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Table of Contents
ORIGINAL ARTICLE
Year : 2021  |  Volume : 7  |  Issue : 4  |  Page : 456-466

Network pharmacology-based exploration of the mechanism of guanxinning tablet for the treatment of stable coronary artery disease


Department of Emergency, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China

Date of Submission13-Oct-2020
Date of Acceptance12-Jan-2021
Date of Web Publication13-May-2021

Correspondence Address:
Dr. Yong-Yue Xian
Xiyuan Playground No. 1, Haidian District, Beijing 100091
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/wjtcm.wjtcm_25_21

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  Abstract 


Objective: Network pharmacology was utilized to explore the mechanism of Guanxinning (GXN) tablet for the treatment of stable coronary artery disease (SCAD). Materials and Methods: First, active ingredients and therapeutic targets were predicted by databases and gene chip. Then, we constructed the compound-target (C-T) network and target-disease (T-D) network to screen hub compounds and therapeutic targets based on contribution index (CI), degree, closeness, betweenness, and coreness in the networks. Enrichment analysis was performed on hub therapeutic targets, and finally, the verification of hub ingredients and hub therapeutic targets was performed through molecular docking. Results: With “oral bioavailability ≥30%, druglikeness ≥0.18, and half-life ≥4 h” as screening conditions, 58 active ingredients were obtained. Seven hundred and seventeen compound targets and 636 SCAD targets were retrieved using databases and gene chip, and the intersection of both (139 targets) was defined as therapeutic targets. According to CI, degree, betweenness, closeness, and coreness, 2 hub compounds and 13 hub therapeutic targets were chosen from the C-T network and T-D network, respectively. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that GXN treated SCAD from several aspects including inflammatory reaction, oxidative stress, nutritional metabolism, blood pressure regulation, ventricular remodeling, vascular smooth muscle proliferation, angiogenesis, and platelet aggregation. Tissue enrichment analysis revealed that the therapeutic targets were enriched in multiple organs and tissues. The excellent binding force between the hub compounds and hub therapeutic targets was verified by molecular docking. Conclusions: The treatment of SCAD by GXN has the characteristics of multiple ingredients, multiple targets, and multiple approaches. Consequently, it may theoretically treat SCAD from multiple angles and levels.

Keywords: Gene chip, Guanxinning tablet, molecular docking, network pharmacology, stable coronary artery disease


How to cite this article:
Sheng S, Yang QN, Zhu HN, Xian YY. Network pharmacology-based exploration of the mechanism of guanxinning tablet for the treatment of stable coronary artery disease. World J Tradit Chin Med 2021;7:456-66

How to cite this URL:
Sheng S, Yang QN, Zhu HN, Xian YY. Network pharmacology-based exploration of the mechanism of guanxinning tablet for the treatment of stable coronary artery disease. World J Tradit Chin Med [serial online] 2021 [cited 2021 Nov 29];7:456-66. Available from: https://www.wjtcm.net/text.asp?2021/7/4/456/328766




  Introduction Top


Stable coronary artery disease (SCAD) is defined as a common clinical syndrome of acute myocardial ischemia and hypoxia caused by increased myocardial load on the basis of fixed epicardial coronary artery stenosis, which includes stable angina pectoris, ischemic cardiomyopathy, and stable stage after acute coronary syndrome.[1] The conventional application of beta blocker, nitric acid ester, calcium antagonist, antiplatelet drug, lipid lowering drug, angiotensin converting enzyme inhibitors, angiotensin Ⅱ receptor antagonist blocker can alleviate symptoms of myocardial ischemia and reduce the occurrence of adverse cardiovascular events such as myocardial infarction and death. However, among the patients receiving conventional Western medicine treatment, some still experienced acute coronary events or even died,[2] suggesting that SCAD is probably a complex pathological process in which multiple factors are involved and regulated.

Guided by the holistic view, traditional Chinese medicine (TCM) can prevent and control SCAD from multiple perspectives and levels and improve clinical efficacy theoretically. TCM believes that angina pectoris and coronary heart disease belong to “chest bi-syndrome,” “heartache,” and “true heart pain” and the main etiology and pathogenesis are blood stasis and heart vessel blockage stasis.[3] Guanxinning (GXN) tablet is composed of Radix Salviae (Danshen, DS) and Chuanxiong Rhizoma (Chuanxiong, CS), which aims to promote blood circulation, removing blood stasis, and nourishing the heart. Previous studies have revealed that GXN has an accurate anti-effect on SCAD, including dilating blood vessels, increasing coronary flow, preventing platelet aggregation, protecting vascular endothelial cells, resisting oxidation, and inhibiting inflammatory response through laboratory approach[4],[5],[6],[7],[8] as well as a significant clinical effect in the treatment for SCAD.[9],[10],[11] However, due to the complexity and diversity of Chinese herbal medical ingredients and its multi-component, multi-target, multi-link, and multi-pathway pharmacological effects, it is difficult to scientifically explain the material basis of the therapeutic effects of GXN on SCAD by conventional research method, which is characterized by single component and single target.[12] By contrast, network pharmacology is an emerging discipline based on the system biology theory and biological system network analysis which focus on the mechanism of interactions between drugs and human body from an overall perspective. Network pharmacology has been widely accepted for the screening of active ingredients of TCM, target prediction, and mechanism research because of its advantage on integrity and systematicness.[13] Therefore, in the present study, we explored the mechanism of GXN in the treatment for SCAD from an overall perspective through network pharmacology in order to provide a theoretical basis for the systematic and in-depth study of GXN. The flowchart for the study is presented in [Figure 1].
Figure 1: The flowchart for Guanxinning in the treatment of stable coronary artery disease based on network pharmacology

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  Materials and Methods Top


Screening active ingredients of Guanxinning

TCMSP (https://tcmspw. com/tcmsp.php) is a research platform for network pharmacology of TCM, which includes 499 Chinese herbs and 29384 compounds. In addition, it provides pharmacokinetic information such as druglikeness (DL), oral bioavailability (OB), and half-life period (HL) for each compound. Therefore, it can be used to further screen the compounds that have good medicinal property and pharmacokinetic for research.[14] In the present study, the chemical ingredients of DS and CX were obtained from TCMSP, and the eligible active compounds were further screened with the criteria of OB ≥30%, DL ≥0.18, and HL ≥4 h (up to June 29, 2020).

Target acquisition of active ingredients

In order to obtain comprehensively possible potential targets of active ingredients, our study adopted several approaches and methods to predict targets, such as pharmacophore matching, ingredient-target interaction, and structural similarity. First of all, Mol2 files of active ingredients were downloaded from TCMSP and later submitted to PharmMapper (http://www.lilab-ecust.cn/pharmmapper/, v2017).[15] “Maximum Generated Conformations” and “Select Targets Set” were set as “300” and “Druggable Pharmacophore Models (v2017, 16159),” respectively. Only the targets with “Normalized Fit Score ≥0.9” were retained. Second, InChI chemical formula of each active compound downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov/) was successively submitted to BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/)[16] and “Score cutoff” was set as “20.” Third, we submitted SMILES chemical formula of each active ingredient downloaded from PubChem to SwissTargetPrediction (http://www.swisstargetprediction.ch/, v2019)[17] and DrugBank (https://www.drugbank.ca/,v5.1.4),[18] respectively. Then, “Choose an organism” option in SwissTargetPrediction and “Similarity threshold” option in DrugBank were set as “Homo sapiens” and “0.90,” respectively (up to June 29, 2020).

Acquisition of stable coronary artery disease targets

The SCAD targets were obtained from databases and Affymetrix gene chip in GEO (https://www.ncbi.nlm.nih.gov/). We used “Stable Coronary Artery Disease,” “Stable Coronary Arteriosclerosis,” “Stable Coronary Arterioscleroses,” “Stable Coronary Atherosclerosis,” “Ischemic Cardiomyopathy,” “Stable Angina Pectori,” “Stable Angina Pectoris,” “Chronic Stable Anginas,” “Chronic Stable Angina,” “Stable Angina,” and “Stable Anginas” as keywords to mine seven databases including NCBI Gene (https://www.ncbi.nlm.nih.gov/gene/),[19] GeneCards (https://www.genecards.org/, v4.10),[20] DisGeNET (http://www.disgenet.org/, v6.0),[21] DrugBank,[18] TTD (http://db.idrblab.net/ttd/, v2020),[22] PharmGKB (https://www.pharmgkb.org/),[23] and GAD (https://geneticassociationdb.nih.gov/)[24] for SCAD targets. Moreover, the species were limited to “H. sapiens.” Besides, we also mined disease targets from gene chip.[25] GSE98583 is an RNA gene expression dataset of peripheral venous blood from North Indian population provided by Sanjay Gandhi Graduate School of Medicine. The dataset included 12 blood samples of SCAD patients without diabetes and 6 healthy controls. The median age of 18 participants was 52.0 ± 7.4, and all performed coronary angiography for the diagnosis of SCAD. First, we converted gene-probe IDs into gene symbols using GPL570 platform annotation file and Perl v5.32.0 (https://www.perl.org/) scripts. If a gene corresponded to multiple probes, scripts averaged the amount of gene expression of multiple probes. Perl scripts are listed in the Supplementary Materials. Second, we used impute package 1.62.0 in R 4.0.0 (https://www.r-project.org/) to impute missing data based on K-nearest neighbor algorithm.[26] Third, limma package v3.44.3 in R was utilized for standardization and screening differential genes by empirical Bayesian model.[27] The threshold of differential genes was set as P < 0.05 and log2 fold change ha. R scripts are listed in the Supplementary Materials. In order to ensure the accuracy of SCAD target prediction, SCAD targets from the above were further overlapped and merely targets that belonged to at least two sources were retained. Eventually, the compound targets of GXN were intersected with SCAD targets again to obtain therapeutic targets of GXN for SCAD namely therapeutic targets (up to June 29, 2020).

Network construction

Cytoscape 3.2.1 (www.cytoscape.org/) is an open-source software used for network visualization, analysis, and editing. A simple network diagram includes nodes and edges. The nodes in the network can be drug compounds, proteins, and genes, while the edges represent interactions between nodes.[28]

The compound-target network construction and topology analysis

The active compounds, therapeutic targets, and their interactions of GXN were introduced into Cytoscape to construct the compound-target (C-T) network. According to a previous study, we introduced the contribution index (CI) to evaluate the contribution degree of each active compound to the C-T network. Considering both network-based efficacy and literature number in PubMed, if the cumulative sum of the top N active compounds was over 85%, these N active compounds were identified as hub compounds that made the biggest contribution to the C-T network.[29] The calculation formula of CI is listed as follows:





In formula (1), n is the number of targets connected to ingredient j and d (i) is the degree of target i connected to ingredient j. In formula (2), m is the number of compounds in the C-T network and c (j) is the number of PubMed literatures related to ingredient j and SCAD. We searched abstract/title section of literature from 1990 to June 29, 2020, in PubMed and used “Stable Coronary Artery Disease,” “Stable Coronary Arteriosclerosis,” “Stable Coronary Arterioscleroses,” “Stable Coronary Atherosclerosis,” “Ischemic Cardiomyopathy,” “Stable Angina Pectori,” “Stable Angina Pectoris,” “Chronic Stable Anginas,” “Chronic Stable Angina,” “Stable Angina,” and “Stable Anginas” as well as common name and synonym of active ingredients as index terms. Take sitosterol, for example, the retrieval strategy is listed as follows: (((((((((((Stable Coronary Artery Disease[Title/Abstract]) OR (Stable Coronary Arteriosclerosis[Title/Abstract])) OR (Stable Coronary Arterioscleroses[Title/Abstract])) OR (Stable Coronary Atherosclerosis[Title/Abstract])) OR (Ischemic Cardiomyopathy[Title/Abstract])) OR (Stable Angina Pectori[Title/Abstract])) OR (Stable Angina Pectoris [Title/Abstract])) OR (Chronic Stable Anginas[Title/Abstract])) OR (Chronic Stable Angina[Title/Abstract])) OR (Stable Angina[Title/Abstract])) OR (Stable Anginas[Title/Abstract]) AND (review[Filter])) AND (((((((((((sitosterol[Title/Abstract]) OR (alexandrin_qt [Title/Abstract])) OR (beta-sitosterol[Title/Abstract])) OR (beta sitosterolmol[Title/Abstract])) OR (b-sitosterol[Title/Abstract])) OR (phytosterol[Title/Abstract])) OR (stigmasta-5-en-3-ol[Title/Abstract])) OR (β-sitosterol[Title/Abstract])) OR (β-sitosterol-3-O-β-D-glucopyranoside _qt[Title/Abstract])) OR (β-stigmasterol[Title/Abstract])) OR (24-ethylcholesterol[Title/Abstract]) AND (review[Filter])) (up to June 29, 2020).

The compound target-disease target network construction and topology analysis

Therapeutic targets, namely the intersection of the compound targets and SCAD targets, were uploaded to STRING (https://string-db.org/).[30] “Organism” and “minimum required interaction score” were set as “H. sapiens” and “medium confidence (>0.400),” respectively. Then, both therapeutic targets and their interactions were introduced into Cytoscape to construct the target-disease (T-D) network. Based on previous literature, the importance of nodes in the T-D networks was evaluated by degree, betweenness, closeness, and coreness.[31] Degree is the number of nodes directly related to the node. Betweenness refers to the ratio of the number of shortest paths from node a to node b through node i to the number of shortest paths from node a to node b. Closeness represents the reciprocal of the sum of the distances from node i to other nodes. Coreness is the K-shell index (Ks) of the network during k-core decomposition. K-core is defined as the remaining maximum node group that all nodes are connected to at least k nodes in the group after deleting the nodes with degree ≤K − 1. When the nodes with degree ≤K − 1 are deleted, newly generated nodes with degree ≤K − 1 will also be deleted. Through this repeated iterative process, a series of core groups will be successively generated. The specific implementation method is listed as follows: it is assumed that there are no isolated nodes with degree = 0 in the network and the nodes with degree = 1 are the most secondary nodes in the network. Therefore, the nodes with degree = 1 and their adjacent edges are first deleted from the network. After deletion, new nodes with degree = 1 will appear in the network and then these nodes with degree = 1 and their adjacent edges will be deleted. Repeat the above operation until the nodes with degree = 1 no longer appear in the network. At this point, all the deleted nodes form 1 shell and Ks of the node is 1. The degree of each node in the remaining network is at least 2. Continue to repeat the above operation and we get 2 shells with Ks equal to 2. And so on repeat process until all nodes are assigned as Ks. In conclusion, coreness can better describe the importance of a node in terms of network propagation dynamics.[32] In general, the importance of a node in the network is proportional to degree, betweenness, closeness, and coreness. According to a previous study, we first screened the targets whose degree is greater than twofold mean value of degree in the network, and then, on this basis, we selected the nodes whose degree, closeness, betweenness, and coreness are above mean value at the same time.[33]

Enrichment analysis

In the present study, R was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The first step was to convert gene symbols of therapeutic targets into unique EntrezIDs by Org.Hs.eg.db package v3.11.4. If corresponding EntrzID cannot be found, it would be deleted and not included in enrichment analysis. Second, ClusterProfiler package v3.16.0 was used for enrichment analysis and significant enrichment terms were chosen with the criteria of adjusted P < 0.05. Third, we adopted enrichplot package v1.8.1 and ggplot2 package v3.3.2 to draw bar and bubble diagrams to demonstrate the results of analysis. R scripts are listed in the Supplementary Materials. TissueEnrich platform(https://tissue.gdcb.iastate.edu/)was used for tissue enrichment analysis and visualization.[34] We selected “H. sapiens” in “organism” option and significantly enriched tissues were screened with the criteria of adjusted P < 0.05.

Molecular docking

GEMDOCK (http://gemdock.life.nctu.edu.tw/dock/, v2.1) is an open-source docking software with fast speed, high accuracy, easy operation and friendly interface for molecular docking, virtual screening, and analysis.[35] Mol2 structure files of hub compounds and PDB structure files of hub therapeutic targets were downloaded from TCMSP and PDB (https://www.rcsb.org/), respectively. Before docking, PDB structure files required processing operation by Discovery Studio 2016 including deleting hydrone, adding polar hydrogen, and cutting original ligands which were saved as mol2 structure files. In “Docking Accuracy Settings” module of GEMDOCK operation interface, “Population Size,” “Generation,” “Number of Solutions,” and “Default Setting” were set as “200,” “7,” “2,” and “Standard Setting” and all other docking parameters were all set as default.

The binding affinity is determined by binding energy. When the binding energy between hub compounds and hub therapeutic targets is stronger than that between original ligands and hub therapeutic targets, it is considered that the binding affinity between hub compounds and hub therapeutic targets is satisfactory.


  Results Top


Screening active ingredients of Guanxinning

In TCMSP, we obtained 377 compounds of GXN and 58 active ingredients were finally screened with the condition of “OB ≥30%, DL ≥0.18, and HL ≥4 h,” including 6 CX ingredients and 52 DS ingredients. The details are provided in Table S1.

Target acquisition of active ingredients of Guanxinning

After removing the duplicates, we obtained 717 compound targets from PharmMapper, BATMAN-TCM, SwissTargetPrediction, and DrugBank, including 153 CX targets, 596 DS targets, and 32 common targets.

Acquisition of stable coronary artery disease targets

Six hundred and thirty-six SCAD targets were after we overlapped targets from 8 sources including 7 databases and 1 gene chip. Then, SCAD targets and compound targets were overlapped again, and finally, a total of 139 therapeutic targets were obtained. Among 139 therapeutic targets, there were 25 CX therapeutic targets, 122 DS therapeutic targets, and 8 common therapeutic targets, which indicated that GXN had their specific targets that could play a role alone in the treatment for SCAD and also had common targets that could work synergistically to enhance efficacy.

The compound-target network construction and topology analysis

After deleting the isolated nodes and adjacent edges, there were 173 nodes and 447 edges in the C-T network, representing 36 compounds, 137 therapeutic targets, and 447 C T interactions [Figure 2]a. The results of CI revealed that the sum of CI of M433 (folic acid [FA]) and M359 (sitosterol) was 95.1% (over 85%), and consequently, both M433 and M359 were hub compounds that contributed the most to the C-T network [Figure 2]b.
Figure 2: The compound-target network and contribution index. (a) The compound-target network. In the figure, red circles represent CX compounds, yellow circles represent DS compounds, blue squares represent therapeutic targets and edges represent compound-target interactions. (b) The result of contribution index. The sum of contribution index of M433 and M359 is >85% and they are hub compounds in the compound-target network

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The target-disease network construction and topology analysis

After removing the isolated nodes and adjacent edges, the T-D network included 138 nodes and 1588 edges, representing 138 therapeutic targets and 1588 interactions [Figure 3]a. First, we screened the targets whose degree was greater than twofold the mean value (degree >23.014) in the T-D network and obtained the hub T-D network 1 including 53 nodes and 750 edges [Figure 3]b. Second, 13 hub therapeutic targets with degree, closeness, betweenness, and coreness above average (degree >40.925, closeness >0.576, betweenness >0.017, and coreness >14.519) were collected from the hub T-D network 1 [Table S2]. These 13 therapeutic targets are Albumin (ALB), Insulin (INS), interleukin (IL)-6, Protein kinase B alpha (AKT1), Prostaglandin endoperoxide synthase-2 (PTGS2), tumor necrosis factor (TNF), G0/G1 switch regulatory protein 7 (FOS), epidermal growth factor receptor (EGFR), IL1B, Renin (REN), Leptin (LEP), peroxisome proliferator-activated receptor gamma (PPARG), and MAPK8, all of which were used to construct the hub T-D network 2 including 13 nodes and 78 edges [Figure 3]c. Third, we extracted 13 hub therapeutic targets with their adjacent nodes in the original T-D network and constructed the hub T-D network 3 in which there were 125 nodes and 1517 edges [Figure 3]d. It indicated that 13 hub therapeutic targets had interactions with 81.2% ((125-13)/138) therapeutic targets in the original T-D network and they were cores in the network. The fact also demonstrated that the original T-D network was a scale-free network which obeyed power-law distribution. In other words, a few hub nodes in the network had many connections, while most of nodes only had a few connections and a few hub nodes played a major role in the stability of the network.[32] Therefore, these 13 hub therapeutic targets could be considered as potential target genes in the future research of GXN for SCAD.
Figure 3: The original target-disease network and 3 hub target-disease networks. (a) The original target-disease network. (b) The hub target-disease network 1(degree >23.014). (c) The hub target-disease network 2 (degree >40.925, closeness >0.576, betweenness >0.017and coreness 14.519). (d) The hub target-disease network 3 (13 hub therapeutic targets and their adjacent nodes extracted from the original target-disease network). In the figure, circles represent therapeutic targets and edges represent interactions. Red circles represent the nodes with high degree, yellow circles represent the nodes with medium degree and green circles represent the nodes with low degree. The size of the circles and font size of the labels are proportional to the degree value of nodes

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Enrichment analysis

GO and KEGG enrichment analysis was performed on 139 therapeutic targets of GXN for SCAD, and 2674 GO terms were selected with the criteria of adjusted P < 0.05. Among them, there are 2489 biological process terms, mainly including regulation of inflammatory response, regulation of blood pressure, reactive oxygen species metabolic process, response to lipopolysaccharide, response to molecule of bacteria origin, regulation of lipid metabolic process, responds to nutrient levels, vascular process in circulatory system, regulation of tube diameter, and regulation of blood vessel diameter; 122 molecular function terms, mainly including steroid binding, steroid hormone activity receptor, receptor ligand activity, signaling receptor activator activity, nuclear receptor activity, ligand-activated transcription factor activity, cytokine activity, organic acid binding, peptide binding, and amide binding; and 63 cellular component terms, mainly including endoplasmic reticulum lumen, collagen-containing extracellular matrix, membrane raft, membrane microdomain, membrane region, Golgi lumen, apical part of cell, blood microparticle, apical plasma membrane, and vesicle lumen [Figure 4]a. Based on adjusted P < 0.05, a total of 115 enriched pathways were obtained. SCAD-related pathways identified by literature in the top 30 enriched pathways include fluid shear stress and atherosclerosis (AS), TNF signaling pathway, IL-17 signaling pathway, endocrine resistance, Th17 cell differentiation, insulin resistance, relaxin signaling pathway, FoxO signaling pathway, adipocytokine signaling pathway, Toll-like receptor signaling pathway, HIF-1 receptor signaling pathway, renin–angiotensin system, regulation of lipolysis in adipocyte, and fat digestion and absorption [Figure 4]b.
Figure 4: The result of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. (a) The result of the top 10 significantly enriched biological process, molecular function, and cellular component terms. In the figure the length of bars represents the number of enriched genes in the terms and the color of bars represents the adjusted P value of the enriched terms. (b) The top 30 significantly enriched Kyoto Encyclopedia of Genes and Genomes pathways. The size of the circle is proportional to the number of enriched genes in pathway. The horizontal axis represents the ratio of the number of enriched genes to the number of all genes in pathway. The color of the circle represents the adjusted P value of pathway

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Most of the remaining top 30 enriched pathways were classified as human disease pathways, suggesting that these diseases might be potentially related to SCAD. For example, the presence of rheumatoid arthritis is associated with an increased risk of SCAD, and they may share many pathological mechanisms, such as inflammatory cells and inflammatory responses.[36] The analysis of KEGG pathway showed that the therapeutic targets of active compounds of GXN for the treatment of SCAD enriched in different pathways and mutual regulation between multi-component and multi-target might be the mechanisms of anti-SCAD. Tissue enrichment analysis revealed that 139 therapeutic targets were enriched in multiple tissues and organs. It indicated that the treatment for SCAD by GXN was the result of comprehensive regulation of multiple tissues and organs throughout the human body, among which liver, small intestine, duodenum, lung, bladder, and other tissues and organs played an important role in the treatment for SCAD by GXN [Figure 5].
Figure 5: The results of tissue enrichment analysis. The horizontal axis represents enriched tissues and organs, and the vertical axis represents the-Log10 (adjusted P) value of the enriched tissues and organs

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Molecular docking

Two hub compounds and 12 processed hub therapeutic targets (IL-6 was not included because the protein structure of IL-6 with the corresponding ligand molecule was not found in PDB) were verified by molecular docking. The structures of them are presented in [Figure 6]a. The results of docking showed that the binding force between M433 (FA), M359 (sitosterol) and each hub therapeutic target was superior to that between original ligand and 12 targets [Figure 6]b, which indicated that 2 hub compounds had stronger binding force with hub therapeutic targets, indirectly proving the accuracy of prediction.
Figure 6: The structures of hub compounds and hub therapeutic targets and the results of molecular docking. (a) The molecular structures of hub compounds and hub therapeutic targets. (b) The heatmap of docking energy. The color is related to docking energy value

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  Discussion Top


SCAD is one of the chronic diseases that have received considerable attention in recent years. Recent studies have revealed that SCAD was not a single pathological process and it included AS-associated vascular stenosis, microvascular dysfunction, and focal or diffuse coronary spasm. Therefore, it is difficult to control the development of SCAD by a specific target or pathway and achieve satisfactory clinical efficacy by a single compound. Chinese herbal medicine compound preparation (GXN) consists of a variety of natural medicines which contain various biologically active compounds and have the characteristic of multiple components, multiple targets, and multiple pathways. Theoretically, it is an ideal treatment for chronic diseases regulated by multiple pathological mechanisms such as SCAD.

Through network pharmacology, gene chip, molecular docking, and other bioinformatics technologies, the present study explored the active ingredients, potential molecular mechanism, and pathways of GXN for the treatment of SCAD from multiple perspectives. The C-T network and the result of CI revealed that FA and sitosterol contributed the most to the network, indicating their significant role and extremely important research value in the treatment for SCAD. Previous studies have confirmed that hyperhomocysteinemia (HHcy) led to vascular endothelial damage as well as promoted oxidation of low-density lipoprotein, platelet aggregation, and vascular smooth muscle cell proliferation to accelerate the occurrence of AS.[37] Compared with healthy people, increased homocysteine level and decreased folic acid level were found in blood of SCAD patients and that taking folic acid could significantly improve HHcy.[38],[39] Sitosterol is a phytosterol with a cholesterol-like chemical structure that competitively inhibited cholesterol absorption, thereby lowering serum cholesterol and preventing AS.[40],[41] The above results of hub compounds provide us with new clues for drug research and development of monomer of Chinese traditional herbs in the treatment for SCAD. The analysis of the T-D network indicated that 13 hub therapeutic targets, namely ALB, INS, IL6, AKT1, PTGS2, TNF, FOS, EGFR, IL1B, REN, LEP, PPARG, and MAPK8, were particularly important in the treatment for SCAD by GXN. ALB is the main protein in plasma whose main functions include plasma osmotic pressure regulation and substance transportation. ALB also plays a role in extracellular antioxidation, inhibition of vascular endothelial cell apoptosis, antiplatelet activation, and antiplatelet aggregation.[42] The main physiological function of INS is to promote glycolipid synthesis and reduce blood glucose, and it is an independent risk factor for coronary artery disease. In addition, INS markedly inhibits intercellular cell adhesion molecule (ICAM), monocyte chemoattractant protein-1, and nuclear factor-kappa B, showing anti-inflammatory effects. During the formation and development of AS, a variety of inflammatory cells and factors are involved from early inflammatory response, lipid deposition, plaque maturation to plaque rupture. IL6 is a major pro-inflammatory cytokine in acute phase and elevated level of IL6 is associated with future cardiac events and mortality in SCAD patients.[43] TNF is a potent pro-inflammatory factor, and a long-term increase in TNF may be a trigger for SCAD advanced to acute coronary syndrome.[44] IL1B is a classic inflammatory cytokine that can decrease the incidence of major adverse cardiovascular events in patients with SCAD complicated with chronic kidney disease after myocardial infarction through IL1B inhibitors.[45] Excessive apoptosis of vascular smooth muscle cells in AS plaque plays a major role in promoting the transition from stable plaque to unstable plaque. Activated Akt1 inhibits apoptosis by regulating CASP9, Bax, and Bcl-2.[46] FOS expression is associated with apoptosis, and FOS expression in SCAD plaques is significantly lower than that in non-ST-segment elevation acute coronary syndrome plaques.[47] PTGS2 can promote the synthesis and release of NO and PGl2 in endothelial cells to expand blood vessels, inhibit platelet aggregation and leukocyte adhesion, and prevent thrombosis.[48] EGFR inhibitor causes T-cell inactivation and reduces inflammatory factor production (LI-2, IL-4, and interferon-γ) and T-cell infiltration of AS plaque in Ldlr−/− mouse. Consequently, EGFR may be a potential therapeutic target for AS and SCAD.[49] REN is a proteolytic enzyme released by juxtaglomerular cell, which can further produce Ang II under the action of REN to promote vasoconstriction, inflammatory cell adhesion, and myocardial fibrosis. Meta-analysis revealed that RAS inhibitor can reduce cardiovascular events and death in SCAD patients without heart failure compared with placebo.[50] LEP is an adipocytokine that can induce oxidative stress and promote angiogenesis. Serum LEP in SCAD patients was significantly higher than that in normal population, and serum LEP level was positively correlated with the severity of coronary artery disease in SCAD patients.[51],[52] PPARG, a ligand-activated nuclear transcription factor, can regulate metabolism, inflammation, immunity and cell differentiation, and so on. It has cardiac protective effects such as improving glucose and lipid metabolism, weakening oxidative stress, and inhibiting inflammatory response.[53] MAPK8 (JNK1) is a serine/threonine kinase, which is associated with cell proliferation, apoptosis, and other physiological and pathological processes. JNK1 gene knockout can inhibit the apoptosis of macrophages, increase cell survival, and accelerate the occurrence and development of early AS in JNK1 (−/−) mouse.[54]

GO and KEGG enrichment analysis showed that GXN could intervene in regulation of inflammatory response, regulation of blood pressure, reactive oxygen species metabolic process, response to lipopolysaccharide, response to molecule of bacteria origin, regulation of lipid metabolic process, responds to nutrient levels, vascular process in circulatory system, regulation of tube diameter and regulation of blood vessel diameter to regulate fluid shear stress and AS, TNF signaling pathway, IL-17 signaling pathway, endocrine resistance, Th17 cell differentiation, insulin resistance, relaxin signaling pathway, FoxO signaling pathway, adipocytokine signaling pathway, Toll-like receptor signaling pathway, HIF-1 receptor signaling pathway, renin–angiotensin system, regulation of lipolysis in adipocyte, and fat digestion and absorption. In other words, GXN prevents and cures SCAD, delays or reverses the transition from stable AS plaque to unstable plaque, and reduces the occurrence of acute events from the perspectives of inflammatory reaction, oxidative stress, nutritional metabolism, blood pressure regulation, ventricular remodeling, vascular smooth muscle proliferation, angiogenesis, and platelet aggregation. Tissue enrichment analysis indicated that the treatment for SCAD by GXN was the result of joint regulation of multiple tissues and organs of the whole body, which was consistent with the concept of organic wholeness in TCM, further supporting the results of GO and KEGG enrichment analysis, that is, GXN preventing and curing SCAD from multiple angles and levels. The results of molecular docking showed that FA and sitosterol had good binding force with 12 hub therapeutic targets, which further indirectly proved the accuracy of prediction by network pharmacology.

However, it should be noted that the research methods of network pharmacology still have some limitations. First of all, different dosages and processing methods of Chinese herbs and individual differences have a great influence on the efficacy of herbs, but they are always ignored in network pharmacology research. Second, compared to previous similar researches, our study includes Chinese herbal medicinal ingredients, ingredient targets, and disease target-related database as many as possible. However, due to the problems such as incomplete information collection, delayed or suspended update in databases, server crash, and failed user log-in, therapeutic targets of GXN for SCAD were not collected completely, thus affecting the results of subsequent analysis. Third, previous studies on some herbs, compounds, biological functions, and pathways have been relatively sufficient, which may lead to research bias to some extent and affect the results of network pharmacology prediction. Therefore, further experiments are urgently needed to verify the predicted results of network pharmacology.


  Conclusion Top


The treatment of SCAD by GXN has the characteristics of multiple ingredients, multiple targets, and multiple approaches. Consequently, it may theoretically treat SCAD from multiple angles and levels.

Acknowledgment

Sheng S completed the whole study and approved the final manuscript. The data used to support the findings of this study are included within the supplementary information files. This paper is supported by the National Natural Science Foundation (No. 81573803).

Financial support and sponsorship

This study was financially supported by the National Natural Science Foundation (No. 81573803), China.

Conflicts of interest

There are no conflicts of interest.



 
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