Ti

JB

a

ARRAA

KETH

1

aeTm

uph

DlktNE

0d

Journal of Ethnopharmacology 137 (2011) 77– 84

Contents lists available at ScienceDirect

Journal of Ethnopharmacology

journa l h o me page: www.elsev ier .com/ locate / je thpharm

CM grammar systems: An approach to aid the interpretation of the molecularnteractions in Chinese herbal medicine

ing Yan, Yun Wang ∗, Si-jun Luo, Yan-jiang Qiaoeijing University of Chinese Medicine, School of Chinese Pharmacy, No. 6, Sourthern Rd., Beijing 100102, China

r t i c l e i n f o

rticle history:eceived 19 October 2010eceived in revised form 23 January 2011ccepted 20 April 2011vailable online 28 April 2011

eywords:ntity grammar systemsraditional Chinese medicineserba Ephedrae Decoction

a b s t r a c t

Ethnopharmacological relevance: Interpreting the molecular interactions in Chinese herbal medicine willhelp to understand the molecular mechanisms of Traditional Chinese medicines (TCM) and predict thenew pharmacological effects of TCM. Yet, we still lack a method which could integrate the concernedpieces of parsed knowledge about TCM.Materials and methods: To solve the problem, a new method named TCM grammar systems was proposedin the present article. The possibility to study the interactions of TCM at the molecular level using TCMgrammar systems was explored using Herba Ephedrae Decoction (HED) as an example.Results: A platform was established based on the formalism of TCM grammar systems. The related molecu-lar network of Herba Ephedrae Decoction (HED) can be extracted automatically. The molecular networkindicates that Beta2 adrenergic receptor, Glucocorticoid receptor and Interleukin12 are the relativelyimportant targets for the anti-anaphylaxis asthma function of HED. Moreover, the anti-anaphylaxisasthma function of HED is also related with suppressing inflammation process. The results show thefeasibility using TCM grammar systems to interpret the molecular mechanism of TCM. Although theresults obtained depend on the database absolutely, recombination of existing knowledge in this method

provides new insight for interpreting the molecular mechanism of TCM.Conclusions: TCM grammar systems could aid the interpretation of the molecular interactions in TCMto some extent. Moreover, it might be useful to predict the new pharmacological effects of TCM. Themethod is an in silico technology. In association with the experimental techniques, this method will playan important role in the understanding of the molecular mechanisms of TCM.

. Introduction

Traditional Chinese medicines (TCM) usually contain complexctive ingredients and exhibit broad pharmacologic effects. How-ver, some of them also contain toxic ingredients. In order to applyCM in a better way, it is necessary to know their mechanisms atolecular level.Nowadays, there are many approaches to study the molec-

lar mechanism of TCM, such as experimental approaches,articularly molecular pharmacological experiments, includingigh-throughput screening (AI-Sayah et al., 2008), high content

Abbreviations: TCM, Traditional Chinese medicines; HED, Herba Ephedraeecoction; EGS, entity grammar systems; ASP, answer set programming; IL1, Inter-

eukin 1; IL1R1, Interleukin 1 receptor 1; IRAK1, Interleukin-1 receptor-associatedinase; TNFR, Tumor Necrosis Factor Receptor; TRAF6, Tumor Necrosis Factor Recep-or Associated Factor 6; IKK, IkB kinase; TNF-alpha, Tumor Necrosis Factor alpha;O, nitric oxide; PKA, Protein Kinase A; AA, arachidonic acid; IL12, Interleukin12;OS, eosinophils; BHR, bronchial hyperresponsiveness.∗ Corresponding author. Tel.: +86 10 84738620.

E-mail address: wangyun@bucm.edu.cn (Y. Wang).

378-8741/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved.oi:10.1016/j.jep.2011.04.057

© 2011 Elsevier Ireland Ltd. All rights reserved.

screening (Nadanaciva et al., 2010), and metabonomics (Barton,2011); and informatics approach, such as virtual screening,including molecular docking (Adane and Bharatam, 2010), andpharmacophore (Deng, 2008). These methods mainly focus onthe identification of targets for chemical constituents of TCM.Because of the complexity of the chemical components of TCM andthe sophisticated mechanisms in vivo, it is hard to elucidate theexact functional mechanisms of TCM and the interaction betweenthe chemical components by these methods. In recent years,the proposed chemical systems biology approach offered a newavenue for the study of the molecular mechanisms of TCM (Vanet al., 2009). However, due to the complex nature of TCM, whichinclude combination of Chinese Herbal Formula apart from theherb itself, there is currently no systematic method to study theinteractions of TCM at the molecular level. We still lack a methodwhich could integrate the concerned pieces of parsed knowledgeabout TCM. In this paper, we present a new method based on the

entity grammar systems to solve the problem and explore thepossibilities to study the interactions of TCM at the molecular levelusing this method. Because this method is proposed to study TCM,we call it TCM grammar system for convenient.

7 pharm

fdrbtuboefpsct

2

2

e

oi

(((

F

t.P

P

o

etAi

VsFoe

L

iiSxu

cP

b

8 J. Yan et al. / Journal of Ethno

Entity grammar systems (EGS) are a formal language proposedor modeling the complex hierarchies in biological systems. Theefinition of EGS has been introduced by Wang (2004). Theelationship between EGS and Chomsky formal language haseen studied in the same paper. Using the EGS as the framework,he formal model of complex systems can be easily constructedsing the method mentioned in the paper. Therefore, EGS haveeen successfully used in the modeling, learning and simulationf biological cells. A recently published paper has discussed themergence in entity grammar systems (Wang et al., 2007). Theormal definition of emergence based on entity grammar systemsrovide formal tool to study the entangled hierarchies in biologicalystems and to control the generation of emergence in certainonditions. In this paper, we use EGS as the framework to constructhe TCM grammar system.

. TCM grammar systems

.1. Brief introduction of entity grammar systems

In this section, we will recall the basic definitions of entities andntity grammar systems.

An alphabet V is a set of symbols. An organizer F is a set ofperations (or functions). The set E(V, F) of all F-entities over V isnductively defined as:

1) � ∈ E(V, F), � is the empty entity with no symbols;2) V ⊆ E(V, F);3) For all f ∈ F, and all �1, . . ., �n ∈ E(V, F), we have f(�1, . . ., �n) ∈ E(V,

F).

The set E+(V, F) of all non-empty F-entities over V is E+(V, F) = E(V,) − {�}.

Suppose f is an n-ary operation in F, the set of positions of f ishe set Pos(f) = {1, 2, . . ., n}. The set of positions of an entity � = f(�1,

. ., �n) ∈ E(V, F), denoted by Pos(�), is inductively defined as: (1)os(�) = ˚; (2) for � ∈ V, Pos(�) = {�}; (3) for an entity � = f(�1, . . ., �n),

os(�) =n⋃

i=1

{ip|p ∈ Pos(�i)}. The size∣∣�

∣∣ of an entity is the cardinality

f Pos(�).For an entity with the form f(�1, . . ., �n), if at least one �i is �, the

ntity is called pseudo-f-entity. For all operations f in F, the set ofhe all pseudo-f-entities is called pseudo-F-entities, denoted by PFE.n operation f : (E(V, F))n → E(V, F) in F is called pseudo-operation,

f for any set A ⊆ E(V, F), f(A) = A.An entity grammar G is a quintuple, G = (VN, VT, F, P, S), where,

N is finite set of non-terminal symbols, VT is finite set of terminalymbols, and VN ∩ VT = ˚, F is finite set of operations, F = {fi|fi : (E(V,))n → E(V, F), 1 ≤ i ≤ m, m, n ∈ N}, where V = VN ∪ VT, P is a finite setf productions → with ∈ E+(V, F) and ∈ E(V, F), S is the startntities.

Let G = (VN, VT, F, P, S) be an entity grammar. Then the set

(G) = {� ∈ E(VT , F)|S⇒∗G�}

s the language generated by G, where S⇒∗G� represents an unspec-

fied number of derivations (including zero) that can be taken from to �. For x, y ∈ E(V, F), f ∈ F we say that y is directly derivable from

in G, denoted by x ⇒ Gy, if and only if for some → in P and, v ∈ E(V, F), we get x = f (u, ˛, v) and y = f (u, ˇ, v).

If the distinction between non-terminals and terminals are not

oncerned about, an entity grammar can be expressed as G = (V, F,, S), where V is the alphabet of the system.

Using the following several steps, a new grammar system cane constructed (Wang, 2004).

acology 137 (2011) 77– 84

(1) Define the structure of a kind of entities and the alphabet of thegrammar. From the definition of entity, any concrete entity hasthe general form f(�1, . . ., �n), which is composed of a set of com-ponents {�1, . . ., �n} and an operation f on the set. The structureof the entity or the relationship of components is determinedby the operation. A concrete form of the operation is corre-sponding to a concrete kind of entities. The operation f might becomposed of many different operations, which operate on dif-ferent subset of {�1, . . ., �n} or define the different relationshipof components.

(2) Define the elements of the set of operations. The set of oper-ations is an important part and the basis of entity grammarsystems. A successful definition of the set of operations is theprerequisite of a useful entity grammar system. The elements ofthe set of operations can be divided into two categories: (1) theoperations to define the basic structure of entities, which hasbeen defined in the first step, (2) the operations to define theinteraction of entities. As defined in Definition 3.1, all the oper-ations are closed in E(V, F), so all of the results of the operationsare also entities. Suppose f(x1, . . ., xn) is a general operation inF, if it is the first type operation, it could be used for all x1, . . .,xn ∈ E(V, F). If it is the second type operations, it satisfies at leastone of xi /∈ V.

(3) Define the production rules of grammar. The basic type ofproduction rules is recursive productions, context-sensitiveproductions, context-free productions and regular productions.For a successful grammar, the productions should reflect thechanging law of the systems being studied.

Usually, if the entities we defined in step 1 is called “X”, the corre-sponding grammar is called “X Grammar” and the system describedby this grammar is called “X Grammar System”. From the definitionof entities grammar system, TCM grammar systems can be derivedfrom it.

2.2. Definition of TCM grammar systems

Definition 1. An entity grammar system G = (V, F, P, S) is calledan TCM grammar system, if

(1) V = V1 ∪ V2 ∪ V3 ∪ V4, where V1 is a set of TCM formula, V2 is aset of TCM herb medicine, V3 is a set of components of TCMmedicine and V4 is a set of endogenous molecules.

(2) F = F1∪ F2 ∪ F3 ∪ F4 ;

F1 = {f1|f1 : (Vi, Vj) → E(V, F), 1 ≤ i ≤ 3, 2 ≤ j ≤ 3},F2 = {f2|f2 : (V3, V4) → E(V, F)},F3 = {f3|f3 : (V4, V4) → E(V, F)},F4 = {f4|f4 : V → E(V, F)}.

(3) P = P1 ∪ P2,

P1 = {p1|p1 : ⇒ ˇ, ∈ E+(V1 ∪ V2 ∪ V3, F1),

∈ E+(V1 ∪ V2 ∪ V3, F1)},P2 = {p2|p2 : ⇒ ˇ, ∈ E+(V3 ∪ V4, F2 ∪ F3, F4),

∈ E+(V3 ∪ V4, F2 ∪ F3 ∪ F4)}

In this definition, F1 is the set of the relationships between TCMformula and its ingredients or herbal medicine and its components.

F2 is the set of the interaction between the components of herbalmedicine and the biological targets. F3 is the set of interactionbetween endogens molecules. F4 is the set of properties of the enti-ties. Using the rules in P1, the relationship chain from formulas,

pharm

hineiFt

tom

(

(

(

tTlsD

3ih

3

ametpo

iLiLt

E

J. Yan et al. / Journal of Ethno

erbs to components can be realized. For example, if formula Anclude a herb called B, and the herb B include a chemical compo-ent called C, then the entity include(A, C) can be derived from twontities, include(A, B) and include(B, C), respectively. Using the rulesn P2, the state transfer among different molecules can be described.or example, the state D of a molecule A (written as D(A)) can beransferred from A to B through the interaction between A and B.

For a concrete system, the name in V, F and P are determined byhe objects being studied and the research purpose. For the purposef interpretation of the molecular interactions in Chinese herbaledicine, the entities include four types,

1) inclusion entity. If a formula A include herb medicine a, b and c,then the entities are include(A, a), include(A, b), include(A, c). Inthe same way, if a herb medicine B include the chemical com-ponent a and b, then the entities are include(B, a) and include(B, b).

2) the interaction between the chemical component and theendogenous molecules or the interaction between endogenousmolecules. For example, the molecule A inhibit the activity ofenzyme B, then the relationship between the two moleculescan be expressed as inhibit(A,B).

3) the properties of entities. For example, the increase of the con-centration for the molecule A can be described as increase(A)formally.

The rules in the set of P can be used as a reasoning engine in theask of interpreting the functional mechanism of herbal medicine.he whole system can be seen as a simulation system. In the fol-owing sections, we will show the application of TCM grammarystem in the interpretation of the mechanism of Herba Ephedraeecoction (HED).

. Application of TCM grammar systems in thenterpretation of the molecular interactions in Chineseerbal medicine

.1. Basic idea

In this section, we use HED as the example to represent thepplication of TCM grammar systems in the interpretation of theolecular interactions in Chinese herbal medicine. The HED is

ffective in treating inflammation and anaphylaxis asthma. In addi-ion, the formula is representative in Chinese herb formula. For theast few years, Chinese herbalist doctors have reached a consensusn the pharmacological effect of HED (Liu and Luo, 2007).

HED is composed of monarch drug Herba Ephedrae (fam-ly Ephedraceae), ministerial drug Ramulus Cinnamomi (familyauraceae), adjuvant drug Semen Armeniacae Amarum (fam-ly Rosaceae) and cionductant drug Radix Glycyrrhizae (familyeguminosae). The “include” relation is used to express these rela-ionship:

include(HED, herba ephedrae).include(HED, ramulus cinnamomi).include(HED, semen armeniacae amarum).include(HED, radix glycyrrhizae).

Taken Herba Ephedrae as example, the natural products such as

phedrine and norephedrine in it can be expressed as follows:

include(herba ephedrae, ephedrine).include(herba ephedrae, norephedrine).

acology 137 (2011) 77– 84 79

The relations between the natural products and the correspond-ing targets can be expressed by “reg”. For instance, Ephedrineactivates the target Beta2 adrenergic receptor, which can beexpressed by code as follow:

reg(ephedrine, beta2 adrenergic receptor).The relationship between molecules and reactions in biological

network include reg(reactant, reaction) and produce(reaction, prod-uct). For example, the reaction of active calmodulin being producedby calcium and calmodulin can be expressed as

reg(calcium, calcium binds calmodulin).reg(calmodulin, calcium binds calmodulin).produce(calcium binds calmodulin, active calmodulin cytosol).

In addition, there is a third class of property entities. Forinstance, the concentration of ephedrine will be increased after tak-ing HED, which cause the activation of �-2 adrenergic receptor. Thisprocess can be expressed as

increase(ephedrine).reg(ephedrine, beta2 adrenergic receptor).

Based on the formal entities, the related biological network ofChinese Herb formula can be derived using the following rules:

(1) include(X,Y), include(Y,Z), → include(X,Z).(2) include(X, Y), increase(X), → increase(Y).(3) reg(X, Y), increase(X), → increase(Y).(4) produce(X, Y), increase(X), → increase(Y).

HED comes into the human body after one takes it. We thinkthat the content of HED in the body is increasing, which isexpressed by increase(HED). If we have known “include(HED, herbaephedrae), include(herba ephedrae, ephedrine), increase(HED)”,then “increase(herba ephedrae), increase(ephedrine)” can bederived according to the rules (1) and (2). And if wealso known “reg(ephedrine, beta 2 adrenergic receptor) and pro-duce(beta 2 adrenergic receptor, active calmodulin cytosol)”, then,“increase(beta 2 adrenergic receptor)” can be derived accordingto the rule(3) and “increase(active calmodulin cytosol)” can bederived by rule (4). Finally, the function network of HED can beapproximately outlined, the molecular mechanism of HED can beelucidated.

3.2. Implementation of TCM grammar system

To use TCM grammar system in practice, a software plat-form is necessary. The platform includes six parts: TCM formuladatabase; TCM chemical composition database; chemical target-ing database; endogenous reaction database; the reasoning engineand the user interface. TCM formula database, TCM chemical com-position database, drug targets database and endogenous reactiondatabase are implemented by MySQL. TCM formula and TCM chem-ical composition come from the commercial version of TCMDdatabase (Min et al., 2001); Chemical targeting database come fromSTITCH database (Kuhn et al., 2010); endogenous reactions comefrom REACTOME database (Vizcaíno et al., 2010). The reasoningengine is implemented by answer set programming (ASP) language.User interface and integration of databases are realized by php5.2.The whole system is issued by Apache server.

3.3. Molecular interactions in HED

Fig. 1 shows the molecular interaction network of HED obtaineddirectly from TCM grammar system. The artwork was modifiedusing Cytoscape (Paul et al., 2003). There are 3438 nodes and

80 J. Yan et al. / Journal of Ethnopharmacology 137 (2011) 77– 84

F s reprt ces to

6pta(cma

aIsp

cIlst1w(apficoca

ig. 1. The molecular network of the functional mechanism of HED. The blue nodehe anti-anaphylaxis asthma signaling molecules. (For interpretation of the referen

105 edges in this network. The nodes include TCM chemical com-ositions, drug targets and endogenous molecules. To analyseshe molecular mechanism of the anti-inflammatory and anti-naphylaxis asthma of HED, which had been proved in literatureLiu and Luo, 2007), the related nodes are marked by differentolor. The blue nodes represent the anti-inflammatory signalingolecules while the red ones demonstrate the anti-anaphylaxis

sthma signaling molecules.Firstly, we focus on the blue nodes which relates to the

nti-inflammatory signaling pathway, which include three parts,nterleukin 1 signaling pathway (Fig. 2), tumor necrosis factorignal pathway (Fig. 3) and multidrug resistance protein1 signalathway (Fig. 4), respectively.

In Fig. 2 we can see that a chemical composition of Radix Gly-yrrhizae named Rutin plays a role in interacting with the targetnterleukin 1 receptor followed by a series of reactions. Inter-eukin 1 (IL1) signals via Interleukin 1 receptor 1 (IL1R1), the onlyignaling-capable IL1 receptor. The recruitment of MyD88 leads tohe recruitment of Interleukin-1 receptor-associated kinase (IRAK)-

and -4, probably via their death domains. They in turn interactith Tumor Necrosis Factor Receptor (TNFR)-Associated Factor 6

TRAF6), which is an E3 ubiquitin ligase (Deng et al., 2000). Thisctivates TAK1, which then activates inhibitor of NF-kappaB (Ikap-aB) kinase 2 (IKK2 or IKKB) within the IKK complex. NF-kB is aamily of transcription factors that play pivotal roles in immune,nflammatory, and antiapoptotic responses. It is sequestered in the

ytosol of unstimulated cells through the interactions with a classf inhibitor proteins, called IkBs, which prevent the nuclear translo-ation of NF-kB and induce the expression of various genes (Bonizzind Karin, 2004).

esent the anti-inflammatory signaling molecules while the red ones demonstrate color in this figure legend, the reader is referred to the web version of the article.)

In Fig. 3, we can see that the three chemical compositionsCoumarin, Isoliquiritigenin and Glycyrrhizic acid play a role ininteracting with the target Tumor Necrosis Factor followed by aseries of reactions.

The Tumor Necrosis Factor alpha (TNF-alpha) mediated apo-ptosis pathway has been implicated in the pathogenesis of anumber of diseases including sepsis, diabetes, cancer, osteoporosis,multiple sclerosis, rheumatoid arthritis and inflammatory boweldiseases. The TNF signaling network provides extensive cross talkbetween the apoptotic pathway, NF-kB pathway and JNK pathwaythat also emanate from TNF-R (Chen and Goeddel, 2002).

CD40 is a member of the Tumor Necrosis Factor Recep-tor family and its ligand CD40L is a type II transmembraneprotein of the TNF superfamily. The latter is expressed pref-erentially on T-cells and platelets. In the immune system,CD40–CD40L interaction affects some key processes such asimmune cell activation, differentiation, proliferation and apo-ptosis. It also up regulates costimulatory molecules (Chen et al.,2006).

In addition, we find that some reactions associated with theendogenous molecules cbl and egfr are linked with the cd40–cd40lcomplex. It was reported that cbl and egfr are both closely relatedwith inflammation and asthma (Jeon et al., 2004; Cao et al., 2006).It also shows that the mechanism of HED anti-anaphylaxis asthmamay be correlated with suppressing inflammation.

In Fig. 4, we can see that a chemical composition of Radix Gly-

cyrrhizae named Rutin plays a role in interacting with the targetmultidrug resistance protein1 and then rises to a series of reactions.

The red arrows represent two cycles about eNOS. And nitricoxide (NO) is produced through the two ongoing cycles. Originally

J. Yan et al. / Journal of Ethnopharmacology 137 (2011) 77– 84 81

Fig. 2. The molecular network of the Interleukin 1 (IL1) signal pathway. The green hexagon represents a chemical composition of Radix Glycyrrhizae named Rutin. The pinktriangle represents the drug target Interleukin 1 receptor. The blue and yellow circles represent endogenous molecules. The blue diamonds represent chemical reactions.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

Fig. 3. The molecular network of the target tumor necrosis factor signal pathway. The three green hexagons respectively represent a chemical composition of RamulusCinnamomi named Coumarin and two chemical compositions of Radix Glycyrrhizae named Isoliquiritigenin and Glycyrrhizic acid. The pink triangle represents the drugtarget tumor necrosis factor. The circles represent endogenous molecules. The diamonds represent chemical reactions. (For interpretation of the references to color in thisfigure legend, the reader is referred to the web version of the article.)

82 J. Yan et al. / Journal of Ethnopharmacology 137 (2011) 77– 84

F pathwn oteinr der is

iNls

Fnc

ig. 4. The molecular network of the target multidrug resistance protein1 signal

amed Rutin. The pink triangle represents the drug target multidrug resistance preactions. (For interpretation of the references to color in this figure legend, the rea

dentified as endothelium-derived relaxing factor, eNOS derivedO is a critical signaling molecule in vascular homeostasis. It regu-

ates blood pressure and vascular tone, and is involved in vascularmooth muscle cell proliferation, platelet aggregation and leuko-

ig. 5. The molecular network of the target Beta2 adrenergic receptor pathway. The two gramed Ephedrine and D-cathine. The pink triangle represents the drug target Beta2 adrenehemical reactions. (For interpretation of the references to color in this figure legend, the

ay. The green hexagon represents a chemical composition of Radix Glycyrrhizae1. The circles represent endogenous molecules. The diamonds represent chemical

referred to the web version of the article.)

cyte adhesion (Oess et al., 2006). eNOS activation and subsequentNO production bring a number of implications. It was reported thatNO played a role in airway relaxation, and participated in immuneregulation (Zhang et al., 2007). Moreover, in the molecular network,

een hexagons respectively represent two chemical compositions of Herba Ephedraergic receptor. The circles represent endogenous molecules. The diamonds represent

reader is referred to the web version of the article.)

J. Yan et al. / Journal of Ethnopharm

Fig. 6. The molecular network of the Glucocorticoid receptor signal pathway. Thegreen hexagon represents a chemical composition of Radix Glycyrrhizae namedGlycyrrhetinic acid. The pink triangle represents the drug target Glucocorticoidrrfi

wcr2

iaFstt

apn(

Eirsiuov

FGr

eceptor. The red circles represent endogenous molecules. The yellow diamondsepresent chemical reactions. (For interpretation of the references to color in thisgure legend, the reader is referred to the web version of the article.)

e found that this process was accompanied by activation of PKC,almodulin, C1r and C1s. It was reported that they were all closelyelated with inflammatory reaction (Idris et al., 2001; Major et al.,010).

So, Figs. 2–4 show us that the HED has the effects of anti-nflammatory and anti-asthma. Moreover, the mechanism of HEDnti-asthma may be correlated with suppressing inflammation.urthermore, the molecular network of the HED anti-inflammatoryignaling pathway tells us that Interleukin 1, Tumor Necrosis Fac-or and multidrug resistance protein1 are the relatively importantargets.

Next, we focus on the red nodes in Fig. 1, which relates to thenti-anaphylaxis asthma signaling pathway, which include threearts, Beta2 adrenergic receptor pathway (Fig. 5), receptor sig-al pathway (Fig. 6) and Interleukin12 beta chain signal pathwayFig. 7), respectively.

In Fig. 5 we can see that two chemical compositions of Herbaphedrae named Ephedrine and D-cathine play a role in interact-ng with the target Beta2 adrenergic receptor followed by a series ofeactions. Beta2 adrenergic receptor couples with G protein alpha-subtype (Wenzel-Seifert et al., 2002), increasing cAMP activity. The

nactive Protein Kinase A (PKA) complex contains 2 regulatory sub-nits and 2 catalytic subunits. The cAMP induces the dissociationf inactive PKA tetramers. And then, the PKA catalytic subunit acti-ates the opening of calcium channels, resulting in heart muscle

ig. 7. The molecular network of the target Interleukin12 beta chain signal pathway. Thlycyrrhizic acid. The pink triangle represents the drug target Interleukin12 beta chain. T

eferences to color in this figure legend, the reader is referred to the web version of the a

acology 137 (2011) 77– 84 83

contraction and smooth muscle relaxation, to achieve the pharma-cological effects of anti-asthma.

Now we focus on another part to get the network of the anti-anaphylaxis asthma signaling pathway about target Glucocorticoidreceptor (Fig. 6).

We can see that a chemical composition of Radix Glycyrrhizaenamed Glycyrrhetinic acid plays a role in interacting with thetarget Glucocorticoid receptor followed by a series of reactionsin Fig. 6. Prostanoid hormones are formed from the precursorarachidonic acid (AA) in the local environment. Leukotrienes arebiologically active molecules formed in response to inflamma-tory stimuli. They cause contraction of bronchial smooth muscles,stimulation of vascular permeability, attraction and activation ofleukocytes. Oxidation of arachidonate by lipoxygenase is the firststep in their synthesis. It was reported that LTC4 was related withallergic rhinitis (Duroudier et al., 2009). Moreover, Leukotrienesand prostaglandins are closely related with inflammation, asthmaand allergic reactions (Boyce, 2008). Fig. 6 shows us that Glucocor-ticoid receptor is a very important target about anti-anaphylaxisasthma.

Now we focus on the last part to get the network of the anti-anaphylaxis asthma signaling pathway about target Interleukin12beta chain (Fig. 7).

We can see that a chemical composition of Radix Glycyrrhizaenamed Glycyrrhizic acid plays a role in interacting with the tar-get Interleukin12 beta chain (IL-12) and then rises to a series ofreactions in Fig. 4.

Regulating the balance of Th1/Th2 cellular immune responseis the most important character of IL-12. IL-12 can be effective inpromoting the production of Th1 type cells such as IFN-�, whilethe IFN-� can also promote the function of IL-12, thus forminga positive feedback adjustment factor section and enhancing theTh1 cell immune response (Kodama et al., 2003). Meanwhile, IL-12 can inhibit the production of Th2 type cells such as IL-4, IL-5,leading to inhibition of Th2 cells in the immune response (Gatelyet al., 1998). Clinical animal studies also show that IL-12 can inhibitthe synthesis of IgE, through promoting the production of IFN-� orinhibiting the production of IL-4, IL-5 (Habu et al., 2001). Moreover,IL-12 can induce the dissociation of EOS and inhibit its aggregationand infiltration (Kawashima et al., 1998). Asthma has the follow-

ing features: eosinophils (EOS) infiltration in airway, bronchialhyperresponsiveness (BHR) and increasing synthesis of IgE in vivo.So we know that Interleukin12 beta chain is an important targettoo.

e green hexagon represents a chemical composition of Radix Glycyrrhizae namedhe red diamonds represent relatively chemical reactions. (For interpretation of therticle.)

8 pharm

ttrHi2u

4

ictp

teIpa

A

FPt

R

A

A

B

B

B

C

C

C

4 J. Yan et al. / Journal of Ethno

To sum up, Beta2 adrenergic receptor, Glucocorticoid recep-or and Interleukin12 beta chain are the relatively importantargets about anti-anaphylaxis asthma. Moreover, they are alsoelated with inflammation. So we infer that the mechanism ofED anti-anaphylaxis asthma may be correlated with suppress-

ng inflammation. We also found a paper referred this view (Liu,005), which indicates that the valuable results can be obtainedsing established TCM grammar systems.

. Conclusion

In this paper, we present a new method which could integratenformation of the functional mechanism of TCM. This methodould aid the interpretation of the molecular interactions in TCMo some extent. Moreover, it might be useful to predict the newharmacological effects of TCM.

This method is an in silico technology. The results obtained usinghis method depend on the database absolutely. The new knowl-dge is emerged from the recombination of existing knowledge.n association with the experimental techniques, this method willlay an important role in the understanding of the molecular mech-nisms of TCM.

cknowledgements

This work is supported and sponsored by the Natural Scienceoundation of China (NSFC 30500643, NSFC 30973946) and Keyrojects in the National Science & Technology Pillar Program duringhe Eleventh Five-year Plan Period (2008BAI51B01).

eferences

dane, L., Bharatam, P.V., 2010. Binding modes of 2,4-diaminoquinazoline and 2,4-diaminopteridine analogs to P. falciparum dihydrofolate reductase enzyme:molecular docking studies. Indian Journal of Pharmaceutical Sciences 72,324–333.

I-Sayah, et al., 2008. High throughput screening of active pharmaceutical ingredi-ents by UPLC. Journal of Separation Science 31, 2167–2172.

arton, R.H., 2011. A decade of advances in metabonomics. Expert Opinion on DrugMetabolism & Toxicology (Epub ahead of print).

onizzi, G., Karin, M., 2004. The two NF-kappaB activation pathways and their rolein innate and adaptive immunity. Trends in Immunology 25, 758–765.

oyce, J.A., 2008. Eicosanoids in asthma, allergic inflammation, and host defense.Current Molecular Medicine 8, 335–349.

ao, et al., 2006. Expression of epidermal growth factor receptor and its ligands inchronic asthma mice and the influence of dexamethasone. Journal of the Fourth

Military Medical University 562, 216–219.

hen, G., Goeddel, D.V., 2002. TNF-R1 signaling: a beautiful pathway. Science 296,1634–1635.

hen, et al., 2006. CD40/CD40L dyad in the inflammatory and immune responses inthe central nervous system. Cellular & Molecular Immunology 3, 163–169.

acology 137 (2011) 77– 84

Deng, et al., 2008. Pharmacophore modelling and virtual screening for identifica-tion of new Aurora: a kinase inhibitors. Chemical Biology & Drug Design 71,533–539.

Deng, et al., 2000. Activation of the IkappaB kinase complex by TRAF6 requiresa dimeric ubiquitin-conjugating enzyme complex and a unique polyubiquitinchain. Cell 103, 351–361.

Duroudier, et al., 2009. Leukotriene pathway genetics and pharmacogenetics inallergy. Allergy 64, 823–839.

Gately, et al., 1998. The interleukin-12/interleukin-12-receptor system: role innormal and athologic immune responses. Annual Review of Immunology 16,495–521.

Habu, Y., et al., 2001. Themechanism of a defective IFN-gamma response to bac-terial toxins in an atopic dermatitismode, l NC/Ngamice, and the therapeuticeffect of IFN-gamma, IL-12, or IL-18 on dermatitis. Immunology 166, 5439–5447.

Idris, I.S., et al., 2001. Protein kinase C activation: isozyme-specific effects onmetabolism and cardiovascular complications in diabetes. Diabetologia 44,84–87.

Jeon, et al., 2004. Essential role of the E3 ubiquitin ligase Cbl-b in T cell anergyinduction. Immunity 21, 167–177.

Kawashima, T., et al., 1998. Interleukin-12 induces tyrosine phosphorylation of an85-kDa protein associated with the interleukin-12 receptor beta 1 subunit. Cel-lular Immunology 186, 39–44.

Kodama, T., et al., 2003. Role of interleukin-12 in the regulation of CD4+T cell apo-ptosis in amousemodel of asthma. Clinical and Experimental Immunology 131,199–205.

Kuhn, M., et al., 2010. STITCH 2: an interaction network database for small moleculesand proteins. Nucleic Acids 552, 37–42.

Liu, Y.g., 2005. Studies on compatibility principal of HED-effects and mechanism ofthe compatibility of HED anti-inflammation and anti-allergic asthma. ChineseTraditional Patent Medicine 27, 345–346.

Liu, Y.G., Luo, J.B., 2007. Effects of among compositions of Herba Ephedrae decoctionon genic expression of 5-lipoxygenase activating protein IL-4 and leukotrieneC4 in asthmatic mice. China Journal of Chinese Material Medical 32, 246–249.

Major, et al., 2010. Calcium-dependent conformational flexibility of a CUB domaincontrols activation of the complement serine protease C1r. Journal of BiologicalChemistry 285, 11863–11869.

Min, et al., 2001. Traditional Chinese medicine database and application on the web.American Chemical Society 41, 273–277.

Nadanaciva et al., 2010. A high content screening assay for identifying lysoso-motropic compounds. Toxicology in Vitro (Epub ahead of print).

Oess, S., et al., 2006. Subcellular targeting and trafficking of nitric oxide synthases.Biochemical Journal 396, 401–409.

Paul, et al., 2003. Cytoscape: a software environment for integrated models ofbiomolecular interaction networks. Genome Research 13, 2498–2504.

Van, et al., 2009. Systems biology guided by Chinese medicine reveals new markersfor sub-typing rheumatoid arthritis patients. Journal of Clinical Rheumatology15, 330–337.

Vizcaíno, J.A., et al., 2010. The proteomics identifications database: 2010 update.Nucleic Acids 736, 80–85.

Wang, Y., 2004. Entity grammar systems: a grammatical tool for studying the hier-archal structures of biological systems. Bulletin of Mathematical Biology 66,447–471.

Wang, et al., 2007. Modeling, Learning and Simulating Biological Cells with EntityGrammar, vol. 4490. Springer-Verlag, Berlin, Heidelberg, pp. 138–141.

Wenzel-Seifert, K., et al., 2002. Similarities and differences in the coupling of humanbeta1- and beta2-adrenoceptors to Gs(alpha) splice variants. Biochemical Phar-macology 64, 9–20.

Zhang, et al., 2007. A novel class of anti-inflammatory and analgesic drugs – no-donating NSAIDs. Acta Pharmaceutica Sinica 42, 352–435.