A Systems Biology Perspective on the Molecular Mechanisms Underlying the Therapeutic Effects of Buyang Huanwu Decoction on Ischemic Stroke

Abstract

Ischemic stroke is the leading cause of adult disability worldwide. The outcome is worse in older patients, especially in terms of disability. Buyang Huanwu decoction (BHD), a famous traditional Chinese medicine formula, has been used extensively in the treatment of ischemic stroke for centuries. However, its pharmacological mechanisms have not been fully elucidated. In this study, 82 putative targets for 411 composite compounds contained in BHD were predicted on the basis of our previously developed target prediction system. On the basis of large-scale molecular docking, more than 80% compound–putative target pairs had medium to strong binding efficiency. The pharmacological networks of BHD were built according to relationships among herbs, putative targets, and known therapeutic targets for ischemic stroke, and 121 major nodes were identified by calculating three topological features—degree, node betweenness, and closeness. Importantly, the pathway enrichment analysis identified several signaling pathways involved with major putative targets of BHD, such as the calcium signaling pathway, vascular smooth muscle contraction, and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, which have not hitherto been reported. These data are expected to help find new therapeutic effects of BHD and optimize clinical use of this formula. Collectively, our study developed a comprehensive systems approach integrating drug target prediction and network and functional analyses to reveal the relationships of the herbs in BHD with their putative targets, and for the first time with ischemic stroke-related pathway systems. This is a pilot study based on bioinformatics analysis; thus, further experimental studies are required to validate our findings.

Introduction

Ischemic stroke is the leading cause of adult disability worldwide. The outcome has been reported as worse in older patients, especially in terms of disability.¹ Hypertension, diabetes, hypercholesterolemia, and smoking are major risk factors for this disease, which starts with sudden cessation of blood flow, oxygen, glucose, and energy in the lesion area, followed by series pathologic biochemical events called “ischemic cascades,” which include glutamate-induced excitotoxicity, calcium influx, inflammation response, blood–brain barrier breakdown, edema, and cell death.² Because it is a debilitating disease leading to physical disability, cognitive dysfunction, vision and hearing loss, and language barrier, ischemic stroke is often a difficult burden for both family and society.³ Current therapeutic strategies for this disease are aimed at dissolving clots and restoring blood flow. Moreover, therapies such as blocking excitatory neurotransmission, preventing the ischemic inflammatory response, or scavenging free radicals have all shown promising therapeutic potential in animal stroke models.⁴ However, there are no approved treatments that can effectively reduce stroke size or neurological disability in humans.

Traditional Chinese medicine (TCM), as an important complementary and alternative medicine modality, has been practiced in China for more than 2000 years. Its application to stroke therapy has a long history. The famous Chinese physician Zhang Zhongjing described the symptoms of acute stroke about 2000 years ago. In 1995, the State Administration of TCM of the People's Republic of China issued standards for the diagnosis of stroke and the evaluation of the efficacy of treatments.^5,6 Herbal medicine therapy has been an alternative and promising strategy for the treatment of ischemic stroke. Because herbal formulae are the main clinical patterns in TCM, various herbal formulae, such as Buyang Huanwu Decoction (BHD), Zhengan Xifeng Decoction, and Dahuang Xiexin Decoction, have been employed extensively to relieve the severity of ischemic stroke.

Among these formulae, BHD, originally recorded in the Yilin Gaicuo (Correction on Errors in Medical Classics) written by Wang Qingren in 1830 during late Qing Dynasty, is a famous TCM formula for benefiting qi and activating blood circulation.⁷ It has been used clinically in Asia to treat stroke-induced disability for centuries. Accumulating evidence demonstrates that BHD can improve the outcome of ischemic stroke in clinical trials.⁸ This formula is comprised of Radix Astragali (Huang Qi), Radix Angelicae Sinensis (Dang Gui), Radix Paeoniae Rubra (Chi Shao), Rhizoma Chuanxiong (Chuan Xiong), Flos Carthami (Hong Hua), Semen Persicae (Tao Ren), and Lumbricus (Di Long), in the ratio of 120:6:4.5:3:3:3: 3 on a dry weight basis, all of which are recorded in the Chinese Pharmacopoeia.⁹

Growing evidence has suggested the clinical efficacy of BHD regarding neuroprotective effects.¹⁰ Recent studies on pharmacological and biochemical actions of BHD and its constituents have also reported mechanisms of BHD acting on ischemic stroke. For example, Shen et al.¹¹ indicated that the neuro-restorative effects of BHD might be associated with angiogenesis and the enhancement of the expression of angiopoietin-1 on chronic brain injury after focal cerebral ischemia. Kong et al.¹² reported that BHD might exert its neuro-protective effects partially by promoting migration of neural precursor cells to ischemic brain areas. Wang et al.¹³ also found that BHD could prevent the ischemia-reperfusion–induced spinal injury in rats, and its protective function might be partially linked with the inhibition of cyclin-dependent kinase 5. However, the pharmacological mechanisms of BHD acting on ischemic stroke have not been fully elucidated due to the lack of appropriate methods.

Because herbal formulae with numerous compositive compounds are too complex to be detected solely by conventional methods, there is an urgent need to develop new and appropriate approaches to address this problem. With the development of systems biology, network biology, and polypharmacology, network pharmacology has been proposed by Andrew L. Hopkins.¹⁴ This is a novel research field that is implicated in the application of “-omics” and systems biology-based technologies.¹⁵ It clarifies the synergistic effects and the underlying mechanisms of multi-component and multi-target agents by analyzing various networks of complex and multi-level interactions.^16

–19 As a major tool in network pharmacology, network analysis based on widely existing databases allows us to form an initial understanding of the mechanisms of action within the context of systems-level interactions. The herbal formula has been considered as a multi-component and multi-target therapeutic that potentially meets the demands of treating a number of complex diseases in an integrated manner; therefore, the methodologies of network pharmacology are suitable for pursuing a priori knowledge about the combination rules embedded in formulae.²⁰

Here we have developed a comprehensive systems approach to investigate the pharmacological mechanisms of BHD acting on ischemic stroke. The steps of our protocol include: (1) Prediction of putative targets for BHD; (2) calculation of the binding efficiency of compound–putative target pairs; and (3) investigation of the relationships of putative targets of BHD with the ischemic stroke imbalanced network and relative signal pathways, which offers a great opportunity for the deep understanding of the pharmacological mechanisms of BHD on reversing this disease-related imbalanced network. The technical strategy of this study is shown in Fig. 1.

FIG. 1.

A schematic diagram of the systems biology–based strategies for deciphering the pharmacological mechanisms of herbal formula Buyang Huanwu Decoction (BHD) acting on ischemic stroke.

Materials and Methods

Data preparation

Compositive compounds of each herb in BHD

Compositive compounds of each herb in BHD were obtained from TCM Database@Taiwan²¹ (http://tcm.cmu.edu.tw/, updated June 28, 2012), which is currently the largest non-commercial TCM database worldwide. In total, we collected the structural information of 22 compounds for Radix Astragali (Huang Qi), 28 compounds for Radix Paeoniae Rubra (Chi Shao), 243 compounds for Rhizoma Chuanxiong (Chuan Xiong), 56 compounds for Radix Angelicae Sinensis (Dang Gui), five compounds for Lumbricus (Di Long), 52 compounds for Flos Carthami (Hong Hua), and 35 compounds for Semen Persicae (Tao Ren). The detailed information on these compositive compounds of each herb in BHD is described in Table S1 (Supplementary Data are available at www.liebertonline.com/rej/).

Known therapeutic targets for the treatment of ischemic stroke

Known therapeutic targets for the treatment of ischemic stroke were collected from the DrugBank database²² (www.drugbank.ca/, v. 3.0) and the Online Mendelian Inheritance in Man (OMIM) database²³ (www.omim.org/, last updated October 31, 2013). For the DrugBank database, we only used those drug–target interactions whose drugs are Food and Drug Administration (FDA)-approved therapeutic targets for the treatment of ischemic stroke and whose targets are human genes/proteins. For the OMIM database, the keyword “ischemic stroke” was used. After deleting redundancy, a total of 70 known therapeutic targets for the treatment of ischemic stroke were collected. The detailed information on these known therapeutic targets is described in Supplementary Table S2.

Protein–protein interaction data

Protein–protein interaction (PPI) data were imported from eight existing PPI databases including Human Annotated and Predicted Protein Interaction Database (HAPPI),²⁴ Reactome,²⁵ Online Predicted Human Interaction Database (OPHID),²⁶ InAct,²⁷ Human Protein Reference Database (HPRD),²⁸ Molecular Interaction Database (MINT),²⁹ Database of Interacting Proteins (DIP),³⁰ and PDZBase.³¹ The detailed information on these PPI databases is described in Supplementary Table S3.

Drug target prediction for BHD

Drug Similarity Search tool in the Therapeutic Targets Database (TTD)³² (http://xin.cz3.nus.edu.sg/group/cjttd/ttd.asp/, v. 4.3.02, released on August 25, 2011) was used to screen drugs similar to chemical compounds of each herb contained in BHD via structural similarity comparisons. We only selected drugs with a high structural similarity score (>0.85, representing similar to very similar). The therapeutic targets of these similar drugs were also included as putative BHD targets.

Molecular docking simulation

To calculate the binding efficiency of putative targets with the corresponding compositive compounds contained in BHD, the molecular docking simulation was performed using the electronic high-throughput screening (eHiTS) docking module, which is a flexible ligand docking system.³³ All the protein structures of putative targets of BHD were obtained from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank³⁴ (www.pdb.org, updated November 11, 2014) and have carefully checked for their resolutions. The three-dimensional structures (*.mol files) of the compositive compounds contained in BHD were obtained from TCM Database@Taiwan¹⁷ (http://tcm.cmu.edu.tw/, updated in June 28, 2012). Docking scores represent the binding energy of the ligand to the receptor and are in units of logKd. The higher the absolute value of docking score is, the stronger the binding efficiency of the ligand to the receptor is. According to previous studies^35,36 and the eHiTS algorithm, the binding efficiencies of compound-target pairs were divided into three levels—strong, the absolute value of docking score is higher than 6.0; medium, the absolute value of docking score is ranging from 3.0 to 6.0; weak, the absolute value of docking score is lower than 3.0.

Network construction

Seven herbs contained in BHD, putative BHD targets, and known therapeutic targets for ischemic stroke treatment were used to construct a BHD herbs–putative targets–known ischemic stroke targets network and a BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network, respectively. PPI data were obtained from eight existing PPI databases, as described in Supplementary Table 3. Navigator software (v. 2.2.1) and Cytoscape (v. 2.8.1) were used to visualize the networks.

BHD herbs–putative targets–known ischemic stroke targets network

The BHD herbs–putative targets–known ischemic stroke targets network was constructed by linking the seven herbs contained in BHD, their putative targets, and known therapeutic targets for ischemic stroke treatment that were interacted with putative targets.

BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network

The BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network was constructed by linking BHD putative targets, known ischemic stroke therapeutic targets and other human proteins that have direct interactions with BHD putative targets and known ischemic stroke therapeutic targets.

Defining the network topological feature set

For each node i in the interaction network, we defined three measures for assessing its topological property: (1) “Degree” is defined as the number of links to node i and reflects how often node i interacts with other nodes. Nodes with an extremely high level of degree tend to be essential in interaction network.³⁷ (2) “Node betweenness” is defined as the number of the shortest paths between pairs of nodes that run through node i. Betweenness centrality reflects the ability of nodes to control the rate of information flow in interaction network. Nodes with high betweenness centrality are recognized as bottlenecks.³⁸ (3) “Closeness” is defined as the inverse of the farness, which is the sum of node i distances to all other nodes. Closeness centrality refers to a measure of how long it will take to spread information from node i to all other nodes sequentially. Degree, node betweenness, and closeness centrality are the most often cited measures of nodes' topological importance in the network. The larger a node's degree/node betweenness/closeness centrality is, the more important the node is in the interaction network.³⁹ According to our previous studies,^40
–42 “degree,” “node betweenness,” and “closeness” were calculated to screen major putative targets, the three features of which were higher than the corresponding median values.

Pathway enrichment analysis

We used Database for Annotation, Visualization and Integrated Discovery⁴³ (DAVID, http://david.abcc.ncifcrf.gov/home.jsp/, v. 6.7) for gene ontology (GO) enrichment analysis. We also performed pathway enrichment analysis using pathway data obtained from the FTP service of Kyoto Encyclopedia of Genes and Genomes,⁴⁴ (KEGG; www.genome.jp/kegg/, last updated October 16, 2012).

Results and Discussion

Putative targets for BHD

On the basis of our previously developed target prediction system,^40,45 252 similar drugs of chemical components in seven herbs contained in BHD and the corresponding 82 known targets of these similar drugs as putative targets for BHD, including 18 for Radix Astragali, 20 for Radix Paeoniae Rubra, 52 for Rhizoma Chuanxiong, 28 for Radix Angelicae Sinensis, 15 for Lumbricus, 27 for Flos Carthami, and 12 for Semen Persicae, were identified. See detailed information on putative targets for BHD in Table S4.

As shown in Table 1, there were different numbers of common putative targets among the seven herbs contained in BHD, implying their synergistic effects. Of note, Rhizoma Chuanxiong and Radix Angelicae Sinensis shared more common putative targets with Radix Paeoniae Rubra (n=15 and 12, respectively; Table 1), Lumbricus (both n=12; Table 1), and Flos Carthami (n=18 and 16, respectively; Table 1), and the two herbs shared the most common potential targets with each other (n=20; Table 1), suggesting their roles in facilitating the effects of other herbs in BHD.

Table 1.

Putative Target Overlaps among Seven Herbs of Buyang Huanwu Decoction

	Radix Astragali (18)	Radix Paeoniae Rubra (20)	Rhizoma Chuanxiong (52)	Radix Angelicae Sinensis (28)	Lumbricus (15)	Flos Carthami (27)	Semen Persicae (12)
Radix Astragali (18)	—	4	5	7	4	4	0
Radix Paeoniae Rubra (20)	4	—	15	12	12	12	5
Rhizoma Chuanxiong (52)	5	15	—	20	12	18	1
Radix Angelicae Sinensis (28)	7	12	20	—	12	16	2
Lumbricus (15)	4	12	12	12	—	12	0
Flos Carthami (27)	4	12	18	16	12	—	5
SemenPersicae (12)	0	5	1	2	0	5	—

As a structure-based method, molecular docking simulation has been used extensively as an invaluable tool in drug discovery and design, and it may play a crucial role in identification of ligand–protein interactions and elucidation of binding mechanisms. In the current study, we used the molecular docking software eHiTS to evaluate the binding efficiency of 394 compound–target pairs. This software systematically covers the part of the conformational and positional search space that avoids severe steric clashes, producing highly accurate docking poses at a speed practical for virtual high-throughput screening.³³ As a result, four compound–target pairs were deleted either because their structural information was unavailable or negative results were output from eHiTS. The positive docking results for other interactions are summarized in Table S5. According to the evaluation method mentioned above, of 390 compound–target pairs, 46 (11.80%) had strong binding free energy, 268 (68.72%) had medium binding free energy, and 76 (19.49%) had weak binding free energy, suggesting the high potentials of putative targets of BHD to bind with the corresponding compounds.

Generally, drug indication for use is determined by functions of its affected targets. In the current study, we collected the known anti-ischemic stroke drug targets from the DrugBank database²² and OMIM database.²³ Among 82 putative targets for BHD, 15 (15/82, 18.29%) were known therapeutic targets for the treatment of ischemic stroke. As shown in Table 2, eight putative targets of Rhizoma Chuanxiong and Radix Angelicae Sinensis, respectively, were known therapeutic targets, suggesting the crucial roles of the two herbs for the treatment of ischemic stroke. To investigate the functions of the 15 putative BHD targets/known therapeutic targets, we performed the enrichment analysis based on GO annotation. Interestingly, we found that there were seven putative BHD targets/known therapeutic targets, including ADRB1, ADRB2, ADRA1B, PTGS2, PTGS1, ATP1A1, and NOS2, which were all involved in the regulation of blood pressure and blood circulation (fold enrichment=63.13, p=2.50E−07; Table S6). Hypertension is the most important and common risk factor for stroke.^46,47 Reduction of blood pressure by lifestyle measures and anti-hypertensive drug therapy may reduce the incidence of stroke. Our data implied a possible therapeutic effect of BHD on hypertension. However, the results of our literature retrieval showed that most of the publications in this research field focused on the neuro-protective effect of BHD. Thus, our data may provide a new research direction for investigating the therapeutic effects and pharmacological mechanisms of BHD acting on ischemic stroke.

Table 2.

Overlaps Between Putative Targets for Buyang Huanwu Decoction (BHD) and Known Therapeutic Targets for the Treatment of Ischemic Stroke

Herbs	Target symbol	Target name	General function	Specific function
Rhizoma Chuanxiong	ADRA1B	Adrenergic, alpha1b-, receptor	Involved in alpha1-adrenergic receptor activity	This alpha-adrenergic receptor mediates its action by association with G proteins that activate a phosphatidylinositol–calcium second messenger system
Lumbricus	ADRA2A	Adrenergic, alpha2a-, receptor	Involved in alpha2-adrenergic receptor activity	Alpha2 adrenergic receptors mediate the catecholamine- induced inhibition of adenylate cyclase through the action of G proteins.
Radix Paeoniae Rubra	ADRB1	Adrenergic, beta1-, receptor	Involved in beta1-adrenergic receptor activity	Beta-adrenergic receptors mediate the catecholamine-induced activation of adenylate cyclase through the action of G proteins. This receptor binds epinephrine and norepinephrine with approximately equal affinity
Rhizoma Chuanxiong
Radix Angelicae Sinensis
Lumbricus
Flos Carthami
Radix Paeoniae Rubra	ADRB2	Adrenergic, beta2-, receptor, surface	Inorganic ion transport and metabolism	This is the catalytic component of the active enzyme, which catalyzes the hydrolysis of ATP coupled with the exchange of sodium and potassium ions across the plasma membrane. This action creates the electrochemical gradient of sodium and potassium ions, providing the energy for active transport of various nutrients
Radix Paeoniae Rubra	ATP1A1	Sodium/potassium-transporting adenosine 5′-triphosphatase (ATPase) subunit alpha1	Inorganic ion transport and metabolism	This is the catalytic component of the active enzyme, which catalyzes the hydrolysis of adenosine triphosphate (ATP) coupled with the exchange of sodium and potassium ions across the plasma membrane. This action creates the electrochemical gradient of sodium and potassium ions, providing the energy for active transport of various nutrients
Rhizoma Chuanxiong
Radix Angelicae Sinensis
Lumbricus
Flos Carthami
Radix Astragali
Radix Angelicae Sinensis	F10	Coagulation factor X	Involved in calcium ion binding	Factor Xa is a vitamin K–dependent glycoprotein that converts prothrombin to thrombin in the presence of factor Va, calcium, and phospholipid during blood clotting
Flos Carthami
Semen Persicae
Lumbricus	F2	Coagulation factor ii (thrombin)	Involved in blood clotting cascade	Thrombin, which cleaves bonds after Arg and Lys, converts fibrinogen to fibrin and activates factors V, VII, VIII, XIII, and, in complex with thrombomodulin, protein C
Rhizoma Chuanxiong	HTR2C	5-Hydroxytryptamine (serotonin) receptor 2C	Involved in rhodopsin-like receptor activity	This is one of the several different receptors for 5- hydroxytryptamine (serotonin), a biogenic hormone that functions as a neurotransmitter, a hormone, and a mitogen. This receptor mediates its action by association with G proteins that activate a phosphatidylinositol-calcium second messenger system
Radix Angelicae Sinensis
Flos Carthami
Semen Persicae	NOS2	Nitric oxide synthase 2, inducible	Inorganic ion transport and metabolism	Produces nitric oxide (NO), which is a messenger molecule with diverse functions throughout the body. In macrophages, NO mediates tumoricidal and bactericidal actions
Radix Angelicae Sinensis	PTGIR	Prostacyclin receptor	Involved in rhodopsin-like receptor activity	Receptor for prostacyclin (prostaglandin I2 or PGI2). The activity of this receptor is mediated by G(s) proteins, which activate adenylate cyclase.
Rhizoma Chuanxiong	PTGS1	Prostaglandin G/H synthase 1	Involved in peroxidase activity	May play an important role in regulating or promoting cell proliferation in some normal and neoplastically transformed cells
Radix Angelicae Sinensis
Flos Carthami
Rhizoma Chuanxiong	PTGS2	Prostaglandin G/H synthase 2	Involved in peroxidase activity	May have a role as a major mediator of inflammation and/or a role for prostanoid signaling in activity-dependent plasticity
Radix Angelicae Sinensis	SERPINC1	Antithrombin (AT) III	Involved in serine-type endopeptidase inhibitor activity	Most important serine protease inhibitor in plasma that regulates the blood coagulation cascade. ATIII inhibits thrombin as well as factors IXa, Xa, and XIa. Its inhibitory activity is greatly enhanced in the presence of heparin
Flos Carthami
Semen Persicae
Radix Paeoniae Rubra	SLC12A1	Solute carrier family 12 member 1	Involved in sodium/potassium transporter activity	Electrically silent transporter system. Mediates sodium and chloride reabsorption. Plays a vital role in the regulation of ionic balance and cell volume
Rhizoma Chuanxiong
Radix Angelicae Sinensis
Lumbricus
Flos Carthami
Radix Astragali
Rhizoma Chuanxiong	VKORC1	Vitamin K epoxide reductase complex, subunit 1	Involved in vitamin K epoxide reductase (warfarin-sensitive) activity	Involved in vitamin K metabolism. Catalytic subunit of the vitamin K epoxide reductase (VKOR) complex, which reduces inactive vitamin K 2,3-epoxide to active vitamin K.

Network analysis

BHD herbs–putative targets–known ischemic stroke targets network

First, we constructed the BHD herbs–putative targets–known ischemic stroke targets network to clarify the relationships of herbs with the corresponding putative targets and known ischemic stroke targets. As shown in Fig. 2A, the network consists of 111 nodes (seven herbs, 67 putative targets, 22 known ischemic stroke targets, and 15 putative targets/known ischemic stroke targets) and 348 edges. Among seven herbs, Rhizoma Chuanxiong had the highest degree distributions following by Radix Angelicae Sinensis, Flos Carthami, and Radix Paeoniae Rubra, the links of which with other nodes were all more than 20. Of note, these four herbs had more interactions with common nodes, leading to their closer distance in the network.

FIG. 2.

(A) Buyang Huanwu Decoction (BHD) herbs–putative targets–known ischemic stroke targets network built and visualized with Navigator. (B) According to the associated biological processes or pathways, the putative targets of BHD and known ischemic stroke targets were all implicated into various molecular mechanisms associated with ischemic stroke. (Green nodes) Seven herbs contained in BHD; (blue nodes) putative targets of BHD; (yellow nodes) known ischemic stroke targets; (blue nodes with red circles) putative targets that also are known ischemic stroke targets.

Because the herbs with a higher degree in the network have been demonstrated to be more pharmacologically important, our data mentioned above showed that Rhizoma Chuanxiong, Radix Angelicae Sinensis, Flos Carthami, and Radix Paeoniae Rubra might be major herbs in this pharmacological network. In addition, the enrichment analysis, as shown in Fig. 2B, indicated that the putative targets for BHD and known ischemic stroke targets were most significantly associated with vascular smooth muscle contraction (KEGG ID: hsa04270, fold enrichment=6.79, p<0.001) and complement and coagulation cascades (KEGG ID: hsa04610, fold enrichment=5.26, p=0.002). The degree of contraction or relaxation of the vascular smooth muscle cells characterizes the general vasomotor tone, which governs the local blood pressure level and distributes the flow according to metabolic needs.⁴⁷ Patients with ischemic stroke often have impaired vasomotor reactivity, especially in the affected cerebral hemisphere, so that they may depend directly on systemic blood pressure to maintain perfusion to vulnerable “at risk” penumbral tissue. Thus, blood pressure variation has been identified as one of the factors that affects the risk of ischemic stroke recurrence and prognosis.⁴⁸ Blood coagulation cascades are very important for normal hemostasis and have been reported to play crucial roles in thrombotic diseases, including ischemic stroke.⁴⁹ Intravenous thrombolysis remains the mainstay treatment for ischemic stroke, but hemorrhage is one of serious complications of this treatment.⁵⁰ BHD has been used for neuro-restorative effects and primary prevention of ischemic stroke. No previous studies have assessed the influence of BHD on the regulation of vascular smooth muscle contraction and coagulation cascades, and this might be worth validating in the future.

BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network

To evaluate the importance of putative targets of BHD, we constructed a BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network, which consists 2272 nodes (67 putative targets, 55 known ischemic stroke targets, 15 putative targets/known ischemic stroke targets, and 2105 other human proteins interacted with putative targets or known ischemic stroke targets) and 4462 edges (Fig. 3A). To screen the major nodes with topological importance, three topological features, “degree,” “node betweenness,” and “closeness” (defined in the Materials and Methods section), were calculated for each node in the network. The median values of degree, node betweenness, and closeness were 4.00, 0.002, and 0.26, respectively. Therefore, we determined that hubs with degree>4.00, node betweenness>0.002, and closeness>0.26 were major nodes. As a result, 121 major nodes were identified (see detailed information on topological features of 121 major nodes in Table S7). After selecting the intersection with putative targets of BHD, 34 major nodes were identified as candidate targets for this formula.

FIG. 3.

(A) Buyang Huanwu Decoction (BHD) putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network built and visualized with Navigator. (B) Network of direct interactions among 121 major nodes in BHD putative targets–known ischemic stroke therapeutic targets–other human proteins PPI network built and visualized with Navigator. According to the associated pathways, the major nodes were divided into different functional modules, which are all implicated in various pathological processes during ischemic stroke. (Green nodes) Seven herbs contained in BHD; (blue nodes) putative targets of BHD; (yellow nodes) known ischemic stroke targets; (blue nodes with red circles) putative targets that also are known ischemic stroke targets; (pink nodes) other human proteins interacted with putative targets of BHD or known ischemic stroke targets; (pink nodes with red circles) major nodes among other human proteins interacted with putative targets of BHD or known ischemic stroke targets.

Moreover, the network of direct interactions among 121 major nodes was constructed and consisted of 76 nodes (seven herbs contained in BHD, 31 putative targets of BHD, six putative targets/known ischemic stroke targets, and 40 other human proteins interacted with putative targets or known ischemic stroke targets) and 156 edges. According to the associated pathways, the major nodes were divided into different functional modules, as shown in Fig. 3B. Importantly, the calcium signaling pathway (KEGG ID: hsa04020, fold enrichment=6.16, p<0.001), neuroactive ligand-receptor interaction (KEGG ID: hsa04080, fold enrichment=5.01, p=0.001), vascular smooth muscle contraction (KEGG ID: hsa04270, fold enrichment=5.95, p=0.01), and vascular endothelial growth factor (VEGF) signaling pathway (KEGG ID: hsa04370, fold enrichment=5.62, p=0.03) were most frequently involved by the major nodes. The calcium signaling pathway plays an important role in a variety of physiological functions; for example, in vascular smooth muscle cells, it is involved in the regulation of agonist-stimulated contraction and myogenic tone.⁵¹ Changes in vascular smooth muscle calcium handling have been implicated in different vascular diseases.⁵² Growing evidence suggests that modification of the calcium signaling pathway may be a novel therapeutic strategy for ischemic stroke. Here, our data found that the major nodes in the pharmacological network of BHD might play a role in the regulation of calcium signaling pathway and vascular smooth muscle contraction, implying that BHD could attenuate ischemic stroke by reversing hypertension-induced cerebro-vascular remodeling, which has not hitherto been reported and should be validated experimentally in the future.

Angiogenesis, the formation of new blood vessels from pre-existing ones, was observed in stroke patients; it correlates with longer survival and positively affects the formation of new neurons, neurogenesis, and functional recovery.⁵³ It is a multistep process, involving extracellular matrix degradation, endothelial cell proliferation, and new vessel formation. Interactions between VEGF and its receptors play a central role in these angiogenic signaling cascades.⁵⁴ Thus, VEGF has been identified as a promising candidate for the treatment of ischemic stroke,⁵⁵ but further insight into the regulatory role of BHD in the VEGF signaling pathway is still needed to fully apprehend its therapeutic potential and its relevance for angiogenesis.

Of note, the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway (KEGG ID: hsa04621, fold enrichment=4.32, p=0.02) was also a pathway significantly associated with the major nodes in the pharmacological network of BHD (Fig. 3B). NOD-like receptors are a class of pattern recognition receptors that are cytosolic and constitute part of different inflammasomes.⁵⁶ These receptors can be activated not only by different pathogens, but also by sterile inflammation or by specific metabolic conditions. Mutations in NOD-like receptors lead to hereditary auto-inflammatory systemic diseases.⁵⁷ In recent years, accumulating studies have reported the important roles of NOD-like receptors in different central nervous diseases, such as stroke, because their abnormal changes can cause redox imbalance in the brain, which significantly contributes to ischemic stroke pathogenesis.⁵⁸ Thus, further studies should be focused on the roles of BHD in the modulation of NOD-like receptors during the treatment of ischemic stroke, which is still unknown according to our literature retrieval.

The major nodes were also frequently involved in the neurotrophin signaling pathway (KEGG ID: hsa04722, fold enrichment=7.39, p<0.001). Neurotrophins have been recognized as mediators of both neurodegenerative and neuroprotective mechanisms in a number of CNS pathologies.⁵⁹ The neurotrophins have been reported to exert angiogenic effects and have been proposed as therapeutic agents for the treatment of neurodegenerative disorders and ischemic injury, especially for stroke recovery.^60
–62 However, the recent clinical trials with several neurotrophins for the treatment of ischemic stroke or neurodegenerative diseases have failed so far, primarily because of their poor blood–brain barrier permeability. To address this problem, a therapeutic strategy that can enhance neurotrophin activity may possibly improve stroke outcome. In the present study, our data showed that one major putative target of BHD interacted directly with genes involved in the neurotrophin signaling pathway; thus, we hypothesize that BHD might provide a neuroprotective effect in the context of ischemic stroke, possibly through increasing neurotrophin activity. This still needs further investigation.

Conclusion and Perspective

Ischemic stroke carries a poor long-term prognosis for death and disability.¹ Due to the limited therapeutic approaches currently available, there is an urgent need for developing novel therapies for this disease. There have been many well-known traditional Chinese herbal prescriptions for the treatment of patients with ischemic stroke. At the molecular level, TCM formulae are multi-component and multi-target agents, essentially acting in the same way as the combination therapy of multi-component drugs, which can produce greater levels of efficacy with fewer adverse effects and toxicities than mono-therapies.²⁰ The fast development of systems biology affords new possibilities for uncovering the molecular mechanisms related to the therapeutic efficacy of TCM formulae from a systematic point of view.

In the current study, we integrated several algorithm-based and network-based computational methods to predict drug targets, construct the target network, and elucidate the molecular synergy of a TCM formula, BHD, the mechanisms of which for ischemic stroke treatment are still unclear. Our main findings are as following: First, 82 putative targets of seven herbs in BHD were predicted, providing clues to investigate the pharmacological mechanisms of this formula for the treatment of ischemic stroke. Interestingly, more than 80% compound-putative target pairs had medium-to-strong binding efficiency.

The pharmacological network of BHD was built according to the relationships among herbs, putative targets, and known therapeutic targets for ischemic stroke, providing insights into the synergetic effects of herbs in this formula. After that, a multilevel network with a combination of ischemic stroke-related imbalanced network and pharmacological network of BHD has been built and can pinpoint the major putative targets of this formula acting on ischemic stroke and the corresponding molecular pathways.

Last, but not least, several novel signaling pathways involved with major putative targets of BHD acting on ischemic stroke, such as the calcium signaling pathway, vascular smooth muscle contraction, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, have been identified. These data are expected to help find new therapeutic effects of BHD and optimize clinical usage of this formula. This pilot study was performed according to our strategy based on bioinformatics analysis; thus, further experimental studies are required to validate our findings.

Footnotes

Acknowledgments

This study was supported by Beijing Natural Science Foundation (7144228) and Beijing Joint Project Specific Funds and the Fundamental Research Funds for the Central Public Welfare Research Institutes (no. ZZ070831).

Author Disclosure Statement

No competing financial interests exist.

N. Lin participated in study design and coordination, material support for obtained funding, and supervised study; Y.Q. Zhang performed network analysis and statistical analysis, and drafted the manuscript; Q.Y. Guo performed data collection, statistical analysis and literature retrieval, and drafted the section of manuscript; Haiyu Xu, guide for molecular docking simulation; M.C. Zhong and X. Mao collected data. All authors read and approved the final manuscript.

References

Manning

, Campbell

, Oxley

, Chapot

. Acute ischemic stroke: Time, penumbra, and reperfusion. Stroke, 2014; 45:640–644.

Hata

, Kiyohara

. Epidemiology of stroke and coronary artery disease in Asia. Circ J, 2013; 77:1923–1932.

Singh

, Kaur

. Endovascular treatment of acute ischemic stroke. J Neurosci Rural Pract, 2013; 4:298–303.

Tymianski

. Novel approaches to neuroprotection trials in acute ischemic stroke. Stroke, 2013; 44:2942–2950.

Chen

. Traditional Chinese herbal medicine and cerebral ischemia. Front Biosci (Elite Ed), 2012; 4:809–817.

Sucher

. Insights from molecular investigations of traditional Chinese herbal stroke medicines: Implications for neuroprotective epilepsy therapy. Epilepsy Behav, 2006; 8:350–362.

Xian-Hui

, Xiao-Ping

, Wei-Juan

. Neuroprotective effects of the Buyang Huanwu decoction on functional recovery in rats following spinal cord injury. J Spinal Cord Med, 2014; in press.

Liu

, Cai

, Yi

, Chen

. Buyang Huanwu Decoction regulates neural stem cell behavior in ischemic brain. Neural Regen Res, 2013; 8:2336–2342.

, Liu

, Li

, Wang

, Shang

, Zheng

. Buyang huanwu decoction for healthcare: Evidence-based theoretical interpretations of treating different diseases with the same method and target of vascularity. Evid Based Complement Alternat Med, 2014; 2014:506783.

10.

Wang

, Huang

, Wu

, Lv

, Zhou

, Jia

. Effect of Buyang Huanwu decoction on amino acid content in cerebrospinal fluid of rats during ischemic/reperfusion injury. J Pharm Biomed Anal, 2013; 86:143–150.

11.

Shen

, Zhu

, Yu

, Fan

, Xiao

, Wu

, Zhang

, Xiong

, Pan

, Zhan

. Buyang Huanwu decoction increases angiopoietin-1 expression and promotes angiogenesis and functional outcome after focal cerebral ischemia. J Zhejiang Univ Sci B, 2014; 15:272–280.

12.

Kong

, Su

, Zhu

, Wang

, Wan

, Zhong

, Li

, Lin

. Neuroprotective effect of buyang huanwu decoction on rat ischemic/reperfusion brain damage by promoting migration of neural precursor cells. Rejuvenation Res, 2014; 17:264–275.

13.

Wang

, Jiang

. Neuroprotective effect of Buyang Huanwu Decoction on spinal ischemia-reperfusion injury in rats is linked with inhibition of cyclin-dependent kinase 5. BMC Complement Altern Med, 2013; 13:309.

14.

Hopkins

. Network pharmacology: The next paradigm in drug discovery. Nat Chem Biol, 2008; 4:682–690.

15.

Liang

, Li

. A novel network pharmacology approach to analyse traditional herbal formulae: The Liu-Wei-Di-Huang pill as a case study. Mol Biosyst, 2014; 10:1014–1022.

16.

Tao

, Xu

, Wang

, Li

, Wang

, Li

, Yang

. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J Ethnopharmacol, 2013; 145:1–10.

17.

Liu

, Sun

. Network pharmacology: New opportunity for the modernization of traditional Chinese medicine. Yao Xue Xue Bao, 2012; 47:696–703.

18.

Zhao

, Li

. Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One, 2010; 5:e11764.

19.

, Zhang

. Network target for screening synergistic drug combinations with application to traditional Chinese medicine. BMC Systems Biol, 2011; 5:S10.

20.

, Zhang

, Jiang

, Wei

, Zhang

. Herb network construction and co-module analysis for uncovering the combination rule of traditional Chinese herbal formula. BMC Bioinformatics, 2010; 11(Suppl):S6.

21.

Chen

CY-C

. TCM Database@Taiwan: The world's largest traditional Chinese medicine database for drug screening in silico. PLoS One, 2011; 6:e15939.

22.

Wishart

, Knox

, Guo

, Cheng

, Shrivastava

, Tzur

, Gautam

, Hassanali

. DrugBank: A knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res, 2008; 36:D901–D906.

23.

Hamosh

, Scott

, Amberger

, Bocchini

, McKusick

. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res, 2005; 33(Database Issue):D514–D517

24.

Chen

, Mamidipalli

, Huan

. HAPPI: An online database of comprehensive human annotated and predicted protein interactions. BMC Genomics, 2009; 10(Suppl 1):S16.

25.

D'Eustachio

. Reactome knowledgebase of human biological pathways and processes. Methods Mol Biol, 2011; 694:49–61.

26.

Brown

, Jurisica

. Online predicted human interaction database. Bioinformatics, 2005; 21:2076–2082.

27.

Aranda

, Achuthan

, Alam-Faruque

, Armean

, Bridge

, Derow

, Feuermann

, Ghanbarian

, Kerrien

, Khadake

, Kerssemakers

, Leroy

, Menden

, Michaut

, Montecchi-Palazzi

, Neuhauser

, Orchard

, Perreau

, Roechert

, van Eijk

, Hermjakob

. The IntAct molecular interaction database in 2010. Nucleic Acids Res, 2010; 38:D525–D531.

28.

Keshava Prasad

, Goel

, Kandasamy

, Keerthikumar

, Kumar

, Mathivanan

, Telikicherla

, Raju

, Shafreen

, Venugopal

, Balakrishnan

, Marimuthu

, Banerjee

, Somanathan

, Sebastian

, Rani

, Ray

, Harrys Kishore

, Kanth

, Ahmed

, Kashyap

, Mohmood

, Ramachandra

, Krishna

, Rahiman

, Mohan

, Ranganathan

, Ramabadran

, Chaerkady

, Pandey

. Human Protein Reference Database—2009 update. Nucleic Acids Res, 2009; 37:D767–D772.

29.

Ceol

, Chatr Aryamontri

, Licata

, Peluso

, Briganti

, Perfetto

, Castagnoli

, Cesareni

. MINT, the Molecular Interaction Database: 2009 update. Nucleic Acids Res, 2010; 38:D532–D539.

30.

Lehne

, Schlitt

. Protein–protein interaction databases: Keeping up with growing interactomes. Hum Genomics, 2009; 3:291–297.

31.

Beuming

, Skrabanek

, Niv

, Mukherjee

, Weinstein

. PDZBase: A protein–protein interaction database for PDZ-domains. Bioinformatics, 2005; 21:827–828.

32.

Zhu

, Shi

, Qin

, Tao

, Liu

, Xu

, Zhang

, Song

, Liu

, Zhang

, Han

, Zhang

, Chen

. Therapeutic target database update 2012: A resource for facilitating target-oriented drug discovery. Nucleic Acids Res, 2012; 40(database issue): D1128–D1136.

33.

Zsoldos

, Reid

, Simon

, Sadjad

, Johnson

. eHiTS: An innovative approach to the docking and scoring function problems. Curr Protein Pept Sci, 2006; 7:421–435.

34.

Rose

, Prlić

, Bi

, Bluhm

, Christie

, Dutta

, Green

, Goodsell

, Westbrook

, Woo

, Young

, Zardecki

, Berman

, Bourne

, Burley

. The RCSB Protein Data Bank: Views of structural biology for basic and applied research and education. Nucleic Acids Res, 2015; 43(database issue):D345–D356.

35.

Lack

, Axerio-Cilies

, Tavassoli

, Han

, Chan

, Feau

, LeBlanc

, Guns

, Guy

, Rennie

, Cherkasov

. Targeting the binding function 3 (BF3) site of the human androgen receptor through virtual screening. J Med Chem, 2011; 54:8563–8573.

36.

Axerio-Cilies

, Lack

, Nayana

, Chan

, Yeung

, Leblanc

, Guns

, Rennie

, Cherkasov

. Inhibitors of androgen receptor activation function-2 (AF2) site identified through virtual screening. J Med Chem, 2011; 54:6197–6205.

37.

Jeong

, Mason

, Barabási

, Oltvai

. Lethality and centrality in protein networks. Nature, 2001; 411:41–42.

38.

Penrod

, Moore

. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics. BMC Syst Biol, 2014; 8:12.

39.

Wang

, Liu

, Li

, Ouyang

, Yu

, Guo

, He

, Wang

. Drug target prediction based on the herbs components: The study on the multitargets pharmacological mechanism of qishenkeli acting on the coronary heart disease. Evid Based Complement Alternat Med, 2012; 2012:698531.

40.

Zhang

, Guo

, Wang

, Li

, Xu

, Liu

, Song

, Lin

, Li

, Lin

. A systems biology-based investigation into the therapeutic effects of Gansui Banxia Tang on reversing the imbalanced network of hepatocellular carcinoma. Sci Rep, 2014; 4:4154.

41.

Zhang

, Li

, Yang

, Wang

, Yu

, Guo

, Lin

. Identification of GRB2 and GAB1 coexpression as an unfavorable prognostic factor for hepatocellular carcinoma by a combination of expression profile and network analysis. PLoS One, 2013; 8:e85170.

42.

Zhang

, Guo

, Yang

, Yu

, Li

, Lin

. Identification of AKT kinases as unfavorable prognostic factors for hepatocellular carcinoma by a combination of expression profile, interaction network analysis and clinical validation. Mol Biosyst, 2014; 10:215–222.

43.

Dennis

Jr , Sherman

, Hosack

, Yang

, Gao

, Lane

, Lempicki

. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol, 2003; 4:P3.

44.

Wixon

, Kell

. The Kyoto Encyclopedia of Genes and Genomes—KEGG. Yeast, 2000; 17:48–55.

45.

Zhang

, Wang

, Tan

, Xu

, Liu

, Lin

. A systems biology-based investigation into the pharmacological mechanisms of Wu Tou Tang acting on rheumatoid arthritis by integrating network analysis. Evid Based Complement Alternat Med, 2013; 2013:548498.

46.

Nordahl

, Osler

, Frederiksen

, Andersen

, Prescott

, Overvad

, Diderichsen

, Rod

. Combined effects of socioeconomic position, smoking, and hypertension on risk of ischemic and hemorrhagic stroke. Stroke, 2014; 45:2582–2587.

47.

Hung

, Wang

, Wu

, Hsieh

, Huang

, Loh

el-W

, Lin

. Resistant hypertension, patient characteristics, and risk of stroke. PLoS One, 2014; 9:e104362.

48.

De Silva

, Brait

, Drummond

, Sobey

, Miller

. Nox2 oxidase activity accounts for the oxidative stress and vasomotor dysfunction in mouse cerebral arteries following ischemic stroke. PLoS One, 2011; 6:e28393.

49.

Skalidi

, Manios

, Stamatelopoulos

, Barlas

, Michas

, Toumanidis

, Vemmos

, Zakopoulos

. Brain edema formation is associated with the time rate of blood pressure variation in acute stroke patients. Blood Press Monit, 2013; 18:203–207.

50.

Pham

, Stoll

, Nieswandt

, Bendszus

, Kleinschnitz

. Blood coagulation factor XII—a neglected player in stroke pathophysiology. J Mol Med (Berl), 2012; 90:119–126.

51.

Moretti

, Ferrari

, Villa

. Neuroprotection for ischaemic stroke: Current status and challenges. Pharmacol Ther, 2015; 146:23–34.

52.

Wang

, Yang

, Zheng

, Zhang

, Tang

, Wang

, Du

, Lv

, Liu

, Zhou

, Guan

. Downregulation of TMEM16A calcium-activated chloride channel contributes to cerebrovascular remodeling during hypertension by promoting basilar smooth muscle cell proliferation. Circulation, 2012; 125:697–707.

53.

Povlsen

, Waldsee

, Ahnstedt

, Kristiansen

, Johansen

, Edvinsson

. In vivo experimental stroke and in vitro organ culture induce similar changes in vasoconstrictor receptors and intracellular calcium handling in rat cerebral arteries. Exp Brain Res, 2012; 219:507–520.

54.

Yang

, Liang

, Cui

, Zhou

, Luo

, Tang

. Angiogenesis opens a way for Chinese medicine to treat stroke. Chin J Integr Med, 2013; 19:815–819.

55.

Adamczak

, Schneider

, Nelles

, Que

, Suidgeest

, van der Weerd

, Löwik

, Hoehn

. In vivo bioluminescence imaging of vascular remodeling after stroke. Front Cell Neurosci, 2014; 8:274.

56.

, Jiang

, Zhu

, Wen

, Xu

, Xie

, Yang

, Xu

, Lan

, Xu

, Liu

. Orosomucoid1: Involved in vascular endothelial growth factor-induced blood-brain barrier leakage after ischemic stroke in mouse. Brain Res Bull, 2014; 109:88–98.

57.

Abais

, Xia

, Li

, Chen

, Conley

, Gehr

, Boini

, Li

. Nod-like receptor protein 3 (NLRP3) inflammasome activation and podocyte injury via thioredoxin-interacting protein (TXNIP) during hyperhomocysteinemia. J Biol Chem, 2014; 289:27159–27168.

58.

Trendelenburg

. Molecular regulation of cell fate in cerebral ischemia: Role of the inflammasome and connected pathways. J Cereb Blood Flow Metab, 2014; 34:1857–1867.

59.

Ishrat

, Mohamed

, Pillai

, Soliman

, Fouda

, Ergul

, El-Remessy

, Fagan

. Thioredoxin-interacting protein: A novel target for neuroprotection in experimental thromboembolic stroke in mice. Mol Neurobiol., 2015; 51:766–778.

60.

. Neuroprotection in experimental stroke with targeted neurotrophins. NeuroRx, 2005; 2:120–128.

61.

Alhusban

, Kozak

, Ergul

, Fagan

. AT1 receptor antagonism is proangiogenic in the brain: BDNF a novel mediator. J Pharmacol Exp Ther, 2013; 344:348–359.

62.

Han

, Pollak

, Yang

, Siddiqui

, Doyle

, Taravosh-Lahn

, Cekanaviciute

, Han

, Goodman

, Jones

, Jing

, Massa

, Longo

, Buckwalter

. Delayed administration of a small molecule tropomyosin-related kinase B ligand promotes recovery after hypoxic-ischemic stroke. Stroke, 2012; 43:1918–1924.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.12 MB

0.01 MB

0.02 MB

0.05 MB

0.01 MB

0.02 MB