Sage Journals: Discover world-class research

Abstract

Background

Curcumae Radix (CR), derived from the dry roots of Curcuma longa L., family Zingiberaceae, is widely used to treat depression. However, the ingredients and mechanisms of CR are still unclear. The purpose of this study was to solve this problem using network pharmacology and molecular docking.

Methods

The active ingredients of CR were screened through TCMSP, and the depression-related genes were obtained through the Genetic Association, GeneCards, and OMIM databases. Then, DisGeNET score was performed to evaluate the correlation between co-genes and depression. Topological analysis was conducted to screen hub genes and proteins, molecular docking was performed to evaluate the binding ability of the hub protein with active ingredients, and gene ontology (Go) function analysis, gene tissue localization, and KEGG pathway analysis were conducted to explore the function and location of genes, as well as the mechanism of CR for treating depression.

Results

Eight ingredients of CR were screened based on pharmacokinetic properties, five of which are closely related to depression, including (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one, oxycurcumenol, β-sitosterol, and sitosterol. They interacted with 45 co-genes and co-proteins with a DisGeNET score ≥0.3. AR, NOS2, PTGS2, and TYK2 were pivot genes. EGFR, PTGS2, HSP90AA1, MAPK8, and ESR1 were hub proteins. PTGS2 was found to have good binding potential with oxycurcumenol, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one and (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene. Go functional analysis indicated that co-genes involved complex biological processes, cellular components and molecular functions. PER2, P2RX7, GRM1, TACR1, MAPK8, HCRTR1, EGFR, and TYK2 were highly expressed in the prefrontal cortex. The potential pathways for CR to exert antidepressant effects were calcium, estrogen, PI3K-Akt and ErbB signaling pathways.

Conclusions

This study revealed the ingredients, effective targets and mechanisms of CR in the treatment of depression, which provides a new perspective for the development of new antidepressants.

Keywords

Chinese medicine protein interaction molecular docking network pharmacology gene localization brain

Depression is a common worldwide mental disorder characterized by the persistence of negative emotions and the diminishment of positive emotions, which could cause serious functional and social disorders.¹ Previous studies have also found that depression is a major cause of global disability and is related to a 10-year reduction in life expectancy.² In addition, a recent study found that 50.4% of health care workers exposed to COVID-19 in Wuhan reported depressive symptoms,³ and Vietnamese patients with COVID-19 were found to have a higher risk of depression (OR 2.88) and lower quality of life than those without COVID-19.⁴ This is undoubtedly a heavy physical and mental burden for more than 150 million patients diagnosed with COVID-19 worldwide. What is worse, a recent network meta-analysis which comprehensively summarized the clinical effectiveness of antidepressants found that the benefits of all antidepressants were small compared to placebo,⁵ especially in adolescents and children.⁶ Therefore, seeking reliable, effective and safe antidepressant drugs has become an urgent requirement for doctors and patients with depression.

Chinese herbal medicine, as one kind of complementary and alternative medicine, has been shown to be superior to placebo in reducing the HAMD-17 score [mean difference (MD) = −4.53, 95% confidence interval (CI):−5.69‐−3.77, P < 0.00001], and the total effective rate is better than that of Western conventional medications alone when combined with Western conventional medications [Risk Ratio (RR): 1.16, 95% CI: 1.07‐1.27, P = 0.0004], and with less adverse reaction.⁷ CR, a Chinese herbal medicine derived from the dry roots of Curcuma longa L., family Zingiberaceae, has been found not only to treat COVID-19,⁸ but also to have antidepressant effects.⁹ For example, a clinical trial showed that CR plays a role in improving the negative emotions and psychological stress of patients with post-stroke depression and improves their quality of life.¹⁰ Animal experiments have also proved that CR can shorten the time of forced swimming and tail suspension in a mice model of depression, and antagonize the hypothermia induced by reserpine.¹¹ However, no study has been conducted to explore the active ingredients, antidepressant targets and systemic pharmacological mechanisms of CR.

Chinese herbal medicine holds the characteristics of multi-ingredient, multi-target, multi-pathway and synergistic effects, leading to an uncertain material basis and unclear pharmacological mechanism.¹² Therefore, it is difficult to understand the underlying molecular mechanism of Chinese herbal medicines through routine experimental research. In order to address this thorny scientific problem, innovative strategies and methods are urgently needed to comprehensively and systematically elucidate the pharmacological mechanisms of their specific therapeutic efficacy. Network pharmacology is a new field of pharmacology and pharmacodynamics, which integrates multi-pharmacology, systems biology, and computational biology to explain the complex regulation of Chinese herbal medicines on human organisms.¹³ It investigates the synergistic effects and potential mechanisms of multiple ingredients by constructing an ingredient-target network, protein-protein target network and drug-disease network.¹² For example, Liu et al. identified 121 active ingredients of Chaihu Shugan powder and 15 targets related to depression through network pharmacology, and predicted its antidepressant mechanism.¹⁴ Lin et al. identified 17 active components, 144 gene targets and 143 protein targets, as well as four important signaling pathways of Drynariae Rhizoma in fracture repair through network pharmacology, reflecting the characteristics of multiple components, multiple targets and multiple pathways of Chinese herbal medicine in promoting fracture healing.¹⁵

In this study, a comprehensive network pharmacology approach was conducted, through drug active ingredient screening, drug and disease target screening, active ingredient-gene network construction, protein-protein interaction network construction, Go functional analysis, KEGG pathway enrichment and gene tissue localization, to explore the active ingredients and complex pharmacological mechanisms of CR for depression (Figure 1). Besides, the DisGeNET score was used to assess the correlation between the common targets and depression, and molecular docking was performed to assess the binding potential of the active ingredients to the corresponding hub proteins. This study provides a valuable reference for exploring the active ingredients of CR and clarifying the mechanism of CR for treating depression, which will be beneficial to patients with depression, especially those affected by COVID-19.

Figure 1

The process of systematically elucidating the mechanism of Curcumae Radix in treating depression.

Materials and Methods

Screening Active Ingredients and Chemical Structures

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform¹⁶ (TCMSP, http://tcmspw.com/, Version 2.3) is a systematic pharmacology platform that can identify the relationship between Chinese herbal medicines, targets and diseases, and is used for new drug discovery. The platform also provides pharmacokinetic properties of natural compounds such as oral bioavailability (OB), drug-likeness (DL), aqueous solubility, and potential to cross the blood-brain barrier (BBB). A compound with a BBB from −0.3 to +0.3 is considered to be moderately penetrating, and greater than 0.3 indicates strong penetrating power. The potential active ingredients of CR were obtained from the TCMSP according to the pharmacokinetic properties of CR, including OB ≥30%, DL ≥0.18 and BBB ≥ −0.3. Then, the chemical structures and their Pubchem Cid of the potential active ingredients of CR were downloaded and stored in the mol2 format.

Protein and Gene Targets of CR’s Potential Active Ingredients

According to Pubchem Cid of the active ingredients, the corresponding SMILES were retrieved in the Pubchem database (https://pubchem.ncbi.nlm.nih.gov/). If there were no canonical SMILES in the Pubchem database, the SMILES of the active ingredients were obtained by searching for the chemical name in Zinc15 (http://zinc.docking.org/substances/home/). Next, the SMILES of the CR’s active ingredients were used to search for the relevant protein targets of the active ingredients in the Swiss Target Prediction (http://www.swisstargetprediction.ch/). The protein targets with a probability ≥0 were included, then the duplicate data were deleted, and the name of the protein targets and their Uniprot IDs were saved. Finally, the UniProt KB search function (http://www.uniprot.org/uniprot/) was used to obtain the gene targets of the CR’s active ingredient.

Screening of Co-Genes for Curcumae Radix and Depression

The following keywords were used to search for genes associated with depression in the Genetic Association Database (GAD, https://geneticassociationdb.nih.gov/), GeneCards database (http://www.genecards.org/), and OMIM database (http://www.ncbi.nlm.nih.gov/omim), such as depression, depressive, antidepressant, and depressed; duplicate data and false positive genes were removed, and then the gene targets of CR were matched to obtain the co-gene targets of CR’s active ingredients and depression, as potential gene targets for the treatment of depression with CR’s active ingredients.

DisGeNET Score of Co-Genes and Depression

The DisGeNET database¹⁷ (http://www.disgenet.org/search, Version 5.0) is one of the platforms that contains the largest available genes and variants associated with human disease. In the database, disease information related to the input gene can be obtained, and genetic information related to the input disease can also be received. A confidence score (DisGeNET score) was performed to navigate the more than 400,000 gene-disease associations in the DisGeNET database, which reflects the recurrence of gene-disease associations across all data sources.¹⁷ In the DisGeNET database, we used co-gene targets of CR’s active ingredients and depression to search, extracted the association results of depression in Mental Disorders, and screened genes with a DisGeNET score ≥0.3.

Construction of Active Ingredient-Gene Network

Cytoscape Version 3.6.0 is an open source software platform that integrates data integration, analysis and visualization and is now widely used to visualize molecular interaction networks and biological pathways.¹⁸ We introduced the gene targets with DisGeNET score ≥0.3 and the active ingredients of CR into Cytoscape Version 3.6.0 software to construct a CR active ingredient-gene target network. Then, we performed a topology analysis to obtain the gene targets with the highest degree values, betweenness centrality and closeness centrality at the same time, as the pivot gene.

Construction of Protein-Protein Interaction (PPI) Networks

The String database (version 11.0, https://string-db.org/, updated January 19, 2019) collects, scores, and integrates all publicly available PPI information, including direct (physical) and indirect (functional) interactions.¹⁹ The co-gene targets with DisGeNET score ≥0.3 were imported into the String database, the species was defined as ‘human’, and the PPI relationship was obtained. The medium confidence was set to 0.4 as the minimum required interaction score, and the updated result was saved in TSV format. Node1, node2 and the combined score were imported into Cytoscape software to draw the PPI network, and topology analysis was performed to obtain the protein targets with the top 5 degrees. The Generate style from statistics tool was used to set the protein targets format, including setting the node size and color to reflect the degree value, and setting the color of the edge to reflect the combined score, and then the PPI network was uploaded.

Molecular Docking

CB-Dock (http://cao.labshare.cn/cb-dock/) integrates the popular docking program Autodock Vina to predict the binding of the target protein to the compound, with an accuracy rate of 70%.²⁰ CB-Dock can automatically identify the protein-ligand binding site, calculate the center and size, customize the size of the docking box according to the query ligand, and then perform molecular docking with AutoDock Vina. The Uniprot ID of the five highest-degree proteins recognized by the PPI network was inputted into the UniProt database (https://www.uniprot.org/), then the corresponding PDB ID with the minimum resolution (Å) in the 3D structure databases was selected, and its protein structure downloaded in PDB format. Finally, CB-Dock was used to dock the CR active ingredients in mol2 format with the corresponding protein targets, and Schrodinger Suites (Version 2018‐1, Materials) was used to show the 3D and 2D map of the corresponding molecular docking.

Gene Ontology Function, KEGG Pathway, and Tissue Localization Analysis of Genes

The Database for Annotation, Visualization and Integrated Discovery (DAVID, https://david. ncifcrf.gov/, Version 6.8) provides systematic, comprehensive biofunctional annotation information for a large number of genes and proteins, enabling the identification of the most significantly enriched biological annotations.²¹ We introduced depression-related genes with a DisGeNET score ≥0.3 into the DAVID database, set the “Select Identifier” to “official gene symbol,” set the “list type” to “gene list,” and defined the species as “homo sapiens” in the “background” and “list.” Then, we performed Go function, KEGG pathway and tissue localization analysis on the gene targets. The most important biological process (BP), cellular component (CC), molecular function (MF), KEGG pathway and tissue localization results were screened and stored according to a threshold P < 0.05. The BP, CC, and MF was designed using GraphPad Prism 7.0 software. The advanced bubble chart for the KEGG pathway was created with Omicshare Tools (http://omicshare.com/tools/Home/Soft/getsoft/type/index).

Brain-Related Gene Localization Analysis

Founded in 2008, BioGPS (http://biogps.org/) is a centralized gene annotation portal with a “Gene expression/activity chart” plugin that contains approximately 6,000 datasets, enabling researchers to access distributed gene annotation resources.²² The gene targets identified above in the brain were input into BioGPS for specific distribution analysis, the raw data were downloaded, the expression value of each gene in each part was calculated, and the gene expression heat map was drawn using GraphPad Prism 7.0 software.

Integrated Signal Pathway

We imported the UniprotID of genes with DisGeNET score ≥0.3 into the KEGG mapper of the KEGG database (http://www.kegg.jp/), and set the species to humans, thus obtaining the signaling pathways of the genes. Finally, screening and integrating was carried out of the signaling pathways related to depression as the potential mechanism for CR treatment of depression.

Results

Screening of Active Ingredients of Curcumae Radix

There were 222 ingredients of CR in the TCMSP database. According to the pharmacokinetic properties of CR, including OB ≥30%, DL ≥0.18, and BBB ≥ −0.3, eight potential active ingredients were selected, as shown in Table 1 (β-sitosterol, sitosterol, (4aR,5R,8R,8aR)−5,8-dihydroxy-3,5,8a-trimethyl-6,7,8,9-tetrahydro-4aH-benzo[f]benzofuran-4-one, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one, (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene, oxycurcumenol, zedoalactone E, and 1,7-diphenyl-3-acetoxy-6(E)-hepten).

Table 1

Main Active Ingredients in Curcumae Radix.

No.	Molecule ID	Molecule name	Chemical formula	OB(%)	DL	BBB
1	MOL000358	β-sitosterol	C29H50O	36.91	0.75	0.99
2	MOL000359	Sitosterol	C29H50O	36.91	0.75	0.87
3	MOL004244	(4aR,5R,8R,8aR)−5,8-dihydroxy-3,5,8a-trimethyl-6,7,8,9-tetrahydro-4aH-benzo[f]benzofuran-4-one	C15H20O4	59.52	0.2	−0.16
4	MOL004260	(E) −1,7-Diphenyl-3-hydroxy-1-hepten-5-one	C19H20O2	64.66	0.18	0.28
5	MOL004263	(E) −5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene	C19H22O2	46.9	0.19	0.21
6	MOL004291	Oxycurcumenol	C15H22O3	67.06	0.18	0.93
7	MOL004309	zedoalactone E	C15H20O4	85.16	0.19	−0.28
8	MOL004316	1,7-Diphenyl-3-acetoxy-6(E)-hepten	C21H24O2	48.47	0.22	0.99

Co-Genes of Curcumae Radix and Depression

Except for (4aR,5R,8R,8aR)−5,8-dihydroxy-3,5,8a-trimethyl-6,7,8,9-tetrahydro-4aH-benzo- [f]benzofuran-4-one and 1,7-diphenyl-3-acetoxy-6(E)-hepten, six active ingredients of CR were able to be used to retrieve the corresponding protein targets in the Swiss Target Prediction database. A total of 239 proteins, and 239 corresponding genes were retrieved by UniProt database.

In terms of disease targets, 235 genes related to depression were retrieved from the GAD database, 24435 from the GeneCards database, and 256 from the OMIM database. Excluding the duplicate data, 7920 genes related to depression were obtained, and then matched with the gene targets of CR’s active ingredients (Supplemental Figure S1); 203 potential co-genes for CR treatment of depression were screened, as shown in Table 2.

Table 2

Potential Gene Targets of Curcumae Radix in the Treatment of Depression.

No.	Uniprot ID	Target	No.	Uniprot ID	Target	No.	Uniprot ID	Target
1	P08183	ABCB1	69	P47871	GCGR	137	P47900	P2RY1
2	O60706	ABCC9	70	P23415	GLRA1	138	P09874	PARP1
3	P00519	ABL1	71	Q9GZN0	GPR88	139	P12004	PCNA
4	P22303	ACHE	72	Q13255	GRM1	140	Q9Y233	PDE10A
5	P00813	ADA	73	Q14416	GRM2	141	O00408	PDE2A
6	P78536	ADAM17	74	P41594	GRM5	142	Q15118	PDK1
7	P30542	ADORA1	75	O43613	HCRTR1	143	O15055	PER2
8	P30556	AGTR1	76	O43614	HCRTR2	144	P42336	PIK3CA
9	Q9UM73	ALK	77	Q9UBN7	HDAC6	145	P42338	PIK3CB
10	P20292	ALOX5AP	78	P04035	HMGCR	146	O00329	PIK3CD
11	Q01433	AMPD2	79	P09601	HMOX1	147	P48736	PIK3CG
12	Q01432	AMPD3	80	Q9Y5N1	HRH3	148	P11309	PIM1
13	Q16853	AOC3	81	Q9H3N8	HRH4	149	Q9P1W9	PIM2
14	P05067	APP	82	P28845	HSD11B1	150	P14555	PLA2G2A
15	P10275	AR	83	P80365	HSD11B2	151	P06746	POLB
16	Q13535	ATR	84	P37059	HSD17B2	152	Q07869	PPARA
17	P37288	AVPR1A	85	P07900	HSP90AA1	153	Q03181	PPARD
18	P06276	BCHE	86	P50406	HTR6	154	P37231	PPARG
19	P46663	BDKRB1	87	P14902	IDO1	155	P50336	PPOX
20	P13497	BMP1	88	P08069	IGF1R	156	P17252	PRKCA
21	P15056	BRAF	89	P40189	IL6ST	157	P05771	PRKCB
22	O60885	BRD4	90	Q08881	ITK	158	Q05655	PRKCD
23	Q16602	CALCRL	91	P23458	JAK1	159	Q02156	PRKCE
24	P29466	CASP1	92	O60674	JAK2	160	P05129	PRKCG
25	P41180	CASR	93	P52333	JAK3	161	P24723	PRKCH
26	P32246	CCR1	94	P22460	KCNA5	162	P78527	PRKDC
27	P30304	CDC25A	95	P15382	KCNE1	163	P49810	PSEN2
28	P06493	CDK1	96	Q14654	KCNJ11	164	P43116	PTGER2
29	P24941	CDK2	97	Q12791	KCNMA1	165	O14684	PTGES
30	Q00535	CDK5	98	P35968	KDR	166	P23219	PTGS1
31	Q15078	CDK5R1	99	P10721	KIT	167	P35354	PTGS2
32	O14757	CHEK1	100	P53667	LIMK1	168	P18031	PTPN1
33	P11229	CHRM1	101	P38571	LIPA	169	P29350	PTPN6
34	P08172	CHRM2	102	Q5S007	LRRK2	170	P06737	PYGL
35	P08173	CHRM4	103	P48449	LSS	171	P04049	RAF1
36	P08912	CHRM5	104	P09960	LTA4H	172	O95267	RASGRP1
37	P21964	COMT	105	Q15722	LTB4R	173	Q13464	ROCK1
38	P34998	CRHR1	106	Q02750	MAP2K1	174	P35398	RORA
39	P07711	CTSL	107	P28482	MAPK1	175	P51449	RORC
40	P49238	CX3CR1	108	P53779	MAPK10	176	Q9UBE0	SAE1
41	P25025	CXCR2	109	Q15759	MAPK11	177	Q15858	SCN9A
42	P15538	CYP11B1	110	Q16539	MAPK14	178	P08185	SERPINA6
43	P19099	CYP11B2	111	P45983	MAPK8	179	P04278	SHBG
44	P05093	CYP17A1	112	P45984	MAPK9	180	Q99720	SIGMAR1
45	P11511	CYP19A1	113	Q00987	MDM2	181	Q99808	SLC29A1
46	P33261	CYP2C19	114	P50579	METAP2	182	P13866	SLC5A1
47	P11712	CYP2C9	115	P03956	MMP1	183	P23975	SLC6A2
48	Q16850	CYP51A1	116	P08253	MMP2	184	Q01959	SLC6A3
49	Q9UBM7	DHCR7	117	P08254	MMP3	185	P31645	SLC6A4
50	P21917	DRD4	118	P22894	MMP8	186	Q00796	SORD
51	P33316	DUT	119	P14780	MMP9	187	P12931	SRC
52	P24530	EDNRB	120	P48039	MTNR1A	188	Q12772	SREBF2
53	P00533	EGFR	121	P49286	MTNR1B	189	P43405	SYK
54	Q9NZJ5	EIF2AK3	122	P42345	MTOR	190	Q96RJ0	TAAR1
55	P07099	EPHX1	123	P35228	NOS2	191	P25103	TACR1
56	P34913	EPHX2	124	Q9UHC9	NPC1L1	192	P21452	TACR2
57	P04626	ERBB2	125	Q15761	NPY5R	193	O14746	TERT
58	O75460	ERN1	126	P55055	NR1H2	194	P36897	TGFBR1
59	P03372	ESR1	127	Q13133	NR1H3	195	P10828	THRB
60	Q92731	ESR2	128	Q14994	NR1I3	196	O75762	TRPA1
61	O00519	FAAH	129	P04150	NR3C1	197	P30536	TSPO
62	P37268	FDFT1	130	P08235	NR3C2	198	P42681	TXK
63	P11362	FGFR1	131	P04629	NTRK1	199	P29597	TYK2
64	P62942	FKBP1A	132	P41143	OPRD1	200	P04818	TYMS
65	P17948	FLT1	133	P41145	OPRK1	201	P16662	UGT2B7
66	P36888	FLT3	134	P41146	OPRL1	202	P55072	VCP
67	P11413	G6PD	135	P35372	OPRM1	203	P11473	VDR
68	P04406	GAPDH	136	Q99572	P2RX7

DisGeNET Score Between Co-Genes and Depression

In order to identify genes strongly related to different depressions, we conducted a DisGeNET score to assess the correlation between co-genes and depression. In the DisGeNET database, 36,617 correlations between co-gene targets and depression were obtained, and 138 genes were associated with depressive disorder, unipolar depression, and major depressive disorder. Among them, 45 genes had a DisGeNET score ≥0.3, which may be those strongly related to depression. The detailed DisGeNET scores of these genes and different types of depression are shown in Table 3. There were seven genes with a DisGeNET score ≥0.6, of which three were related to depressive disorder: SLC6A4, NR3C1, FGFR1; two were associated with unipolar depression: SLC6A4, NR3C1, and two were related to major depressive disorder: SLC6A4, CRHR1.

Table 3

DisGeNET Score of Genes and Depression (DisGeNET Score ≥0.3).

No.	Gene	Disease	DisGeNET score	No.	Gene	Disease	DisGeNET score
1	SLC6A4	Depressive disorder	0.6	41	CYP2C9	Unipolar Depression	0.33
2	NR3C1	Depressive disorder	0.6	42	DRD4	Major Depressive Disorder	0.33
3	FGFR1	Depressive disorder	0.6	43	CYP2C9	Major Depressive Disorder	0.33
4	SLC6A4	Unipolar Depression	0.6	44	EGFR	Major Depressive Disorder	0.33
5	NR3C1	Unipolar Depression	0.6	45	RORA	Depressive disorder	0.32
6	SLC6A4	Major Depressive Disorder	0.6	46	PLA2G2A	Depressive disorder	0.32
7	CRHR1	Major Depressive Disorder	0.6	47	DRD4	Unipolar Depression	0.32
8	PTGS2	Depressive disorder	0.56	48	GRM5	Major Depressive Disorder	0.32
9	PTGS2	Unipolar Depression	0.51	49	FGFR1	Major Depressive Disorder	0.32
10	FGFR1	Unipolar Depression	0.5	50	MTOR	Major Depressive Disorder	0.32
11	PER2	Depressive disorder	0.45	51	TERT	Major Depressive Disorder	0.32
12	COMT	Depressive disorder	0.4	52	ESR2	Major Depressive Disorder	0.32
13	CRHR1	Depressive disorder	0.4	53	PRKCB	Major Depressive Disorder	0.32
14	SLC6A2	Depressive disorder	0.4	54	GRM1	Depressive disorder	0.31
15	DRD4	Depressive disorder	0.4	55	HSD11B1	Depressive disorder	0.31
16	ABCB1	Depressive disorder	0.4	56	LTA4H	Depressive disorder	0.31
17	COMT	Unipolar Depression	0.4	57	GRM5	Unipolar Depression	0.31
18	SLC6A2	Unipolar Depression	0.4	58	ESR2	Unipolar Depression	0.31
19	ABCB1	Unipolar Depression	0.4	59	TERT	Unipolar Depression	0.31
20	COMT	Major Depressive Disorder	0.4	60	PRKCB	Unipolar Depression	0.31
21	NR3C1	Major Depressive Disorder	0.4	61	KDR	Unipolar Depression	0.31
22	SLC6A2	Major Depressive Disorder	0.4	62	HSP90AA1	Unipolar Depression	0.31
23	ABCB1	Major Depressive Disorder	0.4	63	MAP2K1	Unipolar Depression	0.31
24	AR	Depressive disorder	0.39	64	GRM1	Unipolar Depression	0.31
25	NR3C2	Depressive disorder	0.39	65	EGFR	Unipolar Depression	0.31
26	SLC6A3	Depressive disorder	0.38	66	PTGS2	Major Depressive Disorder	0.31
27	ESR1	Depressive disorder	0.37	67	KDR	Major Depressive Disorder	0.31
28	CRHR1	Unipolar Depression	0.35	68	HSP90AA1	Major Depressive Disorder	0.31
29	CYP2C19	Unipolar Depression	0.35	69	MAP2K1	Major Depressive Disorder	0.31
30	CYP2C19	Major Depressive Disorder	0.35	70	TYK2	Major Depressive Disorder	0.31
31	P2RX7	Depressive disorder	0.34	71	TACR1	Depressive disorder	0.3
32	CHRM2	Depressive disorder	0.34	72	MAPK8	Depressive disorder	0.3
33	NR3C2	Unipolar Depression	0.34	73	HCRTR1	Depressive disorder	0.3
34	NR3C2	Major Depressive Disorder	0.34	74	ALK	Depressive disorder	0.3
35	OPRK1	Depressive disorder	0.33	75	HDAC6	Depressive disorder	0.3
36	OPRM1	Depressive disorder	0.33	76	SERPINA6	Depressive disorder	0.3
37	APP	Depressive disorder	0.33	77	MTOR	Unipolar Depression	0.3
38	NOS2	Depressive disorder	0.33	78	MMP8	Unipolar Depression	0.3
39	CYP2C19	Depressive disorder	0.33	79	TYK2	Unipolar Depression	0.3
40	IDO1	Depressive disorder	0.33	80	MMP8	Major Depressive Disorder	0.3

Ingredient-Gene Network of Curcumae Radix

In order to identify the pivotal genes and corresponding active ingredients that are strongly related to depression, we constructed an active ingredient-gene network using Cytoscape software, which is shown in Figure 2. The active ingredient-gene network contains 50 nodes and 80 lines. Only five active ingredients in CR were associated with the 45 genes related to depression. (E)−5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene was linked to 24 genes, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one to 19, oxycurcumenol to 13, and β-sitosterol and sitosterol to 12. Topology analysis showed that there were four genes with the top degree values, betweenness centrality and closeness centrality at the same time: AR, NOS2, PTGS2, TYK2 (Table 4).

Figure 2

Ingredient-gene network of Curcumae Radix. The pink rectangle nodes (■) are the main active ingredients of Curcumae Radix, the light blue oval (●) is a potential gene for treating depression with Curcumae Radix. The line represents the correlation between the active ingredients and the genes.

Table 4

Genes With the Top 4 Degree Values, Betweenness Centrality and Closeness Centrality.

No.	Gene targets	Degree (rank)	Betweenness centrality (rank)	Closeness centrality (rank)
1	AR	5 (1)	0.109 (1)	0.527 (1)
2	NOS2	4 (2)	0.045 (4)	0.434 (4)
3	PTGS2	3 (3)	0.051 (2)	0.476 (2)
4	TYK2	3 (3)	0.051 (2)	0.476 (2)

Protein-Protein Interaction Network

The PPI network of CR shown in Figure 3 involves 45 nodes and 189 lines. The average node degree of the PPI network was 8.4. We performed topological analysis to screen for proteins with top 5 degree values in the PPI network, which is shown in Table 5. The results showed that EGFR had the highest degree of 21; PTGS2 had the second highest degree of 20; HSP90AA1 with a degree of 18; and MAPK8 and ESR1 with a degree of 16; these were considered to be hub proteins in the PPI network.

Figure 3

Protein-protein interaction network of Curcumae Radix. Nodes represent proteins and lines represent associations between proteins; the size and the color of the node represent the value of the degree, and the color of the line indicates the value of the combined score (low values to bright colors, low values to small sizes).

Table 5

Hub Proteins With Top 5 Degree Values in the Protein-Protein Interaction Network.

No.	Degree	Uniprot ID	Target name	PDB ID (resolution)	Databases
1	21	P00533	EGFR	5HG8(1.42）	RCSB PDB
2	20	P35354	PTGS2	5F19(2.04）	RCSB PDB
3	18	P07900	HSP90AA1	5J2X(1.22）	RCSB PDB
4	16	P45983	MAPK8	2XRW(1.33）	RCSB PDB
5	16	P03372	ESR1	3CBP(1.42）	RCSB PDB

Molecular Docking

Molecular docking was performed to evaluate the binding ability of the active ingredients with the corresponding hub-proteins (Table 6). The hub-proteins with the top 5 degree values were molecularly docked with the CR’s active ingredients, and 9 docking results were received and are represented in Figure 4. It is believed that the more negative the vina score, the more stable the binding of the active ingredients and the proteins.²⁰ The vina scores of the active ingredients and core protein targets of CR in the treatment of depression were negative and less than −5.9. The results show that oxycurcumenol, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one and (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene had good binding activities to PTGS2. Oxycurcumenol was fixed in the binding cavity of the protein PTGS2 through π-π bonds with residue ALA 527. (E)−1,7-Diphenyl-3-hydroxy-1-hepten-5-one was fixed in the binding cavity of the protein PTGS2 through π-π bonds with residues PHE 209 and TRP387. (E)−5-Hydroxy-7 -(4-hydroxyphenyl)−1-phenyl-1-heptene was fixed in the binding cavity of the protein PTGS2 through π-π bonds with residues PHE 518 and TRP 387.

Figure 4

Molecular docking of active ingredients with the corresponding hub proteins (Left: 3D structure, right: 2D structure; the green arrows indicate π-π bonds, and the pink arrows indicate H bonds).

Table 6

Molecular Docking of Active Ingredients With the Corresponding Hub Proteins.

No.	Ingredient	Uniprot ID	Protein name	PDB ID (Resolution)	Databases	Vina score
1	(E)−1,7-Diphenyl-3-hydroxy-1-hepten-5-one	P00533	EGFR	5HG8(1.42）	RCSB PDB	−7.6
2	(E)−1,7-Diphenyl-3-hydroxy-1-hepten-5-one	P35354	PTGS2	5F19(2.04）	RCSB PDB	−9.5
3	Oxycurcumenol	P35354	PTGS2	5F19(2.04）	RCSB PDB	−8.5
4	(E)−5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene	P35354	PTGS2	5F19(2.04）	RCSB PDB	−9.1
5	(E)−5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene	P07900	HSP90AA1	5J2X(1.22）	RCSB PDB	−7.1
6	(E)−5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene	P45983	MAPK8	2XRW(1.33）	RCSB PDB	-8
7	Sitosterol	P03372	ESR1	3CBP(1.42）	RCSB PDB	−7.4
8	beta-sitosterol	P03372	ESR1	3CBP(1.42）	RCSB PDB	−7.2
9	(E)−5-Hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene	P03372	ESR1	3CBP(1.42）	RCSB PDB	−7.5

^a https://www.rcsb.org/structure/5J2X

Gene Ontology Function, KEGG Pathway, and Tissue Localization Analysis

The Go enrichment analysis and tissue localization analysis of the potential genes for CR’s active ingredients in the treatment of depression are shown in Figure 5, including BP, CC, and MF. In the BP (Figure 5(A)), signal transduction involved 11 (24.4%) genes of depression, chemical synaptic transmission involved eight (17.8%), protein phosphorylation seven (15.6%), and positive regulation of nitric oxide biosynthetic process six (13.3%). In the CC (Figure 5(B)), plasma membrane involved 27 (60%) gene targets for treatment of depression, nucleus 22 (48.9%), the integral component of the membrane 20 (44.4%), and the integral component of the plasma membrane 17 (37.8%). In the MF (Figure 5(C)), protein binding involved 35 (77.8%) gene targets of depression, ATP binding 12 (26.7%), zinc ion binding 10 (22.2%), and enzyme binding nine (20%).

Figure 5

Enriched gene ontology terms of potential targets for treating depression from main active ingredients of Curcumae Radix. (A) Biological process (BP), (B) Cellular component (CC), (C) Molecular function (MF), (D) Specific distribution of depression-related genes, (E) Specific distribution of brain-related genes, (F). Enriched KEGG pathways of potential targets for treating depression from main active ingredients of Curcumae Radix.

In the tissue localization analysis (Figure 5(D)), 30 depression-related genes were highly expressed in the brain, three in the breast, and 10 in the liver (Supplemental Table S1). Thirty brain-related genes were further analyzed for specific distribution using the BioGPS database (Figure 5(E)). HSP90AA1 was highly expressed in cingulate cortex, hypothalamus, medulla oblongata, occipital lobe, olfactory bulb, pineal (day), and pineal (night). COMT was highly expressed in hypothalamus, olfactory bulb, whole brain, pineal (day), and pineal (night). There were eight genes that were only highly expressed in the prefrontal cortex: PER2, P2RX7, GRM1, TACR1, MAPK8, HCRTR1, EGFR, and TYK2; APP was highly expressed in the cingulate cortex.

The KEGG pathway analysis revealed the potential mechanism of CR in the treatment of depression (Figure 5(F)). The calcium signaling pathway was identified as the most important one involving eight genes, accounting for 17.8%; the estrogen, Ras, and PI3K-Akt signaling pathways involved 7 gene targets, respectively, accounting for 15.6%; and the ErbB, HIF-1, Rap1, and MAPK signaling pathways involved 5 gene targets (11.1%).

Signaling Pathway Integration

Four important signaling pathways for treatment of depression with CR’s active ingredients are shown in Figure 6. The gene targets are marked in light blue, and the potential gene targets for CR active ingredients treatment of depression are marked in dark blue. There were 15 potential gene targets for the treatment of depression in the calcium, ERBB, PI3K-AKT, and estrogen signaling pathways, accounting for 33.3%, suggesting that CR may exerted antidepressant effects at these gene targets. PKC and MEK were recognized to play a role in multiple signaling pathways simultaneously, suggesting that they may be “hub genes.”

Figure 6

Anti-depression pathways of potential targets from main active ingredients of Curcumae Radix. (The color represents the signal pathway, the arrow “→” indicates the promoting effect, and T-arrow “┨” indicates the inhibition).

Discussion

According to the pharmacokinetic properties of CR, including OB, DL, and BBB, eight CR active ingredients were selected in our study, five of which are closely related to depression, namely β-sitosterol, sitosterol, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one, (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene, and oxycurcumenol. The first two are terpenoids, and the latter three are volatile oil components. Mou et al. found that either terpenoids or volatile oils extracted from Shugan Hewei Decoction containing CR could reduce the resting time in the open-field test in depression model rats and increase the content of serotonin (5-HT), γ-aminobutyric acid (GABA), and glutamic acid in the rats’ hypothalamus.²³ Besides, it has been reported that the 95% ethanol, light petroleum and ethyl acetate extracts of CR have a good antidepressant effect, especially the light petroleum and ethyl acetate extracts.²⁴ In the active ingredient-gene network, these five active ingredients of CR have also been identified to be closely linked to depression-related genes. Therefore, the active ingredients of CR may have good antidepressant effects.

In our study, 45 co-genes of CR and depression with DisGeNET score ≥0.3 were associated with depressive disorder, unipolar depression, or major depressive disorder. Topology analysis showed that genes AR, NOS2, PTGS2 and TYK2 with the top 4 degree values, betweenness centrality and closeness centrality at the same time, suggested that AR, NOS2, PTGS2 and TYK2 were closely related to depression. These results are partially consistent with previously published studies. For example, Owens found that decreased AR mRNA expression levels may be associated with depressive symptoms in healthy men.²⁵ Galecki et al. observed that NOS2 transcription was increased in peripheral blood of patients with recurrent depression,²⁶ and that the polymorphism (−1026C/A) in the NOS2 promoter was associated with the risk of recurrent depressive disorder.²⁷ Brkic et al. found that lipopolysaccharide reduced the level of PTGS2 mRNA in the prefrontal cortex of male Wistar depression rats.²⁸ Kalkman suggested that inhibiting TYK2 could reduce depression by blocking IL-6-mediated inflammation, which could be a potential new treatment based on the cause of depression.²⁹ Therefore, CR may reduce the onset and development of depression by regulating these important gene targets.

In our study, 45 proteins and 189 interactions were identified in the PPI network. Topological analysis showed that EGFR, PTGS2, HSP90AA1, MAPK8, and ESR1 have the highest values, which may be the hub protein targets that regulate or affect the progression of depression. These results have been partially confirmed by previous studies. For example, Hu et al. observed by biotin-labeled protein chip technology that the expression of EGFR protein in the hippocampus of rats with chronic stress depression increased significantly.³⁰ Jacobs et al. also confirmed that patients with stage IV non-small cell lung cancer with EGFR mutations showed elevated pro-inflammatory marker TNF-a, but the severity of depression was lower.³¹ Previous studies found that Shugan Hewei Decoction containing CR could increase the content of glutamate in the nucleus accumbens to inhibit depressive-like behavior, which may be related to glutamate promoting the expression of PTGS2 protein in nerve cells.^32,33 Wei et al. found that the expression of HSP90AA1 protein in patients with moderate or severe depression was significantly higher than that in patients with mild depression or without depression.³⁴ Mohammad et al. identified MAPK8 (also called JNK1) as a central repressor of neurogenesis and promoter of depressive-like behavior in the adult hippocampus.³⁵ Besides, Chai also found that MAPK8 protein was highly expressed in the hippocampus of diabetic rats with depression, and the drug containing the Curcuma ingredient could reverse this trend.³⁶ Kajta et al. observed that the depressive-like effects of prenatal exposure to dichlorodiphenyltrichloroethane impair GPER1/ESR1 protein.³⁷ In addition, our molecular docking experiments further confirmed that these hub protein targets have good binding activity with the corresponding CR active ingredients, especially PTGS2 was identified to bind well to oxycurcumenol, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one and (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene. Chen et al. found that cortical PTGS2 protein (also known as cyclooxygenase 2, COX2) is activated in rats with depression-like behavior induced by chronic unpredictable mild stress (CUMS).³⁸ In addition, Song also confirmed that the expression of COX-2 in the hippocampal CA1 area of CUMS-induced depressed rats increased, suggesting that the occurrence of neuroinflammation is related to the depression-like behavior of rats.³⁹ Although the direct effect of oxycurcumol with COX2 is still unclear, it has been confirmed that oxycurcumol could prevent cell damage induced by oxidative stress.⁴⁰ However, few studies have been reported on (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one and (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene. In short, these results indicate that EGFR, PTGS2, HSP90AA1, MAPK8 and ESR1 proteins may be important targets for the active ingredients of CR to regulate depression, but further animal or cell experiments are needed to confirm this.

To identify further the role of genes in the regulation of depression, we performed Go functional analysis. In the BP, signal transduction, chemical synaptic transmission and protein phosphorylation were the most involved biological processes of the genes. Several lines of evidence also prove this finding. For example, a previous study reported that corticosterone-induced depression and anxiety-like behavior reduced intracellular signal transduction in the hippocampus.⁴¹ Lagus et al. confirmed that most people with depressive disorder experience sleep disorders, which were associated with disruption of chemical synaptic transmission.⁴² Rajamanickam et al. found that Akt could regulate the expression and phosphorylation of serotonin transporter, the target of antidepressants.⁴³ In the CC, the plasma membrane and nucleus were identified as sites of gene action. Bowman et al. found that monoamine transporters on the plasma membrane were involved in the steady-state regulation of serotonin in adolescent antidepressant therapy.⁴⁴ Pace et al. confirmed that blocking the transport of glucocorticoid receptors from the cytoplasm to the nucleus and disrupting glucocorticoid receptor-DNA binding may lead to major depression.⁴⁵ In the MF, protein binding and ATP binding were common functions of depression-related gene targets. A previous study found that overexpression of p11 promotes protein binding between P11 and 5-HT receptors and alleviates depression-like behavior.⁴⁶ ATP-binding related genes, such as ABCB1, were also identified as being associated with major depression.⁴⁷

In terms of the pathological part of depression, previous studies have reported that the dysregulated hypothalamus-pituitary-adrenocortical axis and the neurotrophin system of the brain are considered to be important in the pathophysiology of depression, which is closely related to the brain.⁴⁸ In our study, most co-genes were identified as being related to the brain. However, many brain regions are involved in the pathology and development of depression. In order to identify accurately high expression regions of genes, we used BioGPS for further analysis. The results revealed that PER2, P2RX7, GRM1, TACR1, MAPK8, HCRTR1, EGFR and TYK2 were highly expressed in the prefrontal cortex. Previous studies also suggested that PER2 and P2RX7 are associated with stress-induced depression.^49,50 GRM1 was identified as high expression in the prefrontal cortex in female patients with major depressive disorder.⁵¹ However, the expression of TACR1, MAPK8, HCRTR1, EGFR, and TYK2 in the prefrontal cortex has not been verified in patients with depression.

KEGG pathway enrichment analysis indicated that multiple signaling pathways were involved in the pathophysiological processes of depression, such as the calcium, estrogen, Ras, PI3K-Akt, ErbB, and HIF-1 signaling pathways. Previous studies found that Jiawei Wendan decoction containing CR could exert antidepressant effects by inhibiting the massive influx of Ca²⁺ in hippocampal neurons and improving neuroplasticity,⁵² as well as up-regulating Ras protein in the hippocampus to improve the spatial cognitive ability in rats with depression.⁵³ Besides, regulation of the estrogen signaling pathway has also been found to play an important role in the neuroprotection of postpartum depression.⁵⁴ Fan et al. found through network pharmacology that the mechanism of the drug combination Tatarinowii Rhizoma and CR in the treatment of depression is related to the secretion of sex hormones and hippocampal neuronal apoptosis, which is similar to our study.⁵⁵ However, its vague pictures, single data, and lack of in-depth analysis cannot provide more effective information for the treatment of depression. Guo et al. demonstrated that enhancing the PI3K/AKT/FoxO1 pathway to inhibit TLR4 expression could alleviate neuroinflammation and cause depression-like behavior.⁵⁶ Previous studies confirmed that the antidepressant effect of β-sitosterol is mediated by 5-HT, dopamine and GABA-ergic systems, while mGluR1-dependent long-term inhibition in brain dopamine neurons is regulated by the NRG1/ErbB signaling pathway and involves the ErbB2/ErbB4 receptor.^57,58 Chen et al. found that oxycurcumol protection of nerve cells from oxidative stress-induced cell damage may be related to the HIF1α signaling pathway.⁴⁰ Eyre et al. also believed that the onset of depression is associated with impaired neuroplasticity and that the HIF-1α signaling pathway is involved in this process in rodent depression models.⁵⁹

Conclusions

This study revealed the ingredients, effective targets and mechanisms of CR in the treatment of depression, as well as high expression areas of depression-related targets, which provide a new perspective for the development of new antidepressants.

Supplemental Material

Online supplementary file 1 - Supplemental material for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking

Supplemental material, Online supplementary file 1, for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking by Xiaotong Wang, Qiaoru Lin, Meiqing Shen, Haixiong Lin, Junjie Feng, Lulu Peng, Minling Huang, Xiaoxuan Zhan, Ziyin Chen and Tengfei Ma in Natural Product Communications

Supplemental Material

Figure S1 - Supplemental material for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking

Supplemental material, Figure S1, for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking by Xiaotong Wang, Qiaoru Lin, Meiqing Shen, Haixiong Lin, Junjie Feng, Lulu Peng, Minling Huang, Xiaoxuan Zhan, Ziyin Chen and Tengfei Ma in Natural Product Communications

Supplemental Material

Table S1 - Supplemental material for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking

Supplemental material, Table S1, for Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking by Xiaotong Wang, Qiaoru Lin, Meiqing Shen, Haixiong Lin, Junjie Feng, Lulu Peng, Minling Huang, Xiaoxuan Zhan, Ziyin Chen and Tengfei Ma in Natural Product Communications

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported, in part, by the International Program for Postgraduates, Guangzhou University of Chinese Medicine (GZYXB[2021]52), Excellent Doctoral Dissertation Incubation Grant of Guangzhou University of Chinese Medicine (GZYXB2020-18), and Excellent Doctoral Dissertation Incubation Grant of First Clinical School of Guangzhou University of Chinese Medicine (YB201902).

Supplemental Material

Supplemental material for this article is available online.

References

Lopez-Montoyo

Quero

Montero-Marin

et al. Effectiveness of a brief psychological mindfulness-based intervention for the treatment of depression in primary care: study protocol for a randomized controlled clinical trial. BMC Psychiatry. 2019;19(1):301. doi:10.1186/s12888-019-2298-x

31619196

Daly

EJ.

Trivedi

MH.

Janik

et al. Efficacy of Esketamine nasal spray plus oral antidepressant treatment for relapse prevention in patients with treatment-resistant depression: a randomized clinical trial. JAMA Psychiatry. 2019;76(9):893-903.doi:10.1001/jamapsychiatry.2019.1189

31166571

Lai

Wang

et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3(3):e203976.doi:10.1001/jamanetworkopen.2020.3976

32202646

Nguyen

HC.

Nguyen

MH.

et al. People with suspected COVID-19 symptoms were more likely depressed and had lower health-related quality of life: the potential benefit of health literacy. J Clin Med. 2020;9(4):965. doi:10.3390/jcm9040965

32244415

Cipriani

Furukawa

TA.

Salanti

et al. Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet. 2018;391(10128):1357-1366.doi:10.1016/S0140-6736(17)32802-7

29477251

Hetrick

SE.

McKenzie

JE.

Cox

GR.

Simmons

MB.

Merry

. Newer generation antidepressants for depressive disorders in children and adolescents. Cochrane Database Syst Rev. 2012;11:D4851.doi:10.1002/14651858.CD004851.pub3

23152227

Wang

Shi

Y-H.

Zeng

Zheng

G-Q

. Efficacy and safety of Chinese herbal medicine for depression: a systematic review and meta-analysis of randomized controlled trials. J Psychiatr Res. 2019;117:74-91.doi:10.1016/j.jpsychires.2019.07.003

31326751

Health Commission of Guangdong Province, Traditional Chinese Medicine Bureau of Guangdong Province . Chinese Medicine Treatment Protocol for COVID-19 in Guangdong Province (Trial Version 2). 2020. http://szyyj.gd.gov.cn/zwgk/gsgg/content/post_2902010.html

Kwon

. Review of recent clinical trials for depression in traditional Chinese medicine -based on randomized controlled trials and systematic reviews. Korean J Orient Physiol Pathol. 2015;29(6):458-466.doi:10.15188/kjopp.2015.12.29.6.458

10.

Jin

Dai

. Clinical observation on treatment of post-stroke depression with Changpu Yujin decoction and Xiaoyao powder. J New Chin Med. 2016;48(12):17-19.doi:10.13457/j.cnki.jncm.2016.12.007

11.

Han

Yang

et al. Experimental study on anti-depression effect of Curcumae Radix. J Ningxia Med Col. 2008;30(3):275-276.doi:10.3969/j.issn.1674-6309.2008.03.002

12.

Liu

A-lin.

G-hua.

Liu

. Network pharmacology: new guidelines for drug discovery. Yao Xue Xue Bao. 2010;45(12):1472-1477.doi:10.16438/j.0513-4870.2010.12.010

21351485

13.

Zhao

Shan

Gong

. A network pharmacology approach to explore active compounds and pharmacological mechanisms of Epimedium for treatment of premature ovarian insufficiency. Drug Des Devel Ther. 2019;13:2997-3007.doi:10.2147/DDDT.S207823

31692519

14.

Liu

Fan

et al. Study on mechanism of Chaihu Shugan powder for treating depression based on network pharmacology. Chin J Integr Med. 2019:1-8.doi:10.1007/s11655-019-3172-x

15.

Lin

Wang

et al. Identified the Synergistic Mechanism of Drynariae Rhizoma for Treating Fracture Based on Network Pharmacology. Evid Based Complement Alternat Med. 2019;2019:1-19.doi:10.1155/2019/7342635

31781279

16.

Wang

et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. doi:10.1186/1758-2946-6-13

24735618

17.

Piñero

Bravo

Àlex.

Queralt-Rosinach

et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 2017;45(D1):D833-D839.doi:10.1093/nar/gkw943

27924018

18.

Bauer-Mehren

. Integration of genomic information with biological networks using Cytoscape. Methods Mol Biol. 2013;1021:37-61.doi:10.1007/978-1-62703-450-0_3

23715979

19.

Szklarczyk

Morris

JH.

Cook

et al. The string database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(D1):D362-D368.doi:10.1093/nar/gkw937

27924014

20.

Liu

Grimm

Dai

W-T.

Hou

M-C.

Xiao

Z-X.

Cao

. CB-Dock: a web server for cavity detection-guided protein-ligand blind docking. Acta Pharmacol Sin. 2020;41(1):138-144.doi:10.1038/s41401-019-0228-6

31263275

21.

Dennis

Sherman

BT.

Hosack

et al. David: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):P3. doi:10.1186/gb-2003-4-5-p3

12734009

22.

Jin

Tsueng

Afrasiabi

. BioGPS: building your own mash-up of gene annotations and expression profiles. Nucleic Acids Res. 2016;44(D1):D313-D316.doi:10.1093/nar/gkv1104

26578587

23.

Mou

X-J.

Liu

Lin

Chen

Liu

S-L

. Effects of Shugan Hewei decoction and active substance fractions on behavior and neurotransmitter levels in hypothalamus of depression model rats. Zhongguo Zhong Yao Za Zhi. 2019;44(15):3343-3348.doi:10.19540/j.cnki.cjcmm.20190515.301

31602893

24.

Zhao

Zhang

Liu

. Screening of antidepressant fractions of Wenyujin. China J Trad Chin Med Pharm. 2011;26(08):868-869. http://www.cnki.com.cn/article/cjfdtotal-bxyy201108065.htm

25.

Owens

SJ.

Purves-Tyson

TD.

Webster

MJ.

Shannon Weickert

Weickert

. Evidence for enhanced androgen action in the prefrontal cortex of people with bipolar disorder but not schizophrenia or major depressive disorder. Psychiatry Res. 2019;280(5):112503. doi:10.1016/j.psychres.2019.112503

31446215

26.

Gałecki

Gałecka

Maes

et al. The expression of genes encoding for COX-2, MPO, iNOS, and sPLA2-IIA in patients with recurrent depressive disorder. J Affect Disord. 2012;138(3):360-366.doi:10.1016/j.jad.2012.01.016

22331023

27.

Gałecki

Maes

Florkowski

et al. An inducible nitric oxide synthase polymorphism is associated with the risk of recurrent depressive disorder. Neurosci Lett. 2010;486(3):184-187.doi:10.1016/j.neulet.2010.09.048

20869428

28.

Brkic

Petrovic

Franic

Mitic

Adzic

. Male-specific effects of lipopolysaccharide on glucocorticoid receptor nuclear translocation in the prefrontal cortex of depressive rats. Psychopharmacology. 2016;233(18):3315-3330.doi:10.1007/s00213-016-4374-y

27387895

29.

Kalkman

. Novel treatment targets based on insights in the etiology of depression: role of IL-6 trans-signaling and stress-induced elevation of glutamate and ATP. Pharmaceuticals. 2019;12(3):113. doi:10.3390/ph12030113

31362361

30.

Wang

Zhang

et al. Influence of electro-acupuncture on CNTFRα, EGFR protein expression in rats with chronic stress depression. World Chin Med. 2014;9(05):616-618.doi:10.3969/j.issn.1673-7202.2014.05.024

31.

Jacobs

JM.

Traeger

Eusebio

et al. Depression, inflammation, and epidermal growth factor receptor (EGFR) status in metastatic non-small cell lung cancer: a pilot study. J Psychosom Res. 2017;99:28-33.doi:10.1016/j.jpsychores.2017.05.009

28712427

32.

Yue

Chen

et al. Effect of Shugan Hewei decoction on the content of glutamate and γ-GABA in the nucleus accumbens of depression model rats. Lishizhen Med Mater Med Res. 2018;29(04):803-805.doi:10.3969/j.issn.1008-0805.2018.04.011

33.

Liu

. Study on Ferrostatin-1 Prevents Glutamate-Induced Ferroptosis and Molecular Mechanisms in HT-22 Cells [Master Thesis]. Hefei, Anhui Univ Chin Med; 2017. http://cdmd.cnki.com.cn/Article/CDMD-10369-1017199289.htm

34.

Wei

Xiang

Zhao

et al. Expression of HSP90AA1/HSPA8 in hepatocellular carcinoma patients with depression and its clinical significance. China Ca. 2019;28(05):387-394.

35.

Mohammad

Marchisella

Ortega-Martinez

et al. JNK1 controls adult hippocampal neurogenesis and imposes cell-autonomous control of anxiety behaviour from the neurogenic niche. Mol Psychiatry. 2018;23(2):362-374.doi:10.1038/mp.2016.203

36.

Chai

. Study on Apoptosis of Hippocampus Neuron and the Interventional Mechanism of Chinese Medicines in Diabetes Mellitus with Depression Rats [Master Thesis]. Changsha: Hunan Univ Chin Med; 2015. http://cdmd.cnki.com.cn/Article/CDMD-10541-1015563535.htm

37.

Kajta

Wnuk

Rzemieniec

et al. Depressive-like effect of prenatal exposure to DDT involves global DNA hypomethylation and impairment of GPER1/ESR1 protein levels but not ESR2 and AHR/ARNT signaling. J Steroid Biochem Mol Biol. 2017;171:94-109.doi:10.1016/j.jsbmb.2017.03.001

28263910

38.

Chen

Luo

Kuang

et al. Cyclooxygenase-2 signalling pathway in the cortex is involved in the pathophysiological mechanisms in the rat model of depression. Sci Rep. 2017;7(1):488. doi:10.1038/s41598-017-00609-7

28352129

39.

Song

. Hippocampal CA1 βCaMKII Mediates Neuroinflammatory Responses Via COX2-PGE2 Signaling Pathways in Depression [Master Thesis]. Qingdao: Shandong Univ; 2019. http://kns.cnki.net/KCMS/detail/detail.aspx?FileName=1019055564.nh&DbName=CMFD2019

40.

Chen

Sun

et al. Systems pharmacology dissection of the anti-stroke mechanism for the Chinese traditional medicine Xing-Nao-Jing. J Pharmacol Sci. 2018;136(1):16-25.doi:10.1016/j.jphs.2017.11.005

29336875

41.

Shibata

Iinuma

Soumiya

Fukumitsu

Furukawa

. A novel 2-decenoic acid thioester ameliorates corticosterone-induced depression- and anxiety-like behaviors and normalizes reduced hippocampal signal transduction in treated mice. Pharmacol Res Perspe. 2015;3(2):e132. doi:10.1002/prp2.132

26038707

42.

Lagus

Gass

Saharinen

Savelyev

Porkka-Heiskanen

Paunio

. Inter-tissue networks between the basal forebrain, hippocampus, and prefrontal cortex in a model for depression caused by disturbed sleep. J Neurogenet. 2012;26(3-4):397-412.doi:10.3109/01677063.2012.694932

22783900

43.

Rajamanickam

Annamalai

Rahbek-Clemmensen

et al. Akt-mediated regulation of antidepressant-sensitive serotonin transporter function, cell-surface expression and phosphorylation. Biochem J. 2015;468(1):177-190.doi:10.1042/BJ20140826

25761794

44.

Bowman

MA.

Daws

. Targeting serotonin transporters in the treatment of juvenile and adolescent depression. Front Neurosci. 2019;13:156. doi:10.3389/fnins.2019.00156

30872996

45.

Pace

TWW.

Miller

. Cytokines and glucocorticoid receptor signaling. Relevance to major depression. Ann N Y Acad Sci. 2009;1179:86-105.doi:10.1111/j.1749-6632.2009.04984.x

19906234

46.

Seo

J-S.

Wei

Qin

Kim

Yan

Greengard

. Cellular and molecular basis for stress-induced depression. Mol Psychiatry. 2017;22(10):1440-1447.doi:10.1038/mp.2016.118

27457815

47.

Jelen

AM.

Salagacka-Kubiak

Ropicka

et al. Selected ABCB1 single nucleotide polymorphisms and its haplotype - connection with development of depression and treatment efficacy. Int J Hum Genet. 2019;19(1):1-11.doi:10.31901/24566330.2019/19.01.672

48.

Hennings

JM.

Kohli

MA.

Uhr

Holsboer

Ising

Lucae

. Polymorphisms in the BDNF and BDNFOS genes are associated with hypothalamus-pituitary axis regulation in major depression. Prog Neuropsychopharmacol Biol Psychiatry. 2019;95:109686. doi:10.1016/j.pnpbp.2019.109686

31295515

49.

Erburu

Cajaleon

Guruceaga

et al. Chronic mild stress and imipramine treatment elicit opposite changes in behavior and in gene expression in the mouse prefrontal cortex. Pharmacol Biochem Behav. 2015;135:227-236.doi:10.1016/j.pbb.2015.06.001

26051025

50.

Bollinger

JL.

Wohleb

. The formative role of microglia in stress-induced synaptic deficits and associated behavioral consequences. Neurosci Lett. 2019;711:134369. doi:10.1016/j.neulet.2019.134369

31422099

51.

Gray

AL.

Hyde

TM.

Deep-Soboslay

Kleinman

JE.

Sodhi

. Sex differences in glutamate receptor gene expression in major depression and suicide. Mol Psychiatry. 2015;20(9):1057-1068.doi:10.1038/mp.2015.91

52.

Zhang

Jiu-hui

. Effect of Jiawei Wendan decoction on intracellular calciumion concentration in rat depression model. Chin J Exp Tradit Med Form. 2013;19(01):188-191.doi:10.13422/j.cnki.syfjx.2013.01.061

53.

Wang

Xia

Shi

et al. Effect of Jiawei Wendan decoction on ras protein expression in hippocampus of depression model rats at different time periods. E-J Transl Med. 2016;3(6):1-5. http://www.cnki.com.cn/Article/CJFDTOTAL-ZHDZ201606001.htm

54.

Chen

et al. Neuroprotection of reduced thyroid hormone with increased estrogen and progestogen in postpartum depression. Biosci Rep. 2019;39(9):9. doi:10.1042/BSR20182382

31406011

55.

Fan

W-T.

Wang

. Mechanism of Acori Tatarinowii Rhizoma-Curcumae Radix treating depression based on network pharmacology. Zhongguo Zhong Yao Za Zhi. 2018;43(12):2607-2611.doi:10.19540/j.cnki.cjcmm.2018.0084

29950083

56.

Guo

L-T.

Wang

S-Q.

et al. Baicalin ameliorates neuroinflammation-induced depressive-like behavior through inhibition of Toll-like receptor 4 expression via the PI3K/AKT/FoxO1 pathway. J Neuroinflammation. 2019;16(1):95. doi:10.1186/s12974-019-1474-8

31068207

57.

Yin

Liu

et al. The effect of beta-sitosterol and its derivatives on depression by the modification of 5-HT, DA and GABA-ergic systems in mice. RSC Adv. 2018;8(2):671-680.doi:10.1039/C7RA11364A

58.

Ledonne

Mercuri

. mGluR1-dependent long term depression in rodent midbrain dopamine neurons is regulated by neuregulin 1/ErbB signaling. Front Mol Neurosci. 2018;11:346. doi:10.3389/fnmol.2018.00346

30327588

59.

Eyre

Baune

. Neuroplastic changes in depression: a role for the immune system. Psychoneuroendocrinology. 2012;37(9):1397-1416.doi:10.1016/j.psyneuen.2012.03.019

22525700

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.

0.00 MB

0.78 MB

0.29 MB

0.45 MB