Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 7826769 | https://doi.org/10.1155/2019/7826769

Jun-feng Liu, An-na Hu, Jun-feng Zan, Ping Wang, Qiu-yun You, Ai-hua Tan, "Network Pharmacology Deciphering Mechanisms of Volatiles of Wendan Granule for the Treatment of Alzheimer’s Disease", Evidence-Based Complementary and Alternative Medicine, vol. 2019, Article ID 7826769, 12 pages, 2019. https://doi.org/10.1155/2019/7826769

Network Pharmacology Deciphering Mechanisms of Volatiles of Wendan Granule for the Treatment of Alzheimer’s Disease

Academic Editor: Khawaja M. I. Bashir
Received30 Jun 2018
Accepted29 Jan 2019
Published12 Feb 2019


Objective. To explore the mechanisms of the volatiles of Wendan granule (WDG) for the treatment of Alzheimer’s disease, network pharmacology method integrating absorption, distribution, metabolism, and excretion (ADME) screening, target fishing, network constructing, pathway analysing, and correlated diseases prediction was applied. Methods. Twelve small molecular compounds of WDG were selected as the objects from 74 volatiles with the relative abundances above 2 %, and their ADME parameters were collected from Traditional Chinese Medicine Systems Pharmacology platform (TCMSP), and the corresponding targets, genes, pathways, and diseases were predicted according to the data provided by TCMSP, DrugBank, Uniport, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Then the related pathways and correlation analysis were explored by the Kyoto Encyclopedia and Genomes (KEGG) database. Finally, the networks of compound target, target pathway, and pathway disease of WDG were constructed by Cytoscape software. Results. Twelve compounds interacted with 49 targets, of which top three targets were gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1), prostaglandin G/H synthase 2 (PGHS-2), and sodium-dependent noradrenaline transporter. Interestingly, these targets were highly associated with depression, insomnia, and Alzheimer’s disease that mainly corresponded to mental and emotional illnesses. Conclusion. The integrated network pharmacology method provides precise probe to illuminate the molecular mechanisms of the main volatiles of WDG for relieving senile dementia related syndromes, which will also facilitate the application of traditional Chinese medicine as an alternative or supplementary to conventional treatments of AD, as well as follow-up studies such as upgrading the quality standard of clinically applied herbal medicine and novel drug development.

1. Introduction

Alzheimer’s disease (AD), also known as senile dementia, is an age-related progressive neurodegenerative disease that continues to form a huge challenge to the aging community, especially a heavy burden for patients and their family. With the worldwide reduction in birth rates and prolonged life span expectancies, the Alzheimer’s disease together with other dementias was considered to be one of the 10 leading causes of disability among people with the age above 60 globally [1]. The decline of cognitive function of old people happened with the progress of aging; thus, the early detection and early intervention in cognitive dysfunction are important for delaying or preventing the occurrence or progression of dementia, enabling patients to maintain basic cognitive functions and improve their quality of life for a longer period of time [2]. Various medicines have been developed all over the world for the treatment of AD. There are four conventional therapeutics strategies for the treatment of AD using modern clinical medicines: (1) restoration of cognitive impairment, (2) activation of α-secretase, (3) inhibition of β-secretase and γ-secretase, and (4) inhibition of Tau hyperphosphorylation [3]. However, for clinically applied medicines, such as (1) acetylcholinesterase inhibitors: Donepezil, Galantamine, (2) N-methyl-D-aspartate acid receptor: Magnesium Hydrochloride [4], and (3) ABT-126 [5] that only target the symptoms of AD, but not the pathogenesis. Though a certain degree of recovering impairment efficacies these medicines may have, their side effects may also have to be considered. Gastrointestinal reactions, diarrhoea, nausea and vomiting, insomnia, fatigue, and urinary incontinence are the common adverse effects of cholinergic drugs. Magnesium Hydrochloride, which is the only nonacetylcholinesterase inhibitor approved for AD treatment, has the adverse effects such as fatigue, high blood pressure, dizziness, headache, and constipation.

Traditional Chinese medicine (TCM) prescriptions play an important role in the treatment of various serious diseases, especially those the western medicine find it difficult to tackle, because the therapeutic effects of TCM are based on synergistic and holistic theory. Unfortunately, the uncertainty of potential active compounds, explicit targets, and underlying pharmacological mechanisms impeded the modernization of TCM. Network pharmacology [6], an emerging promising subject and an advanced approach to new drug discovery, provides novel tactics for the understanding of the relationship between drugs and diseases at a systematic level.

Wendan granule (WDG) is a hospital preparation of traditional Chinese medicine prescription for the treatment of AD. It is a modern dosage form produced based on a modified prescription of Wendan decoction, which is an ancient and classical prescription with the function of “Hua-Tan (化痰)”. TCM believes that Tan can stay in various parts of the body, including brain, and produce all sorts of diseases. On the other hand, just like the aggregated β-amyloid in brains of AD patients, Tan itself is also a pathological product of various diseases. Therefore, the pharmacological efficacies of WDG are predicted to be reducing the production and promoting the clearance of β-amyloid in brains of AD patients by multiple mechanisms. Our previous study has identified 74 volatiles by employing HS-SPME-GC-MS [7]. However, their specific mechanisms of efficacy are still vague. Hence, in this study, network pharmacology was applied to explore the mechanisms of the main volatiles of WDG for the treatment of AD.

2. Materials and Methods

2.1. Materials
2.1.1. Analysis Platforms and Databases

TCMSP (http://lsp.nwu.edu.cn/index.php) is a systems pharmacology platform and database of Chinese herbal medicines [8]. DrugBank (https://www.drugbank.ca/) is a bioinformatics and cheminformatics resource database [9]. Uniprot (https://www.uniprot.org/) is a comprehensive database about protein [10]. DAVID (https://david.ncifcrf.gov/) is a database to help understand biological meaning behind genes by providing various functional annotations [11]. KEGG (https://www.kegg.jp/) is Kyoto Encyclopedia of Genes and Genomes [12]. GeneMANIA (http://genemania.org/) is a website used for predicting the function of genes and gene sets [13].

2.1.2. Utility Software

ChemBioOffice Ultra 12.0 (Perkin Elmer Inc., Waltham, MA, USA) is applied for candidate compounds structures constructing. Cytoscape 3.5.1 is open source software for visualizing complex networks and integrating them with all sorts of types of attribute data [14].

2.2. Methods
2.2.1. Candidate Components Screen

Our previous study has identified 74 volatiles from WDG by employing HS-SPME-GC-MS. In order to acquire the potential main volatile compounds from the granule, four screening criteria were defined as follows: OB (oral bioavailability)30%, Caco-2 permeability>0, BBB (blood–brain barrier [15])0.3, and relative abundance (RA)0.2. Based on the ADME parameters in TCMSP and RA values obtained from previous work [7], the volatiles which satisfied the principles were selected as the candidate compounds.

2.2.2. Targets Screening

To identify the corresponding targets of main compounds of WDG, several approaches were implemented. First of all, TCMSP and DrugBank database were applied to find out the potential targets. Then, “drug-target” network will be constructed by Cytoscape 3.5.1 software. The candidate targets were mainly screened by degree that represents the number of edges adjacent to a node [3]. Next, for more accurate result obtaining purpose, the targets with degree3 were chosen as candidate targets and others were eliminated.

2.2.3. GeneMANIA Analysis

GeneMANIA was used for predicting the function of genes and gene sets. A relationship network of genes was given after input of a set of gene list and species selected as “homo sapiens.”

2.2.4. GO and Pathway Annotation

The targets were input to the DAVID for further investigation such as Gene Ontology (GO), pathways. Construct the “target-pathway” network. The pathways that are equal to or above degree 3 were analysed for candidate pathways identification.

2.2.5. Identification for Diseases

The KEGG gave you information about related disease when candidate pathways entered and then constructed the “pathway-disease” network and preliminarily speculate the pharmacological mechanisms of WDG with all the information above.

3. Results and Discussion

3.1. Identification of Active Volatility Components

From the former work, 75 volatility components were identified by employing HS-SPME-GC-MS. For the purpose of acquiring the main compounds, four screening criteria were defined as follows: OB30%, Caco-2 permeability>0, BBB0.3, and RA0.2. A total of 12 compounds including γ-Asarone, trans-ligustilide, and senkyunolide A, which showed poor OB but have high abundance, were selected as the candidate compounds for more accurate investigation (Table 1, see Table 1S in the Supplementary Material for the detailed information of the twelve candidate volatiles). For the purpose of tracing back to the original herbal medicines applied to form the WDG prescription, twelve compounds were input into TCMSP to backtrack the corresponding herbs (Figure 1), and two, five, and two were recognized as those of Ginger, Chuanxiong, and Acorus tatarinowii, respectively. Senkyunolide A, one of the main bioactive constituents in the herb Rhizoma Chuanxiong, shows protective effect on the injury of central nervous system and on contractions to various contractile agents in rat isolated aorta [16]. β-Asarone, which shows the highest abundance in WDG and a shared compound of Chuanxiong and Acorus tatarinowii, has been investigated with regard to effects on central nervous system [15, 17]. Thymol, a common ingredient shared by Ginger and Chuanxiong, possesses active antioxidant effect [18]. Butylidenephthalide has been suggested to produce various pharmacological activities in cerebral blood vessels [19]. Butylphthalide could decrease the brain infarct volume and enhance microcirculation, thus benefiting patients [20]. BBB of all the 12 are more than 1.00 which means they all have effect to break through the BBB to cure disease related to CNS.

Compound NameMolecular FormulaMolecular WeightRA (%)OB (%)Caco-2BBB

Senkyunolide AC12H16O21927.27126.561.31.34
Methyl eugenolC11H14O21782.98773.361.471.41

3.2. Analysis of “Compound-Target” Network

AD has been considered as one of the most serious threats of health, although many different types of therapeutic methods have been applied for the management and prevention of AD. Identifying the compound interacting with targets is a good strategy for drug discovery. TCMSP and DrugBank database were applied for predicting the potential targets for each compound. As a result, 201 compound-target interactions were identified between 12 compounds and 49 targets, and the candidate targets were selected according to the degrees that reflect the number of edges of each target (Figure 2, see Table 2S_1-9 in the Supplementary Material for the detailed compound-target information of the twelve candidate volatiles with their corresponding targets). After calculating the value of degree for each target in C-T network, the average value of the degrees was 4.10. Therefore, the targets with degree3 were regarded as candidate targets for further investigation (Table 1).

GABRA1 shows the highest degree, followed by PTGS2, SLC6A2, and CHRM1. Many of them have been verified in previous researches. For instance, GABRA1 was found to play an important role in Alzheimer’s dementia [21], and a previous study reported that gamma-amino butyric acid receptor subunit alpha-1 could inhibit neurotransmission in the brain [22]. GABA receptors might induce a significant impact on brain structures and functions and might also participate in the dysregulation of the balance between excitatory and inhibitory neurotransmission that was observed in AD patients [23]. Both PTGS1 and PTGS2 could convert arachidonate into prostaglandin H2 (PGH2). PTGS1, which is involved in the production of prostanoids particularly in the stomach and platelets, could help promote platelet activation and aggregation, vasoconstriction, and proliferation of vascular smooth muscle cells. PTGS2 was mainly expressed in endothelium, kidney, and brain and in cancers. Some researchers reported that patients with AD have a higher PTGS2 in hippocampal neurons, and it is convinced that PTGS2 played an important role in the pathogenesis of AD for it could convert arachidonate into PGH2 which might induce inflammation in the brain [2426]. Meanwhile, the modern epidemiology also showed that the use of nonsteroidal anti-inflammatory drugs can reduce the incidence of AD; therefore, controlling the inflammatory response became one of the important therapeutic strategies of clinical treatment of AD [27]. SLC6A2 was closely associated with depression. This protein was the target of psychomotor stimulants such as Amphetamines or Cocaine [28, 29]. CHRM1, CHRM2, and CHRM3 were three subtypes of CHRM (muscarinic acetylcholine receptors), one of cholinergic neurotransmitter receptors, that usually participated in the regulation of cognitive function [30, 31]. Relevant pharmacological experiments have indicated the specific reduction of cholinergic neurons on basal forebrain in AD patients, and the rate of hippocampal atrophy in AD patients was halved 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 acetylcholine which is located in the terminal cholinergic neurons in the forebrain [33]. CHRM3 was able to promote various cellular activities by regulating the passage of different signals [34]. In summary, the common functions of these targets were relevant with nervous system diseases like insomnia, AD, which were also the indications of WDG. The relationship between the compounds and targets revealed the truth that multiple compounds and multiple targets interact with each other in molecular system that might break off and jump out of the traditional “one-target-one-compound” model.

3.3. Gene Function Analysis by Using GeneMANIA

Most successful computational methods for compound interacting with targets prediction integrate the prediction of multiple direct targets and multiple indirect targets. GeneMANIA, a useful website to find genes most related to the query genes, is capable of predicting protein functions with the advanced and unique algorithm and is also regarded as a real-time multiple association network integration algorithm for predicting gene function such as coexpression, colocalization, pathways, and protein domain [35]. Figure 3 shows the network generated by GeneMANIA website. The nodes with black colour represent the input genes and the grey nodes represent the associated genes. The edges with different colour are associated with different functions. As shown in the results (Figure 3), 39.24% of the genes shared protein domains and 20.60% had the coexpression.

3.4. Analysis of GO Enrichment

Thirty candidate targets were chosen for further investigation by using DAVID (Table 3, see Table 3S_1-3 in the Supplementary Material for the integrated GO enrichment analysis results of thirty selected candidate targets). Gene ontology enrichment analysis consisted of three parts, BP (biological process), CC (cellular component), and MF (molecular function). Drug binding, protein heterodimerization activity, and epinephrine binding were predicted to be the main biological functions induced by 12 volatiles. Plasma membrane, integral component of plasma membrane, and integral component of membrane were ranked as top three cellular components, which might reflect that most of the small volatiles were targeted to neural cells. The top three biological processes were the G protein coupled receptor signaling pathway, the acetylate cycles activating adrenergic receptor signaling pathway, and the response to drug. G protein coupled receptor signaling pathway was ranked as No. 1, which indicated that G protein coupled receptor might be one of the main drug targets for the treatment. There were previous studies which reported that G protein coupled receptors might serve as the largest pharmacodynamic therapeutic target for AD, because they can directly affect the beta-amyloid signaling cascade in the nervous system by regulating α-, β-, γ-, secretory enzyme secretion, amyloid precursor protein (APP) production, and Aβ degradation[36]. Furthermore, CHRM1, CHRM3, and HTR2A all belonged to the G protein coupled receptor. The abnormality of a variety of signal pathways and signal transmission played important roles in the pathogenesis of AD; moreover, the dysfunction of adenylate cyclase signaling system was considered to be the main cause of AD. G protein-mediated dysfunction of adenylate cyclase signaling system was an important enlightenment for the prevention and treatment of AD [37]. Interestingly, ADRA1B and ADRA1A, an alpha-adrenergic receptor, mediated their effects through binding to the G protein that could activate the phosphatidylinositol-calcium second messenger system [38]. ADRB1 and ADRB2 were β-adrenergic receptors that mediate catecholamine-induced activation of adenylate cyclase through the sensitization of the G protein [39]. ADRA2A and ADRA2C were alpha-2 adrenergic receptors that mediated catecholamine-induced inhibition of adenylate cyclase also through the sensitization of the G protein [40].

3.5. Target-Pathway and Pathway-Disease Networks

For the purpose of systematically deciphering the multiple underlying mechanisms of volatiles of WDG, all of the pathways interacting with candidate targets were extracted from KEGG pathways database using DAVID, and then the “target-pathway” network was constructed (Figure 4). Twenty-three related pathways were found, including neuroactive ligand-receptor interaction, calcium signaling pathway, cGMP-PKG signaling pathway, and cAMP signaling pathway. Signaling pathways have been regarded as one of the most important parts of systems pharmacology [41]. As shown in Figure 3, five targets including DAD1, GRIA2, MAOA, MAOB, and SLC6A3 were all found to be associated with Cocaine addiction, Amphetamine addiction, Dopaminergic synapse, and Alcoholism together, which could help speculate the pharmacokinetic synergistic effects among them. GABA family including GABRA1, GABRA2, GABRA3, and GABRA6 worked together on neuroactive ligand-receptor interaction, Morphine addiction, retrograde endocannabinoid signaling, Nicotine addiction, and GABAergic synapse pathways. ADRA1A, ADRA1B, ADRA1D, ADRB1, and ADRB2 that belonged to adrenergic receptor were all associated with neuroactive ligand-receptor interaction, calcium signaling pathway, cGMP-PKG signaling pathway, salivary secretion, and adrenergic signaling in cardiomyocytes. Neuroactive ligand-receptor interaction might be the main pathway that correlated with the mechanism. For all the pathways, three of them belonged to the cellular processes: regulation of actin cytoskeleton, gap junction, and endocytosis. Five belonged to environmental information processing or signal transduction: calcium signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, and neuroactive ligand-receptor interaction. Five belonged to human diseases or substance dependence: Morphine addiction, Amphetamine addiction, Alcoholism, Cocaine addiction, and Nicotine addiction. Nine belonged to organismal systems: adrenergic signaling in cardiomyocytes, salivary secretion, regulation of lipolysis in adipocytes, renin secretion, retrograde endocannabinoid signaling, serotonergic synapse, dopaminergic synapse, GABAergic synapse, and cholinergic synapse while, the last four also belonged to nervous system.

In Figure 5, there were totally 142 nodes while 24 of which shaped green ‘V’s corresponded to candidate pathways and the remaining 118 red circle nodes represented diseases. Neuroactive ligand-receptor interaction pathways, calcium signaling pathway, retrograde endocannabinoid signaling pathways, and endocytosis were corresponding to 27, 23, 23, and 13 kinds of different diseases. All the corresponding diseases were classified by KEGG while nervous system related disease accounts for largest proportion followed by musculoskeletal disease, developmental disorder, endocrine disease, inherited metabolic disease, and cardiovascular disease.

4. Discussion

TCM prescriptions were prescribed for fighting against diseases from ancient China till now based on the theories of traditional Chinese medicine. Multicomponents, multitargets, and various pharmacological actions within herbs were the superiority, but also the key issues of the modernization studies of traditional Chinese medicine. Here, we presented efficient tactics that contribute to understanding the pharmacological mechanism of TCM prescription by “network pharmacology” in molecular level to reveal the interaction between small molecule compounds with protein targets, pathways, and diseases. The “network pharmacology” was first proposed by Andrew L. Hopkins [42]. Different from the conventional drug discovery mode, the network pharmacology breaks through the “one-drug-one-target” mode to the synergy of multiple targets to identify the therapeutic mechanisms of TCM based on holistic research strategy of multicomponents, multitargets, and multidiseases [43].

Twelve main compounds with favourable ADME were selected from seventy-four volatiles of WDG identified using HS-SPME-GC-MS. Then online databases, TCMSP and DrugBank, were applied to obtain potential interaction targets with the pathways, followed by GO enrichment clustering analysis. The results showed some compounds and targets identified in this research that might be conductive to the therapy and prevention of Alzheimer’s disease had already been reported previously, which highlighted the credibility of the ADME evaluation system and target predicting databases. One target was hit by multiple ingredients, which coincided with the holistic view and synergistic action theory of TCM. Prediction of potential targets might be the first step in drug discovery within the wide application of computational target fishing technology. As shown in Table 2, some targets had already been reported previously. For example, GABRA1 is the most important inhibitory receptor in brain that can produce inhibitory postsynaptic sites to inhibit neuronal discharge by acting on the chloride channel after combining with GABA. GABRA1 and GABRA2 are the components of the heteropentameric receptor for GABA, receptor of GABA (gamma-aminobutyric acid) that is an important inhibitory neurotransmitter in vertebrate brain and plays an essential role in the treatment of insomnia by inhibiting brain neurotransmitters excitement [44]. Moreover, molecular mechanisms of Alzheimer’s disease treatment were made manifest based on the “compound-target” network, “target-pathway” network, and “pathway-disease” network of WDG.

TargetGene symbolDegree

Gamma-aminobutyric acid receptor subunit alpha-1GABRA110

Prostaglandin G/H synthase 2PTGS210

Sodium-dependent noradrenaline transporterSLC6A210

Muscarinic acetylcholine receptor M1CHRM19

Alpha-1B adrenergic receptorADRA1B8

Muscarinic acetylcholine receptor M2CHRM28

Muscarinic acetylcholine receptor M3CHRM38

Alpha-1A adrenergic receptorADRA1A7

Beta-1 adrenergic receptorADRB17

Beta-2 adrenergic receptorADRB27

Prostaglandin G/H synthase 1PTGS17

Sodium-dependent dopamine transporterSLC6A37

Alpha-2A adrenergic receptorADRA2A6

Alpha-2C adrenergic receptorADRA2c6

cAMP-dependent protein kinase inhibitor alphaPKIA6

Gamma-aminobutyric-acid receptor alpha-2 subunitGABRA26

Alpha-2B adrenergic receptorADRA2B5

Gamma-aminobutyric-acid receptor subunit alpha-6GABRA65

Sodium channel protein type 5 subunit alphaSCN5A5

Sodium-dependent serotonin transporterSLC6A45

5-hydroxytryptamine 2A receptorHTR2A4

Alpha-1D adrenergic receptorADRA1D4

CGMP-inhibited 3′,5′-cyclic phosphodiesterase APDE3A4

Dopamine D1 receptorDRD14

Leukotriene A-4 hydrolaseLTA4H4

Amine oxidase [flavin-containing] AMAOA3

Amine oxidase [flavin-containing] BMAOB3

Gamma-aminobutyric-acid receptor alpha-3 subunitGABRA33

Glutamate receptor 2GRIA23

Retinoic acid receptor RXR-alphaRXRA3


BPresponse to drug90.23.20E-083.10E-06
G-protein coupled receptor signalling pathway90.21.00E-042.80E-03
adenylate cyclase-activating adrenergic receptor signalling pathway80.21.00E-153.80E-13
cell-cell signalling70.14.00E-062.50E-04
positive regulation of vasoconstriction60.12.10E-093.90E-07
signal transduction60.14.60E-023.10E-01

CCplasma membrane230.51.70E-095.90E-08
integral component of plasma membrane200.46.90E-154.70E-13
integral component of membrane160.36.60E-033.40E-02
postsynaptic membrane80.23.20E-087.30E-07
cell junction80.25.90E-068.20E-05

MFdrug binding60.11.80E-071.10E-05
protein heterodimerization activity60.11.10E-031.40E-02
protein homodimerization activity60.17.50E-038.00E-02
epinephrine binding50.11.10E-101.30E-08
GABA-A receptor activity40.14.30E-061.80E-04
extracellular ligand-gated ion channel activity40.12.40E-054.90E-04

5. Conclusions

Previous studies had identified that WDG can improve the learning and memory ability by repairing the damage of cholinergic system, inhibiting the cytokine levels of transition-activated microglia and reducing the level of transient phosphorylation Tau protein [4547].

Based on the series of results from the database, platform, and software given, we could preliminarily predict that the mechanism of WDG involved a large network which contained drugs, targets, pathways, and diseases. The main targets were GABRA1, PTGS2, SLC6A2, CHRM1, ADRA1B, CHRM2, CHRM3, ADRA1A, ADRB1, ADRB2, PTGS1, and SLC6A3 that conducted with neuroactive ligand-receptor interaction pathways, signaling pathways, and metabolism pathways related to analgesic, anticonvulsant, and repair reduction of cholinergic neurons and eliminate the inflammatory response to reduce the secretion of neurotoxic inflammatory cytokine to cure diseases related to AD and insomnia.

This study chose a novel strategy for the systematic understanding of the molecular mechanisms of the main volatiles of WDG relieving senile dementia related syndromes, which could facilitate the application of TCM in modern medicine and prescription/dosage form optimization [48]. Further studies, such as molecular biological experiments and clinical investigations, have to be carried out to verify these mechanisms. It might be a long-term and arduous task, but only in this way could we scientifically elaborate and evaluate the mechanisms of efficacy of the clinically approved TCM prescriptions and promote the worldwide application of Chinese medicine [49].

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

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


An abstract of the pilot study results was presented on “2018 International Conference on the Pharmacology of Traditional Medicine of the Belt and Road Initiatives.” Professor Dinesh Kumar Bharatraj and Dr. Amir Hooman Kazemi have given us some valuable advices that facilitated the optimization of the data analysis procedure and the Discussion section of this manuscript. The project was supported by Grants from Natural Science Foundation of Hubei [2015CFB321 and Key Project of National Natural Science Foundation of China [81130064.

Supplementary Materials

The detailed information of the 12 volatile compounds (), compound-target analysis data (), and Gene Ontology enrichment analysis results () used to support the findings of this study is included within the supplementary information files. (Supplementary Materials)


  1. United Nations Department of Economic and Social Affairs Population Division, World Population Ageing, United Nations, 2015, http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf.
  2. J. S. Elkins, V. C. Douglas, and S. C. Johnston, “Alzheimer disease risk and genetic variation in ACE: A meta-analysis,” Neurology, vol. 62, no. 3, pp. 363–368, 2004. View at: Publisher Site | Google Scholar
  3. A. Fisher, Z. Pittel, R. Haring et al., “M1 muscarinic agonists can modulate some of the hallmarks in alzheimer's disease: Implications in future therapy,” Journal of Molecular Neuroscience, vol. 20, no. 3, pp. 349–356, 2003. View at: Publisher Site | Google Scholar
  4. C. Cui, The Effect Evaluation of Different Anti-dementia Drugs treatment in the cognitive of Alzheimer’s disease: A network META-Analysis, Jilin university, 2017.
  5. D. L. Bi, N. Wen, Y. Shen, and W. Xiong, “Development of potential therapeutic targets of and approaches to Alzheimer disease,” Chinese Journal of Pharmacology and Toxicology, vol. 29, no. 4, pp. 507–535, 2015. View at: Google Scholar
  6. A. L. Hopkins, “Network pharmacology: the next paradigm in drug discovery,” Nature Chemical Biology, vol. 4, no. 11, pp. 682–690, 2008. View at: Publisher Site | Google Scholar
  7. J. F. Liu, J. F. Zan, P. Wang et al., “HS-SPME-GC/MS analysis of volatile components in Yi-Zhi-Wen-Dan granules,” Lishizhen Medicine and Material Research, vol. 28, no. 1, pp. 67–70, 2017. View at: Publisher Site | Google Scholar
  8. J. Ru, P. Li, J. Wang et al., “TCMSP: a database of systems pharmacology for drug discovery from herbal medicines,” Journal of Cheminformatics, vol. 6, no. 1, p. 13, 2014. View at: Publisher Site | Google Scholar
  9. D. S. Wishart, Y. D. Feunang, A. C. Guo et al., “DrugBank 5.0: A major update to the DrugBank database for 2018,” Nucleic Acids Research, vol. 46, no. 1, pp. D1074–D1082, 2018. View at: Publisher Site | Google Scholar
  10. UniProt Consortium, “Uniprot: the universal protein knowledgebase,” Nucleic Acids Research, vol. 45, no. D1, pp. D158–D169, 2017. View at: Publisher Site | Google Scholar
  11. D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009. View at: Publisher Site | Google Scholar
  12. M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, and K. Morishima, “KEGG: new perspectives on genomes, pathways, diseases and drugs,” Nucleic Acids Research, vol. 45, no. 1, pp. D353–D361, 2017. View at: Publisher Site | Google Scholar
  13. D. W. Farley, S. L. Donaldson, O. Comes et al., “The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function,” Nucleic Acids Research, vol. 38, no. 2, pp. W214–W220, 2010. View at: Publisher Site | Google Scholar
  14. M. E. Smoot, K. Ono, J. Ruscheinski, P. L. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431-432, 2011. View at: Publisher Site | Google Scholar
  15. R. Daneman and A. Prat, “The blood–brain barrier,” Cold Spring Harbor Perspectives in Biology, vol. 7, no. 1, Article ID 020412, 2015. View at: Google Scholar
  16. S. S.-K. Chan, T.-Y. Cheng, and G. Lin, “Relaxation effects of ligustilide and senkyunolide A, two main constituents of Ligusticum chuanxiong, in rat isolated aorta,” Journal of Ethnopharmacology, vol. 111, no. 3, pp. 677–680, 2007. View at: Publisher Site | Google Scholar
  17. J. D. Sharma, P. C. Dandiya, R. M. Baxter, and S. I. Kandel, “Pharmacodynamical effects of asarone and β-asarone,” Nature, vol. 192, no. 4809, pp. 1299-1300, 1961. View at: Publisher Site | Google Scholar
  18. N. V. Yanishlieva, E. M. Marinova, M. H. Gordon, and V. G. Raneva, “Antioxidant activity and mechanism of action of thymol and carvacrol in two lipid systems,” Food Chemistry, vol. 64, no. 1, pp. 59–66, 1999. View at: Publisher Site | Google Scholar
  19. C. M. Teng, W. Y. Chen, W. C. Ko, and C. H. Ouyang, “Antiplatelet effect of butylidenephthalide,” Biochimica et Biophysica Acta, vol. 924, no. 3, pp. 375–382, 1987. View at: Google Scholar
  20. Y. Ozaki, S. Sekita, and M. Harada, “Centrally acting muscle relaxant effect of phthalides (Ligustilide, Cnidilide and Senkyunolide) obtained from Cnidium officinale Makino,” Yakugaku Zasshi, vol. 109, no. 6, pp. 402–406, 1989. View at: Publisher Site | Google Scholar
  21. N. W. Seidler, “Effect of GABRA1 cytoplasmic peptide on a regulatory protein,” Biophysical Journal, vol. 112, no. 3, p. 502a, 2017. View at: Publisher Site | Google Scholar
  22. H. Deng, W.-J. Xie, W.-D. Le, M.-S. Huang, and J. Jankovic, “Genetic analysis of the GABRA1 gene in patients with essential tremor,” Neuroscience Letters, vol. 401, no. 1-2, pp. 16–19, 2006. View at: Publisher Site | Google Scholar
  23. K. J. Johnson, T. Sander, A. A. Hicks et al., “Confirmation of the localization of the human GABAA receptor α1-subunit gene (GABRA1) to distal 5q by linkage analysis,” Genomics, vol. 14, no. 3, pp. 745–748, 1992. View at: Publisher Site | Google Scholar
  24. Y. L. Huang, M. Y. Wan, X. S. Liang, and F. R. Liang, “Regulation of FAH gene and PTGS2 gene by acupoint selection on the same meridian for controlling migraine,” Journal of Beijing University of Traditional Chinese Medicine, vol. 37, no. 4, pp. 280–284, 2014. View at: Google Scholar
  25. S. F. Kim, D. A. Huri, and S. H. Snyder, “Inducible nitric oxide synthase binds, Snitrosylates, and activates cyclooxygenase-2,” Science, vol. 310, no. 5756, pp. 1966–1970, 2005. View at: Publisher Site | Google Scholar
  26. Y. F. Zhang, Expression QTL and Genetic Regulatory Network Analysis of AD-Related Gene Soat1 and Ptgs2, Nantong University, 2012.
  27. J. N. Trollor, E. Smith, B. T. Baune et al., “Systemic inflammation is associated with MCI and its subtypes: the Sydney Memory and Aging Study,” Dementia and Geriatric Cognitive Disorders, vol. 30, no. 6, pp. 569–578, 2010. View at: Publisher Site | Google Scholar
  28. F. Xiong, Association of EMILIN1, SLC6A2 polymorphisms and occupational stress with hypertension, Xinjiang Medical University, 2014.
  29. T. Pacholczyk, R. D. Blakely, and S. G. Amara, “Expression cloning of a cocaine-and antidepressant-sensitive human noradrenaline transporter,” Nature, vol. 350, no. 6316, pp. 350–354, 1991. View at: Publisher Site | Google Scholar
  30. L. C. Tan, L. Zhao, C. M. Deng et al., “Lovastatin may up-regulate expression of muscarinic acetylcholine receptors and against the neurotoxicity of ß-amyloid peptide on the receptors,” Chinese Journal of Gerontology, vol. 10, no. 37, pp. 2363–2367, 2017. View at: Google Scholar
  31. L. Zhang, H. B. Huan, and X. D. Wen, “Effect of muscarinic cholinergic receptor 3 on invasion and migration in hepatocellular carcinoma cells,” Acta Academiae Medicinae Militaris Tertiae, vol. 39, no. 6, pp. 509–514, 2017. View at: Google Scholar
  32. B. Dubois, M. Chupin, H. Hampel et al., “Donepezil decreases annual rate of hippocampal atrophy in suspected prodromal Alzheimer disease,” Alzheimers Dementia, vol. 11, no. 9, pp. 1041–1049, 2015. View at: Google Scholar
  33. A. J. Wein, “Re: Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study,” The Journal of Urology, vol. 193, no. 6, p. 2035, 2015. View at: Publisher Site | Google Scholar
  34. J. Montojo, K. Zuberi, H. Rodriguez, G. D. Bader, and Q. Morris, “GeneMANIA: Fast gene network construction and function prediction for Cytoscape,” F1000Research, vol. 3, 2014. View at: Google Scholar
  35. S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, and Q. Morris, “GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function,” Genome Biology, vol. 9, no. 1, p. S4, 2008. View at: Google Scholar
  36. X. L. Liu, Y. J. Han, S. Chao et al., “advancement of G protein-coupled Receptors Related to Alzheimers disease,” Progress in Modern Biomedicine, vol. 12, no. 27, pp. 5380–8384, 2012. View at: Google Scholar
  37. B. H. Luo, X. Z. Zhang, L. Zhao et al., “Review of mechanism prevention and treatment of g protein- adenylate cyclase signal pathway in alzheimers disease,” Liaoning Journal of Traditional Chinese Medicine, vol. 39, no. 6, pp. 1184–1188, 2012. View at: Google Scholar
  38. C. D. Wright, Q. Chen, N. L. Baye et al., “Nuclear α1-adrenergic receptors signal activated ERK localization to caveolae in adult cardiac myocytes,” Circulation Research, vol. 103, no. 9, pp. 992–1000, 2008. View at: Publisher Site | Google Scholar
  39. Y. Pak, N. Pham, and D. Rotin, “Direct binding of the β1 adrenergic receptor to the cyclic AMP-dependent guanine nucleotide exchange factor CNrasGEF leads to ras activation,” Molecular and Cellular Biology, vol. 22, no. 22, pp. 7942–7952, 2002. View at: Publisher Site | Google Scholar
  40. C. Li, Y. Fan, T.-H. Lan, N. A. Lambert, and G. Wu, “Rab26 modulates the cell surface transport of α2- adrenergic receptors from the Golgi,” The Journal of Biological Chemistry, vol. 287, no. 51, pp. 42784–42794, 2012. View at: Publisher Site | Google Scholar
  41. R. Iyengar, S. Zhao, S. Chung, D. E. Mager, and J. M. Gallo, “Merging systems biology with pharmacodynamics,” Science Translational Medicine, vol. 4, no. 126, pp. 126-127, 2012. View at: Google Scholar
  42. A. L. Hopkins, “Network pharmacology,” Nature Biotechnology, vol. 25, no. 10, pp. 1110-1111, 2007. View at: Publisher Site | Google Scholar
  43. Q. Xu, R. Bauer, B. M. Hendry et al., “The quest for modernisation of traditional Chinese medicine,” BMC Complementary and Alternative Medicine, vol. 13, no. 132, 2013. View at: Publisher Site | Google Scholar
  44. Z. L. Liu, C. L. Tang et al., “Effect of different intensities of electroacupuncture on the expression of GABA and GABRA1 in hypothalamus of insomnia rats by PCPA,” Life Science Research, vol. 15, no. 3, pp. 236–240, 2011. View at: Google Scholar
  45. H. Hu, P. Wang, M. W. Kong et al., “Effect and potential mechanism of replenishing the kidney and eliminating phlegm therapy on CaMKII-α activities of AD rat model,” China Journal of Traditional Chinese Medicine and Pharmacy, vol. 25, no. 5, pp. 786–788, 2010. View at: Google Scholar
  46. M. W. Kong and P. Wang, “Approach of the relationship between senile dementia and renal deficiency and phlegmatic abundance,” Shanxi Journal of Traditional Chinese Medicine, vol. 27, no. 3, pp. 1–3, 2011. View at: Google Scholar
  47. W. Y. Zhang, H. Hu, P. Wang et al., “Explore the effect and potential mechanism of rat hippocampal cortex cholinergic system on replenishing kidney-essence and removing phlegm therapy,” Journal of Traditional Chinese Medicine, vol. 26, no. 2, pp. 239–2541, 2011. View at: Google Scholar
  48. B. Li, W. Tao, C. Zheng et al., “Systems pharmacology-based approach for dissecting the addition and subtraction theory of traditional Chinese medicine: an example using Xiao-Chaihu-Decoction and Da-Chaihu-Decoction,” Computers in Biology and Medicine, vol. 53, pp. 19–29, 2014. View at: Publisher Site | Google Scholar
  49. S. Li and B. Zhang, “Traditional Chinese medicine network pharmacology: theory, methodology and application,” Chinese Journal of Natural Medicines, vol. 11, no. 2, pp. 110–120, 2013. View at: Publisher Site | Google Scholar

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