Abstract

   Background: Guan-Xin-Shu-Tong capsule (GXSTC) is a traditional Chinese
   medicine (TCM) that has been used to treat coronary heart disease (CHD)
   for many years in China. However, the holistic mechanism of GXSTC
   against CHD is still unclear. Therefore, the purpose of this paper was
   to systematically explore the mechanism of action GXSTC in the
   treatment of CHD rats using a metabolomics strategy.

   Methods: A CHD model was induced by ligation of the left anterior
   descending coronary artery (LAD). In each group, echocardiography was
   performed; the contents of creatine kinase (CK), lactate dehydrogenase
   (LDH) and aspartate transaminase (AST) in serum were determined; and
   the myocardial infarct size was measured. The metabolites in plasma
   were analyzed by UHPLC-MS/MS-based untargeted metabolomics. Then,
   multivariate statistical analysis was performed to screen potential
   biomarkers associated with the GXSTC treatment in the LAD-induced rat
   CHD model. Finally, the MetaboAnalyst 4.0 platform was used for
   metabolic pathway enrichment analysis.

   Results: GXSTC was able to regulate the contents of CK, LDH and AST;
   restore impaired cardiac function; and significantly reduce the
   myocardial infarction area in model rats. Twenty-two biomarkers and
   nine metabolic pathways of GXSTC in the treatment of CHD were
   identified through UHPLC-MS/MS-based untargeted metabolomics analysis.

   Conclusion: GXSTC regulates metabolic disorders of endogenous
   components in LAD-induced CHD rats. The anti-CHD mechanism of GXSTC is
   mainly related to the regulation of amino acid, lipid and hormonal
   metabolism. This study provides an overall view of the mechanism
   underlying the action of GXSTC against CHD.

   Keywords: metabolomics, Coronary heart disease, traditional Chinese
   medicine, Guan-Xin-Shu-Tong capsule, active ingredients

Introduction

   Coronary heart disease is a common cardiovascular disease, mainly due
   to coronary atherosclerosis. In the past century, the incidence of CHD
   has been increasing, and it has become one of the major causes of death
   in many countries ([56]Yang et al., 2019). Currently, the conventional
   drugs used to treat CHD are mainly Western drugs, which usually aim to
   act on individual targets, such as beta-blockers, nitrates, statins,
   calcium channel blockers or angiotensin-converting enzyme inhibitors.
   The side effects of these drugs are obvious in the long term.
   Consequently, researchers have been constantly optimizing strategies
   for the prevention and treatment of CHD. More recently, traditional
   Chinese medicine has received great attention because it has a variety
   of ingredients, can act on multiple targets and pathways and plays an
   overall regulatory role ([57]Hu et al., 2019). Recent studies have
   confirmed that compared to Western medicine, TCM has fewer side effects
   and unique advantages for treating CHD ([58]Zhang et al., 2019;
   [59]Leung and Xu, 2020). Therefore, the exploration of the
   pharmacological mechanisms of TCM may provide insights into the
   development of new drugs.

   Guan-Xin-Shu-Tong capsule, a Chinese medicinal formula, has been
   clinically used to treat CHD for many years. GXSTC is composed of five
   herbal medicines: Fructus Choerospondiatis (Guangzao), Salvia
   Miltiorrhiza (Danshen), Flos Caryophylli (Dingxiang), Borneolum
   (Bingpian) and Concretio Silicea Bambusae (Tianzhuhuang). GXSTC can
   reduce the levels of interleukin-1β (IL-1β), interleukin-6 (IL-6) and
   other inflammatory factors in the serum of rats after myocardial
   infarction and can also prevent apoptosis of myocardial cells
   ([60]Liang et al., 2012). In addition, GXSTC can significantly decrease
   the expression level of NADPH oxidase in the myocardium of ischemic
   rats and play a protective role in the myocardium by exerting
   antioxidative activity ([61]Cao et al., 2014). However, the systematic
   mechanism of action, specific active ingredients and in vivo targets of
   GXSTC remain to be explored.

   Metabolomics is the profiling of metabolites in biofluids, cells and
   tissues and is routinely applied as a tool for biomarker discovery.
   Metabolomics can also be used to understand the system-level effects of
   metabolites ([62]Johnson et al., 2016; [63]Wishart, 2016; [64]McGarrah
   et al., 2018). Metabolomics can be divided into untargeted and targeted
   metabolomics ([65]Cui et al., 2018). The untargeted metabolomics has
   the advantages of high efficiency, sensitivity and comprehensiveness,
   which has led it to be increasingly used in revealing the complex
   action mechanism of TCM ([66]Guijas et al., 2018).

   In this study, we used an UHPLC-MS/MS-based untargeted metabolomics to
   identify the key biomarkers, metabolic pathways and mechanisms of
   action GXSTC in the treatment of CHD.

Materials and Methods

Materials and Reagents

   GXSTC was purchased from Shaanxi Buchang Pharmaceutical Co., Ltd
   (Shaanxi, China; batch number: 20190205). The assay kits for the
   determination of creatine kinase, aspartate transaminase and lactate
   dehydrogenase activities were supplied by the Nanjing Jiancheng
   Bioengineering Institute (Nanjing, China). 2,3,5-Triphenyltetrazolium
   chloride (TTC) and Evans Blue (EB) were purchased from Sigma Chemical
   Co. (St Louis, MO, United States). High-performance liquid
   chromatography (HPLC)-grade methanol, HPLC-grade formic acid and
   ammonium acetate were supplied by Thermo Fisher Scientific (Waltham,
   MA, United States). Ultrapure water was generated by a Millipore system
   (Bedford, MA, United States).

Experimental Animals

   Male Sprague-Dawley rats (200 ± 15g at 6–7weeks) were purchased from
   SPF Biotechnology Co., Ltd (Beijing, China). All animals were housed
   under standard laboratory conditions of temperature (25 ± 2°C),
   constant humidity (55 ± 5%) and light (12h dark/light cycle) with
   freely available food and purified water. After adaptive feeding for
   one week, rats were randomly selected and subjected to sham surgery or
   CHD. The CHD rat model was established by ligation of the left anterior
   descending coronary artery. Briefly, a left thoracotomy was performed
   after rats were anesthetized with 2% isoflurane. The left anterior
   descending coronary artery was then ligated with a 5–0 polypropylene
   suture. After the ligation, the heart was placed back in the chest
   cavity, the air that entered the chest cavity was expelled, the chest
   cavity was closed, and the skin was sutured. For the sham-operated
   group, rat LADs were only threaded and not ligated, and the remaining
   procedures were the same as those in the CHD group. After the
   operation, electrocardiogram monitoring was performed. T wave changes
   and ST segment elevation in the rats in the CHD group indicated that
   the model was successfully constructed; otherwise, it was rejected
   ([67]Chen et al., 2017).

   The CHD-operated rats were divided into the CHD model group (Model, n =
   6) and the GXSTC administration group (Treat, n = 6) according to a
   random number control table. Rats without ligation were placed in the
   sham-operated group (Sham, n = 6). The rats in the Treat group received
   a dose of 1.2 g/kg/d GXSTC at a drug concentration of 0.06 g/ml, which
   was continuously administered for four weeks. The rats in the Sham and
   Model groups were given the same volume of normal saline via
   intragastric administration.

   All experimental rats were handled humanely, and all procedures were
   strictly performed according to the ARRIVE guidelines and approved by
   the Animal Ethics Committee of the Institute of Radiation Medicine,
   Chinese Academy of Medical Sciences (Approval No. IRM-DWLL-2019075,
   Tianjin, China).

Index Detection

Echocardiography Detection

   After two weeks, each rat was subjected to echocardiography using a
   Visual Sonics ultrahigh-resolution small animal ultrasound imaging
   system (VisualSonics, Toronto, Canada), and the left ventricular
   ejection fraction (EF) and left ventricular fractional shortening (FS)
   were two indicators used to evaluate the heart function of rats
   ([68]Sciarretta et al., 2018). After acquiring a myocardial M-shaped
   sample line, ultrasonic index measurements and calculations were
   performed, including left ventricular end diastolic diameter (LVEDd),
   left ventricular end systolic diameter (LVESd), left ventricular end
   diastolic volume (LVEDv), left ventricular end systolic volume (LVESv),
   EF = [(LVEDv−LVESv)/LVEDv]×100% and FS = [(LVEDd−LVESd)/LVEDd]×100%.

Biochemical Index Detection

   One milliliter of blood from each rat s ocular vein was placed into a
   2 ml centrifuge tube and allowed to stand at room temperature for
   30 min to coagulate and layer. Then, the samples were centrifuged at
   3,000 rpm for 15 min at room temperature, and the upper serum was drawn
   into a 1.5 ml centrifuge tube. The contents of CK, AST and LDH in each
   rat serum sample were determined according to the instructions of the
   kit. The serum used to determine the LDH content was diluted 40 times
   with normal saline.

Myocardial Infarct Size Detection

   To more intuitively evaluate the therapeutic effect of GXSTC on CHD
   rats, we produced myocardial infarct slices from rat hearts and
   measured the area of myocardial infarct in each group by EB and TTC
   double staining ([69]Zhu et al., 2015). Rats were injected with 2.5 ml
   EB at a concentration of 2% through the tail vein. Rats were sacrificed
   after 30 min of circulation, and the heart was quickly removed. After
   freezing at −20°C for 30 min, the heart was cut into five pieces on the
   vertical longitudinal center axis. Then, a 1% TTC solution was added,
   and the pieces were wrapped in foil to protect them from light. All the
   pieces were placed in a water bath and incubated at 37°C for 15 min.
   The pieces were then fixed for 12 h, and then, a camera was used to
   take pictures, keeping the distance between the lens and each slice
   unchanged at ×5 magnification. Image-Pro Plus 6.0 image processing
   software was used to process the slice photos. The area stained by EB
   in the slice was marked as the area not at risk (ANAR), and the rest of
   the slice was marked as the area at risk (AAR). The AAR was composed of
   an off-white area (infarct area) and a red area (ischemic but not
   infarct area). Myocardial infarct size = infarct area/ischemic area ×
   100%.

Plasma Samples Preparation for Metabolic Profiling

   Plasma samples were removed and placed at 4°C to slowly thaw. One
   hundred microliters was aspirated and placed in a 1.5 ml centrifuge
   tube, and 400 μL of MS grade methanol was added and vortexed for 30 s.
   After being left to stand in an ice bath for 5 min, the tube was
   centrifuged at 15,000 rpm at 4°C for 10 min in a
   low-temperature/high-speed centrifuge. The supernatant was mixed with
   53% MS-grade methanol. The tube was centrifuged again at 15,000 rpm for
   10 min at 4°C in the low-temperature high-speed centrifuge, and the
   supernatant was collected for subsequent UHPLC-MS analysis.

   The quality control (QC) samples were prepared by mixing equal volumes
   (10 μL) of all the plasma samples. Five QC samples were injected before
   the test sample to monitor the status of the instrument and balance the
   chromatography-mass spectrometry system. During the testing of the test
   samples, four QC samples were randomly injected to evaluate the
   stability of the chromatography-mass spectrometry system and the
   reliability of the experimental data throughout the experiment.

UHPLC-MS/MS Conditions

   Chromatographic separation of all rat plasma samples was completed
   using a Thermo Vanquish Flex Ultra Performance Liquid Chromatograph
   (Thermo Fisher) and Thermo Hyperil Gold column (100 mm ×
   2.1 mm×1.9 µm). The column temperature was 40°C, and the flow rate was
   0.2 ml/min. Eluent A was 0.1% formic acid, and eluent B was methanol.
   The solvent gradient was set as follows: 0–1.5 min, 2% B; 1.5–12.0 min,
   2–100% B; 12.0–14.0 min, 100% B; 14.0–14.1 min 100–2% B; and
   14.1–17 min 2% B.

   Rat plasma samples were separated by UHPLC and analyzed by a Thermo
   Scientific quadrupole Orbitrap mass spectrometer. The electrospray ion
   source (ESI) positive ion (pos) and negative ion (neg) modes were used
   for detection, and the scanning range was m/z 70–1,050, spray voltage
   was 3.2 kV, sheath gas flow rate was 35 arb, aux gas flow rate was 10
   arb, and capillary temperature was 320°C.

Data Analysis

   First, Compound Discoverer 3.0 (CD) software was used to preprocess the
   obtained mass spectrometry data. The specific operations were as
   follows: the peak alignment standard was a retention time deviation of
   0.2 min and mass deviation of 5 ppm. At the same time, peak extraction
   was performed according to the requirements of a mass deviation of
   5 ppm, signal intensity deviation of 30%, signal-to-noise ratio of 3,
   and minimum signal intensity of 100,000. Then, the peak area was
   quantified and normalized.

   Natural separation of all the tested groups was performed by the PCA
   model. The value of variable importance in the projection (VIP) from
   the PLS-DA model, the ratio of the mean of all the biological replicate
   quantitative values in the comparison group of each metabolite (fold
   change, FC), and the p value for univariate analysis (t-test) were used
   as parameters to screen different metabolites from different sample
   groups. Metabolites with VIP >1 and FC > 1.2 or FC < 0.833 and p < 0.05
   were considered to be potential biomarkers. These potential biomarkers
   were then annotated and subjected to pathway analysis performed through
   the online databases mzCloud ([70]https://www.mzcloud.org/), SMPDB
   ([71]https://smpdb.ca/), HMDB ([72]http://www.hmdb.ca/), and KEGG
   ([73]http://www.genome.jp/kegg/).

Statistical Analysis

   All experimental data are expressed as the mean ± standard deviation
   (mean ± SD). GraphPad Prism 5.0 (La Jolla, CA) statistical analysis
   software was used to perform t-tests and one-way ANOVA. p < 0.05
   indicates statistical significance.

Results

Echocardiography Detection

   To evaluate the improved effect of GXSTC on cardiac function in the
   model rats with CHD, echocardiography was performed in all rats after
   two weeks of administration. As shown in [74]Figure 1, the
   echocardiography waveform of the sham group rats was clear and regular,
   and the EF and FS values were 77.96% ± 2.540 and 48.29% ± 2.544,
   respectively. All the data indicated that the systolic and pumping
   functions of the rats were good. Compared with the Sham group, the
   echocardiography waveform of the Model group was significantly changed,
   with EF and FS values decreasing to 39.51% ± 5.960 and 20.54% ± 3.513,
   respectively, indicating that the cardiac function of the Model group
   had been seriously damaged and that the CHD Model was successfully
   constructed. Compared with the Model group, the echocardiography
   waveform of the Treatment group was improved, and the EF and FS values
   increased to 63.94% ± 0.9126 and 35.84% ± 0.6803, respectively, which
   indicated that GXSTC improved cardiac function in rats with CHD.

FIGURE 1.

   [75]FIGURE 1
   [76]Open in a new tab

   (A,B and C) Representative images of echocardiography (D and E) EF and
   FS were measured by echocardiography. Data were expressed as mean ± SD
   (n = 6), **p < 0.01 compared with the Sham group, ##p < 0.01 compared
   with the Model group.

Biochemical Index Detection

   The elevated levels of CK, LDH and AST in serum samples of rats are
   important markers after myocardial infarction because severe ischemia
   and hypoxia of the myocardium can cause myocardial cell necrosis, which
   causes CK, LDH and AST to be released from the myocardium into the
   blood circulation system. As shown in [77]Figure 2, compared with the
   Sham group, the serum levels of CK, LDH and AST were significantly
   increased in the Model group. These results indicated that the CHD
   model in rats after LAD was successfully constructed, but the level of
   autonomous recovery without drug intervention was poor. Additionally,
   in the Treatment group, the CK, LDH and AST levels were significantly
   decreased, which indicated that GXSTC treatment significantly reduced
   these values compared to those in the Model group, which implied that
   GXSTC had a positive therapeutic effect on rats with CHD.

FIGURE 2.

   [78]FIGURE 2
   [79]Open in a new tab

   CK, LDH and AST levels in Sham group, Model group and Treat group. Data
   were expressed as mean ± SD (n = 6), *p < 0.05, **p < 0.01 compared
   with the Sham group, #p < 0.05, ##p < 0.01 compared with the Model
   group.

Myocardial Infarct Size Detection

   In this study, EB/TTC double staining was used to observe myocardial
   infarction of rats in different groups, and the results are shown in
   [80]Figure 3. In the Sham group, no obvious infarctions were observed,
   and more than half of the area was stained by EB as blue-purple,
   indicating that no obvious ischemia occurred in the hearts of the Sham
   group rats, and the hearts remained relatively complete in shape and
   function. The entire myocardium of the Model group was stained brick
   red by TTC, indicating that the heart was basically in a state of
   ischemia. Moreover, significant collapse and atrophy of the left
   ventricle of rats could be observed, and the myocardial infarct area
   reached 16.90% ± 0.83. Compared with the Model group, the area of
   myocardial infarction in the Treatment group was significantly reduced
   (9.97% ± 1.73).

FIGURE 3.

   FIGURE 3
   [81]Open in a new tab

   Representative images of EB/TTC double staining of rat heart sections
   and myocardial infarction area of each group. Data were expressed as
   mean ± SD (n = 6), **p < 0.01 compared with the Sham group, #p < 0.05
   compared with the Model group.

Metabolomic Study of Plasma Samples

Method Validation

   To ensure the stability of the chromatography-mass spectrometry system
   and the reliability of the experimental data during the experiment,
   four QC samples were randomly injected during the testing of the plasma
   samples, and the total ion current diagram was obtained. The response
   intensity and retention time of each chromatographic peak of the four
   QC samples were basically the same, indicating that the stability of
   the chromatography-mass spectrometry system was good throughout the
   experiment. Then, the peak area was used to calculate the Pearson
   correlation coefficient among the four QC samples. The results showed
   that the R ^2 between each sample was greater than 0.985, indicating
   that the QC samples were highly correlated and the metabolome results
   were repeatable and stable.

Multivariate Statistical Analysis

   PCA is an unsupervized data analysis method that can generally reflect
   metabolic differences among samples of different groups and the degree
   of variation among samples of each group. In this work, PCA models of
   the plasma metabolomic data were constructed to determine the natural
   separation of metabolites in different groups. As shown in [82]Figure
   4, the metabolic profile of the rats in the Model group deviated from
   that of the rats in the Sham group, suggesting that significant
   biochemical changes were induced by CHD. Moreover, the mean center of
   spots in the Treatment group was fairly different from that in the
   Model group and was close to that in the Sham group, indicating that
   GXSTC treatment of CHD rats had a positive effect on metabolites. In
   addition, QC samples were concentrated in the middle area of the tested
   samples, which indicated that the QC samples had good repeatability and
   stability.

FIGURE 4.

   [83]FIGURE 4
   [84]Open in a new tab

   PCA score plots of the plasma samples from Sham group, Model group,
   Treat group, QC samples in positive mode (A) and negative modes (B).
   The ellipse represents the confidence interval of 95%.

   To discriminate the differential metabolites between each pair of
   groups, supervised PLS-DA analyses were performed on the Model vs. Sham
   and Model vs. Treatment groups ([85]Figures 5A,B; [86]Supplementary
   Figure S1C,D). At the same time, a 7-fold cross-validation was
   conducted to obtain the model evaluation parameters R ^2 and Q^2 to
   test the reliability of the PLS-DA model. The parameters were R ^2 =
   1.00 and Q^2 = 0.93 between the Sham and Model groups in the positive
   ion mode, R ^2 = 0.99 and Q^2 = 0.85 in the negative ion mode, R ^2 =
   1.00 and Q^2 = 0.94 between the Treatment and Model in the positive ion
   mode, and R ^2 = 0.99 and Q^2 = 0.84 in the negative ion mode, which
   revealed the good stability and high predictability of the results.
   Then, the potential overfitting of the PLS-DA model was assessed by two
   hundred permutation tests, in which all the R ^2 and Q^2 values were
   lower than the original values, indicating the reliability of the
   established discriminant model.

FIGURE 5.

   [87]FIGURE 5
   [88]Open in a new tab

   PLS-DA score plots of the plasma samples from Sham group, Model group
   in positive mode (A) and negative mode (B). The corresponding
   validation plots based on 200 times permutation tests of the PLS-DA
   models in positive mode (A) and negative mode (B). The ellipse
   represents the confidence interval of 95%.

Screening and Identification of Differential Metabolites

   Based on the VIP value of the first principal component of the PLS-DA
   model, FC value and p value, a significant difference in the
   metabolites of different groups was identified. Using VIP >1.0, FC >
   1.2 or FC < 0.833 and p < 0.05 as the threshold, 73 differential
   metabolites were obtained (49 from the positive mode and 24 from the
   negative mode) between the Model and Sham groups. Likewise, 71
   differential metabolites conforming to parameter thresholds were
   obtained (39 from the positive mode and 32 from the negative mode)
   between the Treatment and Model groups. To show the overall
   distribution of the differential metabolites more intuitively, a
   volcano map was constructed. As shown in [89]Figure 6, each dot
   represents a metabolite, with those in red representing a significantly
   upregulated metabolite due to VIP >1.0, p < 0.05 and FC > 1.2 and those
   in green representing a significantly downregulated metabolite due to
   VIP >1.0, p < 0.05 and FC < 0.833.

FIGURE 6.

   [90]FIGURE 6
   [91]Open in a new tab

   Volcano map of every differential metabolite among the Model VS. Sham
   in positive mode (A), Model VS. Sham in negative mode (B), Treat VS.
   Model in positive mode (C) and Treat VS. Model in negative mode (D).
   Each dot represents a metabolite, red represents a significantly
   up-regulated metabolite, green represents a significantly
   down-regulated metabolite, the size represents the VIP.

   Subsequently, a Venn diagram was used to integrate overlapping
   metabolites of each pairwise comparison. The result is shown in
   [92]Figure 7. A total of 33 overlapping differential metabolites were
   obtained between the two compared groups. These 33 differential
   metabolites were preliminarily annotated using the KEGG, HMDB and
   METLIN databases. Some of the identified metabolites were further
   confirmed using authentic reference material. Finally, 22 biomarkers
   were identified as potential biomarkers associated with CHD, including
   13 in the positive mode and nine in the negative ion mode. Detailed
   information of the biomarkers is provided in [93]Table 1. The results
   of the biomarkers showed that all 22 biomarkers in plasma samples of
   the Treatment group had a significant callback compared with the Model
   group. Furthermore, according to the abundance value of every potential
   biomarker, a heat map was constructed to observe the relationship among
   the groups directly. As depicted in [94]Figure 8, these potential
   biomarkers showed distinct segregation between the Treatment and Model
   groups. The content of the potential biomarkers in the Treatment group
   was closer to that of the potential biomarkers in the Sham group than
   to that of the potential biomarkers in the Model group.

FIGURE 7.

   FIGURE 7
   [95]Open in a new tab

   Venn diagram of every differential metabolites between the Model VS.
   Sham and Treat VS. Model.

TABLE 1.

   Potentiale biomarkers associated with GXSTC treatment in rat plasma in
   positive mode and negative mode.
   Mode Metabolites HMDB ID Formula tR/min Mass (m/z) FC[S] FC[T] Model vs
   sham Treat vs model
   pos Palmitoylethanolamide HMDB0002100 C[18]H[37]NO[2] 11.88 299.28
   17.22 0.05 Up[96] ^b Down[97] ^d
   11-Ketoetiocholanolone HMDB0006031 C[19]H[28]O[3] 13.91 286.19 3.25
   0.32 up[98] ^b down[99] ^d
   Melatonin HMDB0001389 C[13]H[16]N[2]O[2] 8.67 232.12 3.20 0.24 up[100]
   ^b down[101] ^d
   Isophorone HMDB0031195 C[9]H[14]O 10.67 138.10 0.02 54.56 down[102] ^b
   up[103] ^d
   2,6-Dimethylaniline HMDB0060677 C[8]H[11]N 5.77 121.09 5.38 0.16
   up[104] ^b down[105] ^d
   Indoleacrylic acid HMDB0000734 C[11]H[9]NO[2] 11.05 187.06 3.25 0.28
   Up[106] ^a Down[107] ^c
   p-Anisic acid HMDB0001101 C[8]H[8]O[3] 9.00 152.05 0.30 2.26 down[108]
   ^a Up[109] ^d
   2-Methylbutyroylcarnitine HMDB0000378 C[12]H[23]NO[4] 9.95 245.16 2.13
   0.42 up[110] ^a down[111] ^d
   All-trans-13,14-dihydroretinol HMDB0011618 C[20]H[32]O 11.09 288.24
   19.50 0.04 up[112] ^b down[113] ^d
   Phenylpyruvic acid HMDB0000205 C[9]H[8]O[3] 12.92 164.05 13.01 0.07
   up[114] ^b down[115] ^d
   N,N-Dimethylsphingosine HMDB0013645 C[20]H[41]NO[2] 13.16 327.31 7.70
   0.11 up[116] ^b down[117] ^d
   Diethylphosphate HMDB0012209 C[4]H[11]O[4]P 9.99 154.04 0.06 14.55
   down[118] ^b up[119] ^d
   Phytosphingosine HMDB0004610 C[18]H[39]NO[3] 12.73 317.29 7.08 0.10
   up[120] ^b down[121] ^d
   neg Polygalitol HMDB0002712 C[6]H[12]O[5] 1.41 224.09 0.54 1.90
   down[122] ^b up[123] ^d
   Methylmalonic acid HMDB0000202 C[4]H[6]O[4] 1.24 118.03 2.68 0.31
   up[124] ^b down[125] ^d
   4-tert-Butylphenol HMDB0032063 C[10]H[14]O 12.32 150.10 0.11 7.86
   down[126] ^b up[127] ^d
   Arachidic acid HMDB0002212 C[20]H[40]O[2] 14.49 312.30 0.35 3.34
   down[128] ^b up[129] ^d
   Lysophosphatidylcholine (22:4) HMDB0010401 C[30]H[54]NO[7]P 14.96
   631.39 0.64 1.66 down[130] ^a up[131] ^c
   N-Acetylserotonin HMDB0001238 C[12]H[14]N[2]O[2] 12.42 218.11 2.07 0.46
   up[132] ^b down[133] ^d
   Estrone sulfate HMDB0001425 C[18]H[22]O[5]S 1.45 350.12 0.39 2.72
   down[134] ^b up[135] ^d
   2-Isopropylmalic acid HMDB0000402 C[7]H[12]O[5] 1.27 1.80 0.56 176.07
   up[136] ^a down[137] ^c
   Agnuside HMDB0036561 C[22]H[26]O[11] 11.55 466.15 11.32 0.08 up[138] ^b
   down[139] ^d
   [140]Open in a new tab

   Note, the FC[s] and FC[T] values represent the fold change of the Model
   group compared to the Sham group or Treat group, respectively.
   ^^a

   p < 0.05.
   ^^b

   p < 0.01 compared with the sham group.
   ^^c

   p < 0.05.
   ^^d

   p < 0.01 compared with the model group.

FIGURE 8.

   [141]FIGURE 8
   [142]Open in a new tab

   Hierarchical cluster analysis heat map of potential biomarkers in each
   group. Horizontal is the clustering of potential biomarkers, vertical
   is the clustering of different groups of samples, red represents
   significantly up-regulated biomarkers, blue represents significantly
   down-regulated biomarkers, and the biomarkers clustered in the same
   cluster have similar expression patterns.

Metabolic Pathway Analysis of Potential Biomarkers

   The metabolic pathways of 22 potential biomarkers were analyzed by
   MetaboAnalyst ([143]https://www.metaboanalyst.ca/) to evaluate the
   importance of the pathways in the efficacy of CHD. As shown in
   [144]Figure 9, these biomarkers were distributed in various metabolic
   pathways of the body, including phenylalanine metabolism, tryptophan
   metabolism, valine, leucine and isoleucine degradation,
   glycerophospholipid metabolism, sphingolipid metabolism, phenylalanine,
   tyrosine and tryptophan biosynthesis, retinol metabolism, biosynthesis
   of unsaturated fatty acids, and steroid hormone biosynthesis. These
   pathways might denote their potential as the targeted pathways of GXSTC
   against CHD.

FIGURE 9.

   FIGURE 9
   [145]Open in a new tab

   Analysis diagram of metabolic pathway of potential biomarkers. Each
   point represents one metabolic pathway. The size of dot and the shade
   of color are positively related to the effect on metabolic pathway.

Discussion

   TCM, which is characterized by multicomponent, multitarget, overall
   regulation and lower adverse reactions than Western medicine, has
   unique advantages for treating CHD, which requires long-term treatment.
   However, the composition of TCM is so complicated that it is too
   difficult and inefficient to explore its molecular mechanisms through
   conventional experimental methods. Metabolomics has holistic and
   systematic characteristics, which are highly consistent with the
   concept of TCM regulating the overall balance of the body. Therefore,
   we used a plasma metabolomics method to reveal the overall treatment
   mechanism of action GXSTC in CHD rats.

   LAD-induced CHD rats exhibited obvious ST-segment elevation along with
   myocardial damage and hypofunction, potentially making them a useful
   model of human CHD. In the present study, decreased EF and FS, as well
   as increased myocardial infarct size and enzyme activities of serum CK,
   LDH and AST were observed in LAD-induced model rats. After treatment
   with GXSTC, EF and FS in the Treatment group were markedly increased.
   The area of myocardial infarction and enzyme activities of serum CK,
   LDH and AST in the Treat group were decreased compared with those of
   the Model group. These results are consistent with previous reports
   ([146]Liu et al., 2014; [147]Yang et al., 2017; [148]Yang et al.,
   2018), indicating that GXSTC has a significant therapeutic effect on
   CHD.

   To deeply explore the therapeutic effect of GXSTC in CHD rats, a
   UHPLC-MS/MS-based plasma metabolomics method combined with a
   multivariate statistical analysis was established. With the
   metabolomics approach, the deviations caused by CHD were significantly
   ameliorated after treatment with GXSTC, suggesting that its
   cardioprotective effects are most likely to ameliorate the metabolic
   disorders induced by LAD. Twenty-two potential biomarkers were
   identified as being related to GXSTC against CHD, and they were
   involved in nine metabolic pathways. These biomarkers were involved in
   a variety of physiological and biochemical processes, especially lipid
   metabolism, amino acid metabolism and hormone metabolism.

   Lipid metabolism disorder is an independent risk factor for CHD and the
   pathological basis of coronary atherosclerosis ([149]Ference et al.,
   2019). Ameliorating lipid metabolism disorder and keeping blood lipids
   at the appropriate level can effectively prevent and treat CHD
   ([150]Lee et al., 2017). Changes in sphingolipid and glycerolipid
   metabolism can well reflect lipid metabolism disorders in the body
   under pathological conditions ([151]Harshfield et al., 2019). In our
   study, compared with the Sham group, the phytosphingosine content was
   increased and lysophosphatidylcholine (22:4) was significantly reduced
   in the Model group, while both metabolites returned the normal levels
   in the Treat group, suggesting that GXSTC has a regulatory effect on
   lipid metabolism disorders.

   As important enzyme substrates and regulators, amino acids play
   important roles in many metabolic pathways, which contain important
   biochemical information reflecting the metabolic and functional state
   of the body and play important roles in the normal operation of the
   immune system and other organs ([152]White and Newgard, 2019). Studies
   have shown that phenylalanine metabolic disorder is related to the
   aggravation of oxidative stress reactions, such as lipid peroxidation
   in the body ([153]Dong et al., 2019). The oxidative stress reaction
   causes vascular endothelial oxidative damage and then leads to vascular
   endothelial dysfunction, which is an indicative response in the early
   stage of many cardiovascular diseases ([154]Zhang et al., 2020). The
   increased phenylalanine content in the Model group indicated that CHD
   resulted in abnormal phenylalanine metabolism in the body, while the
   Treat group raised the phenylalanine content to the normal level and
   inhibited abnormal phenylalanine metabolism. This result indicated that
   GXSTC played a role in the regulation of phenylalanine metabolism. In
   our study, we found that N-acetylserotonin, indoleac acid and melatonin
   were correlated with tryptophan metabolism. Compared with those in the
   Sham group, the levels of the metabolites N-acetylserotonin, indoleacid
   and melatonin in the Model group were elevated, indicating that CHD
   leads to abnormal tryptophan metabolism in the body, which was
   consistent with previous experimental results ([155]Liu et al., 2017).
   All three metabolites in the Treatment group showed a callback trend,
   indicating that GXSTC could inhibit abnormal tryptophan metabolism and
   reduce the damage of CHD to the body.

   Interestingly, our study also found that GXSTC had a specific effect on
   ameliorating CHD in the regulation of hormone levels. For example,
   GXSTC has a certain effect on estrone sulfate levels. Estrone sulfate
   is a form of estrogen. Studies have shown that estrogen may affect the
   coagulation system, vascular endothelial function and other systems,
   directly or indirectly enhancing protective effects on the heart
   ([156]Dai et al., 2015). A recent prospective study of 2,834 subjects
   followed for an average of 12.1 years found that higher estrogen levels
   were associated with a lower risk of CHD ([157]Subramanya et al.,
   2019). Our study results showed that the level of estrone sulfate in
   the body was increased after treatment with GXSTC, indicating that
   GXSTC played a certain role in the regulation of estrogen metabolism.
   In addition, we also found that the level of 11-ketoetiocholanolone, an
   endogenous anabolic androgenic steroid, increased after GXSTC
   treatment. Studies have shown that lower levels of serum endogenous
   testosterone are associated with a higher risk of diseases such as
   atherosclerosis, myocardial infarction, chronic heart failure and
   obesity ([158]Fink et al., 2018; [159]Lorigo et al., 2020), and our
   results indicated that GXSTC also played a role in the regulation of
   androgen metabolism.

   In summary, for the first time, this study revealed the mechanism by
   which GXSTC acts on CHD at a holistic metabolic level. We can assume
   that GXSTC has a unique advantage in the treatment of CHD, and its
   therapeutic mechanism may be the regulation of amino acid metabolism,
   lipid metabolism and hormone levels. Our study provides a certain
   theoretical basis for subsequent research.

Conclusion

   In the present study, biochemical analysis of serum, echocardiography
   and myocardial tissue sections revealed a significant therapeutic
   effect of GXSTC against CHD in rats. Subsequently, 22 biomarkers and
   nine disturbed metabolic pathways in the body during the process of CHD
   were obtained by UHPLC-MS/MS-based untargeted metabolomics. The
   analysis results showed that GXSTC could call back the disordered
   metabolic pathways and thus played a therapeutic role in CHD. We also
   found that these metabolic pathways were related to amino acid
   metabolism, lipid metabolism and hormone levels in the body, which
   fully indicated the overall role of GXSTC in the prevention and
   treatment of CHD through multiple pathways, multiple levels and
   multiple targets. To the best of our knowledge, our study is the first
   to use a metabolomics approach to explain the drug effects of GXSTC on
   CHD rats. Our results can provide a direction for follow-up studies on
   the mechanism of GXSTC in the treatment of CHD, which is conducive to
   the clinical promotion and application of GXSTC.

Acknowledgments