Integrating Metabolomics and Network Pharmacology to Explore the Mechanism of Xiao-Yao-San in the Treatment of Inflammatory Response in CUMS Mice

Abstract

Depression can trigger an inflammatory response that affects the immune system, leading to the development of other diseases related to inflammation. Xiao-Yao-San (XYS) is a commonly used formula in clinical practice for treating depression. However, it remains unclear whether XYS has a modulating effect on the inflammatory response associated with depression. The objective of this study was to examine the role and mechanism of XYS in regulating the anti-inflammatory response in depression. A chronic unpredictable mild stress (CUMS) mouse model was established to evaluate the antidepressant inflammatory effects of XYS. Metabolomic assays and network pharmacology were utilized to analyze the pathways and targets associated with XYS in its antidepressant inflammatory effects. In addition, molecular docking, immunohistochemistry, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), and Western Blot were performed to verify the expression of relevant core targets. The results showed that XYS significantly improved depressive behavior and attenuated the inflammatory response in CUMS mice. Metabolomic analysis revealed the reversible modulation of 21 differential metabolites by XYS in treating depression-related inflammation. Through the combination of liquid chromatography and network pharmacology, we identified seven active ingredients and seven key genes. Furthermore, integrating the predictions from network pharmacology and the findings from metabolomic analysis, Vascular Endothelial Growth Factor A (VEGFA) and Peroxisome Proliferator-Activated Receptor-γ (PPARG) were identified as the core targets. Molecular docking and related molecular experiments confirmed these results. The present study employed metabolomics and network pharmacology analyses to provide evidence that XYS has the ability to alleviate the inflammatory response in depression through the modulation of multiple metabolic pathways and targets.

Keywords: CUMS; Xiao-Yao-San; depression; inflammation; metabolomics; network pharmacology.

Figure 1

Experimental flowchart.

Figure 2

Identification of chemical components of XYS. Total Ion Chromatogram (TIC) in positive (a) and negative (b).

Figure 3

Effect of Xiao-Yao-San on depression-like behaviors in chronic unpredictable mild stress mice. (a) Mouse moulding time flow chart. (b–d) Open Field Test (OFT). (e) Sucrose Preference Test (SPT). (f) Forced Swimming Test (FST). (g) Tail Suspension Test (TST). Data were presented mean ± SD, n = 10 pre group. ** p < 0.01 compared with the control group, ^# p < 0.05, ^## p < 0.01, compared with the CUMS group.

Figure 4

Effect of XYS on the inflammatory response in the serum of CUMS mice. (a) IL-6 level. (b) TNF-α level. (c) IL-1β level. (d) Spleen index. Data were presented mean ± SD, n = 6 pre group. ** p < 0.01 compared with the control group, ^# p < 0.05, ^## p < 0.01, compared with the CUMS group.

Figure 5

Multivariate statistical analysis of metabolic characters of mouse spleen samples. PCA score plot of metabolomic analysis in the ESI+ model (a) and ESI− model (b). The OPLS-DA score graph (c), permutation (d) and S-plot (e) of the control vs. CUMS groups as well as the plot of OPLS-DA (f) permutation (g) and S-plot (h) of CUMS vs. XYS-H for positive ion mode. In the S-plot, the red markers indicate metabolites with VIP values ≥ 1, while the green markers represent metabolites with VIP values < 1.

Figure 6

Heatmap visualization of the 21 potential metabolites expressed in mouse spleens (a). Pathway analysis of the specific metabolites (b): a. Pyrimidine metabolism, b. Glutathione metabolism, c. Phenylalanine, tyrosine and tryptophan biosynthesis, d. Glycine, serine, and threonine metabolism. In the bubble plot, larger and darker bubbles represent a stronger correlation between the metabolic pathway and the differential metabolite.

Figure 7

Network pharmacological analysis of XYS in the treatment of depression inflammation. (a) Venn diagram of overlapping targets. (b) Intersection target PPI viewable. The higher the degree of the node, the darker the node will be. The thickness of the edges is related to the combined score. (c) Top 10 key targets in terms of degree. (d) The KEGG pathway enrichment analysis of the potential targets. (e) GO terms enrichment analysis. (f) The disease-ingredients-target network map, with disease names labeled in green, intersecting targets labeled in purple, drugs labeled in light blue, and chemical components labeled in pink.

Figure 8

Molecular docking results. (a) Heat map of ligand-receptor binding energy. (b–e) Molecular docking pattern diagram.

Figure 9

Immunohistochemistry (magnification 400×, n = 3) (a,c,d), RT-qPCR (e,f) and Western Blot (b,g,h) analysis of key targets. Data are presented as mean ± SD, n = 3 pre group. * p < 0.05, ** p < 0.01 compared with the control group, ^# p < 0.05, ^## p < 0.01, compared with the CUMS group.