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.