Jing Jiang

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning.

Atherosclerosis is a leading cause of death worldwide. Recent studies have emphasized the significance of gut microbiota and lipid metabolism in the development of atherosclerosis. Herein, the effects and molecular mechanisms involving ferulic acid (FA) was examined in atherosclerosis using the ApoE-knockout (ApoE -∕- , c57BL/6 background) mouse model. Eighteen male ApoE −/− mice were fed a high-fat diet (HFD) for 12 weeks and then randomly divided into three groups: the model group, the FA (40 mg/kg/day) group and simvastatin (5 mg/kg/day) group. As results, FA could significantly alleviate atherosclerosis and regulate lipid levels in mice. Liver injury and hepatocyte steatosis induced by HFD were also mitigated by FA. FA improved lipid metabolism involving up-regulation of AMPKα phosphorylation and down-regulation of SREBP1 and ACC1 expression. Furthermore, FA induced marked structural changes in the gut microbiota and fecal metabolites and specifically reduced the relative abundance of Fimicutes , Erysipelotrichaceae and Ileibacterium, which were positively correlated with serum lipid levels in atherosclerosis mice. In conclusion, we demonstrate that FA could significantly ameliorate atherosclerotic injury, which may be partly by modulating gut microbiota and lipid metabolism via the AMPKα/SREBP1/ACC1 pathway.

Non-steroidal anti-inflammatory drugs (NSAIDs) are worldwide used drugs for analgesic, antipyretic, and anti-inflammatory therapeutics. However, NSAIDs often cause several serious liver injuries, such as drug-induced liver injury (DILI), and the molecular mechanisms of DILI have not been clearly elucidated. In this study, we developed a systems pharmacology approach to explore the mechanism-of-action of NSAIDs. We found that the Farnesoid X Receptor (FXR) antagonism of NSAIDs is a potential molecular mechanism of DILI through systematic network analysis and in vitro assays. Specially, the quantitative real-time PCR assay reveals that indomethacin and ibuprofen regulate FXR downstream target gene expression in HepG2 cells. Furthermore, the western blot shows that FXR antagonism by indomethacin induces the phosphorylation of STAT3 (signal transducer and activator of transcription 3), promotes the activation of caspase9, and finally causes DILI. In summary, our systems pharmacology approach provided novel insights into molecular mechanisms of DILI for NSAIDs, which may propel the ways toward the design of novel anti-inflammatory pharmacotherapeutics.

Stigmasterol (ST) has been shown to improve both lipid and bile acid (BA) metabolism. However, the mechanism(s) by which ST prevents dyslipidemia via BA metabolism, and the potential involvement of other regulatory mechanisms, remains unclear. Here, we found that ST treatment effectively alleviates lipid metabolism disorder induced by a high-fat diet (HFD). Moreover, we also show that fecal microbiota transplantation from ST-treated rats displays similar protective effects in rats fed on an HFD. Our data confirm that the gut microbiota plays a key role in attenuating HFD-induced fat deposition and metabolic disorders. In particular, ST reverses HFD-induced gut microbiota dysbiosis in rats by reducing the relative abundance of Erysipelotrichaceae and Allobaculum bacteria in the gut. In addition, ST treatment also modifies the serum and fecal BA metabolome profiles in rats, especially in CYP7A1 mediated BA metabolic pathways. Furthermore, chenodeoxycholic acid combined with ST improves the therapeutic effects in HFD-induced dyslipidemia and hepatic steatosis. In addition, this treatment strategy also alters BA metabolism profiles via the CYP7A1 pathway and gut microbiota. Taken together, ST exerts beneficial effects against HFD-induced hyperlipidemia and obesity with the underlying mechanism being partially related to both the reprogramming of the intestinal microbiota and metabolism of BAs in enterohepatic circulation. This study provides a theoretical basis for further study of the anti-obesity effects of ST and consideration of the gut microbiota as a potential target for the treatment of HFD-induced dyslipidemia.