Evidence-Based Complementary and Alternative Medicine

Evidence-Based Complementary and Alternative Medicine / 2015 / Article

Research Article | Open Access

Volume 2015 |Article ID 497314 | 12 pages | https://doi.org/10.1155/2015/497314

The Mechanism Research of Qishen Yiqi Formula by Module-Network Analysis

Academic Editor: Lixing Lao
Received28 May 2015
Accepted04 Aug 2015
Published24 Aug 2015

Abstract

Qishen Yiqi formula (QSYQ) has the effect of tonifying Qi and promoting blood circulation, which is widely used to treat the cardiovascular diseases with Qi deficiency and blood stasis syndrome. However, the mechanism of QSYQ to tonify Qi and promote blood circulation is rarely reported at molecular or systems level. This study aimed to elucidate the mechanism of QSYQ based on the protein interaction network (PIN) analysis. The targets’ information of the active components was obtained from ChEMBL and STITCH databases and was further used to search against protein-protein interactions by String database. Next, the PINs of QSYQ were constructed by Cytoscape and were analyzed by gene ontology enrichment analysis based on Markov Cluster algorithm. Finally, based on the topological parameters, the properties of scale-free, small world, and modularity of the QSYQ’s PINs were analyzed. And based on function modules, the mechanism of QSYQ was elucidated. The results indicated that Qi-tonifying efficacy of QSYQ may be partly attributed to the regulation of amino acid metabolism, carbohydrate metabolism, lipid metabolism, and cAMP metabolism, while QSYQ improves the blood stasis through the regulation of blood coagulation and cardiac muscle contraction. Meanwhile, the “synergy” of formula compatibility was also illuminated.

1. Introduction

Qishen Yiqi formula (QSYQ), consisting of Radix Salvia miltiorrhiza, Panax notoginseng, Dalbergia odorifera, and Astragalus membranaceus, has the effect of tonifying Qi, promoting blood circulation and relieving pain, and hence it has been widely used to treat the cardiovascular diseases with Qi deficiency and blood stasis syndrome [1, 2]. Pharmacological researches have shown that the mechanism of QSYQ is related to improve myocardial function, inhibit platelet aggregation, prevent enlargement of end-diastolic diameter, and slow down ventricular remodeling [37]. However, the mechanism of QSYQ was mostly elucidated macroscopically by pharmacology indexes of animal experiments or clinical trials. For example, Tong et al. [6] had reported that the myocardial protection function of QSYQ may relate to the reduction of myocardial cell apoptosis in adriamycin-induced cardiomyopathy animal model. The research of Cui et al. [7] had shown that, by detecting clinical indexes which included the right ventricular end-diastolic volume (RVEDV), end-systolic volume (RVESV), stroke volume (SV), and right ventricular ejection fraction (RVEF), QSYQ could significantly improve the right heart function on patients undergoing valve replacement. The findings of these studies explain the action mechanism of QSYQ to some extent, but the further study of QSYQ is still to be done. Up to now, the mechanism of QSYQ to tonify Qi and promote blood circulation is rarely reported at molecular or systems level. In this study, the mechanism of QSYQ was illuminated by the network analysis approach which has the advantage of evaluating TCM’s pharmacological effect as a whole unity at molecular level [8, 9].

Proteins are vital macromolecules, at both cellular and systematic levels, but they rarely act alone. And protein-protein interactions (PPIs) are major bearers of the biological process. So, protein interaction network (PIN) could provide the basis of understanding cellular organization and processes. The GO [10] project is a collaborative effort to construct ontologies which facilitate biologically meaningful annotation of gene products. It provides a collection of well-defined biological terms, spanning biological processes, molecular functions, and cellular components. GO enrichment is a common statistical method used to identify shared associations between proteins and annotations to GO. Module-network and GO analysis may provide an efficient way to illustrate the molecular mechanism of QSYQ.

In this study, a network analysis approach based on functional modules is applied to systematically illuminate the mechanism of QSYQ. The PINs of QSYQ were constructed by Cytoscape, while properties of scale-free, small word, and modularity were analyzed based on topological parameters. Then, the functional modules were identified by gene ontology (GO) enrichment analysis based on Markov Cluster (MCL) algorithm. This study aimed to provide an efficient way to elucidate the mechanism of QSYQ based on functional modules at the molecular level.

2. Materials and Methods

2.1. Targets Mining of Main Active Components of QSYQ

The main active components of QSYQ were used to study the mechanism of QSYQ. By literature retrieval from PubMed and CNKI database, the main active components of QSYQ were obtained based on the principles that components are the main efficacy components, have rich content, and can be absorbed into the blood. The information of the main active components of QSYQ is shown in Table 1.


HerbsActive componentsReference

Salvia miltiorrhizaTanshinone IIA, cryptotanshinone, salvianolic acid A, salvianolic acid B, tanshinol, and protocatechuic aldehyde[4651]
Panax notoginsengDencichine, ginsenoside Rb1, ginsenoside Rg1, and notoginsenoside R1[5254]
Dalbergia odoriferaButein, formononetin, isoliquiritigenin, nerolidol[5558]
Astragalus membranaceusCalycosin, astragaloside Ι, formononetin, and astragaloside IV[59, 60]

The targets’ information of main active components of QSYQ was obtained from two parts: pharmacophore virtual screening and the component-protein interaction database including ChEMBL (https://www.ebi.ac.uk/chembl/#) [11] and STITCH 3.1 (http://stitch.embl.de/) [12]. The 27 pharmacophore models which were applied to virtual screen were constructed by our laboratory team [13, 14]. ChEMBL is a manually curated chemical database which contains compound bioactivity data against drug targets. STITCH is a database in which every interaction has a confidence score, and the interactions with a confidence score > 0.7 were selected.

2.2. Network Construction of Single Herb and Formula

The PPIs information of targets was obtained from the online updated database of String 9.1 (http://string-db.org/) which has a confidence score for every protein interaction [15]. PPIs with a confidence score > 0.7 were applied to construct PIN using Cytoscape which is one of the most popular open-source software tools for the visual exploration of biomedical networks composed of protein, gene, and other types of interactions [16]. Every single herb network is formed only by PPIs involving proteins of this herb, and the formula network is formed only by PPIs involving proteins of this formula.

2.3. Network Analysis

The analysis of topological properties based on topological parameters has become very popular for gaining insight into the organization and structure of the resultant large complex networks [1719]. Therefore, the topological parameters such as degree distribution, average shortest path, and clustering coefficient were analyzed by Network Analyzer [20] in Cytoscape. Properties of scale-free, small word, and modularity of the QSYQ’s PIN were also investigated.

Functional modules of the network were explored by the MCL [21] which simulates a flow on the graph by calculating successive powers of the associated adjacency matrix and the value of the inflation parameter strongly influences the number of clusters. Compared to the other algorithms, for example, RNSC [22], MCODE [23], and SPC [24], the MCL is superior with highlighting the robustness to graph alterations [25]. Based on the identified modules, GO enrichment analysis was utilized to predict possible biological roles of the modules by evaluating the involved biological processes, using the BinGO [26] plugin for Cytoscape.

3. Results and Discussion

3.1. The Analysis of the Main Active Components of QSYQ

The main active components of QSYQ are all related to the effect of tonifying Qi or promoting blood stasis.

Tanshinone IIA, cryptotanshinone, salvianolic acid A, salvianolic acid B, tanshinol, and protocatechuic aldehyde are from Salvia miltiorrhiza which is a classical traditional Chinese medicine (TCM) which can promote blood circulation and remove blood stasis with 1000 years of clinical application [27]. It has been demonstrated that Salvia miltiorrhiza can reduce the area of cerebral infarct of ischemia-reperfusion injury rats which results from blood stasis [28]. The chemical components of Salvia miltiorrhiza are divided into water-soluble and liposoluble components. Among the liposoluble components, tanshinone IIA [29] has been reported to improve blood stasis syndrome of patients with coronary heart diseases by inhibiting the circulating inflammatory markers (including IL-6, TNF α, VCAM-1, CD40, sCD40L, MCP-1, and MMP9). Cryptotanshinone [30] has good pharmacological effects on atherosclerosis, while atherosclerosis is one of the diseases resulting from blood stasis. Salvianolic acids, as the main effective components of water-soluble components including salvianolic acid A, salvianolic acid B, tanshinol, and protocatechuic aldehyde, can inhibit thrombosis, thromboxane B2 formation, and platelet aggregation [31]. This indicated that the main active components from Salvia miltiorrhiza are all associated with blood stasis.

Dencichine, ginsenoside Rb1, ginsenoside Rg1, and notoginsenoside R1 are from Panax notoginseng, which is a highly-valued herb and is able to modulate vascular tone such as the activation of blood circulation, removal of blood stasis, and inhibition of platelet aggregation [32]. The main active components of Panax notoginseng include two types of bioactive molecules: one has been reported to have good hemostatic and antithrombotic effects, such as dencichine [33]. In addition, saponins, as the main blood-activating components, which include ginsenoside Rb1, ginsenoside Rg1, and notoginsenoside R1, have showed significant effectiveness on treating cardiovascular diseases [34, 35].

Butein, formononetin, isoliquiritigenin, and nerolidol are from Dalbergia odorifera. Dalbergia odorifera, as blood-activating and stasis-removing TCM, is widely used for promoting blood circulation, relieving pain, and removing blood stasis, which has the effects on antithrombosis, antiplatelet aggregation, antioxidant, antitumor, and anti-inflammation [36]. Volatile oil and flavonoid compounds are two main chemical components of Dalbergia odorifera. According to Guo et al. [37], the ethyl acetate part of Dalbergia odorifera can significantly shorten the bleeding time and clotting time of mice, and it indicated that volatile oil is the material basis of blood-activation in Dalbergia odorifera, while Nerolidol, as a main active component, accounts for 45.23~69.13% of the volatile oil. Butein, formononetin, and isoliquiritigenin, as flavonoid components, show antioxidant activity, antiplatelet aggregation, anti-inflammatory properties, and the capacity for treating cardiovascular diseases [3840].

Calycosin, astragaloside Ι, formononetin, and astragaloside IV are from Astragalus membranaceus which is a popular Qi-tonifying herb with multiple biological functions, such as antioxidative, antihypertensive, antiaging, and immunomodulatory activities [41]. The main bioactive components including isoflavonoids and triterpene saponins are associated with effects on human health [42]. Isoflavonoids, which are considered “marker components” for the quality control of Astragalus membranaceus including calycosin and formononetin, show strong antioxidant activity, immunoregulation, anti-inflammatory properties, and the capacity for treating cardiovascular diseases [43]. Astragaloside, including astragaloside Ι and astragaloside IV, is the main effective component of astragalus polysaccharides and exerts significant effects on myocardial protection and immunity enhancement [44, 45].

3.2. Targets Information of Active Components of QSYQ

75 targets were obtained from pharmacophore virtual screening. 174 and 65 targets were, respectively, extracted from the ChEMBL and STITCH 3.1. The targets’ number of each active component is listed in Table 2, and the additional targets’ information is shown in Table S1 in Supplementary Material available online at http://dx.doi.org/10.1155/2015/497314.


Active componentstotal

Tanshinone IIA45
Cryptotanshinone34
Salvianolic acid A17
Salvianolic acid B21
Tanshinol8
Protocatechuic aldehyde9
Dencichine7
Ginsenoside Rb115
Ginsenoside Rg118
Notoginsenoside R111
Butein23
Formononetin27
Isoliquiritigenin51
Nerolidol3
Calycosin8
Astragaloside Ι7
Astragaloside IV10

3.3. Construction of Network

PPIs information of the targets from String 9.1 with their confidence score > 0.7 was imported in Cytoscape 2.8.3, and then union calculation was carried out, followed by the removal of duplicated edges of PPIs using Advanced Network Merge [20] of Plugins. The structural information of constructed networks was listed in Table 3.


NetworksNodesEdges

Salvia miltiorrhiza6042362
Panax notoginseng264963
Dalbergia odorifera5882379
Astragalus membranaceus3991294
QSYQ9934215

3.4. Network Analysis
3.4.1. Topological Analysis

All the topological parameters of QSYQ were calculated and they are shown in Table 4.


ParametersPIN of QSYQ

Clustering coefficient0.673
Network diameter (radius)11 (1)
Network centralization0.104
Shortest path804676
Mean path length4.455
Network heterogeneity0.955

Notes. The network diameter is the longest distance between any pair of vertices and the radius of a graph is the minimum eccentricity of any vertex. Network centralization is a network index that measures the degree of dispersion of all node centrality scores in a network. And network heterogeneity quantifies the degree of uneven distribution of the network.

Biological networks have been proposed to have scale-free topology whose degree distribution follows a power law distribution () [61]. As shown in Figure 1(a), the degree distribution of the PIN of QSYQ followed the power law distribution and the equation is . So, the PIN of QSYQ was a scale-free network.

Small world networks have a property that mean path length is short [62]. The shortest path length between any two proteins was calculated, and it turned out to be 4.455. As shown in Figure 1(b), network path length was mostly concentrated in 3–5 steps, which meant that most proteins were closely linked and the PIN of QSYQ was a small world network.

In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. As shown in Figures 1(c) and 1(d), compared with random network whose numbers of nodes and edges are the same as PIN of QSYQ, the clustering coefficient of PIN was higher. It meant the PIN of QSYQ was more modular. These results suggested that the network exhibited the properties of scale-free, small word, and modularity.

3.4.2. Clustering and GO Enrichment Analysis

With the MCL algorithm, 57, 24, 49, 29, and 85 modules were, respectively, identified from salvia miltiorrhiza, Panax notoginseng, Dalbergia odorifera, Astragalus membranaceus, and QSYQ. The modules of QSYQ are shown in Figure 2, and the others are shown in Figures S1–S4.

The results of functional enrichment analysis of QSYQ using BinGO are shown in Table 5, and they show that QSYQ played a pharmacodynamics with the biological processes, such as DNA metabolic process, regulation of cAMP metabolic process, lipid metabolic process, and the regulation of blood coagulation. The results of functional enrichment analysis of salvia miltiorrhiza, Panax notoginseng, Dalbergia odorifera, and Astragalus membranaceus are shown in Tables S2–S5.


Modules valueGO terms

11.97E − 14Regulation of protein metabolic process
21.32E − 32DNA metabolic process
32.01E − 28Regulation of cAMP metabolic process
44.16E − 24G-protein coupled receptor signaling pathway
55.28E − 23DNA-dependent transcription, initiation
63.03E − 25Transmembrane receptor protein tyrosine kinase signaling pathway
74.74E − 23Cellular lipid metabolic process
86.43E − 15Apoptotic process
91.80E − 18Tricarboxylic acid cycle
109.96E − 27G-protein coupled receptor signaling pathway
111.32E − 32Xenobiotic metabolic process
122.74E − 15Toll-like receptor signaling pathway
135.96E − 32Potassium ion transport
141.50E − 20Lipid metabolic process
152.57E − 20Xenobiotic metabolic process
168.66E − 12Positive regulation of RNA metabolic process
172.78E − 27Regulation of blood coagulation
183.33E − 14Inflammatory response
191.94E − 16Immune response-activating signal transduction
203.65E − 19Apoptotic process
211.04E − 11Regulation of blood coagulation
222.64E − 16Nucleotide metabolic process
234.14E − 13Transmembrane receptor protein tyrosine kinase signaling pathway
243.85E − 12Interferon-gamma-mediated signaling pathway
255.59E − 07Regulation of cellular protein metabolic process
264.34E − 06RNA processing
271.19E − 22Cell cycle phase transition
282.71E − 06Regulation of RNA metabolic process
299.03E − 14Regulation of systemic arterial blood pressure by renin-angiotensin
305.05E − 18Cardiac muscle contraction
312.53E − 06Regulation of RNA splicing
321.13E − 09Carbohydrate metabolic process
339.21E − 14Insulin receptor signaling pathway
343.77E − 08Lipid catabolic process
352.50E − 22Cellular amino acid catabolic process
361.93E − 17Regulation of cell cycle
372.42E − 14Lipid metabolic process
383.52E − 17mRNA metabolic process
392.31E − 06Execution phase of apoptosis
401.16E − 15Toll-like receptor signaling pathway
413.70E − 11RNA biosynthetic process
423.65E − 12Cell cycle phase
432.80E − 12Cellular amino acid metabolic process
446.84E − 05Regulation of cell proliferation
451.17E − 07Inflammatory response
464.51E − 09RNA biosynthetic process
473.74E − 11Regulation of interleukin-1 secretion
481.49E − 13DNA repair
491.67E − 18Insulin receptor signaling pathway
503.18E − 06Negative regulation of RNA metabolic process
511.24E − 04Transport
528.34E − 14Regulation of transforming growth factor beta receptor signaling pathway
534.47E − 06Xenobiotic metabolic process
541.16E − 08Negative regulation of inflammatory response
551.37E − 07Positive regulation of RNA metabolic process
565.56E − 07Transmission of nerve impulse
575.79E − 12Mitotic cell cycle
586.69E − 06Negative regulation of protein metabolic process
592.66E − 12Regulation of apoptotic signaling pathway
602.00E − 09Regulation of apoptotic signaling pathway
612.47E − 11NIK/NF-kappaB cascade
624.69E − 15Vascular endothelial growth factor receptor signaling pathway
631.98E − 06DNA metabolic process
642.04E − 04Chromatin organization
651.11E − 05DNA packaging
669.39E − 07Inflammatory response
673.22E − 09Xenobiotic metabolic process
688.09E − 12Insulin receptor signaling pathway
698.52E − 04DNA-dependent transcription, initiation
701.56E − 05DNA replication
715.51E − 07Toll-like receptor signaling pathway
722.16E − 08Copper ion import
732.90E − 08Transmission of nerve impulse
744.63E − 07Regulation of blood coagulation
766.90E − 04Alanine catabolic process
776.76E − 05RNA biosynthetic process
787.60E − 04Lipid metabolic process
792.86E − 05TRIF-dependent toll-like receptor signaling pathway
814.18E − 05Regulation of type I interferon production
835.21E − 03Cellular lipid metabolic process
841.27E − 11Neural crest cell migration
852.10E − 03AMP catabolic process

Notes. value is the probability of obtaining the observed effect under the null hypothesis; a very small value indicates that the observed effect is very unlikely to have arisen purely by chance.

(1) Modules Related to Qi Deficiency. In TCM, Qi refers to the energy which flows within our body, to support a variety of biological functions such as movement, digesting food, and fight against diseases [43]. Qi deficiency is reflected in the lack of energy. Therefore, the regulation of energy metabolism would improve the Qi deficiency. As shown in Table 5, QSYQ participated in the amino acid metabolic process, carbohydrate metabolic process, lipid metabolic process, and cAMP metabolic process which are related to the energy metabolism and also have been demonstrated to play critical roles in cardiovascular diseases [6366]. Among them, amino acid metabolism, carbohydrate metabolism, and lipid metabolism are the main energy source of the body.

Amino acid metabolism (module 35) contained proteins such as GLUD2, GLUD1, and GLS. Glutamate dehydrogenase (GLUD) is an enzyme central to the glutamate and energy metabolism of the cell [67]. GLUD activity is raised in order to increase the amount of α-ketoglutarate produced, which can be used to provide energy by being used in the citric acid cycle to ultimately produce ATP. GLUD2 and GLUD1 are the GLUD’s isozymes that differ in amino acid sequence but catalyze the same chemical reaction. Glutaminase (GLS) is a multifunctional enzyme involved in energy metabolism [68]. And GLS is the GLS2’s isozyme, which regulates cellular energy metabolism by increasing production of glutamate and alpha-ketoglutarate and in turn results in enhanced mitochondrial respiration and ATP generation [69]. This shows that proteins in amino acid metabolism are all involved in the energy metabolism, and QSYQ can improve the Qi deficiency by regulating the amino acid metabolism.

Carbohydrate metabolism is the basis of the body to produce energy. Carbohydrate metabolism (module 32) contained proteins such as GALK1, SORD, and DCXR. Galactokinase 1 (GALK1) is an enzyme (phosphotransferase) that facilitates the phosphorylation of α-D-galactose to galactose 1-phosphate at the expense of one molecule of ATP. Sorbitol dehydrogenase (SORD) is an enzyme in carbohydrate metabolism converting sorbitol, the sugar alcohol form of glucose, into fructose [70]. Dicarbonyl/L-xylulose reductase (DCXR) is involved in carbohydrate metabolism and glucose metabolism which is a highly conserved and phylogenetically widespread enzyme converting L-xylulose into xylitol [71]. This shows that proteins in carbohydrate metabolism make contribution to provide energy for the body by participating in carbohydrate metabolism.

Lipid metabolism (module 83) contained proteins such as ACOT8, AACS. Acyl-coenzyme A thioesterase 8 (ACOT8) is a peroxisomal thioesterase involved more in the oxidation of fatty acids which are in order to generate acetyl-CoA, the entry molecule for the citric acid cycle, the main energy supply of animals [72]. Acetoacetyl-CoA synthetase (AACS) can directly activate ketone bodies for the synthesis of physiologically important lipidic substances such as cholesterol and fatty acid [73]. So, AACS can provide basic substances for energy metabolism. This shows that proteins in lipid metabolism are all related to energy metabolism, and QSYQ can improve the Qi deficiency by regulating the lipid metabolism.

cAMP metabolism (module 3) contained proteins such as GCG, ADCY7, and ADCYAP1. Glucagon (GCG) is a peptide hormone of cAMP metabolic process, which generally elevates the concentration of glucose in the blood by promoting gluconeogenesis and glycogenolysis [74]. Adenylate cyclase type 7 (ADCY7) is a membrane-bound adenylate cyclase that catalyses the formation of cyclic AMP from ATP [75]. ADCYAP1 is also known as pituitary adenylate cyclase-activating polypeptide (PACAP), which stimulates adenylate cyclase and subsequently increases the cAMP level and plays crucial roles in energy metabolism, including lipid metabolism [76].

This indicated that the QSYQ reinforced Qi efficacy by the regulation of the cAMP metabolism, amino acid metabolism, carbohydrate metabolism, and lipid metabolism. And Qi deficiency may be associate with the modules including amino acid metabolism, carbohydrate metabolism, lipid metabolism, and the cAMP metabolism.

(2) Modules Related to Blood Stasis. Blood stasis is caused by disturbance of blood circulation and is reflected in microcirculation relating to vessel and cell function, such as blood viscosity and blood cell adhesion [77]. As shown in Table 5, QSYQ took part in the regulation of blood coagulation and cardiac muscle contraction which can promote blood circulation.

The regulation of blood coagulation (module 17) contained proteins such as GGCX, F2, and SERPIND1. Gamma-glutamyl carboxylase (GGCX) catalyzes the posttranslational modification of vitamin K-dependent proteins which are involved in coagulation [78]. F2 is also known as thrombin (IIa) acts as a serine protease that converts soluble fibrinogen into insoluble strands of fibrin and activation of thrombin is crucial in physiological and pathological coagulation [79]. SERPIND1, known as heparin cofactor II, is a coagulation factor which rapidly inhibits thrombin in the presence of dermatan sulfate or heparin. SERPIND1 deficiency can lead to increased thrombin generation and a hypercoagulable state [80]. This shows that proteins in this module are all involved in the blood coagulation, and QSYQ can improve the blood stasis by the regulation of blood coagulation.

The cardiac muscle contraction (module 30) contained proteins such as MYL2, TNNC1, and TNNI3. MYL2 is also known as myosin regulatory light chain 2, ventricular/cardiac muscle isoform (MLC-2v) which plays a key role in the regulation of cardiac muscle contraction, through its interactions with myosin [81]. TNNC1 is also known as troponin C which is a protein that resides in the troponin complex on actin thin filaments of striated muscle (cardiac) and is responsible for binding calcium to activate muscle contraction [82]. Troponin I (TNNI3) has been shown to interact with TNNC1 [83] and has been reported to have a special role in the control of cardiac contractility [84]. This shows that proteins in this module are all participated in the cardiac muscle contraction. The mechanism of QSYQ has been reported to be related to improve myocardial function [7]. So, QSYQ can promote blood circulation and hence can improve the blood stasis by regulating the cardiac muscle contraction.

This indicated that QSYQ improved the blood stasis through the regulation of blood coagulation and cardiac muscle contraction. And blood stasis may be associated with the modules including the regulation of blood coagulation and cardiac muscle contraction.

3.4.3. The Synergetic Effects of QSYQ

Synergetic effects occur when the efficacy of herbs are combined. The scientific interpretation of these properties is a benefit to the explanation of the compatibility rule and it is further beneficial to the action mechanism of formulae. Synergy refers to the efficacy of combinations of herbs that is greater than the summed responses of each individual herb. As shown in Figure 3, Salvia miltiorrhiza, Panax notoginseng, Dalbergia odorifera, and Astragalus membranaceus all participate in the energy metabolism process, including cAMP metabolic process, carbohydrate metabolic process, and lipid metabolic process, and they hence have the synergetic effect on enhancing the Qi efficacy of QSYQ. The regulation of blood coagulation is involved by four herbs which reinforce the efficacy of promoting the blood circulation of QSYQ. This indicated that the synergy of formula can be illustrated based on the functional modules.

4. Conclusion

In this paper, the PIN of QSYQ exhibited the properties of scale-free, small world, and modularity based on the analysis of topological parameters. A module-based network analysis approach was proposed to expound the mechanism of QSYQ. Qi-tonifying efficacy of QSYQ may be partly attributed to the regulation of amino acid metabolic process, carbohydrate metabolic process, lipid metabolic process, and the cAMP metabolic process, while QSYQ improves the blood stasis through the regulation of blood coagulation and cardiac muscle contraction. A systematic exploration of mechanism of QSYQ based on module-network analysis may bring out the best between research on drug molecules and TCM phenotypic information, so as to facilitate the therapy for the disease.

Further experiments are needed to confirm the conclusions. However, despite the lack of validation of wet experiments, this study provides an efficient way to understand the mechanisms of QSYQ faster and better considering the complexity of TCM analogous formulae. What is more, the scientific intension of “synergy” of TCM can be also illustrated based on the functional modules at the molecular level.

Conflict of Interests

The authors declared that there is no conflict of interests.

Acknowledgments

This research is financially supported by the National Natural Science Foundation of China (nos. 81430094 and 81173522) and the National Key Technology R&D Program (2008BAI51B01) in Beijing University of Chinese Medicine.

Supplementary Materials

Table S1 is about the additional targets’ information of each active components from QSYQ. Tables S2–S5 are respective about the results of functional enrichment analysis of salvia miltiorrhiza, Panax notoginseng, Dalbergia odorifera,and Astragalus membranaceus. Figures S1–S4 are respective about the Modules in the PIN of salvia miltiorrhiza, panax notoginseng, dalbergia odorifera, astragalus membranaceus.

  1. Supplementary Material

References

  1. Y. B. Ge and J. Y. Mao, “Study on the distribution characteristics of the TCM syndromes of 7512 coronary artery disease patients,” Shandong Journal of Traditional Chinese Medicine, vol. 4, pp. 227–229, 2011. View at: Google Scholar
  2. Y. Z. Hou, S. Wang, Z. Q. Zhao et al., “Clinical assessment of complementary treatment with Qishen Yiqi dripping pills on ischemic heart failure: Study protocol for a randomized, double-blind, multicenter, placebo-controlled trial (CACT-IHF),” Trials, vol. 14, article 138, 2013. View at: Publisher Site | Google Scholar
  3. D. X. Xie, X. Z. Huang, and B. Y. Mao, “Mechanisms of Qishen Yiqi dropping pills on ventricular remodeling after myocardial infarction of rat model,” Chinese Journal of Experimental Traditional Medical Formulae, no. 6, pp. 180–183, 2011. View at: Google Scholar
  4. Y. Z. Song, L. P. Guo, H. Shang, and J. Wang, “Effects of Qishenyiqi dripping pills on lipid metabolism in experimental hypercholesterolemia rabbits,” Jilin Journal of Traditional Chinese Medicine, vol. 1, no. 31, pp. 71–73, 2011. View at: Google Scholar
  5. Y. Wang, J. Wang, L. Guo, and X. Gao, “Antiplatelet effects of Qishen Yiqi Dropping Pill in platelets aggregation in hyperlipidemic rabbits,” Evidence-Based Complementary and Alternative Medicine, vol. 2012, Article ID 205451, 5 pages, 2012. View at: Publisher Site | Google Scholar
  6. J.-Y. Tong, Y.-J. Xu, Y.-P. Bian, X.-B. Shen, L. Yan, and X.-Y. Zhu, “Effect and mechanism of Qishen Yiqi Pills on adriamycin-induced cardiomyopathy in mice,” Chinese Journal of Natural Medicines, vol. 11, no. 5, pp. 514–518, 2013. View at: Publisher Site | Google Scholar
  7. Z.-T. Cui, W.-L. Wei, M. Liu, and W.-J. Wang, “Effect of pretreatment with Qishen Yiqi dropping pills on right cardiac function of patients undergoing valve replacement,” Zhongguo Zhongyao Zazhi, vol. 39, no. 5, pp. 916–919, 2014. View at: Publisher Site | Google Scholar
  8. A.-L. Barabási, “Scale-free networks: a decade and beyond,” Science, vol. 325, no. 5939, pp. 412–413, 2009. View at: Publisher Site | Google Scholar
  9. S. C. Janga and A. Tzakos, “Structure and organization of drug-target networks: insights from genomic approaches for drug discovery,” Molecular BioSystems, vol. 5, no. 12, pp. 1536–1548, 2009. View at: Publisher Site | Google Scholar
  10. D. Pal, “On gene ontology and function annotation,” Bioinformation, vol. 1, no. 3, pp. 97–98, 2006. View at: Publisher Site | Google Scholar
  11. A. P. Bento, A. Gaulton, A. Hersey et al., “The ChEMBL bioactivity database: an update,” Nucleic Acids Research, vol. 42, no. 1, pp. D1083–D1090, 2014. View at: Publisher Site | Google Scholar
  12. M. Kuhn, D. Szklarczyk, S. Pletscher-Frankild et al., “STITCH 4: integration of protein-chemical interactions with user data,” Nucleic Acids Research, vol. 42, no. 1, pp. D401–D407, 2014. View at: Publisher Site | Google Scholar
  13. X. Wang, Y. Xiang, Z. Ren, Y. Zhang, and Y. Qiao, “Rational questing for inhibitors of endothelin converting enzyme-1 from Salvia miltiorrhiza by combining ligand-and structure-based virtual screening,” Canadian Journal of Chemistry, vol. 91, no. 6, pp. 448–456, 2013. View at: Publisher Site | Google Scholar
  14. X. Wang, Z. Ren, Y. He, Y. Xiang, Y. Zhang, and Y. Qiao, “A combination of pharmacophore modeling, molecular docking and virtual screening for iNOS inhibitors from Chinese herbs,” Bio-Medical Materials and Engineering, vol. 24, no. 1, pp. 1315–1322, 2014. View at: Publisher Site | Google Scholar
  15. A. Franceschini, D. Szklarczyk, S. Frankild et al., “STRING v9.1: protein-protein interaction networks, with increased coverage and integration,” Nucleic Acids Research, vol. 41, no. 1, pp. D808–D815, 2013. View at: Publisher Site | Google Scholar
  16. G. Su, J. H. Morris, B. Demchak, and G. D. Bader, “Biological network exploration with cytoscape 3,” Current Protocols in Bioinformatics, vol. 47, pp. 8.13.1–8.13.24, 2014. View at: Publisher Site | Google Scholar
  17. A.-L. Barabási and Z. N. Oltvai, “Network biology: understanding the cell's functional organization,” Nature Reviews Genetics, vol. 5, no. 2, pp. 101–113, 2004. View at: Publisher Site | Google Scholar
  18. E. Almaas, “Biological impacts and context of network theory,” The Journal of Experimental Biology, vol. 210, part 9, pp. 1548–1558, 2007. View at: Google Scholar
  19. X. Zhu, M. Gerstein, and M. Snyder, “Getting connected: analysis and principles of biological networks,” Genes and Development, vol. 21, no. 9, pp. 1010–1024, 2007. View at: Publisher Site | Google Scholar
  20. Y. Assenov, F. Ramírez, S.-E. S.-E. Schelhorn, T. Lengauer, and M. Albrecht, “Computing topological parameters of biological networks,” Bioinformatics, vol. 24, no. 2, pp. 282–284, 2008. View at: Publisher Site | Google Scholar
  21. A. J. Enright, S. Van Dongen, and C. A. Ouzounis, “An efficient algorithm for large-scale detection of protein families,” Nucleic Acids Research, vol. 30, no. 7, pp. 1575–1584, 2002. View at: Publisher Site | Google Scholar
  22. A. D. King, N. Pržulj, and I. Jurisica, “Protein complex prediction via cost-based clustering,” Bioinformatics, vol. 20, no. 17, pp. 3013–3020, 2004. View at: Publisher Site | Google Scholar
  23. G. D. Bader and C. W. V. Hogue, “An automated method for finding molecular complexes in large protein interaction networks,” BMC Bioinformatics, vol. 4, no. 1, article 2, 2003. View at: Publisher Site | Google Scholar
  24. M. Blatt, S. Wiseman, and E. Domany, “Superparamagnetic clustering of data,” Physical Review Letters, vol. 76, no. 18, pp. 3251–3254, 1996. View at: Publisher Site | Google Scholar
  25. S. Brohée and J. van Helden, “Evaluation of clustering algorithms for protein-protein interaction networks,” BMC Bioinformatics, vol. 7, article 488, 2006. View at: Publisher Site | Google Scholar
  26. S. Maere, K. Heymans, and M. Kuiper, “BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks,” Bioinformatics, vol. 21, no. 16, pp. 3448–3449, 2005. View at: Publisher Site | Google Scholar
  27. X. Chen, J. Guo, J. Bao, J. Lu, and Y. Wang, “The anticancer properties of Salvia miltiorrhiza Bunge (Danshen): a systematic review,” Medicinal Research Reviews, vol. 34, no. 4, pp. 768–794, 2014. View at: Publisher Site | Google Scholar
  28. C.-J. Lao, J.-G. Lin, J.-S. Kuo et al., “Effect of salvia miltiorrhiza bunge on cerebral infarct in ischemia-reperfusion injured rats,” The American Journal of Chinese Medicine, vol. 31, no. 2, pp. 191–200, 2003. View at: Publisher Site | Google Scholar
  29. Q. Shang, H. Wang, S. Li, and H. Xu, “The effect of sodium tanshinone IIA sulfate and simvastatin on elevated serum levels of inflammatory markers in patients with coronary heart disease: a study protocol for a randomized controlled trial,” Evidence-based Complementary and Alternative Medicine, vol. 2013, Article ID 756519, 8 pages, 2013. View at: Publisher Site | Google Scholar
  30. Z. Liu, S. Xu, X. Huang et al., “Cryptotanshinone, an orally bioactive herbal compound from Danshen, attenuates atherosclerosis in apolipoprotein E-deficient mice: role of LOX-1,” British Journal of Pharmacology, 2015. View at: Publisher Site | Google Scholar
  31. R.-W. Jiang, K.-M. Lau, P.-M. Hon, T. C. W. Mak, K.-S. Woo, and K.-P. Fung, “Chemistry and biological activities of caffeic acid derivatives from Salvia miltiorrhiza,” Current Medicinal Chemistry, vol. 12, no. 2, pp. 237–246, 2005. View at: Publisher Site | Google Scholar
  32. L. Y. Ma and P. G. Xiao, “Effects of Panax notoginseng saponins on platelet aggregation in rats with middle cerebral artery occlusion or in vitro and on lipid fluidity of platelet membrane,” Phytotherapy Research, vol. 12, no. 2, pp. 138–140, 1998. View at: Publisher Site | Google Scholar
  33. J. V. Formica and W. Regelson, “Review of the biology of Quercetin and related bioflavonoids,” Food and Chemical Toxicology, vol. 33, no. 12, pp. 1061–1080, 1995. View at: Publisher Site | Google Scholar
  34. X. J. Zhang, C. He, K. Tian, P. Li, H. Su, and J. Wan, “Ginsenoside Rb1 attenuates angiotensin II-induced abdominal aortic aneurysm through inactivation of the JNK and p38 signaling pathways,” Vascular Pharmacology, 2015. View at: Publisher Site | Google Scholar
  35. C. Jia, M. Xiong, P. Wang et al., “Notoginsenoside R1 attenuates atherosclerotic lesions in ApoE deficient mouse model,” PLoS ONE, vol. 9, no. 6, Article ID e99849, 2014. View at: Publisher Site | Google Scholar
  36. Z.-H. Yang, C. Mei, X.-H. He, and X.-B. Sun, “Advance in studies on chemical constitutions, pharmacological mechanism and pharmacokinetic profile of dalbergiae odoriferae lignum,” Zhongguo Zhong Yao Za Zhi, vol. 38, no. 11, pp. 1679–1683, 2013. View at: Publisher Site | Google Scholar
  37. L. B. Guo, L. R. Huang, L. H. Zhao, and C. P. Tang, “Pharmacological screening of effective parts on Xingqi Zhitong and Huoxue Zhixue in Dalbergia odorifera,” Journal of Chinese Medicinal Materials, vol. 30, no. 6, pp. 696–698, 2007. View at: Google Scholar
  38. Z.-J. Cheng, S.-C. Kuo, S.-C. Chan, F.-N. Ko, and C.-M. Teng, “Antioxidant properties of butein isolated from Dalbergia odorifera,” Biochimica et Biophysica Acta, vol. 1392, no. 2-3, pp. 291–299, 1998. View at: Publisher Site | Google Scholar
  39. C. Zhan and J. Yang, “Protective effects of isoliquiritigenin in transient middle cerebral artery occlusion-induced focal cerebral ischemia in rats,” Pharmacological Research, vol. 53, no. 3, pp. 303–309, 2006. View at: Publisher Site | Google Scholar
  40. J. Sung and J. Lee, “Anti-inflammatory activity of butein and luteolin through suppression of NFκB activation and induction of heme oxygenase-1,” Journal of Medicinal Food, vol. 18, no. 5, pp. 557–564, 2015. View at: Publisher Site | Google Scholar
  41. T.-S. Yeh, H.-L. Chuang, W.-C. Huang, Y.-M. Chen, C.-C. Huang, and M.-C. Hsu, “Astragalus membranaceus improves exercise performance and ameliorates exercise-induced fatigue in trained mice,” Molecules, vol. 19, no. 3, pp. 2793–2807, 2014. View at: Publisher Site | Google Scholar
  42. L.-Z. Lin, X.-G. He, M. Lindenmaier et al., “Liquid chromatography-electrospray ionization mass spectrometry study of the flavonoids of the roots of Astragalus mongholicus and A. membranaceus,” Journal of Chromatography A, vol. 876, no. 1-2, pp. 87–95, 2000. View at: Publisher Site | Google Scholar
  43. W. Zhou and Y. Wang, “A network-based analysis of the types of coronary artery disease from traditional Chinese medicine perspective: potential for therapeutics and drug discovery,” Journal of Ethnopharmacology, vol. 151, no. 1, pp. 66–77, 2014. View at: Publisher Site | Google Scholar
  44. S. Ren, H. Zhang, Y. Mu, M. Sun, and P. Liu, “Pharmacological effects of Astragaloside IV: a literature review,” Journal of Traditional Chinese Medicine, vol. 33, no. 3, pp. 413–416, 2013. View at: Publisher Site | Google Scholar
  45. M. Lu, F. Tang, J. Zhang et al., “Astragaloside IV attenuates injury caused by myocardial ischemia/reperfusion in rats via regulation of toll-like receptor 4/nuclear factor-kappaB signaling pathway,” Phytotherapy Research, vol. 29, no. 4, pp. 599–606, 2015. View at: Publisher Site | Google Scholar
  46. G. Ye, C.-S. Wang, Y.-Y. Li, H. Ren, and D.-A. Guo, “Simultaneous determination and pharmacokinetic studies on (3,4-dihydroxyphenyl)-lactic acid and protocatechuic aldehyde in rat serum after oral administration of Radix Salviae miltiorrhizae extract,” Journal of Chromatographic Science, vol. 41, no. 6, pp. 327–330, 2003. View at: Publisher Site | Google Scholar
  47. T. Lu, J. Yang, X. Gao et al., “Plasma and urinary tanshinol from Salvia miltiorrhiza (Danshen) can be used as pharmacokinetic markers for cardiotonic pills, a cardiovascular herbal medicine,” Drug Metabolism and Disposition, vol. 36, no. 8, pp. 1578–1586, 2008. View at: Publisher Site | Google Scholar
  48. R. Mahesh, H. W. Jung, G. W. Kim, Y. S. Kim, and Y.-K. Park, “Cryptotanshinone from Salviae miltiorrhizae radix inhibits sodium-nitroprusside-induced apoptosis in neuro-2 cells,” Phytotherapy Research, vol. 26, no. 8, pp. 1211–1219, 2012. View at: Publisher Site | Google Scholar
  49. S.-C. Chiu, S.-Y. Huang, S.-F. Chang et al., “Potential therapeutic roles of tanshinone IIA in human bladder cancer cells,” International Journal of Molecular Sciences, vol. 15, no. 9, pp. 15622–15637, 2014. View at: Publisher Site | Google Scholar
  50. T. Zhang, J. Xu, D. Li et al., “Salvianolic acid A, a matrix metalloproteinase-9 inhibitor of Salvia miltiorrhiza, attenuates aortic aneurysm formation in apolipoprotein E-deficient mice,” Phytomedicine, vol. 21, no. 10, pp. 1137–1145, 2014. View at: Publisher Site | Google Scholar
  51. J. Fu, H.-B. Fan, Z. Guo et al., “Salvianolic acid B attenuates spinal cord ischemia-reperfusion-induced neuronal injury and oxidative stress by activating the extracellular signal-regulated kinase pathway in rats,” Journal of Surgical Research, vol. 188, no. 1, pp. 222–230, 2014. View at: Publisher Site | Google Scholar
  52. Y. Wei, P. Li, H. Fan et al., “Metabolism study of notoginsenoside R1, ginsenoside Rg1 and ginsenoside Rb1 of radix panax notoginseng in zebrafish,” Molecules, vol. 16, no. 8, pp. 6621–6633, 2011. View at: Publisher Site | Google Scholar
  53. J. Huang, L. Ding, D. Shi et al., “Transient receptor potential vanilloid-1 participates in the inhibitory effect of ginsenoside Rg1 on capsaicin-induced interleukin-8 and prostaglandin E2 production in HaCaT cells,” Journal of Pharmacy and Pharmacology, vol. 64, no. 2, pp. 252–258, 2012. View at: Publisher Site | Google Scholar
  54. C.-F. Qiao, X.-M. Liu, X.-M. Cui et al., “High-performance anion-exchange chromatography coupled with diode array detection for the determination of dencichine in Panax notoginseng and related species,” Journal of Separation Science, vol. 36, no. 15, pp. 2401–2406, 2013. View at: Publisher Site | Google Scholar
  55. Z.-J. Cheng, S.-C. Kuo, S.-C. Chan, F.-N. Ko, and C.-M. Teng, “Antioxidant properties of butein isolated from Dalbergia odorifera,” Biochimica et Biophysica Acta, vol. 1392, no. 2-3, pp. 291–299, 1998. View at: Publisher Site | Google Scholar
  56. S. H. Lee, J. Y. Kim, G. S. Seo, Y.-C. Kim, and D. H. Sohn, “Isoliquiritigenin, from Dalbergia odorifera, up-regulates anti-inflammatory heme oxygenase-1 expression in RAW264.7 macrophages,” Inflammation Research, vol. 58, no. 5, pp. 257–262, 2009. View at: Publisher Site | Google Scholar
  57. L. Xu, H. Shi, T. Liang et al., “Selective separation of flavonoid glycosides in Dalbergia odorifera by matrix solid-phase dispersion using titania,” Journal of Separation Science, vol. 34, no. 11, pp. 1347–1354, 2011. View at: Publisher Site | Google Scholar
  58. M. P. N. Silva, G. L. S. Oliveira, R. B. F. De Carvalho et al., “Antischistosomal activity of the terpene nerolidol,” Molecules, vol. 19, no. 3, pp. 3793–3803, 2014. View at: Publisher Site | Google Scholar
  59. H.-J. Kwon, J. Hwang, S.-K. Lee, and Y.-D. Park, “Astragaloside content in the periderm, cortex, and xylem of Astragalus membranaceus root,” Journal of Natural Medicines, vol. 67, no. 4, pp. 850–855, 2013. View at: Publisher Site | Google Scholar
  60. W. Li, Y. N. Sun, X. T. Yan et al., “Flavonoids from Astragalus membranaceus and their inhibitory effects on LPS-stimulated pro-inflammatory cytokine production in bone marrow-derived dendritic cells,” Archives of Pharmacal Research, vol. 37, no. 2, pp. 186–192, 2014. View at: Publisher Site | Google Scholar
  61. A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” Science, vol. 286, no. 5439, pp. 509–512, 1999. View at: Publisher Site | Google Scholar | Zentralblatt MATH
  62. S. H. Strogatz, “Exploring complex networks,” Nature, vol. 410, no. 6825, pp. 268–276, 2001. View at: Publisher Site | Google Scholar
  63. Z. Wang and F. Li, “Effects of ilexonin A on cAMP metabolism in platelets,” Chinese Medical Sciences Journal, vol. 8, no. 4, pp. 215–217, 1993. View at: Google Scholar
  64. J. M. Gaziano, H. D. Sesso, J. L. Breslow, C. H. Hennekens, and J. E. Buring, “Relation between systemic hypertension and blood lipids on the risk of myocardial infarction,” American Journal of Cardiology, vol. 84, no. 7, pp. 768–773, 1999. View at: Publisher Site | Google Scholar
  65. J. Mann, “Dietary carbohydrate: relationship to cardiovascular disease and disorders of carbohydrate metabolism,” European Journal of Clinical Nutrition, vol. 61, supplement 1, pp. S100–S111, 2007. View at: Publisher Site | Google Scholar
  66. K. U. Yun, C. S. Ryu, J. M. Oh et al., “Plasma homocysteine level and hepatic sulfur amino acid metabolism in mice fed a high-fat diet,” European Journal of Nutrition, vol. 52, no. 1, pp. 127–134, 2013. View at: Publisher Site | Google Scholar
  67. L. Rosso, A. C. Marques, A. S. Reichert, and H. Kaessmann, “Mitochondrial targeting adaptation of the hominoid-specific glutamate dehydrogenase driven by positive darwinian selection,” PLoS Genetics, vol. 4, no. 8, Article ID e1000150, 2008. View at: Publisher Site | Google Scholar
  68. N. Bae, Y. Wang, L. Li, S. Rayport, and G. Lubec, “Network of brain protein level changes in glutaminase deficient fetal mice,” Journal of Proteomics, vol. 80, pp. 236–249, 2013. View at: Publisher Site | Google Scholar
  69. W. Hu, C. Zhang, R. Wu, Y. Sun, A. Levine, and Z. Feng, “Glutaminase 2, a novel p53 target gene regulating energy metabolism and antioxidant function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 107, no. 16, pp. 7455–7460, 2010. View at: Publisher Site | Google Scholar
  70. O. El-Kabbani, C. Darmanin, and R. P.-T. Chung, “Sorbitol dehydrogenase: structure, function and ligand design,” Current Medicinal Chemistry, vol. 11, no. 4, pp. 465–476, 2004. View at: Publisher Site | Google Scholar
  71. S.-K. Lee, L. T. Son, H.-J. Choi, and J. Ahnn, “Dicarbonyl/l-xylulose reductase (DCXR): the multifunctional pentosuria enzyme,” International Journal of Biochemistry and Cell Biology, vol. 45, no. 11, pp. 2563–2567, 2013. View at: Publisher Site | Google Scholar
  72. M. C. Hunt, M. I. Siponen, and S. E. H. Alexson, “The emerging role of acyl-CoA thioesterases and acyltransferases in regulating peroxisomal lipid metabolism,” Biochimica et Biophysica Acta, vol. 1822, no. 9, pp. 1397–1410, 2012. View at: Publisher Site | Google Scholar
  73. S. Hasegawa, Y. Ikeda, M. Yamasaki, and T. Fukui, “The role of acetoacetyl-CoA synthetase, a ketone body-utilizing enzyme, in 3T3-L1 adipocyte differentiation,” Biological and Pharmaceutical Bulletin, vol. 35, no. 11, pp. 1980–1985, 2012. View at: Publisher Site | Google Scholar
  74. B. Ahrén, “Glucagon—early breakthroughs and recent discoveries,” Peptides, vol. 67, pp. 74–81, 2015. View at: Publisher Site | Google Scholar
  75. K. Hellevuo, R. Berry, J. M. Sikela, and B. Tabakoff, “Localization of the gene for a novel human adenylyl cyclase (ADCY7) to chromosome 16,” Human Genetics, vol. 95, no. 2, pp. 197–200, 1995. View at: Publisher Site | Google Scholar
  76. S. Tomimoto, T. Ojika, N. Shintani et al., “Markedly reduced white adipose tissue and increased insulin sensitivity in Adcyap1-deficient mice,” Journal of Pharmacological Sciences, vol. 107, no. 1, pp. 41–48, 2008. View at: Publisher Site | Google Scholar
  77. J. Hou, J. Wang, C. Lin et al., “Circulating microRNA profiles differ between Qi-stagnation and Qi-deficiency in coronary heart disease patients with blood stasis syndrome,” Evidence-Based Complementary and Alternative Medicine, vol. 2014, Article ID 926962, 9 pages, 2014. View at: Publisher Site | Google Scholar
  78. C. R. King, E. Deych, P. Milligan et al., “Gamma-glutamyl carboxylase and its influence on warfarin dose,” Thrombosis and Haemostasis, vol. 104, no. 4, pp. 750–754, 2010. View at: Publisher Site | Google Scholar
  79. K. Guria and G. T. Guria, “Spatial aspects of blood coagulation: two decades of research on the self-sustained traveling wave of thrombin,” Thrombosis Research, vol. 135, no. 3, pp. 423–433, 2015. View at: Publisher Site | Google Scholar
  80. K. A. Parker and D. M. Tollefsen, “The protease specificity of heparin cofactor II. Inhibition of thrombin generated during coagulation,” The Journal of Biological Chemistry, vol. 260, no. 6, pp. 3501–3505, 1985. View at: Google Scholar
  81. F. Sheikh, R. C. Lyon, and J. Chen, “Getting the skinny on thick filament regulation in cardiac muscle biology and disease,” Trends in Cardiovascular Medicine, vol. 24, no. 4, pp. 133–141, 2014. View at: Publisher Site | Google Scholar
  82. P. J. Townsend, M. H. Yacoub, and P. J. R. Barton, “Assignment of the human cardiac/slow skeletal muscle troponin C gene (TNNC1) between D3S3118 and GCT4B10 on the short arm of chromosome 3 by somatic cell hybrid analysis,” Annals of Human Genetics, vol. 61, no. 4, pp. 375–377, 1997. View at: Publisher Site | Google Scholar
  83. D. G. Ward, S. M. Brewer, C. E. Gallon, Y. Gao, B. A. Levine, and I. P. Trayer, “NMR and mutagenesis studies on the phosphorylation region of human cardiac troponin I,” Biochemistry, vol. 43, no. 19, pp. 5772–5781, 2004. View at: Publisher Site | Google Scholar
  84. R. J. Solaro, P. Rosevear, and T. Kobayashi, “The unique functions of cardiac troponin I in the control of cardiac muscle contraction and relaxation,” Biochemical and Biophysical Research Communications, vol. 369, no. 1, pp. 82–87, 2008. View at: Publisher Site | Google Scholar

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