Integrative network pharmacology and molecular docking to reveal the mechanism of Jinqiaomai tablet on asthma

doi:10.21203/rs.3.rs-2577528/v1

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Integrative network pharmacology and molecular docking to reveal the mechanism of Jinqiaomai tablet on asthma

https://doi.org/10.21203/rs.3.rs-2577528/v1

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Background: Jinqiaomai tabletis widely used to treat asthma in China. Despite this, it is less certain how Jinqiaomai tablet worked.

Objective: To explore the active components and molecular mechanism of Jinqiaomai tablet treating asthma via network pharmacology and molecular docking.

Methods: Through searching the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), we identified the active components and targets of Jinqiaomai tablet (JQM). Pathogenic genes related to asthma were retrieved from DisGeNET and GeneCards database. In turn, the common target genes between JQM and asthma were obtained. Also, GO and KEGG enrichment analysis were conducted on the online platform PANTHER for the common target genes. By utilizing the STRING database, an interaction network for the JQM-asthma common target protein was established. Based on the Cytohubba package, hub genes were calculated. In addition, molecular docking was conducted to confirm whether the targeted compounds bind to potential therapeutic targets.

Results: In total, 15 active components and 208 targets of Jinqiaomai tablet were identified. 3701 pathogenic genes related to asthma were obtained, including 3697 from GeneCards database and 110 from DisGeNET database accompanying 106 repetitions. Then, 128 common target genes between JQM and asthma were obtained. GO and KEGG enrichment analysis of the common target genes were performed. An interaction network for the JQM-asthma common target protein was established and the top ten hub genes were AKT1, VEGFA, PTGS2, MMP9, CASP3, TNF, IL6, EGF, IL1B and NFKBIA. The affinity of the molecular docking between the targeted compound and the potential therapeutic target was analyzed, and the ligands and the overall pictures of partial results of molecular docking above were showed.

Conclusion: The active components and molecular mechanism of Jinqiaomai tablet treating asthma were revealed via network pharmacology and molecular docking.

molecular mechanism

active component

asthma

network pharmacology

molecular docking

Asthma is one of the most prevalent chronic inflammation diseases in the world, affecting more than 300 million people(1). It is a major health issue and has a high mortality rate(2). Chronic airway inflammation, bronchial hyperreactivity, and episodes of bronchoconstriction typically accompany asthma. These symptoms include recurrent coughing, dyspnea, and wheezing(3). Since we currently understand the pathogenesis of asthma, clinical treatment is mainly focused on anti-inflammatory approaches and airway dilation(4). The presence of co-morbidities, underdiagnosis, and undertreatment of asthma, as well as potential adverse effects of long-term asthma medication, undermine the effective treatment of asthma, resulting in a higher risk of exacerbations(5). There is still a need for further improvement in the current treatment regimen to control and treat asthma because of the rising incidence in the developed world(6). The existing treatment of asthma cannot be satisfied by a single-ingredient medicine due to the prevalence of asthma. Drugs that target multiple targets and can remedy the weaknesses of those currently available are urgently needed to treat asthma(7). Traditional Chinese medicine (TCM) preparations contain a number of active ingredients that exhibit synergistic effects on multiple targets at the same time(8). TCM has currently been widely used in China to treat asthma, whether alone or in combination with conventional asthma medications(9).

Fagopyrum cymosum, known as golden buckwheat (Jinqiaomai) in China, is a perennial herb that belongs to the Fagopyrum genus within the Polygonaceae family(10). Due to its medicinal and nutritional properties, especially flavonoids, it has attracted attention(11). It is the major ingredient of Jinqiaomai tablet. Jinqiaomai tablet are widely used in the treatment of asthma in China. As a result of its complex composition, TCM has attributes such as multitarget, multichannel, coordination, and synergism(12). In the same way, the mechanism and pathway of treating asthma by Jinqiaomai tablet remain unclear.

Network pharmacology has emerged as a powerful tool that can integrate with pharmacology in recent years(13). Systems pharmacology is used in network pharmacology to help researchers understand how drugs are able to do what they do(14). Drugs, components, diseases, and targets are modeled using network pharmacology(15). It may be appropriate to use network pharmacology methods to elucidate Jinqiaomai tablet's complex mechanisms on asthma using the core ideas of network pharmacology(16). Molecular docking is a methodology that studies the behavior of molecules when bound to target proteins(17). It has been demonstrated that network pharmacology and molecular docking are effective approaches to discover the therapeutic bio-targets and action pathways of bioactive compounds(18).

Thus, a pharmacology network and molecular docking approach were combined in this study to investigate the effects of Jinqiaomai tablet on asthma and its molecular mechanism.

Identification of active components and targets of Jinqiaomai tablet

The major ingredient of Jinqiaomai tablet is golden buckwheat (Jinqiaomai). The active components and targets of Jinqiaomai were downloaded from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (http://tcmspw.com/tcmsp.php). The active components with oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 were selected. The selected target proteins of Jinqiaomai were converted to their gene names via UniProt database(https://www.uniprot.org/).

Retrieval of pathogenic genes related to asthma

With “asthma” as the keyword in the search line, asthma related pathogenic genes were obtained from GeneCards database (https://www.genecards.org/) and DisGeNET database ( https://www.disgenet.org/ ). In the GeneCards database, genes with a score ≥ median were selected, and 3697 related pathogenic genes were got. In the DisGeNET database, genes with a Score_gda≥maximum×0.3 were selected, and 110 related pathogenic genes were got. Genes from the two databases were merged, and 106 repetitions were removed. Finally, we got 128 common target genes between JQM and asthma.

Venny intersection diagram

Venny intersection diagram between asthma and Jinqiaomai tablet was drawn using Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/). Gene targets related to asthma using “Asthma” were put into List1 and the related gene targets of Jinqiaomai tablet using “Jinqiaomai” were put into List2.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

GO and KEGG enrichment analysis were conducted on the online platform PANTHER (www.pantherdb.org) for the common target genes. Analyses of GO terms enrichment included biological processes (BP), cellular components (CC) and molecular functions (MF). Bioinformatics tools on the website provided results based on the common target genes between JQM and asthma.

Protein-protein interaction network (PPI) analysis

Proteins encoded by the common target genes between JQM and asthma were put into STRING database ( https://cn.string-db.org/ ). Sample type of "homo sapiens" was selected, and the threshold value was set at a combined score of > 0.4. Interaction data between proteins was downloaded.

Hub genes selection

Interaction data downloaded from STRING database was put into Cytoscape 3.9.1, an excellent application for visualization of protein networks. Cytohubba, a plugin for Cytoscape that screens hub genes from a large number of candidates, calculated the top 10 hub genes.

Molecular docking

Hub genes were input into PDB( http://www.wwpdb.org/ ), then the corresponding protein structures were downloaded. 2D structures of the corresponding genes drugs were downloaded from PubChem( https://pubchem.ncbi.nlm.nih.gov/ ), and the 3D structures of the corresponding genes drugs were generated using the YINFO TECHNOLOGY online tool ( http://www.yinfotek.com/ ). Hub gene related proteins were dehydrated, hydrogenated and ligand separated using PyMOL software. Molecular docking was conducted by AutoDockTools 1.5.7 to complete the docking and analysis, with the target protein of the gene as the receptor and the corresponding drug as the ligand. The affinity of the molecular docking was analyzed. The affinity was divided into three levels. Affinity༞-4kcal/mol means weak binding force, -7kcal/mol༜Affinity≤-4kcal/mol means middle binding force, and Affinity≤-7kcal/mol means strong binding force. PyMOL software was used to adjust the color, deal with ligands and residues, and measure hydrogen bonds(19). Lastly, the ligands and the overall pictures were exported.

Cytoscape image of disease-drug-active components-targets-KEGG pathways

Cytoscape was used to generate the network by modifying its color, shape, and position based on the disease-drug-active components-targets-KEGG pathways.

Active components and targets of Jinqiaomai tablet

In total, 15 active components (Fig. 1) and 208 target proteins of Jinqiaomai tablet were identified. A list of converted genes was provided from the target proteins (Table 1). The network between active components and target genes were established (Fig. 2).

Table 1

Gene list of JQM.
PSMD3	CASP9	gyrB	HSP90B	NR3C2	PPARD	RB1
HTR2A	CAT	TOP1	HMOX1	MAPK1	PPARG	RXRA
MMP2	CTSD	TOP2A	HGF	MAPK14	PPARA	RUNX2
HSPA5	CAV1	TOP2	HK2	PRKACA	PTEN	CHEK1
ACHE	CCL2	DRD1	NKX3-1	PTPN1	PIK3CG	CHEK2
ACACA	CD40LG	DUOX2	HAS2	CHRM1	SERPINE1	PON1
AHSA1	CDC2	MDM2	HIF1A	CHRM2	PARP1	STAT1
ADCY2	CDK2	EGFR	IGHG1	CHRM3	KCNH2	SCN5A
AKR1B1	CDK4	SELE	MCL1	CHRM4	HERC5	SLC6A4
ADRA1A	TP53	ESR1	CHUK	OPRM1	PCOLCE	SLC2A4
ADRA1B	PDE3A	ESR2	INSR	MYC	EGF	MMP3
MAOB	CLDN4	STE	IGF2	MPO	PGR	SOD1
APP	F7	ELK1	IGFBP3	NQO1	PCNA	thrombin
AR	F10	EIF6	ICAM1	POR	PTGES	THBD
BAX	COL1A1	FASN	IFNG	CHRNA7	PTGER3	F3
BCL2	COL3A1	CCND1	IRF1	CHRNA2	PTGS1	PLAT
ALOX5	CRP	CCNB1	IL1A	NCF1	PTGS2	pap1
AHR	CXCL10	GABRA1	IL1B	NFKBIA	ACP3	E2F1
ABCG2	CXCL11	GABRA2	IL10	NOS3	RUNX1T1	E2F2
BIRC4	CXCL2	GABRA3	IL2	NOS2	PRKCA	RELA
BIRC5	CCNA2	GABRA5	IL4	NOS3	PRKCB	TGF-beta1
BCL2L1	CDKN1A	GJA1	IL6	NFE2L2	FOS	PRSS1
ADRB1	CDKN2A	GRIA2	IL8	NCOA1	PIM1	TNF
ADRB2	CYP1A1	GSTM1	MMP1	NCOA2	NPEPPS	DIO1
DPEP1	CYP1A2	GSTM2	NUF2	NR1I2	AKT1	TYR
CALM1	CYP1B1	GSTP1	KLF7	NR1I3	RAF1	PLAU
PKIA	CYP3A4	PYGM	LTA4H	ODC1	RASSF1	VCAM1
CASP3	camC	GSK3B	MGAM	SPP1	RASA1	VEGFA
CASP7	DCAF5	HSF1	MMP9	OLR1	ERBB2	XDH
CASP8	DPP4	HSPB1	MAP2	PRXC1A	ERBB3

Pathogenic genes related to asthma

It was found that 3701 pathogenic gene targets related to asthma were identified, and partial genes were shown (Table 2).

Table 2

Partial gene list of asthma.
A2M	ACKR1	CCK	AEBP1	ALG11	ANO1	ELF3
AAA1	ACO1	CCL1	AEBP2	ALG12	ANO3	ELF5
BIRC5	ACO2	CCL11	AFF4	ALG13	DAB2	ELN
BLK	ACP1	ADAR	CFTR	ALG3	DAD1	ASRT4
BATF	ACP5	KIT	AGER	ALG6	DAG1	ASRT6
BAX	ACRV1	KITLG	AGFG1	ALG9	DAP3	ASRT8
BAZ2A	ACSL3	KL	AGL	ALK	DDO	ATAT1
BBIP1	ACTA1	KLB	CHIA	CYBA	DDR1	ATF3
CALCA	ACTA2	ADCY9	CHIT1	CYBB	DDX1	ATF6
CALR	ACTL9	CAV1	CHML	CST1	AOAH	ATF6B
CAMK4	ACTN1	CBL	CHN2	CST3	AOC1	ATG3
CAMP	ACTN3	CBX2	CHP1	CSTA	AP1S3	ATG4B
ABCC1	ACVR1	CBX4	AGTR2	CTCF	AP5B1	ATG5
ABCC4	CASR	CCL5	AHI1	CTSK	APC	ATG7
ABCC8	CAT	CCL7	AHR	CTSL	DGKA	EGF
ABCF1	ADA	CCL8	AICDA	ALPP	DGKG	EGFL8
ABCG2	ADAD1	CCN2	AIF1	ALRH	DGKH	EGFR
BSG	CHUK	CCN4	AIM2	AMBN	APOE	EGR1
BST1	CIAO3	ADH5	AIMP1	AMBP	APOF	EGR2
BST2	CIITA	CR1	CFD	AMFR	APOH	EHF
BTD	CIRBP	ADM	CFH	CTR9	APOM	ATOD9
BTG4	CISH	ADO	CFHR2	CTRL	AQP1	EAPP
BTK	CNR1	CPM	CFL1	CTSB	AQP4	EBI3
ACAA1	CNTLN	CPN1	CFL2	CTSD	AQP5	ECE1
ACADS	CNTN2	CPO	AKT1	CTSE	AR	ECM1
ACAN	CNTRL	CPQ	ALAD	ANK1	AREG	ATP4A
ACBD4	COG6	CPS1	ALB	ANK2	DKK1	DPEP2
ACE	CP	CREG1	CRNN	CTF1	DLC1	DPP10
ACE2	CPA3	CREM	CRP	CTH	ARG1	DPP4
ACHE	CPB2	CRH	ALG1	CTLA4	ARG2	DPP6

Common target genes between JQM and asthma

A total of 128 gene targets intersected in Venny when 3701 pathogenic genes related to asthma were crossed with 208 genes associated with Jinqiaomai tablet. (Fig. 3; Table 3).

Table 3

Co-expressed genes between asthma and JQM.
ABCG2	CAV1	CXCL2	GSTP1	IRF1	NQO1	PTGS1
ACHE	CCL2	CYP1A1	HAS2	LTA4H	NR1I2	PTGS2
ADCY2	CCNB1	CYP1A2	HERC5	MAPK1	NR3C2	RAF1
ADRA1B	CCND1	CYP1B1	HIF1A	MAPK14	ODC1	RB1
ADRB1	CD40LG	CYP3A4	HMOX1	MDM2	OPRM1	RELA
ADRB2	CDK2	DCAF5	HSPB1	MGAM	PARP1	RUNX2
AHR	CDKN1A	DPP4	HTR2A	MMP1	PCNA	SELE
AKR1B1	CDKN2A	EGF	ICAM1	MMP2	PIK3CG	SERPINE1
AKT1	CHEK1	EGFR	IFNG	MMP3	PLAT	SLC6A4
ALOX5	CHRM1	ERBB2	IGF2	MMP9	PLAU	SOD1
AR	CHRM2	ERBB3	IGFBP3	MPO	PON1	SPP1
BAX	CHRM3	ESR1	IGHG1	MYC	PPARA	STAT1
BCL2	CHRNA7	ESR2	IL10	NCOA1	PPARG	THBD
BCL2L1	CHUK	F10	IL1A	NCOA2	PRKCA	TNF
BIRC5	COL1A1	F3	IL1B	NFE2L2	PSMD3	TP53
CASP3	CRP	FOS	IL2	NFKBIA	PTEN	VCAM1
CASP8	CTSD	GJA1	IL4	NOS2	PTGER3	VEGFA
CASP9	CXCL10	GSTM1	IL6	NOS3	PTGES	XDH
CAT	CXCL11

GO and KEGG pathway enrichment analysis

GO and KEGG enrichment analysis of the common target genes were performed. (Fig. 4) The most important biological process was cellular process. The most important cellular component was cellular anatomical entity. The most important molecular function was binding. The KEGG pathways were primarily enriched in CCKR signaling map, inflammation mediated by chemokine and cytokine signaling pathway, apoptosis signaling pathway, and angiogenesis.

PPI Network Analysis

The protein-protein interaction network of the common targets between JQM and asthma was identified via STRING (Fig. 5).

Hub genes screening

Hub genes were calculated by Cytohubba, and we selected genes with the 10 highest degree nodes by using the degree algorithm (Fig. 6). The top ten hub genes were AKT1, VEGFA, PTGS2, MMP9, CASP3, TNF, IL6, EGF, IL1B and NFKBIA.

Results of molecular docking

Molecular docking between hub gene proteins and related active components of Jinqiaomai tablet was performed. The affinity of the molecular docking was analyzed. AKT1, PTGS2, MMP9, CASP3, TNF, IL6, EGF, IL1B and NFKBIA had strong binding force with the related active components of Jinqiaomai tablet. VEGFA had middle binding force with the related active components of Jinqiaomai tablet. Active components, such as quercetin and luteolin had the highest frequency with hub gene proteins. IL6 had the strongest binding force with luteolin. (Table 4) Color was adjusted, residues and ligands were handled, and hydrogen bonds were measured with PyMOL software. The ligands and the overall pictures of partial results of molecular docking above were showed. (Fig. 7)

Table 4

Molecular docking between hub gene proteins and related active components of JQM.
Gene name	Mol ID	Molecule name	Affinity(kcal/mol)
AKT1	MOL000006	luteolin	-7.8
AKT1	MOL000098	quercetin	-7.8
VEGFA	MOL000006	luteolin	-6.8
VEGFA	MOL000098	quercetin	-6.3
PTGS2	MOL000004	Procyanidin B1	-8.1
PTGS2	MOL000006	luteolin	-8.1
PTGS2	MOL000073	ent-Epicatechin	-7.3
PTGS2	MOL000096	(-)-catechin	-7.2
PTGS2	MOL000098	quercetin	-7.9
PTGS2	MOL000354	isorhamnetin	-8
PTGS2	MOL000358	beta-sitosterol	-5
PTGS2	MOL000492	(+)-catechin	-7.1
PTGS2	MOL000569	digallate	-7.7
PTGS2	MOL000631	coumaroyltyramine	-8
PTGS2	MOL002914	Eriodyctiol (flavanone)	-8.5
PTGS2	MOL007280	3-methylquercetin	-8
MMP9	MOL000006	luteolin	-9.2
MMP9	MOL000098	quercetin	-8.9
CASP3	MOL000006	luteolin	-7.8
CASP3	MOL000098	quercetin	-7.7
CASP3	MOL000358	beta-sitosterol	-8.3
TNF	MOL000006	luteolin	-9.3
TNF	MOL000098	quercetin	-9.4
IL6	MOL000006	luteolin	-9.5
IL6	MOL000098	quercetin	-9.2
EGF	MOL000098	quercetin	-9.1
IL1B	MOL000098	quercetin	-7.6
NFKBIA	MOL000006	luteolin	-7.2
NFKBIA	MOL000098	quercetin	-7

Cytoscape image of disease-drug-active components-targets-KEGG pathways

By importing 15 active components, 208 targets, 128 common target genes, and 10 KEGG pathways, we constructed a diagram representing disease, drug, active components, targets, and KEGG pathways (Fig. 8).

In our study, we integrated network pharmacology and molecular docking to reveal the mechanism of Jinqiaomai tablet on asthma. Research on TCM has widely used network pharmacology to predict pharmacological mechanisms so that drug research can be guided(20). Molecular networks have been used to study the interaction between drugs, targets, and diseases in network pharmacology(21). In this study, an investigation of putative active compounds and herb targets was conducted using network pharmacology, and the mechanism of Jinqiaomai tablet against asthma was investigated utilized by network pharmacology. According to a molecular docking study, these active compounds and major targets had good binding interactions.

The pathogenesis of asthma is complex, involving environmental factors and heterogeneous genetic which result in phenotypic heterogeneity among asthmatics(22). Many cytokines are involved in the development of asthma, and these cytokines constitute the cytokine network interactions. It may be possible to modify this cytokine network in order to gain a better understanding of asthma pathogenesis and develop more effective therapeutics(23). This study provided insights into the complex pathogenesis of asthma, starting with functional classification and PPI network analysis of the identified proteins in combination with disease and drug. To our knowledge, it is the first study that incorporates network pharmacology with molecular docking in order to reveal the pharmacological mechanisms of Jinqiaomai tablet against asthma.

In our study, we found 3701 pathogenic genes related to asthma and a total of 15 active components and 208 targets of Jinqiaomai tablet. It was found that the identified proteins were involved in CCKR signaling map, inflammation mediated by chemokine and cytokine signaling pathway, apoptosis signaling pathway, angiogenesis, and so forth in KEGG pathway. This was also consistent with the pathological changes and pathogenesis of asthma that have been found. Airway inflammation, airway hyperresponsiveness and airway remodeling are hallmarks of asthma (24). Airway remodeling involves the degeneration of the epithelium, the formation of subepithelial fibrosis, hypertrophy of mucous cells, proliferation of smooth muscle cells, and angiogenesis(25). Inflammation is usually mediated by cytokines(26). Asthma is mainly driven by cytokines, which are known as inflammation drivers(27). Similarly, apoptosis is involved in the development of asthma. In asthma, T cells and eosinophils produce IFN-gamma and TNF-alpha together to induce apoptosis in bronchial epithelial cells(28).

In our study, we identified AKT1, VEGFA, PTGS2, MMP9, CASP3, TNF, IL6, EGF, IL1B and NFKBIA as potential therapeutic targets in the host system. Many studies had also confirmed the involvement of these targets in the occurrence of asthma. Study found that Akt activation induced hypertrophy without contractile phenotypic maturation in airway smooth muscle. Therefore, Akt1 might be a useful therapeutic target for preventing or perhaps reversing ASM hypertrophy in asthmatic patients(29). VEGFA was identified as promising diagnostic biomarkers of asthma-COPD overlap syndrome with highly specificity(30). VEGFA polymorphisms was associated with asthma treatment outcome in children(31). PTGS2 gene was associated with diisocyanate-induced asthma(32). MMP9 was involved in airway inflammation in cough variant asthma(33). In asthma related airway inflammation, eosinophils were also a source of MMP9 and MMP9 expression correlated with eosinophil counts(34). Increasing level of CASP3 had been reported in bronchial epithelium and lung tissues associated with allergic asthma. CASP3 may be involved in allergic asthma(35). Despite their broad immune effects, TNF and IL-6 can be considered pathogenic in the context of asthma. Combined IL-6/TNF inhibition in severe steroid-resistant asthma was confirmed to have therapeutic potential(36). Similarly, EGF had been shown to play a role in both inflammation and airway remodeling in asthma. It can induce bronchial epithelial cells to drive neutrophil chemotactic and anti-apoptotic activity in asthma(37). Catalpol prevents asthmatic airway remodeling by inhibiting TGF-β1 and EGF(38). Furthermore, genetic studies had found that IL1B TT haplotypes (3962T and − 511T) were associated with atopy and asthma with moderate persistence(39). NFKBIA/IκBα played a central role in transcriptional responses associated with respiratory syncytial virus infection, asthma, and bronchopulmonary dysplasia among children. Airway hyperresponsiveness is associated with immunologically relevant genetic variations in the NFKBIA promoter(40).

In the molecular docking section, we found that the active component luteolin and quercetin had the highest frequency with hub gene proteins. Studies had shown their role in asthma treatment. Luteolin had an anti-allergic effect in a murine model of allergic asthma and rhinitis. In a murine model of allergic asthma and rhinitis, luteolin had an anti-allergic effect(41). Lutein can inhibit allergic asthma autophagy through activation of PI3K/Akt/mTOR signaling and inhibition of Beclin-1-PI3KC3 receptors(42). In experimental mice, luteolin treatment significantly reduced asthmatic features during and after sensitization(43). For quercetin, it can reduce airway hyperreactivity in allergic asthma(44) and can regulate the Th1/Th2 balance in a murine asthma model(45). Quercetin showed anti-inflammatory activity in experimental mice with allergic asthma(46). In addition, it can acutely relax airway smooth muscle and enhance β-agonist-induced relaxation(47). Thus, luteolin and quercetin could be used as either lead molecule for the identification of effective asthma therapies or as means to identify novel asthma targets.

As a result of network pharmacology and molecular docking, we revealed the gene mechanism of Jinqiaomai tablet for treating asthma. Despite these promising findings, more research is needed to uncover the underlying mechanisms.

Acknowledgements

We address a special thank to Yu-qing Zhang in Department of Ultrasound, The Second Hospital of Shandong University.

Ethical Approval

Not applicable.

Competing interests

It is declared that the authors have no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Authors' contributions

Zhen Liu wrote the main manuscript, Shi Qiu prepared the tables, and Wen Liu prepared and the figures. Zhen Liu is the first author, and Wen Liu is the corresponding author.

Funding

Not applicable.

Availability of data and materials

The data used to support the findings of this study are included within the article.

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No competing interests reported.

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Integrative network pharmacology and molecular docking to reveal the mechanism of Jinqiaomai tablet on asthma

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Abstract

Figures

Introduction

Materials And Methods

Identification of active components and targets of Jinqiaomai tablet

Retrieval of pathogenic genes related to asthma

Venny intersection diagram

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses

Protein-protein interaction network (PPI) analysis

Hub genes selection

Molecular docking

Cytoscape image of disease-drug-active components-targets-KEGG pathways

Results

Active components and targets of Jinqiaomai tablet

Pathogenic genes related to asthma

Common target genes between JQM and asthma

GO and KEGG pathway enrichment analysis

PPI Network Analysis

Hub genes screening

Results of molecular docking

Cytoscape image of disease-drug-active components-targets-KEGG pathways

Discussion

Conclusion

Declarations

References

Additional Declarations

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