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Author’s Accepted Manuscript
The therapeutic effect of Ilex pubescens extract on
blood stasis model rats according to serum
metabolomics
Di Cao, Chuncao Xu, Yuanyuan Xue, Qingfeng
Ruan, Bao Yang, Zhongqiu Liu, Hui Cui, Lei
Zhang, Zhongxiang Zhao, Jing Jin
www.elsevier.com/locate/jep
PII:
DOI:
Reference:
S0378-8741(18)31319-9
https://doi.org/10.1016/j.jep.2018.08.026
JEP11484
To appear in: Journal of Ethnopharmacology
Received date: 17 April 2018
Revised date: 15 August 2018
Accepted date: 19 August 2018
Cite this article as: Di Cao, Chuncao Xu, Yuanyuan Xue, Qingfeng Ruan, Bao
Yang, Zhongqiu Liu, Hui Cui, Lei Zhang, Zhongxiang Zhao and Jing Jin, The
therapeutic effect of Ilex pubescens extract on blood stasis model rats according
to
serum
metabolomics, Journal
of
Ethnopharmacology,
https://doi.org/10.1016/j.jep.2018.08.026
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The therapeutic effect of Ilex pubescens extract on
blood
stasis
model
rats
according
to
serum
metabolomics
Di Caob1, Chuncao Xua1, Yuanyuan Xueb, Qingfeng Ruanb, Bao Yangb, Zhongqiu Liub, Hui
Cuib, Lei Zhangb, Zhongxiang Zhaob*, Jing Jina*
a
School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou 510006, China
b
School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou,
510006, China
zzx37@163.com
jinjing@mail.sysu.edu.cn
*
Corresponding author. School of Pharmaceutical Sciences, Guangzhou University of Chinese
Medicine, Guangzhou, 510006, China. Tel.: +86 20 39358325; fax: +86 20 39358253.
*
Corresponding author. School of Pharmaceutical Sciences, Sun Yat-Sen University,
Guangzhou, 510006, China. Tel.: +86 20 39943034; fax: +86 20 39943000.
1
Co-first author.
1
ABSTRACT
Ethnopharmacological relevance
Ilex pubescens Hook. et Arn (MDQ), a traditional Chinese herb, is used in the treatment of
cardiovascular diseases. However, the mechanisms underlying the preventive effect of MDQ
on blood stasis remain unclear.
Aim of the study
In this study, serum metabolomics integrated with a biochemical assay strategy were
established to evaluate the preventive effect and mechanism of action of MDQ on rats with
acute blood stasis.
Materials and methods
Forty-nine rats were divided into seven groups: the control group, model group, aspirin
treatment group (30 mg/kg), clopidogrel treatment group (8 mg/kg) and three MDQ treatment
groups (250, 500 and 1000 mg/kg). A hybrid quadrupole time of flight mass spectrometry
(QTOF/MS) coupled to ultra-high-performance liquid chromatography (UPLC) was applied
for profiling the serum metabolites. The multivariate data analysis techniques using
unsupervised principal component analysis (PCA) and supervised orthogonal projections to
latent structures discriminant analysis (OPLS-DA) were used for pattern recognition and
distinguishing variabilities among groups.
Results
MDQ protected the rats against blood stasis, as evidenced by the restoration of the
anti-platelet aggregation activity, fibrinogen concentration, prothrombin time, thrombin time,
activated partial thromboplastin time, endothelial nitric oxide synthase, endothelin,
thromboxane B2 and 6-keto-prostaglandin F1α. The combination of PCA and OPLS-DA
revealed deviations in eighteen differential biomarkers in serum. The identified biomarkers
were primarily engaged in the metabolic pathways including arachidonic acid metabolism,
glycerophospholipid metabolism, phospholipid biosynthesis and bile acid biosynthesis. The
levels of eleven biomarkers showed significant alterations and a tendency to be restored to
normal values in MDQ-treated blood stasis rats. Moreover, a correlation network diagram was
constructed to show the serum biomarkers perturbed by MDQ.
Conclusions
2
These results suggested that MDQ had preventive effects on blood stasis in rats via
arachidonic acid metabolism and glycerophospholipid metabolism.
Abbreviations: ADP, adenosine diphosphate; ANOVA, one-way analysis of variance; APTT,
activated partial thromboplastin time; ASP, aspirin; CLO, clopidogrel; CVD, cardiovascular
diseases; eNOS, endothelial nitric oxide synthase; ET, endothelin; FIB, fibrinogen
concentration; lysophosphat LPC, lysophosphatidylcholine; LPG, lysophosphatidylglycerol;
MDQ, Mao-Dong-Qing; NMR, nuclear magnetic resonance; OPLS-DA, orthogonal
projections to latent structures discriminant analysis; PAR, platelet aggregation rate; PC,
phosphatidylcholine; PCA, principal component analysis; PE, phosphatidylethanolamine; PG,
phosphatidylglycerol; PT, prothrombin time; QC, quality control; TT, thrombin time; TXB2,
thromboxane B2; UPLC-QTOF/MS, ultra-high-performance liquid chromatography coupled
to quadrupole time of flight mass spectrometry; VIP, variable importance in projection;
Keywords: Ilex pubescens; acute blood stasis; UPLC-QTOF/MS; metabolic phenotyping
3
1. Introduction
Cardiovascular diseases (CVD) represent a leading global disease burden for both
morbidity and mortality (Roth et al., 2017). As a result of the increasing incidence of CVD,
efficient drugs are constantly being sought to both prevent and treat CVD. Traditional
Chinese Medicines (TCM), which possess characteristics of multi-component, multi-channel,
multi-target approaches to treating CVD, have attracted more and more attention (Hao et al.,
2017). TCM have been widely applied for intervention in different diseases for a long time
(Chen et al., 2018; Lin et al., 2015; Wang et al., 2018; Wang et al., 2017a). Researchers have
demonstrated that some TCM, such as Salvia miltiorrhiza Bge, Panax ginseng C. A. Mey,
Astragalus membranaceus (Fisch.) Bunge and Ophiopogon japonicus (L.f) Ker-Gawl were
shown to be effective for the therapy of CVD by improving blood circulation and removing
blood stasis (Layne et al., 2017; Liperoti et al., 2017).
The roots of Ilex pubescens Hook. et Arn, originally known as Mao-Dong-Qing (MDQ),
are a commonly used TCM that is widely thrives throughout southern China. In TCM
practice, it is primarily applied to treat CVD such as coronary heart disease, myocardial
infraction, cerebral thrombosis, thromboangiitis obliterans and thrombophlebitis (Wang et al.,
2008). It is also an indispensable ingredient of many medical prescriptions such as the
Mao-Dong-Qing capsule, Mao-Dong-Qing injection and Mao-Dong-Qing tablet for treating
CVD and thromboangiitis obliterans. Pharmacological researchers have shown that MDQ
extracts possess antithrombotic activity, and they have improved endothelial injuries (Chen et
al., 2015; Han et al., 1987; Xiong et al., 2013). Triterpenoid saponins, including the
ilexsaponin B3, ilexoside D and ilexolic acid isolated from MDQ, reportedly exert
anti-coagulant and anti-thrombotic activities (Han et al., 1987; Han et al., 1993; Xiong et al.,
2012; Zhang et al., 2003). The compound ilexsaponin A1 has also been found to rescue blood
vessels loss in vascular insufficient zebrafish (Li et al., 2017). However, the mechanisms
underlying the anti-thrombosis effect of MDQ remain unclear.
TCM theory held that blood stasis was described as a slowing down of the blood flow
owing to the disruption of the heart Qi, and it was a widely established model for assessing
the antithrombotic property of TCM (Dang et al., 2015; Jin et al., 2016; Zou et al., 2015).
This issue has been explained in pathology as a status resulting from sluggish blood flow in
4
vivo or the permeation of blood into perivascular tissues and fails to disperse (Wang et al.,
2016). In addition, its main clinical manifestations includes haemorheology, platelet function,
vascular endothelial function and coagulation function (Liu et al., 2012).
Metabolomics is a holistic analytical approach to the low-molecular-weight endogenous
metabolites in various biological samples, and it is widely employed to disease diagnosis and
prognosis, disease biomarker discovery, and pharmacological and toxicological evaluation
(Doerr, 2017; Wishart, 2016; Zhang et al., 2017; Zhao et al., 2012). Metabolic phenotyping
showed potential in the bioactivity and toxicity evaluation of TCM, which coincides with the
holism concept of TCM. (Chen et al., 2016; Wang et al., 2017b; Zhang et al., 2015).
Analytical
techniques
such as
nuclear
magnetic resonance
(NMR)
and
liquid
chromatography coupled with mass spectrometry (LC-MS) are primary used for
metabolomic study. LC-MS is less reproducible than NMR, and both approaches are
complementary. NMR-based metabolomics are usually performed to profile the
metabolic mechanism of TCM, but this approach poses the problem of overlapping NMR
signals
that
hinder
robust
metabolite
identification
(Porzel
et
al.,
2014).
Ultra-high-performance liquid chromatography coupled with quadrupole time of flight mass
spectrometry (UPLC-QTOF/MS) has higher sensitivity and specificity for metabolite signals,
and it is recommended for identifying and quantifying the hundreds of molecular species
found in various biological samples (Cheng, 2012; Dunn et al., 2011; Haggarty et al., 2017).
UPLC-QTOF/MS-based metabolomics is particularly suitable for metabolite identification
(Zhao et al., 2014). However, the serum metabolomics using UPLC-QTOF/MS has not been
applied to studying the intervention mechanism of MDQ on blood stasis model rats.
In this study, an acute blood stasis model was established using adrenaline combined with
an ice bath. Biochemical indexes such as haemorheology and coagulation functions were
evaluated for the effects of MDQ on the model rats. Furthermore, UPLC-QTOF/MS-based
serum metabolomics was applied to find differential metabolites and metabolic pathways
associated with blood stasis to elucidate the biochemical mechanism underlying the
anti-thrombosis activity of MDQ.
2. Materials and methods
5
2.1. Experimental reagents
Clopidogrel hydrogen sulphate tablets and adrenaline hydrochloride injections were
manufactured by Sanofi Winthrop Industrie (Paris, France) and Grand Pharmaceutical Co.,
Ltd. (Wuhan, China), respectively. Enteric-coated asprin tablets were produced by Bayer
HealthCare Manufacturing S.r.l. (Leverkusen, Germany). The ELISA kits for detecting
endothelial nitric oxide synthase (eNOS), endothelin (ET), thromboxane B2 (TXB2) and
6-keto-prostaglandin F1α (6-keto-PGF1α) were purchased from Cusabio Biotech Co. Ltd.
(Wuhan, China). High-performance liquid chromatography (HPLC)-grade acetonitrile was
from Fluka (Sigma, USA). The aqueous solutions were prepared with deionized water, which
was purified by a Pall Cascada laboratory water system (USA).
2.2. Experimental rats
A total of 49 male Sprague-Dawley rats (Certificate no. SCXK 2011-0015) with body
weights of 200 ± 20 g were purchased from the laboratory animal centre at Southern Medical
University (Guangdong, China). They were maintained at a constant temperature and
humidity-controlled environment (25 ± 2 °C, 55 ± 10% humidity) under a 12/12 h dark/light
cycle. The animals were fed standard chow and water ad libitum during the study. All the
experiments were strictly conducted according to the Guide for the Care and Use of
Laboratory Animals of Guangzhou University of Chinese Medicine and the National Institute
of Health and Nutrition Guidelines for the Care and Use of Laboratory Animals.
2.3. Herbal preparation
MDQ was obtained from Guangzhou Medicine Company (Guangzhou, China) and
authenticated by Professor Zhongxiang Zhao at Guangzhou University of Chinese Medicine
(Guangzhou, China). MDQ was extracted with 75% ethanol (v/v) for 2 h by
heating reflux under a reduced pressure of 0.5 MPa. The extraction solution was filtered and
concentrated at 70 °C by a rotary evaporator. Eventually, the solution was converted into
powder by freeze drying under a vacuum. The residue was dissolved in saline at
6
concentrations of 25, 50 and 100 mg/mL for the following experiments.
The primary compounds, including ilexoside O (C53H86O22), ilexsaponin B3 (C47H76O18),
pedunculoside (C36H58O10), ilexsaponin B2 (C47H76O17), ilexsaponin A1 (C36H56O11),
ilexsaponin B1 (C41H66O13), ilexgenin A (C30H46O6) and calenduloside E (C36H56O9), were
identified in the MDQ extract by UPLC-QTOF/MS (Cao et al., 2017). The total ion
chromatogram (TIC) of the MDQ extraction is presented in Fig. S6.
2.4. Experimental design
Forty-nine rats were randomly divided into seven groups (n = 7) as follows: a control
group, model group, aspirin group (ASP, 30 mg/kg), clopidogrel group (CLO, 8 mg/kg) and
MDQ groups (250, 500 and 1000 mg/kg). The rats received the treatments by gavage for
seven days consecutively. The animals in the control and model groups were both
administered with saline solution. After the seventh administration, the rats except for control
group subcutaneously injected twice with adrenaline hydrochloride injection at a dose of 0.8
mg/kg at 4 h intervals, and they were made by being soaked in ice-cold water (0 - 4°C) for 5
minutes during the interval between two injections, while keeping their heads above the
surface. All rats were fasted with free access to water for 12 h before the procedures.
2.5. Biochemical assay
2.5.1. Determination of four coagulation tests
Blood was collected into test-tubes treated with 3.8% sodium citrate from the abdomen
aortas of rats. After being centrifuged at 850 RCF for 15 min, the fibrinogen concentration
(FIB), prothrombin time (PT), thrombin time (TT) and activated partial thromboplastin time
(APTT) of the plasma were investigated by an automatic coagulation instrument (CA7000,
Sysmex Medical Electronics Co., Ltd., Shanghai, China). The tests were accomplished
within 3 h after collecting the blood.
7
2.5.2. Platelet aggregation assay in vitro
Platelet-rich plasma (PRP) was received after centrifugation of citrated whole blood at 90
RCF for 10 min, whereas platelet-poor plasma (PPP) was obtained by centrifugation at 850
RCF for another 10 min. PRP and PPP were transferred into the special cuvettes, which were
put into the reaction chamber of the aggregometer. After incubation at 37 °C for 5 min,
platelet aggregation was induced by adding adenosine diphosphate (ADP), and the maximum
aggregation degree of platelets in the PRP was detected by platelet aggregometer
(560-CA, Chrono-Log Corp., State College, PA, USA).
2.5.3. Measurement of the serum level of TXB2 and 6-keto-PGF1α
Blood withdrawn from the abdominal aortas were centrifuged at 630 RCF for 15 min
after the samples were left to standing for an hour at room temperature. For the assay of
TXB2 and 6-keto-PGF1α, the obtained serum samples were assessed by means of
radioimmunoassay and microplate reader (Thermo, Multiskan Spectrum, Vantaa, Finland)
referred to the manuals of the manufacture.
2.5.4. Measurement of the plasma level of ET and eNOS
Blood samples were promptly drawn into tubes pre-coated with EDTA-Na2 from the
abdominal aortas. After being centrifuged at 630 RCF for 15 min, the ET and eNOS levels
were analyzed by the radioimmunoassay kits in accordance with the instructions.
2.6. Preparation of serum samples for metabolic profiling
The blood drawn from the abdominal aortas was centrifuged at 630 RCF for 15 min at
4 °C. The supernatant was then sub-aliquoted (120 μL) into labelled tubes and stored at
-80 °C before metabolic analysis. After all the serum samples were thawed at 4 °C, each
aliquot (100 μL) serum was mixed with methanol (300 μL) and then vortexed for 3 minutes.
The resulting supernatant used for further UPLC/MS analysis was obtained from the mixture
centrifuged at 12000 RCF for 10 min at 4 °C.
8
A mixture consisting of equal volumes (20 μL) of all the serum samples was labelled as
quality control (QC) sample. To monitor the reproducibility and reliability of the analysis
system, QC samples were run before, within and after the analyzed sequence. Blank solvent
samples were arranged alongside the QC samples to identify the impurity of the solvents or
to check the contamination from intense analytes.
2.7. Chromatographic separation
Separation of the metabolites was conducted on a Shimadzu UPLC system (Nexera
UHPLC LC-30A, Japan) equipped with an Acquity BEH C18 column (2.1 mm × 100 mm, 1.7
μm). Mobile phase A was acetonitrile modified by the addition of 0.1% formic acid, while
phase B was 0.1% formic acid-water. A solvent gradient system was used: 70-65% B from 0
to 4.5 min, 65-56% B from 6.5 to 8.0 min, 56% B from 8.0 to 9.0 min, 56-15% B from 9.0
min ~ 12.0 min, 15% B from 12.0 to 14.0 min. The column temperature was 40 C, and the
volume of injection was 3 μL for each run.
2.8. Mass spectrometry
Metabolomic analysis was performed on an AB SCIEX Triple TOFTM 5600+ (Foster City,
CA), equipped with a Duo Spray Ion Source. Analyst®TF 1.7 software (AB Sciex, Foster
City, CA) was employed for acquiring the raw data. The mass scan range was 100 to 1200
Da for TOF-MS and 50 to1000 Da for TOF-MS/MS, and the ion accumulation time was
250.0 ms. The collision energy and collision energy spread were set as 45 eV and 15 eV,
respectively. The source parameters were adjusted to a turbo spray temperature of 550 C,
curtain gas to 35 psi, heater gas to 55 psi, nebulizer gas to 55 psi and declustering potential to
100 V, respectively. The ion spray voltage was 4500 V. The information dependent
acquisition (IDA) with dynamic background subtraction (DBS) was configured to trigger the
acquisition of the MS/MS for trace ingredients. The criteria for IDA was set for the eight
most intense peaks, which exhibited counts higher than 100 cps. Additionally, the
accurateness of the MS and the MS/MS was corrected automatically by calibration delivery
system (CDS) at every 3 h.
9
2.9. Data Processing
2.9.1. Statistical analysis of pharmacological aspects
Experimental data were presented as the means ± standard deviation (SD). All statistical
analyses were carried out using SPSS package (Version 17.0, SPSS Inc., US). The
differences between the groups were assessed by a one-way analysis of variance (ANOVA)
and LSD test. Differences with P < 0.05 indicated statistical significance.
2.9.2. Statistical analysis of metabolic profiling
The LC-MS original datasets were pretreated by MarkerView software (AB SCIEX,
Foster City, CA), which allowed peak detection, alignment and data filtering, yielding a table
of mass, retention time (RT) and associated ion intensities. The primary parameters in
MarkerView were set as follows: RT range, 0.1-14 min; minimum spectral peak width, 30
ppm; minimum RT peak width, 5 scans; subtraction offset, 10; subtraction mult.factor, 1.3;
noise threshold, 100; RT tolerance, 0.1 min; mass tolerance, 10 ppm; remove peaks in less
than, 20 samples; maximum number of peaks, 8000; and area reporting, use areas integrated
from raw data. The aligned data matrix was normalized using total area normalization. In
addition, the drug and MDQ metabolites observed in serum samples were removed from the
dataset before the metabolic profiling.
SIMCA 14.1 software (Umetrics, Sweden) for multivariate statistical analysis was used
for processing the data matrices. The multivariate data analysis techniques using
unsupervised principal component analysis (PCA) and supervised orthogonal projections to
latent structures discriminant analysis (OPLS-DA) were used for pattern recognition and
distinguishing variabilities among groups. S-plots were created to identify variables by
visualizing the covariance and correlation within the OPLS-DA data. Metabolic variables
with high influence and the variable importance in projection (VIP) values > 1.5 was taken
for further analysis. Furthermore, differential biomarker candidates between the model and
control groups were evaluated by independent t-test. An ANOVA with an LSD test was
performed to compare the potential metabolite markers in MDQ-treated groups with those in
10
the other groups. The significant p-values for both the ANOVA and t-tests were adjusted
using the Benjamini-Hochberg method (Benjamini et al., 1995) for multiple-hypothesis
testing with a false discovery rate (FDR) less than 0.05.
The identification of differential metabolites was primarily dependent on online MS
databases (Metlin, HMDB and Lipidmaps) and LipidViewTM 1.2 software (AB Sciex, Foster
City, CA). Several steps for identifying metabolites were performed as follows: (1) an
accurate molecular weight was used to find the possible metabolites within an error of 5 ppm;
(2) MS/MS fragment spectra from an extracted ion chromatogram (EIC) were compared with
MS/MS information on possible metabolites provided by the above databases to confirm an
identified metabolite; and (3) reasonable fragmentation pathways and retention times were
verified to support the correctness of the identified metabolite.
The metabolic pathway analysis based on the identified biomarkers was conducted using
Metabolomic Pathway Analysis (MetPA), which is a comprehensive tool dedicated to
identifying the potential associated metabolic pathways.
3.0. Results
3.1. Coagulation parameters and platelet aggregation
The effects of MDQ on blood coagulation function were measured by assessment of TT,
PT, APTT and FIB contents in the plasma. As illustrated in Table 1, the APTT, TT and PT
levels were significantly shortened, and the FIB level was significantly elevated in model
group rats in comparison with the normal controls (P < 0.01). The MDQ pretreatment at the
1000 mg/kg dose was able to clearly increase the APTT, TT and PT as well as lower the FIB
effectively (P < 0.01).
The effects on the platelet aggregation rate (PAR) in vitro are also shown in Table 1. The
platelet aggregation induced by ADP (P < 0.01) could be distinctly inhibited by the MDQ
treatments at 500 and 1000 mg/kg.
3.2. Effects of MDQ on the ET, eNOS, TXB2 and 6-keto-PGF1α
11
The results for the TXB2, 6-keto-PGF1α, ET and eNOS for each group are presented in
Fig.1. The ET level in plasma of the rats with blood stasis was significantly higher than the
control group (P < 0.01), whereas the eNOS level in model rats was markedly lower than
that of the control group (P < 0.01). The eNOS level was significantly up-regulated, and the
ET level was significantly down-regulated by MDQ (500 and 1000 mg/kg) compared to the
model rats (P < 0.05 and P < 0.01). In Fig. 1C, treating with MDQ (500 and 1000 mg/kg)
could markedly offset the elevation in the TXB2 content (P < 0.05 and P < 0.01) and the
reduction in the 6-keto-PGF1α content in the model rats (P < 0.01).
3.3. UPLC-QTOF/MS metabolomic analysis
As shown in Fig. S1, the TICs of 17 QC samples in both negative and positive mode
overlapped well, which demonstrated that the UPLC-QTOF/MS method had good
reproducibility. A total of ten peaks in negative ion mode were selected to evaluate the
repeatability. The relative standard deviation (RSD) of the retention time and the intensities
of ten negative ions in QC samples for the stability evaluation were lower than 0.6% and
13%, respectively (Table S1). In addition, the acquisition data in positive ion mode showed
good performance as well (Table S2). These results indicated that this analytical method had
good repeatability and stability for metabolic profiling.
The base peak chromatograms (BPC) of serum samples in both positive and negative ion
modes are showed in Fig. 2. An unsupervised PCA analysis showed good separation between
the model and the control groups (Fig. S2), demonstrating that the established model was
capable of differentiating the rats with blood stasis from normal controls according to the
serum metabolite profiles. The R2Y and Q2 values of the OPLS-DA model (Fig. 3C and 3D)
were 0.996 and 0.965 in negative ion mode and 0.973 and 0.930 in positive mode, which
indicated that the approach was good to fit and predict, respectively. The corresponding
S-plot (Fig. 4C and 4D) was constructed from the OPLS-DA model. In Fig. 3A and 3B,
explicit boundaries were observed among the clustering of the control, model, ASP, CLO and
MDQ pretreatment groups. The MDQ2 and MDQ3 groups showed a trend close to that of
normal control and opposite that of the model group in negative ion mode. Moreover, the
12
MDQ2 and MDQ3 groups had a shorter distance from the CLO than the ASP group, which
indicated the similarity between MDQ and CLO in preventing blood stasis in the model rats.
Permutation tests with 200 times yielded intercept values of R2 = 0.80 and Q2 = −0.89 in
negative ion mode, with R2 = 0.24 and Q2 = −0.72 in positive ion mode (Fig. 4A and 4B),
which revealed the good fitness and prediction ability.
A total of eighteen differential metabolites (Table 2) with FDR < 0.05 were identified
according to the mass value within 5 ppm from the high resolution mass spectrometry, the
database (LipidViewTM 1.2, MassBank, HMDB, Lipidmap, Metlin, Chemspider), the
properties of the compounds and the fragment information. For example, the biomarker with
an m/z 564.3307 at 8.7 min was used to illustrate the identification procedure. The VIP value
of the biomarker was noted as 6.06. The phosphate groups of the PC structure, such as
phosphocholines (C5H15NO4P+, m/z 184.072z1) and a neutral loss of m/z 79.0561, were
found in the fragmentation of the ion m/z 520.3366 in positive ion mode (Fig. S4A.). To
define its structure, the ion with the m/z 564.3307 was searched by comparing the extract
mass to those enrolled in the HDMB database. The unique metabolite LPC (18:2) matched
with the accurate mass (< 5 ppm) in the candidate list. The fragmentation pathway was well
matched with the record in LipidViewTM 1.2 (Fig. S4B.). Thus, the ion with the m/z
564.3307 was tentatively identified as LPC (18:2) (12). In Fig. 5, the possible MS/MS
fragmentation pathway in negative mode was deduced.
The identified potential metabolites are related to arachidonic acid metabolism, primary
bile acid biosynthesis, glycerophospholipid metabolism and phospholipid biosynthesis.
Compared with the normal control, the contents of thirteen metabolites in the model group
such as (3) arachidonic acid, (4) allocholic acid, (5) cholic acid, (6) LPE(16:0), (7)
LPE(18:2), (8) LPE(18:0/0:0), (9) LPC(14:0), (10) LPC(16:1), (12) LPC(18:2), (13)
LPC(18:1), (15) LPC(20:5), (17) LPC(20:3) and (18) PC(35:4) were significantly increased,
while (1) L-alloisoleucine, (2) DL-O-tyrosine, (11) LPC(20:1), (14) LPC(18:0) and (16)
LPC(20:4) were significantly reduced. To determine the effects of MDQ groups and
drug-treated groups on prevention against acute blood stasis, the identified eighteen
biomarker levels were compared using the ANOVA with LSD test methods. Among the
identified metabolites, eleven were corrected to different degrees of normality in the treated
13
groups, especially the MDQ3-pretreated groups, such as (1) L-alloisoleucine, (5) cholic acid,
(6) LPE(16:0), (7) LPE(18:2), (8) LPE(18:0/0:0), (9) LPC(14:0), (10) LPC(16:1), (12)
LPC(18:2), (13) LPC(18:1), (15) LPC(20:5) and (18) PC(35:4) (Fig. 6., FDR < 0.05).
Overall, after MDQ pretreatment at doses of 250, 500 and 1000 mg/kg, the levels of these
metabolites including LPE (16:0), LPC(18:2) and LPC(20:5), returned to normal levels, to
some degree. The higher the dose of MDQ was, the more metabolites and the more the
effectiveness could be improved, to some extent, suggesting that the effect of the MDQ on
serum metabolites was dose-dependent. The ability to regulate the level of eleven metabolites
simultaneously using a high dose of MDQ was more effective than the two positive drugs,
which may have contributed to the advantages of the multi-component, multi-target and
multi-level actions of traditional Chinese medicine.
3.4. Metabolic pathways related to blood stasis
The metabolic pathway network associated with rat blood stasis and its corresponding
impact value were analysed by MetPA. The results of the pathway analysis showed that some
pathways, such as arachidonic acid metabolism, glycerophospholipid metabolism, linoleic
acid metabolism, primary bile acid biosynthesis, the biosynthesis of unsaturated fatty acids,
and alpha-linolenic acid metabolism might be perturbed during blood stasis. The summary of
the pathway analysis is displayed in Fig. S5 and Table S3. Those pathways involving in
arachidonic acid metabolism and glycerophospholipid metabolism with impact value more
than 0.1 were considered as the significantly relevant pathways of MDQ versus blood stasis.
The disturbed metabolic pathways are drawn as shown in Fig.7.
3.5. Correlation between biochemical parameters and biomarkers
In order to elucidate any relationship between the potential biomarkers and the
biochemical parameters, Pearson correlation matrix analysis was performed in normal and
model groups, and the results are presented in Fig.8. The size of the Pearson correlation
coefficient (r = from −1 to 1) was reflected by the colour depth. The colour that is closer to
red indicated a stronger positive correlation and the colour that is closer to green indicated a
14
stronger negative correlation. The level |r| ≥ 0.6 was considered the cut-off value for good
correlations. For example, L-alloisoleucine showed clear negative correlations with PAR,
FIB, ET and TXB2 of r = -0.625, -0.910, -0.908 and -0.762 and positive correlations with TT,
APTT, 6-keto-PGF1α and eNOS of r=0.815, 0.937, 0.639 and 0.681. PT showed obviously
negative correlations with PC (35:4) of r = -0.728. The metabolites cholic acid, LPE(16:0),
LPE(18:2), LPE(18:0), LPC(14:0), LPC(16:1), LPC(18:2), LPC(18:1), LPC(20:5) and
PC(35:4) were prominently negatively associated with FIB, ET and TXB2, whereas they
were strongly negatively associated with APTT. PAR was also markedly positively
associated with cholic acid, LPE(16:0), LPE(18:2), LPE(18:0), LPC(14:0), LPC(16:1),
LPC(18:2), LPC(18:1) and LPC (20:5) at r =0.703, 0.770, 0.768, 0.839, 0.729, 0.722, 0.837,
0.600 and 0.754, respectively. TT presented higher negatively correlations with cholic acid,
LPE(16:0), LPE(18:2), LPE(18:0), LPC(14:0), LPC(16:1), LPC(18:2), LPC (20:5) and
PC(35:4) at r = -0.781, -0.757, -0.820, -0.815, -0.880, -0.782, -0.771, -845 and -0.600,
respectively. The 6-keto-PGF1α was prominently negatively associated with LPE(16:0),
LPE(18:2), LPE(18:0), LPC(14:0), LPC(16:1), LPC(18:2), LPC(20:5) and PC(35:4) at r =
-0.784, -0.869, -0.804, -0.715, -0.631, -0.821, -0.531, -0.770 and -0.702. The eNOS was
strongly negatively correlated with LPE(18:0), LPC(14:0) LPC(20:5) and PC(35:4) at r =
-0.642, -0.662, -0.622 and -675. All above correlations between differential metabolites and
biochemical parameters provided a reference to clarify the pathological mechanisms of blood
stasis.
4. Discussion
4.1. Biochemistry analysis
Biochemical parameters have been widely employed for the diagnosis of blood stasis as
described as a slowing or pooling of blood. (Chan et al., 2007; Zou et al., 2015). The blood
coagulation indexes including APTT, TT, PT and FIB were used to evaluate the effectiveness
of antithrombotic activity of MDQ. APTT and PT are the indicators for intrinsic and extrinsic
coagulation pathways, respectively. TT and FIB reflect the third coagulation phase in plasma
(Dang et al., 2015). In our research, TT, APTT and PT were significantly extended after
15
MDQ treatment and the FIB level was markedly reduced, indicating that MDQ had
beneficial effects on the endogenous coagulation pathway and could impede the formation of
fibrin.
TXA2 and PGI2 are involved in the formation, development and seizure of CVD. An
important determinant of the interaction between the platelets and the endothelial wall is the
local equilibrium of production of PGI2 and TXA2, labile eicosanoids with opposing effects
on homeostasis (Hui et al., 2010). MDQ treatment led to a significantly decrease of TXB2
and an increase of 6-keto-PGF1α compared to the model group. TXB2 and 6-keto-PGF1α are
the stable metabolites of TXA2 and PGI2 in the plasma, respectively (Zhu et al., 2005). An
optimal balance of TXB2/6-keto-PGF1α is important in regulation of the vascular resistance
and regional mobility. The consequence indicated that the ameliorative effects of MDQ on
blood stasis could be attributed to the modulation of active substances in vascular
endothelium.
Endothelial dysfunction has been proposed to be an early event of pathophysiologic
importance in thrombosis. The eNOS and the ET are mutually antagonistic biological active
factors that regulate the cardiovascular system function (Dang et al., 2015). Vascular
endothelial damage increased the release of ET and could lead to vasoconstriction. The
consequence indicated that MDQ had a beneficial effect on the vascular endothelium by
lowering the level of ET and up-regulating the eNOS. Additionally, MDQ treatment could
markedly lower the ADP-induced platelet aggregation in vitro, which demonstrated that
MDQ might have a synergetic effect for decreasing platelet aggregation.
4.2. Intervention of MDQ in the metabolic pattern
Clinical observations showed that the thrombosis formed in different parts of the body
can cause various diseases, among which coronary heart disease, myocardial infarction,
blood viscosity, atherosclerosis, etc. Large amounts of studies contributed to clarifying the
involved mechanisms using NMR, LC-MS and GC-MS approaches. However, the complex
pathophysiological process of diseases related to blood stasis remains to be understood
systematically. The mechanism of blood stasis and the efficacy of MDQ could be clarified by
16
monitoring the changes in 18 endogenous metabolites involved in many metabolic pathways
and physiological functions. In addition, arachidonic acid metabolism and phospholipid
biosynthesis were reported for the first time using serum metabolic phenotyping.
4.2.1. Glycerophospholipid metabolism
Lysophosphatidylcholines (LPCs) are produced by the enzyme lecithin cholesterol
acyltransferase, and they can also be continuously generated by phospholipase A2
(PLA2)-mediated hydrolysis of phosphatidylcholine under physiological and pathological
conditions. LPCs are involved in signal transduction, energy metabolism and cellar
membrane integrity (Zhang et al., 2016). In the present study, LPCs were disturbed during
the pathogenesis of blood stasis, which is further confirmed by the findings of previous
studies (Laaksonen, 2016). A large number of studies have demonstrated that LPCs are
associated with various diseases such as obesity, hyperlipidaemia, kidney disease, metabolic
disease and cardiovascular disease in both patients and animal models (Chen et al., 2014;
Rauschert et al., 2017; Zhao et al., 2015). Our results showed that the dysregulation of LPCs
could be significantly restored after MDQ treatment, which indicated that MDQ could
influence glycerophospholipid metabolism during the process of acute blood stasis.
4.2.2. Bile acid biosynthesis
The results indicated that allocholic acid and cholic acid were significantly up-regulated
in the serum of blood stasis rats, which was reported in the previous study (Zhao et al., 2008;
Zou et al., 2015). Bile acids, which are stored in the gallbladder, are also an important
metabolic regulator for the dynamic balance of cholesterol metabolism to facilitate the
excretion, absorption and transport of fats and sterols in the intestines and liver (Porez et al.,
2012). In the model group, the allocholic acid and cholic acid contents were significantly
increased, suggesting the imbalance of bile acid biosynthesis. In comparison with the model
group, the contents of allocholic acid and cholic acid were obviously decreased in the
MDQ-treated groups. Our study also strongly suggested that MDQ could relieve the blood
stasis syndrome by ameliorating bile acid biosynthesis.
17
4.2.3. Arachidonic acid metabolism
Arachidonic acid is present in vivo in an esterified form to cell membrane
glycerophospholipids (Zeldin, 2001). The activation of phospholipases by the protein kinase
pathway (PKC) in turn activates phospholipase A2 (PLA2) after blood stasis is formed, which
accelerates the hydrolytic action of phospholipases (e.g., cytosolic phospholipase A2),
releasing free arachidonic acid from the phospholipid (PL) pools and making it available for
oxidative metabolism, as signalling molecules and in homeostatic function (Akiba et al.,
2002; Li et al., 2011). In the model group, the arachidonic acid level was significantly
increased, resulting in the peripheral blood routine and the function of the blood cells to be
disordered. Thromboxane A2 (TXA2), an arachidonic acid metabolite, is a putative mediator
of platelet aggregation and vasoconstrictor (Ma et al., 2017). Thus, the degree of platelet
aggregation was significantly elevated, which was in agreement with Table1 (Li et al., 2015).
Our research also showed that the level of PC (35:4) involved in arachidonic acid
metabolism was obviously increased in the model group compared to the control group,
suggesting an imbalance in the arachidonic acid metabolism. Compared with the model
group, the PC (35:4) serum level was significantly decreased in the MDQ treatment group.
These results indicated that MDQ could regulate the arachidonic acid metabolism to relieve
blood stasis syndrome.
L-alloisoleucine with a trace amount in serum is typically a stereoisomeric
branched-chain amino acid derived from L-isoleucine, and it plays a neurotoxic role when it
has accumulated to excess. Studies have shown that L-alloisoleucine is used in the diagnosis
of maple syrup urine disease (Heemskerk et al., 2015; Li et al., 2016; Schadewaldt et al.,
1999). The relevance of L-alloisoleucine to blood stasis diseases has not yet been
documented. Thus, the reverse of the down-regulated level of L-alloisoleucine in the model
group to reach the initial condition in response to MDQ treatment for blood stasis remains to
be further clarified.
Conclusions
18
In this study, the protective effects of MDQ on acute blood stasis rats was studied for the
first time using UPLC-QTOF/MS-based metabolomics analysis in combination with clinical
biochemistry assays. The protective effect of MDQ on acute blood stasis was observed in
serum metabolite profiles. Eleven potential biomarkers that exerted significant changes were
characterized and elucidated the preventive effects of MDQ on blood stasis. MDQ was found
to restore the health of the blood stasis rats experiencing metabolic disorders towards normal
by
regulating
the
metabolic
pathways
involving
arachidonic
acid
metabolism,
glycerophospholipid metabolism and bile acid biosynthesis. The present study could provide
evidence that metabolomics, is a powerful tool for exploring the potential pathophysiology
mechanism of CVD related to blood stasis.
Acknowledgements
This research work was financially supported by grants from the National Natural Science
Foundation of China (Nos. 81673565 and 81270054) and the Program of Science and
Technology of Guangzhou (No: 201607010334).
Authors contributions
Conceived and designed the study: Zhongxiang Zhao, Zhongqiu Liu, Jing Jin, Di Cao.
Carried out the experiment and collected the biological samples: Chuncao Xu, Yuanyuan Xue,
Qingfeng Ruan. Acquired data and performed statistical analyses: Di Cao, Bao Yang,
Chuncao Xu. Wrote or contributed to revising the manuscript: Di Cao, Jing Jin, Hui Cui, Lei
Zhang, Zhongxiang Zhao. All authors have read and approved this version of the manuscript.
19
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23
Fig. 1. The effects of MDQ on ET (A), eNOS (B), TXB2 and 6-Keto-PGF1a (C) levels in blood stasis rats.
Fig. 2. Base peak chromatograms (BPCs) from control, model, ASP, CLO, MDQ1, MDQ2 and MDQ3
groups in negative (A) and positive (B) ion mode, respectively.
25
Fig. 3. OPLS-DA score plots of seven groups (A and B) and OPLS-DA score plots of control and model groups based on UPLC-QTOF/MS data (C and D).
negative ion mode; (B) and (D) positive ion mode
26
(A) and (C):
Fig. 4. Validation of OPLS-DA model of seven groups by permutation test (n = 200 times) (A and B) and S-plots of
OPLS-DA between control and model groups (C and D). (A) and (C): negative ion mode; (B) and (D) positive ion
mode
Fig. 5. Fragmentation pathway and MSMS fragments of compound 12 in negative ion mode.
60000
40000
20000
0
**
*
*
**
L -a llo is o le u c in e
* * *
* ** *
**
L P E (1 8 : 0 /0 : 0 )
**
A SP
**
******
L P E (1 8 :2 )
**
M o d e l
**
L P E (1 6 :0 )
**
N o rm a l
**
M D Q 3
**
M D Q 2
* ** *
M D Q 1
**
c h o lic a c id
**
*
C LO
**
*
**
L P C (1 4 :0 )
*
*
*
L P C (1 6 :1 )
*
**
**
**
**
L P C (1 8 :2 )
*
**
*
*
**
L P C (1 8 :1 )
**
*
* *
**
L P C (2 0 :5 )
*
*
**
*
P C (3 5 :4 )
Fig. 6. Comparison of relative intensity of putative potential biomarkers in the blood stasis group associated with MDQ treatment (**P < 0.01, *P < 0.05, compared with
model group).
**
Fig. 7. The integrated metabolic pathways related to blood stasis. (↑represents up-regulated in blood stasis group compared with control group whereas↓indicates down-
regulated, PG, Phosphatidylglycerol; LPG, Lysophosphatidylglycerol; PE, Phosphatidylethanolamine; LPE, Lysophosphatidylethanolamine; PC, Phosphatidylcholine; LPC,
Lysophosphatidylcholine;)
Fig. 8. Correlation analysis between the disturbed potential biomarkers in serum and biochemical parameters
according to their Pearson correlation coefficient.
Graphical abstract
Table 1 Coagulation parameters and effects of MDQ on antiplatelet in rats with blood stasis (mean ± SD, n=7).
Groups
Dose (mg/kg)
TT (s)
PT (s)
APTT (s)
FIB (g/L)
PAR (%)
Control
─
54.59±4.84
10.24 ±0.56
20.75±0.46
1.76±0.18
51.29±3.64
Model
─
40.87±1.14
##
15.49±0.33
ASP
30
41.70±0.76
10.20±0.58**
18.37±0.33**
5.24±0.22**
41.00±3.16**
CLO
8
44.30±1.42**
10.21±0.56**
19.36±0.29**
5.27±0.19**
47.29±1.60**
##
9.56±0.22
##
5.94±0.38
##
61.71±3.25
##
MDQ 1
250
41.73±1.45
9.84±0.05
15.49±0.18
5.80±0.33
63.29±0.95
MDQ 2
500
43.41±1.19*
9.77±0.16
17.87±0.56**
5.50±0.41*
54.57±2.76**
MDQ 3
1000
45.10±1.05**
10.30±0.58**
19.00±0.86**
5.23±0.63**
53.71±2.75**
#
P < 0.05,
##
P < 0.01, compared with control group; *P < 0.05, **P < 0.01, compared with model group;
NO.
(+)
ESI
1.1
1.2
RT(min)
C20H32O2
C9H11NO3
C6H13NO2
Formula
453.2855
305.2465
182.0817
132.1021
Measured
value (Da)
408.2876
408.2876
304.2402
181.0739
131.0946
Theoretical
value (Da)
3.52
3.69
2.69
3.68
7.36
VIP
-3.9
0.7
-5.2
-3.3
-3.0
ppm
2.94E-03
1.70E-02
1.91E-02
1.83E-02
1.40E-03
p-value
6.51E-03
2.46E-02
2.59E-02
2.56E-02
4.19E-03
FDR
↑
↑
↑
↓
↓
Trend
Table 2 Potential biomarkers associated with blood stasis in rat serum.
1
(+)
12.0
C24H40O5
453.2834
11
10
9
8
7
6
(-)/(+)
(+)
(-)/(+)
(-)
(-)
(-)
(-)
9.3
8.7
10.3
8.4
8.1
10.0
8.6
9.0
C26H54NO7P
C26H52NO7P
C26H50NO7P
C28H56NO7P
C24H48NO7P
C22H46NO7P
C23H48NO7P
C23H44NO7P
C21H44NO7P
586.3137
568.3638
566.3461
564.3318
550.3869
538.3142
512.3005
480.3102
476.2772
452.2772
543.3325
541.3168
523.3638
521.3481
519.3325
549.3794
493.3168
467.3012
481.3168
477.2855
453.2855
7.37
2.16
4.15
4.77
6.06
5.01
3.64
1.96
2.62
1.95
2.94
3.6
1.4
4.2
0.5
3.0
0.7
0.6
1.4
2.5
1.1
1.1
3.08E-04
1.19E-03
3.47E-03
8.37E-03
1.78E-04
1.55E-05
1.11E-02
2.94E-03
1.30E-04
2.40E-05
1.17E-04
1.44E-03
3.84E-03
7.30E-03
1.46E-02
1.07E-03
6.53E-04
1.73E-02
6.85E-03
9.13E-04
5.04E-04
9.79E-04
↓
↑
↓
↑
↑
↓
↑
↑
↑
↑
↑
*
*
*
*
**
**
*
**
**
**
*
**
**
c
2
(+)
7.6
C24H40O5
12
(-)
9.8
C28H48NO7P
588.3280
b
3
(-)
7.0
13
(-)
8.3
C28H50NO7P
a
4
(-)
**
5
14
(-)/(+)
8.7
**
15
(-)/(+)
**
16
arachidonic acid
DL-O-Tyrosine
L-alloisoleucine
NA
Arachidonic acid metabolism
Linoleic acid metabolism
NA
NA
Related pathway
allocholic acid
Primary bile acid biosynthesis
Identification
cholic acid
LPC(20:5)
LPC(18:0)
LPC(18:1)
LPC(18:2)
LPC(20:1)
LPC(16:1)
LPC(14:0)
LPE(18:0/0:0)
LPE(18:2)
LPE(16:0)
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
Glycerophospholipid metabolism
NA
NA
Phospholipid Biosynthesis
Secondary bile acid biosynthesis
LPC(20:4)
17
(-)
(-)/(+)
14.4
9.0
C43H78NO8P
C28H52NO7P
826.5605
590.3466
767.5465
545.3481
3.66
1.48
0.9
1.4
2.07E-03
2.37E-02
5.43E-03
2.84E-02
↑
↑
*
**
18
b
PC(35:4)
LPC(20:3)
Glycerophospholipid metabolism
Arachidonic acid metabolism
Glycerophospholipid metabolism
The p value was calculated from t-test; FDR value was obtained from the adjusted p value of FDR correction by Benjamini-Hochberg method;
Compared with the control group: ↑up-regulated, ↓ down-regulated; **P < 0.01, *P < 0.05; (+): detected in positive ion mode; (-): detected in negative ion mode.
a
c
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