network pharmacology deciphering mechanisms of volatiles...

Research ArticleNetwork Pharmacology Deciphering Mechanisms of Volatiles ofWendan Granule for the Treatment of Alzheimerrsquos Disease

Jun-feng Liu 1 An-na Hu1 Jun-feng Zan2 PingWang 3 Qiu-yun You2 and Ai-hua Tan3

1Ministry of Education Key Laboratory of Chinese Medicine Resource and Compound PrescriptionHubei University of Chinese Medicine Wuhan 430065 China2Pharmacy School Hubei University of Chinese Medicine Wuhan 430065 China3Institute of Geriatrics Hubei University of Chinese Medicine Wuhan 430065 China

Correspondence should be addressed to Jun-feng Liu liujf456hotmailcom and Ping Wang pwang54aliyuncom

Received 30 June 2018 Accepted 29 January 2019 Published 12 February 2019

Guest Editor Khawaja M I Bashir

Copyright copy 2019 Jun-feng Liu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Objective To explore the mechanisms of the volatiles ofWendan granule (WDG) for the treatment of Alzheimerrsquos disease networkpharmacology method integrating absorption distribution metabolism and excretion (ADME) screening target fishing networkconstructing pathway analysing and correlated diseases prediction was appliedMethods Twelve small molecular compounds ofWDG were selected as the objects from 74 volatiles with the relative abundances above 2 and their ADME parameters werecollected from Traditional Chinese Medicine Systems Pharmacology platform (TCMSP) and the corresponding targets genespathways and diseases were predicted according to the data provided by TCMSP DrugBank Uniport and the Database forAnnotation Visualization and IntegratedDiscovery (DAVID)Then the related pathways and correlation analysis were explored bythe Kyoto Encyclopedia and Genomes (KEGG) database Finally the networks of compound target target pathway and pathwaydisease of WDG were constructed by Cytoscape software Results Twelve compounds interacted with 49 targets of which topthree targets were gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1) prostaglandin GH synthase 2 (PGHS-2) andsodium-dependent noradrenaline transporter Interestingly these targets were highly associated with depression insomnia andAlzheimerrsquos disease thatmainly corresponded tomental and emotional illnessesConclusionThe integratednetwork pharmacologymethod provides precise probe to illuminate the molecular mechanisms of the main volatiles ofWDG for relieving senile dementiarelated syndromes which will also facilitate the application of traditional Chinese medicine as an alternative or supplementaryto conventional treatments of AD as well as follow-up studies such as upgrading the quality standard of clinically applied herbalmedicine and novel drug development

1 Introduction

Alzheimerrsquos disease (AD) also known as senile dementiais an age-related progressive neurodegenerative disease thatcontinues to form a huge challenge to the aging communityespecially a heavy burden for patients and their family Withthe worldwide reduction in birth rates and prolonged lifespan expectancies the Alzheimerrsquos disease together withother dementias was considered to be one of the 10 leadingcauses of disability among people with the age above 60globally [1] The decline of cognitive function of old peoplehappened with the progress of aging thus the early detectionand early intervention in cognitive dysfunction are important

for delaying or preventing the occurrence or progression ofdementia enabling patients tomaintain basic cognitive func-tions and improve their quality of life for a longer period oftime [2] Various medicines have been developed all over theworld for the treatment of AD There are four conventionaltherapeutics strategies for the treatment of AD using modernclinical medicines (1) restoration of cognitive impairment(2) activation of 120572-secretase (3) inhibition of 120573-secretaseand 120574-secretase and (4) inhibition of Tau hyperphosphory-lation [3] However for clinically applied medicines suchas (1) acetylcholinesterase inhibitors Donepezil Galan-tamine (2) N-methyl-D-aspartate acid receptor MagnesiumHydrochloride [4] and (3) ABT-126 [5] that only target

HindawiEvidence-Based Complementary and Alternative MedicineVolume 2019 Article ID 7826769 12 pageshttpsdoiorg10115520197826769

2 Evidence-Based Complementary and Alternative Medicine

the symptoms of AD but not the pathogenesis Thougha certain degree of recovering impairment efficacies thesemedicines may have their side effects may also have to beconsidered Gastrointestinal reactions diarrhoea nausea andvomiting insomnia fatigue and urinary incontinence arethe common adverse effects of cholinergic drugsMagnesiumHydrochloride which is the only nonacetylcholinesteraseinhibitor approved for AD treatment has the adverse effectssuch as fatigue high blood pressure dizziness headache andconstipation

Traditional Chinese medicine (TCM) prescriptions playan important role in the treatment of various serious diseasesespecially those thewesternmedicine find it difficult to tacklebecause the therapeutic effects of TCM are based on syner-gistic and holistic theory Unfortunately the uncertainty ofpotential active compounds explicit targets and underlyingpharmacological mechanisms impeded the modernization ofTCM Network pharmacology [6] an emerging promisingsubject and an advanced approach to new drug discoveryprovides novel tactics for the understanding of the relation-ship between drugs and diseases at a systematic level

Wendan granule (WDG) is a hospital preparation oftraditional Chinese medicine prescription for the treatmentof AD It is a modern dosage form produced based on amodified prescription of Wendan decoction which is anancient and classical prescription with the function of ldquoHua-Tan (化痰)rdquo TCM believes that Tan can stay in various partsof the body including brain and produce all sorts of diseasesOn the other hand just like the aggregated 120573-amyloid inbrains of AD patients Tan itself is also a pathological productof various diseases Therefore the pharmacological efficaciesof WDG are predicted to be reducing the production andpromoting the clearance of 120573-amyloid in brains of ADpatients by multiple mechanisms Our previous study hasidentified 74 volatiles by employing HS-SPME-GC-MS [7]However their specific mechanisms of efficacy are still vagueHence in this study network pharmacology was applied toexplore the mechanisms of the main volatiles ofWDG for thetreatment of AD

2 Materials and Methods

21 Materials

211 Analysis Platforms and Databases TCMSP (httplspnwueducnindexphp) is a systems pharmacology plat-form and database of Chinese herbal medicines [8] Drug-Bank (httpswwwdrugbankca) is a bioinformatics andcheminformatics resource database [9] Uniprot (httpswwwuniprotorg) is a comprehensive database about pro-tein [10] DAVID (httpsdavidncifcrfgov) is a databaseto help understand biological meaning behind genes byproviding various functional annotations [11] KEGG (httpswwwkeggjp) is Kyoto Encyclopedia of Genes and Genomes[12] GeneMANIA (httpgenemaniaorg) is a website usedfor predicting the function of genes and gene sets [13]

212 Utility Software ChemBioOffice Ultra 120 (PerkinElmer Inc Waltham MA USA) is applied for candidate

compounds structures constructing Cytoscape 351 is opensource software for visualizing complex networks and inte-grating them with all sorts of types of attribute data [14]

22 Methods

221 Candidate Components Screen Our previous study hasidentified 74 volatiles from WDG by employing HS-SPME-GC-MS In order to acquire the potential main volatilecompounds from the granule four screening criteria weredefined as follows OB (oral bioavailability) ⩾30 Caco-2permeabilitygt0 BBB (bloodndashbrain barrier [15])⩾03 and rel-ative abundance (RA) ⩾02 Based on the ADME parametersin TCMSP and RA values obtained from previous work [7]the volatiles which satisfied the principles were selected as thecandidate compounds

222 Targets Screening To identify the corresponding tar-gets of main compounds of WDG several approaches wereimplemented First of all TCMSP and DrugBank databasewere applied to find out the potential targets Then ldquodrug-targetrdquo network will be constructed by Cytoscape 351 soft-ware The candidate targets were mainly screened by degreethat represents the number of edges adjacent to a node [3]Next for more accurate result obtaining purpose the targetswith degree ⩾3 were chosen as candidate targets and otherswere eliminated

223 GeneMANIA Analysis GeneMANIA was used forpredicting the function of genes and gene sets A relationshipnetwork of genes was given after input of a set of gene list andspecies selected as ldquohomo sapiensrdquo

224 GO and Pathway Annotation The targets were input tothe DAVID for further investigation such as Gene Ontology(GO) pathways Construct the ldquotarget-pathwayrdquo networkThe pathways that are equal to or above degree 3 wereanalysed for candidate pathways identification

225 Identification for Diseases The KEGG gave you infor-mation about related disease when candidate pathwaysentered and then constructed the ldquopathway-diseaserdquo networkand preliminarily speculate the pharmacologicalmechanismsof WDG with all the information above

3 Results and Discussion

31 Identification of Active Volatility Components From theformer work 75 volatility components were identified byemploying HS-SPME-GC-MS For the purpose of acquiringthe main compounds four screening criteria were definedas follows OB⩾30 Caco-2 permeabilitygt0 BBB⩾03 andRA⩾02 A total of 12 compounds including 120574-Asarone trans-ligustilide and senkyunolide A which showed poor OBbut have high abundance were selected as the candidatecompounds for more accurate investigation (Table 1 seeTable 1S in the Supplementary Material for the detailed infor-mation of the twelve candidate volatiles) For the purposeof tracing back to the original herbal medicines appliedto form the WDG prescription twelve compounds were

Evidence-Based Complementary and Alternative Medicine 3

Ligusticumchuanxiong Hort

Zingiber officinaleRoscoe

Acorus tatarinowiiSchott

SenkyunolideA

Transligustilide dl-3n-Butyl-phthalide

3-Butyl-idenephthalide Sedanolide

T-Muurolol

Linalool

ymol

-Asarone

Isocalamendiol

Methyl eugenol

-Asarone

OH

O

O

O

OO

O

O

O

H

H OH

HO

HO

O

O

O

O

O

O

O

O O

O

Figure 1 Twelve candidate compounds and their corresponding herbal medicines

Table 1 Candidate compounds ADME values and molecular information

Compound Name Molecular Formula Molecular Weight RA () OB () Caco-2 BBBSenkyunolide A C

12H16O2

192 7271 2656 13 134Trans-ligustilide C

13H18O 190 662 235 128 12

dl-3n-butylphthalide C12H14O2

190 6273 479 13 1323-Butylidenephthalide C

12H12O2

188 4138 4244 132 127Methyl eugenol C

11H14O2

178 2987 7336 147 141T-Muurolol C

15H26O 222 3129 3041 136 144

Sedanolide C12H18O2

194 3522 6246 124 14120574-Asarone C

12H16O3

208 14658 2276 15 133120573-Asarone C

12H16O3

208 15798 3561 145 124Linalool C

10H18O 154 274 3829 129 133

Isocalamendiol C15H26O2

238 2518 5763 094 074Thymol C

10H14O 150 2125 4147 16 168

input into TCMSP to backtrack the corresponding herbs(Figure 1) and two five and two were recognized as thoseof Ginger Chuanxiong and Acorus tatarinowii respectivelySenkyunolide A one of the main bioactive constituents inthe herb Rhizoma Chuanxiong shows protective effect onthe injury of central nervous system and on contractionsto various contractile agents in rat isolated aorta [16] 120573-Asarone which shows the highest abundance in WDG and ashared compound ofChuanxiong andAcorus tatarinowii hasbeen investigated with regard to effects on central nervoussystem [15 17] Thymol a common ingredient shared byGinger and Chuanxiong possesses active antioxidant effect[18] Butylidenephthalide has been suggested to producevarious pharmacological activities in cerebral blood vessels[19] Butylphthalide could decrease the brain infarct volumeand enhance microcirculation thus benefiting patients [20]BBB of all the 12 are more than 100 which means they all

have effect to break through the BBB to cure disease relatedto CNS

32 Analysis of ldquoCompound-Targetrdquo Network AD has beenconsidered as one of the most serious threats of healthalthough many different types of therapeutic methods havebeen applied for the management and prevention of ADIdentifying the compound interacting with targets is a goodstrategy for drug discovery TCMSP and DrugBank databasewere applied for predicting the potential targets for eachcompound As a result 201 compound-target interactionswere identified between 12 compounds and 49 targetsand the candidate targets were selected according to thedegrees that reflect the number of edges of each target(Figure 2 see Table 2S 1-9 in the Supplementary Materialfor the detailed compound-target information of the twelvecandidate volatiles with their corresponding targets) After


Compound Target

Figure 2 Compound-target network analyses results A compound and a target linked if the target protein was hit by the correspondingcompoundNode size was proportional to its degree that correlatedwith the number edges of each targetcompound 12 candidate compoundswere linked with 49 candidate targets The network shows that most of the compounds hit more than one target and vice versa (The detailedcompound-target information of the twelve volatiles with their corresponding targets can be found in Table 2S 1-9 in the SupplementaryMaterial)

calculating the value of degree for each target in C-T networkthe average value of the degrees was 410 Therefore thetargets with degree⩾3 were regarded as candidate targets forfurther investigation (Table 1)

GABRA1 shows the highest degree followed by PTGS2SLC6A2 and CHRM1 Many of them have been verifiedin previous researches For instance GABRA1 was foundto play an important role in Alzheimerrsquos dementia [21]and a previous study reported that gamma-amino butyricacid receptor subunit alpha-1 could inhibit neurotransmis-sion in the brain [22] GABA receptors might induce asignificant impact on brain structures and functions andmight also participate in the dysregulation of the balancebetween excitatory and inhibitory neurotransmission thatwas observed in AD patients [23] Both PTGS1 and PTGS2could convert arachidonate into prostaglandin H2 (PGH2)PTGS1 which is involved in the production of prostanoidsparticularly in the stomach and platelets could help promoteplatelet activation and aggregation vasoconstriction andproliferation of vascular smooth muscle cells PTGS2 wasmainly expressed in endothelium kidney and brain and

in cancers Some researchers reported that patients withAD have a higher PTGS2 in hippocampal neurons and itis convinced that PTGS2 played an important role in thepathogenesis of AD for it could convert arachidonate intoPGH2 which might induce inflammation in the brain [24ndash26] Meanwhile the modern epidemiology also showed thatthe use of nonsteroidal anti-inflammatory drugs can reducethe incidence of AD therefore controlling the inflammatoryresponse became one of the important therapeutic strategiesof clinical treatment of AD [27] SLC6A2 was closely asso-ciated with depression This protein was the target of psy-chomotor stimulants such as Amphetamines or Cocaine [2829] CHRM1 CHRM2 and CHRM3 were three subtypes ofCHRM (muscarinic acetylcholine receptors) one of cholin-ergic neurotransmitter receptors that usually participated inthe regulation of cognitive function [30 31] Relevant phar-macological experiments have indicated the specific reduc-tion of cholinergic neurons on basal forebrain in AD patientsand the rate of hippocampal atrophy in AD patients washalved after cholinesterase inhibitors were given for one year[32] CHRM1 was considered as a key target of AD therapy


The function of CHRM2 was to control the release of acetyl-choline which is located in the terminal cholinergic neuronsin the forebrain [33] CHRM3 was able to promote variouscellular activities by regulating the passage of different signals[34] In summary the common functions of these targetswere relevant with nervous system diseases like insomniaAD which were also the indications of WDG The relation-ship between the compounds and targets revealed the truththat multiple compounds and multiple targets interact witheach other inmolecular system that might break off and jumpout of the traditional ldquoone-target-one-compoundrdquo model

33 Gene Function Analysis by Using GeneMANIA Mostsuccessful computational methods for compound interactingwith targets prediction integrate the prediction of multipledirect targets and multiple indirect targets GeneMANIAa useful website to find genes most related to the querygenes is capable of predicting protein functions with theadvanced and unique algorithm and is also regarded as areal-time multiple association network integration algorithmfor predicting gene function such as coexpression colocal-ization pathways and protein domain [35] Figure 3 showsthe network generated by GeneMANIA website The nodeswith black colour represent the input genes and the greynodes represent the associated genesThe edges with differentcolour are associated with different functions As shown inthe results (Figure 3) 3924 of the genes shared proteindomains and 2060 had the coexpression

34 Analysis ofGOEnrichment Thirty candidate targetswerechosen for further investigation by using DAVID (Table 3see Table 3S 1-3 in the Supplementary Material for theintegrated GO enrichment analysis results of thirty selectedcandidate targets) Gene ontology enrichment analysis con-sisted of three parts BP (biological process) CC (cellularcomponent) and MF (molecular function) Drug bindingprotein heterodimerization activity and epinephrine bindingwere predicted to be the main biological functions inducedby 12 volatiles Plasma membrane integral component ofplasma membrane and integral component of membranewere ranked as top three cellular components which mightreflect that most of the small volatiles were targeted toneural cells The top three biological processes were the Gprotein coupled receptor signaling pathway the acetylatecycles activating adrenergic receptor signaling pathway andthe response to drug G protein coupled receptor signalingpathway was ranked as No 1 which indicated that Gprotein coupled receptor might be one of the main drugtargets for the treatment There were previous studies whichreported that G protein coupled receptors might serve asthe largest pharmacodynamic therapeutic target for ADbecause they can directly affect the beta-amyloid signalingcascade in the nervous system by regulating 120572- 120573- 120574- secretory enzyme secretion amyloid precursor protein(APP) production and A120573 degradation[36] FurthermoreCHRM1 CHRM3 and HTR2A all belonged to the G proteincoupled receptor The abnormality of a variety of signalpathways and signal transmission played important rolesin the pathogenesis of AD moreover the dysfunction of

adenylate cyclase signaling system was considered to bethe main cause of AD G protein-mediated dysfunctionof adenylate cyclase signaling system was an importantenlightenment for the prevention and treatment of AD [37]Interestingly ADRA1B and ADRA1A an alpha-adrenergicreceptor mediated their effects through binding to the Gprotein that could activate the phosphatidylinositol-calciumsecond messenger system [38] ADRB1 and ADRB2 were120573-adrenergic receptors that mediate catecholamine-inducedactivation of adenylate cyclase through the sensitization ofthe G protein [39] ADRA2A and ADRA2C were alpha-2adrenergic receptors that mediated catecholamine-inducedinhibition of adenylate cyclase also through the sensitizationof the G protein [40]

35 Target-Pathway and Pathway-Disease Networks For thepurpose of systematically deciphering the multiple under-lying mechanisms of volatiles of WDG all of the pathwaysinteracting with candidate targets were extracted fromKEGGpathways database using DAVID and then the ldquotarget-pathwayrdquo network was constructed (Figure 4) Twenty-threerelated pathways were found including neuroactive ligand-receptor interaction calcium signaling pathway cGMP-PKGsignaling pathway and cAMP signaling pathway Signalingpathways have been regarded as one of the most importantparts of systems pharmacology [41] As shown in Figure 3five targets including DAD1 GRIA2 MAOA MAOB andSLC6A3 were all found to be associated with Cocaineaddiction Amphetamine addiction Dopaminergic synapseand Alcoholism together which could help speculate thepharmacokinetic synergistic effects among themGABA fam-ily including GABRA1 GABRA2 GABRA3 and GABRA6worked together on neuroactive ligand-receptor interactionMorphine addiction retrograde endocannabinoid signal-ing Nicotine addiction and GABAergic synapse pathwaysADRA1A ADRA1B ADRA1D ADRB1 and ADRB2 thatbelonged to adrenergic receptor were all associated withneuroactive ligand-receptor interaction calcium signalingpathway cGMP-PKG signaling pathway salivary secretionand adrenergic signaling in cardiomyocytes Neuroactiveligand-receptor interaction might be the main pathwaythat correlated with the mechanism For all the pathwaysthree of them belonged to the cellular processes regula-tion of actin cytoskeleton gap junction and endocytosisFive belonged to environmental information processing orsignal transduction calcium signaling pathway cGMP-PKGsignaling pathway cAMP signaling pathway PI3K-Akt sig-naling pathway and neuroactive ligand-receptor interactionFive belonged to human diseases or substance dependenceMorphine addiction Amphetamine addiction AlcoholismCocaine addiction and Nicotine addiction Nine belonged toorganismal systems adrenergic signaling in cardiomyocytessalivary secretion regulation of lipolysis in adipocytes reninsecretion retrograde endocannabinoid signaling seroton-ergic synapse dopaminergic synapse GABAergic synapseand cholinergic synapse while the last four also belonged tonervous system

In Figure 5 there were totally 142 nodes while 24 of whichshaped green lsquoVrsquos corresponded to candidate pathways and


Shared protein domains3924

Physical interactions2149

Coexpression 2060

Pathway923

Colocalization 899

Genetic interactions046

Figure 3 GENEMAIA based network analysis the black nodes represented the input genes and the grey nodes represented the associatedgenes The edges with different colour were associated with different functions (The detailed information of the integrated GO enrichmentanalysis results of thirty selected candidate targets can be found in Table 3S 1-3 in the Supplementary Material)

the remaining 118 red circle nodes represented diseasesNeuroactive ligand-receptor interaction pathways calciumsignaling pathway retrograde endocannabinoid signalingpathways and endocytosis were corresponding to 27 2323 and 13 kinds of different diseases All the correspondingdiseases were classified by KEGG while nervous systemrelated disease accounts for largest proportion followed by

musculoskeletal disease developmental disorder endocrinedisease inheritedmetabolic disease and cardiovascular disease

4 Discussion

TCM prescriptions were prescribed for fighting against dis-eases from ancient China till now based on the theories of


Target Pathway

Figure 4 Target-pathway network analyses resultsThe red triangle nodes represented the targets while the blue circle nodes represented thepathways The target proteins were linked with their corresponding pathways Nodes sizes were proportional to their degrees (abbreviationsSP signaling pathway MB metabolism NAFLD nonalcoholic fatty liver disease and ALS amyotrophic lateral sclerosis)

traditional Chinese medicine Multicomponents multitar-gets and various pharmacological actions within herbs werethe superiority but also the key issues of the modernizationstudies of traditional Chinese medicine Here we presentedefficient tactics that contribute to understanding the phar-macological mechanism of TCM prescription by ldquonetworkpharmacologyrdquo in molecular level to reveal the interactionbetween small molecule compounds with protein targetspathways and diseases The ldquonetwork pharmacologyrdquo wasfirst proposed by Andrew L Hopkins [42] Different fromthe conventional drug discovery mode the network phar-macology breaks through the ldquoone-drug-one-targetrdquo modeto the synergy of multiple targets to identify the therapeuticmechanisms of TCM based on holistic research strategy ofmulticomponents multitargets and multidiseases [43]

Twelve main compounds with favourable ADME wereselected from seventy-four volatiles of WDG identifiedusing HS-SPME-GC-MS Then online databases TCMSPand DrugBank were applied to obtain potential interactiontargets with the pathways followed by GO enrichmentclustering analysis The results showed some compounds andtargets identified in this research that might be conductive tothe therapy and prevention ofAlzheimerrsquos disease had alreadybeen reported previously which highlighted the credibility oftheADMEevaluation system and target predicting databasesOne target was hit by multiple ingredients which coincided

with the holistic view and synergistic action theory of TCMPrediction of potential targets might be the first step in drugdiscovery within the wide application of computational targetfishing technology As shown in Table 2 some targets hadalready been reported previously For example GABRA1 isthe most important inhibitory receptor in brain that canproduce inhibitory postsynaptic sites to inhibit neuronaldischarge by acting on the chloride channel after combiningwith GABA GABRA1 and GABRA2 are the componentsof the heteropentameric receptor for GABA receptor ofGABA (gamma-aminobutyric acid) that is an importantinhibitory neurotransmitter in vertebrate brain and playsan essential role in the treatment of insomnia by inhibitingbrain neurotransmitters excitement [44] Moreover molec-ular mechanisms of Alzheimerrsquos disease treatment weremade manifest based on the ldquocompound-targetrdquo networkldquotarget-pathwayrdquo network and ldquopathway-diseaserdquo network ofWDG

5 Conclusions

Previous studies had identified that WDG can improvethe learning and memory ability by repairing the dam-age of cholinergic system inhibiting the cytokine levelsof transition-activated microglia and reducing the level oftransient phosphorylation Tau protein [45ndash47]


Figure 5 Pathway-disease network analyses results Red nodes represented diseases while the green nodes represented pathways Theedges represented the interaction between them (abbreviations AADC aromatic L-amino acid decarboxylase deficiency ADHD attentiondeficit hyperactivity disorder ADNFLE autosomal dominant nocturnal frontal lobe epilepsy APDS activated PI3K-delta syndrome ATSAndersen-Tawil syndrome BRS Brugada syndrome CAA cerebral amyloid angiopathy CFEOM congenital fibrosis of the extraocularmuscles CMDMDC congenital muscular dystrophies CSGD congenital systemic glutamine deficiency CSNB congenital stationary nightblindness FCD fleck corneal dystrophy FPL familial partial lipodystrophy FTC familial tumoral calcinosis GFND Glomerulopathywith fibronectin deposits HD Hirschsprung disease HLHS hypoplastic left heart syndrome HypoPP hypokalemic periodic paralysisIFSHD isolated follicle-stimulating hormone deficiency IGEs idiopathic generalized epilepsies LADD lacrimo-auriculo-dento-digitalsyndrome LCCS lethal congenital contracture syndrome LCD lattice corneal dystrophies LDS Loeys-Dietz syndrome LGMD limb-girdle muscular dystrophy MmD multiminicore disease NBIA neurodegeneration with brain iron accumulation NDNC nonsyndromiccongenital nail disorder NSIAD nephrogenic syndrome of inappropriate antidiuresis OCD obsessive compulsive disorder OHSS ovarianhyperstimulation syndrome PEO progressive external ophthalmoplegia POH progressive osseous heteroplasia TAAD familial thoracicaortic aneurysm and dissection and WS Waardenburg syndrome)

Based on the series of results from the database platformand software given we could preliminarily predict thatthe mechanism of WDG involved a large network whichcontained drugs targets pathways and diseases The maintargets were GABRA1 PTGS2 SLC6A2 CHRM1 ADRA1BCHRM2 CHRM3 ADRA1A ADRB1 ADRB2 PTGS1 andSLC6A3 that conducted with neuroactive ligand-receptorinteraction pathways signaling pathways and metabolismpathways related to analgesic anticonvulsant and repairreduction of cholinergic neurons and eliminate the inflam-matory response to reduce the secretion of neurotoxic inflam-matory cytokine to cure diseases related toAD and insomnia

This study chose a novel strategy for the systematic under-standing of the molecular mechanisms of the main volatilesof WDG relieving senile dementia related syndromes whichcould facilitate the application of TCM in modern medicineand prescriptiondosage form optimization [48] Furtherstudies such as molecular biological experiments and clin-ical investigations have to be carried out to verify thesemechanisms It might be a long-term and arduous taskbut only in this way could we scientifically elaborate andevaluate the mechanisms of efficacy of the clinically approvedTCMprescriptions and promote theworldwide application ofChinese medicine [49]


Table 2 Information of candidate targets their corresponding gene symbols and their degrees of correlation with compounds

Target Gene symbol DegreeGamma-aminobutyric acid receptor subunit alpha-1 GABRA1 10Prostaglandin GH synthase 2 PTGS2 10Sodium-dependent noradrenaline transporter SLC6A2 10Muscarinic acetylcholine receptor M1 CHRM1 9Alpha-1B adrenergic receptor ADRA1B 8Muscarinic acetylcholine receptor M2 CHRM2 8Muscarinic acetylcholine receptor M3 CHRM3 8Alpha-1A adrenergic receptor ADRA1A 7Beta-1 adrenergic receptor ADRB1 7Beta-2 adrenergic receptor ADRB2 7Prostaglandin GH synthase 1 PTGS1 7Sodium-dependent dopamine transporter SLC6A3 7Alpha-2A adrenergic receptor ADRA2A 6Alpha-2C adrenergic receptor ADRA2c 6cAMP-dependent protein kinase inhibitor alpha PKIA 6Gamma-aminobutyric-acid receptor alpha-2 subunit GABRA2 6Alpha-2B adrenergic receptor ADRA2B 5Gamma-aminobutyric-acid receptor subunit alpha-6 GABRA6 5Sodium channel protein type 5 subunit alpha SCN5A 5Sodium-dependent serotonin transporter SLC6A4 55-hydroxytryptamine 2A receptor HTR2A 4Alpha-1D adrenergic receptor ADRA1D 4CGMP-inhibited 3101584051015840-cyclic phosphodiesterase A PDE3A 4Dopamine D1 receptor DRD1 4Leukotriene A-4 hydrolase LTA4H 4Amine oxidase [flavin-containing] A MAOA 3Amine oxidase [flavin-containing] B MAOB 3Gamma-aminobutyric-acid receptor alpha-3 subunit GABRA3 3Glutamate receptor 2 GRIA2 3Retinoic acid receptor RXR-alpha RXRA 3

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Authorsrsquo Contributions

Jun-feng Liu and An-na Hu contributed equally to this work

Acknowledgments

An abstract of the pilot study results was presented on ldquo2018International Conference on the Pharmacology of Traditional

Medicine of the Belt and Road Initiativesrdquo Professor DineshKumar Bharatraj and Dr Amir Hooman Kazemi have givenus some valuable advices that facilitated the optimization ofthe data analysis procedure and the Discussion section ofthis manuscript The project was supported by Grants fromNatural Science Foundation of Hubei [2015CFB321] and KeyProject of National Natural Science Foundation of China[81130064]

Supplementary Materials

The detailed information of the 12 volatile compounds (S1)compound-target analysis data (S2) and Gene Ontologyenrichment analysis results (S3) used to support the findingsof this study is included within the supplementary informa-tion files (Supplementary Materials)


Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05

proteinheterodimerizationactiv

ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04

extracellularligand-gatedionchannelactivity

401

240

E-05

490E-04


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[2] J S Elkins V C Douglas and S C Johnston ldquoAlzheimerdisease risk and genetic variation in ACE A meta-analysisrdquoNeurology vol 62 no 3 pp 363ndash368 2004

[3] A Fisher Z Pittel R Haring et al ldquoM1muscarinic agonists canmodulate some of the hallmarks in alzheimerrsquos disease Implica-tions in future therapyrdquo Journal of Molecular Neuroscience vol20 no 3 pp 349ndash356 2003

[4] C Cui The Effect Evaluation of Different Anti-dementia Drugstreatment in the cognitive of Alzheimerrsquos disease A networkMETA-Analysis Jilin university 2017

[5] D L Bi N Wen Y Shen and W Xiong ldquoDevelopment ofpotential therapeutic targets of and approaches to Alzheimerdiseaserdquo Chinese Journal of Pharmacology and Toxicology vol29 no 4 pp 507ndash535 2015

[6] A L Hopkins ldquoNetwork pharmacology the next paradigm indrug discoveryrdquoNature Chemical Biology vol 4 no 11 pp 682ndash690 2008

[7] J F Liu J F Zan P Wang et al ldquoHS-SPME-GCMS analysisof volatile components in Yi-Zhi-Wen-Dan granulesrdquo LishizhenMedicine and Material Research vol 28 no 1 pp 67ndash70 2017

[8] J Ru P Li JWang et al ldquoTCMSP a database of systems pharm-acology for drug discovery from herbal medicinesrdquo Journal ofCheminformatics vol 6 no 1 p 13 2014

[9] D S Wishart Y D Feunang A C Guo et al ldquoDrugBank 50 Amajor update to the DrugBank database for 2018rdquoNucleic AcidsResearch vol 46 no 1 pp D1074ndashD1082 2018

[10] UniProt Consortium ldquoUniprot the universal protein knowl-edgebaserdquo Nucleic Acids Research vol 45 no D1 pp D158ndashD169 2017

[11] D W Huang B T Sherman and R A Lempicki ldquoSystematicand integrative analysis of large gene lists using DAVID bioin-formatics resourcesrdquo Nature Protocols vol 4 no 1 pp 44ndash572009

[12] M Kanehisa M Furumichi M Tanabe Y Sato and KMorishima ldquoKEGG new perspectives on genomes pathwaysdiseases and drugsrdquo Nucleic Acids Research vol 45 no 1 ppD353ndashD361 2017

[13] D W Farley S L Donaldson O Comes et al ldquoThe Gene-MANIA prediction server biological network integration forgene prioritization and predicting gene functionrdquoNucleic AcidsResearch vol 38 no 2 pp W214ndashW220 2010

[14] M E Smoot K Ono J Ruscheinski P L Wang and T IdekerldquoCytoscape 28 new features for data integration and networkvisualizationrdquo Bioinformatics vol 27 no 3 pp 431-432 2011

[15] RDaneman andA Prat ldquoThe bloodndashbrain barrierrdquoCold SpringHarbor Perspectives in Biology vol 7 no 1 Article ID 0204122015

[16] S S-K Chan T-Y Cheng and G Lin ldquoRelaxation effectsof ligustilide and senkyunolide A two main constituents ofLigusticum chuanxiong in rat isolated aortardquo Journal of Ethno-pharmacology vol 111 no 3 pp 677ndash680 2007

[17] J D Sharma P C Dandiya R M Baxter and S I KandelldquoPharmacodynamical effects of asarone and120573-asaronerdquoNaturevol 192 no 4809 pp 1299-1300 1961

[18] N V Yanishlieva E M Marinova M H Gordon and VG Raneva ldquoAntioxidant activity and mechanism of action ofthymol and carvacrol in two lipid systemsrdquo Food Chemistry vol64 no 1 pp 59ndash66 1999

[19] C M Teng W Y Chen W C Ko and C H Ouyang ldquoAnti-platelet effect of butylidenephthaliderdquo Biochimica et BiophysicaActa vol 924 no 3 pp 375ndash382 1987

[20] Y Ozaki S Sekita and M Harada ldquoCentrally acting mus-cle relaxant effect of phthalides (Ligustilide Cnidilide andSenkyunolide) obtained from Cnidium officinale MakinordquoYakugaku Zasshi vol 109 no 6 pp 402ndash406 1989

[21] N W Seidler ldquoEffect of GABRA1 cytoplasmic peptide on aregulatory proteinrdquo Biophysical Journal vol 112 no 3 p 502a2017

[22] H Deng W-J Xie W-D Le M-S Huang and J JankovicldquoGenetic analysis of the GABRA1 gene in patients with essentialtremorrdquo Neuroscience Letters vol 401 no 1-2 pp 16ndash19 2006

[23] K J Johnson T Sander A A Hicks et al ldquoConfirmation ofthe localization of the humanGABAA receptor1205721-subunit gene(GABRA1) to distal 5q by linkage analysisrdquo Genomics vol 14no 3 pp 745ndash748 1992

[24] Y L HuangM YWan X S Liang and F R Liang ldquoRegulationof FAHgene and PTGS2 gene by acupoint selection on the samemeridian for controlling migrainerdquo Journal of Beijing Universityof Traditional Chinese Medicine vol 37 no 4 pp 280ndash284 2014

[25] S F Kim D A Huri and S H Snyder ldquoInducible nitric oxidesynthase binds Snitrosylates and activates cyclooxygenase-2rdquoScience vol 310 no 5756 pp 1966ndash1970 2005

[26] Y F Zhang Expression QTL and Genetic Regulatory NetworkAnalysis of AD-Related Gene Soat1 and Ptgs2 Nantong Univer-sity 2012

[27] J N Trollor E Smith B T Baune et al ldquoSystemic inflammationis associated with MCI and its subtypes the Sydney Memoryand Aging Studyrdquo Dementia and Geriatric Cognitive Disordersvol 30 no 6 pp 569ndash578 2010

[28] F Xiong Association of EMILIN1 SLC6A2 polymorphisms andoccupational stress with hypertension Xinjiang Medical Uni-versity 2014

[29] T Pacholczyk R D Blakely and S G Amara ldquoExpressioncloning of a cocaine-and antidepressant-sensitive human nora-drenaline transporterrdquo Nature vol 350 no 6316 pp 350ndash3541991

[30] L C Tan L Zhao C M Deng et al ldquoLovastatin may up-regulate expression of muscarinic acetylcholine receptors andagainst the neurotoxicity of szlig-amyloid peptide on the recep-torsrdquo Chinese Journal of Gerontology vol 10 no 37 pp 2363ndash2367 2017

[31] L Zhang H B Huan and X D Wen ldquoEffect of muscariniccholinergic receptor 3 on invasion and migration in hepato-cellular carcinoma cellsrdquo Acta Academiae Medicinae MilitarisTertiae vol 39 no 6 pp 509ndash514 2017

[32] B Dubois M Chupin H Hampel et al ldquoDonepezil decreasesannual rate of hippocampal atrophy in suspected prodromalAlzheimer diseaserdquoAlzheimersDementia vol 11 no 9 pp 1041ndash1049 2015

[33] A J Wein ldquoRe Cumulative use of strong anticholinergics andincident dementia A prospective cohort studyrdquo The Journal ofUrology vol 193 no 6 p 2035 2015

[34] JMontojo K Zuberi H Rodriguez GD Bader andQMorrisldquoGeneMANIA Fast gene network construction and functionprediction for Cytoscaperdquo F1000Research vol 3 2014


[35] S Mostafavi D Ray D Warde-Farley C Grouios and Q Mor-ris ldquoGeneMANIA a real-time multiple association networkintegration algorithm for predicting gene functionrdquo GenomeBiology vol 9 no 1 p S4 2008

[36] X L Liu Y J Han S Chao et al ldquoadvancement of G protein-coupled Receptors Related to Alzheimers diseaserdquo Progress inModern Biomedicine vol 12 no 27 pp 5380ndash8384 2012

[37] B H Luo X Z Zhang L Zhao et al ldquoReview of mechanismprevention and treatment of g protein- adenylate cyclase signalpathway in alzheimers diseaserdquo Liaoning Journal of TraditionalChinese Medicine vol 39 no 6 pp 1184ndash1188 2012

[38] C D Wright Q Chen N L Baye et al ldquoNuclear 1205721-adrenergicreceptors signal activated ERK localization to caveolae in adultcardiac myocytesrdquo Circulation Research vol 103 no 9 pp 992ndash1000 2008

[39] Y Pak N Pham and D Rotin ldquoDirect binding of the 1205731 adren-ergic receptor to the cyclic AMP-dependent guanine nucleo-tide exchange factor CNrasGEF leads to ras activationrdquoMolec-ular and Cellular Biology vol 22 no 22 pp 7942ndash7952 2002

[40] C Li Y Fan T-H Lan N A Lambert and G Wu ldquoRab26modulates the cell surface transport of 1205722- adrenergic receptorsfrom the GolgirdquoThe Journal of Biological Chemistry vol 287 no51 pp 42784ndash42794 2012

[41] R Iyengar S Zhao S Chung D E Mager and J M GalloldquoMerging systems biology with pharmacodynamicsrdquo ScienceTranslational Medicine vol 4 no 126 pp 126-127 2012

[42] A L Hopkins ldquoNetwork pharmacologyrdquo Nature Biotechnologyvol 25 no 10 pp 1110-1111 2007

[43] Q Xu R Bauer B M Hendry et al ldquoThe quest for moderni-sation of traditional Chinese medicinerdquo BMC Complementaryand Alternative Medicine vol 13 no 132 2013

[44] Z L Liu C L Tang et al ldquoEffect of different intensities ofelectroacupuncture on the expression ofGABA andGABRA1 inhypothalamus of insomnia rats by PCPArdquo Life Science Researchvol 15 no 3 pp 236ndash240 2011

[45] H Hu P Wang M W Kong et al ldquoEffect and potentialmechanism of replenishing the kidney and eliminating phlegmtherapy onCaMKII-120572 activities of AD ratmodelrdquoChina Journalof Traditional ChineseMedicine and Pharmacy vol 25 no 5 pp786ndash788 2010

[46] M W Kong and P Wang ldquoApproach of the relationshipbetween senile dementia and renal deficiency and phlegmaticabundancerdquo Shanxi Journal of Traditional ChineseMedicine vol27 no 3 pp 1ndash3 2011

[47] W Y Zhang H Hu P Wang et al ldquoExplore the effect andpotential mechanism of rat hippocampal cortex cholinergicsystem on replenishing kidney-essence and removing phlegmtherapyrdquo Journal of Traditional Chinese Medicine vol 26 no 2pp 239ndash2541 2011

[48] B Li W Tao C Zheng et al ldquoSystems pharmacology-basedapproach for dissecting the addition and subtraction theory oftraditional Chinese medicine an example using Xiao-Chaihu-Decoction and Da-Chaihu-Decoctionrdquo Computers in Biologyand Medicine vol 53 pp 19ndash29 2014

[49] S Li and B Zhang ldquoTraditional Chinese medicine networkpharmacology theory methodology and applicationrdquo ChineseJournal of Natural Medicines vol 11 no 2 pp 110ndash120 2013

Stem Cells International

Hindawiwwwhindawicom Volume 2018


MEDIATORSINFLAMMATION

of

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Immunology ResearchHindawiwwwhindawicom Volume 2018

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine


Behavioural Neurology

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary andAlternative Medicine

Volume 2018Hindawiwwwhindawicom

Submit your manuscripts atwwwhindawicom


the symptoms of AD but not the pathogenesis Thougha certain degree of recovering impairment efficacies thesemedicines may have their side effects may also have to beconsidered Gastrointestinal reactions diarrhoea nausea andvomiting insomnia fatigue and urinary incontinence arethe common adverse effects of cholinergic drugsMagnesiumHydrochloride which is the only nonacetylcholinesteraseinhibitor approved for AD treatment has the adverse effectssuch as fatigue high blood pressure dizziness headache andconstipation

Traditional Chinese medicine (TCM) prescriptions playan important role in the treatment of various serious diseasesespecially those thewesternmedicine find it difficult to tacklebecause the therapeutic effects of TCM are based on syner-gistic and holistic theory Unfortunately the uncertainty ofpotential active compounds explicit targets and underlyingpharmacological mechanisms impeded the modernization ofTCM Network pharmacology [6] an emerging promisingsubject and an advanced approach to new drug discoveryprovides novel tactics for the understanding of the relation-ship between drugs and diseases at a systematic level

Wendan granule (WDG) is a hospital preparation oftraditional Chinese medicine prescription for the treatmentof AD It is a modern dosage form produced based on amodified prescription of Wendan decoction which is anancient and classical prescription with the function of ldquoHua-Tan (化痰)rdquo TCM believes that Tan can stay in various partsof the body including brain and produce all sorts of diseasesOn the other hand just like the aggregated 120573-amyloid inbrains of AD patients Tan itself is also a pathological productof various diseases Therefore the pharmacological efficaciesof WDG are predicted to be reducing the production andpromoting the clearance of 120573-amyloid in brains of ADpatients by multiple mechanisms Our previous study hasidentified 74 volatiles by employing HS-SPME-GC-MS [7]However their specific mechanisms of efficacy are still vagueHence in this study network pharmacology was applied toexplore the mechanisms of the main volatiles ofWDG for thetreatment of AD

2 Materials and Methods

21 Materials

211 Analysis Platforms and Databases TCMSP (httplspnwueducnindexphp) is a systems pharmacology plat-form and database of Chinese herbal medicines [8] Drug-Bank (httpswwwdrugbankca) is a bioinformatics andcheminformatics resource database [9] Uniprot (httpswwwuniprotorg) is a comprehensive database about pro-tein [10] DAVID (httpsdavidncifcrfgov) is a databaseto help understand biological meaning behind genes byproviding various functional annotations [11] KEGG (httpswwwkeggjp) is Kyoto Encyclopedia of Genes and Genomes[12] GeneMANIA (httpgenemaniaorg) is a website usedfor predicting the function of genes and gene sets [13]

212 Utility Software ChemBioOffice Ultra 120 (PerkinElmer Inc Waltham MA USA) is applied for candidate

compounds structures constructing Cytoscape 351 is opensource software for visualizing complex networks and inte-grating them with all sorts of types of attribute data [14]

22 Methods

221 Candidate Components Screen Our previous study hasidentified 74 volatiles from WDG by employing HS-SPME-GC-MS In order to acquire the potential main volatilecompounds from the granule four screening criteria weredefined as follows OB (oral bioavailability) ⩾30 Caco-2permeabilitygt0 BBB (bloodndashbrain barrier [15])⩾03 and rel-ative abundance (RA) ⩾02 Based on the ADME parametersin TCMSP and RA values obtained from previous work [7]the volatiles which satisfied the principles were selected as thecandidate compounds

222 Targets Screening To identify the corresponding tar-gets of main compounds of WDG several approaches wereimplemented First of all TCMSP and DrugBank databasewere applied to find out the potential targets Then ldquodrug-targetrdquo network will be constructed by Cytoscape 351 soft-ware The candidate targets were mainly screened by degreethat represents the number of edges adjacent to a node [3]Next for more accurate result obtaining purpose the targetswith degree ⩾3 were chosen as candidate targets and otherswere eliminated

223 GeneMANIA Analysis GeneMANIA was used forpredicting the function of genes and gene sets A relationshipnetwork of genes was given after input of a set of gene list andspecies selected as ldquohomo sapiensrdquo

224 GO and Pathway Annotation The targets were input tothe DAVID for further investigation such as Gene Ontology(GO) pathways Construct the ldquotarget-pathwayrdquo networkThe pathways that are equal to or above degree 3 wereanalysed for candidate pathways identification

225 Identification for Diseases The KEGG gave you infor-mation about related disease when candidate pathwaysentered and then constructed the ldquopathway-diseaserdquo networkand preliminarily speculate the pharmacologicalmechanismsof WDG with all the information above

3 Results and Discussion

31 Identification of Active Volatility Components From theformer work 75 volatility components were identified byemploying HS-SPME-GC-MS For the purpose of acquiringthe main compounds four screening criteria were definedas follows OB⩾30 Caco-2 permeabilitygt0 BBB⩾03 andRA⩾02 A total of 12 compounds including 120574-Asarone trans-ligustilide and senkyunolide A which showed poor OBbut have high abundance were selected as the candidatecompounds for more accurate investigation (Table 1 seeTable 1S in the Supplementary Material for the detailed infor-mation of the twelve candidate volatiles) For the purposeof tracing back to the original herbal medicines appliedto form the WDG prescription twelve compounds were





SenkyunolideA



T-Muurolol

Linalool

ymol

-Asarone

Isocalamendiol

Methyl eugenol

-Asarone

OH

O

O

O

OO

O

O

O

H

H OH

HO

HO

O

O

O

O

O

O

O

O O

O




12H16O2


13H18O 190 662 235 128 12



12H12O2

188 4138 4244 132 127Methyl eugenol C

11H14O2

178 2987 7336 147 141T-Muurolol C

15H26O 222 3129 3041 136 144

Sedanolide C12H18O2

194 3522 6246 124 14120574-Asarone C

12H16O3

208 14658 2276 15 133120573-Asarone C

12H16O3

208 15798 3561 145 124Linalool C

10H18O 154 274 3829 129 133


238 2518 5763 094 074Thymol C

10H14O 150 2125 4147 16 168





Compound Target















Coexpression 2060

Pathway923

Colocalization 899





4 Discussion



Target Pathway





5 Conclusions









Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of























SenkyunolideA



T-Muurolol

Linalool

ymol

-Asarone

Isocalamendiol

Methyl eugenol

-Asarone

OH

O

O

O

OO

O

O

O

H

H OH

HO

HO

O

O

O

O

O

O

O

O O

O




12H16O2


13H18O 190 662 235 128 12



12H12O2

188 4138 4244 132 127Methyl eugenol C

11H14O2

178 2987 7336 147 141T-Muurolol C

15H26O 222 3129 3041 136 144

Sedanolide C12H18O2

194 3522 6246 124 14120574-Asarone C

12H16O3

208 14658 2276 15 133120573-Asarone C

12H16O3

208 15798 3561 145 124Linalool C

10H18O 154 274 3829 129 133


238 2518 5763 094 074Thymol C

10H14O 150 2125 4147 16 168





Compound Target















Coexpression 2060

Pathway923

Colocalization 899





4 Discussion



Target Pathway





5 Conclusions









Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















Compound Target















Coexpression 2060

Pathway923

Colocalization 899





4 Discussion



Target Pathway





5 Conclusions









Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of





























Coexpression 2060

Pathway923

Colocalization 899





4 Discussion



Target Pathway





5 Conclusions









Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















Target Pathway





5 Conclusions









Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of


























Data Availability






Acknowledgments






Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















Table3GOenric

hmentanalysesu

singdatabase

DAV

ID

Category

Term

Cou

nt

P-Va

lue

Benjam

ini

BP

respon

seto

drug

902

320E-08

310E-06

G-protein

coup

ledreceptor

signalling

pathway

902

100E

-04

280E-03

adenylatec

yclase-activatinga

drenergicr

eceptorsignalling

pathway

802

100E

-15

380E-13

cell-cellsig

nalling

701

400

E-06

250E-04

positiver

egulationof

vasoconstrictio

n6

01

210E-09

390E-07

signaltransdu

ction

601

460

E-02

310E-01

CC

plasmamem

brane

2305

170E

-09

590E-08

integralcompo

nent

ofplasmam

embrane

2004

690E-15

470E-13

integralcompo

nent

ofmem

brane

1603

660

E-03

340

E-02

posts

ynaptic

mem

brane

802

320E-08

730E

-07

celljunctio

n8

02

590E-06

820E-05

MF

drug

bind

ing

601

180E

-07

110E

-05


ity6

01

110E

-03

140E

-02

proteinho

mod

imerizationactiv

ity6

01

750E

-03

800

E-02

epinephrineb

inding

501

110E

-10

130E

-08

GABA

-Areceptor

activ

ity4

01

430E-06

180E

-04


401

240

E-05

490E-04


References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of




















References























































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of







































of




Disease Markers



OncologyJournal of





PPAR Research



Volume 2018


Journal of

ObesityJournal of



















network pharmacology deciphering mechanisms of volatiles...

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