Network Pharmacology-based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

doi:10.21203/rs.3.rs-117498/v1

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Network Pharmacology-based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

https://doi.org/10.21203/rs.3.rs-117498/v1

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Background: Dingji Fumai decoction (DFD), a traditional herbal mixture, has been widely used to ventricular arrhythmia (VA) in clinical practice in China. However, research on the bioactive components and underlying mechanisms of DFD in VA is still scarce.

Methods: Components of DFD were collected from TCMSP, ETCM, and literature. Then, the chemical structures of each component were obtained from PubChem. Next, SwissADME and SwissTargetPrediction were applied for compounds screening and targets prediction of DFD, meanwhile, targets of VA were collected from DrugBank and OMIM. Then, the H-C-T-D network as well as the PPI network were constructed based on the data obtained above. CytoNCA was utilized to filter hub genes and VarElect was used to analyze the relationship between genes and diseases. At last, Metascape was employed for systematic analysis on the potential targets of herbals against VA, and AutoDock was applied for molecular docking to verify the results.

Results: A total of 434 components were collected, 168 of which were qualified, and there were 28 shared targets between DFD and VA. Three function modules of DFD were found from the PPI network. Further systematic analysis of shared genes and function modules explained the potential mechanism of DFD in the treatment of VA, molecular docking has verified the interactions.

Conclusions: DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed DFD could apply for the treatment of VA and promoted the explaining of DFD for VA in the molecular mechanisms.

Clinical Pharmacology

Dingji Fumai Decoction

Ventricular arrhythmia

Network pharmacology

Molecular docking

Traditional Chinese medicine

In recent years, cardiovascular diseases are the leading cause of death in china¹. All cardiac conditions, especially ischemic heart disease, can lead arrhythmias². Among all arrhythmias, ventricular arrhythmia (VA) has the highest mortality. VA is a common but life-threatening disease, mainly including ventricular premature contraction, ventricular tachycardia, ventricular flutter, and ventricular fibrillation, with a clinical presentation ranges from no symptoms to cardiac arrest². VA is usually generated by all-caused enhanced automaticity or abnormal automaticity, myocardial ischemia, delayed afterdepolarizations, and structural heart disease with reentry ^3–5. To prevent adverse events of VA, millions of patients are treated with beta-receptor-blockers, I_Na antagonists, I_Kr antagonists, non-dihydropyridine calcium antagonists, and other drugs suggested by 2017 American Heart Association/American College of Cardiology/Heart Rhythm Society guideline², but the control of VA is still far from ideal.

Traditional Chinese medicine (TCM) has a more than 2,000 years clinical-based development history^6,7. Some researchers mentioned that TCM could be used to treat a variety of diseases, including VA^8–10. Besides, related studies suggested that compared with western medicine only, patients suffering from various diseases can benefit more from a TCM and western medicine combined therapy strategy^11–22. Moreover, several TCM, such as Shensong Yangxin Capsule²³, Wenxin Keli²⁴, have been transformed into commercial products for the treatment of cardiovascular-related diseases. Convenient ways like these greatly promoted the development of TCM worldwide.

Dingji Fumai Decoction (DFD), consisting of Chuanxiong Rhizoma (Chuanxiong), Jujubae Fructus (Dazao), Poria Cocos (Schw.), Wolf (Fuling), Cinnamomi Ramulus (Guizhi), Silktree Albizia Bark (Hehuanpi), Osdraconis (Fossiliaossiamastodi) (Longgu), Ostrea Gigas Thunberg (Muli), Ziziphi Spinosae Semen (Suanzaoren), Radix Polygalae (Yuanzhi), and Licorice (Gancao), is widely used for VA. Our previous work confired its efficacy and safety on VA²⁵, and one of the underling mechanisms is class I antiarrhythmic property²⁶. To make DFD more recognized, it is essential to make the efficacy and safety of DFD clearly. Since multiple components contained in DFD, it can generate interactions on multiple targets. Based on the theory of network pharmacology, we constructed network relationships between "component-target pathway" to explore the mechanisms of drugs or herbs²⁷. Here, we analyzed the mechanisms of DFD in the treatment of VA systematically (Fig. 1). At first, we obtained its components and potential targets against VA. Then, the protein-protein interaction (PPI) of potential targets against VA was constructed. Next, systematic analysis of potential targets and bio-functional modules were conducted. Meanwhile, the interactions between the components of DFD and key targets were confirmed using molecular docking.

2.1 Chemical structures construction

The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP)²⁸ and the Encyclopedia of Traditional Chinese Medicine (ETCM)²⁹ are web-based herb databases, providing comprehensive and standardized information for the commonly used herbs. In this study, the components of each herb in DFD were obtained from TCMSP, ETCM, and published literature. To make the components recognizable for the subsequent analysis work, after deduplicating, the structure of each component was collected from PubChem³⁰.

2.2 Gastrointestinal absorption (GA) and drug-likeness (DL) prediction

Increasingly researches founded that TCM despite of their impressive in vitro findings demonstrates less or negligible in vivo activity, resulting in poor absorption and hence poor bioavailability³¹. The absorption, distribution, metabolism, and excretion (ADME) of the drug must be considered by the researcher and developer³². Bioabsorption is highly multifactorial, but is primarily driven by GA³³. Besides, DL assesses qualitatively the chance for a molecule to be an oral drug with respect to bioavailability³². It was constructed that the estimation of ADME before the drug development studies reduces the possibility of failure³⁴. In the mechanism explaining of DFD, GA and DL were evaluated using SwissADME. SwissADME is a free tool to evaluate DL, GA, pharmacokinetics, and medicinal chemistry friendliness of small molecules³². Uploaded the structure of each compound to SwissADME, if the prediction results of the component were shown both “high” GA and got “yes” in more than 2 of 5 filters in DL prediction, it met our inclusion criteria and were adopted to the next screening³².

2.3 Target prediction and verification

In the treatment of diseases, not all absorbable components work, therefore, we filtered out the components with bioactive from all absorbable components with SwissTargetPrediction, an online tool which can evaluate compounds with a score by fitting a multiple logistic regression on various subsets of known actives to weight structure similarity parameters³⁵. Here, we uploaded the structure of each component to SwissTargetPrediction to predict potential targets of DFD, and all possible targets were adopted.

Online Mendelian Inheritance in Man (OMIM) is a knowledgebase providing the latest information of human genes³⁶, and DrugBank is a freely available and comprehensive web resource providing drug-target as well as drug interaction information³⁷. Taking “ventricular arrhythmia” or “arrhythmia of ventricular origin” as keywords, we recruited VA related targets from OMIM and DrugBank. Taking the intersection of DFD and VA targets, the common targets between DFD and VA were considered the therapeutic targets of DFD against VA, as described previously³⁸.

Protein-protein interactions (PPI) is one of the cores of cellular processing. The analysis of PPI makes the interactions of proteins clearly and helps to explain the function of possible protein complexes or functional modules³⁹. STRING is a web-database providing online analysis of PPI³⁹. Uploaded common targets to STRING to construct the PPI network. Then, the results were imported to Cytoscape (version 3.8.0)⁴⁰, and CytoNCA plugin was applied to analyze centrality and evaluate protein interaction networks⁴¹.

The VarElect online tool can analyze direct and indirect links between genes and diseases⁴².In this study, the link relationships of potential targets of DFD against VA were analyzed with VarElect, the results helped determine which targets will be included in the next molecular docking.

2.4 Biology functional analysis

Since Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) can contribute to the interpretation of system-level data and enable new discoveries⁴³, it is essential to make gene-related information clearly in the DFD mechanism explain. Metascape is a web-based platform providing gene annotation, functional enrichment, and interactome analysis services, monthly database update could keep our analysis results up to date⁴³. In our work, GO and KEGG terms with P < 0.01 were considered significantly enrichment analyses.

2.5 Molecular docking

Molecular docking was used to assess interactions between components and hub targets, the 4 hub targets connected to VA closely were included in. The structures of these targets were collected from Protein Data Bank⁴⁴. AutoDock and PyMOL were employed for molecular docking, PyMOL was used to remove the water molecules, isolate proteins of each molecular⁴⁵. AutoDock was used to add the nonpolar hydrogen and calculate Gasteiger charges of the molecular and add the nonpolar hydrogen for each ligand⁴⁶. At last, molecular docking was conducted using AutoDock to assess binding energy.

3.1 Chemical structures construction

After searching TCMSP, ETCM, and literatures^28,29, a total of 434 compounds were collected. Including 92, 73, 76, 38, 19, 70, 12, 36, and 18 compounds in Licorice, Chuanxiong Rhizoma, Jujubae Fructus, Poria Cocos (Schw.) Wolf. Cinnamomi Ramulus, in Silktree Albizia Bark, Ostrea Gigas Thunberg, Ziziphi Spinosae Semen, and Radix Polygalae. Later, the structure of each component was collected from PubChem.

3.2 GA and DL prediction

Uploaded the structures to SwissADME, after screening the GA and DL and deduplicating 168 components qualified, including 80, 20, 27, 11, 4, 19, 4, 10, and 2 in

Licorice, Chuanxiong Rhizoma, Jujubae Fructus, Poria Cocos (Schw.) Wolf. Cinnamomi Ramulus, in Silktree Albizia Bark, Ostrea Gigas Thunberg, Ziziphi Spinosae Semen, and Radix Polygalae. Interestingly, several qualified components are owned by more than one herbal, more information about the prediction results was placed in the Supplementary materials. All qualified components were adopted for the next screening.

3.3 Target prediction and verification

After deduplicating, 1096 related targets of DFD were collected from SwissTargetPrediction. Meanwhile, 260 VA related targets were collected from OMIM and DrugBank. Taking the intersection of DFD and VA targets, there were 28 shared targets, based on the data obtained above, the Herb-Compound-Targets-Disease (H-C-T-D) network was constructed. The H-C-T-D network was composed of 147 nodes (DFD, VA, 7 herbals, 110 bioactive compounds, and 28 common targets) and 465 edges (Fig. 2).

In further analysis, all 28 common targets were uploaded to STRING to construct the PPI network, the results were imported to Cytoscape to calculate the degree value of each gene using CytoNCA plugin and reconstruct the PPI network according to the degree value (Fig. 3). There are three possible bio-functional modules divided from the PPI network (Fig. 4). After screening by CytoNCA, the top 10 targets were defined as hub targets, the designations and topological parameters of hub targets were shown in Table 1. Besides, we used VarElect to find, among the 28 common targets, 23 targets were related to VA directly, whereas 5 targets were related to VA indirectly (Table 2), Potassium Voltage-Gated Channel Subfamily H Member 2 (KCNH2), Sodium Voltage-Gated Channel Alpha Subunit 5 (SCN5A), Troponin T2-Cardiac Type (TNNT2), and Calmodulin 1 (CALM1) are the most related targets.

Table 1

Designations and topological parameter of hub genes in the PPI network
Gene symbol	Protein Name	Degree	Betweenness Centrality	Closeness Centrality
KCNH2	Potassium voltage-gated channel subfamily H member 2	10	128.5	0.32
CYP3A4	Cytochrome P450 3A4	9	166.8	0.35
CYP2C9	Cytochrome P450 2C9	8	22.2	0.31
PIK3R1	Phosphatidylinositol 3-kinase regulatory subunit alpha	8	146.7	0.31
CHRM1	Muscarinic acetylcholine receptor M1	7	19.2	0.27
CYP2D6	Cytochrome P450 2D6	7	59.1	0.32
EGFR	Epidermal growth factor receptor	7	228.3	0.36
SLCO1B1	Solute carrier organic anion transporter family member 1B1	6	3.9	0.28
ADRA1B	Alpha-1B adrenergic receptor	6	11.1	0.27
ADRB2	Beta-2 adrenergic receptor	6	63.8	0.31

Table 2

The connection information of shared genes.
GENES	DESCRIPTION	RELATIONSHIP	SCORE
SCN5A	Sodium Voltage-Gated Channel Alpha Subunit 5	DIRECTLY	445.8
KCNH2	Potassium Voltage-Gated Channel Subfamily H Member 2	DIRECTLY	409.76
TNNT2	Troponin T2, Cardiac Type	DIRECTLY	293.82
CALM1	Calmodulin 1	DIRECTLY	222.12
SCN4A	Sodium Voltage-Gated Channel Alpha Subunit 4	DIRECTLY	158.41
SCN10A	Sodium Voltage-Gated Channel Alpha Subunit 10	DIRECTLY	132.53
ADRB1	Adrenoceptor Beta 1	DIRECTLY	97.00
ADRB2	Adrenoceptor Beta 2	DIRECTLY	75.14
CYP2C9	Cytochrome P450 Family 2 Subfamily C Member 9	DIRECTLY	53.13
PDGFRB	Platelet Derived Growth Factor Receptor Beta	DIRECTLY	46.86
CHRM2	Cholinergic Receptor Muscarinic 2	DIRECTLY	46.86
MAPK1	Mitogen-Activated Protein Kinase 1	DIRECTLY	43.38
EGFR	Epidermal Growth Factor Receptor	DIRECTLY	43.38
ADRA1D	Adrenoceptor Alpha 1D	DIRECTLY	39.60
ADRB3	Adrenoceptor Beta 3	DIRECTLY	39.60
SCN9A	Sodium Voltage-Gated Channel Alpha Subunit 9	DIRECTLY	35.42
CYP3A4	Cytochrome P450 Family 3 Subfamily A Member 4	DIRECTLY	30.68
CYP2D6	Cytochrome P450 Family 2 Subfamily D Member 6	DIRECTLY	25.05
PON1	Paraoxonase 1	DIRECTLY	25.05
PIK3R1	Phosphoinositide-3-Kinase Regulatory Subunit 1	DIRECTLY	17.71
CYP1A1	Cytochrome P450 Family 1 Subfamily A Member 1	DIRECTLY	17.71
CHRM1	Cholinergic Receptor Muscarinic 1	DIRECTLY	17.71
CYP1A2	Cytochrome P450 Family 1 Subfamily A Member 2	DIRECTLY	17.71
ADRA1A	Alpha-1A adrenergic receptor	INDRECTLY	37.78
ADRA1B	Alpha-1B adrenergic receptor	INDRECTLY	36.23
CHRM3	Muscarinic acetylcholine receptor M3	INDRECTLY	28.09
SLCO1B1	Solute carrier organic anion transporter family member 1B1	INDRECTLY	13.50
SLC47A1	Multidrug and toxin extrusion protein 1	INDRECTLY	11.33
The score is an indication of the strength of the connection between the gene and the disease.

3.4 Biology functional analysis

The enrichment analysis of GO and KEGG of the 28 common targets were analyzed with Metascape, the results were ranked by -logP, and the top 14 of each enrichment items were shown in Fig. 5, besides, the functional analysis of the three potential bio-function modules divided from PPI network were shown in Table 3.

Table 3

GO and KEGG enrichment analysis of the bio-function modules
Function modules	DESCRIPTION	Log10(Pvalue)
Module 1	ko04020,Calcium signaling pathway	-22.3027025
	GO:0001996,positive regulation of heart rate by epinephrine-norepinephrine	-12.3777817
	ko04810,Regulation of actin cytoskeleton	-11.6704785
	GO:0043410,positive regulation of MAPK cascade	-10.5659921
	ko04024,cAMP signaling pathway	-9.71878217
Module 2	GO:0086010,membrane depolarization during action potential	-12.7696584
	GO:0006941,striated muscle contraction	-11.8188992
	ko04261,Adrenergic signaling in cardiomyocytes	-7.33482642
	GO:0019233,sensory perception of pain	-5.53543107
	GO:0055080,cation homeostasis	-3.03192747
Module 3	GO:0016098,monoterpenoid metabolic process	-13.3868655
	GO:0008202,steroid metabolic process	-10.1366162
	GO:0006690,icosanoid metabolic process	-10.0957724
	GO:0008203,cholesterol metabolic process	-9.58364991
	GO:0010035,response to inorganic substance	-3.36527057

In GO and KEGG enrichment analysis, terms with high enrichment scores suggested that the regulation of muslecontrction, regulation of systemic arterial blood pressure by norepinephrone-epinephine, blood circulation, circulatory system process, adrenergicreceptor activity, calcium signaling pathway, adenylate signaling in cardiomyocytes, cGMP-PKG signaling pathway and Neuroactive ligand-receptor interaction could be the most possible ways DFD works. For the 3 protein modules, module 1 can regulate calcium signaling pathway, heart rate as well as cAMP signaling pathway, Module 2 can regulate membrane depolarization during action potential, striated muscle contraction, regulate adrenergic signaling in cardiomyocytes, and cation homeostasis. This work supported that DFD against VA though mechanisms, actually, the mechanisms of DFD against VA has been revealed by this work.

3.5 Molecular docking

Molecular docking was conducted to calculate binding energy between components and hub targets, KCNH2 (PDB ID:1BYW), SCN5A (PDB ID: 4DCK), TNNT2) (PDB ID: 1J1D and CALM1 (PDB ID: 1CDL), the 4 genes connected closely to VA were included in molecular docking. The related information of components docked with key targets were listed in Table 4, and the results suggested that components in DFD are interacting strongly with the hub targets against VA. The top 10 binding energy docking modules were shown in Fig. 6, all 10 components interacted with corresponding targets mainly through hydrogenbond.

Table 4

The related information of components docked with key targets
GENE	Component	PUBCHEM CLD	Origin	Binding energy
KCNH2	Jujubogenin	15515703	Semen	-4.85
KCNH2	acacic acid lactone	6712546	Silktree Albizia Bark	-4.83
KCNH2	stepharine	98455	Jujubae Fructus	-4.52
KCNH2	N-Methylasimilobine	197017	Semen	-4.29
KCNH2	Asimilobine	160875	Jujubae Fructus	-4.2
KCNH2	Caaverine	23335	Semen	-4.16
KCNH2	Nuciferine	10146	Jujubae Fructus	-4.07
KCNH2	7-Acetoxy-2-methylisoflavone	268208	Licorice	-4.02
KCNH2	(S)-Coclaurine	160487	Semen, Jujubae Fructus	-3.42
KCNH2	Juzirine	3085285	Semen	-3.02
KCNH2	(2S)-6-(2,4-dihydroxyphenyl)-2-(2-hydroxypropan-2-yl)-4-methoxy-2,3-dihydrofuro[3,2-g]chromen-7-one	637112	Licorice	-2.96
KCNH2	zizyphusine	102063083	Semen	-2.8
KCNH2	Senkyunone	91726743	Chuanxiong Rhizoma	-2.59
KCNH2	25-Hydroxy-3-Epidehydrotumulosic Acid	10368709	Poria Cocos (Schw.)	-2.09
KCNH2	Ethylpentadecanoate	38762	Chuanxiong Rhizoma	-0.61
KCNH2	AP1	21119850	Silktree Albizia Bark	1.45
TNNT2	kanzonols W	15380912	Licorice	-4.62
TNNT2	Glabridin	124052	Licorice	-4.02
TNNT2	Senkyunolide G	5321250	Chuanxiong Rhizoma	-3.23
TNNT2	Odoratin	13965473	Licorice	-3.05
CALM1	DFV	114829	Licorice	-4.21
CALM1	Licochalcone B	5318999	Licorice	-2.66
SCN5A	Senkyunone	91726743	Chuanxiong Rhizoma	-1.59

The results show that Jujubogenin has the highest binding energy, connected with HIS70 and ASP67 of KCNH2 (Fig. 6A), acacic acid lactone connected with ARG62 of KCNH2 (Fig. 6B), kanzonols W connected with ASP88 of TNNT2 (Fig. 6C), stepharine connected with LEU86 of KCNH2 (Fig. 6D), N-Methylasimilobine connected with HIS70 of KCNH2 (Fig. 6E), DFV connected with ARG86 and ARG90 of CALM1 (Fig. 6F), Asimilobine connected with LYS101 of KCNH2 (Fig. 6G), Caaverine connected with VAL36 of KCNH2 (Fig. 6H), Nuciferine connected with GLU95 of KCNH2 (Fig. 6I) and 7-Acetoxy-2-methylisoflavone connected with LYS93 of KCNH2 (Fig. 6J).

Ventricular arrhythmia is a fatal disease, typical drugs may benefit patients, but its side effects such as respiratory diseases, liver and kidney damage as well as bradyarrhythmia can never be ignored. Fortunately, long-time clinical work was told that DFD is an effective herb mixture to against antiarrhythmic. Since its excellent clinical efficacy, we conducted a Real-World Trial to assess the safety and efficacy of DFD for ventricular arrhythmia and the results demonstrate that DFD combined with metoprolol has better efficacy and safety than placebo combined with metoprolol. Besides, we explored the cellular electrophysiological mechanism of DFD, and DFD indeed has anti-arrhythmic effects based on its antioxidant potential, alleviation of Na⁺-K⁺-ATPase and connexin-43, and class I antiarrhythmic properties by suppressing Nav_1.5dose-dependently with an IC₅₀ of 24.0 ± 2.4 mg/mL. In this study, the bioactive components and underlying mechanisms of DFD in the treatment of VA were analyzed systematically.

Through related information collection and primary screening, we identified 28 potential targets of DFD in the treatment of VA. A PPI network was constructed with STRING and Cytoscape 3.8.0, the top 10 degree value genes were selected as hub gene and 3 function modules were divided based on its interactions. Analyzed all potential genes with VarElect, all 10 hub genes are directly related to the treatment of VA, and among these genes, KCNH2, TNNT2, CALM1 as well as SCN5A has the highest value of scores, in other words, these are the 4 genes with the highest correlation with the VA. Recently, KCNH2 could be a hot gene in the study of ventricular arrhythmia, it could mediate the rapidly activating component of the delayed rectifying potassium current in heart. A research suggested that pathogenic variants in KCNH2 encoding may result in Long QT syndrome⁴⁷. Meanwhile, another research based on quantitative analysis of consortium disease cohorts and population controls pointed out among patients with long QT syndrome, the mutation probability of KCNH2 gene is greater than 85%⁴⁸. Besides, another research mentioned the co-expression of CACNA1C and KCNH2 reduces the arrhythmic events⁴⁹. TNNT2 is another hub gene connected to arrhythmias, a genetic analysis suggested that TNNT2 was co-segregated in ventricular arrhythmias and sudden death⁵⁰. Wu launched a study based on zebrafish embryos, the result shows that zebrafish embryos exposed to procymidone are more likely alter transcription levels of TNNT2, and resulted in arrhythmia as well as increased heart rate finally⁵¹. Raffaele Coppini conducted a cohort study of patients with hypertrophic cardiomyopathy(HCM), the outcome indicated that among patients with HCM, most patients have a mutation in gene TNNT2, and these patients are more likely to Suffer from arrhythmias and HCM in the future⁵². SCN5A plays a vitally important role in the cardiac electrical conduction and arrhythmic risk, a study provided a new effective therapy to reduce arrhythmia through downregulating the expression of SCN5A⁵³. Coincidentally, there is a study reported that a combination of quinidine/mexiletine reduces arrhythmia in patients with SCN5A gene mutation⁵⁴. CALM1 is a regulator of voltage-dependent L-type calcium channels, its mutations are related to congenital arrhythmia⁵⁵. Heterozygosity for the CALM1 mutation is causative of an arrhythmia syndrome⁵⁶. Moreover, it can lead cathecolaminergic polymorphic ventricular tachycardia, idiopathic ventricular fibrillation, long QT syndrome, and even Sudden death⁵⁷.

In the further GO and KEGG analysis, the results elucidated that the regulation of systemic arterial blood pressure by norepinephrone-epinephine, muslecontrction, blood circulation, circulatory system process, adrenergicreceptor activity, calcium signaling pathway, adenylate signaling in cardiomyocytes, cGMP-PKG signaling pathway and Neuroactive ligand-receptor interaction could be the most possible ways DFD works. Here, the regulation of calcium signaling pathway is affected by more hub genes than other pathways. According to a study based on the genomic, transcriptomic, and proteomic data initiated by Dan E Arking, calcium signaling pathway plays an important role both in the depolarization and repolarization of myocardial, particularly in the repolarization, during the plateau phase of the cardiac action potential, prolonged inward Ca²⁺ current leads to delays in ventricular myocyte repolarization⁵⁸. Earlier research mentioned that Ca²⁺ waves can result when the Ca²⁺ ion influx into the cell is increased, and Ca²⁺ waves can generate depolarizations that trigger arrhythmias, It is reasonable to speculate that treatments related to calcium signaling pathways may be effective for arrhythmia⁵⁹. Reports also suggested that the adrenergic signaling can increase the transmural difference between Ca²⁺ ion transients duration and action potential duration, finally, promoting the formation of delayed afterdepolarizations, the regulation of adenylate cyclase-activating adrenergic receptor signaling pathway and adrenergic receptor signaling pathway of DFD for VA may work in this way⁶⁰. Adenylate cyclase-modulating G protein-coupled receptor signaling pathway can result in the regulation of G protein-mediated signaling, which is of great importance for the regulation of heart rate and involved in arrhythmias⁶¹. Besides, as we mentioned above, the potential targets were divided into 3 function modules, as shown in Table 3, it is obvious that the enrich analysis results of module 1 can regulate calcium signaling pathway, heart rate as well as cAMP signaling pathway, and it is to say that module 1 has great potential in the anti-arrhythmia. Module 2 can membrane depolarization during action potential, striated muscle contraction, regulate adrenergic signaling in cardiomyocytes and cation homeostasis, enrichment analysis shows that module 2 also has the possibility of anti-arrhythmia. The enrichment analysis of module 3 may not seem ideal, be careful, and we found 4 genes which connected to VA closest are gathered in function module 3, and it is reasonable to believe module 3 has anti-arrhythmic effects. Furthermore, as is shown in Fig. 5, the mutil-regulation in different aspects may benefit patients suffering from related diseases such as hypertension, cancer, and other diseases.

There are still some limitations in our research, although we tried to find out all components, such as Osdraconis (Fossiliaossiamastodi) (Longgu) and Ostrea Gigas Thunberg (Muli), the shell, mineral drugs, has only several pieces of research, with several components, and excluded for its low possibility and DL, but Osdraconis (Fossiliaossiamastodi) and Ostrea Gigas Thunberg (Muli) played important roles in DFD, according to the theoretical system of traditional Chinese medicine, both Osdraconis (Fossiliaossiamastodi) and Ostrea Gigas Thunberg can tranquilize mind, further studies are needed to confirm the sedative mechanism of Osdraconis (Fossiliaossiamastodi) and Ostrea Gigas Thunberg. Besides, so many components boiled together, researches are needed to determine whether there are some new compounds formed.

As mentioned above, DFD could be employed for VA through mechanisms, including complex interactions between related components and targets, as predicted by network pharmacology and molecular docking. This work confirmed DFD could apply for the treatment of VA and promotes the explain of DFD for VA in the molecular mechanisms, similar results can obtain from previous experiments of cellular electrophysiological mechanism. A systematic analysis in this work can provide a comprehensive consideration for further studies.

DFD: Dingji Fumai decoction

VA: Ventricular arrhythmia

TCM: Traditional Chinese medicine

GA: Gastrointestinal absorption

DL: Drug-likeness

H-C-T-D: herb-compound-target-disease

TCMSP: traditional Chinese medicine systems pharmacology database and analysis platform

ETCM: Encyclopedia of Traditional Chinese Medicine

ADME: absorption, distribution, metabolism, and excretion

PPI: Protein-protein interaction

GO: Gene ontology

KEGG: Kyoto encyclopedia of genes and genomes

KCNH2: Potassium Voltage-Gated Channel Subfamily H Member 2

SCN5A: Sodium Voltage-Gated Channel Alpha Subunit 5

TNNT2: Troponin T2-Cardiac Type

CALM1: Calmodulin 1 ()

HCM: hypertrophic cardiomyopathy

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

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

Competing interests

Not applicable.

Funding Statement

Not applicable.

Authors' contributions

YL and BL conceived, designed, and planned the study. YL and XRW acquired and analyzed the data. BL, WC and ZLZ interpreted the results. YL and BL drafted the manuscript and BL and ZLZ contributed to critical revision of the manuscript. All authors read and approved the final manuscript.

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TableS1.docx
Details of qualified compounds in various herbs.
TableS1.docx
Details of qualified compounds in various herbs.

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Network Pharmacology-based Systematic Analysis of Molecular Mechanisms of Dingji Fumai Decoction for Ventricular Arrhythmia

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Abstract

Figures

1. Introduction

2. Methods

2.1 Chemical structures construction

2.2 Gastrointestinal absorption (GA) and drug-likeness (DL) prediction

2.3 Target prediction and verification

2.4 Biology functional analysis

2.5 Molecular docking

3. Results

3.1 Chemical structures construction

3.2 GA and DL prediction

3.3 Target prediction and verification

3.4 Biology functional analysis

3.5 Molecular docking

4. Discussion

5. Conclusion

Abbreviations

Declarations

Ethics approval and consent to participate

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Availability of data and materials

Competing interests

Funding Statement

Authors' contributions

References

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