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. 2025 Feb 16;10(7):6780-6793.
doi: 10.1021/acsomega.4c08802. eCollection 2025 Feb 25.

Chaihu Shugan San Exerts Antidepressant Effects by Regulating Glucocorticoid Metabolism in CUMS Rats and Network Pharmacology Provides Complementary Mechanistic Insights

Yusen Xu  1 , Xuemei Han  1 , Ying Zhu  1 , Bin Deng  1 , Linjie Li  1 , Yujing Du  1 , Yanjie Qin  1 , Yongning Lv  1 , Xuejia Zhai  1
Affiliations

Chaihu Shugan San Exerts Antidepressant Effects by Regulating Glucocorticoid Metabolism in CUMS Rats and Network Pharmacology Provides Complementary Mechanistic Insights

Yusen Xu et al. ACS Omega. .

Abstract

Traditional Chinese medicine Chaihu Shugan San (CSGS) is a classic Chinese herb prescription for improving depression, but its specific molecular mechanism has not been fully clarified. This study integrates network pharmacology and experimental validation to investigate CSGS's antidepressant effects, focusing on its impact on GC metabolism and related pathways. In this research, the antidepressant mechanism of CSGS in relation to the depression model induced by chronic unpredictable mild stress will be discussed. High-performance liquid tandem mass spectrometry was applied for the verification of the grown metabolites' economic vitality in rat plasma and the prefrontal cortex. The revelation of behavioral test results showed that the administration of CSGS improved depression symptoms significantly at the end of the administration period, which was 8 weeks. Network pharmacology was used to assist in verifying and improving the mechanism by which the active ingredients of CSGS affect the glucocorticoid metabolic pathway to exert antidepressant effects. CSGS significantly improved glucocorticoid (GC) metabolism by reducing corticosterone (CORT) levels and increasing dehydrocorticosterone (11-DHCORT) and the 11-DHCORT/CORT ratio in plasma and PFC. It regulated GC metabolism in the liver and PFC by downregulating GC synthase (11β-HSD1) and upregulating GC metabolic enzymes (11β-HSD2). Additionally, CSGS restored GC signaling by upregulating GR and HSP-90α, downregulating FKBP51 and HSP-70, and alleviating inflammation by inhibiting NF-κB P65 and HAT expression. These effects, particularly in the liver and PFC, were stronger than those with fluoxetine. Network pharmacology revealed that CSGS targets multiple pathways including PI3K-Akt, FoxO, HIF-1, and mTOR. These results indicate that CSGS can improve the depressive state of rats by regulating glucocorticoid metabolism and other related pathways as well as downstream signaling proteins.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Flowchart of CUMS modeling experiment (OFT: open-field test, SPT: sucrose preference test; FST: forced swimming test).
Figure 2
Figure 2
Behavioral abnormalities in rats (n = 6) ((a) the total distance, (b) the total score, (c) the immobility time, (d) the sucrose preference index; 0 week: 1 week after adaptive feeding, 4 weeks: 4 weeks after CUMS modeling in the experimental group, 12 weeks: 8 weeks after the drug administration; ## indicates that P < 0.01 compared with Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 3
Figure 3
Concentration of CORT, 11-DHCORT and the ratio of 11-DHCORT/CORT in rat plasma and PFC (n = 6) ((a) GC concentration in plasma, (b) GC concentration in PFC; # indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 4
Figure 4
Effect of CSGS on GC metabolic enzymes in liver (n = 6) (# indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 5
Figure 5
Effect of CSGS on GC metabolic enzymes in PFC (n = 6) ((a) protein expression of 11β-HSD1, (b) protein expression of 11β-HSD2; # indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 6
Figure 6
CSGS on protein expression of the GC downstream signaling pathway in liver (n = 6) (# indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 7
Figure 7
Effect of CSGS on inflammation pathway in liver (n = 6) (# indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 8
Figure 8
CSGS on proteins expression of GC downstream signaling pathway in PFC (n = 6) (# indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01)
Figure 9
Figure 9
Effect of CSGS on inflammation pathway in PFC (n = 6) (# indicates that P < 0.05 compared with the Control group, ## indicates that P < 0.01 compared with the Control group; * means compared with the CUMS group, P < 0.05; ** means compared with the CUMS group, P < 0.01).
Figure 10
Figure 10
Blue rectangle represents the drug target, and the circle represents the active ingredient of CSGS. The depth of the circle color represents the number of active ingredients and targets. The darker the color, the more targets it acts on.
Figure 11
Figure 11
(a) The target predicted by CSGS active ingredients and the repeated target obtained by the intersection of depression targets. These repeated targets may represent the pathway by which CSGS active ingredients exert antidepressant effects. (b) KEGG enrichment results. (c) GO enrichment results.
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