregnenolone 16α-carbonitrile (PCN) and/or carbon tetrachloride (CCl₄). Liver injury was evaluated, and hepatic mRNA levels were determined via quantitative reverse transcription polymerase chain reaction. Reporter assays with wild-type and mutated mouse Cxcl2 promoter-containing reporter plasmids were conducted in 293T cells. Results showed that the hepatic expression of inflammation-related genes was upregulated in CCl₄-treated mice, and PCN treatment repressed the induced expression of chemokine-encoding Ccl2 and Cxcl2 among the genes investigated. Consistently, PCN treatment suppressed the increased plasma transaminase activity and neutrophil infiltration in the liver. In reporter assays, tumor necrosis factor-α-induced Cxcl2 expression was suppressed by PXR. Although an NF-κB inhibitor or the mutation of an NF-κB-binding motif partly reduced PXR-dependent suppression, the mutation of both NF-κB and activator protein 1 (AP-1) sites abolished it. Consistently, AP-1-dependent gene transcription was suppressed by PXR with a construct containing AP-1 binding motifs. In conclusion, the present results suggest that PXR exerts anti-inflammatory effects by suppressing both NF-κB- and AP-1-dependent chemokine expression in mouse liver.

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular, exposure of the liver to environmental metabolism-disrupting chemicals contributes to the development and propagation of steatosis and hepatotoxicity. However, the mechanisms for AhR-induced hepatotoxicity and tumor propagation in the liver remain to be revealed, due to the wide variety of AhR ligands. Recently, quantitative structure–activity relationship (QSAR) analysis using deep neural network (DNN) has shown superior performance for the prediction of chemical compounds. Therefore, this study proposes a novel QSAR analysis using deep learning (DL), called the DeepSnap–DL method, to construct prediction models of chemical activation of AhR. Compared with conventional machine learning (ML) techniques, such as the random forest, XGBoost, LightGBM, and CatBoost, the proposed method achieves high-performance prediction of AhR activation. Thus, the DeepSnap–DL method may be considered a useful tool for achieving high-throughput in silico evaluation of AhR-induced hepatotoxicity.

Background: PXR is a xenobiotic-responsive nuclear receptor that controls the expression of drug-metabolizing enzymes. Drug-induced activation of PXR sometimes causes drug–drug interactions due to the induced metabolism of co-administered drugs. Our group recently reported a possible drug–drug interaction mechanism via an interaction between the nuclear receptors CAR and PPARα. As CAR and PXR are structurally and functionally related receptors, we investigated possible crosstalk between PXR and PPARα. Methods: Human hepatocyte-like HepaRG cells were treated with various PXR ligands, and mRNA levels were determined by quantitative reverse transcription PCR. Reporter assays using the HMGCS2 promoter containing a PPARα-binding motif and mammalian two-hybrid assays were performed in HepG2 or COS-1 cells. Results: Treatment with PXR activators reduced the mRNA levels of PPARα target genes in HepaRG cells. In reporter assays, PXR suppressed PPARα-dependent gene expression in HepG2 cells. In COS-1 cells, co-expression of PGC1α, a common coactivator of PPARα and PXR, enhanced PPARα-dependent gene transcription, which was clearly suppressed by PXR. Consistently, in mammalian two-hybrid assays, the interaction between PGC1α and PPARα was attenuated by ligand-activated PXR. Conclusion: The present results suggest that ligand-activated PXR suppresses PPARα-dependent gene expression by inhibiting PGC1α recruitment.

1. Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.2. The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.3. Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.4. These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance and precise mechanism remain unknown, it is considered an adaptive response resulting from the induction of drug-metabolizing enzymes (DMEs). This study aimed to elucidate the association between centrilobular hepatocyte hypertrophy and DME induction using machine learning on toxicogenomic data. Utilizing publicly available gene expression data and pathological findings from rat livers of 134 compounds, we developed six different types of machine learning models to predict the occurrence of centrilobular hepatocyte hypertrophy based on gene expression data as explanatory variables. Among these, a LightGBM-based model demonstrated the best performance with an accuracy of approximately 0.9. With this model, we assessed each gene’s contribution to predicting centrilobular hepatocyte hypertrophy using mean absolute SHAP values. The results revealed that Cyp2b1 had an extremely significant contribution, while other DME genes also displayed positive contributions. Additionally, enrichment analysis of the top 100 genes based on mean absolute SHAP values identified “Metabolism of xenobiotics by cytochrome P450” as the most significantly enriched term. In conclusion, the current results suggest that the induction of multiple DMEs, including CYP2B1, is crucial for the development of centrilobular hepatocyte hypertrophy.