Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2017 / Article

Research Article | Open Access

Volume 2017 |Article ID 4198035 | https://doi.org/10.1155/2017/4198035

Su Yeon Suh, Won G. An, "Systems Pharmacological Approach of Pulsatillae Radix on Treating Crohn’s Disease", Evidence-Based Complementary and Alternative Medicine, vol. 2017, Article ID 4198035, 21 pages, 2017. https://doi.org/10.1155/2017/4198035

Systems Pharmacological Approach of Pulsatillae Radix on Treating Crohn’s Disease

Academic Editor: Jae Youl Cho
Received20 Jan 2017
Revised28 Feb 2017
Accepted01 Mar 2017
Published01 Jun 2017

Abstract

In East Asian traditional medicine, Pulsatillae Radix (PR) is widely used to treat amoebic dysentery and renowned for its anti-inflammatory effects. This study aimed to confirm evidence regarding the potential therapeutic effect of PR on Crohn’s disease using a system network level based in silico approach. Study results showed that the compounds in PR are highly connected to Crohn’s disease related pathways, biological processes, and organs, and these findings were confirmed by compound-target network, target-pathway network, and gene ontology analysis. Most compounds in PR have been reported to possess anti-inflammatory, anticancer, and antioxidant effects, and we found that these compounds interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by PR are elevated significantly in immunity-related organ tissues, small intestine, and colon. Our results suggest that the anti-inflammatory and repair and immune system enhancing effects of PR might have therapeutic impact on Crohn’s disease.

1. Introduction

Inflammatory bowel disease (IBD) may be categorized clinically as Crohn’s disease or ulcerative colitis [1]. Crohn’s disease usually causes a variety of systemic symptoms, which include chronic inflammation of the bowel [2]. Although any part of the digestive tract from mouth to anus may be affected, usually the small intestine (ileum) and the large intestine (colon) are involved; ileocolic Crohn’s accounts for 50% of cases, ileal Crohn’s for 30%, and colic Crohn’s for 20% of cases [3]. Symptoms vary though its common manifestations include persistent diarrhea, rectal bleeding, abdominal cramps, and pain, though fever, extreme fatigue, and weight loss are also common [1, 2, 4]. Constipation is also a frequent symptom and can lead to bowel obstruction and, thus, increase the risk of bowel cancer. Complications involving other than the gastrointestinal (GI) tract include anemia, arthritis, liver disease, eye inflammation, and skin rashes [2, 4].

According to a statistical report issued by the Health Insurance Review and Assessment Service in Korea in 2016, the number of Crohn’s disease patients increased from 13,920 in 2011 to 18,332 in 2015, an average annual increase of 7.1%. Furthermore, over the same period, total medical cost has increased by 19.4% annually, and more than half of patients are in 20s or 30s. A systematic review about the economic and quality-of-life burden of Crohn’s disease reported that, in the USA, Germany, France, UK, Italy, and Spain, in all countries combined, Crohn’s medical costs totaled €30 billion annually and that patient quality of life was substantially diminished by the physical, emotional, and social effects of the disease [5].

Although Crohn’s disease is a global health problem [5], its pathology remains poorly understood [1, 6]. Nevertheless, it has been established that its etiology is associated with complex interactions between environmental, immune, microbial, and genetic factors [4, 7], though a number of authors have suggested that the primary defect in Crohn’s disease is one of relative immunodeficiency [6, 8, 9].

A systematic review of publications from 1947 to 2013 involving controlled studies of herbal therapies in IBD indicated that at least 50 percent of IBD patients used some form of complementary and alternative medicine (CAM), since most herbal therapies had been reported to have anti-inflammatory effects and plausible action mechanisms in IBD with minimal adverse effects [10]. Moreover, herbal medicines are being increasingly used and requested by IBD patients not only in Asia, but also in Western countries [11].

Of the anti-inflammatory herbal medicines, Pulsatillae Radix (Baekduong, PR) is worth considering as a potential treatment for Crohn’s disease as it was widely used in traditional medicine to treat amoebic dysentery and has also been demonstrated scientifically to have anti-inflammatory effects experimentally [12, 13]. PR is traditional Korean herbal medicine prepared from the roots of Pulsatilla koreana Nakai and contains several phytochemicals, including anemonin, hederagenin, oleanolic acid, and deoxypodophyllotoxin [14, 15]. Some experimental study results have shown that PR has various biological activities. For instance, aqueous and ethanol extracts of PR have been reported to demonstrate anticancer effects in anaplastic thyroid cancer [16], methanol extract of PR was found to have anti-inflammatory effects in lipopolysaccharide (LPS) exposed rats [17], and PR has also been reported to inhibit adipocyte differentiation and to suppress adipogenesis [13].

According to the meridian tropism theory of traditional medicine, the effects of PR reach to the stomach meridian and the large intestine meridian, which is in accord with the organs commonly affected by Crohn’s disease. In terms of the selection of traditional medication, meridian tropism theory plays an important role, as it is one of the core principles of traditional medicine [18]. Based on meridian tropism theory, each herbal medicine possesses different affinities for certain organs and meridians of the body [19].

In the present study, we sought to confirm the therapeutic effects of PR in Crohn’s disease using system level analysis and a network based in silico approach. A schematic of the network pharmacological study is summarized in Figure 1.

2. Material and Methods

2.1. Identification of Active Compounds

According to the Traditional Chinese Medicine Systems Pharmacology (TCMSP, http://ibts.hkbu.edu.hk/LSP/tcmsp.php) database (a free phytochemical database of herbal medicine), PR contains 57 identified compounds. Parameters related to absorption, distribution, metabolism, and excretion (ADME), namely, human drug-likeness (DL) [20], oral bioavailability (OB) [21], and Caco-2 permeability (Caco-2) [22], were employed to filter out potential active compounds.

2.1.1. Drug-Likeness Evaluation

DL helps filter out “drug-like” compounds in oriental herbs, as DL represents a qualitative concept for valuations based on how “drug-like” prospective compound is [23]. 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 [24]. Calculations of DL in the TCMSP database are based on the Tanimoto coefficient formula [25] as follows:where represents the molecular parameters of herbal compounds and is the average molecular parameter of all compounds in the DrugBank database (http://www.drugbank.ca/) [26]. In the present study, we excluded compounds with a DL value of < 0.18.

2.1.2. Oral Bioavailability (OB) Prediction

Oral bioavailability (OB) is defined as the absorption ratio of an active compound into the systemic circulation, which represents convergence of the ADME process [21]. OB values are dependent on drug dissolution in the gastrointestinal (GI) tract, intestinal and hepatic first-pass metabolisms, and intestinal membrane permeation, and, thus, OB is considered a major pharmacokinetic parameter for drug evaluations [24]. 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 [22]. Surface absorptivity of the small intestine is maximized by villi and microvilli; for this reason, orally administered drugs are mostly absorbed in the small intestine [27]. 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 [28]. In the present study, compounds with OB, DL, and Caco-2 values of >15%, >0.18, and >−0.4, respectively, were regarded as active and subjected to analysis. In addition, we included some compounds with lower ADME profile than above thresholds, for the reason that those were reported to possess anti-inflammatory, antioxidant, anticancer, and antibacterial effects. This study was about only one single herb, and for this reason we did not use a high threshold of ADME profile to filter potential active compounds. Instead, we lowered the standard of OB in order to analyze the most potential targets of PR.

2.2. Target Fishing

Molecular targets of filtered potential active compounds were found in the TCMSP [29], Similarity Ensemble Approach (SEA, http://sea.bkslab.org), and the Binding Database (http://www.bindingdb.org). In addition, filtered compound-target interaction profile mapping was performed using the UniProt database (http://www.uniprot.org/) [30].

2.3. Gene Ontology (GO) Analysis

Biological process (BP) of gene ontology (GO) analysis was employed to determine the biological properties of target genes [31]. GO annotation provides statistical analyses 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 PR and targets, two types of networks were built: (1) a compound-target network (C-T network), in which nodes represented either compounds or target proteins, and edges indicated compound-target connections; and (2) a target-pathway network (T-P network), which was used to extract pathways from the KEGG database (http://www.genome.jp/kegg/) and to filter out terms highly associated with Crohn’s disease and values of < 0.01. Related targets were mapped onto relevant pathways to produce the T-P network. Both networks were generated in Cytoscape 3.4.0, an open-source biological network visualization and data integration software package [32].

2.5. Target Organ Location Network

Tissue-specific patterns of mRNA expression can indicate important associations with particular biological events or gene functions [33]. Thus, to explore the beneficial effects of PR on Crohn’s disease, it was important to determine the tissue mRNA expression profiles of target proteins at the organ level [34]. The target organ location network was used with the Dataset: GeneAtlas U133A, gcrma (http://biogps.org). The BioGPS database provides expression data acquired by direct measurements of gene expressions by microarrays analysis [35]. First, the mRNA expression patterns of each target gene in 84 parts of organ tissues were obtained. Second, average values were calculated for each gene. Third, above average mRNA expressions in relevant organ tissues were extracted and arranged by frequency. Finally, a target organ location network was constructed using Cytoscape 3.4.0 and organ-specific, Crohn’s disease related, gene overexpression data.

2.6. GEO2R Analysis

Using Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo), we compared mRNA expression pattern of normal groups and Crohn’s disease groups. GEOquery and limma R packages of GEO2R tool were employed to identify highly expressed genes.

2.7. Network Pathway

In order to elucidate the action mechanisms of PR in Crohn’s disease, filtered target proteins were input into the pathway map of inflammatory bowel disease acquired from the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) database.

3. Results

3.1. Identification of Active Compounds

Of the 57 compounds (as shown in Supplementary Table S1 in Supplementary Material available online at https://doi.org/10.1155/2017/4198035) in PR acquired from the TCMSP, excluding compounds with no target information, 19 compounds with a known target met the criteria OB ≥ 15%, Caco-2 ≥ −0.4, and DL ≥ 0.18. Additionally 13 compounds reported to have anti-inflammatory, antioxidant, anticancer, and antibacterial effects were added, and finally 32 compounds were analyzed (as shown in Table 1).


IDActive compoundsStructureOB (%)Caco-2DL

C13-Methylcoumarin19.661.270.05

C25,6,7-Trimethoxycoumarin32.540.940.12

C3AIDS04570321.370.550.87

C4Androstane-3,11,17-triol13.19−0.040.38

C5Anemosapogenin17.870.070.77

C6Anemoside A315.46−1.60.15

C7Aureusidin53.420.070.24

C8Beta-sitosterol36.911.320.75

C9Betulonic acid16.830.650.78

C10Campesterol5.571.60.72

C11Cauloside A6.84−0.820.4

C12Cernuoside2.69−1.51−2.18

C13Dauricine (8CI)23.650.90.37

C14Ergosterol14.291.470.72

C15Fraxinol24.190.70.1

C16Hederagenol22.420.10.74

C17Isorhamnetin49.60.310.31

C18LAN42.121.520.75

C19Lignoceric acid14.91.240.33

C20Mairin55.380.730.78

C21Oleanolic acid29.020.590.76

C22Oleanolic acid deriv.14.240.650.7

C23Pinoresinol4.250.520.52

C24Pulchinenoside A_qt16.910.120.77

C25Scoparone74.750.850.09

C26Sitogluside20.63−0.140.62

C27Sitosteryl acetate40.391.390.85

C28Stigmasterol43.831.440.76

C29Tricosanoic acid15.291.180.3

C30Ursolic acid16.770.670.75

C31ZINC0161530756.380.530.87

C32β-Sitosterol5.841.420.71

A number of these 32 compounds have been shown experimentally to have various biological activities. For example, antioxidative effect of cernuoside (C12; DL = 0.79, OB = 2.69, Caco-2 = −1.51) was experimentally identified [36]. Pinoresinol (C23; DL = 0.52, OB = 4.25, Caco-2 = 0.52) was reported to have anti-inflammatory properties [37]. β-Sitosterol (C32; DL = 0.71, OB = 5.84, Caco-2 = 1.42) and campesterol (C23; DL = 0.72, OB = 5.57, Caco-2 = 1.6) were reported to have the protecting effect by natural and synthetic antioxidants during heating [38]. Antiallergic effects of scoparone (C25; DL = 0.09, OB = 74.75, Caco-2 = 0.85) was experimentally demonstrated in rat model, which attenuates IgE-mediated allergic response in mast cells [39]. Aureusidin (C7; DL = 0.24, OB = 53.42, Caco-2 = 0.07) was reported to have marked antioxidant activity and to be useful for the treatment of several diseases [40, 41], and anemosapogenin (C5; DL = 0.77, OB = 17.87, Caco-2 = 0.07) has antitumor effects [42, 43]. Betulonic acid (C9; DL = 0.78, OB = 16.83, Caco-2 = 0.65) possesses various medical effects, such as antiviral (HIV-1), anticancer, and immunomodulatory activities [44]. Cauloside (C11; DL = 0.4, OB = 6.84, Caco-2 = −0.82) from blue cohosh was reported to inhibit proinflammatory cytokine induction by LPS [45]. Dauricine (C13; DL = 0.9, OB = 23.65, Caco-2 = 0.37) from Asiatic moonseed was reported to have significant antibacterial and anti-inflammatory effects [46], and ergosterol (C14; DL = 0.72, OB = 14.29, Caco-2 = 1.47) [47] from the mushroom and isorhamnetin (C17; DL = 0.31, OB = 49.6, Caco-2 = 0.31) [48] were both found to have anti-inflammatory effects. Furthermore, oleanolic acid (C21; DL = 0.76, OB = 29.02, Caco-2 = 0.59) and ursolic acids (C30; DL = 0.75, OB = 16.77, Caco-2 = 0.67) have been reported to have antioxidative and anti‐inflammatory effect [49, 50]. As mentioned above, PR contains many compounds, which are ubiquitous in plants, herbs, and fruits, with anti-inflammatory, anticancer, and antioxidative effects.

3.2. Target Fishing

These 32 identified active compounds interact with 182 target proteins (Table 2); that is, on average, they interact with 5.7 target genes, which does much to explain the polypharmacological and synergistic effects of PR on multiple targets [51].


UniProt IDTargetGene name

Q92887Canalicular multispecific organic anion transporter 1ABCC2
P12821Angiotensin-converting enzymeACE
P22303AcetylcholinesteraseACHE
P24666Low molecular weight phosphotyrosine protein phosphataseACP1
P00326Alcohol dehydrogenase 1CADH1C
P35348Alpha-1A adrenergic receptorADRA1A
P35368Alpha-1B adrenergic receptorADRA1B
P25100Alpha-1D adrenergic receptorADRA1D
P08913Alpha-2A adrenergic receptorADRA2A
P18825Alpha-2C adrenergic receptorADRA2C
P08588Beta-1 adrenergic receptorADRB1
P07550Beta-2 adrenergic receptorADRB2
P15121Aldose reductaseAKR1B1
O60218Aldo-keto reductase family 1 member B10AKR1B10
P09917Arachidonate 5-lipoxygenaseALOX5
P09923Intestinal-type alkaline phosphataseALPI
P04746Pancreatic alpha-amylaseAMY2A
P10275Androgen receptorAR
P15336Cyclic AMP-dependent transcription factor ATF-2ATF2
P05023Sodium/potassium-transporting ATPase subunit alpha-1ATP1A1
P15291Beta-1,4-galactosyltransferase 1B4GALT1
Q07812Apoptosis regulator BAXBAX
P10415Apoptosis regulator Bcl-2BCL2
Q07817Bcl-2-like protein 1BCL2L1
O15392Baculoviral IAP repeat-containing protein 5BIRC5
O43570Carbonic anhydrase 12CA12
Q8N1Q1Carbonic anhydrase 13CA13
P00918Carbonic anhydrase IICA2
P22748Carbonic anhydrase IVCA4
P35218Carbonic anhydrase 5A, mitochondrialCA5A
Q16790Carbonic anhydrase VICA9
P62158CalmodulinCALM1
P29466Caspase-1CASP1
P42574Caspase-3CASP3
Q14790Caspase-8CASP8
P55211Caspase-9CASP9
P13500C-C motif chemokine 2CCL2
P20248Cyclin-A2CCNA2
P24385G1/S-specific cyclin-D1CCND1
P30279G1/S-specific cyclin-D2CCND2
P60033CD81 antigenCD81
P24941Cell division protein kinase 2CDK2
P11802Cell division protein kinase 4CDK4
Q00534Cell division protein kinase 6CDK6
P38936Cyclin-dependent kinase inhibitor 1CDKN1A
O14757Serine/threonine-protein kinase Chk1CHEK1
P11229Muscarinic acetylcholine receptor M1CHRM1
P08172Muscarinic acetylcholine receptor M2CHRM2
P20309Muscarinic acetylcholine receptor M3CHRM3
P08173Muscarinic acetylcholine receptor M4CHRM4
Q15822Neuronal acetylcholine receptor subunit alpha-2CHRNA2
P36544Neuronal acetylcholine receptor protein, alpha-7 chainCHRNA7
O15111NF-kappa-B inhibitor alphaCHUK
P16220Cyclic AMP-responsive element-binding protein 1CREB1
P15509Granulocyte-macrophage colony-stimulating factorCSF2RA
P17538Chymotrypsinogen BCTRB1
P07858Cathepsin BCTSB
P10145Interleukin-8CXCL8
Q16850Lanosterol 14-alpha demethylaseCYP51A1
Q9UBM77-Dehydrocholesterol reductaseDHCR7
P27487Dipeptidyl peptidase IVDPP4
P21728Dopamine D1 receptorDRD1
Q9NRD8Dual oxidase 2DUOX2
Q6UWV6Intestinal alkaline sphingomyelinaseENPP7
Q99814Endothelial PAS domain-containing protein 1EPAS1
P03372Estrogen receptorESR1
Q92731Estrogen receptor betaESR2
P00742Coagulation factor XaF10
P00734ThrombinF2
P08709Coagulation factor VIIF7
P48023Tumor necrosis factor ligand superfamily member 6FASLG
P49327Fatty acid synthaseFASN
P05230Fibroblast growth factor 1FGF1
P09038Heparin-binding growth factor 2FGF2
P01100Proto-oncogene c-FosFOS
P14867Gamma-aminobutyric acid receptor subunit alpha-1GABRA1
P47869Gamma-aminobutyric-acid receptor alpha-2 subunitGABRA2
P34903Gamma-aminobutyric-acid receptor alpha-3 subunitGABRA3
P31644Gamma-aminobutyric-acid receptor alpha-5 subunitGABRA5
P17677NeuromodulinGAP43
Q8TDU6G-protein coupled bile acid receptor 1GPBAR1
P42262Glutamate receptor 2GRIA2
P49841Glycogen synthase kinase-3 betaGSK3B
Q9UII4Probable E3 ubiquitin-protein ligase HERC5HERC5
P09601Heme oxygenase 1HMOX1
P01112GTPase HRasHRAS
P28845Corticosteroid 11-beta-dehydrogenase isozyme 1HSD11B1
P80365Corticosteroid 11-beta-dehydrogenase isozyme 2HSD11B2
P08238Heat shock protein HSP 90HSP90AB1
P282235-Hydroxytryptamine 2A receptorHTR2A
P460985-Hydroxytryptamine receptor 3AHTR3A
P05362Intercellular adhesion molecule 1ICAM1
P01857Ig gamma-1 chain C regionIGHG1
P01584Interleukin-1 betaIL1B
P60568Interleukin-2IL2
P05231Interleukin-6IL6
O15357Phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 2INPPL1
P05412Transcription factor AP-1JUN
Q12809Potassium voltage-gated channel subfamily H member 2KCNH2
Q12791Calcium-activated potassium channel subunit alpha 1KCNMA1
O75164Lysine-specific demethylase 4AKDM4A
P35968Vascular endothelial growth factor receptor 2KDR
Q99732Lipopolysaccharide-induced tumor necrosis factor-alpha factorLITAF
P09960Leukotriene A-4 hydrolaseLTA4H
P21397Amine oxidase [flavin-containing] AMAOA
P27338Amine oxidase [flavin-containing] BMAOB
P11137Microtubule-associated protein 2MAP2
Q16539Mitogen-activated protein kinase 14MAPK14
P45983Mitogen-activated protein kinase 8MAPK8
Q13387C-Jun-amino-terminal kinase-interacting protein 2MAPK8IP2
Q07820Induced myeloid leukemia cell differentiation protein Mcl-1MCL1
P14174L-Dopachrome tautomeraseMIF
P03956Interstitial collagenaseMMP1
P09238Stromelysin-2MMP10
P0825372 kDa type IV collagenaseMMP2
P08254Stromelysin-1MMP3
P14780Matrix metalloproteinase-9MMP9
P42345Serine/threonine-protein kinase mTORMTOR
P14598Neutrophil cytosol factor 1NCF1
Q15788Nuclear receptor coactivator 1NCOA1
Q15596Nuclear receptor coactivator 2NCOA2
Q9GZQ4Neuromedin-U receptor 2NMUR2
P35228Nitric oxide synthase, inducibleNOS2
P29474Nitric oxide synthase, endothelialNOS3
P15559NAD(P)H dehydrogenase [quinone] 1NQO1
P04150Glucocorticoid receptorNR3C1
P08235Mineralocorticoid receptorNR3C2
P01111GTPase NRasNRAS
P78380Oxidized low-density lipoprotein receptor 1OLR1
P35372Mu-type opioid receptorOPRM1
P07237Protein disulfide-isomeraseP4HB
Q14432CGMP-inhibited 3′,5′-cyclic phosphodiesterase APDE3A
P16284Platelet endothelial cell adhesion moleculePECAM1
P06401Progesterone receptorPGR
P42336Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoformPIK3CA
P48736Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit, gamma isoformPIK3CG
P11309Proto-oncogene serine/threonine-protein kinase Pim-1PIM1
P61925cAMP-dependent protein kinase inhibitor alphaPKIA
P00749Urokinase-type plasminogen activatorPLAU
P191741-Phosphatidylinositol 4,5-bisphosphate phosphodiesterase gamma-1PLCG1
P09884DNA polymerase alpha catalytic subunitPOLA1
P06746DNA polymerase betaPOLB
P54098DNA polymerase catalytic subunitPOLG
P27169Serum paraoxonase/arylesterase 1PON1
Q03181Peroxisome proliferator activated receptor deltaPPARD
P37231Peroxisome proliferator activated receptor gammaPPARG
P17612mRNA of PKA catalytic subunit C-alphaPRKACA
P17252Protein kinase C alpha typePRKCA
P05129Protein kinase C gamma typePRKCG
P07477Trypsin-1PRSS1
P43115Prostaglandin E2 receptor EP3 subtypePTGER3
O14684Prostaglandin E synthasePTGES
P23219Prostaglandin G/H synthase 1PTGS1
P35354Prostaglandin G/H synthase 2PTGS2
P18031Protein-tyrosine phosphatase 1BPTPN1
P17706T-cell protein-tyrosine phosphatasePTPN2
P29350Hematopoietic cell protein-tyrosine phosphatasePTPN6
P11217Glycogen phosphorylase, muscle formPYGM
Q04206Transcription factor p65RELA
P19793Retinoic acid receptor RXR-alphaRXRA
Q14524Sodium channel protein type 5 subunit alphaSCN5A
P16581E-selectinSELE
Q9HAS3Solute carrier family 28 member 3SLC28A3
O43826Glucose-6-phosphate translocaseSLC37A4
P13866Sodium/glucose cotransporter 1SLC5A1
P31639Sodium/glucose cotransporter 2SLC5A2
Q9NY91Low affinity sodium-glucose cotransporterSLC5A4
P23975Sodium-dependent noradrenaline transporterSLC6A2
Q01959Sodium-dependent dopamine transporterSLC6A3
P31645Sodium-dependent serotonin transporterSLC6A4
Q9Y6L6Solute carrier organic anion transporter family member 1B1SLCO1B1
P40763Signal transducer and activator of transcription 3STAT3
P59538Taste receptor type 2 member 31TAS2R31
P01137Transforming growth factor beta-1TGFB1
P01375Tumor necrosis factorTNF
P11388DNA topoisomerase II alphaTOP2A
P04637Cellular tumor antigen p53TP53
P17752Tryptophan 5-hydroxylase 1TPH1
P14679TyrosinaseTYR
P176435,6-dihydroxyindole-2-carboxylic acid oxidaseTYRP1
P15692Vascular endothelial growth factor AVEGFA
P47989Xanthine dehydrogenase/oxidaseXDH

3.3. GO Analysis

For the filtered 182 target genes, 469 biological process terms with values of < 0.01 were sorted using the functional annotation chart of the DAVID 6.8 Gene Functional Classification Tool and values were adjusted using Benjamini-Hochberg method. This process resulted in the identification of 25 biological process terms. GO analysis showed that the 182 genes were highly related to inflammation, proliferation, oxidation reduction, and the regulations of apoptosis and signal transduction (Figure 2).

In detail, phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit, gamma isoform (PIK3CG), interleukin-6 (IL6), tumor necrosis factor (TNF), C-C motif chemokine 2 (CCL2), prostaglandin E2 receptor EP3 subtype (PTGER3), oxidized low-density lipoprotein receptor 1 (OLR1), prostaglandin G/H synthase 2 (PTGS2), and others are related to “inflammatory response.”

Androgen receptor (AR), interleukin-6 (IL6), heparin-binding growth factor 2 (FGF2), GTPase HRas (HRAS), hematopoietic cell protein-tyrosine phosphatase (PTPN6), and signal transducer and activator of transcription 3 (STAT3) are related to both the “positive regulation and negative regulation of cell proliferation.”

Xanthine dehydrogenase/oxidase (XDH), 5,6-dihydroxyindole-2-carboxylic acid oxidase (TYRP1), prostaglandin G/H synthase 2 (PTGS2), neutrophil cytosol factor 1 (NCF1), lanosterol 14-alpha demethylase (CYP51A1), amine oxidase [flavin-containing] A (MAOA), dual oxidase 2 (DUOX2), and others are associated with “oxidation-reduction process.”

Bcl-2-like protein 1 (BCL2L1), interleukin-6 (IL6), mitogen-activated protein kinase 8 (MAPK8), and cellular tumor antigen p53 (TP53) are associated with both the “positive regulation and negative regulation of apoptotic process.”

To summarize, it is likely that the therapeutic effect of PR in Crohn’s disease is due to its anti-inflammatory and repair process and immune system enhancing effects.

3.4. Network Construction and Analysis

To visualize more conveniently the multitargeted effects of PR, network analysis was used to investigate its actions within the context of the whole human genome [52, 53]. As shown in Figure 3, constructed (A) C-T and (B) T-P network demonstrated multicompound and multitargeted effects and relations between various pathways and targets. Circular nodes represent compounds and targets in the C-T network and triangles and circular nodes show pathways and compounds in the T-P network. In both networks, node size was regulated by degree centrality and edges showed interactions between nodes.

The C-T network showed 415 interactions between the 182 targets and 32 active compounds of PR. Ursolic acid (C30, degree = 55) had the highest number of interactions with targets, followed by beta-sitosterol (C8, degree = 37) and isorhamnetin (C17, degree = 36), and, thus, these results demonstrated that single molecules can target multiple receptors [54]. Likewise, prostaglandin G/H synthase 2 (PTGS2, degree = 15) displayed the most affinitive connections with compounds, followed by prostaglandin G/H synthase 1 (PTGS1, degree = 13) and nuclear receptor coactivator 2 (NCOA2, degree = 11). According to betweenness centrality results, protein-tyrosine phosphatase 1B (PTPN1, betweenness = 0.11) was second highest followed by PTGS2 (betweenness = 0.12). 28 (88%) Of the 32 active compounds were connected with more than two targets and 86 (47%) of the 182 targets interact with more than one compound. This network analysis results clearly demonstrated the multitargeting natures of herbal compounds; besides it showed that ursolic acid (C30) is the most essential compound in PR.

In addition, 40 pathways related to Crohn’s disease were extracted to construct the T-P network. According to degree centrality, “pathways in cancer” (degree = 49) had the highest number of connections with the targets, followed by the “PI3K-Akt signaling pathway” (degree = 34) and “hepatitis B” (degree = 33). In the same manner, phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA, degree = 33), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform (PIK3CG, degree = 33), and nuclear factor NF-κB p65 subunit (RELA, degree = 25) demonstrate higher affinitive connections with pathways. Betweenness centrality and degree centrality results were similar; there was only little difference. “Pathways in cancer” (betweenness = 0.19) had the highest betweenness among the targets, which concurred with degree centrality, and this was followed by “neuroactive ligand-receptor interaction” (betweenness = 0.15) and “PI3K-Akt signaling pathway” (betweenness = 0.10). Regarding highest betweenness targets, PIK3CA (betweenness = 0.10), PIK3CG (betweenness = 0.10), and prostaglandin E2 receptor EP3 subtype (PTGER3, betweenness = 0.04) showed most affinitive connections with pathways.

3.5. Target Organ Location Network

The tissue mRNA expression profiles of target genes at the organ level were investigated to identify effects of PR on Crohn’s disease. No mRNA expression information of three of genes, muscarinic acetylcholine receptor M1 (CHRM1), G-protein coupled receptor TGR-1 (NMUR2), and taste receptor type 2 member 31 (TAS2R31), was found in the BioGPS. In total, the mRNA expression profiles of 179 of 182 genes were analyzed (Supplementary Table S2). 158 Of the genes displayed above average mRNA expressions in 17 relevant organ tissues, namely, in BDCA4+ dendritic cells, bone marrow, CD14+ monocytes, CD19+ B cells, CD33+ myeloid cells, CD34+ hematopoietic stem cells, CD4+ T cells, CD56+ NK cells, CD8+ T cells, colon, colorectal adenocarcinoma, liver, lymph nodes, lymphoblasts, small intestine, smooth muscle, and thymus. In addition, these 17 organ tissues, retina, prefrontal cortex, pineal, amygdala, cardiac myocyte, heart, whole blood, and other tissues, were also associated with relatively high mRNA expressions. Networks of the tissue mRNA expressions of 158 target genes and PR compounds are shown in Figure 4, nodes represent organs and genes, and node sizes indicate the number of interactions between nodes.

In detail, among 84 organ tissues, CD33+ myeloid showed the most overexpressed mRNA expression, 104 genes of 158 target genes were overexpressed in CD33+ myeloid, followed by 97 genes in lymphoblasts, 95 in each of smooth muscle and CD34+ hematopoietic stem cells, 91 genes in liver and CD56+ NK cell, 84 in bone marrow and colorectal adenocarcinoma, 82 in BDCA4+ dendritic cells, 75 in CD14+ monocytes, 73 in small intestine, 72 in CD4+ T cells, 70 in CD19+ B cells, 67 in colon, 38 in CD8+ T cells, 23 in thymus, and 22 in lymph nodes. It is evident that most genes were overexpressed in several organs at the same time.

Additionally, intestinal alkaline sphingomyelinase (ENPP7), DNA polymerase catalytic subunit (POLG), and carbonic anhydrase 13 (CA13) recorded beyond average mRNA expressions in all 17 organs. Furthermore, more than 146 (92%) of 158 target genes were overexpressed in two or more organ tissues, suggesting that these organs and compounds in PR are closely related. Furthermore, because the above 17 organs are highly related to immunity, our study results indicate that the therapeutic effects of PR on Crohn’s disease are due to its targeting and activating the immune system.

The other 21 target genes, such as acetylcholinesterase (ACHE), pancreatic alpha-amylase (AMY2A), and muscarinic acetylcholine receptor M3 (CHRM3), did not show above average mRNA expression in the 17 organs.

3.6. GEO2R Analysis

Comparison data between normal tissue and Crohn’s disease patients’ mRNA expression pattern from Gene Expression Omnibus (GEO) was collected. We employed GEOquery and limma R packages of GEO2R tool to identify highly expressed genes in 6 datasets. Dataset accession numbers are as follows: GSE24287, GSE60083, GSE6731, GSE36807, GSE68570, and GSE72780. To sum up, 86 normal samples and 149 Crohn’s disease samples were analyzed in each dataset. GEO2R presented the top 250 highly expressed genes in Crohn’s disease group compared to the control group and we deleted overlaps, so, in the end, 1182 genes were sorted out.

We found out that there were 23 common genes (Table 3) between target genes of PR and highly expressed genes of Crohn’s disease dataset from GEO.


UniProt IDTargetGene name

Q92887Canalicular multispecific organic anion transporter 1ABCC2
O60218Aldo-keto reductase family 1 member B10AKR1B10
P09917Arachidonate 5-lipoxygenaseALOX5
P62158CalmodulinCALM1
Q00534Cell division protein kinase 6CDK6
P10145Interleukin-8CXCL8
Q99814Endothelial PAS domain-containing protein 1EPAS1
P01100Proto-oncogene c-FosFOS
P47869Gamma-aminobutyric-acid receptor alpha-2 subunitGABRA2
P49841Glycogen synthase kinase-3 betaGSK3B
P09601Heme oxygenase 1HMOX1
P05362Intercellular adhesion molecule 1ICAM1
P01584Interleukin-1 betaIL1B
P05231Interleukin-6IL6
P03956Interstitial collagenaseMMP1
Q15788Nuclear receptor coactivator 1NCOA1
P35228Nitric oxide synthase, inducibleNOS2
P01111GTPase NRasNRAS
P11309Proto-oncogene serine/threonine-protein kinase Pim-1PIM1
P61925cAMP-dependent protein kinase inhibitor alphaPKIA
P00749Urokinase-type plasminogen activatorPLAU
P35354Prostaglandin G/H synthase 2PTGS2
P40763Signal transducer and activator of transcription 3STAT3
P17752Tryptophan 5-hydroxylase 1TPH1

3.7. Network Pathway

In order to investigate further the effect of PR in Crohn’s disease, we performed pathway enrichment analysis (Figure 5). Using the IBD pathway provided by the KEGG pathway database, we confirmed the pathway mapping effect of PR in Crohn’s disease by inputting the filtered human target genes into the pathway. The KEGG pathway was constructed according to the current knowledge of the pathogenesis IBD.

The synthesis of inflammatory cytokines, such as IL-1, IL-6 and TNF-α, is mediated by NF-κB, which is a key regulator of inflammation [55, 56]. We found that oleanolic acid derivative (C22) targets IL-1; ursolic acid (C30) targets all of IL-1, IL-6, and TNF; isorhamnetin (C17), scoparone (C25), and ursolic acid (C30) target nuclear factor NF-κB p65 subunit (RELA); and scoparone (C25) and ursolic acid (C30) target NF-κB inhibitor alpha (CHUK), which suggests that these compounds affect NF-κB activity. Cernuoside (C12) targets IL-2. Beta-sitosterol (C8) targets transforming growth factor beta-1 (TGFB1) and transcription factor AP-1 (JUN), and ursolic acid (C30) also targets transcription factor AP-1 (JUN) and signal transducer and activator of transcription 3 (STAT3). Furthermore, NF-κB and AP-1 in combination are highly related to the initial inflammatory response and to the development of acquired immunity [57]. Moreover, IL-6-mediated STAT3 activation on mucosal T cells may has been suggested to play a role in the development of IBD [58].

4. Discussion

In this study, a network pharmacology method with active compounds filtration, multiple drug target prediction, gene ontology, network analysis, relevant organ location network, and pathway enrichment analysis were employed to determine the targets of PR in relation to Crohn’s disease. Our study shows that PR is highly connected to the pathways, biological processes, and organs of Crohn’s disease. A pharmacological approach was used to identify the actions of PR at the systems network level.

In this study, pathway mapping result showed that the target genes of PR overlap more with Crohn’s disease than with ulcerative colitis. Experimental study also suggested that the markers of both diseases are different from each other [59, 60]. The clinical symptoms of these diseases also differ; for instance, Crohn’s disease affect any region of the entire gastrointestinal (GI) tract and all layers of the bowel wall, whereas ulcerative colitis affects only the mucosa and submucosa of colon [61]. Furthermore, ulcerative colitis can be cured by surgery, but Crohn’s disease of any part of GI tract tends to relapse after surgery [62]. For this reason, long term management using herbal medicines might be highly recommendable treatment option for Crohn’s disease, since herbal medicines have advantages for managing chronic diseases [63, 64].

In addition, IBD is usually referred to as an autoimmune disorder [65], whereas Crohn’s disease does not meet the criteria of an autoimmune disorder; rather it is associated with immune deficiency or a secondary immune response to altered intestinal microbiota [65]. Furthermore, ulcerative colitis is a mucosal disease where autoimmune autoantibodies are commonly detected [66], whereas Crohn’s disease is a transmural disease, in which pathological changes in gut wall are thought to result from submucosal inflammatory changes [67]. Accordingly, the areas targeted for treatment in these two diseases should be differentiated.

In the present study, we focused on the use of PR as a potential therapy for Crohn’s disease. However, herb pairs and combinations are more commonly prescribed and are regarded to be more effective and safer [68]. In terms of the Gunsinjwasa theory of traditional medicine combinations, there are four different roles for each herb in the formula. First, the major component targets the main symptom. Second, the supportive component assists the effect of the major component or targets the secondary symptoms. Third, the neutralizing component allays the side effects or toxins of the major and the supportive component. Fourth, the deliver/retaining component guides the medicine to the targeting part of the body [69, 70]. This combination principle enables not only the enhancement of synergistic medicinal effects but also potentially reduces toxicities [69]. In order to induce better effects and reduce toxicities, an extended analysis of the Pulsatillae Radix (Baekduong, PR), Phellodendri Cortex (Hwangbaek), Coptidis Rhizoma (Hwangryeon), and Citrus reticulata (Jinpi) herb combination (a widely prescribed formula, known as Baekduong decoction) should be explored in the future.

Through GO analysis, we found out that targets of PR are associated with liver diseases such as hepatitis B, hepatitis C, and nonalcoholic fatty liver. In addition, the mRNA expression of 91 of 179 genes in liver was overexpressed according to the target organ location network result. Liver inflammation is as common extraintestinal symptom of Crohn’s disease [71], and the number of liver abscesses in Crohn’s disease patients is 15 to 20 times higher than that found in the general population [72]. Besides biochemical liver dysfunction [73] and hepatic fibrosis [74] are also frequently found in Crohn’s disease, and a number of drugs used to treat IBD have been reported to be associated with liver injury [75]. Furthermore, in an experimental study using a mice model of Crohn’s disease-like ileitis, it was found that TLR9 plays an important role in hepatic involvement in IBD [71]. More detailed pathways and relations between liver and Crohn’s disease should be discussed in the future.

This study demonstrated that 73 and 67 of 158 targets of PR were highly expressed in small intestine and colon, respectively, results which were accordance with not only the organs commonly affected by Crohn’s disease but also the properties of PR in terms of meridian tropism theory. However stronger evidence with another research design is required to support this result.

We confirmed a multicompound and multitarget interaction through the C-T network, which showed that 88% of the active compounds were connected with more than two targets and 47% of the targets interacted with more than one compound. Although it demonstrated the synergetic network of multitarget actions, we should explore more differentiated drug action based on degree centrality and how different the drug actions are when more compounds target one gene in simultaneous way compared to the case of one compound targeting one gene.

5. Conclusion

Previous studies indicate that most compounds in PR have anti-inflammatory, anticancer, and antioxidant effects. In the present study, we found that these compounds interact with multiple targets in a synergetic manner and that PR is highly connected to Crohn’s disease related pathways, biological processes, and organs. C-T and T-P network results demonstrated complex multicompound and multitarget drug actions and the relations between targets and various pathways. Furthermore, target genes were found to be overexpressed in organs highly related to immunity. These findings suggest the anti-inflammatory effects of PR, and its enhancements of repair processes and immune system might be of therapeutic benefit in Crohn’s disease.

Conflicts of Interest

The authors have no conflicts of interest to declare.

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

Supplementary Table S1 showed the ADME profiles of 57 compounds in PR, and supplementary Table S2 demonstrated the mRNA expression profiles of 179 of 182 genes.

  1. Supplementary Material

References

  1. D. C. Baumgart, Crohn's Disease and Ulcerative Colitis: From Epidemiology and Immunobiology to a Rational Diagnostic and Therapeutic Approach, Springer Science & Business Media, 2012.
  2. D. C. Baumgart and W. J. Sandborn, “Crohn's disease,” The Lancet, vol. 380, no. 9853, pp. 1590–1605, 2012. View at: Publisher Site | Google Scholar
  3. W. C. Tan and R. N. Allan, “Diffuse jejunoileitis of Crohn's disease,” Gut, vol. 34, no. 10, pp. 1374–1378, 1993. View at: Publisher Site | Google Scholar
  4. R. Dessein, M. Chamaillard, and S. Danese, “Innate immunity in Crohn’s disease: the reverse side of the medal,” Journal of Clinical Gastroenterology, vol. 42, supplement 3, pp. S144–S147, 2008. View at: Google Scholar
  5. D. N. Floyd, S. Langham, H. C. Séverac, and B. G. Levesque, “The economic and quality-of-life burden of crohn’s disease in Europe and the United States, 2000 to 2013: a systematic review,” Digestive Diseases and Sciences, vol. 60, no. 2, pp. 299–312, 2016. View at: Publisher Site | Google Scholar
  6. D. J. B. Marks, F. Z. Rahman, G. W. Sewell, and A. W. Segal, “Crohn's disease: an immune deficiency state,” Clinical Reviews in Allergy & Immunology, vol. 38, no. 1, pp. 20–31, 2010. View at: Publisher Site | Google Scholar
  7. T. Stefanelli, A. Malesci, A. Repici, S. Vetrano, and S. Danese, “New insights into inflammatory bowel disease pathophysiology: paving the way for novel therapeutic targets,” Current Drug Targets, vol. 9, no. 5, pp. 413–418, 2008. View at: Publisher Site | Google Scholar
  8. B. Hayee, F. Z. Rahman, G. Sewell, A. M. Smith, and A. W. Segal, “Crohn's disease as an immunodeficiency,” Expert Review of Clinical Immunology, vol. 6, no. 4, pp. 585–596, 2010. View at: Publisher Site | Google Scholar
  9. A. N. Ananthakrishnan, R. J. Xavier, and D. K. Podolsky, “Inflammatory bowel diseases: pathogenesis,” Yamada's Textbook of Gastroenterology, pp. 1364–1377, 2016. View at: Publisher Site | Google Scholar
  10. S. C. Ng, Y. T. Lam, K. K. F. Tsoi, F. K. L. Chan, J. J. Y. Sung, and J. C. Y. Wu, “Systematic review: the efficacy of herbal therapy in inflammatory bowel disease,” Alimentary Pharmacology and Therapeutics, vol. 38, no. 8, pp. 854–863, 2013. View at: Publisher Site | Google Scholar
  11. M. Sałaga, H. Zatorski, M. Sobczak, C. Chen, and J. Fichna, “Chinese herbal medicines in the treatment of IBD and colorectal cancer: a review,” Current Treatment Options in Oncology, vol. 15, no. 3, pp. 405–420, 2014. View at: Publisher Site | Google Scholar
  12. S.-H. Kim, “Anti-inflammatory effect of Pulsatilla koreana,” pp. 1–49, 2008. View at: Google Scholar
  13. L. Y.-M. K. Hyo-Jin, “Pulsatilla koreana ethanol extract suppress adipocyte differentiation and adipogenesis via down-regulation of PPAR-γ and C/EBPs,” pp. 1–47, 2014. View at: Google Scholar
  14. W. Li, Y. Ding, Y. N. Sun et al., “Oleanane-type triterpenoid saponins from the roots of Pulsatilla koreana and their apoptosis-inducing effects on HL-60 human promyelocytic leukemia cells,” Archives of Pharmacal Research, vol. 36, no. 6, pp. 768–774, 2013. View at: Publisher Site | Google Scholar
  15. W. Li, Y. Ding, Y. N. Sun et al., “Triterpenoid saponins of pulsatilla koreana root have inhibition effects of tumor necrosis factor-α secretion in lipopolysaccharide-induced RAW264.7 cells,” Chemical and Pharmaceutical Bulletin, vol. 61, no. 4, pp. 471–476, 2013. View at: Publisher Site | Google Scholar
  16. B. H. Park, K. H. Jung, M. K. Son et al., “Antitumor activity of Pulsatilla koreana extract in anaplastic thyroid cancer via apoptosis and anti-angiogenesis,” Molecular Medicine Reports, vol. 7, no. 1, pp. 26–30, 2013. View at: Publisher Site | Google Scholar
  17. S. H. Lee, E. Lee, and Y. T. Ko, “Anti-inflammatory effects of a methanol extract from Pulsatilla koreana in lipopolysaccharide-exposed rats,” BMB Reports, vol. 45, no. 6, pp. 371–376, 2012. View at: Publisher Site | Google Scholar
  18. Y. Zhao, Y. Li, X. Wang, and W. J. Sun, “The experimental study of Cortex Eucommiae on meridian tropsim: the distribution study of aucubin in rat tissues,” Journal of Pharmaceutical and Biomedical Analysis, vol. 46, no. 2, pp. 368–373, 2008. View at: Publisher Site | Google Scholar
  19. Y.-X. Chang, Y.-G. Sun, J. Li et al., “The experimental study of Astragalus membranaceus on meridian tropsim: The distribution study of astragaloside IV in rat tissues,” Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, vol. 911, pp. 71–75, 2012. View at: Publisher Site | Google Scholar
  20. M. Shen, S. Tian, Y. Li et al., “Drug-likeness analysis of traditional Chinese medicines: 1. property distributions of drug-like compounds, non-drug-like compounds and natural compounds from traditional Chinese medicines,” Journal of Cheminformatics, vol. 4, no. 1, article 31, 2012. View at: Publisher Site | Google Scholar
  21. X. Xu, W. Zhang, C. Huang et al., “A novel chemometric method for the prediction of human oral bioavailability,” International Journal of Molecular Sciences, vol. 13, no. 6, pp. 6964–6982, 2012. View at: Publisher Site | Google Scholar
  22. I. Hubatsch, E. G. E. Ragnarsson, and P. Artursson, “Determination of drug permeability and prediction of drug absorption in Caco-2 monolayers,” Nature Protocols, vol. 2, no. 9, pp. 2111–2119, 2007. View at: Publisher Site | Google Scholar
  23. W. Tao, X. Xu, X. Wang et al., “Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease,” Journal of Ethnopharmacology, vol. 145, no. 1, pp. 1–10, 2013. View at: Publisher Site | Google Scholar
  24. J. Zhang, Y. Li, S.-S. Chen et al., “Systems pharmacology dissection of the anti-inflammatory mechanism for the medicinal herb Folium eriobotryae,” International Journal of Molecular Sciences, vol. 16, no. 2, pp. 2913–2941, 2015. View at: Publisher Site | Google Scholar
  25. P. Willett, J. M. Barnard, and G. M. Downs, “Chemical similarity searching,” Journal of Chemical Information and Computer Sciences, vol. 38, no. 6, pp. 983–996, 1998. View at: Publisher Site | Google Scholar
  26. H. Liu, J. Wang, W. Zhou, Y. Wang, and L. Yang, “Systems approaches and polypharmacology for drug discovery from herbal medicines: an example using licorice,” Journal of Ethnopharmacology, vol. 146, no. 3, pp. 773–793, 2013. View at: Publisher Site | Google Scholar
  27. K. S. Pang, “Modeling of intestinal drug absorption: Roles of transporters and metabolic enzymes (for the gillette review series),” Drug Metabolism & Disposition, vol. 31, no. 12, pp. 1507–1519, 2003. View at: Publisher Site | Google Scholar
  28. T. Pei, C. Zheng, C. Huang et al., “Systematic understanding the mechanisms of vitiligo pathogenesis and its treatment by Qubaibabuqi formula,” Journal of Ethnopharmacology, vol. 190, pp. 272–287, 2016. View at: Publisher Site | Google Scholar
  29. H. Yu, J. Chen, X. Xu et al., “A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data,” PLoS ONE, vol. 7, no. 5, Article ID e37608, 2012. View at: Publisher Site | Google Scholar
  30. C. H. Wu, R. Apweiler, A. Bairoch et al., “The Universal Protein Resource (UniProt): an expanding universe of protein information,” Nucleic Acids Research, vol. 34, no. Database issue, pp. D187–D191, 2006. View at: Publisher Site | Google Scholar
  31. G. Bindea, B. Mlecnik, H. Hackl et al., “ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks,” Bioinformatics, vol. 25, no. 8, pp. 1091–1093, 2009. View at: Publisher Site | Google Scholar
  32. 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
  33. J.-B. Pan, S.-C. Hu, D. Shi et al., “PaGenBase: a pattern gene database for the global and dynamic understanding of gene function,” PLoS ONE, vol. 8, no. 12, Article ID e80747, 2013. View at: Publisher Site | Google Scholar
  34. W. Zhang, Q. Tao, Z. Guo et al., “Systems pharmacology dissection of the integrated treatment for cardiovascular and gastrointestinal disorders by traditional chinese medicine,” Scientific Reports, vol. 6, p. 32400, 2016. View at: Publisher Site | Google Scholar
  35. Z. Prevoršek, G. Gorjanc, B. Paigen, and S. Horvat, “Congenic and bioinformatics analyses resolved a major-effect Fob3b QTL on mouse Chr 15 into two closely linked loci,” Mammalian Genome, vol. 21, no. 3-4, pp. 172–185, 2010. View at: Publisher Site | Google Scholar
  36. K. Senthil Kumar and R. Kumaresan, “A comparative study on the antioxidant properties of bractein and cernuoside by the DFT method,” Monatshefte fur Chemie, vol. 144, no. 10, pp. 1513–1524, 2013. View at: Publisher Site | Google Scholar
  37. T. Guo, Y.-X. Deng, H. Xie et al., “Antinociceptive and anti-inflammatory activities of ethyl acetate fraction from Zanthoxylum armatum in mice,” Fitoterapia, vol. 82, no. 3, pp. 347–351, 2011. View at: Publisher Site | Google Scholar
  38. D. Kmiecik, J. Korczak, M. Rudzińska, J. Kobus-Cisowska, A. Gramza-Michałowska, and M. Hȩś, “β-Sitosterol and campesterol stabilisation by natural and synthetic antioxidants during heating,” Food Chemistry, vol. 128, no. 4, pp. 937–942, 2011. View at: Publisher Site | Google Scholar
  39. Y. H. Choi and G. H. Yan, “Anti-allergic effects of scoparone on mast cell-mediated allergy model,” Phytomedicine, vol. 16, no. 12, pp. 1089–1094, 2009. View at: Publisher Site | Google Scholar
  40. K. S. Kumar and R. Kumaresan, “A quantum chemical study on the antioxidant properties of aureusidin and bracteatin,” International Journal of Quantum Chemistry, vol. 111, no. 15, pp. 4483–4496, 2011. View at: Publisher Site | Google Scholar
  41. M. Roussaki, A. Gaitanarou, P. C. Diamanti et al., “Encapsulation of the natural antioxidant aureusidin in biodegradable PLA nanoparticles,” Polymer Degradation and Stability, vol. 108, pp. 182–187, 2014. View at: Publisher Site | Google Scholar
  42. Q. Yanping, “Effects of velvet for the tumor and immune function in tumor-bearing mice,” Journal of Heilongjiang Bayi Agricultural University, vol. 1, p. 17, 2012. View at: Google Scholar
  43. F. Dan and Z. C. Bin, “The anti-tumor effect of the extract from Radix Pulsatillae,” Chinese Journal of Hospital Pharmacy, vol. 9, p. 009, 2003. View at: Google Scholar
  44. S. F. Vasilevsky, A. I. Govdi, I. V. Sorokina et al., “Rapid access to new bioconjugates of betulonic acid via click chemistry,” Bioorganic and Medicinal Chemistry Letters, vol. 21, no. 1, pp. 62–65, 2011. View at: Publisher Site | Google Scholar
  45. Y. Lee, J.-C. Jung, Z. Ali, I. A. Khan, and S. Oh, “Anti-inflammatory effect of triterpene saponins isolated from blue cohosh (caulophyllum thalictroides),” Evidence-based Complementary and Alternative Medicine, vol. 2012, Article ID 798192, 8 pages, 2012. View at: Publisher Site | Google Scholar
  46. N. Chen, Y.-B. Ji, D.-X. Song, C.-R. Xu, H. Song, and J. Li, “Effects of Dauricine concentration in asiatic moonseed by different extraction solvents and methods,” Applied Mechanics and Materials, vol. 411–414, pp. 3162–3165, 2013. View at: Publisher Site | Google Scholar
  47. L. Ma, H. Chen, P. Dong, and X. Lu, “Anti-inflammatory and anticancer activities of extracts and compounds from the mushroom Inonotus obliquus,” Food Chemistry, vol. 139, no. 1–4, pp. 503–508, 2013. View at: Publisher Site | Google Scholar
  48. C. Boesch-Saadatmandi, A. Loboda, A. E. Wagner et al., “Effect of quercetin and its metabolites isorhamnetin and quercetin-3-glucuronide on inflammatory gene expression: role of miR-155,” Journal of Nutritional Biochemistry, vol. 22, no. 3, pp. 293–299, 2011. View at: Publisher Site | Google Scholar
  49. J. Liu, “Oleanolic acid and ursolic acid: research perspectives,” Journal of Ethnopharmacology, vol. 100, no. 1-2, pp. 92–94, 2005. View at: Publisher Site | Google Scholar
  50. S.-J. Tsai and M.-C. Yin, “Antioxidative and anti-inflammatory protection of oleanolic acid and ursolic acid in PC12 cells,” Journal of Food Science, vol. 73, no. 7, pp. H174–H178, 2008. View at: Publisher Site | Google Scholar
  51. J. Liu, T. Pei, J. Mu et al., “Systems pharmacology uncovers the multiple mechanisms of Xijiao Dihuang decoction for the treatment of viral hemorrhagic fever,” Evidence-Based Complementary and Alternative Medicine, vol. 2016, Article ID 9025036, 17 pages, 2016. View at: Publisher Site | Google Scholar
  52. S. I. Berger and R. Iyengar, “Network analyses in systems pharmacology,” Bioinformatics, vol. 25, no. 19, pp. 2466–2472, 2009. View at: Publisher Site | Google Scholar
  53. P. Li, L.-W. Qi, E.-H. Liu, J.-L. Zhou, and X.-D. Wen, “Analysis of Chinese herbal medicines with holistic approaches and integrated evaluation models,” TrAC—Trends in Analytical Chemistry, vol. 27, no. 1, pp. 66–77, 2008. View at: Publisher Site | Google Scholar
  54. L. M. Espinoza-Fonseca, “The benefits of the multi-target approach in drug design and discovery,” Bioorganic and Medicinal Chemistry, vol. 14, no. 4, pp. 896-897, 2006. View at: Publisher Site | Google Scholar
  55. P. P. Tak and G. S. Firestein, “NF-κB: a key role in inflammatory diseases,” Journal of Clinical Investigation, vol. 107, no. 1, pp. 7–11, 2001. View at: Publisher Site | Google Scholar
  56. W. Xiao, D. R. Hodge, L. Wang, X. Yang, X. Zhang, and W. L. Farrar, “NF-κB activates IL-6 expression through cooperation with c-Jun and IL6-AP1 site, but is independent of its IL6-NFκB regulatory site in autocrine human multiple myeloma cells,” Cancer Biology & Therapy, vol. 3, no. 10, pp. 1007–1017, 2004. View at: Publisher Site | Google Scholar
  57. C. K. Glass and K. Saijo, “Nuclear receptor transrepression pathways that regulate inflammation in macrophages and T cells,” Nature Reviews Immunology, vol. 10, no. 5, pp. 365–376, 2010. View at: Publisher Site | Google Scholar
  58. K. Mitsuyama, S. Matsumoto, J. Masuda et al., “Therapeutic strategies for targeting the IL-6/STAT3 cytokine signaling pathway in inflammatory bowel disease,” Anticancer Research, vol. 27, no. 6A, pp. 3749–3756, 2007. View at: Google Scholar
  59. K. Yamazaki, J. Umeno, A. Takahashi et al., “A genome-wide association study identifies 2 susceptibility loci for Crohn's disease in a Japanese population,” Gastroenterology, vol. 144, no. 4, pp. 781–788, 2013. View at: Publisher Site | Google Scholar
  60. F. Costa, M. G. Mumolo, L. Ceccarelli et al., “Calprotectin is a stronger predictive marker of relapse in ulcerative colitis than in Crohn's disease,” Gut, vol. 54, no. 3, pp. 364–368, 2005. View at: Publisher Site | Google Scholar
  61. M. E. Ament, “Inflammatory disease of the colon: ulcerative colitis and Crohn's colitis,” The Journal of Pediatrics, vol. 86, no. 3, pp. 322–334, 1975. View at: Publisher Site | Google Scholar
  62. I. Kristo, A. Stift, M. Bergmann, and S. Riss, “Surgical recurrence in Crohn's disease: are we getting better?” World Journal of Gastroenterology, vol. 21, no. 20, pp. 6097–6100, 2015. View at: Publisher Site | Google Scholar
  63. C. Elder, C. Ritenbaugh, M. Aickin et al., “Reductions in pain medication use associated with traditional Chinese medicine for chronic pain,” Issues, vol. 16, pp. 18–23, 2016. View at: Google Scholar
  64. M. Tan, M. Win, and S. A. Khan, “The use of complementary and alternative medicine in chronic pain patients in Singapore: a single-centre study,” Annals of the Academy of Medicine, Singapore, vol. 42, no. 3, pp. 133–137, 2013. View at: Google Scholar
  65. M. A. Behr, M. Divangahi, and J.-D. Lalande, “What's in a name? The (mis)labelling of Crohn's as an autoimmune disease,” The Lancet, vol. 376, no. 9736, pp. 202-203, 2010. View at: Publisher Site | Google Scholar
  66. K. M. Das and L. Biancone, “Is IBD an autoimmune disorder?” Inflammatory Bowel Diseases, vol. 14, no. S2, pp. S97–S101, 2008. View at: Google Scholar
  67. B. B. Crohn, L. Ginzburg, and G. D. Oppenheimer, “Regional ileitis: a pathologic and clinical entity,” Journal of the American Medical Association, vol. 99, no. 16, pp. 1323–1329, 1932. View at: Publisher Site | Google Scholar
  68. A. Riaz, R. A. Khan, S. Ahmed, and S. Afroz, “Assessment of acute toxicity and reproductive capability of a herbal combination,” Pakistan Journal of Pharmaceutical Sciences, vol. 23, no. 3, pp. 291–294, 2010. View at: Google Scholar
  69. H. U. Kim, J. Y. Ryu, J. O. Lee, and S. Y. Lee, “A systems approach to traditional oriental medicine,” Nature Biotechnology, vol. 33, no. 3, pp. 264–268, 2016. View at: Publisher Site | Google Scholar
  70. H.-Y. Hsieh, P.-H. Chiu, and S.-C. Wang, “Epigenetics in traditional chinese pharmacy: a bioinformatic study at pharmacopoeia scale,” Evidence-based Complementary and Alternative Medicine, vol. 2011, Article ID 816714, 10 pages, 2011. View at: Publisher Site | Google Scholar
  71. S. Omenetti, M. Brogi, R. Garg et al., “Essential role for Toll-like receptor 9 in the pathogenesis of liver inflammation in a murine model of Crohns disease-like ileitis (P3147),” The Journal of Immunology, vol. 190, no. 1 supplement, pp. 35–43, 2013. View at: Google Scholar
  72. S. H. Mir-Madjlessi, M. C. McHenry, and R. G. Farmer, “Liver abscess in Crohn's disease. Report of four cases and review of the literature,” Gastroenterology, vol. 91, no. 4, pp. 987–993, 1986. View at: Publisher Site | Google Scholar
  73. A. D. Perrett, G. Higgins, H. H. Johnston et al., “The liver in Crohn's disease,” Gastroenterology, vol. 40, no. 158, pp. 187–209, 1971. View at: Publisher Site | Google Scholar
  74. M. N. Eade, W. T. Cooke, and I. A. Williams, “Liver disease in crohn's disease: a study of 100 consecutive patients,” Scandinavian Journal of Gastroenterology, vol. 6, no. 3, pp. 199–204, 1971. View at: Publisher Site | Google Scholar
  75. J. Carvalheiro, S. Mendes, and C. Sofia, “Infliximab induced liver injury in Crohn's disease: a challenging diagnosis,” Journal of Crohn's and Colitis, vol. 8, no. 5, pp. 436-437, 2014. View at: Publisher Site | Google Scholar

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.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views1338
Downloads500
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.