Elucidating the anti-aging mechanism of Si Jun Zi Tang by integrating network pharmacology and experimental validation in vivo

Abstract

Si Jun Zi Tang (SJZT) is a classic Traditional Chinese Medicine (TCM) prescription used to treat aging-related diseases. However, the potential molecular mechanisms of the anti-aging effects of the bioactive compounds and their targets remain elusive. In this study, we combined network pharmacology and molecular docking with in vivo experiments to elucidate the anti-aging molecular mechanism of SJZT. A series of network pharmacology strategies were used to predict potential targets and therapeutic mechanisms of SJZT, including compound screening, pathway enrichment analysis and molecular docking studies. Based on the network pharmacology predictions and observation of outward signs of aging, the expression levels of selected genes and proteins and possible key targets were subsequently validated and analysed using qRT-PCR and immunoblotting. Using a data mining approach, 235 effective targets of SJZT and aging were obtained. AKT1, STAT3, JUN, MAPK3, TP53, MAPK1, TNF, RELA, MAPK14 and IL6 were identified as core genes in the Protein-Protein Interaction Networks (PPI) analysis. The results of the effective target Gene Ontology (Go) functional enrichment analysis suggested that SJZT may be involved aging and antiapoptotic biological processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the anti-aging mechanism of SJZT may be associated with the PI3K-AKT and P38 MAPK signalling pathways. Molecular docking analysis suggested that kaempferol and quercetin could fit in the binding pockets of the core targets. In addition, SJZT alleviated the aging symptoms of mice such as osteoporosis and hair loss. In conclusion, the anti-aging effect of SJZT was associated with the inhibition of the PI3K-AKT and P38 MAPK signalling pathways, and these findings were consistent with the network pharmacology prediction.

Exploring the potential mechanism of Taohong Siwu decoction in the treatment of avascular necrosis of the femoral head based on network pharmacology and molecular docking

“…[ 30 , 31 ] Additionally, the top 10 nodes ranked by degree score were chosen as hub genes using the CytoHubba algorithm plug-in of the Cytoscape software. [ 32 , 33 ] Lastly, Cytoscape software (version 3.9.1) was used to create the “THSWD-drug-active compound-intersection gene-hub gene-AVNFH” network diagram to illustrate the disease-drug interaction. [ 34 ]…”

Modified Chaishao Liujunzi Decoction inhibits bile acid-induced gastric intestinal metaplasia: from network prediction to experimental verification

“…In this study, we used the main active components of MCLD, quercetin, kaempferol, isorhamnetin, luteolin, and naringenin, to define the binding properties with the top ten core targets (AKT1, TNF, IL6, VEGFA, EGFR, STAT3, IL1B, SRC, CASP3, and MAPK3). In general, the lower the binding energy between the ligand and the receptor, the more stable the binding conformation [28]. Binding energy less than -4.25 kcal/mol (1 kcal ≈418,585 kJ), -5.0 kcal/mol, or -7.0 kcal/mol indicates a certain, good, or strong binding activity between the ligand and the receptor, respectively [28].…”

“…In general, the lower the binding energy between the ligand and the receptor, the more stable the binding conformation [28]. Binding energy less than -4.25 kcal/mol (1 kcal ≈418,585 kJ), -5.0 kcal/mol, or -7.0 kcal/mol indicates a certain, good, or strong binding activity between the ligand and the receptor, respectively [28]. Our research results indicate that the main active components of MCLD have a good binding ability to the ten core targets mentioned above (Table 1).…”

“…Finally, AutoDock Vina was used to calculate the docking activity of target and compound structures. The lower the binding energy, the more stable the binding conformation of the receptor and the ligand [28].…”

“…Next, SYBR Green Master Mix (Thermo Scientific, USA) and a Real-Time PCR Detection system were used for qPCR. Finally, using GAPDH as the internal reference gene, we used the 2 -∆∆Ct method to calculate the relative mRNA expression of each gene to be tested [28]. The PCR primers were as follows: CDX2, 5'-GGTTT CAGAACCGCAGAGCA-3'-(Forward), and 5'-GAA GACACCGGACTCAAGGG-3'-(Reverse); MUC2, 5'-TCAAAAGCAGCGTGTTCAGC-3'-(Forward), and 5'-AGCAGAAGCACTCACAGTCC-3'-(Reverse); EGFR, 5'-CAGATCGCAAAGGGCATGAA-3'-(Forward), and 5'-TTGCCTCCTTCTGCATGGTA-3'-(Reverse); PI3K, 5'-TTATAAACGAGAACGTGTG-3'-(Forward), and 5'-AATAGCTAGATAAGCC-3'-(Reverse); AKT, 5'-CAGCATCGCTTCTTTGCCGGTA-3'-(Forward), and 5'-CCTGGTGTCAGTCTCCGACGTGA-3'-(Reverse); mTOR, 5'-GTGGTGGCAGATGTGCTTAG-3'-(Forward), and 5'-TTCAGAGCCACAAACAAGGC-3'-(Reverse); GAPDH, 5'-CACCATCTTCCAGGAGCGA GA-3'-(Forward), and 5'-CATGACGAACATGGGGG CAT-3'-(Reverse).…”