Gene ontology enrichment analysis of PPAR-γ modulators from Cassia glauca in diabetes mellitus

System biology mediated assessment of molecular mechanism for sinapic acid against breast cancer: via network pharmacology and molecular dynamic simulation

Sinapic acid is a hydroxycinnamic acid widespread in the plant kingdom, known to be a potent anti-oxidant used for the treatment of cancer, infections, oxidative stress, and inflammation. However, the mode of action for its chemotherapeutic properties has yet not been unleashed. Hence, we aimed to identify potential targets to propose a possible molecular mechanism for sinapic acid against breast cancer. We utilized multiple system biology tools and databases like DisGeNET, DIGEP-Pred, Cytoscape, STRING, AutoDock 4.2, AutoDock vina, Schrodinger, and gromacs to predict a probable molecular mechanism for sinapic acid against breast cancer. Targets for the disease breast cancer, were identified via DisGeNET database which were further matched with proteins predicted to be modulated by sinapic acid. In addition, KEGG pathway analysis was used to identify pathways; a protein-pathway network was constructed via Cytoscape. Molecular docking was performed using three different algorithms followed by molecular dynamic simulations and MMPBSA analysis. Moreover, cluster analysis and gene ontology (GO) analysis were performed. A total of 6776 targets were identified for breast cancer; 95.38% of genes predicted to be modulated by sinapic acid were common with genes of breast cancer. The 'Pathways in cancer' was predicted to be modulated by most umber of proteins. Further, PRKCA, CASP8, and CTNNB1 were predicted to be the top 3 hub genes. In addition, molecular docking studies revealed CYP3A4, CYP1A1, and SIRT1 to be the lead proteins identified from AutoDock 4.2, AutoDock Vina, and Schrodinger suite Glide respectively. Molecular dynamic simulation and MMPBSA were performed for the complex of sinapic acid with above mentioned proteins which revealed a stable complex throughout simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to regulation of multiple proteins like CTNNB1, PRKCA, CASP8, SIRT1, and cytochrome enzymes (CYP1A1 & CYP3A4); the majorly regulated pathway was predicted to be 'Pathways in cancer'. This indicates the rationale for sinapic acid to be used in the treatment of breast cancer. However, these are predictions and need to be validated and looked upon in-depth to confirm the exact mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.

Ficus benghalensis promotes the glucose uptake- Evidence with in silico and in vitro

Background

Ficus benghalensis L. is traditionally used to manage diabetes; also used in various herbal formulations, and is indicated as an insulin sensitizer. Hence, present work attempted in identifying the probable lead hits to promote glucose uptake via computational approach followed by experimental evaluation of hydroalcoholic extract of Ficus benghalensis L. bark in yeast cells.

Methods

The in vitro assay for glucose uptake was performed in the baker yeast whereas in-silico study involved retrieving the phytoconstituents from open sources, and predicting for probable targets of diabetes followed by drug-likeness score, probable side effects, and ADMET profile. Homology modeling was performed to construct the target protein glucose transporter-2. In addition, the binding affinity of each ligand with glucose transporter was predicted using AutoDock 4.2.

Results

A total of 17 phytoconstituents from F. benghalensis were identified to possess the anti-diabetic effects. Among them, 4-methoxybenzoic acid scored the highest drug-likeness score and lupeol acetate had the maximum binding affinity of -8.02 kcal/mol with 9 pi-interactions via Tyr324, Phe323, Ile319, Ile200, Ile28, Phe24, and Ala451. Similarly, the extract showed the highest glucose uptake efficacy in yeast cells at 500 µg/mL.

Conclusion

Herein the present study reflected the probable activity of the phytoconstituents from F. benghalensis in promoting the glucose uptake via the in silico and in vitro approaches.

Exploring the therapeutic mechanisms of Cassia glauca in diabetes mellitus through network pharmacology, molecular docking and molecular dynamics

Cassia glauca is reported as anti-diabetic medicinal plant and is also used as an ethnomedicine. However, its mode of action as an anti-diabetic agent has not been clearly elucidated. Hence, the present study investigated the probable mechanism of action of C. glauca to manage diabetes mellitus via network pharmacology and molecular docking and simulations studies. The reported bioactives from C. glauca were retrieved from an open-source database, i.e. ChEBI, and their targets were predicted using SwissTargetPrediction. The proteins involved in the pathogenesis of diabetes were identified from the therapeutic target database. The targets involved in diabetes were enriched in STRING, and the pathways involved in diabetes were identified concerning the KEGG. Cytoscape was used to construct the network among bioactives, proteins, and probably regulated pathways, which were analyzed based on edge count. Similarly, molecular docking was performed using the Glide module of the Schrodinger suite against majorly targeted proteins with their respective ligands. Additionally, the drug-likeness score and ADMET profile of the individual bioactives were predicted using MolSoft and admetSAR2.0 respectively. The stability of these complexes were further studied via molecular dynamics simulations and binding energy calculations. Twenty-three bio-actives were retrieved from the ChEBI database in which cassiarin B was predicted to modulate the highest number of proteins involved in diabetes mellitus. Similarly, GO analysis identified the PI3K-Akt signaling pathway to be primarily regulated by modulating the highest number of gene. Likewise, aldose reductase (AKR1B1) was majorly targeted via the bioactives of C. glauca. Similarly, docking study revealed methyl-3,5-di-O-caffeoylquinate (docking score −9.209) to possess the highest binding affinity with AKR1B1. Additionally, drug-likeness prediction identified cassiaoccidentalin B to possess the highest drug-likeness score, i.e. 0.84. The molecular dynamics simulations and the MMGBSA indicate high stability and greater binding energy for the methyl-3,5-di-O-caffeoylquinate (ΔG_bind = −40.33 ± 6.69 kcal mol⁻¹) with AKR1B1, thus complementing results from other experiments. The study identified cassiarin B, cassiaoccidentalin B, and cinnamtannin A2 as lead hits for the anti-diabetic activity of C. glauca. Further, the PI3K-Akt and AKR1B1 were traced as majorly modulated pathway and target, respectively.

Cassia glauca is reported for anti-diabetic action and is also used as an ethnomedicine.