Cardioprotective effects of arjunolic acid in LPS-stimulated H9C2 and C2C12 myotubes via the My88-dependent TLR4 signaling pathway.

CONTEXT: Arjunolic acid (AA) is a triterpenoid saponin found in Terminalia arjuna (Roxb.) Wight & Arn. (Combretaceae). It exerts cardiovascular protective effects as a phytomedicine. However, it is unclear how AA exerts the effects at the molecular level. OBJECTIVE: This study investigates the cardioprotective effects of arjunolic acid (AA) via MyD88-dependant TLR4 downstream signaling marker expression. MATERIALS AND METHODS: The MTT viability assay was used to assess the cytotoxicity of AA. LPS induced in vitro cardiovascular disease model was developed in H9C2 and C2C12 myotubes. The treatment groups were designed such as control (untreated), LPS control, positive control (LPS + pyrrolidine dithiocarbamate (PDTC)-25 µM), and treatment groups were co-treated with LPS and three concentrations of AA (50, 75, and 100 µM) for 24 h. The changes in the expression of TLR4 downstream signaling markers were evaluated through High Content Screening (HCS) and Western Blot (WB) analysis. RESULTS: After 24 h of co-treatment, the expression of TLR4, MyD88, MAPK, JNK, and NF-κB markers were upregulated significantly (2-6 times) in the LPS-treated groups compared to the untreated control in both HCS and WB experiments. Evidently, the HCS analysis revealed that MyD88, NF-κB, p38, and JNK were significantly downregulated in the H9C2 myotube in the AA treated groups. In HCS, the expression of NF-κB was downregulated in C2C12. Additionally, TLR4 expression was downregulated in both H9C2 and C2C12 myotubes in the WB experiment. DISCUSSION AND CONCLUSIONS: TLR4 marker expression in H9C2 and C2C12 myotubes was subsequently decreased by AA treatment, suggesting possible cardioprotective effects of AA.

Discovery of new nanomolar peroxisome proliferator-activated receptor γ activators via elaborate ligand-based modeling.

Peroxisome Proliferator-Activated Receptor γ (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. With this in mind, we explored the pharmacophoric space of PPARγ using seven diverse sets of activators. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent and predictive quantitative structure-activity relationship (QSAR) (r2(71)=0.80, F=270.3, r2LOO=0.73, r2PRESS against 17 external test inhibitors=0.67). Three orthogonal pharmacophores emerged in the QSAR equation and were validated by receiver operating characteristic (ROC) curves analysis. The models were then used to screen the national cancer institute (NCI) list of compounds. The highest-ranking hits were tested in vitro. The most potent hits illustrated EC50 values of 15 and 224 nM.