Research on the Mechanism of Guizhi to Treat Nephrotic Syndrome Based on Network Pharmacology and Molecular Docking Technology

Abstract

Objective: Nephrotic syndrome (NS) is a common glomerular disease caused by a variety of causes and is the second most common kidney disease. Guizhi is the key drug of Wulingsan in the treatment of NS. However, the action mechanism remains unclear. In this study, network pharmacology and molecular docking were used to explore the underlying molecular mechanism of Guizhi in treating NS.

Methods: The active components and targets of Guizhi were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Hitpick, SEA, and Swiss Target Prediction database. The targets related to NS were obtained from the DisGeNET, GeneCards, and OMIM database, and the intersected targets were obtained by Venny2.1.0. Then, active component-target network was constructed using Cytoscape software. And the protein-protein interaction (PPI) network was drawn through the String database and Cytoscape software. Next, Gene Ontology (GO) and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID database. And overall network was constructed through Cytoscape. Finally, molecular docking was conducted using Autodock Vina.

Results: According to the screening criteria, a total of 8 active compounds and 317 potential targets of Guizhi were chosen. Through the online database, 2125 NS-related targets were identified, and 93 overlapping targets were obtained. In active component-target network, beta-sitosterol, sitosterol, cinnamaldehyde, and peroxyergosterol were the important active components. In PPI network, VEGFA, MAPK3, SRC, PTGS2, and MAPK8 were the core targets. GO and KEGG analyses showed that the main pathways of Guizhi in treating NS involved VEGF, Toll-like receptor, and MAPK signaling pathway. In molecular docking, the active compounds of Guizhi had good affinity with the core targets.

Conclusions: In this study, we preliminarily predicted the main active components, targets, and signaling pathways of Guizhi to treat NS, which could provide new ideas for further research on the protective mechanism and clinical application of Guizhi against NS.

Figure 1

A flow diagram based on a cohesive integration strategy of network pharmacology and molecular docking.

Figure 2

The Venny plot of 93 potential targets.

Figure 3

The active component-target network. The network formed with 101 nodes and 169 edges. The dark purple circles represented active compounds; the light purple inverted triangles represented intersecting targets. The edges represented the connection between active component and targets.

Figure 4

The PPI network diagram (a) and the bar graph of the top 10 intersecting targets with degree values in PPI network (b). In PPI network, nodes represented intersecting targets, and edges represented interactions between targets, and the size reflected the value of degree. In the bar graph, the top 10 targets were selected according to the degree value.

Figure 5

Go (a) and KEGG enrichment analysis (b). The top 10 items of BP, CC, and MF and the top 30 KEGG signal pathways were selected according to the P value to draw the GO and KEGG bubble diagram. The color of the bubbles changes from purple to red indicating that the P value decreases from large to small. Gene ratio is the number of targets that located in the pathway. The higher the gene ratio is, the more targets were enriched.

Figure 6

The overall network of the top 30 significant KEGG signaling pathways with their corresponding targets. The yellow represents Guizhi, pink represents active compounds, purple represents core targets, green represents signaling pathways, and dark green represents NS.

Figure 7

The binding energy of the main active components of Guizhi and core targets.

Figure 8

The molecular docking of active compounds and core targets: (a) cinnamaldehyde-PTGS2, (b) (-)-taxifolin-PTGS2, (c) (+)-catechin-PTGS2, (d) (-)-taxifolin-VEGFA, (e) (+)-catechin-SRC, (f) (+)-catechin-VEGFA, (g) ent-Epicatechin-PTGS2, (h) ent-Epicatechin-SRC, (i) peroxyergosterol-PTGS2, (j) taxifolin-PTGS2, (k) taxifolin-VEGFA, and (l) taxifolin-SRC.