In Silico and In Vivo Studies on the Mechanisms of Chinese Medicine Formula (Gegen Qinlian Decoction) in the Treatment of Ulcerative Colitis

Abstract

Ulcerative colitis (UC) is a chronic inflammatory bowel disease, and Gegen Qinlian Decoction (GQD), a Chinese botanical formula, has exhibited beneficial efficacy against UC. However, the mechanisms underlying the effect of GQD still remain to be elucidated. In this study, network pharmacology approach and molecular docking in silico were applied to uncover the potential multicomponent synergetic effect and molecular mechanisms. The targets of ingredients in GQD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) database, while the UC targets were retrieved from Genecards, therapeutic target database (TTD) and Online Mendelian Inheritance in Man (OMIM) database. The topological parameters of Protein-Protein Interaction (PPI) data were used to screen the hub targets in the network. The possible mechanisms were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to verify the binding affinity between the active compounds and hub targets. Network pharmacology analysis successfully identified 77 candidate compounds and 56 potential targets. The targets were further mapped to 20 related pathways to construct a compound-target-pathway network and an integrated network of GQD treating UC. Among these pathways, PI3K-AKT, HIF-1, VEGF, Ras, and TNF signaling pathways may exert important effects in the treatment of UC via inflammation suppression and anti-carcinogenesis. In the animal experiment, treatment with GQD and sulfasalazine (SASP) both ameliorated inflammation in UC. The proinflammatory cytokines (TNF-α, IL-1β, and IL-6) induced by UC were significantly decreased by GQD and SASP. Moreover, the protein expression of EGFR, PI3K, and phosphorylation of AKT were reduced after GQD and SASP treatment, and there was no significance between the GQD group and SASP group. Our study systematically dissected the molecular mechanisms of GQD on the treatment of UC using network pharmacology, as well as uncovered the therapeutic effects of GQD against UC through ameliorating inflammation via downregulating EGFR/PI3K/AKT signaling pathway and the pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6.

Keywords: gegen qinlian decoction (GQD); inflammatory bowel disease; molecular docking; network pharmacology; ulcerative colitis.

FIGURE 1

The flowchart of network pharmacology and molecular docking-based strategy for deciphering the underlying mechanisms of GQD on the treatment of UC.

FIGURE 2

The results of gene ontology (GO) biological process analysis. The X-axis represents gene count, while the Y-axis represents the categories of biological process (p-value ≤ 0.05).

FIGURE 3

Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The X-axis represents the enrichment rate of these genes in total genes, while the Y-axis represents the enrichment pathways of the target genes (p-value ≤ 0.05). The depth of the color represents the size of the value, and the size of circle represents the enrichment counts of these pathways.

FIGURE 4

The interaction network between compounds and hub targets. The hexagon represents the herbal compounds, the circles stand for the potential targets, and the edges represent the interactions between them. The depth of color and the size of circle are proportional to their degree value. The ID of the compounds was elaborated in Table 1.

FIGURE 5

Compound–target–pathway network. The rectangle, triangle and circle represent the potential targets, major pathways and botanical compounds, respectively. The rhombus represents the four botanical drugs in GQD. Edges represent the interaction between them. For the potential targets and pathways, the change in color depth reflect the degree value. For the botanical compounds, the circle size is proportional to their degree value. GG, Pueraria lobata (Willd.) Ohwi; HQ, Scutellaria baicalensis Georgi; HL, Coptis chinensis Franch; GC, Glycyrrhiza uralensis Fisch.

FIGURE 6

Molecular docking analysis of the binding affinity of the four active compounds toward to the hub target EGFR. (A) Puerarin, vina score = −8.4; (B) Baicalein, vina score = −7.8; (C) Berberine, vina score = −7.8; (D) Glabridin, vina score = −7.9. (E) SASP, vina score = −8.2.

FIGURE 7

HPLC profiles of the main active components of GQD. (A) The mixed standard sample. (B) The four active components. (C) The chemical formula of the four active compounds. 1. puerarin; 2. baicalein; 3. berberine; 4. glabridin.

FIGURE 8

GQD ameliorated DSS-induced body weight loss, DAI score and colonic shortening. (A) Change of body weight in four groups. (B) DAI score during experimental colitis. (C) Colon length in four groups. Values presented as mean ± SEM. ^## p < 0.01 vs. control; ^** p < 0.01 vs. model.

FIGURE 9

Representative images of colonic tissues with HE staining (× 100 and × 200 magnification).

FIGURE 10

The influence of GQD and SASP on proinflammatory cytokines TNFα (A), IL-1β (B), and IL-6 (C) in the colonic tissues. Values presented as mean ± SEM. ^## p < 0.01 vs. control; ^** p < 0.01 vs. model.

FIGURE 11

Western blot and quantitative analysis of EGFR, PI3K and p-AKT in the colon tissues. Values presented as mean ± SEM. ^## p < 0.01 vs. control; ^** p < 0.01 vs. model.