Abstract
Background
GuanXinNing tablet (GXNT), a traditional Chinese patent medicine, has
been found to have remarkable antithrombotic effects and can
effectively inhibit pro-thrombotic factors in previous studies.
However, the mechanism of its antithrombotic effects remains little
known.
Methods
In this study, we first determined and identified the sources of each
main compound in GXNT using liquid chromatography-mass spectrometry
(LC-MS). Through the approach of network pharmacology, we predicted the
action targets of the active components, mapped the target genes
related to thrombus, and obtained potential antithrombotic targets for
active ingredients. We then performed gene ontology (GO) enrichment
analyses and KEGG signaling pathway analyses for the action targets,
and constructed networks of active component–target and active
component–target–pathway for GXNT. Additionally, we evaluated the
pharmacodynamic effects of GXNT on thrombus using the rat thrombus
model induced by FeCl[3], observed the effects of antiplatelet
aggregation via platelet assay, and further verified the results
predicted by network pharmacology via Western blot.
Results
In total, 14 active ingredients were identified in GXNT, and 83 action
targets were predicted, 17 of which are antithrombotic targets that
potentially participate in processes including response to oxidative
stress and positive regulation of blood vessel endothelial cell
migration. KEGG pathway analyses revealed that the predicted action
targets were involved in multiple signal pathways, such as MAPK, IL-17,
and platelet activation. Pharmacodynamics study found that GXNT could
significantly reduce the thrombus length and weight, lower platelet
aggregation function, and decrease the levels of Fbg and PAI-1. In
addition, GXNT could significantly increase 6-keto-PGF1α content and
regulate the ratio of TXB[2]/6-keto-PGF1α, while not having dramatic
effects on TXB[2]. GXNT was also observed to visibly inhibit maximum
platelet aggregation. Herein, we further studied the thrombus-related
MAPKs signaling pathway and found that GXNT could significantly reduce
the phosphorylation levels of p38MAPK, ERK, and JNK proteins in
platelet.
Conclusions
This study revealed the pharmacodynamic material basis of GXNT and its
potential multicomponent–multitarget–multipath pharmacological effects,
confirmed the antithrombotic effects of GXNT, and showed that its
mechanism may be related to inhibiting phosphorylation of p38, ERK, and
JNK proteins in MAPKs signaling pathway, partially verifying the
results from network pharmacology. The results from this study could
provide a theoretical basis for the development and clinical
application of GXNT.
Keywords: GuanXinNing tablet; network pharmacology; thrombus; Danshen,
Chuanxiong; MAPKs signal pathway
Introduction
Thrombus is a common pathophysiological basis for various
cardiovascular diseases in the clinic, such as acute myocardial
infarction, stroke, and coronary heart disease ([39]Sadowski et al.,
2014; [40]Wang, 2018). Traditional Chinese medicine (TCM) believes that
cold coagulation and blood stasis plays an important role in thrombotic
diseases ([41]Gu, 2010). Warming collaterals and activating blood
circulation therapy is a general principle for treating cold
coagulation and blood stasis syndrome, according to the Yellow
Emperor's Internal Classic. As a consequence, Chinese medicine with the
function of activating blood circulation and removing blood stasis is
often used to prevent and cure thromboembolic diseases.
GuanXinNing is a classical Chinese herbal formula preparation, which is
composed of two well-established Chinese herbs that activate blood
circulation and remove blood stasis: Salvia miltiorrhiza Bge. (Chinese
name Danshen, DS) and Ligusticum chuanxiong Hort. (Chinese name
Chuanxiong, CX). This preparation has the effect of activating blood
circulation, removing blood stasis, dredging arteries, and nourishing
the heart. To improve patient convenience and compliance, GuanXinNing
tablet (GXNT) is a novel preparation developed from the widely used
GuanXinNing injection with an improved extraction process. GXNT
consists of extracts from Danshen and Chuanxiong at the ratio of 1:1
([42]Chen et al., 2005), and has already been approved for listing by
the China Food and Drug Administration (CFDA approval no. Z20150028).
Danshen, the dry roots of Salvia miltiorrhiza Bge., is beneficial to
heart and liver with a bitter taste and a slightly cold property.
Studies have demonstrated that Danshen has significant anti-arrhythmia
effects via reducing myocardial infarct size, protecting myocardial
injury ([43]Chang et al., 2016), and improving myocardial ischemia
([44]Zhang et al., 2013). The second Chinese herb component,
Chuanxiong, is the dry rhizome of Ligusticum chuanxiong Hort.
Chuanxiong is known to protect the liver, gallbladder, and pericardium
with a mild property and an acrid taste, and has the effects of
activating blood circulation, moving qi, dispelling wind, and relieving
pain ([45]Chen et al., 2018b). Modern pharmacological studies have
shown that Chuanxiong has antioxidation, anti-inflammation,
neuroprotection, and anti-bacteria activities ([46]Chen et al., 2018b;
[47]Shan et al., 2018). Moreover, our previous studies have found that
GXNT could reduce platelet aggregation, scavenge free radicals,
ameliorate blood coagulation in rats with qi stagnation and blood
stasis, protect the vascular endothelium ([48]Chen et al., 2005), and
have antithrombotic activities with multiple-target effects ([49]Wang
et al., 2016). Nevertheless, TCM is a complex chemical composition
system of multiple components, with multiple targets, multiple links,
and multiple effects. Therefore, a holistic view of “multiple
components-multiple targets-multiple pathways” is needed to study the
material basis and action mechanism of GXNT on thrombus.
Network pharmacology uses high-throughput omics data analysis, virtual
computing, and network database retrieval to construct an interaction
network of “compound-gene-disease” and to provide a holistic
understanding of the relationship between drugs and targets.
Integrating with systems biology, multi-directional pharmacology and
bioinformatics, and network pharmacology offers new approaches and
strategies for designing and developing new drugs ([50]Hopkins, 2008;
[51]Li, 2013). In particular, it has unique advantages and potential in
predicting and identifying the active ingredient clusters and action
targets of Chinese medicines, and in discovering new indications
through active molecule screening, target prediction, network
construction, and analysis. The systemic and holistic traits of network
pharmacology are in line with the complexity of TCM, making it widely
adopted in studying the pharmacodynamic material basis and action
mechanism of TCM preparations, such as XinShengHua granule ([52]Pang et
al., 2018), MaZiRen wan ([53]Huang et al., 2018), YinHuangQingFei
capsule ([54]Yu et al., 2017), YangXinShi tablet ([55]Chen et al.,
2018a), etc.
In this study, we used network pharmacology to predict the targets of
active ingredients in GXNT and investigate its action mechanism.
Firstly, the main active components of GXNT were identified and
screened based on liquid chromatography-mass spectrometry (LC-MS)
combined with traditional Chinese medicine system pharmacology
technology platform (TCMSP). Then, active ingredient targets were
predicted using Swiss Target Prediction web server to construct the
active ingredient-target, protein interaction, and
component–target–pathway network for analyzing the pharmacodynamic
basis and action mechanism of GXNT. Next, the common carotid artery
thrombus model in rats induced by FeCl[3] was adopted to further verify
the antithrombotic effects of GXNT, followed by the antiplatelet study.
Finally, we examined the protein expressions in the predicted
thrombus-related signaling pathways via Western blot to verify the
antithrombotic mechanism of GXNT. The results from this study provided
a theoretical reference for the development and utilization of GXNT.
Materials and Methods
Materials and Regents
GXNT (GXN extract powder, raw drug dosage of 12.8 g/g), Danshen and
Chuanxiong were all provided by Chiatai Qinchunbao Pharmaceutical co.,
LTD. (Hangzhou, China). The herbal medicines of Danshen and Chuanxiong
in GXNT were collected from Linyi City (Shandong Province, China) and
Dujiangyan City (Sichuan Province, China) respectively, and were
authenticated correspondingly by Prof. Yuqing Ye (Chinese Medicine
Resource Research and Development Center, Shanghai Institute of
Traditional Chinese Medicine) and Prof. Guihua Jiang (School of
Pharmacy, Chengdu Chinese Medical University). The voucher specimens
were deposited in the Quality Department of Chiatai Qinchunbao
Pharmaceutical co., LTD. Reference standards of tanshinol sodium
(110855-200809, purity=100%), protocatechualdehyde (110810-201608,
purity=99.3%), chlorogenic acid (110753-200413, purity=100%), caffeic
acid (110885-200102, purity=100%), ferulic acid (110773-201012,
purity=100%), rosmarinic acid (111871-201505, purity=98.5%), and
salvianolic acid B (111562-201514, purity=93.7%) were all purchased
from National Institute for the Control of Pharmaceutical and
Biological Products (Beijing, China). LC-MS grade acetonitrile and
formic acid with ≥98.0% of the purity were purchased from Merck
(Darmstadt, Germany). Other reagents were all of analytical grade.
Ultrapure water purified by Millipore Ultra-pure Water Purifier
(Millipore, Milford, MA, USA) was used. Clopidogrel bisulfate was
purchased from Sanofi-Aventis Pharmaceutical Co., Ltd. (Hangzhou,
China). Fibrinogen (Fbg) kit was purchased from Dade Behring Marburg
GmbH (Marburg, Germany). Plasminogen activator inhibitors (PAI-1),
6-keto-prostaglandin F1α (6-keto-PGF1α), and thromboxane B[2] (TXB[2])
assay kits were all purchased from Nanjing Jiancheng Bioengineering
Research Institute Co., Ltd. (Nanjing, China). Aspirin enteric-coated
tablets were purchased from Bayer healthcare Co., Ltd. (Leverkusen,
Germany). Adenosine diphosphate (ADP) and albumin from bovine serum
(BSA) were purchased from Sigma-Aldrich (St. Louis, Missouri, USA).
KeyGEN total protein extraction kit, BCA protein assay kit and western
stripping buffer were purchased from Beyotime Biosciences (Shanghai,
China). Primary antibodies against Phospho-p44/42 MAPK (Erk1/2, #4370),
Phospho-p38 MAPK (#4511), Phospho-SAPK/JNK (#9255), and GAPDH were all
purchased from Cell Signaling Technology (Boston, MA, USA). All male
Sprague-Dawley (Gfeller et al.) rats, weighing 300 to 350 g, were
purchased from Shanghai SLAC Laboratory Animal Co., Ltd (Certification
No: SCXK [Hu] 2012-002; Shanghai, China). Prior to the experiment, the
animals were housed in individually ventilated cages (IVC) with two
rats in each cage under a 12-h light/dark cycle, and were provided with
food and water ad libitum. All experiments were carried out strictly
according to the requirements of the Institutional Animal Care and Use
Committee of Zhejiang Chinese Medical University, and was approved by
the Laboratory Animal Research Center of Zhejiang Chinese Medical
University (Certification No: SYXK [Zhe] 2013-184).
Preparation for the Control Sample
The appropriate amount of each standard sample was accurately weighed
using 1/100,000 precision analytical balance (Sartorius Group, German),
and was diluted with 50% methanol solution to a constant volume for
preparing mother liquor. It was then diluted to a series of
concentrations and filtered with a 0.45-μm needle filter, from which
the filtrate was obtained finally.
Preparation for GXNT Testing Sample
We took a mixture of Danshen and Chuanxiong (10 kg each), added it with
160 L water, and boiled it for 2 h. Afterwards, the extracted solution
was poured out, and the remaining residue was extracted twice, in which
120 L water was added each time and boiled for 1.5 h. The three
extracts were merged to concentrate to 15 L at 60°C in a single-effect
concentrator, and the solution after concentration was transferred to a
rotary evaporator to concentrate to approximately 9 L at 60°C. About 35
L of 95% ethanol solution was added to the concentrated liquor, and was
allowed to stand overnight. The supernatant was taken and concentrated
to about 5 L at 60°C using a rotary evaporator. The concentrate was
placed into a vacuum drying oven, dried thoroughly at 60°C, and
powdered homogeneously with a powder machine to obtain solid powder of
about 1.5 kg (raw dose of 12.85 g/g). Subsequently, 1.0 g of the powder
was accurately weighed, brought to 20 mL with 50% methanol added, and
sonicated for 10 min. It was then filtered through a 0.45-μm
microporous filter column, from which an appropriate amount was
injected into HPLC-MS instrument for analysis (Shimadzu LC-20A liquid
chromatograph, Shimadzu, Japan; API 3200 LCMS/MS Mass Spectrometry
System, American AB SCIEX, USA).
Preparation for Danshen Testing Sample
The preparation was performed in accordance with GXNT technology. We
took 6 kg of Danshen and decocted it with water three times. In
specific, 48 L was added and boiled for 2 h for the first time, and 36
L was added and boiled for 1.5 h for the second and third time. The
three extracts were merged to concentrate to 3.2 L at 60°C in a
single-effect concentrator. About 8.5 L of 95% ethanol solution was
added into the concentrated liquor, and was allowed to stand overnight.
The supernatant was taken and concentrated to about 1 L at 60°C using a
rotary evaporator. The concentrate was placed into a vacuum drying
oven, dried thoroughly at 60°C, and powdered evenly to obtain about 402
g of solid powder (raw dose of 15.0 g/g). Subsequently, 0.5 g of the
powder was weighed accurately, brought to 20 mL with 50% methanol, and
sonicated for 10 min. After that, it was filtered through a 0.45-μm
microporous filter column, from which an appropriate amount was
injected into HPLC-MS instrument for analysis.
Preparation for Chuanxiong Testing Sample
The preparation was conducted according to GXNT technology. We took 3.5
kg of Chuanxiong, and decocted it with water three times. In specific,
28 L was added and boiled for 2 h for the first time, and 21 L was
added and boiled for 1.5 h for the second and third time. The three
extracts were merged to concentrate to 2 L at 60°C in a single-effect
concentrator. About 7.5 L of 95% ethanol solution was added into the
concentrate, and was allowed to stand overnight. The supernatant was
taken and concentrated to about 1 L at 60°C using a rotary evaporator.
The concentrate was placed into a vacuum drying oven, dried thoroughly
at 60°C, and powdered evenly to obtain about 538 g of solid powder (raw
dose of 6.5 g/g). Subsequently, 1.0 g of powder was weighed accurately,
brought to 20 mL with 50% methanol, and sonicated for 10 min. After
that, it was filtered through a 0.45-μm microporous filter column, from
which an appropriate amount was taken to inject into HPLC-MS instrument
for analysis.
LC-MS Analysis
LC-MS analysis of samples was carried out on a Shimadzu LC-20A liquid
chromatograph (Shimadzu, Japan). An Agilent ZORBAX SB-C18 column
(250×4.6 mm i.d., 5 μm, Agilent, USA) was used for column separation.
The column temperature was maintained at 40°C, and the flow rate was
kept at 1 mL/min, with acetonitrile as the mobile phase A and 0.1%
formic acid in water as the mobile phase B. The gradient running
procedure was programmed as follows: 0~5 min, 40~40% A; 5~25 min,
40~69% A; 25~30 min, 69~100% A. The injection volume was 5 μl. In
addition, the mass spectrometer was an API 3200 LCMS/MS system. The
detection mode was Q1 scan profile mode. The total scan time was 5 s
per cycle with 599 cycles, and data was collected in the positive mode.
The capillary voltage was 4500 V, and the mass range was from m/z 100
to 1000. Curtain Gas, Atomized Gas (Gas1), and Auxiliary Gas (Gas2)
were nitrogen, and the pressure was set to 15 psi, 30 and 30 psi,
respectively. We used 4500 V for the spray voltage, 450°C for the
atomization temperature, 10 V for the collision chamber inlet voltage,
and 70 V for the de-clustered voltage (DP). All data collection and
processing were performed using Analyst software (version 1.6). The
chemical structures of main compounds identified in GXNT were drawn
with ChemDraw from CambridgeSoft. The chemical drawing software is
capable of performing accurate mass analyses for LC/MS (electrospray),
such as adducts and protonated molecules.
Establishment of SMILES Format File for Active Ingredients in GXNT
The molecular structure of the active compound was mapped with ChemBio
Draw Ultra 14.0 software, and was saved in the MDL sdf. format. All
chemical structures were converted to Mol2 format using ChemBio 3D
Ultra software in order to establish an active molecular library. The
active molecular Mol2 file was converted to a SMILES file using Open
Babel GUI software for subsequent analyses.
Prediction of Potential Targets for Active Ingredients in GXNT
Swiss Target Prediction ([56]http://www.swisstargetprediction.ch/) is a
web server to accurately predict the action targets of bioactive
molecules based on the similarity of two dimension and three dimension
of known ligands, providing valuable insights into the action mechanism
of active molecules ([57]Gfeller et al., 2014). The SMILE format files
of active ingredients identified by LC-MS were uploaded to the Swiss
Target Prediction server, and “Homo sapiens” was selected as the
species. Then, the potential drug targets were searched using the
active small molecules as probes. The target prediction results were
sorted from high to low according to “Probability”, and the official
names of drug targets were retrieved through the UniProtKB search
function in the UniProt database ([58]http://www.uniprot.org/).
Mapping of Thrombus-Related Targets
Reported genes, possibly related to thrombus, were searched by the
keyword “thrombus” in CooLGeN ([59]http://ci.smu.edu.cn/CooLGeN/) and
in the GeneCards database. Comparing these with the targets obtained
from Swiss Target Prediction server, we obtained the potential targets
of the active ingredients in GXNT that are potentially involved in the
antithrombotic mechanism.
Functional Enrichment Analysis of the Potential Action Targets
Bioscape Annotation Database Metascape ([60]http://metascape.org) is a
reliable, effective, and intuitive online bioinformatics annotation
tool for understanding the biological functions of genes and protein
lists on a large scale for biomedical researchers. Gene ontology (GO)
enrichment and KEGG pathway annotation analyses of the basic ontology
term were performed using Metascape for the potential targets of
GuanXinNing, and “P < 0.05” was considered as the statistically
significant screening condition.
Construction of Component–Target Network and Component–Target–Pathway Network
The action targets and related signaling pathways were predicted
according to the active component candidates identified from GXNT, and
were then imported into Cytoscape software for constructing
compound-target networks, and compound-target-path networks to explore
the overall pharmacological mechanisms of GXNT. The importance of every
node in the network was determined by the degree of topological
parameters. The degree of a node refers to the number of edges
connected to that node, i.e. the higher the degree is, the more nodes
it is directly connected to, and the more importance the node has in
the network. Edges represent the interactions between the compounds and
the targets in the network.
Thrombus Animal Experiment
Animal Administration and Modeling
After 3 to 5 days of adaptive breeding, 48 SD rats were randomly
divided into six groups, namely, the control group, the model group,
the GXNT low, medium, and high groups with doses of 75, 150, and 300
mg/kg, and the positive group (n = 8). Each GXNT group was given the
corresponding dose of GXN extract powder solution by oral
administration. The positive group was given 12.5 mg/kg of clopidogrel
solution orally, and the control group and the model group were
intragastrically administrated with 10 mL/kg of distilled water. After
1 h of administration, thrombus model operation induced by FeCl[3] was
conducted in SD rats. Briefly, rats were anesthetized by
intraperitoneal injection of 3% sodium pentobarbital solution (0.15
mL/kg). The rats were fixed on a 37°C insulated operating platform with
neck hair shaved and neck skin disinfected. Next, the right common
carotid artery was carefully separated, and a plastic paper with a
width of 1 cm was placed on the bottom of the right common carotid
artery. Then, the 1 ×1 cm filter paper, added with 10 µl of 35%
FeCl[3], was wrapped around the common carotid artery rapidly for 15
min of external application. Afterwards, we removed the plastic paper
and the filter paper, ligated both ends of the thrombus, and cut the
embolus.
Measurement of Thrombus Length and Weight
Before SD rats were sacrificed, the emboli of the rats were quickly
cut, and the redundant blood was absorbed by clean filter paper. The
length of the thrombus was accurately measured using vernier caliper
and recorded as L[right]. The weight of the thrombus was also
accurately weighed with an analytical balance and recorded as M[right].
Then, a proper length of the left common carotid artery of SD rats was
taken, with the blood in the vessels absorbed by filter paper. The
length of the blood vessel was precisely measured by the vernier
caliper and recorded as L[left]. The weight of the thrombus was weighed
using an analytical balance and recorded as M[left]. The weight of the
thrombus was calculated with the following formula:
[MATH:
L(le
ft)M(left)<
/mo>=L(<
/mo>right
)M(right)-
M(thrombus),M(th
rombus
)=M(ri<
/mi>ght)-(M(left)<
mi>L(left
))×L(right) :MATH]
Preparation of Platelet-Rich Plasma and Platelet-Poor Plasma
Blood of rats in each group was taken from the abdominal aorta,
transfused into PE tubes containing 3.8% sodium citrate anticoagulant
(9:1, v/v), and then repeatedly inverted several times to fully mix the
blood and anticoagulant. Part of the anticoagulant blood in the PE tube
was centrifuged at 3,500 rpm for 15 min, and plasma was taken and
stored at −80°C for further usage. The rest of the anticoagulant blood
was taken and centrifuged at 1000 rpm for 10 min at room temperature,
and the supernatant, namely platelet-rich plasma (PRP), was obtained to
detect platelet aggregation rates and protein expressions. The
remaining part was further centrifuged at 3000 rpm for 10 min, and the
resulting supernatant, namely platelet-poor plasma (PPP), was taken.
Determination of Fbg, PAI-1, 6-keto-PGF1α, and TXB[2] in the Blood
Anti-coagulated plasma at −80°C was taken and thawed. Fbg was measured
using CA500 automatic blood coagulation analyzer (Sysmex, Japan). The
specific procedures were carried out in strict accordance with the
commercially available kit. The PAI-1, 6- 6-keto-PGF1α, and TXB[2]
assay kits were used to detect the expression levels of PAI-1,
6-keto-PGF1α, and TXB[2] in samples by the enzyme linked immunosorbent
assay (ELISA) method under the guidance of corresponding kit
instructions.
Platelet Aggregation Assay
According to optical principles, the platelet aggregation rate was
measured using Chrono-log platelet aggregation instrument (CHRONO-LOG,
USA). We took PRP and PPP into turbidity tubes, and placed them in
preheating holes. Platelet aggregation assay was performed after
incubation at 37°C for 5 min, and PPP was applied to zero setting
during measurement. Then, ADP (10 μM) as the agonist was added to PRP
with magnetic stirrer stirring, and the aggregation curve was traced.
The inhibition rate of platelet aggregation was calculated by the
following formula:
[MATH:
Inhibit
ion ra
te (%)={(aggregation <
/mtext>rate of the control group−aggreg
ation rate of the administ
ration group)/aggregation <
/mtext>rate of the control group}×100% :MATH]
Determination of MAPKs Signaling Pathway-Related Protein Expressions in
Platelets by Western Blot
Prepared PRP was taken, induced with ADP, incubated for 20 min at 37°C,
and centrifuged at 3000 rpm for 5 min. The supernatant was discarded,
and the precipitated fraction was used for the extraction of total
platelet protein, which was carried out according to the instructions
of the KeyGEN total protein extraction kit. Then, protein
concentrations were quantified using the BCA protein assay kit. After
protein samples were mixed with sample loading buffer (4:1, v/v), they
were boiled for 5 min. Next, proteins (10 μg) were separated by
SDS-PAGE electrophoresis, and transferred to PVDF membranes. The
membranes were blocked with 3% albumin from bovine serum at room
temperature and then incubated overnight with primary antibodies
(p-P38, p-ERK1/2, and p-JNK of 1:1000 dilution; GAPDH of 1:200
dilution; all diluted with 3% BSA) at 4°C. After 48 h, the membranes
were washed with TBST for 4 times, and were incubated with the
secondary antibodies at 37°C for 2 h. Finally, the membranes were
washed with TBST for four times and with TBS for 1 min, and were
scanned in the odyssey infrared fluorescence scanner (Thermo company,
USA). Afterwards, the membranes containing p-P38, p-ERK1/2, p-JNK
proteins were washed with western stripping buffer to further detect
P38, ERK1/2, and JNK proteins by the procedures as mentioned above.
Statistical Analysis
All data were statistically analyzed using SPSS 22.0 software, and
expressed as mean ± standard error (
[MATH: χ¯ :MATH]
± SEM). Statistical analysis was conducted via one-way analysis of
variance (ANOVA) for comparison between groups and via L-S-D test for
pairwise comparison. Drawings of statistical graph were done using
GraphPad Prism 6.0 software. P < 0.05 indicates statistical
significance.
Results
Optimization of Fingerprint of GXNT
Non-volatile phosphoric acid was used as the mobile phase additive in
the fingerprint of GXNT, which was established by the institute
previously ([61]Lin et al., 2017). Since this condition is not suitable
for LC-MS system, the analytical method needs to be properly optimized.
When no additive was used (i.e. pure water was used as the water
phase), the compound had a wider peak shape, poor symmetry, and a
certain degree of tailing. After a certain amount of formic acid was
added, the peak shape could be visibly improved, and the mass spectral
response was also enhanced. Hence, 0.1% formic acid was used as the
aqueous phase. The optimized analysis conditions were used to analyze
the GXNT samples. The mass spectrum TIC map (in positive and negative
modes) and the ultraviolet chromatogram at 280 nm are both shown in
[62]Figure 1. Since the mass spectrometry detector was a broad-spectrum
detector, many compounds that were limited to ultraviolet response
could be detected, and thus more complete material information could be
obtained. It was observed that the main ion peaks of TIC images under
the positive and negative modes of the mass spectrum were all reflected
in the fingerprint, indicating that the currently established
fingerprint map basically satisfied the principle of compound
information maximization.
Figure 1.
Figure 1
[63]Open in a new tab
Chromatograms of GXNT and mixed standard. (A) TIC diagram of GXNT mass
spectrometry in the positive mode. (B) TIC diagram of GXNT mass
spectrometry in the negative mode. (C) Chromatogram of GXNT at 280 nm.
(D) Chromatogram of mixed standard at 280 nm. (1) Phenylalanine; (2)
Tanshinol; (3) Senkyunolide B; (4) Protocatechualdehyde; (5)
Chlorogenic acid; (6) Caffeic acid; (7) Ferulic acid; (8) Salvianolic
acid D; (9) Senkyunolide I; (10) Rosemary acid; (11) Isosalvianolic
acid A; (12) Salvianolic acid B; (13) Salvianolic acid A; (14)
Isosalvianolic acid C.
Maps of GXNT and Single Chinese Herb
From the chromatographic comparative map at 280 nm (as shown in
[64]Figure 2), it could be seen that most compounds in GXNT were from
Danshen, while a small amount came from Chuanxiong, since most
components in Chuanxiong were volatile oils and the extraction rate of
the water extraction process was lower. Among all the compounds, peaks
1 and 6 were the common peaks of the two drugs. Though the fingerprint
had only one peak at 60 min, we can see from the mass spectrometry of
Danshen (peak 10) and Chuanxiong (peak 9) separately that both
compounds had peaks at the same retention time, meaning that the single
peak consists of two superimposed peaks from Danshen and Chuanxiong and
that these two compounds are not the same substance. This is difficult
to see in the ultraviolet chromatogram alone. However, it could be
effectively identified in the mass spectrum, and distinguished and
quantified separately by extracting ion peaks.
Figure 2.
Figure 2
[65]Open in a new tab
Comparison of chromatogram of each extract at 280 nm. (A) Ultraviolet
chromatogram of GXNT. (B) Ultraviolet chromatogram of Danshen. (C)
Ultraviolet chromatogram of Chuanxiong. (2) Tanshinol; (3) Senkyunolide
B; (4) Protocatechualdehyde; (5) Chlorogenic acid; (6) Caffeic acid;
(7) Ferulic acid; (8) Salvianolic acid D; (9) Senkyunolide I; (10)
Rosemary acid; (11) Isosalvianolic acid A; (12) Salvianolic acid B;
(13) Salvianolic acid A; (14) Isosalvianolic acid C.
Identification of Active Ingredients in GXNT
The mass spectrometry information of the materials obtained from the
experiment was compared with the related literature reports. As a
result, 14 compounds were identified in GXNT, of which 7 major
compounds were verified by standard products. The specific
identification results were summarized in [66]Table 1, and the mass
spectrum and the structure of each compound are shown in [67]Figure 3.
In specific, peak 1 was identified as phenylalanine, in which one
molecule of NH[3] and/or HCOOH was removed to form a responsive
fragment ion in the mass spectrum ([68]Ying et al., 2013b). Peak 2 was
identified as tanshinol, which responded weakly in the positive mode,
with one molecule of H[2]O as well as HCOOH removed to form the major
fragment ion in the negative mode ([69]Chen et al., 2011). Peak 3 only
responded in the positive mode of mass spectrometry, mainly forming a
dehydrated ion peak. Senkyunolide B and C, corresponding to molecular
weights and compounds that could form dehydrated ion peaks in
Chuanxiong, were mainly obtained via searching the literature. Given
the polarity according to the peak time and the structure of the
compound, it was preliminarily presumed to be senkyunolide B ([70]Hu et
al., 2012). Peak 4 was identified as protocatechuic aldehyde, which
responded only in the negative mode, where one molecule of CO was
removed to form the major fragment ion of m/z 109 ([71]Chen et al.,
2011). Peak 5 was identified as chlorogenic acid, which mainly formed
[M+H-192]+ by removing one molecule of quinic acid in the positive mode
and fragment ions of quinic acid (m/z 191) in the negative mode
([72]Ying et al., 2013a). Peak 6 was identified as caffeic acid, where
molecule of H[2]O was removed in the positive mode of mass spectrometry
and one molecule of CO[2] was removed in the negative mode,
corresponding to the carboxyl group of structure ([73]Chen et al.,
2011). Peak 7 was identified as ferulic acid, which could remove one
molecule of H[2]O, CO, and CH[3]OH to form corresponding fragment ions
in the positive mode, remove CH[3]• in the methoxy group on the benzene
ring to form radical ions of m/z 178 in the negative mode, and remove
CO[2] of carboxyl group on the side chain to form the fragment ion of
m/z 134 ([74]Hu et al., 2012). Peak 8 was identified as salvianolic
acid D, of which the main fragment ions were formed by firstly removing
one molecule of CO[2] and then removing one molecule of tanshinol (198
of molecular weight) in the positive and negative modes of mass
spectrometry ([75]Chen et al., 2011). Peak 9 was identified as
senkyunolide I, of which the dehydrated ion peak (m/z 207) was obtained
by removing one hydroxyl group from cyclohexane in the positive mode
and the corresponding fragment ions were obtained by further removing
the second hydroxyl group on the ring or the ester bond in the lactone
ring ([76]Hu et al., 2012). Peak 10 was identified as rosmarinic acid,
and its structure was formed by the condensation of one molecule of
caffeic acid and one molecule of tanshinol. Therefore, it could remove
one molecule of caffeoyl group (162 of molecular weight) or caffeic
acid (180 of molecular weight), and a molecule of tanshinol (198 of
molecular weight) to form each fragment ion in the mass spectrometry
([77]Chen et al., 2011). Peaks 11, 12, 13, and 14 were identified as
isosalvianolic acid A, salvianolic acid B, salvianolic acid A, and
isosalvianolic acid C, respectively. These compounds were similarly
cleaved in the mass spectrometry, with single or multiple molecules of
tanshinol (198 of molecular weight) removed to form each of the major
fragment ions ([78]Chen et al., 2011).
Table 1.
Identification results of main compounds in GXNT.
Peak No.^a RT (min) Main ions in the positive mode Main ions in the
negative mode Molecular weight Identification result Source
1 9.7 166[M+H]+;149[M+H-NH3]+;120[M+H-HCOOH]+;103[M+H-HCOOH-NH3]+
164[M-H]−;147[M-H-NH3]− 165 Phenylalanine Common
2 14.1 199[M+H]+ 197[M-H]-;179[M-H-H2O]-;133[M-H-H2O-HCOOH]- 198
Tanshinol^c Danshen
3 19.2 205[M+H]+;187[M+H-H2O]+ n.d.^b 204 Senkyunolide B Chuanxiong
4 21.7 n.d. 137[M-H]-;109[M-H-CO]- 138 Protocatechualdehyde^c Danshen
5 27.9 355[M+H]+、163[M+H-192]+ 707[2M-H]-;353[M-H]-;191[M-H-162]- 354
Chlorogenic acid^c Chuanxiong
6 29.1 181[M+H]+;163[M+H-H2O]+ 179[M-H]-;135[M-H-CO2]- 180 Caffeic
acid^c Common
7 42.9 195[M+H]+;177[M+H-H2O]+;149[M+H-H2O-CO]+;117[M+H-H2O-CO-CH3OH]+
193[M-H]-;178[M-H-CH3·]-•;134[M-H-CH3·-CO2]-• 194 Ferulic acid^c
Chuanxiong
8 55.3 419[M+H]+;177[M+H-CO2-198]+
835[2M-H]-;417[M-H]-;373[M-H-CO2]-;175[M-H-CO2-198]- 418 Salvianolic
acid D Danshen
9 60.6 225[M+H]+;207[M+H-H2O]+;189[M+H-H2O-H2O]+;161[M+H-H2O-HCOOH]+
n.d. 224 Senkyunolide I Chuanxiong
10 60.9 361[M+H]+;181[M+H-180]+;163[M+H-198]+
359[M-H]-;197[M-H-162]-;179[M-H-180]-;161[M-H-198]- 360 Rosemary acid^c
Danshen
11 64.4 495[M+H]+;297[M+H-198]+ 493[M-H]-;295[M-H-198]- 494
Isosalvianolic acid A Danshen
12 71.3 719[M+H]+;521[M+H-198]+;323[M+H-198-198]+
717[M-H]-;519[M-H-198]-;321[M-H-198-198]- 718 Salvianolic acid B^c
Danshen
13 81.9 495[M+H]+;297[M+H-198]+ 987[2M-H]-;493[M-H]-;295[M-H-198]- 494
Salvianolic acid A Danshen
14 95.6 n.d. 491[M-H]-;293[M-H-198]- 492 Isosalvianolic acid C Danshen
[79]Open in a new tab
^aPeak No. is consistent with the label of [80]Figure 1; ^bn.d. (not
detected) means not detected, no or below the detection limit;
^cCompound has been confirmed by reference standard.
Figure 3.
[81]Figure 3
[82]Open in a new tab
Mass spectrums and chemical structures of main compounds in GXNT.
Content Quantification of Seven Main Active Ingredients in GXNT
We took two samples each of GXNT, Danshen and Chuanxiong herbs in
parallel, calculated the amounts of the seven compounds confirmed by
reference standards using the corresponding standard curves listed in
[83]Table 2, and investigated the changes of the seven compounds in
each single Chinese herb and the Chinese compound formula. The results
are shown in [84]Table 3 in detail. In each extract, the seven
compounds accounted for about 6.5% of the total amount of extract from
GXNT. The amount of salvianolic acid B was much higher in the extract
of GXNT and Danshen than that in the extract of Chuanxiong. The current
quality standard of GXNT uses salvianolic acid B and ferulic acid to
control the quality of Danshen and Chuanxiong, respectively. However,
our results indicated that ferulic acid only accounts for 0.316% of the
Chuanxiong extract, which is not enough to reflect the quality of the
whole Chuanxiong extract. Therefore, the quality control indexes for
Chuanxiong need to be improved. In terms of raw drug content, the
amount of each compound is similar in the medicinal materials compared
to that in the compound formula, with some individual compounds having
a slightly lower amount in the compound formula.
Table 2.
Standard curves of seven main compounds.
Peak No. Compound name RT (min) Linear relation R^2 Linear range
(μg/mL)
2 Tanshinol sodium 13.7 y = 0.031x - 0.0658 0.9999 40.8~916.0
4 Protocatechualdehyde 21.6 y = 0.2065x - 0.5731 0.9999 23.1~462.4
5 Chlorogenic acid 27.6 y = 0.1021x - 0.2181 0.9999 21.8~436.8
6 Caffeic acid 28.9 y = 0.2006x - 0.4056 0.9999 21.0~419.2
7 Ferulic acid 42.6 y = 0.1855x - 0.4126 0.9999 21.4~428.8
10 Rosmarinic acid^a 60.7 y = 0.6489x + 5.0937 0.9744 40.2~803.2
12 Salvianolic acid B 71.3 y = 0.0673x - 0.1907 0.9999 198.4~3968
[85]Open in a new tab
^aRosmarinic acid was quantified by extracting ion peaks by mass
spectrometry.
Table 3.
Determination results of seven main compounds.
Peak No. Compound name Content in extract (%) Content of raw drug (%)
GXNT Danshen Chuanxiong GXNT Danshen Chuanxiong
2 Tanshinol 0.580 1.882 n.d. 0.090 0.125 n.d.
4 Protocatechualdehyde 0.135 0.763 n.d. 0.021 0.051 n.d.
5 Chlorogenic acid 0.084 n.d. 0.111 0.013 n.d. 0.017
6 Caffeic acid 0.097 0.082 0.069 0.015 0.005 0.011
7 Ferulic acid 0.362 n.d. 0.316 0.056 n.d. 0.049
10 Rosmarinic acid 0.551 1.547 n.d. 0.086 0.103 n.d.
12 Salvianolic acid B 4.676 12.337 n.d. 0.728 0.822 n.d.
Total 6.485 16.611 0.496 1.009 1.107 0.076
[86]Open in a new tab
n.d. (not detected) means not detected, no or below the detection
limit.
Screening of Potential Antithrombotic Targets for Active Ingredients in GXNT
[87]Table 4 shows that different active compounds in GXNT could act on
the same target, and the same active compound could also act on
different targets, reflecting multi-component and multi-target action
mode of GXNT. In specific, 83 unique targets were predicted from the 14
identified components using the Swiss Target Prediction analysis
platform. The targets were also mapped to 743 targets possibly related
to the occurrence and development of thrombus in the CooLGeN database,
and to 725 targets associated with the occurrence and development of
thrombus in the GeneCards database. Among them, 23 targets were also
found in the GeneCards database, and 25 targets were found in the
CooLGeN database. The intersection of these targets resulted in a total
of 17 potential antithrombotic targets of GXNT (shown in [88]Figure 4),
including MAPT, EGFR, KDR, MMP2, MMP9, MMP13, MMP1, MMP10, PTPN1, FYN,
SRC, PRKCA, PTGS1, PTGS2, JUN, EDNRA, and ALOX12.
Table 4.
Potential targets from active ingredient candidates of GXNT.
Active ingredient candidates Predicted targets
Phenylalanine CA12, CA1, CA2, ALPL, CA3, ALPI, CA6, CA5A, CA7,
CACNA2D1, CA9, CA14, PLAA, CA5B, CA13
Tanshinol CA12, CA1, CA2, CA3, CA6, CA5A, CA7, CA13, TDP1, CA14, CA5B,
MAPT, EGFR, ERBB2, LCK
Senkyunolide B MBNL1, MBNL2, MBNL3, MAPT, CYP19A1, FLT1, FLT4, KDR,
ESR1,ESR2, CDK1, CDK2, CDK4, CDK3, CDK6
Protocatechualdehyde COMT, CA1, CA2, CA3, CA5A, CA7, CA5B, CA13, TYR,
TDP1, MAPT, CA9, KDM4E, KDM4A, KDM4B
Chlorogenic acid AKR1B10, AKR1B1, AKR1B15, AKR1A1, AKR1E2, MMP2, MMP9,
MMP12, MMP13, MMP1, MMP3, MMP10, MMP27,MMP20, TDP1
Caffeic acid CA12, CA1, CA2, CA3, PTPN2, PTPN1, CA6, CA5A, CA7, CA9,
CA13,TDP1, CA14, CA5B, CA4
Ferulic acid CA12, CA1, CA2, CA3, CA6, CA5A, CA7, CA9, CA13, CA14,
CA5B, TDP1, AKR1B10, AKR1B1, AKR1B15
Salvianolic acid D EGFR, ERBB2, ERBB4, ERBB3, FYN, YES1, FGR, SRC, FRK,
ESR1, ESR2, MAPT, AKR1B10, AKR1B1, AKR1B15
Senkyunolide I PRKCG, PRKCB, PRKCA, PRKCQ, PRKCD, PTGS1, PTGS2, RELA,
REL, JUN, JUNB
JUND, CRYZ, ADORA1, EDNRA
Rosmarinic acid AKR1B10, AKR1B1, AKR1B15, TDP1, AKR1A1, AKR1E2, MMP1,
MMP2, MMP3, MMP9
MMP12, MMP13, MMP10, MMP27, FYN
Isosalvianolic acid A MMP1, MMP2, MMP3, MMP9,MMP8, MMP12, MMP13, MMP10,
MMP27, AKR1B10, AKR1B1, AKR1B15, AKR1A1, AKR1E2, TDP1
Salvianolic acid B MMP1, MMP2, MMP3, MMP9, MMP8, MMP12, MMP13, MMP10,
MMP27, PTGS1
PTGS2, AKR1B10, AKR1B1, AKR1B15, AKR1A1
Salvianolic acid A FYN, SRC, YES1, FGR, FRK AKR1B10, AKR1B1, AKR1B15,
AKR1A1, AKR1E2, TDP1
MMP1, MMP2, MMP3, MMP9
Isosalvianolic acid C AKR1B10, AKR1B1, AKR1B15, AKR1A1, AKR1E2, ALOX15,
ALOX12, TOP1, TOP1MT
EGFR, ERBB2, ERBB4, ERBB3, PTGS1, PTGS2
[89]Open in a new tab
Figure 4.
Figure 4
[90]Open in a new tab
Screening of targets for the identified components of GXNT on thrombus.
83 Predicted targets (in the blue circle) were mapped to 743
thrombus-related targets in CooLGeN (in the green circle), and to 725
thrombus-related targets in the GeneCards database (in the yellow
circle), respectively. Among them, 23 targets were found in the
GeneCards database, and 25 targets were found in the CooLGeN database.
The intersection of these targets resulted in a total of 17 potential
antithrombotic targets of GXNT.
Functional Pathway Notes of Active Component Candidate Targets in GXNT
We performed GO enrichment analysis for the above mentioned 17
candidate targets of the active ingredients from GXNT via Metascape
biomolecular function annotation system, which includes the analysis of
biological process, molecular function and cellular components, and
KEGG pathway annotation. And the top 10 terms with P < 0.05 are shown
in [91]Table 5. According to the screening results, the candidate
targets for the active components in GXNT were involved in the
biological process of response to oxidative stress, response to toxic
substance, and response to inorganic substance. In terms of the
molecular function, the targets were mainly related to
metalloendopeptidase activity, endopeptidase activity, and protein
domain specific binding. As for the cellular components, the
extracellular matrix and membrane microdomain were the main components
related to the targets. In addition, 34 pathways were revealed from the
KEGG pathway enrichment analysis, including TNF signaling pathway,
IL-17 signaling pathway, focal adhesion, MAPK signaling pathway, and
platelet activation. These findings suggest that GXNT may play an
antithrombotic role by regulating multi-dimensional signal cascades. In
specific, MAPK signaling pathway is known to play an important role in
the signal transduction in vivo and in maintaining the body's
biological metabolic balance ([92]Lien et al., 2017; [93]Manne et al.,
2018). Therefore, MAPK signaling pathway was selected in this study to
further explore the molecular mechanism of GXNT.
Table 5.
Gene Ontology (GO) and pathway enrichment analysis for active component
candidate targets of GXNT.
Category Term Number of the targets P-value
GO Biological process Response to oxidative stress 9 5.324E-12
Positive regulation of epithelial cell migration 7 1.274E-11
Response to inorganic substance 8 1.630E-09
Circulatory system process 7 4.640E-08
Regulation of mitochondrial membrane potential 4 1.496E-07
Positive regulation of blood vessel endothelial cell migration 4
2.702E-07
Response to toxic substance 6 1.001E-06
Superoxide anion generation 3 1.559E-06
Cellular response to amino acid stimulus 3 1.289E-05
GO Molecular Function Metalloendopeptidase activity 5 7.598E-09
Ephrin receptor binding 3 8.383E-07
Metallopeptidase activity 5 1.322E-07
Endopeptidase activity 5 9.480E-06
Peptidase activity, acting on L-amino acid peptides 5 4.938E-05
Peptidase activity 5 5.929E-05
Serine-type endopeptidase activity 3 0.0001949
Serine-type peptidase activity 3 0.0002867
Serine hydrolase activity 3 0.0003050
Protein domain specific binding 4 0.0012516
GO Cellular Components Membrane raft 6 5.15E-08
Membrane microdomain 6 5.24E-08
Membrane region 6 6.53E-08
Extracellular matrix 5 2.40E-05
Cytoplasmic side of membrane 3 0.000232
Perinuclear region of cytoplasm 4 0.0012385
Early endosome 3 0.0017933
Side of membrane 3 0.00704918
Postsynapse 3 0.0085988
KEGG Pathway Focal adhesion 6 3.33E-09
GnRH signaling pathway 5 4.29E-09
IL-17 signaling pathway 5 4.53E-09
VEGF signaling pathway 4 7.46E-08
Adherens junction 4 1.68E-07
ErbB signaling pathway 4 3.44E-07
Rap1 signaling pathway 4 1.21E-05
MAPK signaling pathway 4 2.59E-05
TNF signaling pathway 3 5.65E-05
Platelet activation 3 8.32E-05
[94]Open in a new tab
Construction of Component–Target Network
The active component–target network for GXNT on thrombus was
constructed using Cytoscape 3.6.1 software. The results showed that a
total of 98 nodes and 210 edges were in the identified GXNT
component–target network. Network topology analysis showed that the
degree and betweenness centrality of Isosalvianolic acid C were the
highest (degree=15, betweenness centrality=0.24921196), whereas the
degree and betweenness centrality of PTGS1, a target of Isosalvianolic
acid C, also ranked among the top (degree=3, betweenness centrality
=0.1234151), shown in [95]Figure 5.
Figure 5.
[96]Figure 5
[97]Open in a new tab
Component–target network for the identified components of GXNT on
thrombus. The component–target network was constructed by linking the
14 identified components and their potential targets. The green nodes
represent the potential targets and the pink nodes represent the
identified components. Edges represent the interactions between the
compounds and the targets in the network.
Construction of Component–Target–Pathway Network
The Cytoscape software was used to construct the identified
component–target–pathway network for GXNT on thrombus. As shown in
[98]Figure 6, the results showed that multiple targets were associated
with multiple components, indicating that different components in GXNT
had a synergistic effect in the process of exerting efficacy. The
action targets of active ingredients in GXNT were distributed in
different pathways and were coordinated with each other, suggesting
that the action mechanism of GXNT may be related to the currently known
effects of GXNT, such as anti-oxidative stress, anti-inflammation,
vascular expansion, anti-platelet aggregation, and protection of
vascular endothelium. The component–target–pathway network of GXNT
revealed through multiple pathways that GXNT has the characteristic of
multiple dimensions and functions for treating thrombotic
cardiovascular disease.
Figure 6.
[99]Figure 6
[100]Open in a new tab
Component–target–pathway network for the identified components of GXNT
on thrombus. The green nodes represent the identified components, the
blue nodes represent pathways, and the yellow nodes represent targets.
Edges represent the interactions between the compounds and the targets
in the network.
Effects of GXNT on the Length and Weight of Thrombus
We observed changes of thrombus lengths and weights in each group of
rats. As shown in [101]Figures 7A and B, we observed obvious thrombus
and significant weight increases in rats of the model group after
induction by FeCl[3]. When compared to the model group, GXNT with
incremental dosages (75, 150, and 300 mg/kg) could significantly reduce
the length and weight of thrombus (P < 0.01). In particular, 300 mg/kg
of GXNT was superior to clopidogrel in suppressing thrombus length. In
addition, the weight of thrombus decreased significantly as the dosage
of GXNT increased (P < 0.01).
Figure 7.
Figure 7
[102]Open in a new tab
Effects of GXNT on the length and weight of thrombus. (A) The length
and (B) the weight of thrombus were observed after GXNT administration
(75, 150, and 300 mg/kg) for 1 h. Data were expressed as the mean ± SEM
(± SEM, n=8). **P < 0.01 vs. model group.
Effects of GXNT on the Expressions of Fbg, PAI-1, 6-keto-PGF1α, and TXB[2]
Fbg is one of the indicators for anticoagulant system activity, and
PAI-1, 6-keto-PGF1α, and TXB[2] are the active markers in fibrinolytic
system ([103]Figures 8A–E). In [104]Figures 8A, B, we can see that the
Fbg and PAI-1 expression levels in the model group were significantly
increased (P < 0.05; P < 0.01), compared with those in the control
group. The expression levels of Fbg and PAI-1 were reduced in all GXNT
groups and the positive group (12.5 mg/kg clopidogrel), compared with
those in the model group (P < 0.01, P < 0.05; P < 0.01). As shown in
[105]Figure 8C, compared to the control group, the expression level of
6-keto-PGF1α in the model group was significantly decreased (P < 0.05),
and there was no significant change in TXB[2] expression (P > 0.05).
Furthermore, the 150-mg/kg GXNT group markedly increased the level of
6-keto-PGF1α (P < 0.01) compared to that in the model group, and no
significant change was observed in TXB[2] expression (P > 0.05).
Meanwhile, the 150- and 300-mg/kg GXNT group significantly decreased
the ratio of TXB[2]/6-keto-PGF1α compared with that in the model group
(P < 0.01, P < 0.01; [106]Figure 8E).
Figure 8.
Figure 8
[107]Open in a new tab
Effects of GXNT on Fbg, PAI-1, 6-keto-PGF1α, TXB2, and
TXB2/6-keto-PGF1α. (A) Fbg was measured using automatic blood
coagulation analyzer. Expression levels of (B) PAI-1, (C) 6-keto-PGF1α,
and (D) TXB2 were detected by the enzyme linked immunosorbent assay
(ELISA) method. (E) The ratio of TXB2/6-keto-PGF1α was then calculated.
Data were expressed as the mean ± SEM (± SEM, n=8). ^#P < 0.05, ^##P <
0.01 vs. normal group; *P < 0.05, **P < 0.01 vs. model group.
Effects of GXNT on Platelet Aggregation
The effect of GXNT on maximum platelet aggregation rate in rats is
shown in [108]Table 6. The results showed that GXNT could inhibit
maximum platelet aggregation rate induced by ADP. Compared with the
control group, maximum platelet aggregation rate significantly
increased in the model group (P < 0.01). Compared with the model group,
GXNT with different dosages could decrease maximum platelet aggregation
rate to varying degrees. Among them, 150 and 300 mg/kg of GXNT could
markedly reduce maximum platelet aggregation rate in rats (P < 0.01).
Table 6.
Effect of GXNT on maximum platelet aggregation rat.
Groups Drug and Doses Maximum platelet aggregation rate (%)
Control 10 mL/kg NS 49.33 ± 1.69
Model 10 mL/kg NS 69.17 ± 1.60^##
The low-dose group 75 mg/kg GXNT 54.83 ± 6.64
The middle-dose group 150 mg/kg GXNT 53.17 ± 2.39**
The high-dose group 300 mg/kg GXNT 52.00 ± 3.34**
The positive group 12.5 mg/kg Clopidogrel 41.50 ± 3.38**
[109]Open in a new tab
Data were expressed as the mean ± SEM (± SEM, n=8). ^##P < 0.01 vs.
normal group; **P < 0.01 vs. model group.
Effects of GXNT on Proteins Related to MAPK Signaling Pathway in Platelet
We observed expression changes of p-P38, p-ERK1/2 and p-JNK proteins in
the MAPK signaling pathways in each group. From [110]Figures 9A–C, it
could be seen that after platelet was induced by ADP, phosphorylation
levels of P38, ERK1/2, and JNK proteins were significantly increased
compared with those in the control group (P < 0.01; P < 0.05). Compared
with the model group, 150 mg/kg GXNT significantly decreased the
phosphorylation levels of P38 and ERK1/2 (P < 0.05), while 300 mg/kg
GXNT significantly lowered the phosphorylation levels of P38, ERK1/2,
and JNK (P < 0.01; P < 0.05).
Figure 9.
[111]Figure 9
[112]Open in a new tab
Effects of GXNT on the expressions of p-P38, p-ERK1/2, and p-JNK
proteins in platelets. The expressions of (A) p-P38, (B) p-ERK1/2, and
(C) p-JNK proteins in platelets were determined by western blotting
analysis after GXNT administration (75, 150, and 300 mg/kg). Data were
expressed as the mean ± SEM (± SEM, n=3). ^#P < 0.05, ^##P < 0.01 vs.
normal group; *P < 0.05, **P < 0.01 vs. model group.
Discussion
GXNT consists of extracts from two Chinese herbal medicines, Danshen
and Chuanxiong, with the effect of promoting blood circulation and
removing blood stasis. The two herbs are compatible with each other to
make the blood and qi of human body run smoothly ([113]Zhang, 2017).
GXNT has excellent treatment effects on stable or unstable coronary
heart diseases and angina pectoris of qi stagnation and blood stasis
type triggered by thrombus in the clinic ([114]Huo et al., 2016;
[115]Yang et al., 2017; [116]Li et al., 2018). In this study, the
pharmacological active substances of GXNT were analyzed, and the action
mechanism of these active ingredients was predicted by the approach of
network pharmacology, revealing that multiple pathways (such as MAPKs,
VEGF, and TNF) are related to the antithrombotic mechanism. The
experiments not only further confirmed the antithrombotic effect of
GXNT, but also proved that the MAPKs pathway is an important action
target. Thus, it showed that network pharmacology could provide
high-value insights and reference information for studying the action
mechanism of traditional Chinese medicines with complex components.
Thrombosis is an important pathophysiological process involved in
various cardiovascular diseases ([117]Otsuka et al., 2016; [118]Ten et
al., 2017), and its formation is a complicated process of multifactor
participation and gradual development. Abnormal coagulation of blood
occurs in the state of flow, due to activation of platelets and
clotting factors ([119]Xu et al., 2009). The conditions of thrombosis
include vascular intimal injury, changes in blood state, and increased
coagulation. Therefore, inhibiting platelet function and preventing
blood coagulation can prevent thrombosis. Currently, antiplatelet and
antithrombin drugs are often used clinically for treating thrombus,
such as platelet cyclooxygenase inhibitor aspirin, platelet ring
nucleotide inhibitor dipyridamole, ADP P2Y12 receptor antagonist
clopidogrel, and GPIIb/IIIa receptor inhibitor abicximab. However, most
of these drugs only have a single target, therefore often require a
combination therapy with the risk of causing gastrointestinal
hemorrhage ([120]Rocca and Husted, 2016; [121]Dregan et al., 2018).
Hence, developing antithrombotic agents with multiple targets and a low
risk of hemorrhage from medicinal herbs is of great significance. Our
previous studies have shown that GXNT has anti-platelet aggregation and
antithrombotic effects ([122]Chen et al., 2005; [123]Wang et al.,
2016), which was again validated using the FeCl[3]-induced rat thrombus
model in this experiment. Common carotid artery thrombus model in rats
induced by FeCl[3] is widely used in the preparation of arterial models
and in the research of antithrombotic drugs ([124]Kurz et al., 1990).
The results of this experiment showed that the length and weight of
thrombus were increased significantly after the formation of common
carotid artery thrombus in rats. The administration of GXNT could
significantly reduce the length and weight of thrombus. Furthermore,
the antithrombotic effect was dose-dependent, indicating that GXNT is
strongly resistant to FeCl[3]-induced thrombus. Fe^3+ can damage
vascular endothelial cells and cause platelet activation and
aggregation, due to a joint action of the coagulation system and the
hemolysis system. When endothelial cells are damaged, the internal and
external coagulation systems are activated. Thrombin activates
platelets and converts Fbg to fibrin, which in turn activates the
coagulation response system. Thus, Fbg plays a major role in the blood
coagulation process, while PAI plays a major role in regulating plasma
fibrinolytic activities. In addition, TXA[2] is a biologically active
substance that strongly promotes vasoconstriction and platelet
aggregation, whereas PGI[2] can dilate blood vessels and increase
platelet cAMP to inhibit platelet aggregation. The dynamic balance
between TXA[2] and PGI[2], i.e. ratio of TXA[2]/PGI[2], plays an
important role in maintaining the function of platelet and vessels and
the regulation of thrombosis. Because of the instability of TXA[2] and
PGI[2], their stable metabolites (TXB[2] and 6-keto-PGF1α) were used as
detection indicators in this study ([125]Xie et al., 2017; [126]Cui et
al., 2018). Our results showed that the level of Fbg was increased
significantly in the thrombus model rats, whereas GXNT reduced the
levels of Fbg and PAI, and regulated the balance of 6-keto-PGF1α and
TXB[2]. The results demonstrated that GXNT could ameliorate the
hypercoagulable state of the body blood, and maintain the balance of
the body's coagulation system and anticoagulant system, thereby
achieving an antithrombotic effect.
In this study, 14 potential active components were identified using
LC-MS technology. In specific, 8 were from Danshen, 4 were from
Chuanxiong, and 2 were common components in both. In general, most
compounds had a certain response in both positive and negative modes,
but the negative mode response was slightly higher than the positive
mode response. This is mainly due to the fact that GXNT contains more
salvianolic acids from Danshen, which is more prone to give protons in
the negative mode to obtain higher ion response. Nevertheless, some of
the compounds only responded in the positive mode, mainly due to the
fact that the lactones from Chuanxiong could only bind to protons and
are difficult to give protons. Among these components, salvianolic acid
B, salvianolic acid A, ferulic acid, chlorogenic acid, caffeic acid,
rosemary acid, tanshinol, and protocatechualdehyde have already shown
antithrombotic effects to some extent in previous studies ([127]Jiang
et al., 2005; [128]Moon et al., 2012; [129]Chen et al., 2018b). The
modern pharmacological research showed that tanshinol could dilate
blood vessels, promote fibrinolysis, reduce blood viscosity, and
promote local blood circulation, thereby exerting antithrombotic
effects ([130]Dang et al., 2015). Salvianolic acid could inhibit
arterial thrombosis by restraining platelet adhesion, aggregation, and
downstream Ca^2+ and cAMP signaling pathways ([131]Hong et al., 2016).
In addition, the antithrombotic effect of chlorogenic acid may be
closely related to the adenosine A[2A] receptor/adenylate
cyclase/cAMP/PKA signaling pathway ([132]Fuentes et al., 2014). Thus,
it can be seen that the multi-level and multi-target effects were
determined by multiple active ingredients of GXNT. We will confirm
whether these active ingredients have synergistic effects in our future
experimental studies.
To understand the antithrombotic mechanism of GXNT, we used network
pharmacology for the analysis. It turned out that the 14 components in
GXNT were closely related to 83 targets, of which 17 potential
antithrombotic targets were obtained through functional enrichment
analysis and are involved in pathways including MAPK signaling pathway,
TNF signaling pathway, and VEGF signal transduction pathway. Based on
this finding, MAPKs signaling pathway, which is known to be strongly
linked to thrombotic diseases ([133]Endale et al., 2012; [134]Lien et
al., 2017; [135]Manne et al., 2018), was selected to verify the
antithrombotic mechanism of GXNT. The MAPKs signaling pathway exists in
most cells. Recent studies have shown that the MAPKs signaling pathway
is an important platelet activation pathway that can be activated by
collagen and thrombin to mediate platelet deformation, adhesion, and
aggregation reaction, thereby participating in thrombus formation
([136]Lien et al., 2017; [137]Manne et al., 2018). The signaling
pathway includes three subfamilies: extracellular signal regulated
protein kinase (ERK), c-Juc amino-terminal kinase (JNK), and p38
protein kinase (p38 MAPK) ([138]Lanna et al., 2017). The study by
Yacoub ([139]Yacoub et al., 2006) has shown that the activation of ERK
and p38 MAPK plays an important role in the release of TXA[2] mediated
by PKC. After being stimulated from collagen and thrombin, the
phosphorylation level of PKCδ was increased in platelets, leading to an
activation of p38 MAPK and ERK as well as a release of TXA[2].
Simultaneously, the p38 MAPK signaling pathway is involved in the
synthesis of platelet cells and backbone proteins. Activated p38 MAPK
can induce regeneration and reorganization of actin and dynamic changes
of platelet cytoskeleton via regulating the activity of heat shock
protein (HSP27) and the level of downstream vasodilation-stimulated
phosphoprotein (VASP), and can therefore cause platelet degeneration
and promoting thrombosis ([140]Mazharian et al., 2007). It has been
found that JNK1 is involved in platelet aggregation and thrombosis. An
in vitro study using rats with JNK1 deficiency ([141]Adam et al., 2010)
has shown an activation of integrin αIIbβ3 by PKC, a reduction of
platelet aggregation, and an occurrence of platelet secretion disorder,
indicating that JNK1 may play a key role in platelet biology and
thrombosis. Moreover, some active components in GXNT such as caffeic
acid ([142]Lu et al., 2015), salvianolic acid B ([143]Li et al., 2010),
and ferulic acid ([144]Hong et al., 2016) may be involved in the
regulation of MAPKs signaling pathway. In our study, platelet
aggregation was promoted by ADP as the inducer, and the phosphorylation
levels of ERK, p38, and JNK proteins in MAPKs signal pathway of
platelets were determined by Western Blot. The results showed that GXNT
inhibited ADP-induced platelet aggregation. In addition, the
phosphorylation levels of p38 MAPK, ERK, and JNK in rat platelets were
all significantly increased compared to those in the control group,
suggesting that ADP may affect platelet function by influencing the
phosphorylation levels of p38 MAPK, ERK, and JNK proteins in the MAPKs
signaling pathway. After the intervention with GXNT, the
phosphorylation levels of p38 MAPK, ERK, and JNK proteins were all
decreased, especially the phosphorylation level of p38 MAPK protein.
Hence, GXNT had clear anti-platelet aggregation and antithrombotic
effects, which may be achieved through reducing the phosphorylation
levels of p38 MAPK, ERK, and JNK in MAPKs signaling pathway.
Conclusion
In conclusion, 14 active ingredients of GXNT were identified in this
study, and the antithrombotic and antiplatelet aggregation effects of
GXNT were further confirmed. Through the approach of network
pharmacology, 34 signal pathways were predicted to be involved in
thrombus (including MAPKs, VEGF, and TNF), and the role of MAPKs signal
pathway in thrombotic diseases was verified. We further showed that the
antithrombotic mechanism of GXNT may be associated with suppressing the
phosphorylation of p38MAPK, ERK, and JNK in the MAPKs signaling
pathway. The results from this study provided a reference for future
studies on the action mechanism of GXNT for treating thrombotic
diseases, as well as demonstrated that network pharmacology approaches
can be used to predict the action mechanism of traditional Chinese
medicine with complex components.
Data Availability Statement
All datasets generated for this study are included in the
article/supplementary material.
Ethics Statement
The animal study was reviewed and approved by Institutional Animal Care
and Use Committee of Zhejiang Chinese Medical University.
Author Contributions
M-LC contributed to the design concepts of this whole study. X-HY,
Z-WZ, M-LW, and Y-YL carried out the study and collected important
background information. M-LW, Y-YL, Q-QY, and Y-SW drafted the
manuscript. Q-QY and Q-XM carried out literature search, data
acquisition and analysis, and manuscript revision and edition. Q-YS
helped perform the analysis with constructive discussions. All authors
have read and approved the content of the manuscript.
Funding
This research was funded by Key Projects of Zhejiang Provincial
Administration of Traditional Chinese medicine (2015ZZ009) and Zhejiang
Science and Technology Department Public Welfare (Experimental Animal
Platform) Project (2018C37129).
Conflict of Interest
M-LW, X-HY, Z-WZ, were employed by company Chiatai Qingchunbao
Pharmaceutical Co., Ltd.
The remaining authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
References