Drug-target networks for Tanshinone IIA identified by data mining

Chinese Journal of Natural Medicines 2015, 13(10): 07510759

Chinese Journal of Natural Medicines

Drug-target networks for Tanshinone IIA identified by data mining CHEN Shao-Jun* Department of Traditional Chinese Medicine, Zhejiang Pharmaceutical College, Ningbo 315100, China Available online 20 Oct., 2015

[ABSTRAC T] Tanshinone II A is a pharmaco lo gically active compo un d isolated from Dansh en (Salvia miltio rrh iza), a traditional Ch ine se her ba l medicine for the managem ent of cardiac diseases and other disor der s. But its un der lyin g molecular m echanism s of action are still unclear. T he present investigation utilized a data min in g approach based on n etwork pharmaco lo gy to uncover the potential protein tar gets of Tan shinone IIA. Network pharm acolo gy, an integrated m ultidiscip linary st udy, incorp orates system s biolo gy, net work analy sis, conn ectivity, redun dancy, an d pleiotropy, providin g po werf ul n ew tools an d in sights into elucidatin g the fine details of dr ug-tar get interaction s. In the present st udy, t wo separate dr ug-target network s for Tanshinone IIA were constr ucted usin g the Agilent Literature Search ( AL S) an d ST IT CH (search tool for interactions of chemicals) methods. Analy sis of the AL S-con structed net work revealed a target network with a scale -free topology an d fiv e top nodes (protein target s) corr espon din g to Fo s, Jun, Src, phosphatidy lino sitol-4, 5- bispho sphate 3-k inase, catalytic subunit alpha (PIK3CA), an d m itogen-activated protein kinase k inase 1 (MAP2K1), wher eas analy sis of the ST IT CH- constructed net work reveale d three top nodes correspon din g to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), an d n uc lear factor kappa B1 (NFκB1). T he discr epancies were pro bably due to the differen ces in the diver gent computer m inin g tools an d data ba se s emp loyed by the two metho ds. Ho wever, it is conceivable that all eight proteins mediate important bio logical f un ctions of Tanshinone IIA, contributin g to its overall dr ug-target network. In conclusion, the current results may assist in dev eloping a comprehen sive un der standin g of the molecular mechanism s an d signalin g path ways of in a simple, comp act , an d v isual manner. [KEY WO RDS] Data mining; Network analysis; Network pharmacology; Tanshinone IIA;

[CLC Number] R965

[Document code] A

[Article ID] 2095-6975(2015)10-0751-09

Introduction Tanshinone IIA (Fig. 1) is one of the most abundant active compounds in Danshen (Salvia miltiorrhiza, or red sage), a traditional Chinese medicine that has been clinically used for more than 2 000 years in China and other Asian cou ntries for the prevention and treatment of cardiovascular diseases [1-2]. Tanshinone IIA can stop or slow the progression of a wide spectrum of disorders in addition to cardiovascular diseas e [2-3], including prostate and breast cancers [4-5], neonatal hypoxic ischemic encephalopathy [3, 6] and neuron regeneration [7]. Tanshinone IIA is a multi-target drug, with numerous molecular targets embracing trans cription factors,

[Re ceived on]  14-July-2014 [Research funding] This work was supported by the Foundation of Zhejiang Province Educational Committee (No. Y201330180). [*Corresponding author] E-mail: chenshaojun@ hotmail.com These authors have no conflict of interest to declare. Copyright © 2015, China Pharmaceutical University. Published by Elsevier B.V. All rights reserved

scavenger receptors, ion channels, kinas es, pro- and anti-apoptotic proteins , growth factors, inflammatory mediators, microRNAs, and others [1] . However, the molecular mechanisms and pathways that mediate the actions of Tanshinone IIA are still unclear and must be addressed to take full advant age of the s alutary effects of the drug. Network pharmacology provides an integrated approach to drug design that encompasses systems biology, network analys is, connectivity, redundancy and pleiotropy . This emerging field of study affords a novel network model of “multiple targets, multiple effects, and complex diseases”, and offers a new opportunity for the modernization of traditional Chines e medicine [8–10]. Network pharmacology has been successfully used to identify three new molecular targets for rhein, a natural compound isolated from Rheum rhabarbarum (rhubarb) [11] . M oreover, the results of recent investigation employing net work pharmacology indicated that Taohong Siwu decoction, a formulation prescribed in traditional Chinese medicine for women’s health and various other conditions, may potentially be effective against at least 69 diseases [ 12] . In ad dit ion, an e xp l orat ion of t h e n et w ork

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Fig. 1 Che mical structure of Tanshinone IIA (Pubchem CID: 164676)

pharmacology properties of various natural product-target networks demonstrated that polypharmacology was highly relevant for compounds with a large degree of chemical divers ity and high betweenness centrality (a measure of the load and importance of an identified drug target node) [13]. Currently, network pharmacology is regarded as the next paradigm in drug dis covery [9] . Indeed, network description and analysis permits a systems-level understanding of drug actions and dis ease complexity that is heretofore unachievable [14]. The aim of the present study was to facilitate a sy stems-level understanding of the drug targets and molecular mechanisms of Tanshinone IIA. To that end, two separate drug-target networks for Tanshinone IIA were constructed using a data mining approach assisted by the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals), where the relevant data were collected from the literature and applicable databases. The properties of these networks were then analyzed utilizing network-related tools [15-16] .

Methods and Materials Drug-target network construction via ALS A drug-target network for Tanshinone IIA was first constructed using ALS version 2.76 in Cytoscape. Cytoscape is an open-source software (freely available at http://www. cytoscape.org/) that is most powerful when used in conjunction with large databases of protein-protein, protein- DNA, kinase-substrate and genetic interactions [15-16], as well as functional relationships between drugs and target moelcules. ALS and other Cytoscape plugins are utilized to extend the Cytoscape software [16]. ALS is among the most popular of the Cytoscape plugins and provides biologists with a meta-search tool for automatically querying multiple public and proprietary text-based search engines. This plugin has been employed for network construction in previous stroke and coronary heart disease research [17], and in a report focused on angiogenesis [18]. Accordingly, ALS offers an alternative to the daunting task of manually searching the literature and available databases and then extracting associations among genes/proteins of interest [19]. Tanshinone IIA was entered into the ALS search panel as the query. The “context” was set to blank. The maximum number of engine matches was set to 500. Other parameters were set to default values.

Network topological parameters assessed by NetworkAnalyzer NetworkAnalyzer is a versatile and user-friendly tool for the analysis of biological and other types of networks. The NetworkAnalyzer plugin is well integrated into Cytoscape and computes and displays a comprehensive list of simple and complex topology parameters using efficient graph algorithms [20-21] . These parameters include the number of nodes, edges, and connected components; the network diameter, radius, and density; the network centralization and heterogeneity values; the clustering coefficients; the characteristic path length; and the distribution coefficients, neighborhood connectivity, average clustering coefficients, and shortest path length between nodes [21]. The ALS-constructed network was imported into NetworkAnalyzer and treated as undirected. Other parameters were set to default values. Identification of important nodes by cyto-Hubba Cyto-Hubba is a Cytoscape plugin designed for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory [22]. Cyto-Hubba permits analysis of proteinprotein interaction (PPI) network topologies by providing a variety of topological analysis algorithms, i.e., Degree, Bottleneck, Edge Percolated Component, M aximum Neighborhood Component (MNC), Density of M aximum Neighborhood Component (DMNC), and the double screening scheme of M NC/DMNC, which combines two algorithms to select essential proteins by different network topological characteristics [22]. Cyto-Hubba analysis has been effectively employed in an earlier study to show that signal transducer and activator of transcription 3, the insulin receptor, C-C chemokine receptor 5, peroxisome proliferator-activated receptor alpha, and interleukin 1 beta, are the members of the adiponectin drug-target network, play ing particularly important roles as facilitators of the biological activities and functions of adiponectin [23]. The ALS-constructed drug-target network for Tanshinone IIA was imported into cyto-Hubba for evaluation of its topology. M atthews Correlation Coefficient (M CC) assessment was chosen as the ranking method, and the ten top-ranking nodes were returned. Other parameters were set to default values. Cluster identification via ClusterONE Clustering with overlapping neighborhood expansion (ClusterONE) is a Cytoscape plugin for detecting potentially overlapping protein complexes from PPI data [24]. Dense regions identified by ClusterONE in PPI networks usually correspond to protein complexes or fractions of protein complexes [25]. This plugin has recently been used to identify protein clusters in the PPI network for epilepsy patients with brain cancer [25] and in the PPI network for patients with early onset colorectal cancer [26]. The ALS-constructed, Tanshinone IIA-associated network was imported into ClusterONE for the identification of pro-

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tein clusters participating in the agent’s molecular mechanisms of action. The minimum cluster size was set to six, and the minimum cluster density was set to 0.5. Other parameters were set to default values. Drug-protein interactions revealed by STITCH STITCH (accessible at http://stitch.embl.de/.), which includes an aggregated database of interactions connecting more than 300 000 chemicals and 2.6 million proteins from 1 133 organisms via evidence obtained from the literature, integrates information about interactions from metabolic pathways, crystal structures, binding experiments, and drugtarget relationships [27-28]. Li and colleagues have identified 280 chemicals in the STITCH database that interacted with three anti-human immunodeficiency virus (HIV) drugs targeted against core human proteins involved in HIV-host interactions [29]. Furthermore, Kuhn et al. have identified a number of individual protein targets in the STITCH dat abase that elicit complex side effects from approved therapeutic drugs [30] . The database has also been used for the construction of a chemical-protein interaction network associated with psoriasis [31]. Tanshinone IIA was entered into the STITCH search panel as the query. The medium confidence of the required confidence (score) was set to 0.400. The maximum number of drug-target interactions was set to 50. Other parameters were set to default values.

Fig. 2 Tanshinone IIA drug-target network constructe d by Agilent Literature Search (ALS). (A) Default view. (B) Edge weighted, force -directed layout. Edges: interactions ; nodes: specific target proteins Table 1 Topological parameters of the ALS-constructe d Tanshinone IIA drug-target network Parameter

Results and Discussion ALS-mediated construction of the Tanshinone IIA drug-target network Drug-target and other n etworks can be obt ained in several w ays, including manual construct ion and querying of public or proprietary databases [32 ]. A LS mines lit erature abstracts from sources s uch as M edline, the OM IM (Online M endelian Inheritance in M an) dat abas e and the U nit ed Stat es patent dat abas e to identify putat ive molecular interact ions and apply thes e interact ions to automatically construct a network [ 16, 1 9] . The primary drug-target network of Tanshinone IIA described in the present study w as construct ed by ALS and corrected manually on D ecember 28, 2013. As shown in F ig. 2A (default view) and F ig. 2B (edge-w eight ed, force-direct ed layout), t he ALS-constructed network consist ed of 258 nodes (protein t argets) and 957 edges (Table 1). Network models are crucial for shaping our understanding of complex networks and can help explain the origin of the observed network charact eristics [ 33] . A scale-free network s ignifies the abs ence of typical nodes in the network [33]. The results show n in Figs . 2A and 2B indicated that the drug-t arget network for Tanshinone IIA, like most other biological networks, had a scale-free topology.

Network

Number of nodes

258

Number of edges

957

Characteristic path length

3.163

Network diameter

9

Avg. number of neighbors

7.419

Network density

0.029

Network centralization

0.273

Clustering coefficient

0.600

Network heterogeneity ALS, Agilent Literature Search

1.416

Topological parameters of the ALS-constructed Tanshinone IIA drug-target network An understanding of the topological properties of drugtarget networks aids in classification of protein functions, elucidation of molecular mechanisms of disease, exploration of potential drug targets, and design of new drugs [23, 33] . The topological properties of the ALS-constructed Tanshinone IIA network were therefore investigated in depth by using NetworkAnalyzer and are shown in Fig. 3 and Table 1. The node degree distribution function is employed to distinguis h between different class es of networks [33] . Fig. 3A shows that the node degree distribut ion for the Tanshinone IIA drug-target network decreas ed follow ing a power law, which again s ignified that the A LS-constructed target network was scale-free, in agreement w ith the res ults shown in F ig. 2. The average clustering coefficient is the average of the clustering coefficients for all of the proteins that form clusters within the network [23]. The average number of neighbors indicates the average connectivity of a protein within the network [23], whereas the characteristic path length gives the expected distance between two connected nodes [23, 33] . These p aramet ers are given in F igs . 3B–3D and Table 1.

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Fig. 3 Topological properties of the Agilent Literature Search (ALS)-constructed Tanshinone IIA network showing (A) the numbe r/de gree of nodes, (B) the average clustering coefficient/number of neighbors, (C) the topology coefficient/numbe r of neighbors, and (D) the frequency/path length

Additional parameters, including network diameter, density, centralization, and heterogeneity, are given in Table 1. Important nodes in the ALS-constructed drug-target network identified by cyto-Hubba analysis Proteins with special network characteristics in an interactome are termed essential or top nodes and may play critical roles in controlling or regulating cellular responses to a specific drug or a physiological stimulus. These essential nodes/hubs may therefore serve as potential drug targets that can be exploited for the development of novel therapies for human diseases [22] . The identification of such nodes/hubs also provides a means to decipher the critical keys that form the foundation of drug-associated biochemical pathways or complex networks [22]. To identify the essential nodes in the Tanshinone IIA network, Cyto-Hubba analysis was next performed. The top 10 nodes returned by the analysis and ranked by MCC scores corresponded to Fos, Jun, Src, Akt1 kinase, mitogen-activated protein kinase 3 (MAPK3), mitogen- activated protein kinase 8 (M APK8), phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), mitogen-activated protein kinase kinase 1 (M AP2K1), platelet-derived growth factor receptor beta (PDGFRB), and pyrophosphatase 1 (PPA1) (Fig. 4). The top five nodes corresponded to F os, J un, Src, PIK3CA, and MAP2K1 (Table 2). Fos, Jun and Src are indispensable transcription factors, and MAP2K1 and PIK3CA are involved in many important signaling cascades [34-45] . As noted above, these five proteins may critically participate in the mediation of TanshinoneⅡA drug actions, and, if s o,

Fig. 4 The top 10 nodes identified in the Agilent Literature Search (ALS)-constructed Tanshinone IIA network, ranke d by Matthews Correlation Coefficient (MCC) scores. MAP2K1, mitogen-activated protein kinase kinase 1; MAPK3, mitogen activated protein kinase 3; MAPK8, mitogen -activated protein kinase 8; PIK3CA phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha; PDGFRB, platelet-de rive d growth factor receptor beta, PPA1, pyrophosphatase 1. Note : This was the terminology returned by the software Table 2 The top five ranking nodes in the ALS-constructed Tanshinone IIA drug-target network Ranking

Node

1

Fos

2

Jun

3

Src

4

PIK3CA

5 MAP2K1 ALS, Agilent Literature Search MAP2K1, mitogen-activated protein kinase kinase 1 PIK3CA, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha

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these nodes are expected to be highly influential in regulating the upstream and downstream activities of all of the proteins in the entire drug-target network. M ajor activating protein 1 (AP-1, Jun/Fos) is composed of members of the Jun (c-Jun, Jun B and Jun D) and Fos (c-Fos, FosB, Fra1 and Fra2) subfamilies of DNA-binding transcription factors [34-35]. AP-1 has diverse functions in cell proliferation, differentiation, transformation, and apoptosis, and in inflammation and wound healing [34-35] . Notably, Tanshinone IIA reduces c-Fos gene expression in the brains of zebrafish larvae with pentylenetetrazol-induced seizure activity, and also exhibits anticonvulsant activity in zebrafish and mouse seizure models [36] . In addition, Tanshinone IIA abolishes the proliferation of fetal bovine serum-stimulated vascular smooth muscle cells in culture, and reduces intimal hyperplasia by inhibiting MAPK signaling pathways and by down-regulating c-Fos expression [37] . Furthermore, Tanshinone IIA has successfully been used as a selective inhibitor of AP-1 in experiments performed to investigate the molecular mechanisms underlying tumor necrosis factor-α (TNF-α) induced cytosolic phospholipase A2 expression and prostaglandin E2 synthesis in human lung epithelial cells [38], as well as in experiments performed to investigate the mechanisms underlying TNF-α-induced matrix metalloprotease-9 expression in osteoblasts [39] . These results would facilitate the identification of Jun and Fos as major protein targets of Tanshinone IIA. SRC, arguably the oldest oncogene, has been implicated in signaling pathways that regulate cell proliferation, angiogenes is, bone metabolis m, and tumor invasion/ metastas is [40] . Addition of Tanshinone IIA to a culture of osteoclast precursor cells can significantly decrease the mRNA expression levels of the calcitonin receptor, c-Src, and integrin β3 [41] , consistent with the identification of Src as another key target of the drug. M oreover, phosphoinositide 3-kinase (PI3K) belongs to a conserved family of lipid kinas es that phos p h o r y l a t e t h e 3 ′ - h y d r o xy l g r o u p o f

Fig. 5

phosphoinositides [42] , and sodium Tanshinone IIA sulfonate protects the rat myocardium against ischemia-reperfusion injury via activation of the PI3K/Akt/Forkhead box O3A/Bim pathway [43] . Tanshinone IIA may also exert neuroprotective actions against beta-amyloid(25-35)-induced apoptosis, including activation of PI3K/Akt and phosphorylation of glycogen synthase kinase 3β in the affected cells [44]. Extracellular signal-regulated kinase (ERK) is a MAPK that functions as the major effector of the Ras oncoprotein [45] . The Ras-ERK and PI3K-mammalian target of rapamycin (PI3K-mTOR) signaling pathways are the chief mechanisms for controlling cell survival, differentiation, proliferation, metabolism, and motility in cancer cells in response to extracellular cues [45] . Importantly, Tanshinone IIA induces autophagic cell death in KBM -5 leukemia cells via activation of AM P-activated protein kinase and ERK, as well as by inhibition of mTOR and p70 S6 kinase [46]. In addition, Tanshinone IIA treatment decreases the number of apoptotic cells, reversed changes in the expression levels of the pro-apoptotic factors, B cell lymphoma-2 (Bcl-2) and caspase-3, and upregulated the activation of Akt and ERK1/2 in seawater-challenged rats [47] . Taken together, an understanding of the functions of these important nodes (Fos, Jun, Src, PIK3CA/PI3K, and ERK/ MAPK) is likely to be helpful in deciphering the roles of the numerous other proteins in the ALS-constructed Tanshinone IIA drug-target network, and analyzing the differential actions of Tanshinone IIA in pathological vs normal cells. ClusterONE analysis of overlapping protein complexes ClusterOne was used to identify the protein clusters in the ALS-constructed, Tanshinone IIA-associated network. Thirteen protein clusters were discerned, with a minimum size of six and a minimum density of 0.5. As shown in Fig. 5, five clusters with a significance level of P < 0.05 were extracted from the network. These clusters represent proteins with similar sequences and/or functions that can interact with Tanshinone IIA and hypothetically facilitate its activities.

The five protein clusters identified in the Agilent Literature Search (ALS)-constructed Tanshinone IIA network

Tanshinone IIA-protein interactions revealed by STITCH STITCH is a search tool that links molecular, cellular, and phenotypic data related to s mall molecules [27-28]. STITCH allows easy navigation in and visualization of networks composed of large collections of associations b etween chemicals and proteins [27-28]. Thus, STITCH

represents a useful resource for both in -depth and large-scale projects in chemical biology [27-28]. The drug-target interaction network for Tanshinone IIA, as constructed by STITCH on December 30, 2013, is shown in Fig. 6. The drug is represented by a red oblong, and the target proteins (nodes) are represented by spheres. Associated

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Fig. 6 Tanshinone IIA drug-target network constructed by STITCH (se arch tool for interactions of chemicals ). O blong: Tanshinone IIA; spheres: target protein s. Thicker lines indicate stronger associations. Protein -protein interactions are shown by blue lines, and Tanshinone IIA-protein interactions are shown by gree n lines

nodes are joined by lines, where thicker lines indicate stronger associations. Several important nodes in the STITCHconstructed network, such as Jun, Akt, nuclear factor kappa B1 (NFκB1) and M APK (Fig. 6), were also identified in the ALS-constructed network by cyto-Hubba and ClusterONE analyses (Figs. 4 and 5). According to the confidence scores (Table 3), the top three nodes in the STITCH-constructed network corresponded to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and NFκB1. Cytochrome P450 (CYP) enzymes are found in all biological kingdoms and a majority of phyla examined so far [48] . M any human P450s metabolize drugs used to treat human diseases. Others are necessary for synthesis of endogenous compounds essential for human physiology [48]. Notable, Danshen multiple components have complicated effects on CYPs [49]. Cryptotanshinone, Tanshinone I, and TanshinoneIIA are competitive inhibitors of human CYP1A2, CYP2C9, CYP2D6, and CYP3A4 in vitro [49-50]. Given that CYP1A2, 2C9, 2E1 and 3A4 are responsible for the metabolism and disposition of a large number of drugs currently used, the potential herb-drug interactions of Danshen preparations containing the major Tanshinones with drugs which are substrates of these CYPs are important to both therapeutic effects and host toxicity [49]. The nuclear factor–κB (NF-κB) signaling pathway serves a crucial role in regulating the transcriptional responses of physiological processes that include cell division, cell survival, differentiation, immunity and inflammation [51].

Table 3 Confidence scores assessed by STITCH Rank

Name

1

CYP3A4

Cytochrome P450, family 3, subfamily A, polypeptide 4

Name

Score 0.923

2

NFKB1

Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1

0.884

3

CYP1A1

Cytochrome P450, family 1, subfamily A, polypeptide 1

0.860

4

SPG7

Spastic paraplegia 7

0.856

5

TNF

Tumor necrosis factor

0.846

6

FSD1

Fibronectin type III and SPRY domain containing 1

0.840

7

HMGB1

High-mobility group box 1

0.835

8

TP53

Tumor protein p53

0.829

9

KCNE1

Potassium voltage-gated channel, Isk-related family, member 1

0.827 7

10

CD40

CD40 molecule, T NF receptor superfamily member 5

0.821

11

NR1I2

Nuclear receptor subfamily 1, group 1, member 2

0.819

12

KCNQ1

Potassium-voltage-gated channel, KQT-like subfamily, member 1

0.819

13

NPM1

Nucleophosmin (nucleolar phosphoprotein B23)

0.816

14

PT GS2

Prostaglandin-endoperoxide synthsase 2

0.814

15

END1

Endothlein 1

0.812

16

NFKBIA

Nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha

0.812

17

ERBB2

v-erb-b2 erythroblastic leukemia viral oncogene homolog 2

0.810

18

HMOX1

Heme oxygenase (decycling) 1

0.710

19

JUN

Jun oncogene

0.674

20

PRDM2

PR domain containing 2, with ZNF domain

0.674

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Continued

Table 3 Confidence scores assessed by STITCH Rank

Name

Name

Score

21

CYP1A2

Cytochrome P450, family 1,subfamily A, polypeptide 2

0.651

22

BCL2

B-cell CLL/lymphoma 2

0.615

23

BAX

BCL2-associated X protein

0.610

24

MAPK8

Mitogen-activated protein kinase 8

0.565

25

AKT1

v-akt murine thymoma viral oncogen homolog 1

0.546

26

PARP4

Poly (ADP-ribose ) polymerase family, member 4

0.534

27

T SN

Translin

0.526

28

NANOS2

Nanos homolog 2 (Drosophila)

0.502

29

ISYNA1

Inositol-3-phosphate synthase 1

0.502

30

CASP3

Caspase 3, apoptosis-related cysteine peptidase

0.486

31

TNFSF11

Tumor necrosis factor (ligand) superfamily, member 11

0.461

32

PARP1

Poly (ADP-ribose) polymerase 1

0.446

33

SRM

Supermidine synthase

0.442

Note: The data correspond to the drug-target network shown in Fig. 6

Tanshinone IIA inhibits breast cancer stem cell growth in vitro and in vivo, through attenuation of IL-6/STAT3/NF-κB signaling pathways [5]. These results are in agreement with the identification of CYPs and NF-κB as the important targets of Tanshinone IIA. The differences in the results returned by ALS and STITCH regarding the Tanshinone IIA drug-target network (Figs. 2 and 6) and the top nodes were probably due to the divergent computer mining tools and databases employed by the two search tools. However, it is plausible that the five top nodes returned by ALS (Fos, Jun, Src, PIK3CA and MAP2K1) and the three top nodes returned by STITCH (CYP3A4, CYP1A1 and NFκB1) mediate important biological functions of Tanshinone IIA and contribute to its overall drug- target network. On the other hand, it is also conceivable that some of the top nodes may represent false positives stemming from the computer mining process.

Conclusions With the explosion of information in the molecular biology and biochemical literature, computational approaches that assimilate and integrate the vast amounts of available data are now imperative to the systemic understanding of drug-perturbed molecular and physiological processes. These computational approaches permit a broader view of the fundamental molecular mechanisms of drug actions than previously recognized [19, 52]. The current study adopted this notion to construct a drug-protein target network for Tanshinone IIA by using the ALS plugin in Cytoscape. The properties of the ALS-constructed network were further analyzed with the Cytoscape NetworkAnalyzer, cyto-Hubba and ClusterONE plugins. Another view of the Tanshinone IIA drug-target network was achieved through the use of STITCH. Despite the possibility of false-positive nodes resulting from computer mining, this study provided a novel and in-depth view of the

molecular mechanisms and signaling pathways that may mediate Tanshinone IIA actions in a simple, compact, and visual manner.

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Cite this article as: CHEN Shao-Jun. Drug-target networks for Tanshinone IIA identified by data mining [J]. Chinese Journal of Natural Medicines, 2015, 13(10): 751-759.

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