The Mechanism Underlying the Hypoglycemic Effect of Epimedin C on Mice with Type 2 Diabetes Mellitus Based on Proteomic Analysis

Abstract

Type 2 diabetes mellitus (T2DM) has become a worldwide public health problem. Epimedin C is considered one of the most important flavonoids in Epimedium, a famous edible herb in China and Southeast Asia that is traditionally used in herbal medicine to treat diabetes. In the present study, the therapeutic potential of epimedin C against T2DM was ascertained using a mouse model, and the mechanism underlying the hypoglycemic activity of epimedin C was explored using a label-free proteomic technique for the first time. Levels of fasting blood glucose (FBG), homeostasis model assessment of insulin resistance (HOMA-IR), and oral glucose tolerance, as well as contents of malondialdehyde (MDA) and low-density lipoprotein cholesterol (LDL-C) in the 30 mg·kg^-1 epimedin C group (EC30 group), were significantly lower than those in the model control group (MC group) (p < 0.05), while the contents of hepatic glycogen, insulin, and high-density lipoprotein cholesterol (HDL-C), as well as activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) in the EC30 group were notably higher than those in the MC group (p < 0.05). The structures of liver cells and tissues were greatly destroyed in the MC group, whereas the structures of cells and tissues were basically complete in the EC30 group, which were similar to those in the normal control group (NC group). A total of 92 differentially expressed proteins (DEPs) were enriched in the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. In the EC30 vs. MC groups, the expression level of cytosolic phosphoenolpyruvate carboxykinase (Pck1) was down-regulated, while the expression levels of group XIIB secretory phospholipase A2-like protein (Pla2g12b), apolipoprotein B-100 (Apob), and cytochrome P450 4A14 (Cyp4a14) were up-regulated. According to the KEGG pathway assay, Pck1 participated in the gluconeogenesis and insulin signaling pathways, and Pla2g12b, Apob, and Cyp4a14 were the key proteins in the fat digestion and fatty acid degradation pathways. Pck1, Pla2g12b, Apob, and Cyp4a14 seemed to play important roles in the prevention and treatment of T2DM. In summary, epimedin C inhibited Pck1 expression to maintain FBG at a relatively stable level, promoted Pla2g12b, Apob, and Cyp4a14 expressions to alleviate liver lipotoxicity, and protected liver tissues and cells from oxidant stress possibly by its phenolic hydroxyl groups.

Keywords: epimedin C; label-free proteomic technique; mechanism of action; mice; type 2 diabetes mellitus.

Figure 1

Effects of epimedin C on the fasting blood glucose (FBG) level and the hepatic glycogen content. (A) FBG levels at 0 d (before administration); (B) changes in FBG levels from 0 to 28 d; (C) FBG levels at 28 d; (D) hepatic glycogen content at 28 d. Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 1

Effects of epimedin C on the fasting blood glucose (FBG) level and the hepatic glycogen content. (A) FBG levels at 0 d (before administration); (B) changes in FBG levels from 0 to 28 d; (C) FBG levels at 28 d; (D) hepatic glycogen content at 28 d. Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 2

Effects of epimedin C on insulin content in serum (A) and homeostasis model assessment of insulin resistance (HOMA-IR) (B). Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 3

Effects of epimedin C on oral glucose tolerance. (A) FBG levels from 0 to 2 h and (B) area under the curve (AUC). Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 4

Effects of epimedin C on oxidative stress. (A) Superoxide dismutase (SOD) activity in serum; (B) malondialdehyde (MDA) content in serum; (C) glutathione peroxidase (GSH-Px) activity in serum; (D), SOD activity in livers; (E) MDA content in livers; (F) GSH-Px activity in livers. Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 4

Effects of epimedin C on oxidative stress. (A) Superoxide dismutase (SOD) activity in serum; (B) malondialdehyde (MDA) content in serum; (C) glutathione peroxidase (GSH-Px) activity in serum; (D), SOD activity in livers; (E) MDA content in livers; (F) GSH-Px activity in livers. Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 5

Effects of epimedin C on blood lipid levels. (A) High-density lipoprotein cholesterol (HDL-C) content in serums; (B) low-density lipoprotein cholesterol (LDL-C) content in serums; (C) HDL-C content in livers; (D), LDL-C content in livers. Different letters on the top of columns indicate significant differences between groups (p < 0.05).

Figure 6

Effects of epimedin C on liver histopathological changes. Structures of liver cells and tissues in the NC group (A), the MC group (B), the PC1 group (C), the PC2 group (D), the EC30 group (E), the EC10 group (F), and the EC5 group (G).

Figure 7

Differentially expressed protein (DEP) analyses in different groups. (A) The numbers of down-regulated and up-regulated DEPs in the NC group vs. the MC group, the NC group vs. the EC30 group, and the EC30 group vs. the MC group. (B) Venn diagram of DEPs in the NC group vs. the MC group, the NC group vs. the EC30 group and the EC30 group vs. the MC group. (C) Hierarchical clustering analysis of DEPs in the NC, MC, and EC30 groups.

Figure 8

Gene ontology (GO) (A) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses (B) of DEPs.

Figure 9

Protein–protein interaction (PPI) networks and degree of protein–protein connectivity of DEPs. (A) PPI network of 92 DEPs screened with a hierarchical clustering analysis. (B) PPI network of 14 DEPs characterized with GO and KEGG assays. Red nodes represent up-regulated DEPs, and blue nodes represent down-regulated DEPs. The size of each node reflects the degree of interaction. Line thickness indicates the interaction score between two DEPs, and only DEPs with an interaction score >0.4 are displayed.

Figure 9

Protein–protein interaction (PPI) networks and degree of protein–protein connectivity of DEPs. (A) PPI network of 92 DEPs screened with a hierarchical clustering analysis. (B) PPI network of 14 DEPs characterized with GO and KEGG assays. Red nodes represent up-regulated DEPs, and blue nodes represent down-regulated DEPs. The size of each node reflects the degree of interaction. Line thickness indicates the interaction score between two DEPs, and only DEPs with an interaction score >0.4 are displayed.