Exploring Anti-Type 2 Diabetes Mellitus Mechanism of Gegen Qinlian Decoction by Network Pharmacology and Experimental Validation

Abstract

Purpose: Gegen Qinlian Decoction (GGQL) has been employed to treat type 2 diabetes mellitus (T2DM) in the clinical practice of traditional Chinese medicine. However, the underlying mechanism of GGQL in the treatment of T2DM remains unknown. This study was aimed at exploring the pharmacological mechanisms of GGQL against T2DM via network pharmacology analysis combined with experimental validation.

Methods: The effective components of GGQL were screened, and the target was predicted by using traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP). The candidate targets of GGQL were predicted by network pharmacological analysis, and crucial targets were chosen by the protein-protein interaction (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed to predict the core targets and pathways of GGQL against T2DM. Then, T2DM mice were induced by a high-fat diet combined with streptozotocin. The model and GGQL groups were given normal saline and GGQL aqueous solution (10 and 20 g/kg/d) intragastric administration, respectively, for 8 weeks. The mice in the GGQLT groups were administered with GGQLT at 10 and 20 g/kg/d, respectively. The pathological changes in liver tissues were observed by hematoxylin-eosin staining. The protein expression of TNF-α and NF-κB was verified by western blotting.

Results: A total of 204 common targets of GGQL for the treatment of T2DM were obtained from 140 active ingredients and 212 potential targets of T2DM. GO and KEGG enrichment analysis involved 119 signaling pathways, mainly in inflammatory TNF signaling pathways. Animal experiments showed that GGQL significantly reduced the serum levels of body mass, fasting blood glucose, fasting insulin, HOMA-IR, TNF-α, and IL-17. The liver pathological section showed that GGQL could improve the vacuolar degeneration and lipid deposition in the liver of T2DM mice. Mechanistically, GGQL downregulated the mRNA expression of TNF-α and NF-κB.

Conclusions: This study demonstrated that GGQL may exert antidiabetic effects against T2DM by suppressing TNF-α signaling pathway activation, thus providing a basis for its potential use in clinical practice and further study in treating T2DM.

Figure 1

Potential GGQL targets treat T2DM and network analysis. (a) Venn diagram summarizing the intersection targets of the GGQL and T2DM. (b) Network of targets shared between GGQL and T2DM. The ring represented the composition, and the rectangle represented target genes.

Figure 2

PPI network construction and key targets. (a) PPI network of potential targets of GGQL for the treatment of T2DM. (b) Obtaining 18 core proteins of GGQL for T2DM.

Figure 3

Top 10 GO terms of hub genes.

Figure 4

Top 30 KEGG pathways of hub genes.

Figure 5

The key KEGG pathway: TNF signaling pathway. The red nodes represent overlapping targets between GGQL and T2DM.

Figure 6

(a) Body weight was measured every week during the treatment period. The expression of serum (b) glucose and (c) insulin concentration. (d) HOMA-IR was calculated at the end of the experiment. ^∗P < 0.05; ^##P < 0.01. Data are expressed as the means ± SEM of at least three independent experiments.

Figure 7

Effects of GGQL on hepatic pathological changes. Histological observation of the hematoxylin and eosin (H&E) sections (original magnification ×400). Macrovesicular steatosis was observed in the livers of mice.

Figure 8

Western blot and densitometric analysis of the expression of TNF-α, NF-κB, and β-action in mice. ^∗∗P < 0.01 compared to the NFD group, which was considered statistically significant.