A network pharmacology study of Sendeng-4, a Mongolian medicine

Chinese Journal of Natural Medicines 2015, 13(2): 01080118

Chinese Journal of Natural Medicines

A network pharmacology study of Sendeng-4, a Mongolian medicine ZI Tian, YU Dong* Department of Natural Medicinal Chemistry, College of Pharmacy, Inner Mongolia Medical University, Hohhot 010110, China Available online 20 Feb. 2015 [ABSTRACT] We collected the data on the Sendeng-4 chemical composition corresponding targets through the literature and from DrugBank, SuperTarget, TTD (Therapeutic Targets Database) and other databases and the relevant signaling pathways from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database and established models of the chemical composition-target network and chemical composition-target- disease network using Cytoscape software, the analysis indicated that the chemical composition had at least nine different types of targets that acted together to exert effects on the diseases, suggesting a "multi-component, multi-target" feature of the traditional Mongolian medicine. We also employed the rat model of rheumatoid arthritis induced by Collgen Type II to validate the key targets of the chemical components of Sendeng-4, and three of the key targets were validated through laboratory experiments, further confirming the anti-inflammatory effects of Sendeng-4. In all, this study predicted the active ingredients and targets of Sendeng-4, and explored its mechanism of action, which provided new strategies and methods for further research and development of Sendeng-4 and other traditional Mongolian medicines as well. [KEY WORDS] Network pharmacology; Mongolian medicine; Sendeng-4; Drug targeting; Disease pathway networks; Rheumatoid arthritis; In vivo evaluation

[CLC Number] R965

[Document code] A

[Article ID] 2095-6975(2015)02-0108-11

Introduction The primary purpose of drug discovery and development has long been to search for highly efficient, single target compounds. Despite rapid advances in new drug screening and drug synthesis technologies, it has become increasingly difficult to find and launch an efficient, single-target drug. This may have directly resulted in nearly a decade of international embarrassment for pharmaceutical companies engaged in drug development. While investment in research and development has been increasing, there appears to be a downward trend in the number of new drugs approved. Especially during phase II and phase III clinical trials, due to the problems of treatment efficacy and safety, the failure rate

[Received on] 013-May-2014 [Research funding] This work was supported by the National Natural Science Foundation of China (No. 81160550), Inner Mongolia Natural Science Foundation (No. 2013JQ03) and 2010 Science and Technology Project of social development in Inner Mongolia. * [ Corresponding author] E-mail: [email protected] The authors have no any conflict of interest to declare. Published by Elsevier B.V. All rights reserved

of new drugs may reach 30% [1]. To solve the problems, some scholars have begun to reconsider the "one gene, one drug, one disease" guiding ideology of drug development. For a given drug, there may be a large number of drug action targets such as receptors, ion channels, transmembrane signal transduction molecules, and enzymes. Complex networks exist between the drugs and their targets and between the targets. The analysis of these complex network topologies is based on the complex modern theory of systems biology knowledge and innovative bioinformatics tools. Thus network pharmacology combines genomics, proteomics, complex network theory, systems biology, bioinformatics, and other disciplines. Although network pharmacology has arisen late in drug discovery and has not yet formed a mature research model, there is an agreement that most complex diseases, such as cancer, autoimmune diseases, and diabetescannot be treated by an intervention at a single target. Li [2] has revealed that these complex diseases are related to the deregulation of biological networks, and the current tendency is to treat complex diseases as the operation of multiple perturbed networks and pathways. Network pharmacology [3-4] can help provide a better understanding of the "drugs-target-disease" interaction network, which may be closer to the complex nature of the disease.

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Sendeng-4, a Mongolian medicine, is commonly used as an anti-rheumatic therapy in the clinic, which is recorded in "The People's Republic of China Ministry of Health Standards for Medicines (Mongolian branch)". This formula includes four medicinal plants, Xanthoceras sorbifolium Bunge (Xanthoceraceae), Melia toosendan Siebold and Zucc. (Meliaceae), Terminalia chebula Retz. (Combretaceae), and Gardenia jasminoides J.Ellis (Rubiaceae) [5]. Sendeng-4 is often used as a heat dampness agent for the treatment of joint inflammation, edema, and other diseases. Although the pharmacological research of the constituents has made some progress [6-9], its multi-component and multi-target interaction mechanisms remain to be revealed. Network pharmacology has emerged as a new field of pharmacology study in recent years [10]. It aims to study the myriad relationships among proteins, drugs, and diseases. The existence of interactions between drugs and network reactions suggests a novel approach to discovering drug targets. Molecular connectivity maps between drugs and genes or proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic profiles and the toxicological profiles of candidate drugs. Since the inception of network pharmacology, the idea has been to build a "drug-target-disease" network, providing a new strategy for drug discovery and development [11], which is expected to bring new breakthroughs in the mechanistic study of both single and multi-component Mongolian medicines. In the diagnosis and treatment of human diseases, the comprehensive and dialectical views of Mongolian medicine are also consistent with the concept of network pharmacology. It is believed that the multi-component and multi-target interaction network of Mongolian medicine can regulate/modulate the non-equilibrium state of the body to a new equilibrium state, ultimately healing the illness [12-13].

Materials and Methods In the present study, we employed computational pharmacology, biological network technologies, and other network pharmacology methods to construct the network relationships of the component-target and component-targetdisease, allowing the simulation study of the Sendeng-4 constituents and the related targets interactions and predicting both active ingredients and mechanism of action. Data collection and collation To identify potential targets, it is necessary to determine initially the compounds present in each medicinal plant. Reviewing the related literature about the chemical constituents in Sendeng-4 generated the list of identified compounds, and a further literature review of all the compounds helped identify the corresponding targets. The next step was retrieval of the related targets of the compounds

from the DrugBank [14] database, the SuperTarget database, and the Therapeutic Targets Database (TTD) [15]. Retrieval TTD and other databases, provided the rheumatoid arthritis disease corresponding targets. The TTD and the KEGG [16-18] (Kyoto Encyclopedia of Genes and Genomes) databases were then searched to retrieve the related signal pathway. Network construction and analysis Cytoscape [19-21] is an open source software platform for visualizing molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles, and other data. Although Cytoscape was originally designed for biological research, it is now a general tool for complex network analysis and visualization. Using the Cytoscape software, the chemical composition-target and chemical composition-target-disease networks were constructed and the corresponding network analysis conducted. Meanwhile, the signal transduction of the particular signal pathway was analyzed. Experimental validation A rat model of rheumatoid arthritis induced by Collgen Type II (C-7806, Sigma, St. Louis. MO, USA) was established to validate the key targets of the chemical components of Sendeng-4. Fifty Sprague-Dawley rats (180−220 g, half male and half female) were purchased from the Experimental Animal Center of Inner Mongolia University and were housed five per cage in a room maintained at 25 °C with an alternating 12 h light-dark cycle. The rats were divided into five groups: normal control, model control, and three treated groups (Sendeng-4, Tripterygium, and Celecoxib). To establish the rat model, an emulsion (0.2 mL) of Freund’s complete adjuvant was injected into the hind feet of each rat except for the normal control group. An emulsion (0.1 mL) of Freund’s complete adjuvant was injected at the same place of rats 20 days later. The drug treatments were given by gavage, starting on Day 21 and and lasting for 20 days. Sendeng-4 (90 mg·kg−1) and the indicated doses for Tripterygium and Celecoxib were given, respectively. The normal and model control groups were given distilled water. The heart blood sample of each rat was taken on Day 42, and the serum levels of TNF-α, IL-6, and PGE-2 were determined by ELISA. Statistical analysis The results were expressed as mean ± SD. The significance of differences between groups was analyzed by one-way ANOVA using the SPSS 13.0 statistical software. P-value of less than 0.05 was considered statistically significant.

Results Construction and analysis of chemical composition-target network The information on compounds and targets is

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summarized in Table 1. The results of the importation of the compounds and corresponding targets to the Cytoscape software to construct a chemical composition-target network of Sendeng-4 are shown in Fig. 1. The network was built and visualized with Cytoscape. The yellow nodes represent chemical components, the blue nodes represent targets, and the edges represent interactions. As shown by the network, multiple compounds acted on a same target, which was very different from the Western medicine philosophy which considers one compound to act on one particular target. The Cytoscape plugin Network Analyzer was used to calculate the number of nodes, network density, network heterogeneity, average number of neighbors, characteristic path length, shortest

[22] paths, and network centralization . The composition-target network consisted of 205 edges and 165 nodes, and included 32 compounds and 133 targets. Li et al [23] have demonstrated that the network parameters such as degree and shortest paths can be used to measure directly the targeted key druggable proteins or protein interactions, and indirectly the targeted key undruggable proteins, using network propagation. The results of the analysis are shown in Table 2. We also tested whether the network was scale-free, just like other biological networks. As a result, the node degree distribution of the component suited well a power law degree (R2 = 0.838), indicating that the network that was built was scale-free, as shown in Fig. 2.

Table 1 Summary of the constituents and their corresponding targets Compounds Targets (PubChem CID) Chrysophanol P2Y purinoceptor 12 (10208) Bone marrow serine protease

Compounds (PubChem CID) Hydroxybenzaldehyde(126)

NADPH dehydrogenase 1

Physcion (10639) P2Y purinoceptor 12

Hydroxynitrilase

Bone marrow serine protease

Cresolase

Intercellular Adhesion Molecule 1

Acetylcholinesteras

Cysteinyl aspartate specific proteinase Eugenol (3314)

Prostaglandin G/H synthase 1

Tumor necrosis factor

COX-2

Interleukin-1 P2Y purinoceptor 12

Androgen receptor Hydroxybenzoate (54675850)

Carbonic anhydrase 12

Fibroblast collagenase

CA-I

Cathepsin B

CA-II

APP secretase

CA-IV

Activator protein 1

CA-IX

Casein kinase II subunit alpha

(S)-3-Amino-2-methylpropionate transaminase

Estrogen receptor

4-Hydroxybenzoate hydroxylase

Estrogen receptor beta

4-Hydroxybenzoate 3-monooxygenase

Bone marrow serine protease

FcbC1

Cytosolic sialidase

Aldehyde dehydrogenase family 5 member A1

Transforming growth factor Interleukin-6 Rutin (5280805)

(S)-3-Amino-2-methylpropionate transaminase Aldehyde dehydrogenase family 5 member A1

Protein kinase A

Emodin (3220)

Targets

Chorismate--pyruvate lyase Geniposide (16760120)

Fibroblast growth factor

Nuclear factor kappa-B

Tumor necrosis factor

Cytosolic sialidase

Hypoxia inducible factor

CYPIID6 CYPIIC8

Interleukin-1 Imperatorin (10212)

COX-2

Albendazole monooxygenase

Prostaglandin E2

(R)-limonene 6-monooxygenase

Acetylcholinesterase

Aldo-keto reductase family 1 member C3 Isoimperatorin (68081)

COX-2

Alpha-2C adrenergic receptor

Prostaglandin E2

Alpha-2A adrenergic receptor

Acetylcholinesterase

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Continued Compounds (PubChem CID)

Oleanolicacid (10494)

Esculetin (5281416)

Epicatechin (72276)

Quercetin (5280343)

Compounds (PubChem CID)

Targets Xanthine dehydrogenase/oxidase

Crocetin (5281232) Prostaglandin G/H synthase 1

Nuclear factor kappa-B

Crocin (1983)

CA-I

Bile acid receptor

CA-II

Prostaglandin G/H synthase 1

CA-IV

COX-2

CA-IX

Protein-tyrosine phosphatase 1B

CYPIA1

Hematopoietic cell protein-tyrosine phosphatase

CYPIA2

Glycogen phosphorylase, muscle form

Cytochrome P450 2A6

Xanthine dehydrogenase/oxidase

CYPIIC8

Prostaglandin G/H synthase 1

(R)-limonene 6-monooxygenase

Aldehyde reductase

CYPIID6

Xanthine dehydrogenase/oxidase

4-Nitrophenol 2-hydroxylase

Prostaglandin G/H synthase 1

Albendazole monooxygenase

COX-2

Anandamide amidohydrolase 1

5-HT-1A

Insulin

Adenosine A3 receptor

Prostaglandin G/H synthase 1

Alcohol dehydrogenase [NADP+]

COX-2

Aldose reductase

CYP2E1 protein

Aldehyde reductase

Cytochrome P450 2E1

Beta-lactamase

Putative cytochrome P450 2E1

Alpha-amylase 1

UDP-glucuronosyltransferase

Amine oxidase [flavin-containing] A

UDP-glucuronosyltransferase variant

Amine oxidase [flavin-containing] B

UDP glycosyltransferase 1 family polypeptide 9

Protein kinase c

HCG2039726, isoform CRA_e

Carbonic anhydrase 12

UDP glycosyltransferase 1 family polypeptide A7

CA-I

UDP glycosyltransferase 1 family polypeptide A6

CA-II

HCG2039726, isoform CRA_g

CA-IV

Hypoxia inducible factor

CA-IX

UDP glucuronosyltransferase 1 family, polypeptide A4

Cell division protein kinase 2

Vanillic acid (8468) Chorismate--pyruvate lyase

Cell division protein kinase 4

Hikimic acid (8742) Nuclear factor kappa-B 3-Phosphoshikimate1-carboxyvinyltransferase

Cell division protein kinase 6

Gallic acid (370)

Aromatase

Myricetin (5281672)

Carbonic anhydrase 12

DNA polymerase beta

Cell division protein kinase 5

Dihydroquercetin (10185)

Targets

CA-I CA-II

Transforming growth factor

CA-III

CYPIA1

CA-IV

COX-2

CA-IX

Prostaglandin G/H synthase 1

CA-VI

PKC-A

CA-XII

Alpha-amylase 1 Canalicular multispecific transporter 1

CA-XIV organic

anion

Cysteinyl aspartate specific proteinase

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Continued Compounds (PubChem CID)

Targets

Compounds (PubChem CID)

Aromatase

Targets P-selectin

CD220

Chebulin (16143735)

PKC-A

Aldoketomutase

Benzoic acid (243)

Hydroxyquinol 1,2-dioxygenase

Lipase

Chlorocatechol 1,2-dioxygenase

Cytosolic sialidase Proto-oncogene serine/threonine-protein kinase Pim-1 Phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit gamma isoform Beta-trypsin

Cocaine esterase Heat-labile enterotoxin B chain ATP-dependent transcriptional activator malT NR

Cysteinyl aspartate specific proteinase

D-Amino-acid oxidase

Xanthine dehydrogenase/oxidase

Hydrogen peroxide-inducible genes activator

Pinoresinol (234817) Prostaglandin G/H synthase 1

2-Hydroxy-6-oxo-7-methylocta-2, 4-dienoate hydrolase

Ferulic acid (445858) Carbonic anhydrase 12

Tautomerase pptA

CA-I

Alu corepressor 1

CA-II

Chloride peroxidase

CA-IV

Replication protein

CA-IX

Rab-9A

Nuclear factor kappa-B

Cresolase

Transforming growth factor

14.5 kDa Translational inhibitor protein

Interleukin-15

1, 4-Beta-D-xylan xylanohydrolase 2

Interleukin-23

β-sitosterol (222284)

B-cell lymphoma-2

B-cell lymphoma-2

Hyal-1 Methyl gallate (7428) B-cell lymphoma-2 Cysteinyl aspartate specific proteinase

Albendazole monooxygenase Arjunolicprime (73641) Ethyl gallate (13250)

Xanthine dehydrogenase/oxidas Chebulinicacid (452240) Dihydromyricetin (161557)

Bile acid receptor B-cell lymphoma-2 Cysteinyl aspartate specific proteinase

PKC-A Tumor necrosis factor

CYPIID6

Squalene monooxygenase Chlorogenicacid (1794427)

Transforming growth factor

B-cell lymphoma-2 Cysteinyl aspartate specific proteinase

Nuclear factor kappa-B

Construction and analysis of chemical composition-targetdisease network The availability of a disease-specific network model structure would help provide a better understanding of the effects of drugs on the disease. The target information on rheumatoid arthritis disease is summarized in Table 3. As shown in Fig. 3, The corresponding chemical compositiontarget-disease network of Sendeng-4 was constructed and visualized by Cytoscape software. In the network, the yellow nodes represent chemical components, the blue nodes represent targets, and the red node represents rheumatoid arthritis disease target. The edges represent interactions. The data and network analysis showed that there were nine categories of common targets between the chemical

composition of Sendeng-4 and rheumatoid arthritis, including prostaglandin E2, prostaglandin G/H synthase 1, xanthine dehydrogenase/oxidase, aldose reductase, nuclear factor kappa-B, tumor necrosis factor-α, interleukin-1, interleukin-6, and interleukin-15. Fig. 3 also reflects the correspondence of chemical composition-target-disease, indicating that: 1. Geniposide and physcion may act on interleukin-1 simultaneously to exert an anti-inflammatory effect; 2. Emodin and ferulic acid can act on interleukin-6 and interleukin-15 respectively to exert an anti-inflammatory effect; 3. Physcion, emodin and dihydromyricetin may act on tumor necrosis factor-α simultaneously to exert an antiinflammatory effect; 4. Shikimic acid, dihydromyricetin, rutin, emodin, and ferulic acid can act on nuclear transcription factor

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Fig. 1 Chemical composition-target interaction network of Sendeng-4. The yellow nodes represent a chemical component, and the blue nodes represent targets. The edges represent interactions. Built and visualized with Cytoscape Table 2 Chemical composition-target interaction network parameters of Sendeng-4 Network parameters

Values

Number of nodes

165

Network density

0.015

Network heterogeneity

1.458

Average number of neighbors

2.485

Characteristic path length

5.565

Shortest paths

27 060 (100%)

Network centralization

0.164

Kappa B to provide an anti-inflammatory effect; 5. Rutin, myricetin, epicatechin, and esculetin can act on xanthine dehydrogenase/oxidase simultaneously to exert an anti-inflam

Fig. 2 The degree distribution. The degree of nodes in the network followed a power-law distribution, indicating that the network constructed was scale-free

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Table 3 Targets of rheumatoid arthritis disease target Targets of disease

Targets of disease

Interleukin-1

T-cell surface glycoprotein CD4

Interleukin-2

T-cell receptor beta chain V region CTL-L17

Interleukin-4

T-lymphocyte activation antigen CD80

Interleukin-6

T-lymphocyte activation antigen CD86

Interleukin-12

Toll-like receptor 9

Interleukin-13

Transcription factor AP-1

Interleukin-15

Thioredoxin reductase, cytoplasmic

Interleukin-17

Vascular endothelial growth factor

Interleukin-18

5-Aminoimidazole-4-carboxamidoribonucleotide transformylase

Interleukin-6 receptor

C-C chemokine receptor type 5

Interleukin-1 receptor

C-C motif chemokine 2

Tyrosine-protein kinase

C-C chemokine receptor type 1

Tyrosine-protein kinase JAK2

C-C chemokine receptor type 2

Tyrosine-protein kinase JAK3

Prostaglandin G/H synthase 1

Tyrosine-protein kinase SYK

Prostaglandin G/H synthase 2

Tumor necrosis factor

Dihydroorotate dehydrogenase, mitochondrial

Tumor necrosis factor receptor

Myeloid differentiation primary response protein MyD88

Tumor necrosis factor receptor superfamily member 1B

Granulocyte-macrophage colony-stimulating factor

Tumor necrosis factor ligand superfamily member 11

Inhibitor of nuclear factor kappa-B kinase

Tumor necrosis factor ligand superfamily member 13B

Vascular endothelial growth factor B

Integrin alpha-4

HIV-1 Tat protein

Integrin beta-1

Retinoic acid receptor

Integrin beta-7

92 kDa type IV collagenase

Integrin alpha V beta 3

Cathepsin K

Neutrophil collagenase

Interferon gamma

Adenosine A3 receptor

Lymphotoxin alpha

Aldose reductase

MAP kinase p38

B-lymphocyte antigen CD20

Macrophage migration inhibitory factor

Complement C5

P2X purinoceptor 7

Corticosteroid-binding globulin

ADAM 17

Leukotriene B4 receptor 1

Beta-lactoglobulin-1

Leukemia inhibitory factor

C-Jun N-terminal kinase

mRNA of TNF alpha

CAMP-specific 3',5'-cyclic phosphodiesterase 7

Nuclear factor kappa-B

CASP8 and FADD-like apoptosis regulator

Mitogen-activated protein kinase 11

Glandular kallikrein 1

Mitogen-activated protein kinase 14

Matrix metalloproteinase

Cyclooxygenase

Heparin-binding growth factor 2

Xanthine dehydrogenase/oxidase

Platelet activating factor

Fc gamma receptor III

Prostaglandin E2

Glucocorticoid receptor

Proteinase activated receptor 2

Peptidylarginine deiminase

Protein tyrosine phosphatase, nonreceptor-type 22

CD244 antigen

Solute carrier family 22

DR beta-1

MHC class II transactivator

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Fig. 3 The chemical composition-target-disease interaction network of Sendeng-4. The yellow nodes represent chemical components, the blue nodes represent targets, and the red node represents rheumatoid arthritis disease target. The edges represent interactions. The network was built and visualized with Cytoscape

matory effect; 6. Rutin and quercetin may act on aldose reductase to show an anti-inflammatory effect; 7. Dihydroquercetin, escu- letin, crocin, epicatechin, oleanolic acid, and eugenol may act on prostaglandin G/H synthase 1 to provide an anti-inflammatory effect; and 8. Imperatorin and isoimperatorin may act on prostaglandin E2 to exert an anti-inflammatory effect. Levels of TNF-α, IL-6 and PGE-2 Among the key targets of the chemical constituents, tumor necrosis factor-α, interleukin-6, and prostaglandin E2 were chosen as candidate molecular targets to validate the anti-

inflammatory effects of Sendeng-4. These are all pro-inflammatory cytokines and changes in their level in vivo can be a good indication of the effectiveness of a treatment for inflammation. The test results are shown in Table 4. Compared with the model group, the Sendeng-4 group had significantly higher levels of the examined factors (P < 0.01), while there were no significant differences between Sendeng-4 group and the normal control groups (P > 0.05), indicating that the chemical components exert an anti-inflammatory effect, and that the changes in the level of these three factors could serve for evaluation of the anti-inflammatory effects. Consequently, this

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experiment also illustrated the importance of these three cytokines as key targets for the drug. Table 4 Levels of TNF-α, IL-6, and PGE-2 for different groups of rats (mean (+–) SD, n = 10) TNF-α (ng·L−1) IL-6 (ng·L−1)

Groups

PGE-2 (ng·L−1)

Normal

54.94 ± 6.94

24.75 ± 4.03

114.27 ± 14.80

Model

90.27 ± 7.65

44.05 ± 2.88

216.27 ± 27.24

61.18 ± 8.75**

27.14 ± 4.91**

134.99 ± 17.97**

**

**

152.38 ± 17.79**

27.08 ± 3.83**

144.76 ± 16.60**

Sendeng-4a b

Tripterygium Celecoxibc

59.34 ± 6.74

55.75 ± 6.60**

29.01 ± 3.34

*

P < 0.05, **P < 0.01 vs model group Dosage levels: a, 90 mg·kg−1, b, 6.25 mg·kg−1, and c, 20.83 mg·kg−1

Molecular functional and pathway analysis associated with rheumatoid arthritis Rheumatoid arthritis (RA) is a systemic, chronic inflammatory disease that is manifested in destructive polyarthritis in association with serological evidence of autoreactivity; it is characterized by chronic inflammation and

progressive impairment of the joints [24]. Meanwhile, RA is an immune system disorder disease mediated by T lymphocytes, especially helper T lymphocytes subsets Th1 [25-26], and there are many signaling pathways and cytokines associated with its immunopathology. Based onthe mechanistic studies of traditional Chinese medicine and Mongolian medicine for rheumatoid arthritis, several important signal transduction pathways were involved in rheumatoid arthritis, including TNF, MAPK, interleukin-1, Interleukin-18, and NF-B pathways. After retrieving data from the TTD (Therapeutic Targets Database) and KEGG (Kyoto Encyclopedia of Genes and Genomes) signaling pathway databases, the cytokine-induced inflammatory response and T cell inflammatory responses pathway signal diagram about rheumatoid arthritis was constructed ( Fig. 4). Using the TNF signal transduction pathway as an example, tumor necrosis factor-α is is synthesized and released by a class of monocytes-macrophages and other cells, and it may induce a systemic inflammatory response. The large amount of generation and release of TNF-α will destroy immune balance, resulting in various pathological injuries [27]. TNF-α inhibitors can act directly on the target, blocking signal transduction associated with immune cells, inhibiting

Fig. 4 Cytokine-induced inflammatory response and T-cell inflammatory response signal pathway

its activity, and thereby regulating inflammation processes. The TNF-α receptor regulates cell proliferation, differentiation, and apoptosis by binding to TNFR1 and TNFR2. TNFR1 has a death region, is expressed in most tissues and cells, and mainly mediates apoptosis; TNFR2 has no death region, is only expressed in immune cells [28], and regulates cell function mainly by recruiting TNF receptor associated factor 2, nuclear factor-κB, and c-Jun N-terminal kinase. Fig. 4 shows that TNF receptor associated factor 2 participates in the NF-κB activation by having an effect on NIK, activating IκB Kinase and phosphate inhibitory κB proteins, which eventually leads to NF-κB expression of target genes to nuclear translocation, resulting in the degradation of cartilage and joint damage [29-30].

As shown in Fig. 4, the chemical components of the Mongolian medicine Sendeng-4 had joint actions on multiple targets, i.e., multiple compounds in the medicinal plants could act at multiple nodes in the network simultaneously. Network analysis showed that different targets were interrelated, and that the disease was also caused by multiple abnormalities. Consequently, the treatment of a complex disease can not just rely on single-target drugs to obtain better results, and "multi-component, multi-target" drug efficacy is even better than the sum of single-target drug efficacy, which means that the combinational effect can be greater than the sum of the individual effects [31-33]. Thus it is also believed that the combination of the chemical components may reduce side effects and overcome adaptive

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resistance in a synergistic manner. The complex chemical composition of the Mongolian medicine Sendeng-4 can therefore play a therapeutic role in diseases by affecting to multiple targets. Building the chemical composition-targetdisease network improves the understanding of the efficacy of Mongolian medicine, and also provides new ideas for the innovation of Mongolian medicines.

Discussion

References

[2]

[3]

[5]

[6]

[7]

The first step of a traditional research strategy of Mongolian medicine is often chemical separation, analysis, and identification of key components, in combination with pharmacological experiments, to determine the activity-oriented main ingredients. Such separation and analysis work sometimes provides no benefit, and is a tedious, long cycle of blindness and expense. Network pharmacology integrates chemistry, medicine, bio-data, network simulation, and modeling to predict pharmacological properties, using known information. In exploring the possible mechanism of Mongolian medicine, this strategy can be used to find some of the potential targets, and provide a new way [34] for the modern research of Mongolian medicine. Li et al [35] have applied the network pharmacology method to the research of the classic herbal formula Liu-Wei-Di-Huang (LWDH), and revealed the core molecular targets and bioprocess network of the pharmacological effects, and inferred its therapeutic indications. In the present study, analysis of the chemical composition-target-disease network of Sendeng-4 showed that different components of the prescription affected different targets, and the correlation between the targets showed a synergistic effect, which reflected the consolidation adjustment feature of the Mongolian medicine Sendeng-4, revealing the mechanisms of action for the whole formula. In summary, this research studied the Mongolian medicine Sendeng-4 on the basis of the overall chemical composition. The possible targets and pathways of the ingredients were examined by means of network pharmacology, and the main pathway associated with inflammation was confirmed by systems biology methods. The preliminary in vivo experimental results were in good agreement with the network forecasting. Therefore, a "drug-target-pathway-network" model for the network pharmacology research was established, which revealed regulatory networks of the anti-inflammatory effects of Sendeng-4, and afforded new methods for the research and development of other Mongolian medicines in general.

[1]

[4]

[8]

[9]

[10]

[11] [12]

[13]

[14]

[15] [16] [17]

[18] [19]

[20]

[21]

[22]

Kola I, Landis J. Can the pharmaceutical industry reduce attrition rates? [J]. Nature Rev Drug Discov, 2004, 3 (8): 711715. Li S. Network systems underlying traditional Chinese medicine syndrome and herb formula [J]. Curr Bioinformat, 2009, 4: 188196. Hopkins AL. Network pharmacology:the next paradigm in drug

[23]

[24]

– 117 –

discovery [J]. Nature Chem Biol, 2008, 4 (11): 682-690. Hopkins AL. Network pharmacology [J]. Nature Biotechnol, 2007, 25 (10): 1110-1111. Pharmacopoeia Commission of People's Republic of China. Drug Standards of People's Republic of China Ministry of Health. Mongolian branch [S]. 1998: 185. Ma CM, Nakamura N, Hattori M, et al. Inhibitory effects on HIV-1 protease of constituents form the wood of Xanthocer sorbifolia Bunge [J]. J Nat Prod, 2002, 63 (2): 238-242. Zhang Q, Shi Y, Liu XT, et al. Minor limonoids from Melia toosendan and their antibacterial activity [J]. Planta Med, 2007, 73 (12): 1298-1303. Koo HJ, Lim KH, Jung HJ, et al. Anti inflammatory evaluation of gardenia extract, geniposide and genipin [J]. J Ethnopharmacol, 2006, 103 (3): 496-500. Ahn MJ, Kim CY, Lee JS, et al. Inhibition of HIV-1 integrase by galloyl glucoses from Term inalia Chebula and flavonol glycoside gallates from Euphorbia pekinensis [J]. Planta Med, 2002, 68 (5): 457-459. Huang H, Wu X, Pandey R, et al. C2Maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships [J]. BMC Genom, 2012, 13: S17. Liu AL, Du GH. Network pharmacology: new guidelines for drug discovery [J]. Acta Pharm Sin, 2010, 45 (12): 1472-1477. Xue R, Fang Z, Zhang M, et al. TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis [J]. Nucleic Acids Res, 2013, 41: 1089-1095. Liu ZH, Sun XB. Network pharmacology: new opportunity for the modernization of traditional Chinese medicine [J]. Acta Pharm Sin, 2012, 47 (6): 696-703. Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs [J]. Nucleic Acids Res, 2011, 39 (1): D1035-D1041. Zhu F, Han B, Kumar P, et al. Update of TTD: therapeutic target database [J]. Nucleic Acids Res, 2010, 38: D787-791. Kanehisa M. The KEGG database [J]. In silico simulation of biological processes, 2002, 247: 91-103. Ji HF, Kong DX, Shen L, et al. Distribution patterns of smallmolecule ligands in the protein universe and implications for origin of life and drug discovery [J]. Genome Biol, 2007, 8: R176. Park K, Kim D. Binding similarity network of ligand [J]. Proteins, 2008, 2: 960-971. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks [J]. Genome Res, 2003, 13 (11): 2498-2504. Lopes CT, Franz M, Kazi F, et al. Cytoscape Web: an interactive web-based network browser [J]. Bioinformatics, 2010, 26 (18): 2347-2348. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization [J]. Bioinformatics, 2011, 27 (3): 431-432. Assenov Y, Ramírez F, Schelhorn SE, et al. Computing topological parameters of biological networks [J]. Bioinformatics, 2008, 24 (2): 282-284. Li S, Zhang B. Traditional Chinese medicine network pharmacology: theory, methodology and application [J]. Chin J Nat med, 2013, 11 (2): 110-120. Bultink IE, Vis M, van der Horst-Bruinsma IE, et al. Inflammatory rheumatic disorders and bone [J]. Curr

ZI Tian, et al. / Chin J Nat Med, 2015, 13(2): 108118

Rheumatol Rep, 2012, 14 (3): 224-230. [25] Cope AP. T cells in rheumatoid arthritis [J]. Arthrit Res Ther, 2008, 10: S1. [26] Cope AP, Schulze-koops H, Aringer M. The central role of T cells in rheumatoid arthritis [J]. Clin Exptl Rheumato, 2007, 25: 4-11. [27] Scheinfeld N. A comprehensive review and evaluation of the side effects of the tumor necrosis factor alpha blockers etanercept, infliximab and adalimumab [J]. J Dermatol Treat, 2004, 15 (5): 280-294. [28] Boissier MC. Cell and cytokine imbalances in rheumatoid synovitis [J]. Joint Bone Spine, 2011, 78 (3): 230-234. [29] Vallabhapurapu S, Karin M. Regulation and function of NF-κB transcription factors in the immune system [J]. Ann Rev Immunol, 2009, 27: 693-733. [30] Marcu KB, Otero M, Olivotto E, et al. NF-κB Signaling: Multiple angles to target OA [J]. Curr Drug Targ, 2010, 11 (5): 599-613.

[31] Li S, Zhang B, Jiang D, et al. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formulae [J]. BMC Bioinformat, 2010, 11 (Suppl 11): S6. [32] Zimmermann GR, Lehár J, Keith CT. Multi-target therapeutics: when the whole is greater than the sum of the parts [J]. Drug Discov Today, 2007, 12: 34-42. [33] Li S, Zhang B, Zhang N. Network target for screening synergistic drug combinations with application to traditional Chinese medicine [J]. BMC Syst Biol, 2011, 5 (Suppl 1): S10. [34] Zhu W, Qiu XH, Xu XJ, et al. Computational network pharmacological research of Chinese medicinal plants for chronic kidney disease [J]. Sci China Chem, 2010, 53: 23372342. [35] Liang X, Li H, Li S. A novel network pharmacology approach to analyse traditional herbal formulae: the Liu-Wei-Di-Huang pill as a case study [J]. Mol BioSyst, 2014, 10 (5): 1014-1022.

Cite this article as: ZI Tian, YU Dong. A network pharmacology study of Sendeng-4, a Mongolian medicine [J]. Chinese Journal of Natural Medicines, 2015, 13 (2): 108-118.

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