Study on the Mechanism of ND for Treating Depression Based on “Blood Components-Network Pharmacology”

Abstract

Objective

To identify blood-absorbed components of Ninglung Deshee (ND) using liquid chromatography-mass spectrometry (LC-MS), and to investigate the mechanism of ND for treating depression using network pharmacology and molecular docking methods.

Methods

Ultra-high-performance liquid chromatography coupled with quadrupole electrostatic field orbitrap tandem high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS) was used to identify the components of ND in the blood. Potential target sites of these chemical components were identified using the SwissADME database, while disease-related targets for depression were selected from the GeneCards database. Intersecting targets between the two databases were then identified. Cytoscape software was used to construct a “component-target-disease” network to screen the core components of ND relevant to the treatment of depression. The STRING platform was used to construct a protein-protein interaction (PPI) network of the intersecting targets to identify the core targets of ND for treating depression. Gene ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID platform. Schrödinger software was used to determine molecular docking of the core components and core target proteins.

Results

Seventy-six blood components associated with 747 drug targets were identified,. Of the 2,000 depression-related targets, 212 overlapped with those of ND. A total of 16 core targets and 16 core components were identified. The GO function and KEGG pathway enrichment analyses indicated that treatment of depression with ND^’s may be related closely to the mitogen-activated protein kinase (MAPK) signaling and Prion disease signaling pathways. The molecular docking results showed that piperine had the highest binding energy with tumor necrosis factor (TNF).

Conclusion

The core components of ND such as piperine, liquiritigenin, and piperanine may act on targets including TNF, mitogen-activated protein kinase 3(MAPK3), estrogen receptor 1 (ESR1, and interleukin-6(IL-6), resulting in regulation of signaling pathways such as MAPK, TNF, and Prion disease involved in the treatment of depression. Accordingly, TNF as a therapeutic target and signaling pathway for ND to treat depression warrants further study, while other potential mechanisms and effector molecules of ND for treating depression require further detailed investigation.

Keywords

Ninglung Deshee blood-active components depression network pharmacology moleculardocking

1. Introduction

Depression is a major mental disorder¹ that is manifested clinically by a persistent low mood and loss of pleasure. The disorder is often accompanied by cognitive impairments such as lack of concentration and slow thinking, and physical discomforts such as sleep disturbances and decreased energy.² In severe cases, depression can lead to self-harm or suicidal thoughts and behaviors.³ According to the Global Burden of Disease Assessment Report released by the World Health Organization (WHO), depression had already become the second leading cause of disease burden and disability by 2020, and was expected to become the leading cause of global disease burden by 2030.⁴

Currently, the drugs used to treat depression mainly include tricyclic antidepressants, monoamine oxidase inhibitors, tetracyclic antidepressants, serotonin and norepinephrine reuptake inhibitors, norepinephrine, and dopamine reuptake inhibitors. In addition, newly discovered target drugs have been introduced in recent years, such as neurosteroids, indoleamine 2,3-dioxygenase inhibitors, and hormone drugs. However, these classic antidepressants generally have adverse effects such as high dependency, a potential for addiction, and easy development of resistance.^5,6 Therefore, from the perspective of traditional medicine, there is significant practical value to develop antidepressants that are safe, reliable, and have minimal side effects and consistent quality.

ND is a modified traditional Tibetan medicine, based on the “Srogzin-11” formula.⁷ The medicine was modified and formulated by Professor Tsering Namgyal (pinyin: CaiRang Nanjia) according to Tibetan medical theory and years of clinical experience. The formula consists of ten medicinal ingredients, including nutmeg [Myristica fragrans Houtt.], agarwood [Aquilaria sinensis (Lour.) Spreng.], Aucklandiae radix [Dolomiaea souliei (Franch.), C.Shih, (Rhodiola rosea L.), (Licorice [Glycyrrhiza uralensis Fisch. ex DC.]), (Terminalia chebula Retz.), (Fructus chaerospondiatis [Choerospondias axillaris (Roxb.) B.L.Burtt & A.W.Hill]), (Corydalis impatiens (Pall.) Fisch. ex DC.), (caraway [Carum carvi L.]), and (Schisandra chinensis (Turcz.) Baill.). According to Tibetan medicine theory, both ND and Srogzin-11 are used to treat“heart-lung”disease. In Tibetan medicine, the“lung”is a core concept fundamental to sustaining life, with its imbalance manifesting as a range of complex physical and mental disorders.⁸ The syndrome described as“heart-lung” disease in Tibetan medicine closely resembles depression, and it is generally considered that depression falls within the scope of“heart-lung” disease.^9,10 Based on this concept, both ND and Srogzin-11 are used to treat depression. Pharmacological studies have also confirmed the antidepressant effects of Srogzin-11.^11-14 However, the mechanism of action and pharmacodynamic basis of ND remains unclear.

In order to obtain further information on ND the current study used UHPLC-Q-Orbitrap HRMS technology to analyze the constituents present in serum after administration of ND. These constituents were then used to investigate the pharmacodynamic material basis of the beneficial effects of ND on depression.¹⁵ This involved constructing a “drug-target-disease-pathway” network to investigate the multi-component, multi-target, and multi-pathway characteristics of ND and elucidate the mechanisms of action of the absorbed components. This involved predicting the core components and targets of ND and also verifying the binding ability of these core components to the core targets using molecular docking. Taken together, these analyses provided a reference for understanding the pharmacodynamic material basis and mechanisms of action of ND.

2. Materials and Methods

2.1. Reagents and Materials

The study used the following equipment: Q Exactive Plus Orbitrap high-resolution liquid chromatography-mass spectrometer (Thermo Fisher); U3000 ultra-high-performance liquid chromatograph with automatic sampler (Thermo Fisher); Vortex-2 Genie vortex mixer (Scientific Industries); 5810R low-temperature centrifuge (Eppendorf); WD-9415C ultrasonic cleaner (Beijing 61 Instrument Factory); and a Waters ACQUITY UPLC HSS T3 C18 column 2.1 mm × 100 mm, 1.8 μm (Waters).

ND was provided by Tongren Drangsong Tibetan Medicinal Bath Hospital. Methanol, acetonitrile, and formic acid were purchased from Thermo Fisher, ammonium acetate from Sigma, and β-glucuronidase from Shanghai Yuanye Bio-Technology Co., Ltd. Deionized water was prepared using a Milli Q Advantage A10 ultrapure water system. Laboratory hardware, such as 1.5 mL centrifuge tubes, 15 mL centrifuge tubes, and sample vials were all purchased from Axygen.

2.2. Animals and Drug Administration

Specific pathogen-free (SPF)-grade female Sprague-Dawley rats (weighing 180-220 g) were provided by SPF (Beijing) Biotechnology Co., Ltd. (Beijing, China, approval number: SCHK-2024-0001) and housed in an animal room (24 ± 2°C, relative humidity 60 ± 5%) with a 12-h light/dark cycle. The rats were acclimated for one week on standard feed and water before being divided randomly into control and experimental groups (n=6 per group). ND was administered orally at 1.67 g/mL, 1 mL/100 g body weight, twice daily for three consecutive days. The control group received the same volume of physiological saline.

2.3. Collection and Preparation of Drug-Containing Serum

The day before the last day of administration, the rats were fasted for 12 h, but had free access to water. Blood samples were collected from the orbital sinus at 0.5, 1, and 2 h after the last administration. Blood collection at 0.5 and 1 h (0.2 - 0.4 mL each time) was performed quickly by skilled personnel without the use of anesthetics in order to avoid chemical interference in the analysis of blood components. For the final 2-h blood collection (large volume, followed by euthanasia), the rats were anesthetized by an intraperitoneal injection of pentobarbital sodium (30 mg/kg) to reduce stress and ensure the safety of the procedures. The collected blood samples were placed in centrifuge tubes and centrifuged at 3500 rpm for 20 min at 4°C. The supernatant serum was transferred to cryovials, labeled, and stored at -80°C for future use.

To measure the concentration of ND 150 μL of pH 5.0 sodium acetate buffer, 100 μL of the serum sample, and 10 μL of β-glucuronidase (concentration 1000 U/mL) were added sequentially into a 5 mL centrifuge tube, and then incubated with shaking at 37°C for 3 h. β-glucuronidase hydrolyze glucuronide conjugates produced by phase II metabolism in the body, and convert polyhydroxy compounds into their free forms. This ensures that the test results accurately reflect the parent drug concentration in the serum.

After incubation, a 100 μL aliquot of the above mixture was added to 400 μL of a methanol-acetonitrile mixed solution (volume ratio 1:1), and then vortexed to mix thoroughly, followed by centrifugation at 13000 rpm for 10 min at 4°C. The methanol-acetonitrile mixture effectively precipitated proteins in the serum and improved the extraction rate of the analyte. 450 μL of the supernatant was then collected, evaporated to dryness, and reconstituted with 50 μL of 80% methanol solution to obtain the sample for testing.

2.4. UHPLC-Q-Orbitrap HRMS Analysis

The ultra-performance liquid chromatography conditions were as follows. Serum sample analysis was performed on a Thermo Fisher Ultimate 3000 UPLC system, with separation carried out on a Waters ACQUITY UPLC HSS T3 C18 column (2.1 mm × 100 mm, 1.8 μm). The mobile phase consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B), with gradient elution as follows: 0-10 min, 100% B; 10-20 min, 100%-70% B; 10-25 min, 70%-60% B; 25-30 min, 60%-50% B; 30-40 min, 50%-30% B; 40-45 min, 30%-0% B; 45-60 min, 0% B; 60-60.1 min, 0%-100% B; 60.1-70 min, 100% B. The flow rate for sample separation was 0.2 mL/min, injection volume 10 μL, and column temperature 35°C.

The mass spectrometry data were acquired using a Thermo Fisher Scientific Q Exactive Orbitrap high-resolution mass spectrometer. The detection mode was Full MS-ddMS2, with separate scanning for positive and negative ion modes. The scan range was m/z 100-1500, with MS1 resolution set to 70,000 and MS2 resolution set to 17,500. The ion source voltage was 3.2 kV, capillary temperature 320°C, auxiliary gas heater temperature 350°C, sheath gas flow rate 40 L/min, and the auxiliary gas flow rate 15 L/min. The AGC target was set to 1e6, TopN was set to 5, while the collision energy for triggering MS2 scans used a stepped normalized collision energy (NCE) set at 30, 40, and 50.

2.5. Data Processing

Compound Discover 3.3 software was used for feature peak extraction of the raw mass spectrometry data, element matching of feature peaks, molecular formula prediction, and isotope distribution matching, with all the mass deviations set within 5 ppm. Feature peaks were identified using the online mzCloud database and a locally built mzVault traditional Chinese medicine natural product database (https://www.mzcloud.org/).¹⁶ Identification criteria included a mass deviation of <5 ppm, compliance with isotope distribution, and an mzVault best match database score >70. Relevant literature and PubChem databases (https://pubchem.ncbi.nlm.nih.gov/) were consulted,¹⁷ and the results then compared with the original components reported for ND (Figures S1–S10) and the original components of individual herbal medicines (Figure S11). The identification results were then manually cross-checked and confirmed.

2.6. Prototype Components and Disease Target Prediction

Due to the components absorbed into the blood being more likely to have pharmacological effects, the prototype components identified by UHPLC-Q-Orbitrap HRMS were used to construct the compound information database of ND to support network pharmacology research. The molecular files of the prototype compounds were downloaded from Chemical Book (https://www.chemicalbook.com/ProductIndex.aspx) and PubChem.¹⁷ In addition, the relevant targets of these compounds were obtained from the SwissTargetPrediction database (https://www.swisstargetprediction.ch/).¹⁸ To improve the reliability of the predicted structures, drug targets of the ND compounds with a score value > 0 were selected.

Screening of the depression-related targets from the GeneCards database (https://www.genecards.org/).¹⁹ using a corresponding threshold of ≥16.518 identified the top 2,000 targets ranked by relevance score which were then selected as the research subjects. This threshold was chosen to ensure that the disease target set for subsequent network analysis was focused and of high confidence.

2.7. Network Diagram Construction

The HD component targets and disease targets were jointly imported into Venny 3.10.3 to draw a Venn diagram that showed their intersections. Excel was used to construct a network and type file of active components and intersecting targets that were then imported into Cytoscape 3.10.3 software for visualization of the interaction network and selection of core drug components based on their degree values.

The intersecting targets of ND and depression were then imported into the STRING database (https://stringdb.org/),²⁰ followed by selection of multiple proteins, setting the species to “Homo sapiens” and the interaction score threshold to 0.4 to generate the PPI network.²¹ Cytoscape 3.10.3 software was used to visualize the protein interaction analysis data, with the CytoHubba plugin applied using the following seven algorithms to analyze the data: betweenness, degree, edge percolation component (EPC), maximal clique centrality (MCC), maximum neighborhood component (MNC), radiality, and stress.²² The top 30 targets from each algorithm were obtained, with the intersection used to identify the core targets, which were then visualized using the MicroBioInfo online bioinformatics analysis and visualization platform (https://www.bioinformatics.com.cn/).²³

2.8. GO and KEGG Enrichment Analysis

The GO and KEGG enrichment analyses of the intersecting targets between ND components and depression were performed using the DAVID database (https://davidbioinformatics.nih.gov/),²⁴ with the species set to “Homo sapiens”. Fisher’s exact test was used and the Benjamini-Hochberg correction applied to account for multiple testing, witha FDR <0.05 used as the screening threshold.

2.9. Molecular Docking

Protein receptors with no mutations, a resolution of <2.5 Å, and a complete pocket structure were selected from the RCSB PDB database (https://www.rcsb.org/).²⁵ To validate the reliability of the molecular docking protocol, the co-crystallized ligands of the selected target proteins were re-docked into their respective binding pockets using Discovery Studio 2019 Client. The heavy atom root-mean-square deviation (RMSD) values between the re-docked ligand conformations and the original crystal structures were calculated, using a RMSD < 2.0 Å as the criterion for an effective docking protocol. The selected protein receptors were processed using the Protein Preparation Wizard module of Schrödinger software, which involved steps including import and process, refined hydrogen addition, structure optimization, and energy optimization. The molecular docking pocket was generated using the Receptor Grid Generation module. The 2D structures of the core components were obtained from the PubChem database¹⁷ and imported into Schrödinger software for processing using the LigPrep module. Finally, molecular docking was performed using the Ligand Docking module of Schrödinger software, followed by the generation of molecular docking binding mode diagrams.^26-28

2.10. Statistical Analysis

All data processing and analyses were performed using the bioinformatics tools described in the corresponding sections. Identification of blood components was based on mass spectrometry data, processed using Compound Discoverer 3.3 software, with quality control parameters set as described in Section 2.5. Screening of depression-related targets from the GeneCards database used a threshold of relevance score ≥16.518, which corresponded to the top 2000 targets. This threshold was chosen to ensure that the disease target set for subsequent network analysis was focused and of high confidence. For GO and KEGG pathway enrichment analysis, the DAVID database was used to perform a Fisher’s exact test. An adjusted P-value <0.05 (corrected for multiple testing using the Benjamini-Hochberg method) was adopted as the statistical significance threshold to identify significantly enriched terms and pathways. The STRING database was used for protein interaction network analysis, with the interaction score threshold set at 0.4 (medium confidence). Core targets were screened using the CytoHubba plugin of Cytoscape software and involved seven topological algorithms (betweenness, degree, edge percolation component [EPC], maximal clique centrality [MCC], maximum neighborhood component [MNC], radiality, and stress). The intersection of the top 30 targets from each algorithm were selected as the core targets. Schrödinger software was used to calculate the binding energies for molecular docking. The binding energy values were used directly to assess the potential binding affinity between compounds and targets, with lower values indicating stronger predicted binding.

3. Results

3.1. Analysis of Blood Components in Rats Treated With ND

Serum samples were collected at 0.5, 1, and 2 h after the last oral administration of ND to the rats. The total ion chromatograms of the serum samples collected at these times, compared to those of the control serum, are shown in Figure S12.

A total of 76 components were identified in the blood samples. These components were identified by comparison with reference compounds and by analyzing their molecular formulas, fragment ions, and chemical structure information from the literature and the Xcalibur 4.5, Compound Discoverer 3.3, and mzCloud databases. Information on all the components identified is shown in Table 1.

Table 1.

Basic Information of the 76 Compounds in the Blood Samples

NO.	RT [min]	Name	Formula	Reference ion	Source
1	1.537	Choline	C₅H₁₃NO	[M+H]⁺¹	A、B、C、D、E、F、G、H、I、J、K、L、M、N
2	13.578	Catechol	C₆H₆O₂	[M-H]^-1	A、C、D、E、F、G、H、J
3	3.325	D-α-Hydroxyglutaric acid	C₅H₈O₅	[M-H]^-1	C、E
3	3.325	D-α-Hydroxyglutaric acid	C₅H₈O₅	[M-H]^-1	G、H、I
4	1.522	L-Aspartic acid	C₄H₇NO₄	[M-H]^-1	F、G、I
5	31.395	o-Veratraldehyde	C₉H₁₀O₃	[M+H]⁺¹	A、C、I、J
6	63.6	Palmitoyl sphingomyelin	C₃₉H₇₉N₂O₆P	[M+H]⁺¹	H
7	36.056	Phthaldialdehyde	C₈H₆O₂	[M+H]⁺¹	A、E
8	1.567	β-D-Glucopyranuronic acid	C₆H₁₀O₇	[M-H]^-1	B
9	52.791	Erucamide	C₂₂H₄₃NO	[M+H]⁺¹	B、D、H
10	36.074	Piperine	C₁₇H₁₉NO₃	[M+H]⁺¹	E、H、I
11	1.615	Betaine	C₅H₁₁NO₂	[M+H]⁺¹	A、B、E、F、G、H、I、J
12	5.857	L-Tyrosine	C₉H₁₁NO₃	[M+H]⁺¹	B、C、D、E、F、G、I
13	2.996	4-Guanidinobutyric acid	C₅H₁₁N₃O₂	[M+H]⁺¹	A、B、C、D、F、H、I
14	3.988	Uric acid	C₅H₄N₄O₃	[M-H]^-1	A、E、I
15	1.75	Citric acid	C₆H₈O₇	[M-H]^-1	A、B、C、D、E、F、G、H、I、J、K
16	1.826	DL-Arginine	C₆H₁₄N₄O₂	[M+H]⁺¹	B、C、E、F
17	4.85	Succinic acid	C₄H₆O₄	[M-H]^-1	A、C、D、E、F、H、I、J
18	22.878	Agarotetrol	C₁₇H₁₈O₆	[M+H]⁺¹	A、B、E、G
19	1.627	Trigonelline	C₇H₇NO₂	[M+H]⁺¹	C、E、G、H、J
20	44.808	18 β-Glycyrrhetintic Acid	C₃₀H₄₆O₄	[M+H]⁺¹	C、E、G、H
21	1.873	4-Oxoproline	C₅H₇NO₃	[M-H]^-1	A、B、C、D、E、H、I、J
22	45.262	Schizandrin A	C₂₄H₃₂O₆	[M+H]⁺¹	J、E
23	37.434	Sedanolide	C₁₂H₁₈O₂	[M+H]⁺¹	E、H
24	34.126	Ligustilide	C₁₂H₁₄O₂	[M+H]⁺¹	A、B、C、D、E、G
25	35.51	Isoalantolactone	C₁₅H₂₀O₂	[M+H]⁺¹	B、C、D、F
26	1.552	Gluconic acid	C₆H₁₂O₇	[M-H]^-1	A、B、C、H、I、J
27	1.627	L-(-)-Malic acid	C₄H₆O₅	[M-H]^-1	A、B、C、D、F、G、H、I、J
28	45.215	Hexadecanamide	C₁₆H₃₃NO	[M+H]⁺¹	A、D
29	1.614	D-(-)-Fructose	C₆H₁₂O₆	[M-H]^-1	C、D、E、J、K
30	26.972	Liquiritigenin	C₁₅H₁₂O₄	[M-H]^-1	A、E
31	48.016	Cyanuric acid	C₃H₃N₃O₃	[M-H]^-1	A、B、C、D、E、I、J
32	24.846	Azelaic acid	C₉H₁₆O₄	[M-H]^-1	A、B、C、D、E、F、G、H、I、J
33	2.849	Nicotinic acid	C₆H₅NO₂	[M+H]⁺¹	A、B、C、D、G、H、I、J
34	32.54	Artemisinic acid	C₁₅H₂₂O₂	[M+H]⁺¹	B、C、I
35	23.087	3-Phenyllactic acid	C₉H₁₀O₃	[M-H]^-1	A、C
36	1.749	2-Furoic acid	C₅H₄O₃	[M-H]^-1	A、C、D、E、F、G、H、I、J
37	2.966	Uracil	C₄H₄N₂O₂	[M+H]⁺¹	A、C、E、H、I
38	33.639	Dehydrodiisoeugenol	C₂₀ H₂₂ O₄	[M+H]⁺¹	A、B、C、D、F、G、H、I、
39	18.906	5-Hydroxymethyl-2-furaldehyde	C₆H₆O₃	[M+H]⁺¹	B、D、F、G、H、J
40	1.618	D-(-)-Quinic acid	C₇H₁₂O₆	[M-H]^-1	A、B、C、G、H、I、J
41	35.712	Piperanine	C₁₇H₂₁NO₃	[M+H]⁺¹	I
42	20.877	Caffeic acid	C₉H₈O₄	[M-H]^-1	D、G、H、I
43	18.257	L-Phenylalanine	C₉H₁₁NO₂	[M-H]^-1	A、B、C、D、F、H
44	44.079	Octadecanedioic acid	C₁₈H₃₄O₄	[M-H]^-1	D、F、G、I
45	30.315	(15Z)-9,12,13-Trihydroxy-15-octadecenoic acid	C₁₈H₃₄O₅	[M-H]^-1	A、C、D、E、G、H、I、J
46	19.685	Salidroside	C₁₄H₂₀O₇	[M+FA-H]^-1	D、H
47	27.519	Senkyunolide A	C₁₂H₁₆O₂	[M+H]⁺¹	G
48	25.441	2-(1H-indol-3-yl)acetic acid	C₁₀H₉NO₂	[M+H]⁺¹	A、H
49	23.812	Indole-3-lactic acid	C₁₁H₁₁NO₃	[M+H]⁺¹	A
50	22.475	3-(4-Hydroxyphenyl)propionic acid	C₉H₁₀O₃	[M-H]^-1	B
51	28.62	1,2,3,4-Tetramethyl-1,3-cyclopentadiene	C₉H₁₄	[M+H]⁺¹	A、B、C、D、G
52	26.55	Daidzein	C₁₅H₁₀O₄	[M-H]^-1	E
53	42.702	Schisanhenol	C₂₃H₃₀O₆	[M+H]⁺¹	J
54	1.644	N-Acetylornithine	C₇H₁₄N₂O₃	[M+H]⁺¹	H
55	44.814	Glabrolide	C₃₀H₄₄O₄	[M+H]⁺¹	D、E、F、G、J
56	33.516	4-Methoxycinnamaldehyde	C₁₀H₁₀O₂	[M+H]⁺¹	A、B、C、D、F、G、H、I
57	1.781	Glutaconic acid	C₅H₆O₄	[M-H]^-1	A、B、D、H
58	39.273	Hexadecanedioic acid	C₁₆H₃₀O₄	[M-H]^-1	D、F、G、H、I、J
59	21.667	2,4-Dihydroxybenzoic acid	C₇H₆O₄	[M-H]^-1	A、C、I、
60	23.022	Phenylacetaldehyde	C₈H₈O	[M-H]^-1	A、C、D
61	21.021	Ferulic acid	C₁₀H₁₀O₄	[M-H]^-1	C、H、I
62	23.097	Liquiritin	C₂₁H₂₂O₉	[M-H]^-1	E
63	20.705	6-Methylquinoline	C₁₀H₉N	[M+H]⁺¹	C、D、E、F、I
64	29.245	Naringenin chalcone	C₁₅H₁₂O₅	[M-H]^-1	A
65	20.237	2-Isopropylmalic acid	C₇H₁₂O₅	[M-H]^-1	A、B、C、D、E、F、G、H、I、J
66	32.534	Butyl benzoate	C₁₁H₁₄O₂	[M+H]⁺¹	A、B、D、E、G、H、I
67	20.721	4-Coumaric acid	C₉H₈O₃	[M-H]^-1	G、J
68	5.337	5-Aminolevulinic acid	C₅H₉NO₃	[M-H]^-1	B、C、E、H、I
69	34.727	Abscisic acid	C₁₅H₂₀O₄	[M-H]^-1	A、G、H、J
70	31.635	Glycyrrhizic acid	C₄₂H₆₂O₁₆	[M-H]^-1	E
71	31.713	Formononetin	C₁₆H₁₂O₄	[M-H]^-1	E
72	21.587	Parthenolide	C₁₅H₂₀O₃	[M+H]⁺¹	B、C
73	22.93	Benzoic acid	C₇H₆O₂	[M-H]^-1	A、B、C、D、H
74	52.281	Anacardic acid	C₂₂H₃₆O₃	[M-H]^-1	F
75	31.761	(-)-Caryophyllene oxide	C₁₅H₂₄O	[M+H]⁺¹	B、C、F、G、H、I、J
76	21.766	Resorcinol monoacetate	C₈H₈O₃	[M-H]^-1	A、E、G、I

Note. A, nutmeg; B, agarwood; C, Aucklandiae radix; D, Rhodiola rosea; E, Licorice; F, Terminalia chebula; G, Fructus chaerospondiatis; H, Corydalis impartiens; I, caraway; J, Schisandra chinensis.

3.2. Screening of the Chemical Components and Potential Targets

The compounds used in the network pharmacology analysis were selected from the 76 absorbed compounds. A total of 21 ingredients were excluded, including 17 prototype components detected in the rat serum (i.e., choline, betaine, L-tyrosine, 4-guanidinobutyric acid, and uric acid), three components whose compound targets had no overlap with the disease targets (L-aspartic acid, palmitoyl sphingomyelin, and trigonelline), and cyanuric acid for which relevant targets could not be identified. Finally, 55 blood-absorbed ingredients of the ND formula were included, corresponding to 747 unique drug-related targets. The structure of all the compounds used in the subsequent network pharmacology analysis is shown in Figure S13.

We then searched for targets related to depressive disorders using the GeneCards database, and obtained a total of 16,799 targets. The top 2,000 targets with a relevance score ≥ 16.518 were selected as the study subjects. Of these 2000 disorder targets, 212 overlapped with 747 drug targets. A Venn diagram of the overlapping targets is shown in Figure 1.

Figure 1.

Intersection targets of 55 absorption components of compound ND with depression

3.3. Compound-Intersection Target Network Construction

The 212 intersecting targets identified in the previous step, along with the 55 components in the blood that corresponded to these targets, were imported into Venny 3.10.3 software to construct a “ND chemical components-potential targets for depression” network. Statistical analysis by the software showed that this network consisted of 267 nodes and 736 edges. Network analysis revealed that individual ND compounds interacted with multiple targets, while several targets were influenced by more than one compound. As shown in Table S2, 16 core components (degree ≥ 20) were identified. The interaction network of the core components and intersecting targets is shown in Figure 2.

Figure 2.

Interaction network of core components and overlapping targets

3.4. PPI Network Construction and Core Target Screening

As shown in Figure 3, the PPI network contained a total of 212 nodes and 2,942 edges. By applying these intersections, 16 core targets were ultimately obtained, including ESR1, AKT serine/threonine kinase 1 (AKT1), axin1/beta-catenin (CTNNB1), glycogen synthase kinase-3 beta (GSK3B), and epidermal growth factor receptor erbB1 (EGFR) (Figure 4).

Figure 3.

PPI network diagram.

Figure 4.

Distribution of the core targets.

3.5. GO Function and KEGG Pathway Enrichment Analysis

GO enrichment analysis was performed on the 212 intersecting targets of depression and blood-absorbed components using FDR < 0.05 as the threshold. A total of 549 GO terms were identified, including 334 biological process (BP) terms, 78 cellular component (CC) terms, and 137 molecular function (MF) terms. The top 10 terms in each category were selected for annotation analysis. Representative BP terms included the insulin-like growth factor receptor signaling pathway (GO:0048009), epidermal growth factor receptor signaling pathway (GO:0007173), and positive regulation of the ERK1 and ERK2 cascade (GO:0070374). Representative CC terms included the plasma membrane (GO:0005886), postsynaptic membrane (GO:0045211), and dendrite (GO:0030425). Representative MF terms included identical protein binding (GO:0042802), protein kinase activity (GO:0004672), and protein tyrosine kinase activity (GO:0004713). The detailed results are shown in Figure 5.

Figure 5.

GO enrichment analysis

KEGG pathway enrichment analysis was performed on the 16 core targets derived from blood-absorbed components using the DAVID database. FDR < 0.05 was used as the screening threshold and 123 significantly enriched pathways were identified. As shown in Figure 6, the top 10 pathways with the smallest FDR values were selected for visualization. In the bubble plot, bubble size represents the number of enriched genes, while bubble color represents the FDR value (redder colors indicate higher significance), and the x-axis represents the fold enrichment. Smaller FDR values indicate stronger associations with depression, and larger bubble sizes indicate more target genes enriched in the pathway.

Figure 6.

KEGG pathway enrichment analysis of core targets

Based on the above results, the ND components in the blood samples, potential targets for depression, and the corresponding signaling pathways were imported into Cytoscape 3.10.3 software to construct a “compound-target-pathway” network (Figure 7). In this figure, green represents the chemical components of ND, red represents core targets, and orange represents signaling pathways. The connections indicate the relationships between signaling pathways, targets, and components. Network visualization analysis of the results identified 15 core protein targets, including GSK3B, IL6, interleukin-1 beta (IL1B), TNF, and MAPK3, corresponding to 28 components such as 18β-glycyrrhetintic acid, daidzein, liquiritigenin, naringenin chalcone, and sedanolide, and 10 pathways including pathways for cancer, inflammatory bowel disease, the C-type lectin receptor signaling pathway, breast cancer, and hepatitis C.

Figure 7.

Compound-key target-pathway network

3.6. Molecular Docking

Based on the PPI network analysis, four core targets (ESR1, IL1B, MAPK3, and TNF) were selected. The heavy atom root-mean-square deviation (RMSD) values for these targets were 1.55 Å, 0.83 Å, 1.73 Å, and 0.97 Å, respectively, all of which were below the commonly accepted threshold of 2.0 Å. This finding confirmed the reliability of the molecular docking method and the parameters used in this study. In addition, six core components (dehydrodiisoeugenol, liquiritigenin, naringenin chalcone, piperanine, piperine, and sedanolide) were selected. As shown in Figure 8 molecular docking was performed using Schrödinger software to obtain the binding energies between the ligands and receptors. It is generally believed that the lower the binding energy of molecular docking, the more stable the binding between the ligand and receptor, and also the greater binding ability.²⁹ The docking results of the six groups with the lowest binding energies are shown in Figure 9.

Figure 8.

Heatmap of binding energy between the components and targets

Figure 9.

Well-bound results in the molecular docking analysis.

4. Discussion

Depression is essentially a complex disease driven by multiple factors, with its pathogenesis involving dysregulation of multiple molecular controls that cause abnormalities in multiple cellular signaling pathways.³⁰ These changes lead ultimately to depression with the high heterogeneity and complex clinical phenotypes creating challenges for precise treatment (Figure 10).

Figure 10.

Schematic overview of the research workflow and antidepressant mechanism of Ninglung Deshee (ND)

In Tibetan medicine, depression falls under the category of “lung disease”, with its clinical symptoms being very similar to those of Ninglung disease. By drawing from clinical experience, Tibetan medicine often uses formulas to treat ND based on medicines that contain ingredients such as nutmeg, agarwood, Aucklandiae radix, and Fructus chaerospondiatis. Following the early stages of investigation, these treatments have gained considerable clinical recognition.

Our analysis identified MAPK3 as a key node in the ND-target network, consistent with the findings of previous reports.³¹ Notably, the co-enrichment of the MAPK signaling pathway and the Prion disease signaling pathway suggests that the ND compound may exert its therapeutic effects by regulating multiple mechanisms related to neuronal survival and function. The MAPK pathway is involved mainly in cell proliferation, differentiation, and stress responses, while the Prion disease pathway is closely associated with neurodegenerative changes. The interplay between these two pathways provides a new perspective for further understanding the neurobiological mechanisms of depression and further confirms the significant role of the MAPK signaling pathway in the treatment of depression using ND.

There is substantial evidence of a strong association between ESR1, a key target of the compound ND, and the treatment of depression. Research has indicated that upregulation of miR-211 and miR-744 in the exosomes of postpartum depression patients may contribute to disease development by targeting ESR1.³² Furthermore, ESR1 gene variants have been linked to reproductive disorders and the risk of depression, underscoring the importance of estrogen signaling in reproductive and mental health.³³

During the progression of major depressive disorder (MDD) and bipolar disorder (BD), the activation of pro-inflammatory immune responses and upregulation of the inflammatory setpoint are significantly associated with the onset of major depressive episodes (MDE) and severity of the disease.³⁴ Tumor necrosis factor α (TNF-α), as a key pro-inflammatory factor, not only synergistically regulates apoptosis and inflammatory responses through activation of TNF signaling and NF-κB pathways,³⁵ but can also be involved in the depressive process by modulating other inflammatory factors such as IL-6. These findings suggest ND may exert anti-inflammatory effects via modulation of TNF-α and IL-6, a possibility that aligns with the holistic approach of Tibetan medicine.

The results of the molecular docking analysis showed that the six core active components in the ND compound exhibited good binding characteristics with both TNF and ESR1 target proteins. Of these components, piperine showed the optimal binding characteristics with TNF, a finding consistent with those of previous studies on the anti-inflammatory effects of piperine.³⁶ When considering the central role of TNF-α in neuroinflammation associated with depression, it is likely that piperine may alleviate neuroinflammatory responses by directly acting on TNF and inhibiting the activation of downstream inflammatory pathways such as NF-κB.

Although this study provides a reference for the potential mechanisms of ND for treating depression, several limitations should be acknowledged. (1) Network pharmacology relies on the accuracy and completeness of public databases, with database bias possibly affecting the predicted results; (2) The bioactive compounds among the 76 blood-absorbed components that truly exert therapeutic effects remain to be confirmed. In this regard, the presence of a compound in the blood does not guarantee its activity at the target site; (3) Molecular docking is a computational prediction, and docking scores may not always correspond to actual in vivo biological efficacy; (4) The PPI and enrichment analyses were based on specific thresholds and different thresholds may have yielded different results.

5. Conclusions

This study used UPLC coupled with UHPLC-Q-Orbitrap HRMS to identify a total of 76 components of ND that entered the circulation. Combined with network pharmacology analysis, 16 core targets and 15 core components of ND were identified for treating depression. Pathway enrichment analysis showed that the MAPK signaling pathway and Prion disease signaling pathway may be key pathways through which ND exerts its effects. In addition, molecular docking results indicated that TNF and ESR1 play important roles in the antidepressant mechanism of ND. However, these computational predictions require further experimental validation to confirm the roles of these bioactive components and pathways in the treatment of depression.

Supplemental Material

Supplemental material - Study on the Mechanism of ND for Treating Depression Based on “Blood Components-Network Pharmacology”

Supplemental material for Study on the Mechanism of ND for Treating Depression Based on “Blood Components-Network Pharmacology” by Ma-jia Wan, Yecuo Lamao, Name Zhaxi, Jie-ben Jiao, Cairang Nanjia in Natural Product Communications

Footnotes

Acknowledgement

The authors thank Beijing Keweite Animal Technology Co., Ltd. for their support in the ethics review of the animal experimental procedures and animal quality certification (License No. SCXK (Jing) 2024-0001). We also appreciate the assistance provided by the animal facility personnel during the in vivo experiments. The authors gratefully acknowledge EditSprings () for their professional language editing services.

ORCID iDs

Ma-jia Wan

Jie-ben Jiao

Ethical Considerations

This study was approved by the Laboratory Animal Welfare and Ethics Committee of Beijing Keweite Animal Technology Co., Ltd.

Consent to Participate

There are no human subjects in this article and informed consent is not applicable.

Author Contributions

Ma-Jia Wan; conceptualization, methodology, investigation, data curation, visualization, writing-original draft, and writing-review and editing. Lamao-Yecuo; data curation, validation, and writing-review and editing. Zhaxi-Name; data curation, validation, writing-review and editing. Jie-ben Jiao; data curation, and validation. Cairang-Nanjia; conceptualization, writing, review, editing, and supervision.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Fund project (Grant Nos: 20XMZ026) and the Key Laboratory for Tibet Plateau Phytochemistry of Qinghai Province.

Declaration of Conflicting Interests

This manuscript has been confirmed by all authors and has not been published or submitted for review in any domestic or international journals, nor is it currently under review by any other journal. All the authors have reviewed and agreed to the final content of the manuscript and the order of authorship, and declare that there are no conflicts of interest.

Data Availability Statement

Data will be made available on request.*

Statement of Human and Animal Rights

The animal experimental procedures involved in this study were conducted in accordance with the guidelines for animal experiments of Beijing Keweite Animal Technology Co., Ltd. and approved by its Laboratory Animal Welfare and Ethics Committee.

Supplemental Material

Supplemental material for this article is available online.

Appendix

References

Jordaan

. Mental Health Update—Update on Depression with a Focus on Vortioxetine. S Afr Pharm J. 2024;91(6):26-30. doi:10.36303/SAPJ.1141.

Yan

Cui

Liu

, et al. Abnormal Default-Mode Network Homogeneity in Melancholic and Nonmelancholic Major Depressive Disorder at Rest. Neural Plast. 2021;2021:6653309. doi:10.1155/2021/6653309.

Guo

Jiang

Jia

, et al. Altered gray matter volumes and plasma IL-6 level in major depressive disorder patients with suicidal ideation. Neuroimage Clin. 2023;38:103403. doi:10.1016/j.nicl.2023.103403.

Zhang

Jia

Yang

Sun

Shi

Wang

. Change in the global burden of depression from 1990-2019 and its prediction for 2030. J Psychiatr Res. 2024;178:16-22. doi: 10.1016/j.jpsychires.2024.07.054.

Elias

Zhang

Manners

. Novel Pharmacological Approaches to the Treatment of Depression. Life (Basel). 2022;12(2):196. doi:10.3390/life12020196.

Kovich

Kim

Quaste

. Pharmacologic Treatment of Depression. Am Fam Physician. 2023;107(2):173-181.

The Pharmacopoeia Commission of the Ministry of Health of the People’s Republic of China . Drug Standards of the Ministry of Health of the People’s Republic of China: Tibetan Medicine Volume (First Volume) [M]. Beijing: People’s Medical Publishing House; 1995:174.

Gyu thog yon tan mgon po . The Four Medical Treatises of Tibetan Medicine [M]. Tibetan edition. Lhasa: Xizang People’s Publishing House; 2009.

Dangzeng

Wenchang

Ren

, et al. Research on the Clinical Positioning of YM Disease (Depression) Based on Tibetan Medicine Constitution and Multi-center Survey Data. Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology. 2022;24(5):2125-2136.

10.

Suo

Zhang

Zha

, et al. Tibetan medicine's understanding and treatment of depression. China Journal of Traditional Chinese Medicine and Pharmacy. 2020;35(6):3032-3034.

11.

Wang

, et al. Study on the Anti-depression Mechanism of Tibetan medicine Shi-Yi-Wei-Wei-Ming capsule Based on BDNF/CREB Signaling Pathway. Yunnan Journal of Traditional Chinese Medicine and Materia Medica. 2024;45(8):61-67. doi: 10.16254/j.cnki.53-1120/r.2024.08.013.

12.

Wang

Sun

, et al. Effects of Shi-Yi-Wei-Wei-Ming Capsule on Hippocampal Astrocytes and Glutamate Metabolism Related Proteins in Depression Mice. Guiding Journal of Traditional Chinese Medicine and Pharmacy. 2023;29(3):30-35. doi:10.26973/d.cnki.gbjzu.2023.001239.

13.

Douzhou

Cairang

. Clinical Observation of Tibetan Medicine in Treating Depression. Electronic Journal of Clinical Medical Literature. 2017;4(37):7193. doi:10.16281/j.cnki.jocml.2017.37.045.

14.

Daojie

. Observation of the Effect of Tibetan Medicine Treatment in Patients with Depression. Electronic Journal of Clinical Medical Literature. 2019;6(57):94. doi:10.16281/j.cnki.jocml.2019.57.070.

15.

Zhang

Zheng

Liu

Song

. Comprehensive investigation of in-vivo ingredients and action mechanism of iridoid extract from Gardeniae Fructus by liquid chromatography combined with mass spectrometry, microdialysis sampling and network pharmacology. J Chromatogr B Analyt Technol Biomed Life Sci. 2018;1076:70-76. doi:10.1016/j.jchromb.2018.01.023.

16.

Sheldon

Mistrik

Croley

. Determination of Ion Structures in Structurally Related Compounds Using Precursor Ion Fingerprinting. J Am Soc Mass Spectrom. 2009;20(3):370-376. doi:10.1016/j.jasms.2008.10.017.

17.

Kim

Chen

Cheng

, et al. PubChem 2025 update. Nucleic Acids Res. 2025;53(D1):D1516-D1525. doi:10.1093/nar/gkae1059.

18.

Daina

Michielin

Zoete

. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47(W1):W357-W364. doi:10.1093/nar/gkz382.

19.

Stelzer

Rosen

Plaschkes

, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. 2016;54:1.30.1-1.30.33. 1.30.1-1.30.33. doi:10.1002/cpbi.5.

20.

Szklarczyk

Gable

Nastou

, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605-D612. doi:10.1093/nar/gkaa1074.

21.

Zuo

Zhong

Liu

, et al. Mechanism of Zingiberis Rhizoma mixed and triturated with Schisandra chinensis for treating phlegm asthma based on UPLC-Q-Exactive Orbitrap-MS and network pharmacology combined with animal experiments. China Journal of Chinese Materia Medica. 2026;51(4):1090-1098. doi:10.19540/j.cnki.cjcmm.20251024.801.

22.

Zhu

Luo

, et al. Exploration on Mechanism of Compound Banmao Capsules in Treating Gastric Cancer Based on Network Pharmacology, Bioinformatics Analysis and Molecular Docking. New Chinese Medicine. 2025;57(6):109-117. doi:10.13457/j.cnki.jncm.2025.06.020.

23.

Tang

Chen

Huang

, et al. SRplot: A free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. doi:10.1371/journal.pone.0294236.

24.

Huang

Sherman

Lempicki

. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44-57. doi:10.1038/nprot.2008.211.

25.

Burley

Berman

Bhikadiya

, et al. RCSB Protein Data Bank: biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Research. 2019;47(D1):D464-D474. doi:10.1093/nar/gky1004.

26.

Liu

Wei

Pei

. Mechanism of Hedysarum multijugum-Rhizoma dioscoreae herb pair for the treatment of acute lung injury based on network pharmacology and molecular docking technique: an exploration study. Guangxi Medical Journal. 2025;47(1):91-99. doi:10.13457/j.cnki.jncm.2025.06.020.

27.

Yang

Zhou

Yuan

, et al. Potential mechanism of isoliquiritigenin in treatment of hyperuricemia. Journal of Binzhou Medical University. 2025;48(1):50-57. doi:10.19739/j.cnki.issn1001-9510.2025.01.011.

28.

. Exploration on the mechanism of Tanreqing injection in treating bronchiectasis based on network pharmacology and molecular docking. Clinical Journal of Chinese Medicine. 2024;16(27):7-13.

29.

Yue

Shui

. Discussion on the mechanism of Diyu-Shengma couplet medicinals in the treatment of myelosuppression based on network pharmacology and molecular docking technology. Clinical Journal of Chinese Medicine. 2024;16(25):6-11.

30.

Wang

Liu

Wang

Min

Chen

. Uncovering the active components, prospective targets, and molecular mechanism of Baihe Zhimu decoction for treating depression using network pharmacology-based analysis. J Ethnopharmacol. 2021;281:114586. doi:10.1016/j.jep.2021.114586.

31.

Zhang

Teng

Wang

Chen

. Bioinformatics and computational chemistry approaches to explore the mechanism of the anti-depressive effect of ligustilide. Sci Rep. 2023;13(1):5417. doi:10.1038/s41598-023-32495-7.

32.

Tang

Liu

Chen

, et al. Upregulation of Circulating MiR-211 and MiR-744 is Related to Postpartum Depression via Estrogen Receptor 1 Targeting. J Biol Regul Homeost Agents. 2023;37(6). doi:10.23812/j.biol.regul.homeost.agents.20233706.314.

33.

Kiewa

Mortlock

Meltzer-Brody

Middeldorp

Wray

Byrne

. A Common Genetic Factor Underlies Genetic Riskfor Gynaecological and Reproductive Disorders and Is Correlated with Risk to Depression. Neuroendocrinology. 2023;113(10):1059-1075. doi:10.1159/000533413.

34.

Benedetti

Zanardi

Mazza

. Antidepressant psychopharmacology: is inflammation a future target? Int Clin Psychopharmacol. 2022;37(3):79-81. doi:10.1097/YIC.0000000000000403.

35.

Wei

Tan

Zhu

, et al. Study on the Mechanism of Modified Guomin Decoction in the Treatment of Atopic Dermatitis Based on UHPLC-QE-MS Combined with Network Pharmacology and Molecular Docking Methods. Journal of Chengdu Medical College. 2024;19(2):193-200.

36.

Gupta

Motiwala

Dumore

Danao

Ganjare

. Effect of piperine on inhibition of FFA induced TLR4 mediated inflammation and amelioration of acetic acid induced ulcerative colitis in mice. JEthnopharmacol. 2015;164:239-246. doi:10.1016/j.jep.2015.01.039.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

1.54 MB

0.00 MB