network pharmacology-based approach to investigate the...

Research ArticleNetwork Pharmacology-Based Approach to Investigate theMechanisms of Mahai Capsules in the Treatment ofCardiovascular Diseases

Minjuan Shi,1 Bo Li,1 Qiuzhen Yuan,1 Xuefeng Gan,1 Xiao Ren,1 Shanshan Jiang,1

and Zhuo Liu 2

1Department of Pharmacy, Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi’an 710000, Shaanxi, China2Department of Pharmacy, Xi’an Children’s Hospital, Xi’an 710000, Shaanxi, China

Correspondence should be addressed to Zhuo Liu; [email protected]

Received 5 September 2019; Accepted 16 April 2020; Published 13 May 2020

Academic Editor: Armando Zarrelli

Copyright © 2020 Minjuan Shi et al. *is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. Mahai capsules (MHC) have been deemed to be an effective herb combination for treatment of cardiovasculardiseases (CVD) development and improvement of the life quality of CVD patients. To systematically explore the mechanisms ofMHC in CVD, a network pharmacology approach mainly comprising target prediction, network construction, biological processand pathway analysis, and related diseases was adopted in this study. Methods. We collected the bioactive compounds andpotential targets of MHC through the TCMSP servers. Candidate targets related to CVD were collected from*erapeutic TargetsDatabase and PharmGkb database and analyzed using ClueGO plugin in Cytoscape. KEGG pathway was enriched and analyzedthrough the EnrichR platform, and protein-protein interaction networks were calculated by STRING platform. *e compound-target, target-disease, and compound-target-disease networks were constructed using Cytoscape. Results. A total of 303 targets ofthe 57 active ingredients in MHC were obtained. *e network analysis showed that PTGS2, PTGS1, HSP90, Scn1a, estrogenreceptor, calmodulin, and thrombin were identified as key targets of MHC in the treatment of CVD. *e functional enrichmentanalysis indicated that MHC probably produced the therapeutic effects against CVD by synergistically regulating many biologicalpathways, such as PI3K-Akt, TNF, HIF-1, FoxO, apoptosis, calcium, T-cell receptor, VEGF, and NF-kappa B signaling pathway.Conclusions. In summary, the analysis of the complete profile of the pharmacological properties, as well as the elucidation oftargets, networks, and pathways, can further illuminate that the underlying mechanisms of MHC in CVD might be stronglyassociated with its synergic regulation of inflammation, apoptosis, and immune function, and provide new clues for its futuredevelopment of therapeutic strategies and basic research.

1. Background

Cardiovascular diseases (CVD) are a class of the degener-ative chronic diseases such as atherosclerosis, heart failure,hypertensive, aneurysms, and thromboembolism [1]. CVDmarkedly impairs the quality of life of patients and has beenthe leading cause for morbidity and mortality. It has beenreported that CVD deprives more than 10 million humanlives each year, and the mortality is projected to be 23.6million in 2030 [2]. *e prevention and treatment of car-diovascular medicine have been dramatically progressed inthe past years. Currently, the major pharmacologic options

for CVD include angiotensin-converting enzyme inhibitors,sodium channel blockers, nitrate esters, and variousthrombolytic agents [3, 4]. However, as a result of thecomplicated pathogenesis involved in CVD, single targetedtherapies may not be sufficient and several certain inevitableside effects still exist. *e medical failures of some patientswith CVD might be due to the incomplete understanding ofthe complex underlying pathophysiology. With the enor-mous development of medical science, researchers graduallyfound that most diseases are usually caused by multipletargets instead of single gene. Hence, multicomponent drugsrepresented by traditional Chinese medicine (TCM), which

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2020, Article ID 9180982, 15 pageshttps://doi.org/10.1155/2020/9180982

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https://doi.org/10.1155/2020/9180982

had been widely used in health maintenance, have drawnincreasing attention in CVD treatment [5]. TCM is a wholemedical system with rich practice experience for thousandsof years and has attracted a lot of attention in recent yearsbecause of valid treatment effects and fewer adverse reac-tions. *e treatment of complex diseases using TCM hasbeen considered as a complexity whole that confronts an-other whole, and it focuses on the state of the whole or-ganisms by regulating all the elements within the body [6].Based on the characteristics of multi-ingredients and mul-titargets feature, TCM treatment has enormous potential intreating chronic complex diseases including CVD.

In China, various TCM have achieved great success inthe prevention and treatment of CVD. Danshen drippingpills and Danhong injection are notable examples that wereconfirmed by clinical trials in their protective effects againstCVD [7–9]. Similarly, Mahai capsules (MHC) have beendeemed to be a crucial strategy for treatment of CVD de-velopment and improvement of the life quality of CVDpatients. MHC is an effective herb combination that consistsof HedysarumMultijugumMaxim (Huangqi, HQ), StrychniSemen (Maqianzi, MQ), Angelicae Sinensis Radix (Danggui,DG), Caulis Piperis Kadsurae (Haifengteng, HFT), Homa-lomena Occulta (Lour.) Schott (Qiannianjian, QN), andRadix Rhei Et Rhizome (Dahuang, DH). *is formula isapplied to inhibit CVD developments such as atheroscle-rosis, stroke, deep vein thrombosis, and cerebral infarction.An increasing number of clinical researches emphasize thepositive effect of HQ in treating CVD, for example, HQ, hasbeen proven to protect cardiovascular disease throughmultiple mechanisms, including anti-inflammatory andlipid lowering effects [10–12]. Several recent clinical studiesshowed that HQ can enhance myocardial contractility andmyocardial cell excitation-contraction coupling and gen-erate significant cardiotonic effect with the treatment ofacute myocardial infarction [13, 14]. Besides, the efficacy andsafety of HQ preparation in control of heart failure fromcardiac dysfunction and metabolic alterations have also beenproven [10]. DG has been widely used to treat blood defi-ciency disease in China, and the ameliorative effect of DGagainst heart injury and myocardial infarction has beenstudied [15, 16]. However, the complex active ingredientsand underlying mechanisms of MHC on CVD have not beenidentified, and it complicates the modernization and clinicalusage of MHC. *us, it is necessary to identify the bioactivesubstances of MHC and understand their synergistic actionsin and the exact effects on multiple targets.

Even though there was considerable benefit with suchmulticomponent drugs, understanding the scientific mate-rial basis and underlying mechanism of TCM herbal for-mulas are still required in the treatment of CVD at themolecular level and from a systematic perspective. Mostherbal medicines containing enormous bioactive com-pounds and lots of related multiple targets also complicatethe pharmacological research, making it difficult to clarifytheir pharmacological mechanism only by traditional ex-perimental approaches. With the rapid development of lifescience and computer science, various virtual screening toolsand bioinformatic database have been developed to explore

the interactions between the complex ingredients and thehuge amount of target genes in TCM [17, 18]. *e networkpharmacology-based approach provides guidance to in-vestigate potential pharmacological actions and clarifycomplex molecular mechanisms of TCM [19]. TraditionalChinese Medicine Systems Pharmacology (TCMSP),PharmMapper, and many databases have been developed topredict the validated and potential targets [20, 21]. Mean-while, there are several bioinformatic software and servers toanalysis the biological and mechanical properties of com-pound targets, such as David, String, EnrichR, andCytoscape.

In this paper, we aim to employ virtual screening da-tabases and integrate bioinformatics analyses to explore therelationships between MHC and targets, which show thenetwork of drug and related targets on whole level. *ismight enable us to investigate the pharmacological mech-anism of how MHC exerts the protective effects on CVD.

2. Methods

2.1. Screening of Active Components and PharmacokineticAbsorption,Distribution,Metabolism, andExcretion (ADME)Evaluation. Candidate compounds for Hedysarum Multi-jugum Maxim (Huangqi, HQ), Strychni Semen (Maqianzi,MQ), Angelicae Sinensis Radix (Danggui, DG), CaulisPiperis Kadsurae (Haifengteng, HF), Homalomena Occulta(Lour.) Schott (Qiannianjian, QN), and Radix Rhei EtRhizome (Dahuang, DH) were retrieved from the TCMSPdatabase (ref.). Identification of ADME (absorption, dis-tribution, metabolism, and excretion) properties by theTCMSP database was employed to screen the compositecompounds. In the current study, OB (prediction of oralbioavailability) and DL (prediction of drug-likeness) identifythe potential bioactive compounds of MHC.*e ingredientssatisfying the criteria of OB ≥30% and DL ≥0.18 are retainedand treated as candidate molecules of MHC for subsequentanalysis.

2.2. Identification of Candidate Compounds and TargetGenes. To gather information on interactions between ac-tive functional compounds in MHC and associated genes,the bioactive compounds and potential targets were collectedthrough the TCMSP servers. Known CVD-related targetswere collected from existing resources, including *erapeuticTargets Database and PharmGkb database, which providecomprehensive information on bioactive compounds andtargets interactions, and relationships among compounds,targets, and diseases. All obtained proteins were subjected toPharmGkb or TTD to detect the relationships betweencandidate molecules, targets, and CVD.

2.3. Network Construction and Analysis. To investigate re-lationship between the compounds inMHC and their targetsin CVD, we construct the network through network visu-alization software in Cytoscape 3.6.1. *is software was usedto integrate data and analysis and visualize complex inter-action networks. In networks, nodes represent compounds

2 Evidence-Based Complementary and Alternative Medicine

or proteins and edges indicate compound-target gene in-teractions. *ree types of networks, for example, com-pounds-targets (C-T), targets-diseases (T-D), andcompounds-targets-diseases (C-T-D) networks, were con-structed and visualized using Cytoscape. For nodes in thecomplex network, three indicators were calculated to revealits features.

2.4.GeneOntology (GO)Analysis. ClueGO, which is a pluginintegrated in Cytoscape, can be employed to conduct GOanalysis to create functionally organized term networks andcomprehensively visualize functionally grouped terms forunderstanding the biological significances [22]. *e p valuewas used to examine the significance of the GO terms en-richment. *e GO terms that have a p value of ≤0.05 wereregarded as significant and interesting.

2.5. Kyoto Encyclopedia of Genes and Genomes (KEGG)Pathway Analysis. Enrichr is an integrative web-basedplatform that includes new gene-set libraries, an alternativeapproach to rank enriched terms, and various interactivevisualization approaches to display enrichment results [23].*e KEGG pathway was enriched and ranked based uponthe combined score which is calculated by the EnrichRplatform. An adjusted p value threshold of 0.05 was used forpathway discovery. In this study, we chose the top ten KEGGterms to explore the related pathways.

2.6. Protein-Protein Interaction (PPI) Networks. String(https://string-db.org/) was employed to construct PPInetworks with the species limited to “homo sapiens” [24].String is a known platform and forecasts the interactions ofproteins, and it defines PPI with confidence ranges for datascores.

3. Results

3.1. 8e Candidate Compounds and Putative Target Proteins.Using in silico prescreening models, the main componentsof MHC with favorable pharmacokinetic characteristicswere determined. From the 444 native MHC compoundscollected from the TCMSP database, 75 candidate com-pounds were screened from the 6 herbs by ADME andprepared for further study as the candidate compounds asshown in Table 1.

We compared the 303 putative target proteins forcommonality and properties, and the results are shown inFigure 1(a).*e distribution of the biochemical classificationindicates that the target space mainly consists of tran-scription factor, receptor, nucleic acid binding, hydrolase,oxidoreductase, transferase, enzyme modulator, transporter,and signaling molecule. Remarkably, the obtained drugtargets are enriched in transcription factor (15.3%), receptor(14.4%), and nucleic acid binding (14%), highlighting thecritical roles of targets in drug discovery. Among the targets,32 targets are receptors, 21 are transferases, and 14 aretransporters (Figure 1(b)).

3.2. C-TNetworkAnalysis. To uncover the synergistic effectsof multicomponents and multitargets in MHC, a global viewof the C-T network was generated. After removing 18compounds with no target proteins, a graph of C-T inter-actions (Figure 1(a)) was constructed using 57 candidatecompounds and their 303 potential targets. *e averagenumber of targets per compound is 5.3, showing the mul-titarget features and polypharmacology properties of con-stituents in MHC. *e C-Tnetwork contains 360 nodes and1056 ligand-target interactions. For most active compounds,quercetin, beta-sitosterol, and stigmasterol have high degreedistributions, and each of them hits more than 90 potentialtargets. For instance, quercetin has the highest degree (154),followed by beta-sitosterol with 114 drug-target interactionsand stigmasterol possessing 93 target proteins.

By further observation of the C-Tnetwork, we found thatmany targets are hit by different numbers of compounds,implying the multicomponent characteristics of herbs.Among these targets, PTGS2 possesses the largest degree(degree� 40), followed by PTGS1 (degree� 31), HSP90(degree� 31), Scn1a (degree� 28), estrogen receptor(degree� 16), calmodulin (degree� 16), and thrombin(degree� 15), demonstrating their potential therapeuticeffects for treating CVD. Among them, the target prosta-glandin G/H synthase 2 (PTGS2) with the highest degree(40) and prostaglandin G/H synthase 1 (PTGS1) with adegree of 31 can be modulated by the compounds in MHC,indicating their vital role in helping to treat CVDs. Similarly,heat shock protein 90 (HSP90), a key enzyme in the bloodcoagulation system, was predicted to be regulated by 31chemicals. *e details of the relationship between activatecompounds and targets are described in Table S1. All theseresults imply the probably different binding properties ofactive chemicals in MHC with the active substances andsuggest that individual compounds may act on the sametargets synergistically, thus exerting therapeutic effects onCVDs.

3.3. T-D Network and C-T-D Network Analysis. To gainbetter insight into the diseases that could be modified byMHC, a T-D network was constructed on the strength ofpredicted targets and the corresponding diseases (Figure 2,Table S2). *e gene entries related to CVD were collectedfrom the TTD and PharmGkb database and converted intoUniProtKB IDs to determine the correlation between theputative target proteins and CVD. Targets with the mostdegrees among the 49 targets were as follows: thrombin,prostaglandin G/H synthase 2, eNOS, estrogen receptor, andβ-adrenergic receptor; the top diseases that were most rel-evant to MHC were as follows: atherosclerosis, cardiovas-cular disease, hypertension, thrombosis, andneurodegenerative diseases, implying that MHCmay be alsoeffective in the treatment of these diseases.*rombin, a maintarget ofMHC, was associated with coronary atherosclerosis,thrombosis, and thrombotic disease in the T-D network.Based on these findings, MHC contained numerous effectivesubstances with different pharmacologic properties that mayact on multiple targets with potential synergistic effects.

Evidence-Based Complementary and Alternative Medicine 3

https://string-db.org/

Table 1: *e candidate compounds and putative target proteins of MHC.

Code Molecule name MW OB (%) DLM1 Mairin 456.78 55.38 0.78M2 Jaranol 314.31 50.83 0.29M3 Hederagenin 414.79 36.91 0.75

M4 (3S,8S,9S,10R,13R,14S,17R)-10,13-Dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol 428.82 36.23 0.78

M5 Isorhamnetin 316.28 49.6 0.31M6 3,9-Di-O-methylnissolin 314.36 53.74 0.48M7 5′-Hydroxyiso-muronulatol-2′,5′-di-O-glucoside 642.67 41.72 0.69M8 7-O-Methylisomucronulatol 316.38 74.69 0.3M9 9,10-Dimethoxypterocarpan-3-O-β-D-glucoside 462.49 36.74 0.92M10 (6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol 300.33 64.26 0.42M11 Bifendate 418.38 31.1 0.67M12 Formononetin 268.28 69.67 0.21M13 Isoflavanone 316.33 109.99 0.3M14 Calycosin 284.28 47.75 0.24M15 Kaempferol 286.25 41.88 0.24M16 FA 441.45 68.96 0.71M17 (3R)-3-(2-Hydroxy-3,4-dimethoxyphenyl)chroman-7-ol 302.35 67.67 0.26M18 Isomucronulatol-7,2′-di-O-glucosiole 626.67 49.28 0.62M19 1,7-Dihydroxy-3,9-dimethoxy pterocarpene 314.31 39.05 0.48M20 Quercetin 302.25 46.43 0.28M21 (2R)-5,7-Dihydroxy-2-(4-hydroxyphenyl)chroman-4-one 272.27 42.36 0.21M22 (S)-stylopine 323.37 51.15 0.85M23 Ziziphin_qt 472.78 66.95 0.62M24 Icaride A 404.5 48.74 0.43M25 Isostrychnine N-oxide (I) 352.47 35.45 0.8M26 Isostrychnine N-oxide (II) 350.45 37.33 0.8M27 Lokundjoside_qt 406.57 32.82 0.76M28 Vomicine 408.54 47.56 0.65M29 Brucine-N-oxide 410.51 49.17 0.38M30 Isobrucine 334.45 33.58 0.8M31 Brucine N-oxide 410.51 52.63 0.38M32 Stigmasterol 412.77 43.83 0.76M33 (+)-Catechin 290.29 54.83 0.24M34 Beta-sitosterol 414.79 36.91 0.75M35 Stigmasterol 412.77 43.83 0.76M36 (2R,3R,3aS)-3a-Allyl-2-(1,3-benzodioxol-5-yl)-5-methoxy-3-methyl-2,3-dihydrobenzofuran-6-one 340.4 59.99 0.43M37 Denudatin B 356.45 61.47 0.38M38 Futokadsurin C 356.45 61.09 0.45M39 Galgravin 372.5 57.12 0.39M40 (2S,3S,4S,5S)-2,5-Bis(3,4-dimethoxyphenyl)-3,4-dimethyltetrahydrofuran 372.5 57.12 0.39M41 Hancinone 340.4 39.31 0.44M42 Isofutoquinol A 354.43 59.2 0.48

M43 (1R,5S,6R,7R,8R)-3-Allyl-6-(3,4-dimethoxyphenyl)-8-hydroxy-1-methoxy-7-methyl-4-bicyclo[3.2.1]oct-2-enone 358.47 64.65 0.35

M44 Bicyclo(3.2.1)oct-3-ene-2,8-dione, 7-(4-hydroxy-3-methoxyphenyl)-5-methoxy-6-methyl-3-(2-propenyl)-, (1R-(6-endo,7-exo))- 342.42 94.67 0.32

M45 Acetic acid [(1R,5S,6R,7R,8R)-3-allyl-6-(3,4-dimethoxyphenyl)-1-methoxy-7-methyl-4-oxo-8-bicyclo[3.2.1]oct-2-enyl] ester 400.51 59.93 0.46

M46 (2S,3S)-2-(3,4-Dimethoxyphenyl)-7-methoxy-3-methyl-2,3-dihydrobenzofuran-5-carbaldehyde 328.39 42.15 0.32

M47 Kadsurenone 356.45 54.72 0.38M48 Kadsurin A 372.45 56.83 0.5M49 Kadsurin B 358.47 30.55 0.46

M50 (4R)-2-Allyl-4-[(E)-2-(4-hydroxy-3-methoxyphenyl)-1-methylvinyl]-4,5-dimethoxy-1-cyclohexa-2,5-dienone 356.45 55.14 0.3

M51 Piperkadsin B 430.54 55.44 0.41M52 Piperlactam S 295.31 40.44 0.4M53 Stigmasterol 412.77 43.83 0.76M54 N-Coumaroyltyramine 283.35 85.63 0.2


*en, we mapped all candidate compounds with theircorresponding targets onto these diseases. After discardingthe targets without participating in any related CVDS andthe corresponding compounds, a compounds-targets-dis-eases network was constructed with 87 nodes (13 com-pounds, 49 targets, and 25 diseases) and 127 edges(Figure 3). For instance, 3 compounds such as quercetin,isorhamnetin, and kaempferol are referred to as regulatingimportant targets in atherosclerosis; isorhamnetin, 3,9-di-O-methylnissolin, and stigmasterol are referred to as regulatingmain targets in hypertension; quercetin, Jaranol iso-rhamnetin, and (+)-catechin are referred to as regulating keytargets in neurodegenerative diseases. Our results suggestthat different ingredients of MHC may be involved in dif-ferent diseases.

3.4. GO Enrichment Analysis. To clarify the multiplemechanisms of MHC on CVD from a systematic level, weperformed an enrichment analysis for the biological process(BP), molecular function (MF), and cellular component (CC)of the retrieved protein targets ofMHC. As shown in Figure 4,the significantly enriched BP terms were mainly involved inregulation of apoptotic process, positive regulation of nucleicacid-templated transcription, cellular response to cytokinestimulus, cytokine-mediated signaling pathway, positiveregulation of transcription, DNA-templated positive regula-tion of intracellular signal transduction, positive regulation ofgene expression, positive regulation of transcription fromRNA polymerase II promoter, and positive regulation ofprotein phosphorylation. *e most frequently occurringprotein targets were TP53, IL-6, TGFB1, TNF, IKBKB, EGFR,CHUK, AKT1, RELLA, VEGFA, FOS, SIRT1, MYC, HIF1A,NKX3-1, STAT1, and IL-4.

Figure 5 lists the significantly enrichedMF terms of thesetargets. *e results suggested that targets of MHC were

strongly correlated with the molecular functions such asprotein binding, enzyme binding, receptor binding, identicalprotein binding, protein dimerization activity, G-proteincoupled amine receptor activity, signal transducer activity,binding, molecular transducer activity, protein hetero-dimerization activity, steroid hormone receptor activity,drug binding, and signaling receptor activity. As shown inFigure 6, the top five cellular components were plasmamembrane region (20.93%), extracellular space (13.95%),cytoplasmic part (12.4%), and membrane raft (11.63%).*ese abovementioned observations are valued in improvedunderstanding of the mechanism of MHC.

3.5. Pathway Enrichment Analysis. To investigate the un-derlying mechanism of MHC, the targets were furthermapped to pathways, and the top 10 pathways are listed inFigure 7. Among the 177 enriched pathways, severalpathways have been verified as important and accurate targetpathways for curing CVDs, such as AGE-RAGE signalingpathway in diabetic complications (hsa04933), PI3K-Aktsignaling pathway (hsa04151), TNF signaling pathway(hsa04668), HIF-1 signaling pathway (hsa04066), FoxOsignaling pathway (hsa04068), apoptosis (hsa04210), cal-cium signaling pathway (hsa04020), T-cell receptor signal-ing pathway (hsa04660), MAPK signaling pathway(hsa04010), Toll-like receptor signaling pathway (hsa04620),focal adhesion (hsa04510), NOD-like receptor signalingpathway (hsa04621), VEGF signaling pathway (hsa04370),and NF-kappa B signaling pathway (hsa04064). Amongthem, the PI3K-Akt and TNF signaling pathways have thehighest combined scores, which imply the vital roles in thetreatment and prevention of CVDs. In addition, 6 signalingpathways including HIF-1, FoxO, VEGF, TLR, MAPK, andNF-κB signaling pathways are also important pathwayscapable of regulating anti-inflammatory, neuroprotective,

Table 1: Continued.

Code Molecule name MW OB (%) DLM55 Wallichinine 370.48 61.64 0.33M56 Futoquinol 354.43 59.83 0.36M57 Beta-sitosterol 414.79 36.91 0.75M58 Acetylbullatantriol 298.47 40.21 0.18M59 Maristeminol 322.54 30.64 0.38M60 Eupatin 360.34 50.8 0.41M61 Mutatochrome 552.96 48.64 0.61M62 Physciondiglucoside 608.6 41.65 0.63M63 Procyanidin B-5,3′-O-gallate 730.67 31.99 0.32M64 Rhein 284.23 47.07 0.28M65 Sennoside E_qt 524.5 50.69 0.61M66 Torachrysone-8-O-beta-D-(6′-oxayl)-glucoside 480.46 43.02 0.74M67 Toralactone 272.27 46.46 0.24M68 Emodin-1-O-beta-D-glucopyranoside 432.41 44.81 0.8M69 Sennoside D_qt 524.5 61.06 0.61M70 Daucosterol_qt 386.73 35.89 0.7M71 Palmidin A 510.52 32.45 0.65M72 Beta-sitosterol 414.79 36.91 0.75M73 Aloe-emodin 270.25 83.38 0.24M74 Gallic acid-3-O-(6′-O-galloyl)-glucoside 484.4 30.25 0.67M75 (-)-Catechin 290.29 49.68 0.24


and antioxidative effects. *is suggests that the potentialtargets of MHC may be involved in various pathways,showing their specific mechanism of action to modulateCVDs.

3.6. Protein-Protein Interactions. *e protein-protein net-work was constructed via mapping the putative targets intothe String platform. After excluding isolated nodes, theprotein interaction network induced by MHC was

Eukaryotic translation initiation factor Beta-1 adrenergic receptor

Kadsurin B

(-)-Catechin Beta-1 adrenergic receptorHepatocyte growth factor receptor

Poly [ADP-ribose] polymerase 1

Procollagen C-endopeptidase enhancer 1

Pro-epidermal growth factor

Probable E3 ubiquitin-protein ligase HERC5

Plasminogen activator inhibitor 1

Cellular tumor antigen p53

Heme oxygenase 1

Prostatic acid phosphatase

Stromelysin-1

Myeloperoxidase

G2/mitotic-specific cyclin-B1

G1/S-specific cyclin-D1

Protein CBFA2T1

NAD(P)H dehydrogenase [quinone] 1

Protein kinase C beta type

Puromycin-sensitive aminopeptidase

Collagen alpha-1(III) chain

Glutathione S-transferase Mu 2

RAF proto-oncogene serine/threonine-protein kinase


Heat shock protein beta-1

Proto-oncogene c-Fos

RAC-alpha serine/threonine-protein kinase

Apoptosis regulator Bcl-2

Tissue factor

�rombomodulin

Mitogen-activated protein kinase 1

Interferon regulatory factor 1

Tissue-type plasminogen activator

Baculoviral IAP repeat-containing protein 5

Cathepsin D

Maltase-glucoamylase, intestinal

Ornithine decarboxylase

Collagen alpha-1(I) chain

DNA gyrase subunit B

ETS domain-containing protein Elk-1

ATP-binding cassette sub-family G member 2

Maristeminol

Lokundjoside_qt

Bcl-2-like protein 1 DNA topoisomerase 1

DNA topoisomerase 2-alpha

Estrogen sulfotransferase CD40 ligand

Dual oxidase 2

Mineralocorticoid receptor

Peroxisome proliferator-activated receptor delta

Interferon gamma

Peroxisome proliferator-activated receptor gamma

Phosphatidylinositol-3,4,5-trisphosphate3-phosphatase and dual-specificity

protein phosphatase PTEN

72 kDa type IV collagenase

Activator of 90 kDa heat shock protein ATPase homolog 1

Acetyl-CoA carboxylase 1

Gap junction alpha-1 protein

Nitric-oxide synthase, endothelial

Interleukin-4

Amine oxidase [flavin-containing] B

isofutoquinol A

Transcription factor AP-1

Acetylcholinesterase

Neuronal acetylcholine receptor protein, alpha-7 chain

Cytochrome P450 1A2 Tumor necrosis factor

Myc proto-oncogene protein

Apoptosis regulator BAX

Brucine N-oxide

Alcohol dehydrogenase 1C

Brucine-N-oxide

mRNA of PKA Catalytic Subunit C-alpha

DNA topoisomerase II

Cell division control protein 2 homolog

Hyaluronan synthase 2


Xanthine dehydrogenase/oxidase

Coagulation factor VII

1,7-Dihydroxy-3,9-dimethoxypterocarpene

Peroxisome proliferator activated receptor gamma

Sodium channel protein type 5 subunit alpha

Piperkadsin B

Isostrychnine N-oxide (I)


Isostrychnine N-oxide (II)

Dopamine D1 receptor

Gamma-aminobutyric acid receptor subunit alpha-1

5-hydroxytryptamine 7 receptor

5-hydroxytryptamine 2A receptor

Alpha-1B adrenergic receptor

Neuronal acetylcholine receptor subunit alpha-2

Delta-type opioid receptor

Mu-type opioid receptor

6

Gamma-aminobutyric-acid receptor alpha-3 subunit

Isobrucine

Glycogen synthase kinase-3 beta

Serum paraoxonase/arylesterase 1

Serine/threonine-protein kinase Chk1

Cell division protein kinase 2

Estrogen receptor beta

Aldose reductase

Coagulation factor Xa

Beta-2 adrenergic receptor

Dipeptidyl peptidase IV

Rhein

Retinoic acid receptor RXR-alpha

acetic acid [(1R,5S,6R,7R,8R)-3-allyl-6-(3,4-dimethoxyphenyl)-1-methoxy-7-methyl-4-oxo-8-bicyclo[3.2.1]oct-2-enyl]

ester

Vascular endothelial growth factor receptor 2

Alpha-1A adrenergic receptor

EUPATIN

Wallichinine

Potassium voltage-gated channel subfamily H member 2

(2R,3R,3aS)-3a-Allyl-2-(1,3-benzodioxol-5-yl)-5-methoxy-3-methyl-2,3-dihydrobenzofuran-6-one

(3S,8S,9S,10R,13R,14S,17R)-10,13-dimethyl-17-[(2R,5S)-5-propan-2-yloctan-2-yl]-2,3,4,7,8,9,11,12,14,15,16,17-dodecahydro-1H-cyclopenta[a]phenanthren-3-ol

Peroxisome proliferator activated receptor delta


FA

Proto-oncogeneserine/threonine-protein kinase

Pim-1

Nuclear receptor coactivator 2

Carbonic anhydrase II

Prostaglandin G/H synthase 1


piperlactam S


Mairin

5'-Hydroxyiso-muronulatol-2',5'-di-O-glucoside

Trypsin-1

Progesterone receptor

Daucosterol_qt

n-Coumaroyltyramine

Kadsurin A

Intercellular adhesion molecule 1

Nuclear receptor subfamily 1 group I member 2


Solute carrier family 2, facilitated glucose transporter member 4

Cytochrome P450 1B1

Transcription factor p65

Inhibitor of nuclear factor kappa-B kinase subunit beta

Signal transducer and activator of transcription 1-alpha/beta

RAC-alpha serine/threonine-protein kinase

Cytochrome P450 1A1

Type I iodothyronine deiodinase

kaempferol

Cyclin-dependent kinase inhibitor 1

Torachrysone-8-O-beta-D-(6'-oxayl)-glucoside

Aldo-keto reductase family 1 member C3

isomucronulatol-7,2'-di-O-glucosiole

DNA topoisomerase II



Physciondiglucoside

Neutrophil cytosol factor 1


Oxidized low-density lipoprotein receptor 1

Glycogen phosphorylase, muscle form

mRNA of Protein-tyrosine phosphatase, non-receptor type 1

Peroxisome proliferator-activated receptor gamma

isorhamnetin

Proliferating cell nuclear antigen

Fatty acid synthase

Cellular tumor antigen p53

Interleukin-1 beta

Krueppel-like factor 7

Protein kinase C delta type G2/mitotic-specific cyclin-B1

Dipeptidyl peptidase IV

Protein kinase C epsilon type

Aloe-emodin

Antileukoproteinase

Serine/threonine-proteinphosphatase 2B catalytic subunit

alpha isoform

Heme oxygenase 1


Activator of 90 kDa heat Shock protein ATPase homolog 1

Peroxidase C1A

Tumor necrosis factor

Emodin-1-O-beta-D-glucopyranoside

Protein kinase C alpha type

Transforming growth factor beta-1

Caspase-8

Glutamate receptor 2

Caspase-9

Caspase-3cAMP-dependent protein kinase

inhibitor alpha



Glycogen synthase kinase-3 beta Androgen receptor


Calycosin Estrogen receptor beta

Cyclin-A2Jaranol


3,9-di-O-methylnissolinHeat shock protein HSP 90


Calmodulin


Kadsurenone

Futoquinol

Retinoic acid receptor RXR-beta

(2S,3S)-2-(3,4-dimethoxyphenyl)-7-methoxy-3-methyl-2,3-dihydrobenzofuran-5-carbaldehydeSodium-dependent serotonin

transporter

Calmodulin

Bifendate

Gamma-aminobutyric acid receptor subunit alpha-1

Delta-type opioid receptor

(2S,3S,4S,5S)-2,5-bis(3,4-dimethoxyphenyl)-3,4-dimethyltetrahydrofuran

�rombin

Icaride A

7-O-methylisomucronulatol

Estrogen receptor

Trypsin-1


Beta-lactamase

Nitric oxide synthase, inducible

Calcium-activated potassium channel subunit alpha 1 Mitogen-activated protein kinase 14

Toralactone

Epidermal growth factor receptor Interleukin-2Solute carrier family 2, facilitated glucose transporter member 4 C-X-C motif chemokine 2 Matrix metalloproteinase-9 Interleukin-1 betaCyclin-dependent kinase inhibitor 1 quercetin

Interleukin-8Claudin-4 NADPH--cytochrome P450

reductaseTranscription factor E2F1 Transcription factor E2F2

Signal transducer and activator of transcription 1-alpha/beta Interleukin-1 alpha Interleukin-10Caveolin-1

Type I iodothyronine deiodinase

Insulin-like growth factor II Cyclin-dependent kinase inhibitor

2A, isoforms 1/2/3 Nitric oxide synthase, endothelial


NF-kappa-B inhibitor alpha Transcription factor p65

Vascular endothelial growth factor A

Cytochrome P450 1B1

Superoxide dismutase [Cu-Zn]

Heat shock factor protein 1

C-X-C motif chemokine 11Receptor tyrosine-protein kinase erbB-3

Intercellular adhesion molecule 1 Retinoblastoma-associated protein

Insulin-like growth factor-binding protein 3

Leukotriene A-4 hydrolase



Cytochrome P450 1A1

DDB1- and CUL4-associated factor 5


Prostaglandin E2 receptor EP3 subtype

Interleukin-6

Peroxidase C1A Peroxisome proliferator-activated

receptor alpha

3 beta-hydroxysteroid dehydrogenase/Delta

5-->4-isomerase type 1

3 beta-hydroxysteroid dehydrogenase/Delta

5-->4-isomerase type 2

NADH-ubiquinone oxidoreductase chain 6

ATP synthase subunit beta, mitochondrial

NAD-dependent deacetylase sirtuin-1

formononetin

Ras association domain-containing protein 1

78 kDa glucose-regulated protein

Hexokinase-2

Nuclear factor erythroid 2-related factor 2

Receptor tyrosine-protein kinase erbB-2

Homeobox protein Nkx-3.1

Ras GTPase-activating protein 1

C-C motif chemokine 2

C-reactive protein

Runt-related transcription factor 2

Osteopontin

Hypoxia-inducible factor 1-alpha

C-X-C motif chemokine 10

Inhibitor of nuclear factor kappa-B kinase subunit alpha

Glutathione S-transferase P

Urokinase-type plasminogen activator

26S proteasome non-ATPase regulatory subunit 3

Aryl hydrocarbon receptor E-selectin

Cytochrome P450 3A4

Arachidonate 5-lipoxygenase

Insulin receptor

Vascular cell adhesion protein 1

Interstitial collagenase

Chymotrypsinogen B

Amine oxidase [flavin-containing] A


Stigmasterol

Apoptosis regulator BAX

Caspase-8

Transforming growth factor beta-1

Muscarinic acetylcholine receptor M2

Microtubule-associated protein 2


Caspase-9


Beta-sitosterol

Lysozyme

Apoptosis regulator Bcl-2

Gamma-aminobutyric-acid receptor subunit alpha-6


Alcohol dehydrogenase 1B

Glutamate receptor 2Hederagenin



Nicotinate-nucleotide--dimethylbenzimidazolephosphoribosyltransferaseCytochrome P450-cam

Mineralocorticoid receptor Sodium-dependent noradrenaline transporter

Progesterone receptor

Vomicine

Mu-type opioid receptor


Alpha-1B adrenergic receptor

Ig gamma-1 chain C region

Bicyclo(3.2.1)oct-3-ene-2,8-dione,7-(4-hydroxy-3-methoxyphenyl)-5-methoxy-6-methyl-3-(2-propenyl)-,

(1R-(6-endo,7-exo))-


Beta-secretase

Futokadsurin C

Retinoic acid receptor RXR-beta

Glucocorticoid receptor

mRNA of PKA Catalytic Subunit C-alpha

Caspase-3

Nuclear receptor coactivator 1Cytochrome P450-cam


CatalaseRetinoic acid receptor RXR-alpha

(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one

5-hydroxytryptamine 2A receptor5-hydroxytryptamine receptor 3A

Carbonic anhydrase II

Protein kinase C alpha type CGMP-inhibited 3',5'-cyclic phosphodiesterase A (S)-Stylopine

Sodium-dependent dopamine Transporter

Phosphatidylinositol-4,5-bisphosphate3-kinase catalytic subunit, gamma

isoformHeat shock protein HSP 90



(1R,5S,6R,7R,8R)-3-Allyl-6-(3,4-dimethoxyphenyl)-8-hydroxy-1-methoxy-7-methyl-4-bicyclo[3.2.1]oct-2-enone

Alpha-1D adrenergic receptor

Alpha-2C adrenergic receptor


CGMP-inhibited 3',5'-cyclic Phosphodiesterase A

Galgravin

(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol

(+)-catechin

(a)

Transcription factor (PC00218)Enzyme modulator (PC00095)Oxidoreductase (PC00176)

Receptor (PC00197)Transfer/carrier protein (PC00219)Signaling molecule (PC00207)Transferase (PC00220)

Isomerase (PC00135)Hydrolase (PC00121)Nucleic acid binding (PC00171)Transporter (PC00227)

Cell junction protein (PC00070)Calcium-binding protein (PC00060)Lyase (PC00144)Cytoskeletal protein (PC00085)

0 5 10 15 20 25 30 35Genes

Cate

gory

(b)

Figure 1: *e C-T network of MHC and the targets class. (a) *e compound in MHC and the potential target network. Different colorsrepresent the nodes with different attributions. Yellow nodes represent the candidate compounds; blue represent the predicted targets. (b)*e distribution of the candidate targets.


NAD-dependentdeacetylase sirtuin-1

Coronary heart disease

Neurologicaldiseases


Tissue-type plasminogen activator

CGMP-inhibited 3',5'-cyclic phosphodiesterase A


C-C motif chemokine 2


Atherosclerosis

Peroxisome proliferator activated receptor gamma

Caveolin-1Acute coronary syndromes

Coronary artery disease

Vascularimpairment

associated with diabetes


Mineralocorticoid receptor

Cardiac arrhythmias

Hypertension

Brain injury




Catalase


Arachidonate 5-lipoxygenase Arterial embolism; �rombosis

5-hydroxytryptamine 2A receptor

Caspase-9

Potassium voltage-gated channel subfamily H member 2

Brain injury associated with seizures


Cardiac dysrhythmias


Vasospasm




Caspase-8

Vascular injury response

Myeloperoxidase



Alzheimer's disease

Glycogen synthase kinase-3 betaHeme oxygenase 1

Vascular diseases

Cathepsin D



Beta-secretase

�rombomodulin



Vascular lesion regression

Interleukin-8Arthritis

Muscarinicacetylcholinereceptor M1Stromelysin-1


Muscarinicacetylcholinereceptor M2

Myocardial infarction

Coronary syndromesEstrogen receptor beta

Estrogen receptorCardiovascular disease

Serum paraoxonase/arylesterase 1

Neurodegenerative diseases

Tissue factor

�rombosis

�rombin

Coronary atherosclerosis


�rombotic disease

Figure 2: T-D network: a potential targets-CVD network and nodes represent targets and diseases.

Heme oxygenase 1Catalase

Arachidonate5-lipoxygenase

Mitogen-activatedprotein kinase 1

Caspase-8 Peroxisome proliferator activated receptor gamma


Interleukin-8C-C Motif chemokine 2

Stromelysin-1

Tissue factor

Caspase-9

Estrogen receptor




Muscarinicacetylcholine receptor

M1

Isorhamnetin

Jaranol

Kaempferol

7-O-Methylisomucronulatol

3,9-di-O-Methylnissolin

Hederagenin

Quercetin




Formononetin

Myeloperoxidase


Nitric-oxidesynthase, endothelial


5-Hydroxytryptamine 2A receptor

Thrombomodulin

Caveolin-1

Gamma-aminobutyric-acidreceptor alpha-5 subunit

CGMP-inhibited3',5'-cyclic

phosphodiesterase A




Mitogen-activatedprotein kinase 14

Cardiac dysrhythmias

Brain injury associated with seizures

ThrombosisNeurological diseases

Thrombotic diseaseVascular diseases Coronaryatherosclerosis

Brain injury

Serumparaoxonase/arylesterase

1

Glycogen synthase kinase-3 beta

Tissue-typeplasminogen activator

Cathepsin D

Muscarinicacetylcholine receptor

M2

Thrombin

Transcriptionfactor AP-1

Beta-secretase

Estrogen receptor beta

(+)-Catechin

Potassiumvoltage-gated channel

subfamily H member 2 Neuronal acetylcholine

receptor protein, alpha-7 chain


Mineralocorticoidreceptor


Arthritis

(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one

Cardiovascular disease

Vascular injury response Neurodegenerativediseases

Vascular impairment associated with

diabetes

Myocardial infarction

Cardiac arrhythmiasAtherosclerosis

HypertensionArterial embolism; Thrombosis


(6aR,11aR)-9,10-Dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol

Futokadsurin C

Vasospasm

Alzheimer's disease

Stigmasterol

Vascular lesion regression

Coronary arterydisease

NAD-dependentdeacetylase sirtuin-1 Coronary syndromes Coronary heart disease

Acute coronary syndromes

Figure 3: C-T-D network: a compounds-targets-CVD network and nodes represent compounds and targets.


TGFB1

VEGFAAKT1EGFR

IL1B

BCL2L1IL1A

BCL2

IL6

RELA

SIRT1

TP53

MYC

IKBKB

IL10

IL4

TNF

ERBB2

NKX3-1STAT1

MMP9

MET

CHUK

CXCL10EGF

IRF1

NFKBIA

FOS

HIF1A

JUNNFE2L2

E2F1NCF1

HMOX1RUNX2

ELK1AHR

NCOA1AKR1B1

PPARG

Regulat

ion of..

Positive

reg..

Cellular

resp..

Cytokine-m

e..

Positive

reg..

Positive

reg..

Positive

reg..

Positive

reg..

Positive

reg..

Cellular

resp..

(a)

Regulation of apoptotic process(GO:0042981)

Positive regulation of nucleic acid-templated transcription…

Cellular response to cytokinestimulus (GO:0071345)

Cytokine-mediated signalingpathway (GO:0019221)

Positive regulation oftranscription, DNA-templated…

Positive regulation of intracellularsignal transduction (GO:1902533)

Positive regulation of geneexpression (GO:0010628)

Positive regulation oftranscription from RNA…

Positive regulation of proteinphosphorylation (GO:0001934)

Cellular response to oxidativestress (GO:0034599)Positive regulation of cell

proliferation (GO:0008284)

Apoptotic process (GO:0006915)

Positive regulation ofphosphorylation (GO:0042327)

Cellular response to reactiveoxygen species (GO:0034614)

Positive regulation ofmacromolecule metabolic…

Negative regulation ofprogrammed cell death…

Cellular response to cadmium ion(GO:0071276)

Positive regulation of cellularprocess (GO:0048522)

Negative regulation of apoptoticprocess (GO:0043066)

Protein kinase B signaling(GO:0043491)

0

10

20

30

40

50

60

Gene counts–log P (p-value)

(b)

Figure 4: *e GO BP analysis of predicted targets of MHC. EnrichR analysis was performed to identify the most significantly enriched GOBP terms.


composed of 221 nodes (proteins) and 3865 edges (Figure 8).*e topological properties of the network rewired by theMHC were analyzed with the network analyzer plugin.Among these properties, the node degree can be used todistinguish between random and scale-free network topol-ogies.*ree topological features of each node in the networkwere calculated to find the major nodes. Finally, 22 nodeswere selected as major nodes, namely, TP53, JUN, AKT1,IL6, TNF, VEGFA, EGF, MAPK1, FOS, PIK3CG, MYC,BCL2, ESR1, EGFR, MAPK8, IL8, PTGS2, CASP3,HSP90AA1, MMP9, NOS3, and CCND1. *us, these targetswere likely to be the key or central proteins that MHC maydirectly act on them to treat CVDs.

4. Discussion

*e efficacy of MHC has been verified through accumulatedconsiderable clinical experiences. However, it is difficult toilluminate the mechanism of MHC from the perspective ofmodern medicine because of the complex composition. *enetwork pharmacological analysis provides new approachesand perspectives for the study of complicated Chinesemedicine formula. In the present study, we used the networkpharmacology approach to illuminate the scientific materialbasis and multiple underlying mechanisms of MHC inCVDs treatment from a systematic perspective. *e phar-macodynamic compounds and potential targets, networkanalysis of elements such as active ingredients and potentialtargets, GO and KEGG pathway enrichment analysis, andprotein-protein interaction were used to investigate therelationships between active molecules and related proteinsof CVD.

HQ, MQ, DG, HFT, QN, and DH are the most com-monly used herbal medicines to treat CVDs. In our work,

with the help of the ADME evaluation system, 444 activeingredients were identified, 75 of which could interact with303 direct targets by drug targeting. Recent researches haveshown that some active ingredients in MHC have biologicalactivity against CVDs, which confirm the bioinformaticsdata analyzed in our study and highlights the credibility ofthe network pharmacology system. For instance, iso-rhamnetin has been proven to exert cardiovascular pro-tective effects through multiple mechanisms, includingantioxidative, anti-inflammatory, and antiproliferative ef-fects. Various pharmacological studies showed that iso-rhamnetin protects against cardiac hypertrophy and canexhibit positive effect on hypoxia/reoxygenation-inducedinjury by attenuating apoptosis and oxidative stress [25, 26].Kaempferol can inhibit inflammatory responses and exertprotective effect in LPS-induced microvascular endothelialcells [27]. Besides, the protective effect of kaempferol onheart in isoproterenol-induced heart failure has also beenproven [28, 29]. Quercetin is a flavonoid that possessespharmacological effects including antitumor, antioxidant,anti-inflammatory, immunosuppressive, and cardiovascularprotection activities [30–34]. In a recent study, it has beenreported that the beta-sitosterol exerts thrombus-preventingactivity by dose-dependent inhibition of thrombin in mousemodel [35].

As we know, MHC probably exerts its therapeutic effecton CVD by binding and regulating particular protein targets.*e analytical result of the C-Tnetwork displayed an averagedegree of 13 per compounds and 5.3 per target proteins,respectively. Among the 57 compounds with correspondingtargets, 99 were capable of acting on more than 2 targets and44 linked with more than 13 target proteins. *e key nodeproteins were suggested to be important targets in thetreatment of CVD. Among them, the targets prostaglandin

Cell adhesion molecule binding

Ubiquitin-likeprotein ligase

bindingTranscription

coactivator activity

Ammonium ion binding

Proteinactivity

Identical protein binding

Protease binding

Heat shock protein binding

Transcription factor binding

DNA-bindingtranscription

activator activity, RNA polymerase

II-specificProtein

homodimerizationactivityheterodimerizationHistone deacetylase

binding

Transcription factor activity, RNA polymerase II

proximal promoter sequence-specific

DNA binding

Transferase activity

Enzyme bindingMolecular function

regulator

Protein kinase regulator activity

Kinase regulator activity

Enzyme activator activity

DNA binding

Organic cyclic compound binding

Neurotransmitterreceptor activity

DNA-binding transcriptionfactor activity

Regulatory region nucleic acid binding

DNA-binding transcription factor activity, RNA polymerase II-specific

Phosphatasebinding

Cytokine activityBindingKinase activity

Transferase activity, transferring phosphorus-containing groups

Cytokine receptor bindingProtein tyrosine kinase activity

Antioxidant activity

Ion bindingSignaling receptor activity

Enzyme regulator activity

Anion binding

Cofactor binding

Moleculartransducer activity

Heterocyclic compound binding

Drug binding

Protein binding Small molecule binding

Serine-type peptidase activity

Protein domain specific binding

Growth factor receptor binding

Protein kinase activity

Purine ribonucleoside triphosphate binding

RNA polymerase II transcription factor

binding

Integrin bindingLipid binding

Kinase binding Phosphotransferase activity, alcohol group as acceptor

Ribonucleotide bindingAmide binding

Peptide binding

Transcriptioncoregulator activity

Nucleotide binding

Receptor ligand activity

Tetrapyrrole binding

Growth factor activity

Transmembrane signaling receptor activity

Receptor regulator activity

ATP binding

Chromatin binding

Inorganic anion transmembrane

transporter activity

Ion gated channel activity

Passive transmembrane transporter activity

Nuclear hormone receptor binding

Ion channel activityIon transmembrane transporter

activity

Heme binding

Oxidoreductaseactivity

Metal ion binding

Cation binding

Proteinserine/threoninekinase activity

Sulfur compound binding

Transcription factor activity, direct ligand regulated sequence-specific

DNA binding Nuclear receptor

activity

Heparinbinding

Inorganic molecular entity transmembrane transporter activity

Channel activity

Transmembranetransporter activity

Carbohydratederivative binding Steroid hormone

receptor activityOxidoreductaseactivity, acting on

paired donors, with incorporation or

reduction of molecular oxygen

Protein dimerization activitySerine hydrolase

activityHormone receptor binding

Growth factor binding

Steroid bindingSignaling receptor

bindingPeptidase activity

Protein-containingcomplex binding

Peptidase activity, acting on L-amino

acid peptides

Catalytic activity, acting on a protein Catalytic activity

Nucleosidephosphate binding

Figure 5: *e GO MF analysis of predicted targets of MHC. ClueGO was used to identify the most significantly enriched GO MF terms.


G/H synthase 2 (PTGS2, COX-2) with the highest degree(40) and prostaglandin G/H synthase 1 (PTGS1, COX-1)with a degree of 31 can be modulated by the compounds inMHC, indicating their vital role in helping to treat CVDs.COX-2 and COX-1 are constitutively expressed in the en-dothelium, brain and various tissues in physiological con-ditions, and convert arachidonate to prostaglandin H2,

which is responsible for production of inflammatoryprostaglandins. Previous relevant studies have defined thatthe increased expression of COX-2 and COX-1 may con-tribute to the development of inflammatory diseases due tothe fact that they can facilitate the transcription of severalcytokines associated with disease progression. COX-2 andCOX-1 have been acknowledged as classic therapeutic

% Terms per group

Plasma membrane region 20.93%∗∗

Cytoplasmic part 12.4%∗∗

Extracellular space 13.95%∗∗

Perinuclear region of cytoplasm 0.78%∗∗

Multivesicular body 0.78%∗∗

Chromosome, telomeric region 0.78%∗∗

Cell surface 0.78%∗∗

Apical part of cell 1.55%∗∗

Extracellular matrix 1.55%∗∗

Microtubule organizing center 1.55%∗∗

Protein kinase complex 2.33%∗∗

Transcription factor complex 2.33%∗∗

Endomembrane system 3.1%∗∗

Cytoplasmic vesicle 3.88%∗∗

Membrane protein complex 3.88%∗∗

Integral component of postsynaptic membrane 4.65%∗∗

Nucleoplasm 6.2%∗∗

Mitochondrion 6.98%∗∗

Membrane ra� 11.63%∗∗

Extracellularvesicle

Membrane region

Plasma membrane ra�

Caveola

Membrane ra�

Secretory granule lumen

Cytoplasmic vesicle part

Secretory vesicleCytoplasmicvesicle lumen

Extracellular space

Vesicle

Endomembranesystem

Protein-containingcomplex

Cellular_component

Platelet alphagranuleSecretory granule

Platelet alphagranule lumen

Vesicle lumen

Extracellular region part

Extracellularorganelle

Plasma membrane

Intrinsic component of plasma Plasma membrane partmembrane

Organelle membrane

Integral component of plasma Intrinsic component of membranemembrane

Cyclin-dependentprotein kinase

holoenzyme complex

TransferasecomplexCatalytic

complex

Transferase complex,transferring phosphorus-

containing groups

Collagen-containingextracellular

matrixApical part of cell

Endoplasmic reticulum partEndoplasmic reticulum

Cell surface

Perinuclear region of cytoplasm

Transcription factor complex

Nucleartranscription

factor complex

Endoplasmic reticulum lumen

RNA polymerase II transcription factor complex Apical plasma membrane

Extracellular matrix

Endosome

Intracellular vesicle

Cytoplasmic vesicle

Ficolin-1-richgranule lumen

Ficolin-1-richgranule

Late endosome Multivesicularbody

Extracellular exosome

Dendritic tree

Cell projection part

Cell periphery

Cell body

Neuron part

Postsynaptic membrane

Chromosome,telomeric region

Integralcomponent

of presynaptic membrane

Integral component of postsynaptic

membraneIntrinsic

component ofsynaptic

membrane

PresynapseIntrinsic

componentof presynaptic

membrane

Plasma membrane regionIntegral

componentof membrane

Membranemicrodomain

Organelle

Non-membrane-bounded organelle

Spindle

Intracellularnon-membrane-bounded organelle

Membrane-bounded organelle

Membrane-enclosed lumenOrganelle part

Intrinsic component of postsynaptic membrane

Synapse part

Synaptic membrane

Somatodendritic compartment

Axon

Dendrite

Cell projection

Neuron projection

Neuronal cell body

Presynaptic membrane

Intracellular

Nuclear chromosome part

Intracellular organelle part

Chromosomal part

Cytoplasm

Cytoplasmic part

Intracellular organelle

Chromosomal region

Intrinsic component of postsynaptic specialization membrane

Ion channel complex

Transmembrane transporter complex

Membrane part

Intracellular membrane-bounded organelle

Organelle lumen

Nuclear partCytosol Nuclear lumen

Nucleus

Nucleoplasm

Chromosome

Plasma membrane bounded cell projection part

Integralcomponent ofpostsynaptic specializationmembrane

Plasma membrane bounded cell projection

Integralcomponentof synaptic membrane

Postsynapse

Outer membrane

Whole membrane

Organelle outer membrane

Mitochondrial envelope

Mitochondrion

Mitochondrial membrane

Mitochondrial outer membrane

Protein kinase complex

Chromatin

Microtubuleorganizing center

Nuclear chromosome

Nuclear chromatin

Intracellular part

Cytoskeletal part

Cell part

Envelope

Organelle envelope

Mitochondrial part

Transporter complex

Chloride channel complex

Plasma membrane protein complex

Membrane protein

Complex

Membrane

Figure 6: *e GO CC analysis of predicted targets of MHC. ClueGO was used to identify the most significantly enriched GO CC terms.


targets of nonsteroidal anti-inflammatory drugs (NSAIDs),which bind the site corresponding to the COX active site.Inhibition of the COX with NSAIDs acutely reduces in-flammation, pain, and fever, and long-term use of thesedrugs reduces fatal thrombotic events, as well as the de-velopment of colon cancer and Alzheimer’s disease. A recentresearch suggested that the inhibition of COX-2may activatethe MAPK pathway and reduce inflammatory response andimprove myocardial remodeling in mice with myocardialinfarction. Various activity compounds in TCM exertprotective effects in the vascular or cardiac injury via thesignaling or downstream processing of COX-2 [36–39]. Inaddition, heat shock protein 90 (HSP 90), the key enzyme inthe blood coagulation system, has been identified to be thetarget of 31 chemicals. A recent study has shown that Hsp90modulates cardiac ventricular hypertrophy through acti-vating the MAPK pathway in cardiomyocytes [40]. SinceHSP 90 serves as a regulator in the TGF-β signaling pathway,the downregulation of HSP 90 can inhibit the activation ofmyocardial fibroblast, which is a pathological signature ofmyocardial fibrosis [41]. In light of recent evidence, theactivity compounds in various TCM formulas can producetherapeutic effects on atherosclerosis, cardiac injury, hy-pertension, thrombosis, and neurodegenerative diseasesthrough inhibiting the expression of HSP 90 in vitro or invivo. *us, the bioactive ingredients from MHC interactingwith COX-2, COX-1, and HSP 90 may be the key factors inthe treatment of the fibroblast activation in patients withCVD.

By analyzing the GO-enriched results, we found thatMHCmay have certain effects in regulating cytokine-relatedbiological processes, such as “cellular response to cytokinestimulus (GO:0071345)” and “cytokine-mediated signalingpathway (GO:0019221)”. Cytokines (such as IL-1β, IL-6, andTNF) derived from the vessel wall or blood have been provento be associated with vascular risk and predictive of futurecardiovascular events. Inflammatory processes and theirfailure to resolve is firmly established as central to theprogress of cardiovascular diseases. Several emerging lines ofevidence support the hypothesis that inhibition of the in-flammatory processes by targeting of the cytokines mightserve as an efficient system to attenuate myocardial andarterial injury, reduce disease progression, and promotehealing. Our results have shown that the protective effects ofMHC may be related to the inhibition of cytokines therebysuppressing chronic inflammatory process. Besides, one ofthe key reasons for the occurrence and development of CVDis the apoptotic process. In the present study the “regulationof apoptotic process (GO:0042981)” and “apoptotic process(GO:0006915)” are both enriched in the top ranked GO BPterms. Apoptosis is defined as a highly regulated form of celldeath and can be regulated by genetic or pharmacologicinterventions. Regulation of apoptosis is a hopeful choice oftreatment for CVDs and disorders such as myocardial in-farction, ischemia/reperfusion injury, chemotherapy car-diotoxicity, and heart failure. In the process of apoptotic,TP53, IL-6, TGF-β, TNF-α, IKBKB, and EGFR are con-sidered to be important related proteins. *e “cellular

HTLV-I infection_Homo sapiens_hsa05166PI3K-Akt signaling pathway_Homo sapiens_hsa04151

Small cell lung cancer_Homo sapiens_hsa05222

TNF signaling pathway_Homa sapiens_hsa04668

Prostate cancer_Homo sapiens_hsa05215AGE-RAGE signaling pathway in diabetic complications_Homo sapiens_hsa04933

Pancreatic cancer_Homo sapiens_hsa05212Pathways in cancer_Homo sapiens_hsa05200

Hepatitis B_Homo sapiens_hsa05161Proteoglycans in cancer_Homo sapiens_hsa05205

Small cell lung cancer_Homo sapiens_hsa05222

HTLV-I infection_Homo sapiens_hsa05166

TNF signaling pathway_Homo sapiens_hsa04668

Proteoglycans in cancer_Homo sapiens_hsa05205

Pancreatic cancer_Homo sapiens_hsa05212

Hepatitis B_Homo sapiens_hsa05161

Prostate cancer_Homo sapiens_hsa05215

PI3K-Akt signaling pathway_Homo sapiens_hsa04151

AGE-RAGE signaling pathway in diabetic complications_Homo sapiens_hsa04933

Pathways in cancer_Homo sapiens_hsa05200

Figure 7: *e KEGG enrichment analysis of predicted targets of MHC.


response to oxidative stress (GO:0034599)” and “cellularresponse to reactive oxygen species (GO:0034614)” were alsoenriched in our results. It is established that elevated oxi-dative stress could be a key factor in the development ofcomplex biochemical, structural, and functional changesassociated with CVD. Recently, different research groupshave provided evidences that pharmacological approaches tocounteract excessive accumulation of ROS are sufficient inthe prevention or treatment of heart failure. Besides, en-dothelial dysfunction and vascular remodeling caused by IL-6, TGF-β, TNF-α, EGFR, and VEGFA are also some of thecore features of CVD.

Our results showed that MHC integrated various sig-naling pathways which have been testified as accurate targetpathways to modulate CVD, such as TNF, PI3K-Akt, HIF-1,FoxO, apoptosis, calcium, MAPK, Toll-like receptor, VEGF,and NF-κB pathways. Most signaling pathways significantlyenriched by targets were associated with multiple chronicinflammatory diseases, not merely CVD, which is consistent

with the enriched GO terms in our study. Among 177pathways, PI3K-Akt and TNF signaling as critical pathwaysregulate the process of apoptosis, inflammation, and oxi-dative stress in the development and severity of CVD. Forinstance, the master factors contributing to the initiation andevolution of inflammatory responses in cardiovascular tis-sues include the elevated level of TNF and the activation ofNF-κB in the TNF signaling pathway. Notably, our researchindicated that the main biological function of MHC in CVDis negatively regulating the TNF signaling pathway by theinteraction between bioactivity compounds and the po-tential targets including TNF, VCAM1, COX-2, MMP3,MMP9, IKBKB, IL-1β, IL-6, CCL2, CXCL2, CXCL10, cas-pase-3, and caspase-8. Moreover, our results also show thatMHC can exert the protective effect by modulating thePI3K-Akt signaling pathway. In the cardiovascular system, agreat number of studies targeting PI3K/Akt have elucidatedthe contribution of this pathway to cardiac and vascularfunction regulation both in the normal and diseased states.

PCOLCECOL3A1

KLF7CTRB1

COL1A1

ACPPPRSS1 MT-ND6

SLPINKX3-1 HERC5 CLDN4AHSA1 OLR1ALOX5

MMP3HAS2F7 DUOX2

LTA4HCTSD RUNX2HSF1 IGFBP3

CASP9 MMP1SPP1 PTGS1HSPB1PARP1 DIO1LYZFASN IGF2 PLATMMP2CHEK2 CASP8BAX CRPTGFB1

CCNB1 STAT1 F10PPARD BCL2L1 SERPINE1NR3C2 MMP9HMOX1TOP1 IL10SIRT1PCNA DPP4IL4 KCNH2NCF1ERBB2HIF1A IL1APPARG CCL2 GJA1CHEK1CASP3 IFNGPTGS2BIRC5 MPORXRB

VEGFAHSPA5 F3NPEPPS THBDRB1E2F1 PTEN VCAM1BCL2 IL6 MAPK14 IRF1TOP2BMYC IL1BAR TNFAKT1TP53 MAPK1 CD40LGIL2CDKN1ANCOA1 SELEEGF SCN5ACXCL10RXRA NOS3 PLAUCCNA2 CCND1 MAPK8ESR1TOP2A CDK2

JUN ERBB3IL8HSP90AA1 KCNMA1RELAEGFRPIM1 AHRABCG2ATP5B FOS PTGER3METNCOA2 PIK3CG BACE1NOS2 ELK1

CAV1E2F2 CDK1 CXCL2PPARA F2RASSF1 CATNFKBIAKDRPGR PRKCD CXCL11 CHRM4SOD1 GSK3B EPXINSRAKR1B1

NR3C1SLC2A4AKR1C3 PRKACAIKBKBHK2 RAF1CHUKNR1I2 ADRA2CPRKCA CA2

CALM2 PRKCENR1I3 ESR2CYP1A1 PKIACHRM2PSMD3ACACAADRA2APRKCBNQO1 PTPN1CYP1B1 NFE2L2ODC1DCAF5 OPRD1OPRM1 ADRA1DRASA1

CHRNA2CHRM1HSD3B2 ADRA1BADRB2CYP1A2 PON1

CYP3A4ACHE CHRM5HSD3B1 MGAM CHRM3 CHRNA7PPP3CA MAP2GSTP1

HTR2APORADRA1APDE3ASLC6A4

GRIA2 GABRA5GSTM1

GABRA1RUNX1T1 PYGM ADRB1SLC6A3XDHHTR7

SLC6A2DRD1SULT1E1HTR3A GABRA2GSTM2

GABRA6

ADH1BMAOA GABRA3

MAOB

Figure 8: Network pharmacology analysis through the protein interaction of predicted protein targets of MHC. *e network nodes werepredicted proteins and the edges represented the functional associations.


GSK-3β, AKT1, eNOS, and VEGFA, which have beenscreened as the targets of MHC, are considered to modulateimportant cellular physiological processes in the vessels,heart, and brain. *e eNOS protein shows pharmacologicalproperties by producing NO, which can rapidly diffuseacross cell membranes to act as a potent paracrine mediator.*e PI3K/Akt signaling pathway has been proven to beinvolved in the resistance response to hypoxia ischemia. Itcan regulate the expression of HIF-1α, which furthermodulates the expression of downstream targets that relatedto glucose metabolism and angiogenesis to facilitate ische-mic adaptation [42]. In cardiomyocytes and smooth musclecells, calcium fluxes are the best characterized receptor-regulated signaling events. Recent studies have proven thatactivation of PI3K/Akt signaling is interconnected withcalcium signaling in cardiovascular system, which isemerging to make great influence in disease development[43]. Based on the results of our study, in this complexsystem with MHC-compounds-targets-CVDs interactions,the abovementioned active ingredients, targets, and path-ways are associated with the pharmacological mechanisms ofMHC in the treatment process of CVDs.

5. Conclusion

*e current study uses a network pharmacology approachthat combines active compounds, potential targets, GO, andKEGG enrichment analysis to investigate the molecularmechanism of MHC against CVDs from a systematic per-spective. Among these crucial biological functions, 303targets were identified as key active factors involved in the177 related pathways. We found that our prediction-basedresults were generally consistent with previous research onpathways and diseases treated with MHC extracts. Fur-thermore, we can suggest more comprehensive mechanismsof therapeutic effects of MHC in terms of target proteins,pathways, and diseases thanmanual reviews of the literature.Nonetheless, more experimental researches were warrantedto validate these hypotheses, and experimental verification ofthe potential effective compounds after candidate screeningis needed, which will lay a foundation for further experi-mental research and clinical rational application of MHC.

Abbreviations

MHC: Mahai capsulesCVD: Cardiovascular diseasesTCM: Traditional Chinese medicineHQ: Hedysarum Multijugum Maxim (Huangqi)MQ: Strychni semen (Maqianzi)DG: Angelicae Sinensis Radix (Danggui)HFT: Caulis Piperis Kadsurae (Haifengteng)QN: Homalomena Occulta (Lour.) Schott

(Qiannianjian)DH: Radix Rhei Et Rhizome (Dahuang)TCMSP: Traditional Chinese Medicine Systems

PharmacologyADME: Absorption, distribution, metabolism, and

excretion

OB: prediction of oral bioavailabilityDL: Prediction of drug-likenessGO: Gene ontologyPPI: Protein-protein interaction.

Data Availability

We have presented all our main data in the form of figuresand additional file. *e datasets supporting the conclusionsof this article are included within the article.

Conflicts of Interest

*e authors declare that they have no conflicts of interest.

Authors’ Contributions

MS and Bl contributed equally to this work. ZL conceivedand designed the experiments; MS and QY performed theexperiments and wrote the paper; XG, XR, and SJ analyzedthe data. All authors read and approved the final manuscript.

Supplementary Materials

Table S1: targets of the 75 candidate compounds of Haima.Table S2: targets-diseases. (Supplementary Materials)

References

[1] G. K. Hansson, “Inflammation, atherosclerosis, and coronaryartery disease,” New England Journal of Medicine, vol. 352,no. 16, pp. 1685–1695, 2005.

[2] E. J. Benjamin, S. S. Virani, C. W. Callaway et al., “Heartdisease and stroke statistics—2018 update: a report from theAmerican heart association,” Circulation, vol. 137, no. 12,pp. e67–e492, 2018.

[3] P. M. Ridker, B. M. Everett, T. *uren et al., “Antiin-flammatory therapy with canakinumab for atheroscleroticdisease,” 8e New England Journal of Medicine, vol. 377,no. 12, pp. 1119–1131, 2017.

[4] J. W. Eikelboom, S. J. Connolly, J. Bosch et al., “Rivaroxabanwith or without aspirin in stable cardiovascular disease,” 8eNew England Journal of Medicine, vol. 377, no. 14,pp. 1319–1330, 2017.

[5] T. O. Cheng, “Cardiovascular effects of danshen,” Interna-tional Journal of Cardiology, vol. 121, no. 1, pp. 9–22, 2007.

[6] W.-Y. Jiang, “*erapeutic wisdom in traditional Chinesemedicine: a perspective from modern science,” Trends inPharmacological Sciences, vol. 26, no. 11, pp. 558–563, 2005.

[7] Y. Jia, F. Huang, S. Zhang, and S.-W. Leung, “Is danshen(Salvia miltiorrhiza) dripping pill more effective than iso-sorbide dinitrate in treating angina pectoris? A systematicreview of randomized controlled trials,” International Journalof Cardiology, vol. 157, no. 3, pp. 330–340, 2012.

[8] M. Sun, J.-J. Zhang, J.-Z. Shan et al., “Clinical observation ofdanhong injection (herbal TCM product from radix Salviaemiltiorrhizae and Flos Carthami tinctorii) in the treatment oftraumatic intracranial hematoma,” Phytomedicine, vol. 16,no. 8, pp. 683–689, 2009.

[9] Y. He, H. Wan, Y. Du et al., “Protective effect of danhonginjection on cerebral ischemia-reperfusion injury in rats,”


http://downloads.hindawi.com/journals/ecam/2020/9180982.f1.zip

Journal of Ethnopharmacology, vol. 144, no. 2, pp. 387–394,2012.

[10] Y. Liu, W. Xu, Y. Xiong, G. Du, and X. Qin, “Evaluations ofthe effect of HuangQi against heart failure based on com-prehensive echocardiography index and metabonomics,”Phytomedicine, vol. 50, pp. 205–212, 2018.

[11] J. Gu, Y. Liu, H.Wu, H. Li, and K. Liu, “Huangqi shengmai yinprotects against radiation-induced cardiac fibrosis injury byregulating the TGF-β1/smads and MMPs,” Evidence-BasedComplementary and Alternative Medicine, vol. 2019, ArticleID 1358469, 10 pages, 2019.

[12] W.-K. Li, G.-F. Wang, T.-M. Wang et al., “Protective effect ofherbal medicine Huangqi decoction against chronic chole-static liver injury by inhibiting bile acid-stimulated inflam-mation in DDC-induced mice,” Phytomedicine, vol. 62,Article ID 152948, 2019.

[13] K. Wang, J. Wu, X. Duan et al., “Huangqi injection in thetreatment of chronic heart failure: a systematic review andmeta-analysis,”Medicine, vol. 96, no. 39, Article ID e8167, 2017.

[14] Y. Zhang, J. Wu, S. Guo et al., “*e clinical efficacy and safetyof the Chinese herbal medicine astragalus (Huangqi) prep-aration for the treatment of acute myocardial infarction: asystematic review of randomized controlled trials,” Medicine,vol. 98, no. 16, Article ID e15256, 2019.

[15] G. Hu, P. Yang, Y. Zeng, S. Zhang, and J. Song, “DangguiBuxue decoction promotes angiogenesis by up-regulation ofVEGFR1/2 expressions and down-regulation of VEGFR1/2expression in myocardial infarction rat,” Journal of the Chi-nese Medical Association, vol. 81, no. 1, pp. 37–46, 2018.

[16] K. Liu, X.-M. Ren, Q.-S. You et al., “Ameliorative effect ofdangguibuxue decoction against cyclophosphamide-inducedheart injury in mice,” BioMed Research International,vol. 2018, Article ID 8503109, 10 pages, 2018.

[17] E. Guney, J. Menche, M. Vidal, and A.-L. Barabasi, “Network-based in silico drug efficacy screening,” Nature Communi-cations, vol. 7, no. 1, 2016.

[18] M. Kibble, N. Saarinen, J. Tang, K. Wennerberg, S. Makela,and T. Aittokallio, “Network pharmacology applications tomap the unexplored target space and therapeutic potential ofnatural products,” Natural Product Reports, vol. 32, no. 8,pp. 1249–1266, 2015.

[19] A. Lavecchia and C. Cerchia, “In silico methods to addresspolypharmacology: current status, applications and futureperspectives,” Drug Discovery Today, vol. 21, no. 2,pp. 288–298, 2016.

[20] X.Wang, Y. Shen, S.Wang et al., “PharmMapper 2017 update:a web server for potential drug target identification with acomprehensive target pharmacophore database,” NucleicAcids Research, vol. 45, no. W1, pp. W356–W360, 2017.

[21] J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systemspharmacology for drug discovery from herbal medicines,”Journal of Cheminformatics, vol. 6, no. 1, p. 13, 2014.

[22] G. Bindea, B. Mlecnik, H. Hackl et al., “ClueGO: a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networks,” Bioinformatics, vol. 25, no. 8,pp. 1091–1093, 2009.

[23] M. V. Kuleshov, M. R. Jones, A. D. Rouillard et al., “Enrichr: acomprehensive gene set enrichment analysis web server 2016update,” Nucleic Acids Research, vol. 44, no. W1, pp. W90–W97, 2016.

[24] D. Szklarczyk, A. Franceschini, S. Wyder et al., “STRING v10:protein-protein interaction networks, integrated over the treeof life,” Nucleic Acids Research, vol. 43, no. D1, pp. D447–D452, 2015.

[25] I. O. Ishola, M. O. Osele, M. C. Chijioke, and O. O. Adeyemi,“Isorhamnetin enhanced cortico-hippocampal learning andmemory capability in mice with scopolamine-induced am-nesia: role of antioxidant defense, cholinergic and BDNFsignaling,” Brain Research, vol. 1712, pp. 188–196, 2019.

[26] T.-T. Zhao, T.-L. Yang, L. Gong, and P. Wu, “Isorhamnetinprotects against hypoxia/reoxygenation-induced injure byattenuating apoptosis and oxidative stress in H9c2 car-diomyocytes,” Gene, vol. 666, pp. 92–99, 2018.

[27] Y. Bian, P. Liu, J. Zhong et al., “Kaempferol inhibits multiplepathways involved in the secretion of inflammatory mediatorsfrom LPS-induced rat intestinal microvascular endothelialcells,” Molecular Medicine Reports, vol. 19, no. 3,pp. 1958–1964, 2019.

[28] L. Zhang, Z. Guo, Y. Wang, J. Geng, and S. Han, “*eprotective effect of kaempferol on heart via the regulation ofNrf2, NF-κβ, and PI3K/Akt/GSK-3β signaling pathways inisoproterenol-induced heart failure in diabetic rats,” DrugDevelopment Research, vol. 80, no. 3, pp. 294–309, 2019.

[29] K. Suchal, S. Malik, N. Gamad et al., “Kampeferol protectsagainst oxidative stress and apoptotic damage in experimentalmodel of isoproterenol-induced cardiac toxicity in rats,”Phytomedicine, vol. 23, no. 12, pp. 1401–1408, 2016.

[30] I. C. Chis, D. Baltaru, A. Dumitrovici et al., “Protective effectsof quercetin from oxidative/nitrosative stress under inter-mittent hypobaric hypoxia exposure in the rat’s heart,”Physiology International, vol. 105, no. 3, pp. 233–246, 2018.

[31] R. V. Patel, B. M. Mistry, S. K. Shinde, R. Syed, V. Singh, andH.-S. Shin, “*erapeutic potential of quercetin as a cardio-vascular agent,” European Journal of Medicinal Chemistry,vol. 155, pp. 889–904, 2018.

[32] L. Y. Dong, F. Chen, M. Xu, L. P. Yao, Y. J. Zhang, andY. Zhuang, “Quercetin attenuates myocardial ischemia-reperfusion injury via downregulation of the HMGB1-TLR4-NF-κB signaling pathway,” American Journal of TranslationalResearch, vol. 10, no. 5, pp. 1273–1283, 2018.

[33] X. Chen, X. Peng, Y. Luo et al., “Quercetin protects car-diomyocytes against doxorubicin-induced toxicity by sup-pressing oxidative stress and improving mitochondrialfunction via 14-3-3c,” Toxicology Mechanisms and Methods,vol. 29, no. 5, pp. 344–354, 2019.

[34] J. Tang, L. Lu, Y. Liu et al., “Quercetin improve ischemia/reperfusion-induced cardiomyocyte apoptosis in vitro and invivo study via SIRT1/PGC-1α signaling,” Journal of CellularBiochemistry, vol. 120, no. 6, pp. 9747–9757, 2019.

[35] D. Gogoi, A. Pal, P. Chattopadhyay, S. Paul, R. C. Deka, andA. K. Mukherjee, “First report of plant-derived β-sitosterolwith antithrombotic, in vivo anticoagulant, and thrombus-preventing activities in a mouse model,” Journal of NaturalProducts, vol. 81, no. 11, pp. 2521–2530, 2018.

[36] M. A. Ahmed, H. M. Kamal, A. M. Taha, and S. F. Abd-Allateef, “Folic acid protects against experimental prenatalnicotine-induced cardiac injury by decreasing inflammatorychanges, serum TNF and COX-2 expression,” Pathophysiol-ogy, vol. 25, no. 2, pp. 151–156, 2018.

[37] F. Al-Rashed, D. Calay, M. Lang et al., “Celecoxib exertsprotective effects in the vascular endothelium via COX-2-independent activation of AMPK-CREB-Nrf2 signalling,”Scientific Reports, vol. 8, no. 1, p. 6271, 2018.

[38] A. Riba, L. Deres, B. Sumegi, K. Toth, E. Szabados, andR. Halmosi, “Cardioprotective effect of resveratrol in apostinfarction heart failure model,” Oxidative Medicine andCellular Longevity, vol. 2017, Article ID 6819281, 10 pages,2017.


[39] L. Chen, P. Liu, X. Feng, and C. Ma, “Salidroside suppressingLPS-induced myocardial injury by inhibiting ROS-mediatedPI3K/Akt/mTOR pathway in vitro and in vivo,” Journal ofCellular and Molecular Medicine, vol. 21, no. 12, pp. 3178–3189, 2017.

[40] S. Tamura, T. Marunouchi, and K. Tanonaka, “Heat-shockprotein 90 modulates cardiac ventricular hypertrophy viaactivation of MAPK pathway,” Journal of Molecular andCellular Cardiology, vol. 127, pp. 134–142, 2019.

[41] R. A. Caceres, T. Chavez, D. Maestro et al., “Reduction ofcardiac TGFβ-mediated profibrotic events by inhibition ofHsp90 with engineered protein,” Journal of Molecular andCellular Cardiology, vol. 123, pp. 75–87, 2018.

[42] Z. Zhang, L. Yao, J. Yang, Z.Wang, and G. Du, “PI3K/Akt andHIF-1 signaling pathway in hypoxia-ischemia (Review),”Molecular Medicine Reports, vol. 18, no. 4, pp. 3547–3554,2018.

[43] A. Ghigo, M. Laffargue, M. Li, and E. Hirsch, “PI3K andcalcium signaling in cardiovascular disease,” CirculationResearch, vol. 121, no. 3, pp. 282–292, 2017.


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