Objective:
To investigate the mechanism of action of Datura metel in the treatment of sinus bradycardia based on network pharmacology and molecular docking.
Methods:
The active ingredients and targets of Datura metel were collected by TCMSP database, and the Cytoscape software was used to map to show the interrelationship. Use 5 databases: GeneCards, PharmGKB, OMIM, DisGeNET, and Drugbank to obtain targets related to sinus bradycardia; establish a protein-to-protein interaction network with the help of the STRING platform; GO and Kyoto Encyclopedia of Genes and Genomes analysis of the selected core targets using the Metascape platform; Finally, the AutoDock platform was used for molecular docking and the results were displayed through Pymol.
Results:
27 kinds of active ingredients of the drug were screened, including 10 kinds of main ingredients; 198 drug targets and 1059 disease targets. There are 54 targets of action in the treatment of sinus bradycardia, of which 19 targets such as AKT1, IL6, TNF, and VEGFA are the core targets of Datura metel in the treatment of sinus bradycardia. Kyoto Encyclopedia of Genes and Genomes obtained 18 results suggesting that AGE-RAGE, hepatitis C, relaxin, and JAK-STAT may be key signaling pathways. Molecular docking shows that most components of the drug have good docking ability with the core target, indicating that the prediction results have certain reliability.
Conclusion:
This study preliminarily explores the potential active ingredients and possible mechanisms of action of Datura metel in the treatment of sinus bradycardia and provides a basis for in-depth investigation of its medicinal material basis and mechanism of action.
Keywords: Datura metel, molecular docking, network pharmacology, sinus bradycardia
1. Introduction
Sinus bradycardia (SB or DH) refers to a sinus rhythm with a frequency of fewer than 60 beats/min,[1] which is characterized by varying degrees of palpitations, chest tightness, dizziness, weakness, chills and cold extremities, or even syncope, cardiogenic shock, and sudden death. Asymptomatic sinus bradycardia often requires no treatment, but in modern medicine, western drugs such as atropine have short-lived and unstable effects, as well as the high cost of pacemakers.
Chinese medicine is a unique treasure trove of medicine in China and has significant and irreplaceable advantages in the prevention and treatment of many diseases such as sinus bradycardia.[2] Modern pharmacological results have confirmed that scopolamine, the active ingredient of Datura metel (DM or YJH), can increase heart rate better at higher doses,[3,4] and clinical reports of formulas using Datura metel as one of the key drugs to increase sinus rhythm have been increasing year by year. The clinical observation found that the combination of Heart Treasure Pill with Datura metel as the main ingredient and the basic treatment of western medicine can increase the improvement rate of sinus bradycardia with ventricular anterior contraction by more than 15%.[5] In addition, it has been clinically found that the combination of Heart Treasure Pill and Irbesartan can better increase heart rate while improving cardiac function.[6] Although Datura metel is effective in increasing sinus rhythm, few studies use big data systems to deeply explore the mechanisms of its components. From the idea of the holistic concept of traditional Chinese medicine, the author has used modern network pharmacology[7] and molecular docking methods to reveal the main chemical components, targets, and mechanisms of Datura metel to increase heart rate, to provide a more reliable theoretical basis for the use of Datura metel in the treatment of sinus bradycardia. The corresponding workflow is shown in Fig. 1.
Figure 1.
Flowchart of this study.
2. Materials and methods
2.1. Data sheet
Web-based pharmacology and molecular docking studies require the use of web-based databases and software platforms, and the URLs of the databases used in this paper are listed in Table 1.
Table 1.
Database name and URL.
| Database name | Database URL |
|---|---|
| TCMSP | http://tcmspw.com/tcmsp.php |
| UniProt | http://www.uniprot.org/ |
| GeneCards | https://www.genecards.org/ |
| OMIM | https://omin.org/ |
| DrugBank | https://www.drugbank.ca/ |
| DisGeNET | https://www.disgenet.org/ |
| PharmGkb | https://www.pharmgkb.org/ |
| Venny 2.1 | http://bioinfogp.cnb.csic.es/tools/venny/index.html |
| String | https://string-db.org/ |
| Metascape | http://metascape.org/gp |
| Bioinformatics | http://swyxzj.com |
| PDB | https://www.rcsb.org/ |
| PubChem | https://pubchem.ncbi.nlm.nih.gov/ |
2.2. Methods
2.2.1. Screening of active ingredients and target genes of Datura metel.
All the chemical components of Datura metel were firstly obtained from the TCM Systematic Pharmacology (TCMSP) database,[8] and then the active ingredients and related targets of Datura metel were screened with the conditions of oral bioavailability ≥ 30%, and drug-like properties ≥ 0.18. The active ingredients and the corresponding targets were compiled in Excel and entered into the UniProt database[9] one by one, with “Reviewed” and “Human” as constraints, to obtain the gene abbreviations of all targets. After de-duplication, the gene results of the targets of the active ingredients of Datura metel were obtained.
2.2.2. Sinus bradycardia-related target search and intersection target acquisition.
Using “sinus bradycardia” as a keyword, searches were conducted in the 5 classic databases GeneCards,[10] Online Human Mendelian Genetic,[11] DrugBank,[12] DisGeNET,[13] Pharmacogenomics Knowledge Base,[14] and the results were collated in Excel format and deduplicated for the desired The results are then deduplicated to the desired disease target. GeneCards were used to retain results for genes greater than the median. The corresponding targets of the active ingredient and the disease target were imported into Venny2.1[15] to obtain the intersecting targets.
2.2.3. Protein interactions network construction and core target screening for sinus bradycardia treatment with Datura metel.
Protein interactions were analyzed using the String[16] database. The intersecting targets from 2.2.2 were imported into the string database, species were selected as “Homo sapiens” and free nodes were hidden, other parameters were set to default values, PPI networks were constructed, and the “tsv” file was exported. The built-in plug-ins Bisogenet and CytoNCA of Cytoscape 3.7.1[17] were used to filter for degree centrality, proximity centrality, and intermediate centrality to derive core targets.
2.2.4. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)) signaling pathway enrichment analysis.
GO functional annotation and KEGG pathway enrichment analysis were performed for the intersection targets of Homo sapiens and sinus bradycardia based on the Metascape database.[18] The biological process (BP), cellular component (CC), molecular functions (MF), and KEGG pathways were obtained and collated in Excel format. The results of the data were plotted using the Bioinformatics online mapping website as GO triple bar graphs and KEGG bubble plots.
2.2.5. “Drug-component-pathway-target-disease” network construction.
To visualize the modern pharmacological rationale for the treatment of sinus bradycardia with Datura metel, the Cytoscape 3.7.1 software was used to construct a “drug-key component-pathway-intersection target-disease” network diagram.
2.2.6. Molecular docking validation of “drug component-core target.”
The 3D structures of the top 4 core targets (AKT serine/threonine kinase 1 (AKT1), IL6, vascular endothelial growth factor (VEGFA), tumor necrosis factor (TNF) in the PDB database[19] were obtained in PDB format, and the water molecules and ligands were removed in Pymol 2.4.0 software; the mol2 structures of the top 4 core targets in the PubChem database[20] were obtained in mol2 format; molecular docking was performed by AutoDockTools 1.5.6 software. Docking was performed by Autodock software to obtain the highest binding energy sites; visualization was performed by Pymol software.
3. Results and analysis
3.1. Results of the collection of constituents and targets of Centaurea
A total of 27 chemical components of Datura metel were obtained in TCMSP (Table 2), and the absorption, distribution, metabolism, excretion (ADME) criteria are shown in Table 3. By searching the 27 components separately, it was found that a total of 10 components had corresponding targets, and the final screening yielded 10 and 198 active components and targets of Datura metel (Table 4).
Table 2.
The active ingredient of Datura metel.
| MOL ID | MOL NAME | OB | DL |
|---|---|---|---|
| MOL011093 | Apohyoscine | 59.68 | 0.25 |
| MOL001554 | Scopolamine | 67.97 | 0.27 |
| MOL011470 | 12-Deoxywithastramonolide | 109.97 | 0.75 |
| MOL011484 | Datuarmeteloside A | 36.13 | 0.25 |
| MOL011486 | Datuarmeteloside B | 31.74 | 0.26 |
| MOL011487 | Datuarmeteloside B_qt | 62.06 | 0.75 |
| MOL011488 | Datuarmeteloside C | 41.16 | 0.25 |
| MOL011490 | Datumetelin | 56.04 | 0.68 |
| MOL011491 | Datumetine | 84.74 | 0.18 |
| MOL011495 | Daturametelin A_qt | 42.04 | 0.89 |
| MOL011497 | (6R)-6-[(1R)-2-hydroxy-1-[(8S,9S,10R,13S,14S,17R)-1-keto-10,13-dimethyl-4,7,8,9,11,12,14,15,16,17-decahydrocyclopenta[a] phenanthren-17-yl]ethyl]-4-methyl-3-methylol-5,6-dihydropyran-2-one | 53.86 | 0.90 |
| MOL011498 | (1R,2R,5R,6S)-2-[(8S,9S,10R,13S,14S,17R)-10,13-dimethyl-1-oxo-4,7,8,9,11,12,14,15,16,17-decahydrocyclopenta[a]phenanthren-17-yl]-6 -(methoxymethyl)-5-methyl-4,8-dioxabicyclo[3.3.1]nonan-7-one | 81.28 | 0.68 |
| MOL011499 | Daturameteline A | 50.40 | 0.73 |
| MOL011505 | Daturameteloside E | 36.00 | 0.37 |
| MOL011507 | Daturameteloside F | 45.10 | 0.35 |
| MOL011519 | Hyoscine | 49.84 | 0.27 |
| MOL011520 | hypaconitine | 31.39 | 0.26 |
| MOL011531 | Secowithamerclin | 50.21 | 0.89 |
| MOL011539 | Withametelin | 83.59 | 0.77 |
| MOL011540 | withanolide D | 58.29 | 0.76 |
| MOL003283 | (2R,3R,4S)-4-(4-hydroxy-3-methoxy-phenyl)-7-methoxy-2,3-dimethylol-tetralin-6-ol | 66.51 | 0.39 |
| MOL003644 | Withaferine | 33.14 | 0.73 |
| MOL000422 | Kaempferol | 41.88 | 0.24 |
| MOL005406 | Atropine | 45.97 | 0.19 |
| MOL000631 | Coumaroyltyramine | 112.90 | 0.20 |
| MOL007923 | 2-(4-hydroxyphenyl)ethyl (E)-3-(4-hydroxyphenyl)prop-2-enoate | 93.36 | 0.21 |
| MOL000098 | Quercetin | 46.43 | 0.28 |
Table 3.
ADME-Tox prediction.
| Mol ID | Molecule Name | MW | AlogP | Hdon | Hacc | OB (%) | Caco-2 | BBB | DL | FASA- | HL |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MOL011093 | Apohyoscine | 285.37 | 1.93 | 0 | 4 | 59.68 | 0.84 | 0.68 | 0.25 | 0.33 | 2.71 |
| MOL001554 | Scopolamine | 303.39 | 0.82 | 1 | 5 | 67.97 | 0.37 | -0.01 | 0.27 | 0.3 | 4.16 |
| MOL011470 | 12-Deoxywithastramonolide | 472.68 | 3.2 | 2 | 6 | 109.97 | -0.02 | -0.48 | 0.75 | 0.24 | 6.34 |
| MOL011484 | Datuarmeteloside A | 650.84 | 0.36 | 6 | 12 | 36.13 | -1.54 | -2.09 | 0.25 | 0.23 | 10.7 |
| MOL011486 | Datuarmeteloside B | 632.82 | 1.43 | 5 | 11 | 31.74 | -1.3 | -1.94 | 0.26 | 0.26 | 10.5 |
| MOL011487 | Datuarmeteloside B_qt | 470.66 | 3.18 | 2 | 6 | 62.06 | 0.03 | -0.44 | 0.75 | 0.26 | 6.85 |
| MOL011488 | Datuarmeteloside C | 648.82 | 0.33 | 6 | 12 | 41.16 | -1.71 | -2.42 | 0.25 | 0.27 | 10.6 |
| MOL011490 | Datumetelin | 468.69 | 3.64 | 0 | 5 | 56.04 | 0.52 | 0.11 | 0.68 | 0.25 | 4.88 |
| MOL011491 | Datumetine | 275.38 | 2.3 | 0 | 4 | 84.74 | 0.99 | 0.85 | 0.18 | 0.29 | 3.16 |
| MOL011495 | Daturametelin A_qt | 438.66 | 5.11 | 1 | 4 | 42.04 | 0.37 | -0.12 | 0.89 | 0.28 | 6.67 |
| MOL011497 | (6R)-6-[(1R)-2-hydroxy-1-[(8S,9S,10R,13S,14S,17R)-1-keto-10,13-dimethyl-4,7,8,9,11,12,14,15,16,17-decahydrocyclopenta[a]phenanthren-17-yl]ethyl]-4-methyl-3-methylol-5,6-dihydropyran-2-one | 454.66 | 3.89 | 2 | 5 | 53.86 | -0.01 | -0.73 | 0.9 | 0.26 | 8.14 |
| MOL011498 | (1R,2R,5R,6S)-2-[(8S,9S,10R,13S,14S,17R)-10,13-dimethyl-1-oxo-4,7,8,9,11,12,14,15,16,17-decahydrocyclopenta[a]phenanthren-17-yl]-6-(methoxymethyl)-5-methyl-4,8-dioxabicyclo[3.3.1]nonan-7-one | 468.69 | 3.64 | 0 | 5 | 81.26 | 0.46 | 0.01 | 0.68 | 0.26 | 5.45 |
| MOL011499 | Daturameteline A | 486.66 | 2 | 3 | 7 | 50.4 | -0.52 | -0.95 | 0.73 | 0.3 | 7.33 |
| MOL011505 | Daturameteloside E | 650.84 | 0.67 | 7 | 12 | 36 | -1.6 | -2.43 | 0.37 | 0.27 | 12.04 |
| MOL011507 | Daturameteloside F | 666.84 | -0.43 | 8 | 13 | 45.1 | -2.05 | -2.79 | 0.35 | 0.27 | 12.48 |
| MOL011519 | Hyoscine | 303.39 | 0.82 | 1 | 5 | 49.84 | 0.21 | -0.19 | 0.27 | 0.3 | 3.44 |
| MOL011520 | Hypaconitine | 615.79 | -0.1 | 2 | 11 | 31.39 | -0.15 | -0.4 | 0.26 | 0.19 | 22.36 |
| MOL011531 | Secowithamerclin | 468.69 | 4.3 | 1 | 5 | 50.21 | 0.27 | -0.2 | 0.89 | 0.27 | 7.07 |
| MOL011539 | Withametelin | 436.64 | 4.34 | 0 | 4 | 83.59 | 0.65 | 0.37 | 0.77 | 0.3 | 6.38 |
| MOL011540 | Withanolide D | 454.66 | 4.34 | 1 | 5 | 58.29 | 0.16 | -0.58 | 0.76 | 0.29 | 6.02 |
| MOL003283 | (2R,3R,4S)-4-(4-hydroxy-3-methoxy-phenyl)-7-methoxy-2,3-dimethylol-tetralin-6-ol | 360.44 | 2.25 | 4 | 6 | 66.51 | -0.2 | -1.17 | 0.39 | 0.23 | 1.26 |
| MOL003644 | Withaferine | 470.66 | 3.25 | 2 | 6 | 33.14 | -0.21 | -1.07 | 0.73 | 0.3 | 6.1 |
| MOL000422 | Kaempferol | 286.25 | 1.77 | 4 | 6 | 41.88 | 0.26 | -0.55 | 0.24 | 0 | 14.74 |
| MOL005406 | Atropine | 289.41 | 1.72 | 1 | 4 | 45.97 | 0.43 | 0.01 | 0.19 | 0.28 | 2.75 |
| MOL000631 | Coumaroyltyramine | 283.35 | 2.88 | 3 | 4 | 112.9 | 0.6 | -0.22 | 0.2 | 0.41 | 5.63 |
| MOL007923 | 2-(4-hydroxyphenyl)ethyl (E)-3-(4-hydroxyphenyl)prop-2-enoate | 284.33 | 3.52 | 2 | 4 | 93.36 | 0.68 | -0.28 | 0.21 | 0.42 | 5.24 |
| MOL000098 | Quercetin | 302.25 | 1.50 | 5 | 7 | 46.43 | 0.05 | -0.77 | 0.28 | 0.38 | 14.40 |
Table 4.
Components and corresponding targets.
| Ingredients | Target name |
|---|---|
| Apohyoscine | CHRM3, CHRM1, DRD5, SCN5A, CHRM5, ADRA2C, CHRM4, OPRD1, HTR2A, SLC6A2, ADRA1A, HTR2C, CHRM2, ADRA2B, ADRA1B, SLC6A3, ADRB2, SLC6A4, DRD2, OPRM1, GABRA1, DPP4, CHRNA7 |
| Datumetine | drd1, adrb1, scn10a, adra2a |
| Daturametelin A_qt | PGR, NR3C1 |
| Withametelin | NR3C2 |
| (2R,3R,4S)-4-(4-hydroxy-3-methoxy-phenyl)-7-methoxy-2,3-dimethylol-tetralin-6-ol | ESR1, AR, PPARG, F10, PTGS2, NOS3, CA2, F7, TOP2A, ESR2, MAPK14, GSK3B, HSP90AB1, CHEK1, PIM1, NCOA2, CAMKK2, CCNA2, PTGS1, IGHG1 |
| Kaempferol | NOS2, PIK3CG, PRKACA, PRSS1, F2, GABRA2, ACHE, TOP2A, RELA, IKBKB, AKT1, BCL2, BAX, TNF, JUN, AHSA1, CASP3, MAPK8, XDH, MMP1, STAT1, CDK1, HMOX1, CYP3A4, CYP1A2, CYP1A1, ICAM1, SELE, VCAM1, NR1I2, CYP1B1, ALOX5, HAS2, GSTP1, AHR, PSMD3, SLC2A4, NR1I3, INSR, DIO1, PPP3CA, GSTM1, GSTM2, AKR1C3, SLPI |
| Atropine | HTR1B, HRH1, HTR1A |
| Coumaroyltyramine | LTA4H, MAOB, PKIA |
| 2-(4-hydroxyphenyl)ethyl (E)-3-(4-hydroxyphenyl)prop-2-enoate | PPARG |
| Quercetin | AKR1B10, KCNH2, MMP3, RXRA, EGFR, VEGFA, CCND1, BCL2L1, FOS, CDKN1A, EIF6, CASP9, PLAU, MMP2, MMP9, MAPK1, IL10, EGF, RB1, IL6, CHEK1, TP53, ELK1, NFKBIA POR, ODC1, CASP8, TOP1, RAF1, SOD1, PRKCA, HIF1A, RUNX1T1, HSPA5, ERBB2, ACACA, CAV1, MYC, F3, GJA1, IL1B, CCL2, PTGER3, CXCL8, PRKCB, BIRC5, DUOX2 HSPB1, TGFB1, SULT1E1, MGAM, IL2, CCNB1, PLAT, THBD, SERPINE1, COL1A1, IFNG, PTEN, IL1A, MPO, NCF1, ABCG1, NFE2L2, NQO1, PARP1, COL3A1, CXCL11, CXCL2, DCAF5, CHEK2, CLDN4, PPARA, PPARD, HSF1, CRP, CXCL10, CHUK, SPP1, RUNX2, RASSF1, E2F1, E2F2, ACP3, CTSD, IGFBP3, IGF2, CD40LG, IRF1, ERBB3, PON1, PCOLCE npepps, hk2, nkx3-1, rasa1 |
3.2. Sinus bradycardia target collection and intersection target acquisition results
Retrieved from 5 major databases, GeneCards, Online Human Mendelian Genetic, DrugBank, DisGeNET, and Pharmacogenomics Knowledge Base, respectively, “sinus bradycardia “277, 20, 9, 900, and 114 corresponding targets were obtained, and 1059 targets remained after all targets were combined and deweighted. The intersecting targets were obtained by importing the drug targets and disease targets together into Venny2.1, suggesting that the 54 intersecting targets (Fig. 2) may be the main targets for the treatment of sinus bradycardia with Datura metel.
Figure 2.
The intersection of Datura metel sinus bradycardia targets.
3.3. Protein interaction network map and core target screening results
The 54 intersecting targets were imported into the String bioanalysis platform, and the PPI network after hiding 1 free node (Fig. 3), which showed a total of 54 nodes and 490 edges (the expected number of edges was 182), with an average degree value of 18.1 per node; then speaking of importing Cytoscape 3.7.1 software, with the help of Bisogenet and CytoNCA for degree centrality, near-centrality, and mediated centrality were screened, resulting in 19 nodes and 159 edges (Fig. 4), suggesting that these 19 nodes are the core targets of Datura metelbe for sinus bradycardia (Table 5).
Figure 3.
Intersectional target interaction network. The more a target is connected to other targets, the more important the target is.
Figure 4.
Core target screening. Note: The box on the right is the core target, where the larger the node, the higher the degree value.
Table 5.
Information about the core target of the treatment of sinus bradycardia.
| Serial number | Core targets | Protein name | Degree |
|---|---|---|---|
| 1 | AKT1 | RAC-alpha serine/threonine-protein kinase | 40 |
| 2 | IL6 | Interleukin-6 | 38 |
| 3 | VEGFA | Vascular endothelial growth factor A | 37 |
| 4 | TNF | Tumor necrosis factor | 37 |
| 5 | IL1B | Interleukin-1 beta | 36 |
| 6 | MMP9 | Matrix metalloproteinase-9 | 36 |
| 7 | TP53 | Cellular tumor antigen p53 | 35 |
| 8 | EGF | Pro-epidermal growth factor | 33 |
| 9 | ESR1 | Estrogen receptor | 31 |
| 10 | MMP2 | 72 kDa type IV collagenase | 30 |
| 11 | MYC | Myc proto-oncogene protein | 29 |
| 12 | CXCL8 | Interleukin-8 | 29 |
| 13 | IL10 | Interleukin-10 | 28 |
| 14 | CASP8 | Caspase-8 | 28 |
| 15 | PPARG | Peroxisome proliferator activated receptor gamma | 28 |
| 16 | TGFB1 | Transforming growth factor beta-1 | 27 |
| 17 | PTEN | Phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN | 26 |
| 18 | CRP | C-reactive protein | 22 |
| 19 | F2 | Thrombin | 18 |
3.4. GO and KEGG signaling pathway enrichment results
3.4.1. GO enrichment analysis.
A total of 4443 GO entries (P < .01) were identified by importing 54 intersecting targets into the Metascape database, with the species selected as “Homo sapiens.” Of these, 3700 entries were for BP, mainly related to the cellular response to organic cyclic compound, negative regulation of cell population proliferation, blood circulation, response to cytokine, and regulation of system process. There are 285 entries in CC, mainly dealing with the extracellular matrix, synaptic membranes, plasma membrane protein complex, membrane raft, transcription regulator complex, and other CCs. MF has 458 entries, mainly related to cytokine receptor binding, protein domain specific binding, endopeptidase activity, protein kinase binding, G protein-coupled neurotransmitter receptor activity, and other MFs. The top 10 of BP, the top 9 of CC, and the top 10 of MF were taken for display (Table 6 and Fig. 5).
Table 6.
GO enrichment analysis.
| GOterm | Subgroup | Gene count |
|---|---|---|
| Cellular response to organic cyclic compound | Biological processes | 20 |
| Negative regulation of cell population proliferation | Biological processes | 19 |
| Blood circulation | Biological processes | 18 |
| Response to cytokine | Biological processes | 18 |
| Regulation of system process | Biological processes | 17 |
| Regulation of ion transport | Biological processes | 17 |
| Regulation of proteolysis | Biological processes | 17 |
| Gland development | Biological processes | 16 |
| Cellular response to lipid | Biological processes | 16 |
| Positive regulation of cytokine production | Biological processes | 15 |
| Extracellular matrix | Cellular components | 10 |
| Synaptic membrane | Cellular components | 8 |
| Plasma membrane protein complex | Cellular components | 8 |
| Membrane raft | Cellular components | 7 |
| Transcription regulator complex | Cellular components | 7 |
| Side of membrane | Cellular components | 7 |
| Platelet alpha granule lumen | Cellular components | 4 |
| Apical part of cell | Cellular components | 4 |
| Cytoplasmic side of plasma membrane | Cellular components | 3 |
| Cytokine receptor binding | Molecular functions | 13 |
| Protein domain specific binding | Molecular functions | 11 |
| Endopeptidase activity | Molecular functions | 7 |
| Protein kinase binding | Molecular functions | 7 |
| G protein-coupled neurotransmitter receptor activity | Molecular functions | 6 |
| Transcription coregulator binding | Molecular functions | 6 |
| Protease binding | Molecular functions | 6 |
| Protein homodimerization activity | Molecular functions | 6 |
| Scaffold protein binding | Molecular functions | 5 |
| Kinase activator activity | Molecular functions | 5 |
Figure 5.
GO analysis results. Note: The x-axis demonstrates the top 9-10 significantly enriched BP, CC, and MF categories, while the y-axis shows the number of enriched genes for these terms (P < .05). GO = gene ontology, BP = biological process, CC = cellular component, MF = molecular function.
3.4.2. KEGG pathway analysis.
The 54 intersecting targets were imported into the Metascape database, and the species selected was “Homo sapiens,” and KEGG enrichment analysis was performed. 209 key signaling pathways were identified (P < .01), of which the first 18 were significant pathways as shown in Table 7 and Fig. 6, mainly involving Pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, hepatitis C, relaxin signaling pathway, and JAK-STAT signaling pathway, etc. Among them, the Calcium signaling pathway, a possible important pathway of Datura metel for sinus bradycardia, is shown in detail in Fig. 7, where the green area in the pathway diagram is signal down-regulation.
Table 7.
KEGG signalling pathway.
| Serial number | Passage ID | Description | Corrected P-value | Number of genes |
|---|---|---|---|---|
| 1 | Hsa05200 | Pathways in cancer | 7.94328E-30 | 25 |
| 2 | Hsa04933 | AGE-RAGE signaling pathway in diabetic complications | 2.51189E-21 | 13 |
| 3 | Hsa05219 | Bladder cancer | 3.0903E-17 | 9 |
| 4 | Hsa05160 | Hepatitis C | 4.16869E-15 | 11 |
| 5 | Hsa04926 | Relaxin signaling pathway | 2.88403E-14 | 10 |
| 6 | Hsa05207 | Chemical carcinogenesis—receptor activation | 4.2658E-12 | 10 |
| 7 | Hsa04630 | JAK-STAT signaling pathway | 1.25893E-11 | 9 |
| 8 | Hsa04660 | T cell receptor signaling pathway | 1.38038E-11 | 8 |
| 9 | Hsa04659 | Th17 cell differentiation | 1.86209E-11 | 8 |
| 10 | Hsa04020 | Calcium signaling pathway | 4.2658E-10 | 9 |
| 11 | Hsa05202 | Transcriptional misregulation in cancer | 1.86209E-09 | 8 |
| 12 | Hsa04024 | cAMP signaling pathway | 2.75423E-06 | 6 |
| 13 | Hsa04115 | p53 signaling pathway | 9.12011E-06 | 4 |
| 14 | Hsa04350 | TGF-beta signaling pathway | 2.45471E-05 | 4 |
| 15 | Hsa04261 | Adrenergic signaling in cardiomyocytes | 0.000151356 | 4 |
| 16 | Hsa04976 | Bile secretion | 0.000549541 | 3 |
| 17 | Hsa04928 | Parathyroid hormone synthesis, secretion and action | 0.000912011 | 3 |
| 18 | Hsa05014 | Amyotrophic lateral sclerosis | 0.004073803 | 4 |
Figure 6.
Bubble map of 18 KEGG pathway. Note: The y-axis demonstrates the top 18 significantly enriched KEGG pathways, while the x-axis shows the number of enriched genes for these terms (P < .05), the colors and the sizes indicate different P value ranges; the redder and bigger it is, the more significantly enriched it is. KEGG = Kyoto Encyclopedia of Genes and Genomes.
Figure 7.
Signal pathway targets of Datura metel in the treatment of sinus bradycardia.
3.5. “Drug-component-pathway-target-disease” network diagram
The interrelationship data files and attribute files of drug and key components, key components and intersecting targets, intersecting targets and pathways, and intersecting targets and diseases were prepared in Excel, and both were imported into Cytoscape 3.7.1 line optimization to obtain the final visual network (Fig. 8), where the more lines a node has with other nodes indicates the more important the node is in the network.
Figure 8.
“Drug-component-pathway-target-disease” network diagram. Note: Different colors represent different information. Chocolate yellow represents the medicinal Datura metel, light green color indicates the active active ingredient of the drug, red indicates the disease sinus bradycardia, dark blue represents disease targets, and yellow represents the pathway of action of the drug in the treatment of the disease.
3.6. Molecular docking results of the active ingredients of Datura metel for sinus bradycardia
Since kaempferol and quercetin are the 2 most important components of Datura metel, and AKT1, TNF, IL6, and VEGFA are the top 4 most important ranked core targets screened by degree value among the intersection targets of drug disease, so the molecular docking validation of the most important components and the most important targets has better credibility. Molecular docking of the top 4 Degree values of the core targets and their corresponding components was carried out by AutoDock software and the binding energy is shown in Table 8. The force field used for the energy minimization of small molecule ligands in this study mainly used the MMFF94 force field. In molecular docking, the results were imported into pymol software for visualization and the molecular docking results showed that hydrogen bonding is the main force in the interaction. Kaempferol forms hydrogen bonds with AKT1 at positions GLU-98, GLU-9, LYS-8, VAL-7, and ILE-6; Kaempferol forms hydrogen bonds with TNF at positions GLY-24, GLN-25, GLU-135; quercetin forms hydrogen bonds with IL6 at positions ASN-144, ASP-140, THR-137 positions; quercetin forms hydrogen bonds with VEGFA at GLU-42, GLU-38, ASP-41, ASN-75, and see Fig. 9 for details.
Table 8.
The binding energy of main active ingredients and key target proteins.
| No. Number |
Active ingredients Active ingredient |
Protein name Protein name |
Combined with the ability to Binding energy (kJ/mol) |
|---|---|---|---|
| Mol000422 | Kaempferol | AKT1 | −5.45 |
| Mol000422 | Kaempferol | TNF | −3.83 |
| Mol000098 | Quercetin | IL6 | −4.32 |
| Mol000098 | Quercetin | VEGFA | −4.8 |
Figure 9.
Molecular docking of major component targets.
4. Discussion
Sinus bradycardia belongs to the category of “palpitation,” or “chest stuffiness and pains” in Chinese medicine, The causes, mechanisms, and treatment of this disease have been described in ancient works such as Yellow Emperor’s Classic of Internal Medicine, Treatise on Febrile Diseases. Modern medicine has confirmed that the causes of this disease are[21] mostly increased excitability of the vagus nerve, impaired function of the sinus node itself, numerous organic heart diseases, etc. It is also related to the long-term use of certain drugs. The pathogenesis[22] is usually related to abnormalities in myocardial ion channel function and autonomic dysfunction. The development of sinus bradycardia is complex, with multiple cells and pathways of action interacting with each other, thus forming a complex network system of interactions. Single-target, single-pathway therapy is not effective, while Chinese medicine has the natural qualities of multiple components, multiple targets, and multiple pathways of action, so it can regulate the body systematically. It has been used for a long time to treat illnesses, with the famous doctor Hua Tuo using it as the main ingredient to make mafei san in the late Eastern Han Dynasty. Abroad, it has a wider range of uses,[23–25] including heart disease, convulsions, diarrhea, hemorrhoids, dysmenorrhea, eczema, and toothache. Although the Chinese Pharmacopoeia 2020 edition[26] does not state that the drug can increase heart rate, several studies[27] have shown that important chemical components of the flower, such as quercetin, have a significant effect on improving heart function, and call for quercetin to be taken seriously in the treatment of many cardiovascular diseases. As research into the chemical composition of the drug has become more advanced, clinical formulations using it as a major component to increase sinus heart rate have been frequently reported, with the Heart Treasure Pill[28–31] being widely used clinically. Although there are many reports, the specific molecular regulatory mechanism of the effect of Datura metel on sinus bradycardia remains unclear. In this study, by searching the TCMSP database, we found that quercetin, apohyoscine, kaempferol, datumetine, atropine, and other active ingredients have more target genes and are the main active ingredients of Datura metel. We obtained the interrelationship network between the above components and sinus bradycardia-related targets through network pharmacology and validated quercetin and kaempferol among them by molecular docking.
After screening by oral bioavailability and drug-like properties parameters, 27 active ingredients were obtained, and a total of 10 active ingredients were identified after eliminating those that could not be found as targets. From the “drug-component-pathway-key target-disease network diagram,” it can be seen that there are 6 major active ingredients directly connected to the key targets, accounting for 60% of the total number of corresponding target ingredients, which are flavonoids and alkaloids. Flavonoids are mostly found in the flowers and leaves of the woody herb and are effective in a variety of diseases of the cardiovascular system. In addition to effectively improving the number of episodes of arrhythmias due to myocardial ischemia, they also have multiple effects such as antiatherosclerosis, anticoagulation, antilipid, and vasodilatation,[32] Chen Qiuhong and others suggest that they may protect against ischemia-reperfusion injury by increasing Na+/K+-ATPase activity and thus improve arrhythmias. The compounds have been shown to improve cardiac arrhythmias by increasing Na+/K+-ATPase activity.[33] These compounds have been shown to exert anti-arrhythmic effects on several ion channels, including potassium, sodium, and calcium, in the myocardial cell membrane.[34] Animal studies have reported that flavonoids have a strong effect on the reduction of inward rectifier potassium currents caused by aconitine.[35] Pei et al[36] found that quercetin had a protective effect on myocardial damage caused by adriamycin in mice. This shows that quercetin and kaempferol, as flavonoids in Chinese medicine, play a “multi-component, multi-target” role in the treatment of sinus bradycardia. In addition, alkaloids also have a role in the improvement of sinus bradycardia. In particular, it is widely known that higher doses of atropine can increase heart rate by relieving vagal inhibition.[37] Studies have shown that scopolamine and scopolamine, which are contained in Hypericum, can improve blood microcirculation by blocking α1 and M receptors, increasing cell membrane fluidity, reducing blood viscosity, promoting tissue healing, increasing heart rate, enhancing myocardial contractility, and inhibiting ectopic excitability of the heart.[38–40]
The results of the network screening of core targets showed that AKT1, IL6, VEGFA, and TNF were the top 4 core target proteins in terms of degree value, and maybe the most core targets for the treatment of sinus bradycardia with Yohimbe. A study showed that resveratrol targeting AKT1 inhibited inflammatory vesicle activation in cardiomyocytes under sympathetic stress, inhibited β-adrenergic receptor-mediated NLRP3 inflammatory vesicle activation in cardiomyocytes and mouse hearts, and the resulting cardiac inflammation.[41] IL-6 and VEGFA are closely related to the progression of cardiac hypertrophy and coronary artery disease, while VEGFA is widely distributed in the brain, VEGFA is widely distributed in the brain, heart, and other tissue cells, and has an important regulatory role in angiogenesis and atherosclerosis.[42] Studies have shown that MSCs can reduce inflammation in the heart and lungs through TNF signaling.[43] Mathematical modeling of TNF-α overexpression in transgenic mouse hearts has revealed an effect on cardiac arrhythmias.[44] All of the above suggests a strong association between these targets and the development of arrhythmias.
The analysis of the BPs of the key core targets of C. elegans revealed that C. elegans was highly correlated with the BPs of blood circulation, regulation of smooth muscle cell proliferation, and response to peptides in the cardiovascular system, which were also reflected in the key targets of C. elegans for the treatment of cardiac arrhythmias. The KEGG pathway enrichment results showed that Hepatitis C, AGE-RAGE signaling pathway, relaxin signaling pathway, JAK-STAT signaling pathway, T cell receptor signaling pathway, calcium signaling pathway, p53 signaling pathway, and adrenergic signaling in cardiomyocytes as the main regulatory pathways. This suggests that the same regulatory mechanisms may exist between different diseases. Evidence suggests a relationship between viral invasion and the development of arrhythmias, such as hepatitis B and C.[45,46] Numerous previous studies have shown that relaxin can hinder fibrosis through many cell targets and signaling pathways.[47] The AGE-RAGE signaling pathway was found to affect the migration and differentiation of cardiac fibroblasts, which in turn affected ventricular reconstruction and cardiac function.[48] In addition, the JAK-STAT signaling pathway plays an important role in myocardial ischemia, infarction, myocarditis, and other cardiac injuries, and its blockade is effective in reducing the inflammatory response in the myocardium and preventing the formation of myocardial fibrosis.[49] Some components, such as arginine pressors, can regulate current by affecting the T-type calcium signaling pathway, increasing intracellular calcium ion concentrations, and triggering prolonged action potential duration and differences in the spatial distribution of action potentials, leading to arrhythmias.[50]
In summary, through the use of network pharmacology combined with molecular docking, and the comprehensive discussion and analysis of various networks and databases, the composition targets and mechanisms of action of Datura metel. in the treatment of sinus bradycardia through “multi-component, multi-target, and multi-pathway” have been more fully explored. The results of this study should be investigated in cellular or animal studies to provide further ideas and references for basic and clinical research.
Author contributions
Conceptualization: Feifei Yang.
Data curation: Pihong Liu, Xiaosi Zhang.
Formal analysis: Feifei Yang, Pihong Liu, Xiaosi Zhang.
Investigation: Zhe Zhang.
Methodology: Feifei Yang, Hao Lu.
Project administration: Naizhi Geng.
Resources: Naizhi Geng.
Software: Feifei Yang.
Supervision: Xiaosi Zhang, Zhe Zhang, Hao Lu.
Validation: Naizhi Geng.
Visualization: Feifei Yang, Xiaosi Zhang, Zhe Zhang, Hao Lu.
Writing—original draft: Feifei Yang.
Writing—review and editing: Naizhi Geng, Xiaosi Zhang.
Correction
The article type has been changed from Narrative Review to Observational Study. “A Review” has been removed as the subtitle of the article title.
Abbreviations:
- AKT1 =
- AKT serine/threonine kinase 1
- BP =
- biological process
- CC =
- cellular component
- MF =
- molecular functions
- TCM =
- traditional Chinese medicine
- TCMSP =
- TCM Systematic Pharmacology
- GO =
- gene ontology
- KEGG =
- Kyoto Encyclopedia of Genes and Genomes
- VEGFA =
- vascular endothelial growth factor
- TNF =
- tumor necrosis factor
- URL =
- uniform resource locator
FY, PL, and XZ contributed equally to this work.
All analyses were based on database thus no ethical approval and patient consent are required.
The authors state that the study was done independently by the team and did not involve any commercial or financial relationships.
This article is supported by the Natural Science Foundation of Heilongjiang Province (D201267).
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
The authors have no conflicts of interest to disclose.
How to cite this article: Yang F, Liu P, Zhang X, Zhang Z, Lu H, Geng N. Mechanism of Datura metel on sinus bradycardia based on network pharmacology and molecular docking. Medicine 2022;101:49(e32190).
Contributor Information
Feifei Yang, Email: 396354360@qq.com.
Pihong Liu, Email: 2270578380@qq.com.
Xiaosi Zhang, Email: 1140822988@qq.com.
Zhe Zhang, Email: 1140822988@qq.com.
Hao Lu, Email: 18846838215@139.com.
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