No abstract

Hepatitis B virus x protein (HBx) serves an important role in the pathogenesis of the hepatitis B virus infection. Previous studies have reported that the interaction between HBx and hepatocyte mitochondria is an important factor leading to liver cell injury and apoptosis, ultimately inducing the formation of liver cancer. In the present study, a mouse model expressing HBx was constructed using hydrodynamic in vivo transfection based on the interaction between HBx and cytochrome c oxidase (COX) subunit III. The specific mechanism of HBx-induced oxidative stress in mouse hepatocytes and the subsequent effect on mitochondrial function and inflammatory injury was assessed. The results demonstrated that HBx reduced the activity of COX and the expression of superoxide dismutase and upregulated the expression of malondialdehyde, NF-κB and phospho-AKT, thus increasing oxidative stress. In addition, HBx induced an increase in interleukin (IL)-6, IL-1β and IL-18 expression levels, which created an inflammatory microenvironment in the liver, further promoting hepatocyte inflammatory injury. Therefore, it was proposed that HBx may affect hepatocyte mitochondrial respiration by reducing the activity of cytochrome c oxidase, leading to mitochondrial dysfunction and inducing hepatocyte inflammation and injury.

Telomerase is reactivated in over 90% of tumors and plays critical roles in tumor progression. The mechanisms by which telomerase is up-regulated in cancer cells are poorly understood. Here we showed that a bioactive lipid, lysophophatidic acid (LPA), up-regulated the expression of human telomerase reverse transcriptase (hTERT) and telomerase activity in serous ovarian adenocarcinoma cell lines SKOV3, A2780, and HEY, but not in OCC1, a clear cell ovarian cancer cell line. This cell type specific effect of LPA on telomerase regulation may reflect distinctive genetic backgrounds in different histological subtype of ovarian cancer cells. Our data further suggest that the phosphatidylinositol 3-phosphate kinase (PI3K) pathway and hypoxia-inducible factor-1alpha (HIF-1alpha) are likely to be involved in LPA-induced hTERT expression. Targeting human telomerase by LPA is potentially involved in its role of promoting tumor progression.

Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.

Background and Aim: More than half of the small-molecule kinase inhibitors (KIs) induced liver injury clinically. Meanwhile, studies have shown a close relationship between mitochondrial damage and drug-induced liver injury (DILI). We aimed to study KIs and the binding between drugs and mitochondrial proteins to find factors related to DILI occurrence.Methods: A total of 1,223 oral FDA-approved drugs were collected and analyzed, including 44 KIs. Fisher’s exact test was used to analyze DILI potential and risk of different factors. A total of 187 human mitochondrial proteins were further collected, and high-throughput molecular docking was performed between human mitochondrial proteins and drugs in the data set. The molecular dynamics simulation was used to optimize and evaluate the dynamic binding behavior of the selected mitochondrial protein/KI complexes.Results: The possibility of KIs to produce DILI is much higher than that of other types (OR = 46.89, p = 9.28E-13). A few DILI risk factors were identified, including molecular weight (MW) between 400 and 600, the defined daily dose (DDD) ≥ 100 mg/day, the octanol–water partition coefficient (LogP) ≥ 3, and the degree of liver metabolism (LM) more than 50%. Drugs that met this combination of rules were found to have a higher DILI risk than controls (OR = 8.28, p = 4.82E-05) and were more likely to cause severe DILI (OR = 8.26, p = 5.06E-04). The docking results showed that KIs had a significant higher affinity with human mitochondrial proteins (p = 4.19E-11) than other drug types. Furthermore, the five proteins with the lowest docking score were selected for molecular dynamics simulation, and the smallest fluctuation of the backbone RMSD curve was found in the protein 5FS8/KI complexes, which indicated the best stability of the protein 5FS8 bound to KIs.Conclusions: KIs were found to have the highest odds ratio of causing DILI. MW was significantly related to the production of DILI, and the average docking scores of KI drugs were found to be significantly different from other classes. Further analysis identified the top binding mitochondrial proteins for KIs, and specific binding sites were analyzed. The optimization of molecular docking results by molecular dynamics simulation may contribute to further studying the mechanism of DILI.