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.

Keywords: SJZT; aging; experimental validation; network pharmacology.

Figure 1

Determination of active components of SJZT and common targets related to aging. (A) SJZT-compounds-target network: The blue round rectangle stand for targets of SJZT compounds, red ellipse and green ellipse stand for SJZT compounds, the edge represented the interaction between nodes, degree indicated nodes sizes stand for interacting with the number of targets. Pink Hexagonal represents SJZT formula. (B) SJZT-target-aging network. The SJZT-target-aging network contains 241 nodes and 478 edges.

Figure 2

PPI analysis of common targets of SJZT to identify core targets. (A) PPI network of SJZT anti-aging related protein from STRING database. (B) Higher degrees indicated larger node sizes and the edge thickness represents the connection score. Screening of the Top 10 core targets. Nodes size and red color depth are proportional to their degree.

Figure 3

Molecular docking between small molecule ligands and core targets protein. The 3D surface structure of protein receptors and small molecular ligands is shown on the left. The right side shows the binding pattern of the small molecular ligand to the core target protein. AKT1-PDBID: 4gv1, STAT3-PDBID: 6njs, MAPK14-PDBID: 3Zs5, RELA-PDBID: 6nv2, IL6-PDBID: 1alu. (A) Quercetin (B) Kaempferol.

Figure 4

Bioinformatics analysis of predicted targets. (A) The distribution of GO entries in biological process, molecular function and cell composition (top 12 according to FDR < 0.05) are shown. (B) The top 20 KEGG pathways. The color scale indicates the different thresholds for the p-values, and the size of the dots represents the number of genes corresponding to each term.

Figure 5

SJZT can improve the aging-related phenotype of aging mice. (A) Body weight of mice after SJZT or distilled water treatment. (B) Representative photos of the old mice and after SJZT treatment with considerable differences in skin and in hair. And μCT of the isolated shin bones to assess bone porosity. (C) The protein levels of P16, P21 and P53 determined by Western blots in mice liver tissue. (D) The mRNA levels of p16 and IL6 determined by qPCR in mice Liver tissue. (E, F) The protein expression levels of liver tissues were detected by western blot analysis. Data represent the means ± standard deviation. *P<0.05, **P<0.01 or ***P<0.001 (n=3).

Figure 6

The mechanisms of SJZT in the treatment of aging by the network pharmacology approach and experimental validation.