Ramulus Mori (Sangzhi) Alkaloids Ameliorate Obesity-Linked Adipose Tissue Metabolism and Inflammation in Mice

Abstract

Obesity has become a global epidemic disease as it is closely associated with a chronic low-grade inflammatory state that results in metabolic dysfunction. Ramulus Mori (Sangzhi) alkaloids (SZ-A) derived from Morus alba L. were licensed to treat type 2 diabetes (T2DM) in 2020. In this study, we explored the effect of SZ-A on adipose tissue metabolism and inflammation using an obesity model induced by a high-fat diet (HFD). C57BL/6J mice were fed high fat for 14 weeks and followed by SZ-A 400 mg/kg treatment via gavage for another six weeks, during which they were still given the high-fat diet. The results showed that SZ-A notably reduced body weight and serum levels of lipid metabolism-related factors, such as triglycerides (TG) and total cholesterol (TC); and inflammation-related factors, namely tumor necrosis factor alpha (TNFα), interleukin 6 (IL6), fibrinogen activator inhibitor-1 (PAI-1), angiopoietin-2 (Ang-2), and leptin (LEP), in the HFD-induced mice. SZ-A increased the protein and mRNA expression of lipid metabolism-related factors, including phosphorylated acetyl coenzyme A carboxylase (p-ACC), phosphorylated hormone-sensitive triglyceride lipase (p-HSL), adipose triglyceride lipase (ATGL), and peroxisome proliferator-activated receptor-alpha (PPARα), in adipose tissue. Immunohistochemistry results demonstrated that SZ-A significantly reduced the infiltration of pro-inflammatory M1-type macrophages in epididymal fat. The data also suggested that SZ-A down-regulates the transcriptional levels of inflammatory factors Il6, Tnfα, monocyte chemoattractant protein-1 (Mcp1), and F4/80, and up-regulates interleukin 4 (Il4), interleukin 10 (Il10), and interleukin 13 (Il13) in adipose tissue. Overall, the results indicate that SZ-A exhibits potential in regulating lipid metabolism and ameliorating obesity-linked adipose inflammation.

Keywords: Ramulus Mori alkaloids; adipose inflammation; adipose tissue; lipid metabolism; obesity.

Figure 1

(A) Schematic diagram of the experimental procedure (n = 10/group). (B) SZ-A (400 mg/kg i.g.) significantly inhibited HFD-induced weight gain. (n = 10/group) (C–E) Effects of SZ-A (400 mg/kg) on serum levels of TGs (C), TC (D) and HDL-C/LDL-C (E) (n = 6/group) (F) Representative images of hematoxylin-eosin (H&E) staining of eWAT, iWAT, and BAT to observe lipid droplet size. (Scale bar, 100 μm in red and 50 μm in blue). (G) Total fat mass. (H) Quantification of eWAT and iWAT adipocyte area (n = 3~5/group). Values represent mean ± SEM. (# p < 0.05, ## p < 0.01, ### p < 0.001 HFD vs. NC and * p < 0.05, *** p < 0.001 HFD vs. SZ-A).

Figure 2

(A) Protein expressions of PPARα and ATGL in eWAT were determined using western blotting. (B) Protein expressions of ACC, p-ACC, HSL, and p-HSL in eWAT were determined using western blotting (n = 5~6/group). (C) and (D) Protein expression histograms. (E) and (F) Relative mRNA expression levels of genes, including Atgl, Hsl, Pparα, and Cpt1a (n = 4~6/group). Values represent mean ± SEM. (# p < 0.05, ## p < 0.01, ### p < 0.001, #### p < 0.0001, HFD vs. NC and * p < 0.05, ** p < 0.01, *** p < 0.001 HFD vs. SZ-A).

Figure 3

(A) Heatmap of NC, HFD, and SZ-A groups differential genes (Padj < 0.05, |log2(foldchange)|> 1.5) in eWAT (B) Volcano plot of the differentially expressed genes (Padj < 0.05, |log2(foldchange)| > 1.5) in eWAT of HFD control and SZ-A group (n = 7/group). Red and blue represent the high and low expression of genes in the SZ-A group, respectively. (C) GO analysis on the down-regulated genes in the SZ-A group. (D) KEGG analysis on the down-regulated genes in the SZ-A group. (E) Protein-protein interaction network analysis of the top 100 differential genes using String database.

Figure 4

Inflammatory factors TNFα (A), IL6 (B), PAI-1 (C), Ang-2 (D), LEP (E) and ADPN (F) in the serum of the three groups of mice (n = 6/group). Representative images of F4/80 (G) and CD86 (H) immunohistochemical staining of the eWAT (Scale bar, 100 µm, and 200 µm). The histograms indicate quantification of F4/80 or CD86 positive area per field (n = 6/group). Values represent mean ± SEM. (# p < 0.05, ## p < 0.01, ### p < 0.001 HFD vs. NC and * p < 0.05, ** p < 0.01, *** p < 0.001 HFD vs. SZ-A).

Figure 5

(A–C) Relative mRNA expression levels of the pro-inflammatory-related factors, including F4/80, Mcp1, and Tnfα, in the three groups. (D–F) Relative mRNA expression levels of the anti-inflammatory-related factors, including Il4, Il10, and Il13, in the three groups. (G,H) Relative mRNA expression levels of Tlrs and downstream genes, including Tlr2, Tlr7, Tlr8, MyD88, Trif, and Irf8. (n = 6/group). Values represent mean ± SEM. (# p < 0.05, ## p < 0.01, ### p < 0.001 HFD vs. NC and * p < 0.05, ** p < 0.01, *** p < 0.001 HFD vs. SZ-A).