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. 2022 Oct 28;101(43):e29323. doi: 10.1097/MD.0000000000029323

Network pharmacology and molecular docking analysis on the mechanism of Baihe Zhimu decoction in the treatment of postpartum depression

Qiong Zhao a,b, Wengu Pan c, Hongshuo Shi a, Fanghua Qi b, Yuan Liu b, Tiantian Yang b, Hao Si d, Guomin Si a,b,*
PMCID: PMC9622608  PMID: 36316904

Abstract

Baihe Zhimu decoction (BZD) has significant antidepressant properties and is widely used to treat mental diseases. However, the multitarget mechanism of BZD in postpartum depression (PPD) remains to be elucidated. Therefore, the aim of this study was to explore the molecular mechanisms of BDZ in treating PPD using network pharmacology and molecular docking. Active components and their target proteins were screened from the traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The PPD-related targets were obtained from the OMIM, CTD, and GeneCards databases. After overlap, the targets of BZD against PPD were collected. Protein–protein interaction (PPI) network and core target analyses were conducted using the STRING network platform and Cytoscape software. Moreover, molecular docking methods were used to confirm the high affinity between BZD and targets. Finally, the DAVID online tool was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping targets. The TCMSP database showed that BZD contained 23 active ingredients in PPD. KEGG analysis showed that overlapping genes were mainly enriched in HIF-1, dopaminergic synapses, estrogen, and serotonergic synaptic signalling pathways. Combining the PPI network and KEGG enrichment analysis, we found that ESR1, MAOA, NR3C1, VEGFA, and mTOR were the key targets of PPD. In addition, molecular docking confirmed the high affinity between BZD and the PPD target. Verified by a network pharmacology approach based on data mining and molecular docking methods, the multi-target drug BZD may serve as a promising therapeutic candidate for PPD, but further in vivo/in vitro experiments are needed.

Keywords: Baihe Zhimu decoction, molecular docking, network pharmacology, postpartum depression, signaling pathway

1. Introduction

Postpartum depression (PPD) can be defined as non-psychotic depression occurring within a year of childbirth, characterized by low mood, unusual thoughts, feelings of guilt, unexplained anxiety, worthlessness, and other depressive symptoms.[1] The rate of PPD among women may be as high as 15% and result in a high death rate from self-harm. The rising numbers could make matters worse.[2–5] Thus, PPD is a significant public health problem.

However, the aetiology of PPD remains unclear. Various factors are associated with PPD, such as low socioeconomic status, prenatal depression, cultural factors, medication use, excessive stress, and anxiety.[6,7] Hormonal, genetic, and psychological effects can all lead to PPD, resulting in a series of physical, mental, and behavioral changes.[8] There is no consensus on drugs for the treatment of PPD, although there are reports of the use of antidepressants and antipsychotics in severe cases.[9] Big data fails to confirm whether antidepressant treatment produces adverse effects on breastfed infants.[10,11]

Traditional Chinese medicine (TCM) is an oriental traditional medicine that is characterized by a holistic concept. It was the main medical method used in ancient China, with thousands of years of accumulated practical experience.[12] In ancient China, TCM was used to treat many mental illnesses, such as restlessness, lily disease, and depression. Reports show that many TCMs, such as Baihe Zhimu decoction (BZD), Chaihu decoction, and Zhizichi decoction Xiaoyao san, are used to treat PPD.

BZD is a classic traditional Chinese medicine prescription, which was first recorded in Treatise on Febrile and Miscellaneous Diseases (200–210 ad). Consisting of two herbs, Lilii Bulbus (Baihe BH) and Anemarrhenae Rhizoma (Zhimu ZM), BZD has become a classic Chinese medicine formulation for treating depression, insomnia, anxiety and other mental and neurological diseases.[13] Some studies have shown that BZD can increase 5-hydroxytryptamine, norepinephrine, and dopamine levels and improve the behavioral indicators of depression.[14–17] Clinical data indicate that Morita therapy combined with BZD can improve the symptoms, quality of life, and ability of daily living for first-episode depression, which may be related to the decrease in serum BDNF and an increase in DOPAC levels.[18] However, the potential mechanisms underlying the treatment of PPD are not fully understood.

Network pharmacology is an emerging method that analyses the components, targets, diseases, and related pathways of traditional Chinese medicine. It combines pharmacology, molecular biology, electronic technology, and bioinformatics and has great advantages for studying the complex components, targets, and pathways of traditional Chinese medicine prescriptions.[19–21]

Therefore, this research aims to apply network pharmacology to identify the active ingredients and targets of BZD and to discover the common targets and possible signaling pathways of BZD in the treatment of PPD. The corresponding workflow is shown in Figure 1.

Figure 1.

Figure 1.

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Workflow of the study design.

2. Materials and Methods

2.1. Chemical ingredient acquisition and processing

The components of the two herbs were found in the pharmacology of Traditional Chinese Medicine Systems Pharmacology (TCMSP, https://tcmspw.com/tcmsp.php)[22] and then screened by integrating oral bioavailability (OB ≥ 30%) and drug-likeness (DL ≥ 0.18).[23] Next, each target of the active ingredient was obtained from this website. The aggregated target was input into UniProt (https://www.uniprot.org/) to obtain information, such as gene symbols and gene IDs.[24,25]

2.2. Related targets of PPD

PPD targets were obtained from the Online Mendelian Inheritance in Man (OMIM, https://www.omim.org/),[26] Comparative Toxicogenomics Database (CTD, http://ctd.mdibl.org/),[27] and GeneCards (https://www.genecards.org/).[28,29] We searched for “postpartum depression” in the GeneCards, CTD, and OMIM databases. Finally, all targets were unified as gene names on UniProt.

2.3. Construction of drug–compound–target genes network

We input the information obtained above of BZD-related drugs, components, and targets into Cytoscape 3.7.2 software to construct a visualized network diagram of the drug-component-target. The graph depicts drugs, ingredients and targets as nodes and the lines connecting them as edges.

2.4. Venn diagram of targets between drugs and disease

The targets of BZD and PPD-related target gene lists were input into Venny 2.1.0 (http://bioinformatics.psb.ugent.be/webtools/Venn/) to obtain their intersection with a Venn diagram.

2.5. Protein–protein interaction data

A protein–protein interaction (PPI) network for disease and drug mapping targets was constructed using the STRING database (https://string-db.org/, version 10.5). According to the corresponding calculation method, the species was set to “Homo sapiens,” and the confidence score was set to >0.4.[30]

2.6. Gene ontology and pathway enrichment analysis

The Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/)[31,32] was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.

2.7. Molecular docking

Based on the results of the GO and KEGG pathway analyses, we selected the key protein receptor and ligand associated with the protein receptor. PubChem (https://pubchem.ncbi.nlm.gov/)[33] was used to obtain the 2D chemical structures of the small-molecule ligands. ChemOffice software was used to construct the 3D chemical structures of small molecular ligands,[34] and the 3D chemical structures of protein receptors were acquired from PDB (http://www.rcsb.org/).[35] After the molecular ligands and water molecules of the protein receptors were removed using PyMol 2.4.0 software (https://pymol.org.),[36] the format of the protein receptors and small-molecule ligands was transformed into pdbqt format.[37] AutoDock Vina was used for molecular docking.[38] Based on the binding energy value, the lowest binding energy value was selected as the docking affinity. Finally, PyMol software was used to visualize the 3D structures of the molecular ligand and protein receptor bonding.

3. Results

3.1. Chemical components of BZD

The active compounds of BZD were retrieved from TCMSP. There were 165 related components in BZD; Baihe contained 84 (50.9%), and Zhimu had 81 (49.1%) components. The values of OB and DL (OB ≥ 30% and DL ≥ 0.18) were used to screen potential active compounds from Baihe and Zhimu, and 22 active compounds met the screening standards. The properties of the compounds are listed in Table 1. The 364 targets and the corresponding Uniprot IDs are listed in Table 2.

Table 1.

Compounds of Baihe Zhimu decoction.

Mol ID Molecule name OB (%) DL Herbs
MOL009471 26-O-ß-D-glucopyranosyl-3ß,26-dihydroxy-cholestan-16,22-dioxo-3-O-a-L-rhamnopyranosyl-(1?2)-ß-D-glucopyranoside_qt 32.43 0.8 BH
MOL009465 26-O-ß-D-glucopyranosyl-3ß,26-dihydroxy-5-cholesten-16,22-dioxo-3-O-a-L-rhamnopyranosyl-(1?2)-ß-D-glucopyranoside_qt 35.11 0.81 BH
MOL009458 3-Demethylcolchicine 39.34 0.57 BH
MOL009449 26-O-beta-D-Glucopyranosyl-3beta,26-dihydroxy-choleslen-16,22-dioxo-3-O-alpha-L-rhamnopyranosyl-(1-2)-beta-D-glucopyranoside_qt 32.43 0.8 BH
MOL002039 Isopimaric acid 36.2 0.28 BH
MOL000449 Stigmasterol 43.83 0.76 BH
MOL000358 beta-Sitosterol 36.91 0.75 BH
MOL001677 Asperglaucide 58.02 0.52 ZM
MOL001944 Marmesin 50.28 0.18 ZM
MOL003773 Mangiferolic acid 36.16 0.84 ZM
MOL000422 Kaempferol 41.88 0.24 ZM
MOL004373 Anhydroicaritin 45.41 0.44 ZM
MOL004489 Anemarsaponin F_qt 60.06 0.79 ZM
MOL004492 Chrysanthemaxanthin 38.72 0.58 ZM
MOL004497 Hippeastrine 51.65 0.62 ZM
MOL004514 Timosaponin B III_qt 35.26 0.87 ZM
MOL000449 Stigmasterol 43.83 0.76 ZM
MOL004528 Icariin I 41.58 0.61 ZM
MOL004540 Anemarsaponin C_qt 35.5 0.87 ZM
MOL004542 Anemarsaponin E_qt 30.67 0.86 ZM
MOL000483 (Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide 118.35 0.26 ZM
MOL000546 Diosgenin 80.88 0.81 ZM
MOL000631 Coumaroyltyramine 112.9 0.2 ZM
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Table 2.

Information of 364 targets of Baihe Zhimu decoction.

No. Target UniprotID No. Target UniprotID No. Target UniprotID No. Target UniprotID
1 ESR1 P03372 92 PTGS2 P35354 183 F2 P00734 274 ESR1 P03372
2 AR P10275 93 CA2 P00918 184 CHRM1 P11229 275 AR P10275
3 PGR P06401 94 GABRA2 P47869 185 AR P10275 276 ADRB1 P08588
4 NR3C1 P04150 95 CHRM4 P08173 186 PPARG P37231 277 SCN5A Q14524
5 ESR1 P03372 96 ACHE P22303 187 NOS3 P29474 278 PPARG P37231
6 AR P10275 97 PDE3A Q14432 188 CA2 P00918 279 PTGS2 P35354
7 NR3C2 P08235 98 HTR2A P28223 189 F7 P08709 280 NOS3 P29474
8 NR3C1 P04150 99 GABRA5 P31644 190 GABRA2 P47869 281 ADRA2A P08913
9 NOS2 P35228 100 ADRA1A P35348 191 ACHE P22303 282 CA2 P00918
10 PTGS1 P23219 101 GABRA3 P34903 192 SLC6A2 P23975 283 RXRA P19793
11 F2 P00734 102 PGR P06401 193 PGR P06401 284 ACHE P22303
12 KCNH2 Q12809 103 CHRM2 P08172 194 CHRM2 P08172 285 HTR2A P28223
13 ESR1 P03372 104 ADRA1B P35368 195 ADRA1B P35368 286 SLC6A2 P23975
14 AR P10275 105 PTPN1 P18031 196 PTPN1 P18031 287 ADRA1A P35348
15 PTGS2 P35354 106 ADRB2 P07550 197 GABRA1 P14867 288 GABRA3 P34903
16 DPP4 P27487 107 CHRNA2 Q15822 198 DPP4 P27487 289 PGR P06401
17 CHEK1 O14757 108 SLC6A4 P31645 199 MAPK14 Q16539 290 CHRM2 P08172
18 ESR1 P03372 109 OPRM1 P35372 200 GSK3B P49841 291 ADRA1B P35368
19 AR P10275 110 ESR2 Q92731 201 CDK2 P24941 292 SLC6A3 Q01959
20 NR3C2 P08235 111 NR3C1 P04150 202 PIK3CG P48736 293 NR3C2 P08235
21 NR3C1 P04150 112 GABRA1 P14867 203 LACTB P83111 294 ADRB2 P07550
22 NOS2 P35228 113 DPP4 P27487 204 CHEK1 O14757 295 AKR1B1 P15121
23 F2 P00734 114 MAPK14 Q16539 205 PRKACA P17612 296 NR3C1 P04150
24 ESR1 P03372 115 GSK3B P49841 206 PRSS1 P07477 297 GABRA1 P14867
25 AR P10275 116 CDK2 P24941 207 PIM1 P11309 298 DPP4 P27487
26 PTGS2 P35354 117 PIK3CG P48736 208 CCNA2 P20248 299 PLAU P00749
27 RXRA P19793 118 LACTB P83111 209 NCOA2 Q15596 300 CDK2 P24941
28 ACHE P22303 119 CHRNA7 P36544 210 NOS2 P35228 301 LACTB P83111
29 PGR P06401 120 CHEK1 O14757 211 PTGS1 P23219 302 LTA4H P09960
30 NR3C1 P04150 121 PRKACA P17612 212 CHRM3 P20309 303 MAOB P27338
31 NCOA2 Q15596 122 PRSS1 P07477 213 F2 P00734 304 MAOA P21397
32 NCOA1 Q15788 123 PIM1 P11309 214 KCNH2 Q12809 305 CHRNA7 P36544
33 NOS2 P35228 124 CCNA2 P20248 215 CHRM1 P11229 306 PRKACA P17612
34 PTGS1 P23219 125 NCOA2 Q15596 216 ESR1 P03372 307 ADH1C P00326
35 CHRM3 P20309 126 NOS2 P35228 217 AR P10275 308 IGHG1 P01857
36 F2 P00734 127 F2 P00734 218 SCN5A Q14524 309 CTRB1 P17538
37 CHRM1 P11229 128 KCNH2 Q12809 219 PPARG P37231 310 PRSS1 P07477
38 ESR1 P03372 129 ESR1 P03372 220 F10 P00742 311 NCOA2 Q15596
39 AR P10275 130 PPARG P37231 221 CHRM5 P08912 312 NCOA1 Q15788
40 ADRB1 P08588 131 F10 P00742 222 PTGS2 P35354 313 ESR1 P03372
41 SCN5A Q14524 132 PTGS2 P35354 223 NOS3 P29474 314 AR P10275
42 PPARG P37231 133 PRSS1 P07477 224 CA2 P00918 315 PGR P06401
43 PTGS2 P35354 134 NOS2 P35228 225 F7 P08709 316 NR3C2 P08235
44 NOS3 P29474 135 PTGS1 P23219 226 KDR P35968 317 NR3C1 P04150
45 ADRA2A P08913 136 F2 P00734 227 RXRA P19793 318 AR P10275
46 CA2 P00918 137 CHRM1 P11229 228 ACHE P22303 319 PTGS1 P23219
47 RXRA P19793 138 ESR1 P03372 229 ADRA1B P35368 320 F2 P00734
48 ACHE P22303 139 AR P10275 230 PTPN1 P18031 321 ESR1 P03372
49 HTR2A P28223 140 PTGS2 P35354 231 ADRB2 P07550 322 AR P10275
50 SLC6A2 P23975 141 CA2 P00918 232 ESR2 Q92731 323 PPARG P37231
51 ADRA1A P35348 142 RXRA P19793 233 DPP4 P27487 324 PTGS2 P35354
52 GABRA3 P34903 143 CHRM2 P08172 234 MAPK14 Q16539 325 PDE3A Q14432
53 PGR P06401 144 PTPN1 P18031 235 GSK3B P49841 326 ADRA1B P35368
54 CHRM2 P08172 145 ADRB2 P07550 236 CDK2 P24941 327 ADRB2 P07550
55 ADRA1B P35368 146 SLC6A4 P31645 237 CHEK1 O14757 328 DPP4 P27487
56 SLC6A3 Q01959 147 DPP4 P27487 238 RXRB P28702 329 MAPK14 Q16539
57 NR3C2 P08235 148 MAPK14 Q16539 239 IGHG1 P01857 330 CDK2 P24941
58 ADRB2 P07550 149 CDK2 P24941 240 PRSS1 P07477 331 LTA4H P09960
59 AKR1B1 P15121 150 PIK3CG P48736 241 PIM1 P11309 332 CHEK1 O14757
60 NR3C1 P04150 151 LACTB P83111 242 CCNA2 P20248 333 PRSS1 P07477
61 GABRA1 P14867 152 LTA4H P09960 243 NCOA2 Q15596 334 PIM1 P11309
62 DPP4 P27487 153 CHEK1 O14757 244 NCOA1 Q15788 335 ESR1 P03372
63 PLAU P00749 154 PRKACA P17612 245 KCNMA1 Q12791 336 PTGS2 P35354
64 CDK2 P24941 155 PIM1 P11309 246 ESR1 P03372 337 PLA2G4A P47712
65 LACTB P83111 156 ESR1 P03372 247 AR P10275 338 FASN P49327
66 LTA4H P09960 157 AR P10275 248 NR3C1 P04150 339 MTOR P42345
67 MAOB P27338 158 NOS2 P35228 249 F2 P00734 340 SOD1 P00441
68 MAOA P21397 159 INSR P06213 250 CHRM3 P20309 341 TP63 Q9H3D4
69 CHRNA7 P36544 160 ESR1 P03372 251 CHRM1 P11229 342 CAT P04040
70 PRKACA P17612 161 BCL2 P10415 252 ESR1 P03372 343 VEGFA P15692
71 ADH1C P00326 162 ALOX5 P09917 253 AR P10275 344 AR P10275
72 IGHG1 P01857 163 PTGS2 P35354 254 SCN5A Q14524 345 PGR P06401
73 CTRB1 P17538 164 AKR1C3 P42330 255 OPRD1 P41143 346 NR3C2 P08235
74 PRSS1 P07477 165 TNFSF15 O95150 256 ADRA1B P35368 347 NR3C1 P04150
75 NCOA2 Q15596 166 ESR2 Q92731 257 OPRM1 P35372 348 PTGS1 P23219
76 NCOA1 Q15788 167 MMP1 P03956 258 GABRA1 P14867 349 F2 P00734
77 BCL2 P10415 168 JUN P05412 259 DPP4 P27487 350 CHRM1 P11229
78 PON1 P27169 169 SELE P16581 260 GSK3B P49841 351 ESR1 P03372
79 JUN P05412 170 CDK1 P06493 261 CDK2 P24941 352 ADRB1 P08588
80 MAP2 P11137 171 VCAM1 P19320 262 PIK3CG P48736 353 PPARG P37231
81 NOS2 P35228 172 XDH P47989 263 CHRNA7 P36544 354 PTGS2 P35354
82 PTGS1 P23219 173 CYP3A4 P08684 264 CCNA2 P20248 355 ADRB2 P07550
83 DRD1 P21728 174 MAPK8 P45983 265 ESR1 P03372 356 DPP4 P27487
84 CHRM3 P20309 175 CYP1A2 P05177 266 AR P10275 357 MAPK14 Q16539
85 F2 P00734 176 GSTP1 P09211 267 PGR P06401 358 GSK3B P49841
86 KCNH2 Q12809 177 HMOX1 P09601 268 NR3C1 P04150 359 CDK2 P24941
87 CHRM1 P11229 178 GSTM1 P09488 269 NOS2 P35228 360 LTA4H P09960
88 ESR1 P03372 179 AHR P35869 270 PTGS1 P23219 361 MAOB P27338
89 AR P10275 180 GSTM2 P28161 271 CHRM3 P20309 362 PRKACA P17612
90 SCN5A Q14524 181 PPP3CA Q08209 272 F2 P00734 363 PRSS1 P07477
91 PPARG P37231 182 PTGS1 P23219 273 CHRM1 P11229 364 PKIA P61925
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3.2. Screening targets of PPD

We searched for PPD targets in several publicly available databases: CTD, OMIM, and GeneCards. After deleting duplicate values, we finally obtained 663 disease targets.

3.3. Drug–compound–target network

The drugs, components, and targets of BZD were drawn into a drug-component-target network diagram using the Cytoscape software to show the relationship between them more intuitively (Fig. 2). In this figure, orange represents two drugs, green rectangle represents 22 active ingredients, and yellow circle represents 364 target genes. Among the three circles, the degree value of the inner circle is higher than that of the outer circle, which has a higher correlation. In the drug-compound-target network, according to the degree value, the top 10 targets were ESR1, AR, F2, PTGS2, NR3C1, DPP4, PGR, NOS2, PTGS1, and CDK2.

Figure 2.

Figure 2.

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Drug-Compound-Target network. The orange color indicates the drugs; the green color indicates the chemical composition; the yellow color indicates the targets.

3.4. Target protein cross-validation

Figure 3 shows the set relationship between the targets of BZD and PPD. We input the 364 targets of BZD and 663 targets of PPD into the Venny2.1 online software mapping tool platform to draw the Venn diagram. Among them, 41 target proteins were targets of BZD acting on PPD, and 25 were common targets of Baihe, Zhimu, and PPD.

Figure 3.

Figure 3.

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Drug-disease target Venn diagram of Baihe Zhimu decoction.

3.5. PPI network data

Figure 4 shows the PPI diagram of the 41 intersecting target proteins drawn using STRING. Red, evidence of fusion; green, evidence of proximity; yellow, evidence of text mining; light blue, evidence of database; blue, evidence of coexistence; black, evidence of co-expression; purple, evidence of experiment.

Figure 4.

Figure 4.

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PPI network of core targets.

3.6. GO and pathway enrichment analysis

The KEGG analysis and GO enrichment analysis of biological process (BP), molecular function (MF), and cell component (CC) obtained by DAVID 6.8 for 41 selected target genes are shown in Figure 5, which clarifies the multiple mechanisms of BZD in treating PPD. There is information on the number of genes, selections, and rich factors. P in the figure represents the significance of enrichment; the redder the color, the higher the significance. The five most affected BPs (P < .01) were response to drugs (GO:0042493), oxidation-reduction process (GO:0055114), response to lipopolysaccharide (GO:0032496), response to estradiol (GO:0032355), and response to hypoxia (GO:0001666) (Fig. 5A). The main CC terms (P < .01) were caveola (GO:0005901), plasma membrane (GO:0005886), extracellular space (GO:0005615), peroxisome (GO:0005777), and perinuclear region of the cytoplasm (GO:0048471) (Fig. 5B). The five most common MF terms (P < .01) were enzyme binding (GO:0019899), protein homodimerization activity (GO:0042803), steroid binding (GO:0005496), steroid hormone receptor activity (GO:0003707), and heme binding (GO:0020037) (Fig. 5C). The 41 proteins further resulted in 45 KEGG pathways. The top 20 pathways and their related genes are listed in Table 3. The relevant reference values are shown in Figure 5D. According to Figure 6, HIF-1, neuroactive ligand-receptor interaction, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways, which were filtered out as prominent and conspicuous enriched pathways, contributed significantly to the PPD response.

Figure 5.

Figure 5.

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GO enrichment analysis of key targets and KEGG enrichment analysis. (A) The first 20 significant P values of BP) (B) The first 20 significant P values of CC; (C) The first 20 significant P values of MF; (D) The first 20 significant P values of KEGG pathways. BP = biological process, CC = cellular component; MF = molecular function.

Table 3.

Top 20 signaling pathways with related genes.

Term Pathway Genes
hsa04066 HIF-1 signaling pathway NOS2, NOS3, INSR, BCL2, HMOX1, MTOR, VEGFA
hsa04020 Calcium signaling pathway CHRM2, NOS2, NOS3, ADRB1, DRD1, ADRB2, HTR2A, ADRA1A
hsa04080 Neuroactive ligand-receptor interaction CHRM2, ADRB1, DRD1, ADRB2, HTR2A, OPRM1, F2, NR3C1, ADRA1A
hsa05200 Pathways in cancer GSK3B, AR, JUN, MAPK8, NOS2, BCL2, PPARG, PTGS2, MTOR, VEGFA
hsa04726 Serotonergic synapse MAOB, MAOA, ALOX5, HTR2A, PTGS2, SLC6A4
hsa00380 Tryptophan metabolism MAOB, MAOA, CYP1A2, CAT
hsa04915 Estrogen signaling pathway JUN, NOS3, OPRM1, ESR1, ESR2
hsa05030 Cocaine addiction JUN, MAOB, MAOA, DRD1
hsa00330 Arginine and proline metabolism MAOB, NOS2, MAOA, NOS3
hsa04668 TNF signaling pathway JUN, MAPK8, VCAM1, PTGS2, SELE
hsa04931 Insulin resistance GSK3B, MAPK8, NOS3, INSR, MTOR
hsa04923 Regulation of lipolysis in adipocytes INSR, ADRB1, ADRB2, PTGS2
hsa04024 cAMP signaling pathway CHRM2, JUN, MAPK8, ADRB1, DRD1, ADRB2
hsa05210 Colorectal cancer GSK3B, JUN, MAPK8, BCL2
hsa04728 Dopaminergic synapse GSK3B, MAPK8, MAOB, MAOA, DRD1
hsa05031 Amphetamine addiction JUN, MAOB, MAOA, DRD1
hsa00982 Drug metabolism - cytochrome P450 MAOB, MAOA, CYP1A2, CYP3A4
hsa04261 Adrenergic signaling in cardiomyocytes BCL2, ADRB1, ADRB2, SCN5A, ADRA1A
hsa04917 Prolactin signaling pathway GSK3B, MAPK8, ESR1, ESR2
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Figure 6.

Figure 6.

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The ralated signaling pathways (obtained from KEGG database). (A) HIF-1 signaling pathway Serotonergic synapse; (B) Serotonergic synapse; (c) Estrogen signaling pathway; (D) Dopaminergic synapse.

3.7. Molecular docking analysis

According to the five related signaling pathways from the results of KEGG pathway enrichment analysis, we identified five key genes: ESR1, MAOA, NR3C1, VEGFA, and MTOR. These key genes were mainly bound to diosgenin, isopimaric acid, stigmasterol, and beta-sitosterol, which are the main components of BZDs (Table 4). The molecular docking method verified the binding sites of BZD target genes and their corresponding compounds, showing that the above five target genes have a high affinity for the main components of BZD (Fig. 7).

Table 4.

Results of the molecular docking of the five core genes with compounds of BZD.

Number Core genes Compound Docking affinity
1 ESR1 Diosgenin -9
2 ESR1 Isopimaric acid -6.6
3 ESR1 Stigmasterol -7
4 ESR1 beta-Sitosterol -6.9
5 MAOA Stigmasterol -6.9
6 NR3C1 Isopimaric acid -9.1
7 NR3C1 Diosgenin -7.6
8 NR3C1 Stigmasterol -8.6
9 NR3C1 beta-Sitosterol -9.6
10 VEGFA Diosgenin -8.1
11 MTOR Diosgenin -7.5
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Figure 7.

Figure 7.

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Molecular docking diagrams of PPD related targets with main compounds of BZD.

4. Discussion

PPD is a common and serious mental health problem that causes personal suffering and interferes with parenting. However, the effects of antidepressant medications remain controversial. In the meantime, numerous people worry about the potential adverse effects of antidepressant medications. TCM has been used to treat mental illness in China for thousands of years. Therefore, the application of Chinese medicine can help develop new ideas and methods for the treatment of PPD.

Owing to the multi-target treatment effects of TCM, it can serve as a significant repository to develop drugs for the treatment of PPD. This study utilized network pharmacology and molecular docking simulations to reveal the molecular mechanisms of BZD in PPD treatment. BZD plays a potential role in treating PPD by regulating multiple target genes and pathways.

For this study, we selected 22 main compounds of BZD, among which icariin, timosaponin B-III (TB-III), and others are shown to have antidepressant effects. The potential target genes are ESR1, MAOA, NR3C1, VEGFA, and mTOR. Moreover, GO annotation and KEGG pathway enrichment analyses confirmed that these target genes were associated with HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways, which are closely involved in the treatment of PPD. Molecular docking showed that the five core targets had a certain affinity for the main compounds of BZD.

TB-III is a steroidal saponin isolated from the rhizome of Baihe, which exhibits antidepressant activity through the regulation of inflammatory cytokines, BDNF signaling, and synaptic plasticity. However, this study was only conducted in a mouse model of PPD, and there have been no human reports.[39] Icariin has potential preventive and therapeutic effects in various neurological diseases such as cerebral ischemia, depression, Parkinson’s disease (PD), and multiple sclerosis (MS). The mechanism by which icariin improves depression may be related to the promotion of cell proliferation, peripheral nerve regeneration, improvement of the function of damaged nerve regulation, decrease in glucocorticoid receptors (GRs) and 5-hydroxytryptamine 1A (5-HTR1A) receptors in the hippocampus and prefrontal cortex, regulation of the central neuroendocrine system, or restoration of the negative feedback regulation of the hypothalamic-pituitary-adrenal (HPA) axis.[40–42] Furthermore, icariin may ameliorate prenatal restraint stress-induced depression-like behavior.[43]

The network pharmacology results confirmed that the potential target genes of PPD regulated by BZD were mainly ESR1, MAOA, NR3C1, VEGFA, and mTOR. Simultaneously, the PPI network results showed that these targets had close interactions. ESR1 plays an important role in mediating hormonal differences during pregnancy and postpartum. One clinical experiment suggested a role for ESR1 in the etiology of PPD, possibly through modulation of serotonin signaling.[44] Previous work has demonstrated that exposure to and withdrawal from normal levels of gonadal steroids results in depressive symptomatology in women previously diagnosed with PPD.[45,46] The MAOA gene, located on the short arm of the X-chromosome (Xp11.4-p11.3), has been the focus of research in the field of mental disorders in recent years. Two studies showed that MAOA was positive at 6 weeks postpartum with PPD.[47,48] Women with PPD appear to have an abnormal HPA axis response to stress, which may involve genetic variants, as reported previously. The NR3C1 gene, which is located on the long arm of chromosome 5 (5q31), encodes the GR. Epigenetic studies have shown that decreased NR3C1 gene expression activity caused by methylation can impair the negative feedback regulation of the HPA axis, alter an individual’s response to SLE, and induce depression.[49] However, the mechanisms of interaction between these targets are not clear. Other targets have not been directly documented to improve PPD, but the literature supports an improvement in depression, which follows the same pathway as PPD. For example, VEGFA can affect the complex processes of learning and memory[50] and plays a role in regulating neurite growth and maturation during brain development.[51] The role of VEGFA in neurogenesis may be mediated by its interactions with downstream effector genes.[52] In the present study, our data showed that VEGF mRNA and protein expression in hippocampal tissues and serum were downregulated in depression model rats, suggesting that downregulation of VEGFA plays a key role in depression in rats. mTOR is a serine/threonine kinase that controls related signalling pathways to regulate many integrated physiological functions of the nervous system. Many studies have shown that it is tempting to hypothesize that the activation of mTOR function followed by enhanced mTOR-dependent protein synthesis may underlie the action of antidepressants, such as ketamine.[53–56]

GO annotation and pathway enrichment analyses were conducted to identify the potential biological functions of PPD targets. GO enrichment analysis revealed that the major biological processes included response to drugs, oxidation-reduction processes, response to lipopolysaccharide, response to estradiol, and response to hypoxia. The results of the KEGG enrichment analysis showed that HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways were the leading signaling pathways for the treatment of PPD by BZD. These data suggest that the HIF-1 pathway might play an important role in antidepressant effects, and that altered mRNA expression of HIF-1 and its target genes in peripheral blood cells is associated mainly in a state-dependent manner with mood disorders, especially major depressive disorder (MDD).[57] The estrogen signaling pathway plays an important role in PPD. Studies have shown that changes in gonadal steroid levels, including estrogen levels, may contribute to the depressive symptoms of PPD.[58] Other data suggest that PPD symptoms are affected by estrogen levels.[59,60] Serotonin regulates several basic biological functions relevant to depression, including sleep and appetite.[61] Women with PPD have lower plasma serotonin levels than non-depressed controls, which are modulated by estrogen.[62] Thus, fluctuating estradiol levels during pregnancy and the postpartum period may cause depressive symptoms in vulnerable women by destabilizing the serotonin system. Dopamine is recognized and proven to be an important factor, and diminished dopaminergic function may also play a role in PPD. Studies have shown that dopamine activity and estradiol levels are positively correlated; therefore, they can synergistically affect PPD symptoms.[63]

Molecular docking verified that ESR1, MAOA, NR3C1, VEGFA, and mTOR have high affinities for the main active ingredients of BZD, diosgenin, isopimaric acid, stigmasterol, and beta-sitosterol, providing data support for BZD as a potential drug for the treatment of PD; however, further experimental studies are needed to confirm its effectiveness.

5. Conclusion

In this study, we used network pharmacology combined with molecular docking to elucidate the mechanism by which BZD regulates PPD through multiple targets and channels. The main target proteins of BZD in PPD treatment have been shown, constructing a target protein network. BZD mainly affected HIF-1, dopaminergic synapse, estrogen, and serotonergic synapse signaling pathways while regulating the key target proteins of ESR1, MAOA, NR3C1, VEGFA, and mTOR.

However, this article also has some limitations. The composition of traditional Chinese medicine is complex, the data of this article is obtained through some databases, and the information of the databases needs to be further improved. The validity of this article lacks experimental support, and its mechanism needs further experimental verification.

Author contributions

Qiong Zhao, Wengu Pan, and Guomin Si performed the main analyses and drafted the manuscript. Hongshuo Shi and Fanghua Qi designed the study. Yuan Liu helped with the introduction and discussion. Tiantian Yang and Hao Si assisted in the preparation of the manuscript. All authors wrote, read, and approved the manuscript.

Conceptualization: Guomin Si.

Data curation: Hongshuo Shi, Fanghua Qi and Hao Si.

Formal analysis: Qiong Zhao, Wengu Pan, Yuan Liu, Tiantian Yang.

Funding acquisition: Qiong Zhao, Wengu Pan.

Investigation: Qiong Zhao, Wengu Pan, Guomin Si, Fanghua Qi.

Methodology: Qiong Zhao, Guomin Si, Tiantian Yang.

Project administration: Guomin Si.

Resources: Qiong Zhao, Wengu Pan, Hongshuo Shi, Fanghua Qi, Yuan Liu, Tiantian Yang, and Hao Si

Software: Qiong Zhao, Hao Si.

Supervision: Guomin Si.

Validation: Guomin Si, Wengu Pan, Hongshuo Shi.

Visualization: Wengu Pan, Hongshuo Shi.

Writing – original draft: Qiong Zhao, Wengu Pan, Guomin Si, Fanghua Qi.

Writing – review & editing: Qiong Zhao, Guomin Si.

Abbreviations:

BZD =
Baihe Zhimu decoction
DAVID =
visualization and integrated discovery
DL =
drug-likeness
GO =
Gene Ontology,
KEGG =
Kyoto Encyclopedia of Genes and Genomes,
OB =
oral bioavailability
PPD =
postpartum depression
PPI =
protein–protein interaction
TCM =
traditional Chinese medicine

How to cite this article: Zhao Q, Pan W, Shi H, Qi F, Liu Y, Yang T, Si H, Si G. Network pharmacology and molecular docking analysis on the mechanism of Baihe Zhimu decoction in the treatment of postpartum depression. Medicine 2022;101:43(e29323).

QZ and WP contributed equally to this work.

The authors have no funding for this article.

The authors have no conflicts of interests to disclose.

The current analysis does not require ethical approval because our analysis only collected uploaded data from the public database search. The article is not involved in any patient’s personal data and will not cause any harm to the patient.

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Contributor Information

Qiong Zhao, Email: fanxingqq123@126.com.

Wengu Pan, Email: 296660293@qq.com.

Hongshuo Shi, Email: 592609880@qq.com.

Fanghua Qi, Email: qifanghua2006@126.com.

Yuan Liu, Email: liuyuanly0429@163.com.

Tiantian Yang, Email: ytt@bucm.edu.cn.

Hao Si, Email: sgm977@126.com.

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