Analysis of Antidepressant Activity of Huang-Lian Jie-Du Decoction Through Network Pharmacology and Metabolomics

Abstract

Depressive disorder is a common mental disorder characterized by depressed mood and loss of interest or pleasure. As the Herbal medicines are mainly used as complementary and alternative therapy for depression. This study aimed at exploring antidepressant activity of Huang-lian Jie-du Decoction (HLJDD), and evaluating active components and potential depression-associated targets. HLJDD was administered on chronic unpredictable mild stress-induced (CUMS) depressive mice. Behavior evaluation was performed through force swimming test (FST), novelty-suppressed feeding test (NSF), and open field test (OFT). Active components of HLJDD, potential targets, and metabolic pathways involved in depression were explored through systemic biology-based network pharmacology assay, molecular docking and metabonomics. FST assay showed that CUMS mice administered with HLJDD had significantly shorter immobility time compared with control mice. Further, HLJDD alleviated feeding latency of CUMS mice in NSFand increased moving distance and duration in OFT. In the following network pharmacology assay, thirty-eight active compounds in HLJDD were identified based on drug-like characteristics, and pharmacokinetics and pharmacodynamics profiles. Moreover, forty-eight molecular targets and ten biochemical pathways were uncovered through molecular docking and metabonomics. GRIN2B, DRD, PRKCA, HTR, MAOA, SLC6A4, GRIN2A, and CACNA1A are implicated in inhibition of depressive symptoms through modulating tryptophan metabolism, serotonergic and dopaminergic synaptic activities, cAMP signaling pathway, and calcium signaling pathway. Further network pharmacology-based analysis showed a correlation between HLJDD and tryptophan metabolism. A total of thirty-seven active compounds, seventy-six targets, and sixteen biochemical pathways were involved in tryptophan metabolism. These findings show that HLJDD acts on potential targets such as SLC6A4, HTR, INS, MAO, CAT, and FoxO, PI3K/Akt, calcium, HIF-1, and mTOR signaling pathways, and modulates serotoninergic and dopaminergic synaptic functions. In addition, metabonomics showed that tryptophan metabolism is the primary target for HLJDD in CUMS mice. The findings of the study show that HLJDD exhibited antidepressant effects. SLC6A4 and MAOA in tryptophan metabolism were modulated by berberine, baicalein, tetrahydroberberine, candicine and may be the main antidepressant targets for HLJDD.

Keywords: depression; huang-lian jie-du decoction; metabolomics; network pharmacology; tryptophan metabolism.

FIGURE 1

FST results showing effect of HLJDD on behavior of CUMS mice. (A) Schematic diagram of the experimental process; (B) Body weight of mice in the control group and mice in HLJDD or FXT treatment groups after six weeks (n = 10, Mean ± SEM); (C) Immobility time of mice in the control group and HLJDD or FXT treated groups after carrying out FST 1 h after treatment (n = 10, Mean ± SEM). *p < 0.05, **p < 0.01, compared with Control; ##p < 0.01, compared with CUMS via ANOVA.

FIGURE 2

NSF results showing effect of HLJDD on behavior of CUMS mice. (A) Individual response times for consuming food of mice in the three groups (n = 10); (B) Kaplan–Meier curves of latency in mice in the three groups (n = 10); (C) Latency of approaching and consuming food of mice in the three groups 1 h after administration (n = 10, Mean ± SEM). **p < 0.01, compared with Control; ##p < 0.01, compared with CUMS via ANOVA.

FIGURE 3

OFT results showing effect of HLJDD on behavior of CUMS mice. (A) Trajectory of an individual mouse over a 5-min testing session in the OFT; (B) Total distance covered by mice in the three groups 1 h after treatment; (C) Total time of movement for mice in the three groups taken 1 h before the testing; (D) Distance covered by mice at the center of open field in all groups 1 h after oral gavage; (E) Time spent by mice to move at the center of arena with or without CUMS stress and CUMS mice with the treatment of HLJDD or FXT 1 h after oral gavage. Data are expressed as Mean ± SEM, n = 10 mice per group. **p < 0.01, compared with Control; #p < 0.05, ##p < 0.01, compared with CUMS via ANOVA.

FIGURE 4

Effect of HLJDD on neurotransmitters in different brain regions. Levels of GABA (A), Glu (B), 5-HT (C), DA (D), Ach (E) in the hippocampus, cortex, striatum and amygdala as detected using LC-MS/MS. Data are expressed as Mean ± SEM, n = 10 mice per group. *p < 0.05, **p < 0.01, compared with Control; #p < 0.05, ##p < 0.01, compared with CUMS via ANOVA.

FIGURE 5

Active compounds and targets of HLJDD against depression and tryptophan metabolism. (A) Active ingredients of HLJDD associated with depression; (B) HLJDD target proteins against depression; (C) Active compounds of HLJDD against tryptophan metabolism; (D) Target proteins of HLJDD associated with tryptophan metabolism.

FIGURE 6

(A–C) GO enrichment analysis of potential targets for primary active ingredients from HLJDD against depression. BP, biological process; CC, cellular components; MF, molecular function.; (D) HLJDD active compound-target-pathway network of depression; (E) Interaction network of HLJDD target proteins involved in depression.

FIGURE 7

Tryptophan metabolism network diagrams after HLJDD treatment. (A) HLJDD active ingredient-target network associated with tryptophan metabolism; (B) HLJDD target-pathway network associated with tryptophan metabolism; (C) Core HLJDD target proteins interaction network associated with tryptophan metabolism.

FIGURE 8

(A) Stereoview of substrate/inhibitor binding of SLC6A4 and MAOA with berberine, baicalein, tetrahydroberberine, and candicine; (B) Biding poses of berberine at SLC6A4 and MAOA binding sites.

FIGURE 9

Metabonomics analysis of hippocampus from CUMS mice using LC-Q/TOF-MS. (A) Metabolites associated with CUMS conditions with or without HLJDD; (B) Correlation heatmap of the 18 different metabolites; (C) Metabolic network of the metabolites constructed using MetScape; (D) Metabolic pathways as visualized using MetaboAnalyst. 1, tryptophan metabolism; 2, glycerol phospholipid metabolism; 3, glycine, serine and threonine metabolism; 4, tyrosine metabolism. Data are expressed as Mean ± SEM, n = 10 mice per group. *p < 0.05, **p < 0.01, compared with Control; #p < 0.05, ##p < 0.01, compared with CUMS via ANOVA.

FIGURE 10

Representative metabolic pathways demonstrated in metabonomics analysis, green represents pathways downregulated by HLJDD whereas red represents pathways upregulated by HLJDD.