To Predict Anti-Inflammatory and Immunomodulatory Targets of Guizhi Decoction in Treating Asthma Based on Network Pharmacology, Molecular Docking, and Experimental Validation

Abstract

Asthma, characterized by the continuous inflammatory response caused by a variety of immune cells, is one of the most common chronic respiratory diseases worldwide. Relevant clinical trials proved that the traditional Chinese medicine formula Guizhi Decoction (GZD) had multitarget and multichannel functions, which might be an effective drug for asthma. However, the effective ingredients and mechanisms of GZD against asthma are still unclear. Therefore, network pharmacology, molecular docking, and cell experiments were performed to explore the antiasthma effects and potential mechanisms of GZD. First, we applied the TCMSP database and literature to obtain the bioactivated ingredients in GZD. SwissTargetPrediction, TCMSP, GeneCards, OMIM, PharmGkb, TTD, DrugBank, and STRING database were used to get core genes. In addition, the key pathways were analyzed by the DAVID database. Molecular docking was used to predict whether the important components could act on the core target proteins directly. Finally, qPCR was carried out to verify the network pharmacology results and the possible mechanisms of GZD in the treatment of asthma. We collected 134 active ingredients in GZD, 959 drug targets, and 3223 disease targets. 431 intersection genes were screened for subsequent analysis. Through GO and KEGG analyses, enriched pathways related to inflammation and immune regulation were presented. Through the qPCR method to verify the role of essential genes, we found that GZD had an excellent anti-inflammatory effect. Direct or indirect inhibition of MAPK and NF-κB pathways might be one of the crucial mechanisms of GZD against asthma. GZD might be a promising potential drug for the treatment of asthma. This article provided a reference for the clinical application of GZD.

Figure 1

Graphical abstract. Network pharmacology, molecular docking, and experimental verification methods were used to clarify the pharmacological mechanism of GZD against asthma.

Figure 2

Screening of asthma targets. Venn diagram of targets for asthma from DrugBank (green), GeneCards (pink), OMIM (blue), PharmGkb (purple), and TTD (orange) databases.

Figure 3

Venn diagram of the potential genes in asthma and GZD. The overlapped genes represented core genes (purple). The blue part represented the unique drug targets, and the red part represented the unique disease targets.

Figure 4

PPI network construction. We took the intersection of drug targets and disease targets to obtain 431 intersection genes of GZD against asthma and put them into STRING to analyze the correlation between them, and to build a PPI visualization network, high confidence 0.9 was selected as the confidence interval. The connection represented the correlation between protein and protein.

Figure 5

PPI network of core targets. As the degree increases, the color is darker (closer to red). Innate immune response-related genes are highlighted in the green box; inflammatory response-related genes are highlighted in the red boxes.

Figure 6

Bubble diagram of functional pathway analysis. Pathway enrichment analyses of 94 core targets by DAVID database. The size of the circle represented the number of genes; the shade of the color represented the size of the p value.

Figure 7

Histogram of GO analyses. GO enrichment analyses of 94 core targets by DAVID database. Green represented the biological process, orange represented the cellular component, and blue represented the molecular function.

Figure 8

Ingredient-target-pathway network. We analyze the relationships of vital ingredients, key targets, and top 20 pathways. Purple “V” nodes represented the ingredients in GZD; orange nodes represented the key targets (as the degree increases, the color closed to orange; the targets in the red box were the focus of subsequent experimental validation); green nodes represented the top pathways.

Figure 9

Heat map of molecular docking. The blue group represented the key five components of GZD obtained in Section 3.5, and the pink represented the positive control group (the known ligands for the top nine targets). The greener the area, the lower the binding energy and the more stable the docking result.

Figure 10

Analyses of representative target protein–ingredient docking simulation. (a) Quercetin acts on IKBKB. (b) Glabrene acts on HRAS. (c) Phaseolinisoflavan acts on CHUK. (d) Glabrene acts on MAPK1. (e) Phaseolinisoflavan acts on MAPK3. (f) Ursolic acid acts on NFKB1. (g) Glabrene acts on PIK3CG. (h) Phaseolinisoflavan acts on RAF1. (i) Ursolic acid acts on RELA. The red box on the left indicated the macromolecule binding pocket. The green in the middle picture represented the ligand, and the red represented the amino acid residues connected with hydrogen bonds. The blue box on the right indicated the binding bond of docking, and the cyan rod shape represented the hydrogen bond.

Figure 11

Cell viability test and the effect of GZD on the secretion of inflammatory cytokines in vitro. LPS-induced MH-S cells were employed. MH-S cells were incubated with DMSO (the same dose as GZD) for 24 hr as the control group. MH-S cells were incubated with GZD (0, 1, 10, 100, 200, and 500 μg/mL) only for 24 hr as the GZD group. MH-S cells were incubated with/without LPS (5 μg/mL) in the absence or presence of different concentrations with GZD (0, 1, 10, 100, 200, and 500 μg/mL) for 24 hr. (a, b) The cell viability was examined using a CCK-8 assay. (c–e) The mRNA relative expressions of proinflammatory cytokines (IL-1β, IL-6, and TNF-α) in LPS-stimulated cells treated with/without GZD were detected using qPCR. ^###p < 0.001 vs. the control group, ^∗∗∗p < 0.001, ^∗∗p < 0.01, ^∗p < 0.05 vs. the model group.

Figure 12

The Effect of GZD on the secretion of key genes' mRNA relative expression in vitro by using qPCR. MH-S cells were incubated with DMSO (the same dose as GZD) for 24 hr as the control group. MH-S cells were incubated with/without LPS (5 μg/mL) in the absence or presence of different concentrations with GZD (0, 10, 100, 200 μg/mL) for 24 hr. (a–i) mRNA relative expression of MAPK1, NFKB1, CHUK, MAPK3, IKBKB, PIK3CG, RAF1, NRAS, and RELA in different groups. ^###p < 0.001, ^##p < 0.01, ^#p < 0.05vs. the control group, ^∗∗∗p < 0.001, ^∗∗p < 0.01, ^∗p < 0.05 vs. the model group.

Figure 13

Representative pathway diagram. MAPK signaling pathway and NF-κB signaling pathway were the greater representative pathways in GZD against asthma based on the results of network pharmacology. The genes in red represented important targets verified in this study. The genes in green represented other genes in these pathways.