Abstract
Background and purpose: Tripterygium wilfordii Hook F (TwHF) has been used in traditional Chinese medicines for treating cardiovascular disease (CVD). However, the underlying pharmacological mechanisms of the effects of TwHF against CVD remain to be elucidated. The aim of the present study is to reveal the pharmacological mechanisms of TwHF acting on CVD based on a pharmacology approach.
Experimental approach: The active compounds were screened by Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) according to the absorption, distribution, metabolism, and excretion (ADME). The potential targets of TwHF were predicted by SwissTargetPrediction database. The CVD-related therapeutic targets were obtained by the DrugBank, the OMIM database and the GeneCards database. Protein–protein interaction (PPI) network was constructed by STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by R package. The network of drug‐targets-diseases-pathways was constructed by Cytoscape software.
Key results: A total of 51 effective ingredients of TwHF and the 178 common targets of TwHF and CVD-related were collected. AKT1, amyloid precursor protein (APP), Mitogen-activated protein kinase 1 (MAPK), phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) and cellular tumor antigen p53 (TP53) was identified the core targets involved in the action of TwHF on CVD. Top ten GO (biological processes, cellular components and molecular functions) and KEGG pathways were identified with a P value ≤ 0.01. Finally, we constructed the network of TwHF-targets-CVD-GO-KEGG.
Conclusion and implications: Our results demonstrated that the main active compound of TwHF exerts cardiovascular protective effects and the core targets and pathways associated with the effects of TwHF on CVD. By the construction of the network of TwHF-targets-CVD-GO-KEGG, network pharmacology uncovered the pharmacological mechanisms of the action of TwHF on CVD and indicated a novel perspective to identify the intricate interactions among TwHF, candidate targets and related pathways.
Introduction
Cardiovascular disease (CVD) is a collective term for cardiovascular and cerebrovascular diseases, which is the first cause of death in the world[1]. The burden of CVD is on the rise globally, especially for low and middle income countries (LMIC)[2, 3]. In 2013, the World Health Organization (WHO) launched the 25×25 Global Action Plan, an ambitious road map for countries to reduce premature mortality that related to noncommunicable diseases (CVD, cancer, diabetes mellitus, and chronic respiratory diseases, etc.) by 25% by 2025[4]. Although Western medicine have made good progress in reducing the risk of cardiovascular events and total mortality, patients with long-term cardiovascular treatment still have trade-offs in adherence that might lead to discontinuation of these drugs. This can be attributed to the adverse reactions caused by multiple pharmacologic agents and some drugs that beyond the affordability of LMIC[3, 5]. Therefore, strategies that develop new drugs are urgently needed for CVD therapies. Traditional Chinese medicine (TCM) has evolved over thousands of years and has gained widespread clinical applications. Chinese, especially elders, has a special place in their heart for Traditional Chinese Medicine (TCM). As a critical component of complementary and alternative medicine, TCM medications has been used for primary and secondary prevention of CVD[6].
Tripterygium wilfordii Hook F (TwHF), also known as Leigongteng and Thunder God Vine in traditional Chinese medicine (TCM), has possesses a variety of pharmacological activities such as anti-cancer, anti-inflammation, anti-fibrosis, anti-atherosclerosis and anti-autoimmune disorders[7-9]. Recently, several fundamental researches have indicated that low-dose TwHF can prevent cardiovascular diseases. Low-dose TwHF can improve the inflammatory reaction, reduce myocardial injury, and optimize acute coronary syndrome (ACS) rat’s condition with inhibition of myocardial apoptosis[10]. TwHF extracts were shown to have cardioprotection effects by inducing the activation of Nrf2/HO-1 defense pathway, inhibiting the activation of NF-KB pathway and reducing the expression of NLRP3 inflammasome[11-13]. In addition, extracts can not only improve the vascular function in atherosclerosis, but may also aid in the prevention of in‑stent restenosis formation following endovascular treatment of lower‑extremity artery disease[14, 15]. However, a systematical understanding of how the multiple therapeutic targets work together to exert therapeutic effects on CVD has not been fully elucidated.
Network pharmacology is an innovative method to analyze the complex relationship between drug and disease at the system level, which can provide clues for discovering new drug[16]. This approach integrates and constructs the complex networks among drug targets, disease targets, and biological processes[17]. With the help of network pharmacology, it is possible to reveal potential drug-target-disease interactions and realize novel therapeutic application beyond the traditional TCM application[18]. In this paper, pharmacokinetic evaluation, target prediction, network, and pathway analysis using multiple available public databases and bioinformatics resources, have systematically elucidated the mechanisms of therapeutic effects of TwHF on CVD.
2. Materials and Methods
2.1. Active Components Screening
Traditional Chinese Medicine Systems Pharmacology Database (TCMSP,https://tcmspw.com/tcmsp.php) is an efficient systems pharmacology platform, which can be used to assess the pharmacokinetics of TCMs or related compounds[19]. It can provide information on the absorption, distribution, metabolism, and excretion (ADME) properties of compounds, such as oral bioavailability (OB), drug likeness (DL), Caco-2 permeability (Caco-2), blood–brain barrier (BBB), and so on. OB represents to the speed and degree of absorbing drugs into the circulatory system, which is a reliable indicator to evaluate the intrinsic quality of drugs objectively. DL is calculated by comparing the functional or physical properties of compounds with those of the majority of known drugs, which refers to the sum of the pharmacokinetic properties and safety[20]. In present study, the compound name “leigongteng” was entered to the search box and active ingredients with OB \(\geq\) 30% and DL \(\geq\) 0.18 were selected as candidate components for subsequent analysis. In addition, the two/three dimensional (2D/3D) structure, canonical SMILES, and PubChem ID of candidate components were prepared and calibrated using the Traditional Chinese Medicines Integrated Database (TCMID,http://www.megabionet.org/tcmid/)[21] and the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database[22].
2.2. Targets fishing
2.2.1. Identified and Predicted Targets of TwHF
The target of active components in TwHF were collected from the SwissTargetPrediction (http://www.swisstargetprediction.ch), which is a free public resource designed to accurately predict targets for bioactive molecules[23]. Potential therapeutic targets were predicted by inputting these components SMILES into SMILES string (s) and searching for their similar molecules. The screening condition was limited to “Homo sapiens” and high probability targets (probability P<0.05) were collected after duplicate contents were eliminated.
2.2.2. Target Identification of Known Therapeutic Targets Acting on CVD
The CVD-related therapeutic targets were found from the DrugBank (http://www.drugbank.ca)[24], the OMIM database (https://omim.org)[25], and the GeneCards database (https://www.genecards.org)[26]. DrugBank is a freely available network database, which provides molecular information about drugs, drug targets, drug effects, and drug interactions. OMIM database, a comprehensive web resource, is focusing on genes, genetic phenotypes, and their relationships. In addition, GeneCards is an integrative database that provides comprehensive information on all annotated and predicted human genes. The query “Cardiovascular disease” was used as the keyword to search for CVD-related targets among three databases.
2.3. Protein–Protein Interaction (PPI) Network Construction and Analysis
The identified targets were uploaded to the STRING database v11.0 (http://string-db.org)[27] to obtain the protein–protein interaction information, including the physical and functional associations. The protein interactions were limited to confidence score of 0.9 or higher. The core target genes were determined based on criterion of the highest interactive scores and the most interaction.
2.4. GO and KEGG Pathway Enrichment Analyses
GO analysis can supplies evidence-supported annotations to describe gene product biological functions, including biological pathways, cellular components, and molecular functions[28]. KEGG analysis can give functional meaning to genes or genomes at molecular and higher levels[29]. Enrichment analyses of GO of core target genes and KEGG were performed using R (version 3.6.0 for Windows), including biological function (BP), cellular component (CC), and molecular function (MF). By using a cut-off value adjusted to P < 0.05, top ten GO enrichments and KEGG pathways were screened.
2.5. Construction of network relationships
Cytoscape is an open source software project, which can integrate biomolecular interaction networks with high-throughput expression data and other molecular states into a versatile and interactive visualization framework[30]. The core targets of TwHF against CVD were constructed for KEGG-GO enrichment visualization by Cytoscape (v3.7.1) software[31]. In interactive network, nodes represent components, targets, GO and pathways, and edges represent the interaction of each other.
2.6. Molecular Docking
In addition, the crystal structures of target proteins were collected from the Protein Data Bank (http: //www.pdb.org/) and decorated by removing the ligands and water motifs, adding hydrogen, and optimizing the mutation sites by the Pymol (version 2.3). The 3D chemical structural formulas of candidate compounds were collected from PubChem and energy minimizing by using ChemBioDraw 3D (version 14). The binding ability, sites, and interactions between compounds and targets were analyzed by Pymol, AutoDockTools (version 1.5.6), and Discovery Studio 2020 Client[32, 33]. Autodock vina (1.1.2) was used to conducte docking between compounds and target proteins.