Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2017 / Article
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Modernization of Traditional Oriental Medicine: New Dosage Forms and Medical Instruments

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Research Article | Open Access

Volume 2017 |Article ID 7236436 | https://doi.org/10.1155/2017/7236436

Su Yeon Suh, Won G. An, "Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition", Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 7236436, 15 pages, 2017. https://doi.org/10.1155/2017/7236436

Systems Pharmacological Approach to the Effect of Bulsu-san Promoting Parturition

Academic Editor: Gihyun Lee
Received28 Jul 2017
Accepted25 Sep 2017
Published29 Oct 2017

Abstract

Bulsu-san (BSS) has been commonly used in oriental medicine for pregnant women in East Asia. The purpose of this research was to elucidate the effect of BSS on ease of parturition using a systems-level in silico analytic approach. Research results show that BSS is highly connected to the parturition related pathways, biological processes, and organs. There were numerous interactions between most compounds of BSS and multiple target genes, and this was confirmed using herb-compound-target network, target-pathway network, and gene ontology analysis. Furthermore, the mRNA expression of relevant target genes of BSS was elevated significantly in related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland. This study used a network analytical approach to demonstrate that Bulsu-san (BSS) is closely related to the parturition related pathways, biological processes, and organs. It is meaningful that this systems-level network analysis result strengthens the basis of clinical applications of BSS on ease of parturition.

1. Introduction

The name of Bulsu-san (BSS) originated from its therapeutic effects that help to promote easy labor as if being touched by merciful Buddha’s hand [1]. BSS is composed of Angelicae Sinensis Radix (Danggui, DG) and Cnidium officinale Makino (Cheongung, CG), which is one of the most commonly used herb pairs in Traditional Medicine of East Asia and the usual component ratio is 2 : 3 (CG : DG) or 1 : 1 [2]. BSS is widely used in women’s medicine in East Asia; its recognized therapeutic effects are as follows: removal of impure blood, blood making, easy parturition, acceleration of labor, elimination of dead fetus or placenta, amelioration of pain, nourishing blood, and promoting blood circulation [3].

What is more, recent experimental research on the CG-DG herb pair indicated that they affect the nourishment of blood [4], activate blood circulation, and prevent blood stasis [5]. In addition, the CG-DG herb pair showed significant inhibitory effects on the proliferation and protein synthesis of vascular smooth muscle cells [6]. It was suggested BSS could affect the activities of Akt kinase and eNOS by increasing intracellular Ca2+ and reducing ROS levels [7] and regulate menstruation and provide relief from pain by enabling the management of uterine smooth muscle contractions [8]. Although BSS has therapeutic effects on various pathological symptoms in pregnant or childbearing aged women, this research focused on the molecular mechanisms and impact of BSS on easing parturition and the acceleration of labor.

In terms of parturition onset, numerous studies have described the complex hormone interactions between estrogen, progesterone, oxytocin, corticosteroid, and prostaglandin. Among these, corticotrophin releasing hormone (CRH) is regarded as a trigger that initiates the labor [9]. The placenta releases substantial amounts of CRH, which stimulates the pituitary glands of both mother and fetus to secrete adrenocorticotropin hormone [10]. This in turn induces the release of estrogen precursor, which is converted into estrogen by the placenta that induces smooth muscle contraction [10]. Additionally, dilatation of cervical connective tissue and smooth muscle is induced by the following changes: a shift from progesterone to estrogen dominance, increased responsiveness to oxytocin via the upregulation of myometrial oxytocin receptor, increased prostaglandins synthesis in uterus, increased myometrial gap junction formation, decreased nitric oxide activity, and increased influx of calcium into myocyte [11].

The hypothesis of this study was that BSS may promote the positive-feedback of hormone loops as well as a series of myometrial and cervical changes to ease parturition and safely accelerate labor. A network based in silico approach was used to identify the effect of BSS on parturition related systems and the aim of this study was to elucidate the effect of BSS on the parturition by system-level analysis. The workflow of the network pharmacological study is summarized in Figure 1.

2. Material and Methods

2.1. Identification of Active Compounds

Compounds in CG and DG were identified using a phytochemical database that is the Traditional Chinese Medicine Systems Pharmacology (TCMSP, http://ibts.hkbu.edu.hk/LSP/tcmsp.php). We applied parameters related to absorption, distribution, metabolism, and excretion (ADME), namely, human drug-likeness (DL) [12], oral bioavailability (OB) [13], and Caco-2 permeability (Caco-2) to screen the Potential active compounds in BSS [14].

2.1.1. Drug-Likeness Evaluation

DL helps filter “drug-like” compounds in oriental herbs, as DL represents a qualitative concept for valuations based on how “drug-like” a prospective compound is [15]. Accordingly, a high DL may lead to a greater possibility of therapeutic success, and compounds with a higher DL value are more likely to possess certain biological properties [16]. The calculations of DL in TCMSP database were based on Tanimoto coefficient formula [17] as follows:where represents the molecular parameters of herbal compounds and is the average molecular parameters of all compounds in the Drugbank database (http://www.drugbank.ca/) [18]. In the present study, we excluded compounds with a DL of <0.08. Other previous researches of herbal formulas set a higher threshold in the range of 0.1 to 0.18. However, we found out that most compounds of DG have low DL. In detail, only 36 compounds of 125 in DG show higher or equal DL value than 0.08. For this reason, this study sets a lower threshold of DL than other previous researches to see the most potential targets of BSS.

2.1.2. Oral Bioavailability (OB) Prediction

OB is defined as the ratio of active compounds’ absorption into the systemic circulation, which represents the convergence of the ADME process [13]. OB values are dependent on drug dissolution in the gastrointestinal (GI) tract and hepatic and intestinal first-pass metabolism, as well as on intestinal membrane permeation, which makes it a major pharmacokinetic parameter for drug evaluations [16]. In this study, the OB threshold was set as ≥15%.

2.1.3. Caco-2 Permeability Screening

Caco-2 permeability is used to predict the absorption of an orally administered drug [14]. Surface absorptivity of the small intestine is maximized with the presence of villi and microvilli, for this reason most orally administered drug absorption occurs in the small intestine [19]. Moreover, the movement of orally administered drugs across the intestinal epithelial barrier determines the rate and extent of human absorption and ultimately affects drug bioavailability [20]. In the present study, compounds with OB, DL and Caco-2 values of greater than 15%, 0.08, and >−0.4, respectively, were regarded as active compounds and subjected to further analysis.

2.1.4. Lipinski’s Rule (LR) Screening

In addition, the screening standard used was defined based on Lipinski’s rule (LR), which identifies druggable compounds as having molecular weight (MW) of ≤500 Da (MW ≤ 500), chemical composition with ≤5 hydrogen-bond donors, ≤10 hydrogen-bond acceptors, and an octanol-water partition coefficient, AlogP of ≤ 5 [21]. AlogP can be used to estimate local hydrophobicity, to produce molecular hydrophobicity maps, and to evaluate hydrophobic interactions in protein-ligand complexes [22]. Hdon and Hacc are the number of possible hydrogen-bond donors and acceptors, and the hydrogen-bonding capacity of a drug solute is recognized as a crucial determinant of permeability; moreover high hydrogen-bonding potential is often related to low permeability and absorption [23]. Eventually, in the present study, we selected active compounds satisfying the following criteria: OB ≥ 15%; DL ≥ 0.08; Caco-2 ≥ −0.4; MW ≤ 500; H-bond donors ≤ 5; H-bond acceptors ≤ 10; AlogP ≤ 5.

2.2. Target Fishing

Aside from filtering active compounds, we also sought to identify the molecular targets of these active compounds. Compound-target interaction profiles were established based on a systematic prediction of multiple drug-target interactions tool which employs random forest (RF) and support vector machine (SVM) methods and integrates chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale [24]. Compound-target interactions satisfying SVM score ≥ 0.8 and RF score ≥ 0.7 were selected for further study. Additionally, filtered compound-target interaction profile mapping was performed using the UniProt database (http://www.uniprot.org/) [25].

2.3. Gene Ontology (GO) Analysis

Biological process (BP) of gene ontology (GO) analysis was employed to determine the biological properties of target genes [26]. GO annotation indicates the possibility of direct statistical analysis on gene function information. In this research, GO BP terms with values < 0.01 were employed and the data was collected using the DAVID 6.8 Gene Functional Classification Tool (http://david.abcc.ncifcrf.gov/).

2.4. Network Construction and Analysis

In order to understand the multiscale interactions between the active compounds of BSS and targets, two types of networks were built: (1) the herb-compound-target network (H-C-T network), in which nodes represent either compounds, target genes, or herbs and edges indicate herb-compound-target connections; and (2) the target-pathway network (T-P network) to extract the pathways from KEGG database (http://www.genome.jp/kegg/), and the terms highly associated with parturition with values < 0.05 were selected as the related pathways of targets in this work. Related targets were mapped onto relevant pathways, which resulted in the T-P network. Both networks were generated in Cytoscape 3.5.1, an open-source biological network visualization and data integration software package [27].

2.5. Target Organ Location Map

Tissue-specific patterns of mRNA expression can indicate important associations with biological events or gene functions [28]. To explore the beneficial effects of BSS during parturition, it is important that the tissue mRNA expression profiles of target genes at the organ level be known [29]. The target organ location map was built according to the Dataset: GeneAtlas U133A, gcrma (http://biogps.org). BioGPS database provides expression data acquired by direct measurements of gene expression obtained by microarrays analysis [30]. First, the mRNA expression patterns of each target gene in 176 parts of organ tissues were obtained. Second, average values were calculated for each gene. Third, frequency of above average mRNA expression tissue organs was inspected. Forth, based on the result from the third step and parturition mechanism theory, mRNA expression data of relevant organ tissues were extracted and categorized into 6 groups, namely, uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood.

3. Results

3.1. Identification of Active Compounds

314 compounds of BSS were identified, including 189 molecules in CG and 125 in DG (as shown in Supplementary Material Table S1 in Supplementary Material available online at https://doi.org/10.1155/2017/7236436) and active compounds met the criteria OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.08, as well as the standards of Lipinski’s rule (LR) (as shown in Table 1). In detail, 60 active compounds were initially chosen, but 8 compounds were present in both herbs, namely, 3-butylidene-7-hydroxyphthalide, adenine, BdPh, beta-selinene, palmitic acid, senkyunolide-C, senkyunolide-D, and senkyunolide-E, and 14 had no target protein information and were thus excluded from the list of active compounds, whereas 27 compounds with lower ADME properties than above thresholds were included, which were reported to be related to oxytocin. In total, 65 active compounds were filtered.


IDActive compoundsOB (%)Caco-2DLHerb

C1()-alpha-Terpineol46.31.280.03DG
C2()-Aromadendrene55.741.810.1CG
C3()-Terpinen-4-ol81.411.360.03CG
C4(+)-alpha-Funebrene52.871.790.1CG
C5(+)-Ledol16.961.430.12DG
C6(1R,5R,7S)-4,7-Dimethyl-7-(4-methylpent-3-enyl)bicyclo3.1.1hept-3-ene16.231.860.09CG
C7(1S,4aR,8aR)-1-Isopropyl-7-methyl-4-methylene-2,3,4a,5,6,8a-hexahydro-1H-naphthalene19.81.860.08DG
C8(1S,4E,8E,10R)-4,8,11,11-tetramethylbicyclo8.1.0undeca-4,8-diene21.691.860.08CG
C9(3E)-3-butylidene-7-hydroxy-2-benzofuran-1-one42.171.030.08DG
C10(L)-alpha-Terpineol48.81.390.03CG
C11(R)-Linalool39.81.330.02CG
C12(Z)-Ligustilide53.721.30.07CG
C131-Acetyl-beta-carboline67.121.180.13CG
C141-beta-Ethylacrylate-7-aldehyde-beta-carboline28.530.450.31CG
C151H-Cycloprop(e)azulen-7-ol, decahydro-1,1,7-trimethyl-4-methylene-, (1aR-(1aalpha,4aalpha,7beta,7abeta,7balpha))-82.331.370.12CG
C161-Terpineol49.831.240.03CG
C172,6-Di(phenyl)thiopyran-4-thione69.131.740.15DG
C182-[(2S,5S,6S)-6,10-Dimethylspiro4.5dec-9-en-2-yl]propan-2-ol37.621.440.09CG
3-Butylidene-7-hydroxyphthalide62.6810.08CG&DG
C204,7-Dihydroxy-3-butylphthalide106.090.690.1CG
C2149070_FLUKA85.511.290.12CG
C224-Hydroxy-3-butylphthalide70.310.90.08CG
C2358870_FLUKA49.011.820.1CG
Adenine62.810.03CG&DG
C25ADO15.980.18CG
C26alpha-Cubebene16.731.830.11CG
C27alpha-Selinene31.811.820.1CG
C28Aromadendrene oxide 265.11.560.14CG
BdPh42.441.320.07CG&DG
C30beta-Chamigrene31.991.820.08DG
beta-Selinene24.391.830.08CG&DG
C32beta-Cubebene32.161.820.11CG
C33Cadinene17.121.880.08DG
C34Caffeic acid25.760.210.05CG
C35Carotol149.031.460.09CG
C36Cedrene51.141.820.11CG
C37Chuanxiongol22.190.940.1CG
C38cis-Thujopsene56.431.840.12DG
C39Coniferyl ferulate4.540.710.39DG
C40Crysophanol18.640.620.21CG
C41FA68.960.71CG
C42Ferulic acid (CIS)54.970.530.06DG
C43InChI=1/C15H24/c1-10-7-8-15-9-12(10)14(3,4)13(15)6-5-11(15)2/h7,11-13H,5-6,8-9H2,1-4H55.561.790.1DG
C44L-Bornyl acetate65.521.290.08CG
C45Methyl palmitate18.091.370.12CG
C46Myricanone40.60.670.51CG
C47Nicotinic acid47.650.340.02DG
C48Oleic acid33.131.170.14CG
Palmitic acid19.31.090.1CG&DG
C50Perlolyrine65.950.880.27CG
C51PLO14.070.690.43CG
C52Scopoletol27.770.710.08DG
C53Senkyunolide A26.561.30.07CG
C54Senkyunolide G39.520.630.08CG
Senkyunolide-C46.80.870.08CG&DG
Senkyunolide-D79.130.120.1CG&DG
Senkyunolide-E34.40.550.08CG&DG
C58Senkyunolide-K61.750.520.08CG
C59Sinapic acid64.150.480.08CG
C60Sphingomyelin0.310.51DG
C61Stearic acid17.831.150.14CG
C62Stigmasterol43.831.440.76DG
C63Succinic acid29.620.01DG
C64Sucrose7.170.23CG
C65Wallichilide42.310.820.71CG

Although ligustilide and ferulic acid have a DL of <0.08, both were included in this study. Since ligustilide (C12, DL = 0.07, OB = 53.72, Caco-2 = 1.3) was reported to be the main compound of DG in uterine contraction [31], and ferulic acid (C42, DL = 0.06, OB = 54.97, Caco-2 = 0.53) has been reported to be useful for the treatment of vascular diseases [6, 32] and blood deficiency syndrome [33] in China and to suppress inflammatory responses and tumor progression [34]. Some other compounds also have been shown experimentally to have various biological activities; for example, crysophanol (C42, DL = 0.21, OB = 18.64, Caco-2 = 0.62) can be used to treat menorrhagia and thrombocytopenia [35]. Perlolyrine (C52, DL = 0.27, OB = 65.95, Caco-2 = 0.88) was confirmed to have a protective effect on injured human umbilical vein endothelial cells [36], and myricanone (C48, DL = 0.51, OB = 57.61, Caco-2 = 0.67) was found to best inhibit mouse skin tumor progression [37].

3.2. Target Fishing

The 65 active compounds interact with 185 target proteins, as shown in Table 2; in other words, on average, each compound on average interacts with 2.85 target proteins. This result confirms the polypharmacological character of oriental medicine and demonstrates the synergistic effects of multiple compounds on multiple targets [38]. Different compounds in CG and DG can directly affect common targets, for example, the target protein “calmodulin (CALM1)” interacts with crysophanol from CG and coniferyl ferulate from DG at the same time, which implies the synergetic or cumulative effects of herbal medicine.


UniProt IDTarget nameGene Name

P804044-aminobutyrate aminotransferase, mitochondrialABAT
P33121Long-chain-fatty-acid--CoA ligase 1ACSL1
O60488Long-chain-fatty-acid--CoA ligase 4ACSL4
P00813Adenosine deaminaseADA
P07327Alcohol dehydrogenase 1AADH1A
P00325Alcohol dehydrogenase 1BADH1B
P00326Alcohol dehydrogenase 1CADH1C
P29274Adenosine A2a receptorADORA2A
P35348Alpha-1A adrenergic receptorADRA1A
P35368Alpha-1B adrenergic receptorADRA1B
P25100Alpha-1D adrenergic receptorADRA1D
P08913Alpha-2A adrenergic receptorADRA2A
P18089Alpha-2B adrenergic receptorADRA2B
P18825Alpha-2C adrenergic receptorADRA2C
P08588Beta-1 adrenergic receptorADRB1
P07550Beta-2 adrenergic receptorADRB2
Q5SY84Adenylosuccinate synthetaseADSS
P21549Serine--pyruvate aminotransferaseAGXT
O43865Putative adenosylhomocysteinase 2AHCYL1
P15121Aldose reductaseAKR1B1
P13716Delta-aminolevulinic acid dehydrataseALAD
P51649Succinate semialdehyde dehydrogenase, mitochondrialALDH5A1
P04745Alpha-amylase 1AMY1A
P04746Pancreatic alpha-amylaseAMY2A
P04114Apolipoprotein B-100APOB
P10275Androgen receptorAR
P06576ATP synthase subunit beta, mitochondrialATP5B
P06276CholinesteraseBCHE
P10415Apoptosis regulator Bcl-2BCL2
Q06187Tyrosine-protein kinase BTKBTK
P00915Carbonic anhydrase ICA1
P62158CalmodulinCALM1
P42574Caspase-3CASP3
P04040CatalaseCAT
P06307CholecystokininCCK
P20248Cyclin-A2CCNA2
P30305M-phase inducer phosphatase 2CDC25B
P24941Cell division protein kinase 2CDK2
P11597Cholesteryl ester transfer proteinCETP
P28329Choline O-acetyltransferaseCHAT
O14757Serine/threonine-protein kinase Chk1CHEK1
P36222Chitinase-3-like protein 1CHI3L1
P11229Muscarinic acetylcholine receptor M1CHRM1
P08172Muscarinic acetylcholine receptor M2CHRM2
P20309Muscarinic acetylcholine receptor M3CHRM3
Q15822Neuronal acetylcholine receptor subunit alpha-2CHRNA2
P36544Neuronal acetylcholine receptor protein, alpha-7 chainCHRNA7
Q99966Cbp/p300-interacting transactivator 1CITED1
P02452Collagen alpha-1(I) chainCOL1A1
Q02388Collagen alpha-1(VII) chainCOL7A1
P17538Chymotrypsinogen BCTRB1
P07339Cathepsin DCTSD
P04798Cytochrome P450 1A1CYP1A1
P05177Cytochrome P450 1A2CYP1A2
Q9ULA0Aspartyl aminopeptidaseDNPEP
P27487Dipeptidyl peptidase IVDPP4
P21728Dopamine D1 receptorDRD1
P14416D(2) dopamine receptorDRD2
P25101Endothelin-1EDNRA
Q07075Glutamyl aminopeptidaseENPEP
Q6UWV6Ectonucleotide pyrophosphatase/phosphodiesterase family member 7ENPP7
P04626Receptor tyrosine-protein kinase erbB-2ERBB2
P03372Estrogen receptorESR1
Q92731Estrogen receptor betaESR2
P00742Coagulation factor XaF10
P00734ThrombinF2
P08709Coagulation factor VIIF7
P07148Fatty acid-binding protein, liverFABP1
P01100Proto-oncogene c-FosFOS
P15408Fos-related antigen 2FOSL2
P35575Glucose-6-phosphataseG6PC
P14867Gamma-aminobutyric acid receptor subunit alpha-1GABRA1
P47869Gamma-aminobutyric-acid receptor alpha-2 subunitGABRA2
P34903Gamma-aminobutyric-acid receptor alpha-3 subunitGABRA3
P48169Gamma-aminobutyric-acid receptor subunit alpha-4GABRA4
P31644Gamma-aminobutyric-acid receptor alpha-5 subunitGABRA5
Q16445Gamma-aminobutyric-acid receptor subunit alpha-6GABRA6
P17677NeuromodulinGAP43
P47871GlucagonGCGR
P14136Glial fibrillary acidic proteinGFAP
Q2TU84Growth-inhibiting protein 18GIG18
P23415Glycine receptor alpha-1 chainGLRA1
P00367Glutamate dehydrogenase 1, mitochondrialGLUD1
P17174Aspartate aminotransferase, cytoplasmicGOT1
P00505Aspartate aminotransferase, mitochondrialGOT2
P42262Glutamate receptor 2GRIA2
P49841Glycogen synthase kinase-3 betaGSK3B
Q15486Putative beta-glucuronidase-like protein SMA3GUSBP1
P19367Hexokinase-1HK1
P040353-hydroxy-3-methylglutaryl-coenzyme A reductaseHMGCR
P00738HaptoglobinHP
O14756OxidoreductaseHSD17B6
P08238Heat shock protein HSP 90HSP90AB1
P282235-hydroxytryptamine 2A receptorHTR2A
P01344Insulin-like growth factor IIIGF2
P01857Ig gamma-1 chain C regionIGHG1
P22301Interleukin-10IL10
P05231Interleukin-6IL6
P01308InsulinINS
Q12809Potassium voltage-gated channel subfamily H member 2KCNH2
Q12791Calcium-activated potassium channel subunit alpha 1KCNMA1
P35968Vascular endothelial growth factor receptor 2KDR
P09848Lactase-phlorizin hydrolaseLCT
Q32P28Prolyl 3-hydroxylase 1LEPRE1
Q8IVL6Prolyl 3-hydroxylase 3LEPREL2
P06858Lipoprotein lipaseLPL
P09960Leukotriene A-4 hydrolaseLTA4H
P21397Amine oxidase [flavin-containing] AMAOA
P27338Amine oxidase [flavin-containing] BMAOB
Q16539Mitogen-activated protein kinase 14MAPK14
Q00266S-adenosylmethionine synthetase isoform type-1MAT1A
P31153S-adenosylmethionine synthetase isoform type-2MAT2A
P23368NAD-dependent malic enzyme, mitochondrialME2
Q16798NADP-dependent malic enzyme, mitochondrialME3
Q3SYC22-acylglycerol O-acyltransferase 2MOGAT2
P05164MyeloperoxidaseMPO
Q15788Nuclear receptor coactivator 1NCOA1
Q15596Nuclear receptor coactivator 2NCOA2
P29475Nitric-oxide synthase, brainNOS1
P35228Nitric oxide synthase, inducibleNOS2
P29474Nitric oxide synthase, endothelialNOS3
P16083NRH dehydrogenase [quinone] 2NQO2
Q14994Nuclear receptor subfamily 1 group I member 3NR1I3
P04150Glucocorticoid receptorNR3C1
P08235Mineralocorticoid receptorNR3C2
Q16620BDNF/NT-3 growth factors receptorNTRK2
P04181Ornithine aminotransferase, mitochondrialOAT
P00480Ornithine carbamoyltransferase, mitochondrialOTC
Q9BYC2Succinyl-CoA:3-ketoacid-coenzyme A transferase 2, mitochondrialOXCT2
O15460Prolyl 4-hydroxylase subunit alpha-2P4HA2
P49585Choline-phosphate cytidylyltransferase APCYT1A
Q14432CGMP-inhibited 3′,5′-cyclic phosphodiesterase APDE3A
O00330Pyruvate dehydrogenase protein X component, mitochondrialPDHX
P52945Pancreas/duodenum homeobox protein 1PDX1
P06401Progesterone receptorPGR
P48736Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoformPIK3CG
P11309Proto-oncogene serine/threonine-protein kinase Pim-1PIM1
P61925cAMP-dependent protein kinase inhibitor alphaPKIA
P14618Pyruvate kinase isozymes M1/M2PKM2
P04054Phospholipase A2PLA2G1B
P00749Urokinase-type plasminogen activatorPLAU
P00747PlasminogenPLG
P00491Purine nucleoside phosphorylasePNP
P27169Serum paraoxonase/arylesterase 1PON1
Q07869Peroxisome proliferator-activated receptor alphaPPARA
Q03181Peroxisome proliferator-activated receptor deltaPPARD
P37231Peroxisome proliferator activated receptor gammaPPARG
Q9UBK2Peroxisome proliferator-activated receptor gamma coactivator 1-alphaPPARGC1A
P17612mRNA of PKA Catalytic Subunit C-alphaPRKACA
P05771Protein kinase C beta typePRKCB
P35030Trypsin-3PRSS3
P60484Phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTENPTEN
P43115Prostaglandin E2 receptor EP3 subtypePTGER3
P23219Prostaglandin G/H synthase 1PTGS1
P35354Prostaglandin G/H synthase 2PTGS2
P18031mRNA of Protein-tyrosine phosphatase, non-receptor type 1PTPN1
P10082Peptide YYPYY
P63000Ras-related C3 botulinum toxin substrate 1RAC1
P50120Retinol-binding protein 2RBP2
P08100RhodopsinRHO
P19793Retinoic acid receptor RXR-alphaRXRA
O00767Acyl-CoA desaturaseSCD
Q14524Sodium channel protein type 5 subunit alphaSCN5A
P31040Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrialSDHA
P16109P-selectinSELP
P05121Plasminogen activator inhibitor 1SERPINE1
P14410Sucrase-isomaltase, intestinalSI
O76082Solute carrier family 22 member 5SLC22A5
Q9UBX3Mitochondrial dicarboxylate carrierSLC25A10
P11168Solute carrier family 2, facilitated glucose transporter member 2SLC2A2
P23975Sodium-dependent noradrenaline transporterSLC6A2
Q01959Sodium-dependent dopamine transporterSLC6A3
P31645Sodium-dependent serotonin transporterSLC6A4
P35610Sterol O-acyltransferase 1SOAT1
P00441Superoxide dismutase Cu-ZnSOD1
P08047Transcription factor Sp1SP1
P12931Proto-oncogene tyrosine-protein kinase SRCSRC
P36956Sterol regulatory element-binding protein 1SREBF1
Q12772Sterol regulatory element-binding protein 2SREBF2
Q9P2R7Succinyl-CoA ligase [ADP-forming] beta-chain, mitochondrialSUCLA2
Q99973Telomerase protein component 1TEP1
P01375Tumor necrosis factorTNF
Q16881Thioredoxin reductase, cytoplasmicTXNRD1
P55851Mitochondrial uncoupling protein 2UCP2
P55916Mitochondrial uncoupling protein 3UCP3

3.3. GO Analysis

397 biological process terms with values of <0.01 were sorted using the functional annotation chart of the DAVID 6.8 Gene Functional Classification Tool, based on 185 filtered target genes, and values were adjusted using the Benjamini-Hochberg method. 30 enriched GO BP terms extracted by value and gene counts are displayed in Figure 2. It is meaningful that most of the target genes are significantly related to the various BP involved in parturition. For instance, 30 extracted GO BP terms include “MAPK signaling pathways,” “steroid hormone mediated signaling pathway,” “response to glucocorticoid,” “response to estradiol,” and “positive regulation of ERK1 and ERK2 cascade.” “MAPK signaling pathways” were reported to be activated in human uterine cervical ripening during parturition [39]. “Steroid hormone mediated signaling pathway” is highly related to parturition process as estrogen and progesterone play important roles in pregnancy and parturition, and estrogen induceS the principal stimulatory myometrial contractility [40]. Also, estradiol takes key place in parturition process [41]. It was identified that increased ERK activation is observed at the onset of labor, and it promotes myometrial contractility and development of parturition [42, 43]. To sum up, the target genes of BSS are highly associated with the biological process (BP) of parturition.

3.4. Network Construction and Analysis

Network analysis is an efficient tool for visualizing and understanding multiple targeted drug actions and demonstrates drug actions within the context of the whole genome [44, 45]. For a better insight of therapeutic impacts, H-C-T and T-P networks were constructed and displayed in Figures 3 and 4, respectively. In the H-C-T network, nodes represent herb names, compounds, and targets. Also in the T-P network, circular nodes represent targets and triangle nodes represent pathways. Besides node size is relative to the degree and edges show interactions between nodes.

H-C-T network confirmed that there were 739 interactions between 185 targets and 65 active compounds of CG and DG: oleic acid (C48, degree = 42) with the highest number of interactions with targets, followed by succinic acid (C63, degree = 40) and stigmasterol (C62, degree = 37). It shows that single molecules target multiple receptors [46]. Also, some compounds from CG and DG were found to share common targets. Likewise, prostaglandin G/H synthase 2 (PTGS2, degree = 56) displayed the most affinitive connections with compounds, followed by gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1, degree = 48), prostaglandin G/H synthase 1 (PTGS1, degree = 37), and muscarinic acetylcholine receptor M1 (CHRM1, degree = 37). Except for C60 (PLA2G1B, degree = 1), the rest of the 64 active compounds are connected with more than one target; likewise, 73 (39.5%) target genes out of 185 interacted with more than one compound. This result demonstrates the multicompounds and multitarget properties of herbal compounds and there was a report that compounds with multiple targets could have greater therapeutic efficacy [47].

In addition, the top 40 pathways were extracted based on gene counts and value (<0.05), and value was adjusted by Benjamini-Hochberg method. T-P network using relevant targets of herbal compounds is demonstrated in Figure 4. There were 485 interactions between the top 40 pathways and 135 of 185 target genes. “Metabolic pathways” (degree = 49) and “neuroactive ligand-receptor interaction pathway” (degree = 32) had the highest and the second highest numbers of connections with the targets, followed by “calcium signaling” (degree = 21), “cAMP signaling pathway” (degree = 17), and “cGMP PKG signaling pathway” (degree = 15). These are compelling results that parturition processes are the complex hormone interactions and it is well known that calcium signals within the myometrium are pivotal for uterine contractions [48]. In the same manner, some target genes demonstrated higher degree centrality with top 40 pathways, namely, PI3-kinase subunit gamma (PIK3CG, degree = 23), cAMP-dependent protein kinase catalytic subunit alpha (PRKACA, degree = 20), protein kinase C beta type (PRKCB, degree = 18), and calmodulin (CALM1, degree = 11). We can confirm the same result in the previous researches. For instance, PI3-kinase subunit gamma plays the key role in regulating cAMP, calcium cycling, and beta-adrenergic signaling [49]. Moreover, during the labor, calmodulin-calcium complex activates myosin light-chain kinase, which causes the generation of ATPase activity; eventually, uterine contraction is promoted [50].

H-C-T network explains the multitarget, multicompounds properties and accumulates effect of herbal medicines and T-P network shows that target genes of BSS are highly related to the pathway associated with parturition process.

3.5. Target Organ Location Map

It is important to confirm the tissue mRNA expression profiles of the target genes at the organ level to identify the effects of BSS on parturition. Since there was no mRNA expression information in BioGPS of muscarinic acetylcholine receptor M1 (CHRM1), putative beta-glucuronidase-like protein SMA3 (GUSBP1), and retinol-binding protein 2 (RBP2), excluding these 3 targets from 185 filtered targets, totally 182 genes mRNA expression profiles were analyzed in this study. There were 519 interactions between target genes and organ locations. The networks of target genes tissue mRNA expression profiles and compounds of BSS are shown in Figure 5.

As a result, 159 of 182 target genes displayed beyond average mRNA expression in relevant organ tissues, such as uterus and/or uterus corpus, fetus and/or placenta, hypothalamus and/or pituitary, smooth muscle, and whole blood. The rest of 23 genes of 182 targets did not display above average mRNA expression in above organ tissues, for example, gamma-aminobutyric acid receptor subunit alpha-6 (GABRA6) and coagulation factor X (F10).

Nevertheless, most genes of 159 demonstrated high expression patterns in several organs of parturition related tissues at the same time. In detail, 60 genes showed most significant mRNA expression in the uterus and/or uterus corpus group, 130 for placenta and/or fetus, 86 for hypothalamus and/or pituitary, 82 for smooth muscle, 80 for pituitary, and 81 for whole blood. Besides, 30 of 159 genes showed expression in all of 6 groups. For instance, muscarinic acetylcholine receptor M2 (CHRM2), neuronal acetylcholine receptor subunit α-2 (CHRNA2), gamma-aminobutyric acid receptor subunit alpha-3 (GABRA3), NO synthase, inducible (NOS2), cGMP-inhibited 3′,5′-cyclic phosphodiesterase A (PDE3A), and sodium-dependent dopamine transporter (SLC6A3) recorded beyond average mRNA expression in all six groups. Furthermore, 79% of targets were expressed in two or more organ tissues, which suggests that those organs and target genes of BSS are closely correlated.

4. Discussion

In this study, network pharmacology method with DL, OB, Caco-2, and LR evaluation, multiple drug-target prediction, network analysis, and relevant organ location mapping was used to explain the targets of BSS in relation to the parturition process. There is no denying that network based analysis is powerful approach for identifying the actions of multitargeting herbal medicines at the systems level and our study shows target genes of BSS are strongly connected to parturition related pathways, biological processes, and organs. It was confirmed that 98% of the active compounds of BSS were interacted with more than two targets and 39.5% of the targets related to more than one compound. The synergetic multitarget properties of BSS were visualized, but further discussion about differentiated drug action based on degree centrality and simultaneous targeting effect of more than one compound is required [51]. Also, detailed potential pathways of BSS should be explored deeply in the future.

Similar findings were identified in a few RCT researches in China that using BSS in induction of labor can reduce the delivery time, the amount of bleeding, and the residual rate of placenta [52, 53]. In addition, BSS targets six genes of GABA receptor and NOS, which was reported to be related oxytocin neurons at the time of parturition in rats [54]. Also, BSS targets NOS and NO (nitric oxide) which are involved in the regulation of uterine contractility during pregnancy and is a key factor for the onset of labor [55], and iNOS (inducible nitric oxide synthase) can be upregulated accordantly by similar inflammatory mediators during ripening [11].

In fact, rather than DG, Angelicae Gigantis Radix (Danggwi, AGR) grows naturally in Korea; for that reason, the combination of AGR and CG is commonly used as BSS in Korea. Instead, DG is named as Chinese Danggwi for accurate classification in Korea. Several studies have shown AGR is differs from DG in terms of its main active constituents and genetic form. AGR is mainly composed of water soluble polysaccharide but coumarin, which is liposoluble including nodakenin (1), peucedanone (2), marmesin (3), decursinol (4), 7-hydroxy-6-(2R-hydroxy-3-methylbut-3-enyl) coumarin (5), demethylsuberosin (6), decursin (7), decursinol angelate (8), and isoimperatorin (9) [56]. Of these, decursin and its isomer decursinol angelate have been reported to be the active compounds in AGR [57]. It was identified in the experimental studies that AGR and DG act via different mechanisms in the cardiovascular, central nervous system, and anticancer activity but both have similar pharmacological effects [57]. Since the compositions of DG and AGR differ, further study on BSS with AGR is required. Currently, BSS is commonly prescribed to treat cerebra vascular and cardiovascular diseases in China [33], but, in Korea, BSS is widely applied in obstetrics.

The similarity between cervical ripening during parturition and inflammatory reaction has been pointed out in earlier studies; this has been attributed to the induction of leukocyte migration into tissue, thus promoting cervical remodeling and parturition by estrogen [58]. Further study is needed in terms of the effect of BSS on inflammatory reactions and parturition.

Furthermore, the CG-DG herb pair has other names, such as, Gunggui-tang (weight ratios of 2 : 3 or 1 : 1), Ogeum-san (1 : 1), Iphyo-san (1 : 1), and Sinmyo Bulsu-san (1 : 2), those are prepared at different weight ratios [3]. Accordingly, weight ratio should be determined based on considerations of targeted symptoms for relevant clinical applications.

5. Conclusion

This study results show that Bulsu-san (BSS) is highly connected to the parturition related pathways, biological processes, and organs. Most compounds in BSS work together with multiple target genes in a synergetic way, and this was confirmed using herb-compound-target network and target-pathway network analysis. The mRNA expression of relevant target genes of BSS was elevated significantly in parturition related organ tissues, such as those of the uterus, placenta, fetus, hypothalamus, and pituitary gland.

This study employed the network analytical methods to show the multicompound, multitarget properties of BSS. The results not only support clinical applications of BSS on easing childbirth but also suggest the related target genes and pathways of BSS on promoting parturition according to a systems-level in silico analytic approach. However, detailed mechanisms and other functions of BSS should be discussed further.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059994).

Supplementary Materials

Table S1: 314 Compounds of BSS (189 molecules of CG and 125 of DG).

  1. Supplementary Material

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Copyright © 2017 Su Yeon Suh and Won G. An. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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