Abstract
As a well-known classical Chinese medicine prescription, Shengxian
Decoction (SXD) has been applied for a century to treat cardiovascular
diseases, especially coronary heart disease (CHD), but the potentially
effective compounds and underlying mechanisms remain unclear. With
ultra-performance liquid chromatography-quadrupole-time of flight-mass
spectrometry (UPLC-Q-TOF/MS) and network pharmacology analysis, the
potential effective compounds of SXD and their pharmacological
mechanisms against CHD were identified and revealed. 57 effective
compounds with favorable pharmacokinetic characteristics and biological
activities were screened through UPLC-Q-TOF/MS analysis, database and
literature mining, interacting with 96 CHD-related targets to support
potential synergistic therapeutic actions. Systematic analysis of the
PPI network and microarray data further revealed six core targets,
including TNF, IL-1β, IL-6, TP53, VEGFA and PTGS2, which were mainly
involved in fluid shear stress and atherosclerosis, lipid and
atherosclerosis, PI3K-Akt signaling pathway et al. Moreover, the
proposed contribution indexes of effective compounds indicated these
compounds, including isoferulic acid, quercetin, calycosin, ferulic
acid, kaempferol, calycosin 7-O-glycoside, formononetin, astragaloside
IV and saikosaponin D, as the core compounds of SXD. The molecular
docking results confirmed that those core compound-target pairs
exhibited strong binding energy. Furthermore, we validated that SXD
significantly alleviated myocardial tissue injury in CHD rats and
reversed H/R-induced decreases in H9c2 cell viability by attenuating
the production of TNF, IL-6 and IL-1β, and reducing cardiomyocyte
apoptosis via down-regulating the TP53, caspase3 and cytochrome C mRNA
expression levels as well as caspase3, caspase9 and cytochrome C
protein expression levels according to RT-qPCR and Western blot
results. Our findings explained the pharmacological mechanisms
underlying the effectiveness of SXD in the treatment of CHD, and laid a
foundation for future basic and clinical research of SXD.
Keywords: Coronary heart disease, Shengxian Decoction, UPLC-Q-TOF/MS,
Network pharmacology, Inflammation, Apoptosis
1. Background
Coronary heart disease (CHD) is one of the most common cardiovascular
diseases, which is caused by stenosis, spasm or blockage of the
coronary artery lumen, and leads to myocardial ischemia, hypoxia or
necrosis [[37]1,[38]2]. Chronic ischemia from coronary artery stenosis
or rupture and myocardial infarction can lead to heart failure and even
death [[39]3]. Drugs for treating CHD usually act on individual
targets, such as beta-blockers and angiotensin converting enzyme
inhibitors. The current clinical drugs for CHD have good efficacy, but
may produce inevitable side effects and drug resistance [[40]4]. In
addition, the morbidity and mortality of CHD are on the rise, and
gradually show a younger trend. It is particularly important to seek a
more effective and safe way to combat CHD.
CHD belongs to the category of “chest arthralgia” in traditional
Chinese medicine (TCM), and is characterized by blood stasis, “qi”
stagnation (vital energy retardation), and phlegm obstruction [[41]5].
In TCM theory, the “qi” stagnation is deemed as the main pathogenesis
of CHD [[42]6]. Shengxian Decoction (SXD), a classic TCM prescription
for the treatment of atmospheric subsidence syndrome, was first
recorded in the text “Medical Treatise on the Integration of Chinese
and Western Medicine” by the famous Chinese medicine master Zhang
Xi-chun in the late Qing Dynasty and the early Republic of China
(between the 19th and 20th centuries). SXD is composed of HuangQi (HQ,
roots of Astragalus membranaceus var. mongholicus (Bunge) Hsiao), ZhiMu
(ZM, rhizomes of Anemarrhena asphodeloides Bge.), ChaiHu (CH, roots of
Bupleurum chinense DC.), JieGeng (JG, roots of Platycodon grandiflorum
(Jacq.) A. DC.) and ShengMa (SM, rhizomes of Cimicifuga foetida L.) at
a ratio of 6:3:1.5:1.5:1 [[43]7,[44]8]. In this prescription, HQ is the
emperor, which can play the role of replenishing and raising “qi”; as
the ministers, CH and SM play a supporting and harmonizing role; the
HQ's medicinal properties are slightly hotter, so ZM plays the role of
heat-clearing as the adjuvant herb; JG, as a courier role, can help to
guide the other herbs in the formula to the heart and also modulate or
harmonize the properties of other drugs. Together, these herbs are
particularly effective in treating conditions such as “qi” stagnation,
which can manifest as chest tightness, shortness of breath, fatigue,
and other symptoms similar to those of CHD [[45]9]. And the main
compounds of SXD are flavonoids, phenolic acids, polysaccharides,
saponins and amino acids, which are closely related to regulating
inflammatory factors, oxidoreductases, and cardiovascular indicators,
etc. Based on a metabolomic study, SXD has a cardioprotective effect
against chronic heart failure, which is involved in the metabolism of
energy and sphingolipid [[46]7]. SXD, in particular, has been
clinically used in China to cure a variety of heart diseases, including
chronic heart failure and myocardial ischemia, especially CHD
[[47]10,[48]11]. However, the potential pharmacological mechanism of
SXD in the treatment of CHD, as well as the potential targets and
pathways are still unclear.
TCM has the multi-compound, multi-target and multi-pathway
characteristics, thus it also belongs to polypharmacy therapy [[49]12].
In recent years, due to the complex composition of TCM prescription,
accurate and complete characterization of the prescription is the core
problem of TCM research. The ultra-performance liquid
chromatography-quadrupole-time of flight-mass spectrometry
(UPLC-Q-TOF/MS) is a modern analytical technique to analyze and
identify the chemical composition of TCM prescription, which is
characterized by rapid and effective qualitative analysis of complex
compounds. Besides, network pharmacology can comprehensively and
systematically reveal the complex relationship between the effective
compounds of TCM and their mechanism of action [[50]13]. It has been
successfully applied to interpret the mechanism of the prescription
therapy for cardiovascular disease at the molecular network level, such
as Radix Salviae and Buyang Huanwu Decoction [[51]14,[52]15]. In the
present study, we first analyzed the compounds of SXD by UPLC-Q-TOF/MS,
and based on which the potential compounds and targets for the
treatment of CHD were collected by database and literature analysis.
Then, we tried to establish network pharmacology models including
protein-protein interaction (PPI), compound-target-disease (C-T-D), and
target-pathway (T-P) networks, calculate contribution index (CI) of
each effective compound, which was based on chemical, pharmacokinetic
and pharmacological data, explore the interaction between
compound-target pairs by molecular docking, and construct the rat CHD
model and hypoxia/reoxygenation (H/R)-induced H9c2 cell model for
further validation, so as to uncover the underlying mechanism of SXD in
the treatment of CHD.
2. Methods
2.1. Preparation and analysis of SXD
2.1.1. Preparation of the SXD extract
SXD was prepared as follows: roots of Astragalus membranaceus var.
mongholicus (Bunge) Hsiao (HQ, Batch No. 20220502), rhizomes of
Anemarrhena asphodeloides Bge. (ZM, Batch No. 20221201), roots of
Bupleurum chinense DC. (CH, Batch No. 20221101), rhizomes of Cimicifuga
foetida L. (SM, Batch No. 20221001), and roots of Platycodon
grandiflorum (Jacq.) A. DC. (JG, Batch No. 20230201) were purchased
from Shaanxi Sciendan Pharmaceutical Co., Ltd. (Tongchuan, China) and
identified by Professor Yonggang Yan. The voucher specimens were
deposited at the Key Laboratory of Shaanxi Administration of
Traditional Chinese Medicine for TCM Compatibility, Shaanxi University
of Chinese Medicine (Specimen number: 22-05-02, 22-12-01, 22-11-01,
22-10-01, 23-02-01). Seventeen reference standards including mangiferin
(Batch No. Yz111920), caffeic acid (Batch No. Yz012423), rutin (Batch
No. Yz051924), Calycosin 7-O-glucoside (Batch No. Yz102721), ferulic
acid (Batch No. Yz011422), isoferulic acid (Batch No. Yz1009201),
luteolin (Batch No. 116523), quercetin (Batch No. Yz011823), calycosin
(Batch No. Yz120322), kaempferol (Batch No. Yz110987), isorhamnetin
(Batch No. Yz090422), saikosaponin A (Batch No. Yz050723),
astragaloside Ⅳ (Batch No. Yz081320), saikosaponin D (Batch No.
Yz080522), neomangiferin (Batch No. Yz1027222), timosaponin AIII (Batch
No. Yz111022) and timosaponin BII (Batch No. Yz072323) were obtained
from Nanjing Plant Origin Biological Co., Ltd. (Nanjing, China).
SXD water extraction solution was prepared as follows: the slices of HQ
(22.38 g), CH (5.60 g), JG (5.60 g) and SM (3.73 g) were evenly mixed,
added 10 times water (MiLLi-Q IQ 7000, MA, USA) was added, soaked for
30 min, ZM (11.19 g) was added after boiling for 8 min, and boiled for
another 7 min, and then strained. Next, the residue was added with 8
times the amount of water, boiled for 10 min, and filtrate was mixed
twice [[53]16]. The water extraction was filtered with gauze, and
freeze-dried to obtain SXD freeze-dried extract (Labconco Freezone
4.5L, MO, USA) The SXD freeze-dried extract was diluted with water to a
certain concentration, and then used for subsequent experiments.
SXD ethanol extraction solution was prepared as follows: HQ (22.38 g),
ZM (11.19 g), CH (5.60 g), JG (5.60 g) and SM (3.73 g) were immersed in
485 mL of 70 % ethanol solution for 1 h and refluxed three times for
1 h each [[54]17]. The combined ethanol extraction was filtered by a
0.22 μm filter, and then diluted to 1 mg/mL for chemical fingerprint
analysis.
2.1.2. Chemical fingerprint of SXD by ultra-performance liquid
chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF/MS)
The chemical fingerprint of SXD was determined by UPLC-Q-TOF/MS. The
concentration of SXD water and ethanol extraction solution was 1 mg/mL,
filtered by 0.22 μm filter, and injected into UPLC-Q-TOF/MS for
analysis. The samples were analyzed by Waters Acquity SDS system. The
mobile phases of UPLC were 0.1 % formic acid and acetonitrile on a
ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm) at a flow rate of
0.3 mL/min. Optimized gradient elution was as follows: 0–2 min,
10%–35 % acetonitrile; 2–15 min, 35%–95 % acetonitrile; 15–17 min, 95 %
acetonitrile; 17–18 min, 95%–10 % acetonitrile; 18–20 min, 10 %
acetonitrile. Column temperature was maintained at 35 °C.
The relevant parameters of MS were set as follows: positive ion mode
analysis, capillary voltage of 3.0 kV, source temperature of 100 °C,
desolvent temperature of 350 °C, cone-hole gas flow of 50 L/h, nitrogen
as atomizing gas (600 L/h), scanning mode of full scan, scanning range
m/z 50∼1200 Da; negative ion mode analysis, capillary voltage of
2.5 kV, source temperature of 100 °C, desolvent temperature of 280 °C,
cone-hole gas flow of 50 L/h, nitrogen as atomizing gas (600 L/h),
scanning mode of full scan, scanning range m/z 50∼1200 Da. Data was
collected and analyzed using MassLynx V4.2 software (Waters, USA).
2.2. Network pharmacology
2.2.1. Screening the effective compounds and predicting the potential targets
of SXD
The compounds of SXD were collected from multiple sources, including
the compounds by UPLC-Q-TOF/MS, the Traditional Chinese Medicine System
Pharmacology Database and Analysis Platform (TCMSP,
[55]https://tcmspe.com/), and literature mining. The compounds were
collected using “Huangqi”, “Zhimu”, “Chaihu”, “Shengma” and “Jiegeng”
as search terms in TCMSP [[56]18]. These compounds were screened by
oral bioavailability (OB) ≥ 30 % and drug-likeness (DL) ≥ 0.18, and
their PubChem ID ([57]https://pubchem.ncbi.nlm.nih.gov/) were recorded.
OB represents the oral availability of pharmaceutical compounds, and DL
refers to the similarity between compound and a known drug [[58]19]. At
the same time, the effective compounds of SXD were imported into TCMSP
database to obtain targets. The compounds with fewer targets were
imported into the PubChem database to obtain their Canonical SMILES
numbers and then imported into the Swiss Target Prediction database
([59]http://www.swisstargetprediction.ch/) to predict the possible
targets. All targets were integrated and entered the UniProt database
([60]https://www.uniprot.org/) for verification, and duplicated targets
were deleted to obtain the SXD-related targets.
2.2.2. Seeking out the potential targets of SXD in the treatment of CHD
With “coronary heart disease” as the keyword, GeneCards Database
([61]https://www.genecards.org/), Online Mendelian Inheritance in Man
Database ([62]https://www.omim.prg/), Therapeutic Target Database
([63]http://db.idrblab.net/ttd/) and DisGeNET Database
([64]https://www.disgenet.org/search) were used to search and screen
the known CHD-related targets, and the repeated targets in the search
results were discarded. However, there were too many results in those
databases, so targets with a target score greater than 50 in the
GeneCards database and a Score greater than 0.1 in the DisGeNET
database were reserved. The UniProt knowledge base was used to get the
standard target's name with the organism selected as “Homo sapiens”.
Finally, the potential targets of SXD acting on CHD were obtained by
using the Venny 2.1.0 platform
([65]https://bioinfogp.cnb.csic.es/tools/venny/index.html).
2.2.3. Construction and analysis of the protein-protein interaction (PPI)
network
These potential targets were imported into the STRING database
([66]https://string-db.org/) to construct the PPI network. The organism
was set to “Homo sapiens” and the minimum required interaction score
was “medium confidence (0.4)”, and then the PPI network was
constructed. Subsequently, the PPI network was saved in “tsv” file
format, which was imported into Cytoscape 3.8.2 software
([67]https://cytoscape.org/), an open-source software package project,
to analyze the results by the network topology analysis function. In
the PPI network, the degree value is an important index, and these
targets above the average value usually play an important role. So, we
ranked these targets according to degree value. At the same time, the
important potential targets were screened out according to the average
of degree and betweenness centrality of the node. Then, the target
sub-networks were identified from the PPI network by using the MCODE
plugin. Finally, the overlapping of the core sub-networks cluster and
important potential targets was taken as the key targets.
Furthermore, we analyzed the PPI network obtained with the CytoHubba
plugin. Four target-ranking methods, including Edge Percolated
Component, Eccentricity, Closeness, and Radiality, were used to analyze
targets' weight (S-value) [[68]20]. Each method allowed the targets to
receive a score, and then we ranked the targets according to the score.
Targets with higher scores were ranked higher. For example, the highest
scoring target was ranked first. However, many targets contained the
same score, and it didn't matter how many targets contained the same
score, the rank of the next target increased by one. Subsequently, all
potential targets were ranked according to these four methods. If the
rank of each target exceeded its median rank, the S-value of this
target was increased by 1. Therefore, the maximum S-value of each
target was 4 and the minimum was 0. Finally, we analyzed the targets'
weight according to the S-value of targets for subsequent analysis.
2.2.4. Microarray data analysis of core targets
Since myocardial infarction (MI) is one of the most serious and harmful
diseases in CHD, the published microarray data (accession number
[69]GSE66360
) of circulating endothelial cells of patients with MI and control were
searched through the GEO database to characterize the significance of
key targets ([70]https://www.ncbi.nlm.nih.gov/geo). The gene expression
profile dataset [71]GSE66360 comprised the gene expression data of
circulating endothelial cells samples from 50 healthy donors and 49
patients with MI. This data was based on the [72]GPL570 Affymetrix
Human Genome U133 Plus 2.0 array. For the expression profile dataset,
we first obtained the annotation information of the probes according to
the corresponding platform, mapped the probes to genes, removed
multiple matches, used the mean value as the gene expression when
multiple probes matched to a gene, and finally obtained the gene
expression profile. The RStudio software's Limma package was used for
the identification of the differences in expression level of key
targets between control and patients with MI. The cutoff criteria in
this analysis were set as p < 0.05 and |log FC| > 1. The key targets of
expression difference were regarded as the core target of SXD in the
treatment of CHD.
2.2.5. Construction of the compound-target-disease (C-T-D) network
The effective compounds and their potential targets in SXD were made
into qualified target table and type table, and imported into Cytoscape
3.8.2 software for visualizing and analyzing the interaction networks.
Subsequently, a C-T-D network was constructed, while the size and color
depth of the potential targets were adjusted according to the degree
value. Finally, we analyzed the parameter rank-sum ratio (RSR) of this
network, which was calculated from network topology parameters,
including Betweenness, Closeness, Degree, Eccentricity, Neighborhood
Connectivity and Average Shortest Path Length.
2.2.6. Contribution index (CI) calculation
In order to screen the core compounds of SXD acting on CHD, a CI based
on the intrinsic properties (content) of the effective compound in the
corresponding herb and the importance of the effective compound in the
C-T-D network was calculated [[73]21,[74]22]. The analysis was
organized as follows:
[MATH: Wj=∑iNSiN :MATH]
[MATH: CIj=mi∑inmi×Ci,jMj×OBj×RSRj×Wj×107 :MATH]
where S[i] is the S-value of the potential target i; N is the number of
potential targets acted by compound j; W[j] is the average S-value of
all potential targets acted by compound j; m[i] is the weight of herb i
in SXD; n is the total count of herbs in SXD;
[MATH:
mi∑
inmi<
/mfrac> :MATH]
is the proportion of each herb in SXD (a common compound is the sum of
the proportions of the herbs, which belongs in these herbs); C[i,j] is
the content of the compound j in herb i (the content of effective
compounds was collected by literature mining, which was expressed as
the average content reported in the literature); M[j] is the molecular
weight of the compound j;
[MATH:
Ci,jMj :MATH]
represents the molar concentration of 1 g of the compound in SXD; OB[j]
is the oral bioavailability of compound j; RSR[j] is the rank-sum ratio
of the integrated network topology parameters of the compound j in the
C-T-D network. This method was used to reflect the ratio of the
compounds in SXD and the weight efficiency of their effect on potential
CHD-related targets. Thus, a compound with a higher CI means a higher
probability of the effect on CHD.
2.2.7. Gene ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG)
enrichment analysis and target-pathway (T-P) network construction
The potential targets were imported into the DAVID platform
([75]https://david.ncifcrf.gov/tools.jsp) for GO function enrichment
analysis and KEGG pathway enrichment analysis, and the “select species”
was set to “Homo sapiens”. GO enrichment analysis involved the
biological process (BP), cellular component (CC) and molecular function
(MF) of the potential targets, and the top 10 results were visualized
as bar graphs. Based on the S-values of all potential targets, the
weight of the pathway P was the sum of the corresponding targets.
Pathway analysis was performed with KEGG, and the top 20 results were
visualized as a bubble diagram by using bioinformatics platform
([76]http://www.bioinformatics.com.cn/). Subsequently, the T-P network
was constructed by Cytoscape 3.8.2 software according to the top 20 of
P-weight [[77]23].
2.2.8. Molecular docking
First, we downloaded the 3D structures of the core compounds in SXD
from the PubChem database and saved them in “sdf” format, and then
downloaded the 3D structures of the core targets from the PDB database
([78]https://www.rcsb.org/) and saved them in “pdb” format. We would
use Chem 3D software to construct their 3D structures for some
compounds, which did not have 3D structures in the database. These
compounds and targets were prepared for ligands and receptors by using
AutoDockTools software, which included operations such as dehydration,
hydrogenation, and deionization. These compound-target pairs were then
docked, and the docking scores were made into a heat map. Finally, the
lower binding energy docking results of each core target were
visualized by LigPlot
([79]https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) and PyMOL
([80]https://pymol.org/2/) software.
2.3. In vivo experiments
2.3.1. Experimental animals
Male specific pathogen-free Sprague-Dawley rats (180–200 g) were
purchased from the Beijing Vital River Laboratory Animal Technology Co
Ltd (Beijing, China). All experimentals were conducted according to the
Guide for the Care and Use of Laboratory Animals by the National
Institute of Health (USA). All procedures were strictly approved by the
Animal Experiment Ethics Committee of the Shaanxi University of Chinese
Medicine (Ethics Approval No. SUCMDL20220310006). All rats were housed
under the standard conditions (temperature, 25 ± 2 °C; constant
humidity, 55 ± 5 %; 12 h dark/light cycle; freely available food and
purified water).
After one week of adaptive feeding, all rats were randomly selected for
sham surgery or CHD model group. The rat CHD model was established by
exhaustive swimming, restricted diet and ligation of the left anterior
descending coronary artery. The rats in sham surgery group were fed
normally. On the first day of the experiment, the rats in the CHD model
group swam exhaustively once a day, with the standard of exhaustively
swimming being that the head of the rats could not emerge from the
water level for 10 s. At the same time, the food control was carried
out, and the daily intake was half of the normal diet for 21 days. On
day 22 of the experiment, a left thoracotomy was performed after rats
were anesthetized with 2 % isoflurane. The rats were laid flat and
disinfected, and incised from the front skin of the neck to extract
tissue and muscle, exposing the windpipe, which was then inserted into
the oral cavity and connected to a ventilator. The left third and
fourth intercostal spaces of the rat were then propped open with
hemostatic forceps, and the left anterior descending coronary artery
(LAD) was then ligated with a 6-0 surgical sutures. 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. 0.25 mL penicillin was injected immediately after chest
closure. 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 [[81]24]. All rats were fed normally after modeling. The 24
CHD-operated rats were divided into the CHD model group, the SXD
low-dose group (4.33 g/kg), SXD high-dose group (12.99 g/kg), and
Betaloc group (10 mg/kg, AstraZeneca, UK) according to a random number
control table. Rats without ligation were placed in the sham-operated
group. The rats in the sham and CHD model groups were given the same
volume of normal saline via intragastric administration.
2.3.2. Myocardial infarct size detection
After 28 days of treatment, the rats were given 12 h of fasting and
water, 2 % isoflurane for anesthesia, all the hearts of the rats were
separated, weighed after absorbing water, and the heart weight index
(HWI) was calculated. Three rats in each group were randomly selected
and their hearts were transferred at 0–4 °C PBS and frozen at −20 °C
for 30 min. Slices of the heart were taken with a thickness of 2 mm and
placed in 2 % red tetrazolium solution in a dark water bath at 37 °C
for 30 min. The container was slightly shaken every 5 min to make it
fully stained. The myocardial sections were removed and washed with PBS
solution for 3–5 min. Next, the sections were soaked in 4 %
paraformaldehyde solution for 24 h, and the myocardial infarction size
was evaluated by 2,3,5-triphenyl-2H-tetrazolium chloride (TTC)
staining. Image Pro Plus 6.0 was used to quantify the myocardial
infarction size. Myocardial infarction area divided by total area was
regarded as the percentage of myocardial infarction.
2.3.3. Hematoxylin and eosin (H&E) and Masson's trichrome staining
The myocardial tissue was fixed in 4 % paraformaldehyde solution,
gradient dehydrated with 70–100 % ethanol, embedded in paraffin, and
sliced (4–5 μm). Hematoxylin-eosin (H&E) staining was used to observe
myocardial structural changes under an optical microscope. The paraffin
sections were dewaxed to water, followed by Masson staining of the
sections. After dehydration and sealing, the changes of myocardial
collagen fibers were observed under an optical microscope to evaluate
the degree of myocardial fibrosis.
2.3.4. ELISA
Blood was collected by intubation in the abdominal aorta of rats in
each group, and serum was obtained by centrifugation at 3000 rpm for
15 min. The levels of serum IL-6 (MM-0190R1, MEIMIAN, China), IL-1β
(MM-0047R1, MEIMIAN, China), Aldosterone (ALD, YJ002876, MEIMIAN,
China), Angiotensin II (Ang II, YJ058803, MEIMIAN, China), N-terminal
pro-brain natriuretic peptide (NT-proBNP, YJ003242, MEIMIAN, China)
were assessed at 450 nm was detected by a microplate reader (RT-6100,
Rayto, China) according to the ELISA kits.
2.4. In vitro experiments
2.4.1. Cell culture and the hypoxia/reoxygenation (H/R) cell model
Rat cardiomyocytes (H9c2) were normally cultured in Dulbecco's Modified
Eagle Medium (DMEM) supplemented with 10 % FBS at a 37 °C normoxic
atmosphere with 5 % CO[2]. The DMEM medium containing 10 % FBS was used
as a complete medium. An H/R cell model was used to mimic the
pathological process of cardiac injury. In brief, the H9c2 cells were
in six groups: the control group, H9c2 cells were normally cultured;
the H/R group, H9c2 cells were cultured in D-Hank's medium in a hypoxia
atmosphere at 37 °C with 5 % CO[2] and 95 % N[2] for 8 h, and further
cultured in complete medium at normoxic atmosphere at 37 °C with 5 %
CO[2] and 95 % O[2] for 12 h; the H/R + treatment group, H9c2 cells
were incubated in an anesthesia induction chamber with fresh gas (95 %
O[2] and 5 % CO[2]) and the addition of drug (100 μM Diazoxide,
250 μg/mL SXD, 500 μg/mL SXD, 1 mg/mL SXD) for 24 h, and then treated
with H/R.
2.4.2. Cell viability assay
3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide (MTT)
assay was useded in the current study to evaluate cell viability after
various treatments. After the collection of H9c2 cells, 10 μL MTT
reagents (5 mg/mL) were added into the 96-well plate
(1.0 × 10^5 cells/1 mL) and then incubated for 4 h. After the solution
was discarded, 100 μL of dimethyl sulfoxide was added into the culture
plate. Finally, an Imark microplate reader was used for measuring the
cell absorbance at 570 nm.
2.4.3. Detection of intracellular reactive oxygen species (ROS) generation
Assessment of intracellular ROS changes in cells during apoptosis was
performed using Reactive Oxygen Species Assay Kit (S0033 M, Beyotime,
China). A cell density of 10^5/mL was placed in each 35-mm culture
plate. DCFH-DA was diluted in serum-free medium at 1:1000 to give a
final concentration of 10 μM/L. Cells were collected and suspended in
diluted DCFH-DA at a concentration of 1 million to 20 million cells/mL
and incubated in a cell incubator at 37 °C for 20 min. The mixture was
reversed every 5 min to make full contact between the probe and the
cell. Cells were washed three times with serum-free cell culture medium
and then analyzed by the Beckman CytoFlex Sflow cytometer system.
2.4.4. Quantification of cellular apoptosis
The apoptosis of H9c2 cells was detected using Annexin V-FITC/PI stain
kit (C1062 M, Beyotime, China). In brief, after the cell collection,
the H9c2 cells were washed by PBS twice, centrifuged (500×g) for 5 min,
and then mixed with 500 μL working buffer. The cells were further added
with 5 μL Annexin V binding with FITC and 5 μL PI for 15 min staining.
Lastly, cell signal was measured, and the apoptosis rate was analyzed
by the Beckman CytoFlex Sflow cytometer system.
2.4.5. ELISA
H9c2 cells were inoculated in 6-well plates with a density of
1 × 10^5 cells per well. After the drug intervention, the supernatant
was taken, and the optical density of tumor necrosis factor (TNF,
FU-D1393, BIOFINE, USA), interleukin-6 (IL-6, FU-D1083, BIOFINE, USA)
and interleukin-1 beta (IL-1β, FU-D1032, BIOFINE, USA) at 570 nm was
detected by a microplate reader according to the ELISA kits.
2.4.6. Real time quantitative PCR (RT-qPCR)
Total RNA was isolated from H9c2 cells with a RNeasy mini kit as
described in the manufacturer's manual. These RNA samples were then
reverse transcribed into cDNAs using Revert Ace kit (TOYOBO, Japan) and
the mRNAs were measured through the SYBR-Green RT-qPCR. Cellular tumor
antigen p53 (TP53), Caspase 3 and Cytochrome C expression was
calculated relative to the β-actin expression ([82]Table 1).
Table 1.
Primers sequences used for RT-qPCR.
Genes Forward primer Reverse primer Size (bp)
TP53 AGATGTTCCGAGAGCTGAATGAG TTTTTTATGGCGGGACGTAGA 130
Caspase3 ACTGGAAAGCCGAAACTCTTCATCA GGAAGTCGGCCTCCACTGGTATC 127
Cytochrome C GCTGGATTCTCTTACACAGATGCC GGTCTGCCCTTTCTCCCTTCTT 151
β-actin AGAGGGAAATCGTGCGTGA CATTGCCGATAGTGATGACCT 144
[83]Open in a new tab
2.4.7. Western blot analysis
H/R-induced H9c2 cells were treated with SXD, and then the proteins
from each treatment group were isolated by radio immunoprecipitation
assay buffer (RIPA, P1003B, Beyotime, China). The supernatant was
centrifuged at 14,000 rpm for 15 min at 4 °C, and the total protein
concentration was determined, followed by quantitation with the
bicinchoninic acid assay (BCA) protein assay kit (P0010, Beyotime,
China). After denaturation in sodium dodecyl sulfate-polyacrylamide gel
electrophoresis (SDS-PAGE) gels, the equal amounts of proteins were
transferred to nitrocellulose membrane (HATF00010, Millipore, USA).
Blots were blocked with 5 % fat-free milk in TBS with Tween-20 (TBST)
at room temperature (approximately 25 °C) for 1 h, and then Caspase3
(1:500, 19677-1-AP, Proteintech, USA), Caspase9 (1:500, bs-20773R,
Bioss, Switzerland), Cytochrome C (1:500, bs-0013R, Bioss,
Switzerland), β-actin (1:5000, 200068-8F10, ZEN-BIOSCIENCE, China)
primary antibodies were added in 5 % blocking buffer at 4 °C overnight.
After incubation with the corresponding HRP-conjugated secondary
antibodies (1:2000, SA00001-1 and SA00001-2, Proteintech, USA), the
enhanced chemiluminescence (ECL) kit (WBKLS0500, Millipore, USA) was
used for detection. The grey scale values of protein bands were
analyzed using Image J software (NIH, USA).
2.4.8. Caspase-3 enzymatic activities
The caspase 3 can catalyze substrate acetyl-Asp-Glu-Val-Asp
p-nitroanilide (Ac-DEVD-pNA) to produce yellow p-nitroaniline (pNA), so
as to determine the activity of caspase 3 by using the caspase 3
Activity Assay Kit (C1115, Beyotime, China). The protein concentrations
were determined by Bradford protein assay kit (P0006, Beyotime, China).
Cellular extracts (30 μg) were incubated in a 96-well microtitre plate
with 20 ng Ac-DEVD-pNA for 4 h at 37 °C, and OD[405nm] values were
measured by using a microplate reader (BioTek Instruments, USA).
2.5. Statistical analysis
The study data were analyzed using SPSS 22.0 (SPSS Inc., Chicago, IL,
USA) and GraphPad Prism 8.3 software (GraphPad Software, USA), and
comparisons between the groups were performed using Student's t-test
(two-group comparison) and one-way analysis of variance (ANOVA, more
than two groups). All data were based on at least three independent
experiments. The statistical results are expressed as the
mean ± standard deviation, and then statistical charts were drawn
according to the statistical results. Value of p < 0.05 was considered
to be statistically significant.
3. Results
3.1. Chemical fingerprint of SXD
MassLynx V4.2 software was used to collect the data, and the obtained
data were quickly matched to the database in UNIFI 1.8 (Waters)
platform. Before conducting the matching process, we created a compound
library in-house by gathering chemical formulas for SXD from various
sources such as Chemical Book, PubChem, TCMSP, and relevant literature.
The compounds in the SXD ethanol extraction solution produced the [M +
H]^+, [M + Na]^+, [M + NH4]^+ and [M + K]^+ peaks in the positive ion
mode, and produced the [M − H]^-, [M + CH3COO]^- and [M + HCOO]^- peaks
in the negative ion mode. The chemical fingerprint of SXD was profiled
by UPLC-Q-TOF/MS ([84]Fig. 1). Meanwhile, we identified 113 compounds
in SXD, including flavonoids, organic acids, alkaloids, terpenoids, and
saponins. [85]Table 2 lists the retention time, molecular formula,
adduction ion and fragment ion of all identified compounds, and these
compounds have possible biological activity. Seventeen of them were
unambiguously identified by comparison with reference compounds
([86]Fig. S1).
Fig. 1.
[87]Fig. 1
[88]Open in a new tab
Base peak ion (BPI) chromatogram of SXD based on UPLC-Q-TOF/MS. (A)
Ethanol extraction solution; (B) Reference compounds solution.
Table 2.
Identification of chemical compounds in SXD by UPLC-Q-TOF/MS.
No. Component name Formula Neutral mass (Da) Observed m/z Mass error
(ppm) Adducts Observed RT (min) Fragments (m/z) Source
1 l(+)-Arginine C[6]H[14]N[4]O[2] 174.1117 173.1041 −1.7 [M − H]^- 0.78
131.0821 HQ
2 7-O-Methylmangiferin C[20]H[20]O[11] 436.1005 495.1097 −9.5
[M + CH[3]COO]^- 0.90 435.9157, 351.0805, 245.0365 ZM
3 Ononin C[22]H[22]O[9] 430.1264 429.1228 8.6 [M − H]^- 0.94 431.1304,
269.1013, 224.0434, 118.0476 HQ
4 Nicotinic acid C[6]H[5]NO[2] 123.0320 182.0448 −6.1 [M + CH[3]COO]^-
0.97 – HQ
5 Boc-D-Tyr-OH C[14]H[19]NO[5] 281.1263 299.1596 −1.7 [M + NH[4]]^+
1.01 265.1544, 250.1418, 235.0709 HQ
6 N-Carbobenzoxy-dl-Leucine C[14]H[19]NO[4] 265.1314 283.1665 4.5
[M + NH[4]]^+ 1.35 230.1685, 212.1597 HQ
7 4-Hydroxybenzaldehyde C[7]H[6]O[2] 122.0368 181.0501 −2.7
[M + CH[3]COO]^- 1.46 – ZM
8 Guaiacol C[7]H[8]O[2] 124.0524 123.0451 −0.3 [M − H]^- 1.65 – SM
9 Neomangiferin[89]^a C[25]H[28]O[16] 584.1377 583.1161 – [M − H]^-
1.73 301.0249, 272.0245, 243.0229 ZM
10 4-Hydroxy-3,5-dimethoxycinnamic acid C[11]H[12]O[5] 224.0685
223.0615 1.4 [M − H]^- 1.98 223.0618, 135.0454 SM
11 1-Caffeoylquinic acid C[16]H[18]O[9] 354.0951 353.0878 0.1 [M − H]^-
2.04 353.0810, 85.0263 JG
12 Mangiferin[90]^a C[19]H[18]O[11] 422.3396 421.0559 – [M − H]^- 2.11
301.0249, 271.0113, 258.0078 ZM
13 Cimicifugamide or its isomer C[25]H[31]NO[10] 505.1948 506.2054 6.5
[M+H]^+ 2.18 344.1657, 177.0851, 145.0580 SM
14 Caffeic acid[91]^a C[9]H[8]O[4] 180.1574 179.0247 – [M − H]^- 2.4
165.0556, 135.0451, 134.0369 HQ, SM
15 Kaempferol-3-O-Rutinoside C[27]H[30]O[15] 594.1585 593.1511 −0.1
[M − H]^- 2.43 285.0423, 239.0477 HQ
16 Rutin[92]^a C[27]H[30]O[16] 610.5175 609.1237 – [M − H]^- 2.52
300.0175, 271.0113, 243.0161, 227.0225 CH
17 Calycosin 7-O-glucoside[93]^a C[22]H[22]O[10] 446.4041 491.1012 –
[M + HCOO]^- 2.62 283.0540, 268.0344, 239.0275, 211.0333 HQ
18 Naringin C[27]H[32]O[14] 580.1792 639.1935 0.7 [M + CH[3]COO]^- 2.71
433.0741, 271.0401, 151.0402 HQ
19 Isorhamnetin 3,4′-diglucoside C[28]H[32]O[17] 624.1690 623.1613 −0.7
[M − H]^- 2.74 541.1690, 342.1341, 301.0348 HQ
20 Tuberosine A C[19]H[21]NO[5] 343.1420 342.1340 −2.1 [M − H]^- 2.76
342.1341, 193.0506 CH
21 Genistin C[21]H[20]O[10] 432.1057 477.1036 −0.6 [M + HCOO]^- 2.9
268.0371, 211.0101 HQ
22 Acacetin C[16]H[12]O[5] 284.0685 283.0608 −1.5 [M − H]^- 2.91
226.1017, 212.0438 JG
23 Cimicifugic acid D C[20]H[18]O[10] 418.0900 417.0825 −0.5 [M − H]^-
2.93 255.0475, 179.0306 SM
24 Timosaponin D C[45]H[74]O[19] 918.4824 963.4801 −0.6 [M + HCOO]^-
2.95 755.4228, 593.1825, 191.0169 ZM
25 Ferulic Acid[94]^a C[10]H[10]O[4] 194.1840 193.0453 – [M − H]^- 2.96
178.0206, 149.0545, 134.0313 SM
26 Timosaponin B II[95]^a C[45]H[76]O[19] 920.4981 965.4958 −0.5
[M + HCOO]^- 2.99 919.4790 ZM
27 Isoferulic acid[96]^a C[10]H[10]O[4] 194.1840 193.0393 – [M − H]^-
3.04 178.0306, 134.0363 SM
28 5-Methyl furfural C[6]H[6]O[2] 110.0368 109.0306 9.6 [M − H]^- 3.07
108.0217 SM
29 Paeoniflorin C[23]H[28]O[11] 480.1632 519.1229 −6.6 [M+K]^+ 3.09
449.1431, 327.1342, 165.0415, 121.0984 SM
30 Baicalin C[21]H[18]O[11] 446.0849 445.0781 1 [M − H]^- 3.15
269.0297, 113.5667 HQ
31 Ethyl 4-hydroxy-3-methoxycinnamate C[12]H[14]O[4] 222.0892 221.0818
−0.6 [M − H]^- 3.23 187.0971, 125.0970 SM
32 Cimilactone A C[33]H[50]O[9] 590.3455 591.3516 −1.9 [M+H]^+ 3.26
433.3441, 415.3356, 301.0968 SM
33 Desapioplatycodin D C[52]H[84]O[24] 1092.5353 1091.5288 0.7
[M − H]^- 3.27 1091.5263 JG
34 Puerarin C[21]H[20]O[9] 416.1107 475.1238 −1.8 [M + CH[3]COO]^- 3.31
267.0659, 252.0424, 223. 0388 SM
35 N-p-trans-Coumaroyltyramine C[17]H[17]NO[3] 283.1208 282.1129 −2.5
[M − H]^- 3.51 282.1137, 179.0347, 152.9960 ZM
36 Jaranol C[17]H[14]O[6] 314.0790 373.0926 −0.7 [M + CH[3]COO]^- 3.53
299.0926, 282.1137, 179.0347, 152.9960 HQ
37 Methylnissolin-3-O-glucoside C[23]H[26]O[10] 426.1526 507.1503 −1
[M + HCOO]^- 3.54 483.2362 HQ
38 Platycoside M[3] C[52]H[80]O[24] 1088.5040 1147.5211 2.9
[M + CH[3]COO]^- 3.59 933.4736, 653.3382 JG
39 7,2′-dihydroxy-3′,4′-dimethoxyisoflavane-7-O-glucoside or its isomer
C[23]H[28]O[10] 464.1683 463.1603 −1.4 [M − H]^- 3.61 353.1018,
301.1079 HQ
40 Isochlorogenic acid C C[25]H[24]O[12] 516.1268 517.1384 8.4 [M+H]^+
3.67 451.3312, 269.1081 CH
41 Timosaponin F C[39]H[64]O[15] 772.4245 817.4225 −0.3 [M + HCOO]^-
3.78 771.4069, 609.2312 ZM
42 Luteolin[97]^a C[15]H[10]O[6] 286.2363 285.0276 – [M − H]^- 3.84
145.9290 JG
43 Anemarsaponin B C[45]H[74]O[18] 902.4875 947.4865 0.8 [M + HCOO]^-
3.85 901.4807, 797.4692, 739.4276, 639.3372 ZM
44 Methylnissolin C[17]H[16]O[5] 300.0998 299.0925 −0.1 [M − H]^- 3.86
301.0325, 163.3195, 147.0660, 113.7024 HQ
45 Quercetin[98]^a C[15]H[10]O[7] 302.2357 301.0249 – [M − H]^- 3.91
285.0276, 268.0273 HQ, CH
46 Icariin I C[27]H[30]O[11] 530.1788 569.1412 −1.4 [M+K]^+ 3.93
451.3323, 258.1019, 270.0794 ZM
47 calycosin[99]^a C[16]H[12]O[5] 284.2635 283.0554 – [M − H]^- 3.95
268.0344, 211.0333 HQ
48 Nepasaikosaponin K C[48]H[80]O[18] 944.5345 989.5345 1.9
[M + HCOO]^- 4.06 943.5290, 829.4599, 783.4519, 725.4124, 199.8049 CH
49 Officinalisinin I C[45]H[76]O[19] 920.4981 959.4648 3.7 [M+K]^+ 4.09
741.8390, 417.1752, 273.2432 ZM
50 Isoflavanone C[15]H[12]O[2] 224.0837 269.0817 −0.7 [M + HCOO]^- 4.16
193.0141, 108.0236 HQ
51 6-Methylcoumarin C[10]H[8]O[2] 160.05243 161.0602 −0.19 [M+H]^+ 4.21
133.0875, 105.0690 HQ
52 Anemarrhenasaponin-Ia C[40]H[68]O[14] 772.4609 831.4751 0.4
[M + CH[3]COO]^- 4.49 839.4802, 785.4686, 767.4565 ZM
53 Saikosaponin F C[48]H[80]O[17] 928.5396 973.5380 0.2 [M + HCOO]^-
4.68 927.5326, 781.4735, 619.4216 CH
54 Oroxylin A-7-O-glucuronide C[22]H[20]O[11] 460.1006 483.0868 −6.3
[M+Na]^+ 4.69 423.3765, 405.3687 HQ
55 Kaempferol[100]^a C[15]H[10]O[6] 286.2363 285.0276 – [M − H]^- 4.7 –
CH, ZM, CH
56 Isorhamnetin[101]^a C[16]H[12]O[7] 316.2623 315.0424 – [M − H]^-
4.84 300.0175, 285.0276 HQ, CH
57 Apigenin C[15]H[10]O[5] 270.0528 315.0510 −0.2 [M + HCOO]^- 4.85
251.8408, 241.0652, 225.1435, 151.0348 HQ
58 Astragaloside III C[41]H[68]O[14] 784.4609 829.4586 −0.6
[M + HCOO]^- 5.05 829.4602, 783.4532, 743.4594, 489.3580, 393.1592 HQ
59 Timosaponin I C[39]H[66]O[14] 758.4453 803.4425 −1.1 [M + HCOO]^-
5.13 757.4374, 665.3908, 595.3806, 529.3520 ZM
60 Cubebin C[20]H[20]O[6] 356.1260 355.1181 −1.7 [M − H]^- 5.16
161.0453 CH
61 Isoliquiritigenin C[15]H[12]O[4] 256.0736 255.0663 0.1 [M − H]^-
5.26 213.1294 ZM
62 Anemarrhenasaponin A[2] C[39]H[64]O[14] 756.4296 801.4273 −0.7
[M + HCOO]^- 5.35 635.3797, 593.3678, 375.1848 ZM
63 Sainfuran C[16]H[14]O[5] 286.0841 285.0762 −2.4 [M − H]^- 5.43 – CH
64 Pratensein C[16]H[12]O[6] 300.0634 299.0554 −2.4 [M − H]^- 5.5
223.0397, 196.0490 HQ
65 Formononetin C[16]H[12]O[4] 268.0736 267.0662 −0.4 [M − H]^- 5.54
252.0425, 223.0397, 195.0450, 167.0501 HQ
66 (−)-Catechin hydrate C[15]H[14]O[6] 290.0790 291.0890 9.1 [M+H]^+
5.55 269.1081, 197.0906, 152.0943 CH
67 Astragaloside Ⅱ C[43]H[70]O[15] 826.4715 871.4731 3.9 [M + HCOO]^-
5.71 778.2931, 635.3801, 577.3384, 499.3055 HQ
68 Saikosaponin A[102]^a C[42]H[68]O[13] 780.9815 825.4397 –
[M + HCOO]^- 5.79 779.4495 CH
69 Saikosaponin B[1] C[42]H[68]O[13] 780.4660 825.4650 1 [M + HCOO]^-
5.81 779.4588, 755.4225, 617.4057 CH
70 Saikosaponin B[4] C[43]H[72]O[14] 812.4922 811.4896 5.8 [M − H]^-
5.95 753.4062, 591.3549, 267.0649, 243.8989 CH
71 Cimicifugoside C[37]H[54]O[11] 674.3666 719.3640 −1.1 [M + HCOO]^-
6.1 655.3469, 520.3057, 295.2272 SM
72 Agroastragaloside Ⅰ C[45]H[74]O[16] 870.4977 915.4981 2.4
[M + HCOO]^- 6.2 915.4952, 419.9543, 329.2333 HQ
73 Malonylsaikosaponin A C[45]H[70]O[16] 866.4664 865.4593 0.2
[M − H]^- 6.24 631.3494, 299.0907, 193.0508 CH
74 Platycoside K C[42]H[68]O[17] 844.4457 867.4323 −3 [M+Na]^+ 6.25
787.4346, 478.3487 JG
75 Anemarrhenasaponin III C[39]H[64]O[14] 756.4296 801.4280 0.2
[M + HCOO]^- 6.37 755.4227, 635.3798, 577.3380 ZM
76 Cis-hinokiresinol C[17]H[16]O[2] 252.1150 251.1077 −0.2 [M − H]^-
6.72 235.0763, 162.8401, 117.0348 ZM
77 Hippeastrine C[17]H[17]NO[5] 315.1107 314.1019 −4.7 [M − H]^- 6.73
295.2269, 251.1077, 235.0763 ZM
78 Saikosaponin E C[42]H[68]O[12] 764.4711 809.4690 −0.4 [M + HCOO]^-
6.75 763.4639, 753.4090, 637.3955, 619.3831 CH
79 Anemarsaponin F C[50]H[82]O[23] 1050.5247 1095.5236 0.6 [M + HCOO]^-
6.83 1049.5169, 915.4600, 773.4369, 721.3797, 617.3706 ZM
80 6″-O-Acetylsaikosaponin b3 C[45]H[74]O[15] 854.5028 913.5159 −0.8
[M + CH[3]COO]^- 7.02 885.4846, 677.3909, 617.3681, 559.3280 CH
81 7-O-methylisomucronulatol C[18]H[20]O[5] 316.1311 315.1227 −3.6
[M − H]^- 7.03 283.0609 HQ
82 Astragaloside Ⅳ[103]^a C[41]H[68]O[14] 784.9702 783.3947 – [M − H]^-
7.35 739.4159, 677.3754, 449.2163 HQ
83 25-O-Acetylcimigenol xyloside C[37]H[58]O[10] 662.4030 661.3932 −3.8
[M − H]^- 7.41 659.3799, 513.2282, 389.1742 SM
84 Saikosaponin D[104]^a C[42]H[68]O[13] 780.9815 825.4397 –
[M + HCOO]^- 7.49 779.4495 CH
85 Timosaponin AIII[105]^a C[39]H[64]O[13] 740.4347 785.4329 0
[M + HCOO]^- 7.87 739.4159 ZM
86 Ethyl caffeate C[11]H[12]O[4] 208.0736 226.1073 −0.5 [M + NH[4]]^+
7.96 207.1474, 197.1642 CH
87 Diosgenin C[27]H[42]O[3] 414.3134 415.3168 −9.3 [M+H]^+ 7.97
415.3153, 378.2821, 361.2735 ZM
88 Stigmasterol C[29]H[48]O 412.3705 451.3329 −1.8 [M+K]^+ 8.24
397.2922, 335.2605, 259.2340, 97.0604 ZM, CH, SM
89 6″-O-Acetylsaikosaponin D C[44]H[70]O[14] 822.4766 867.4751 0.4
[M + HCOO]- 8.27 821.4694, 779.4586, 761.4480, 617.4050 CH
90 (+)-Anomalin C[24]H[26]O[7] 426.1679 427.1780 6.6 [M+H]+ 8.45
317.2493, 171.1482 CH
91 27-Dexyactein C[37]H[56]O[10] 660.3874 659.3796 −0.7 [M − H]- 8.51
659.3798, 559.3257, 523.3100, 485.3272 SM
92 Cimigenol 3-O-β-d-Xylopyranoside C[35]H[56]O[9] 620.3924 665.3903
−0.6 [M + HCOO]- 8.75 619.3835, 595.2781, 315.2512 SM
93 Platycoside M[1] C[36]H[54]O[12] 678.3615 696.3953 −0.1 [M + NH[4]]+
8.99 263.2641, 245.2559 JG
94 Phenprobamate C[10]H[13]NO[2] 179.0946 197.1271 −7 [M + NH[4]]+ 9.05
149.0562 HQ
95 Acetytastragaloside C[47]H[74]O[17] 910.4926 955.4914 0.7
[M + HCOO]^- 9.63 909.4861, 821.4706, 603.3378, 279.2325 HQ
96 Coronaric acid C[18]H[32]O[3] 296.2351 295.2279 0.2 [M − H]^- 10.23
277.2175, 195.1389, 116.9286 JG
97 β-Sitosterol C[29]H[50]O 414.3862 453.3479 −3.1 [M+K]^+ 10.82
277.2434, 149.0556 SM
98 7,8-Didehydrocimigenol C[30]H[46]O[5] 486.3345 545.3481 −0.4
[M + CH[3]COO]^- 10.96 485.3272, 445.2909, 393.1357, 185.1178 SM
99 Cimigenol C[30]H[48]O[5] 488.3502 547.3637 −0.5 [M + CH[3]COO]^-
11.26 487.3410, 383.2895, 279.2326, 243.8991 SM
100 Pentadecanoic acid C[15]H[30]O[2] 242.2246 287.2247 6.5
[M + HCOO]^- 11.32 – SM
101 Scoparone C[11]H[10]O[4] 206.0579 265.0732 5.3 [M + CH[3]COO]^-
12.55 265.0752 CH
102 Anhydroicaritin C[21]H[20]O[6] 368.1260 427.1440 9.8
[M + CH[3]COO]^- 13.47 277.2170, 251.1648, 116.9281 ZM
103 Prosaikogenin A C[36]H[58]O[8] 618.4132 641.4019 −0.7 [M+Na]^+
13.68 457.2680, 177.1953, 93.1013 CH
104 Linolenic acid C[18]H[30]O[2] 278.2246 323.2230 0.6 [M + HCOO]^-
14.17 323.2208, 305.2109 HQ
105 Linoleic acid C[18]H[32]O[2] 280.2402 279.2329 0 [M − H]^- 14.62
279.2329, 211.1338, 116.9280 HQ
106 Bifendate C[20]H[18]O[10] 418.0900 419.0984 2.6 [M+H]^+ 14.63
338.3643, 270.3067, 245.2555, 225.0726 HQ
107 Palmitic acid C[16]H[32]O[2] 256.2402 255.2326 −1.5 [M − H]^- 15.56
261.1982, 205.1383, 165.0720, 149.0771, 125.0043 HQ
108 Linoleyl acetate C[20]H[36]O[2] 308.2715 307.2642 0 [M − H]^- 16.16
281.2481, 234.8201, 116.9279 CH
109 Anemarsaponin E C[46]H[78]O[19] 934.5137 973.4805 3.7 [M+K]^+ 16.29
905.4240, 849.4228, 734.4902 ZM
110 Chrysanthemaxanthin C[40]H[56]O[3] 584.4230 607.4104 −2.9 [M+Na]^+
17.14 485.3876, 312.3499, 256.2919 ZM
111 Baohuoside I C[27]H[30]O[10] 514.1839 532.2143 −6.4 [M + NH[4]]^+
17.8 413.2813, 149.0554 ZM
112 Hederagenin C[30]H[48]O[4] 472.3553 531.3695 0.8 [M + CH[3]COO]^-
18.02 416.7036, 316.7786, 216.8533 HQ
113 Mairin C[30]H[48]O[3] 456.3604 455.3531 0.1 [M − H]^- 18.22
455.3526, 400.7358, 339.7611, 222.8403
184.9164 HQ
[106]Open in a new tab
^a
Compounds identified by reference compounds.
3.2. Collecting and screening of effective compounds and the targets in SXD
In the present study, based on UPLC-Q-TOF/MS analysis and the set
conditions in the database, a total of 57 potentially effective
compounds were retrieved and screened from chemical fingerprint, TCMSP
and literature mining, including 17, 11, 12, 8, 5 and 4 form HQ, ZM,
CH, SM, JG and common compounds in SXD ([107]Table 3). Among them,
isorhamnetin (M54) was shared in HQ and CH, kaempferol (M55) was shared
in HQ, ZM and CH, quercetin (M56) was shared in HQ and CH, and
stigmasterol (M57) was shared in ZM, CH and SM. Some compounds that did
not meet both the OB and DL criteria were also selected in the cases of
high bioactivities and huge amounts, including astragaloside IV (M15),
ononin (M16), calycosin 7-O-glucoside (M17), timosaponin B II (M28),
saikosaponin A (M38), saikosaponin D (M39), rutin (M40), caffeic acid
(M46), isoferulic acid (M47), ferulic acid (M48) and platycodin D
(M53). Specifically, astragaloside IV, calycosin 7-O-glucoside,
saikosaponin A, saikosaponin D, timosaponin B II, isoferulic acid and
platycodin D have been chosen as the marker compounds for quality
control of corresponding herbs in Chinese Pharmacopoeia [[108]25].
Similarly, although ononin holds low OB value, it also exhibits
remarkable pharmacological effects by alleviating H[2]O[2]-induced
cardiomyocyte apoptosis and improving cardiac function [[109]26]. In
addition, cardioprotective effects of caffeic acid and ferulic acid are
also good [[110]27,[111]28]. Based on the above considerations, it was
reasonable to believe that 57 compounds could be listed as potentially
effective compounds for SXD. Subsequently, the effective compounds of
SXD were entered into the database to obtain the corresponding targets.
A total of 502 SXD-related targets were screened by correcting and
deleting duplicate values, which were included in the subsequent
analysis.
Table 3.
The effective compounds of SXD.
No. Name PubChem CID 2D structure OB (%) DL Herb
M1 Mairin[112]^a 64971 Image 1 55.38 0.78 HQ
M2 Jaranol[113]^a 5318869 Image 2 50.83 0.29 HQ
M3 Hederagenin[114]^a 73299 Image 3 36.91 0.75 HQ
M4 3,9-di-O-methylnissolin 15689655 Image 4 53.74 0.48 HQ
M5 7-O-methylisomucronulatol[115]^a 15689652 Image 5 74.69 0.30 HQ
M6 Methylnissolin-3-O-glucoside[116]^a 74977390 Image 6 36.74 0.92 HQ
M7 Methylnissolin[117]^a 5319733 Image 7 64.26 0.42 HQ
M8 Bifendate[118]^a 108213 Image 8 31.10 0.67 HQ
M9 Formononetin[119]^a 5280378 Image 9 69.67 0.21 HQ
M10 Isoflavanone[120]^a 160767 Image 10 109.99 0.30 HQ
M11 Calycosin[121]^a 5280448 Image 11 47.75 0.24 HQ
M12 Folic acid 135398658 Image 12 68.96 0.71 HQ
M13 Isomucronulatol-7,2′-di-O-glucosiole 15689653 Image 13 49.28 0.62
HQ
M14 1,7-Dihydroxy-3,9-dimethoxy pterocarpene 5316760 Image 14 39.05
0.48 HQ
M15 Astragaloside IV[122]^a 13943297 Image 15 22.50 0.15 HQ
M16 Ononin[123]^a 442813 Image 16 11.52 0.78 HQ
M17 Calycosin 7-O-glucoside[124]^a 5318267 Image 17 5.49 0.81 HQ
M18 Asperglaucide[125]^a 10026486 Image 18 58.02 0.52 ZM
M19 Mangiferolic acid 45270099 Image 19 36.16 0.84 ZM
M20 Anhydroicaritin[126]^a 5318980 Image 20 45.41 0.44 ZM
M21 Anemarsaponin F_qt[127]^a / Image 21 60.06 0.79 ZM
M22 Chrysanthemaxanthin[128]^a 21160900 Image 22 38.72 0.58 ZM
M23 Hippeastrine[129]^a 441594 Image 23 51.65 0.62 ZM
M24 Icariin I[130]^a / Image 24 41.58 0.61 ZM
M25 Anemarsaponin E_qt[131]^a / Image 25 30.67 0.86 ZM
M26 Diosgenin[132]^a 99474 Image 26 80.88 0.81 ZM
M27 Coumaroyltyramine[133]^a / Image 27 112.9 0.20 ZM
M28 Timosaponin B Ⅱ[134]^a 44575945 Image 28 13.87 0.04 ZM
M29 Linoleyl acetate[135]^a 21159087 Image 29 42.10 0.20 CH
M30 3′,4′,5′,3,5,6,7-Heptamethoxyflavone[136]^a 389001 Image 30 31.97
0.59 CH
M31 Areapillin 158311 Image 31 48.96 0.41 CH
M32 Cubebin[137]^a 117443 Image 32 57.13 0.64 CH
M33 Sainfuran[138]^a 185034 Image 33 79.91 0.23 CH
M34 Troxerutin 252216528 Image 34 31.6 0.28 CH
M35 (+)-Anomalin[139]^a 6450453 Image 35 46.06 0.66 CH
M36 Saikosaponin C_qt / Image 36 30.50 0.63 CH
M37 Petunidin 441774 Image 37 30.05 0.31 CH
M38 Saikosaponin A[140]^a 167928 Image 38 32.39 0.09 CH
M39 Saikosaponin D[141]^a 107793 Image 39 34.39 0.09 CH
M40 Rutin[142]^a 5280805 Image 40 3.20 0.68 CH
M41 Tuberosine A[143]^a 5322166 Image 41 102.67 0.34 SM
M42 Cimicifugic acid 100913813 Image 42 83.02 0.45 SM
M43 Visamminol 5315249 Image 43 50.01 0.23 SM
M44
(20r,24r)-24,25-epoxy-3-beta-(beta-d-xylopyranosyloxy)-9,19-cyclolanost
-7-ene-16,23-dione_qt / Image 44 40.10 0.76 SM
M45 Paeoniflorin[144]^a 442534 Image 45 53.87 0.79 SM
M46 Caffeic acid[145]^a 689043 Image 46 25.76 0.05 SM
M47 Isoferulic acid[146]^a 736186 Image 47 58.83 0.06 SM
M48 Ferulic acid[147]^a 445858 Image 48 54.79 0.06 SM
M49 Acacetin[148]^a 5280442 Image 49 34.97 0.24 JG
M50 Cis-Dihydroquercetin 443758 Image 50 66.44 0.27 JG
M51 Luteolin[149]^a 5280445 Image 51 36.16 0.25 JG
M52 Robinin 5281693 Image 52 39.84 0.71 JG
M53 Platycodin D 162859 Image 53 7.60 0.01 JG
M54 Isorhamnetin[150]^a 5281654 Image 54 49.6 0.31 HQ, CH
M55 Kaempferol[151]^a 5280863 Image 55 41.88 0.24 HQ, ZM, CH
M56 Quercetin[152]^a 5280343 Image 56 46.43 0.28 HQ, CH
M57 Stigmasterol[153]^a 5280794 Image 57 43.83 0.76 ZM, CH, SM
[154]Open in a new tab
^a
Compounds identified by UPLC-Q-TOF/MS.
3.3. Screening of potential targets for SXD treatment of CHD and construction
of PPI network
The GeneCards, OMIM, TTD and DisGeNET databases were used to search for
CHD-related targets, and 610 corresponding targets were screened and
sorted out. Subsequently, the 96 potential targets of SXD for the
treatment of CHD were screened by getting their intersection targets
([155]Fig. 2A). The 96 potential targets in herbs and diseases
accounted for 19.12 % and 15.74 %, respectively. Among the 96 potential
targets, the number of potential targets of each herb in SXD was shown
in [156]Fig. 2B. HQ, ZM, CH, SM and JG accounted for 90.62 %, 44.79 %,
82.29 %, 46.88 % and 27.08 % of the potential targets, respectively,
which implied that the herbs contained potential targets with the same
effect. HQ, as a monarch herb in SXD, contained more potential targets
than other herbs, and had a synergistic effect with the 72 and 21
overlapping targets of CH and SM, respectively. Similarly, ZM and JG,
as assistant and guide herbs in the prescription, could also assist the
monarch herb to exert a therapeutic effect. This was also in line with
the characteristics of TCM prescriptions for the synergistic treatment
of diseases.
Fig. 2.
[157]Fig. 2
[158]Open in a new tab
Analysis of potential targets for SXD against CHD. (A) Venn diagram of
SXD and CHD related targets; (B) Distribution of potential targets of
each herb in SXD; (C) PPI network analysis of potential targets for SXD
against CHD (The red hexagon nodes represent the 15 key targets with
the average of degree and betweenness in the topology analysis. MCODE
was used to reclassify existing potential targets).
The 96 potential targets were imported into the STRING database, and
the medium confidence (0.4) between the targets was set to obtain a PPI
network file, which was imported into Cytoscape 3.8.2 software for
visualization. The PPI network involves 96 nodes and 1671 edges, and
the average degree and betweenness centrality of all nodes are 34.8125
and 0.0132, respectively ([159]Fig. 2C). In order to further understand
the biological role of the PPI network, the constructed PPI network was
used as input to conduct network clustering analysis based on MCODE
cluster, and a total of 3 network clusters were identified. According
to the MCODE score, the largest cluster contained 46 nodes and 885
edges, with a score of 39.333, and the smallest cluster contained 8
nodes and 13 edges, with a score of 3.714. The targets in the PPI
network are greater than the average of degree and betweenness
centrality and included in the core sub-networks cluster as key
targets, which are TNF, IL-6, peroxisome proliferator-activated
receptor alpha (PPARG), TP53, RAC-alpha serine/threonine-protein kinase
(AKT1), angiotensin-converting enzyme 2 (ACE), IL-1β, nitric oxide
synthase, endothelial (NOS3), estrogen receptor (ESR1), epidermal
growth factor receptor (EGFR), vascular endothelial growth factor A
(VEGFA), prostaglandin G/H synthase 2 (PTGS2) and catenin beta-1
(CTNNB1). The key targets possess a higher degree value and are more
likely to play a critical role in the network of SXD acting on CHD.
Meanwhile, the S-value of each key target was the maximum value of 4 by
CytoHubba plugin analysis excluding ESR1, EGFR and CTNNB1, and then the
S-value of each target in the PPI network was analyzed ([160]Table 4).
Table 4.
Potential targets of SXD against CHD and their weight analysis.
No. Target UniProt ID Gene name Degree S-value
T1 RAC-alpha serine/threonine-protein kinase [161]P31749 AKT1 73 4
T2 Tumor necrosis factor [162]P01375 TNF 80 4
T3 Interleukin-8 [163]P10145 IL-8 63 4
T4 Interleukin-6 [164]P05231 IL-6 80 4
T5 Interleukin-1 beta [165]P01584 IL-1β 70 4
T6 Vascular endothelial growth factor A [166]P15692 VEGFA 70 4
T7 Cellular tumor antigen p53 [167]P04637 TP53 67 4
T8 Matrix metalloproteinase-9 [168]P14780 MMP9 64 3
T9 C–C motif chemokine 2 [169]P13500 CCL2 62 4
T10 Signal transducer and activator of transcription 3 [170]P40763
STAT3 56 3
T11 Epidermal growth factor receptor [171]P00533 EGFR 62 3
T12 Vascular cell adhesion protein 1 [172]P19320 VCAM1 54 3
T13 Caspase-3 [173]P42574 CASP3 59 4
T14 Toll-like receptor 4 [174]O00206 TLR4 58 4
T15 Interleukin-10 [175]P22301 IL10 60 3
T16 Fibroblast growth factor 2 [176]P09038 FGF2 55 4
T17 Prostaglandin G/H synthase 2 [177]P35354 PTGS2 60 4
T18 Catenin beta-1 [178]P35222 CTNNB1 58 3
T19 Nitric oxide synthase, endothelial [179]P29474 NOS3 56 4
T20 Intercellular adhesion molecule 1 [180]P05362 ICAM1 57 4
T21 Peroxisome proliferator-activated receptor gamma [181]P37231 PPARG
59 4
T22 Hypoxia-inducible factor 1-alpha [182]Q16665 HIF1A 59 4
T23 72 kDa type IV collagenase [183]P08253 MMP2 51 3
T24 Plasminogen activator inhibitor 1 [184]P05121 SERPINE1 52 4
T25 Interleukin-4 [185]P05112 IL-4 49 3
T26 Interferon gamma [186]P01579 IFNG 53 4
T27 Interleukin-2 [187]P60568 IL-2 45 4
T28 E-selectin [188]P16581 SELE 43 3
T29 GTPase HRas [189]P01112 HRAS 45 3
T30 Estrogen receptor [190]P03372 ESR1 51 3
T31 Osteopontin [191]P10451 SPP1 45 4
T32 Angiotensin-converting enzyme 2 [192]Q9BYF1 ACE 54 4
T33 Heme oxygenase 1 [193]P09601 HMOX1 46 4
T34 Caveolin-1 [194]Q03135 CAV1 46 3
T35 Vascular endothelial growth factor receptor 2 [195]P35968 KDR 42 4
T36 Receptor tyrosine-protein kinase erbB-2 [196]P04626 ERBB2 47 3
T37 Renin [197]P00797 REN 43 4
T38 Myeloperoxidase [198]P05164 MPO 43 3
T39 Interleukin-1 alpha [199]P01583 IL1A 37 4
T40 Peroxisome proliferator-activated receptor alpha [200]Q07869 PPARA
39 3
T41 Mast/stem cell growth factor receptor Kit [201]P10721 KIT 40 3
T42 Stromelysin-1 [202]P08254 MMP3 38 4
T43 Signal transducer and activator of transcription 1-alpha/beta
[203]P42224 STAT1 41 3
T44 Interstitial collagenase [204]P03956 MMP1 36 4
T45 NAD-dependent protein deacetylase sirtuin-1 [205]Q96EB6 SIRT1 41 4
T46 Tissue factor [206]P13726 F3 33 1
T47 Nitric oxide synthase, inducible [207]P35228 NOS2 31 1
T48 Urokinase-type plasminogen activator [208]P00749 PLAU 32 1
T49 Mitogen-activated protein kinase 1 [209]P28482 MAPK1 40 2
T50 Prothrombin [210]P00734 F2 34 2
T51 Tyrosine-protein phosphatase non-receptor type 11 [211]Q06124
PTPN11 33 0
T52 Amyloid-beta precursor protein [212]P05067 APP 40 3
T53 Type-1 angiotensin II receptor [213]P30556 AGTR1 30 0
T54 CD40 ligand [214]P29965 CD40LG 29 0
T55 Neutrophil cytosol factor 1 [215]P14598 NCF1 27 1
T56 Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit
alpha isoform [216]P42336 PIK3CA 33 0
T57 Heat shock protein beta-1 [217]P04792 HSPB1 24 1
T58 Gap junction alpha-1 protein [218]P17302 GJA1 27 1
T59 Androgen receptor [219]P10275 AR 28 1
T60 Estrogen receptor beta [220]Q92731 ESR2 24 0
T61 Telomerase reverse transcriptase [221]O14746 TERT 24 0
T62 Tissue-type plasminogen activator [222]P00750 PLAT 24 0
T63 Superoxide dismutase [Cu–Zn] [223]P00441 SOD1 24 1
T64 Macrophage migration inhibitory factor [224]P14174 MIF 23 1
T65 Polyunsaturated fatty acid 5-lipoxygenase [225]P09917 ALOX5 24 0
T66 Thrombomodulin [226]P07204 THBD 23 1
T67 Fibroblast growth factor 1 [227]P05230 FGF1 24 0
T68 Vitamin D3 receptor [228]P11473 VDR 20 1
T69 Tyrosine-protein kinase ABL1 [229]P00519 ABL1 24 0
T70 RAF proto-oncogene serine/threonine-protein kinase [230]P04049 RAF1
22 0
T71 Oxidized low-density lipoprotein receptor 1 [231]P78380 OLR1 19 1
T72 Phospholipase A2 [232]P04054 PLA2G1B 17 1
T73 Neprilysin [233]P08473 MME 17 0
T74 Beta-2 adrenergic receptor [234]P07550 ADRB2 18 0
T75 Monocyte differentiation antigen CD14 [235]P08571 CD14 14 0
T76 Serum paraoxonase/arylesterase 1 [236]P27169 PON1 15 1
T77 Serine/threonine-protein kinase B-raf [237]P15056 BRAF 12 1
T78 HLA class II histocompatibility antigen, DRB1 beta chain
[238]P01911 HLA-DRB1 10 0
T79 Tyrosine-protein phosphatase non-receptor type 22 [239]Q9Y2R2
PTPN22 9 0
T80 Oxysterols receptor LXR-alpha [240]Q13133 NR1H3 8 0
T81 Coagulation factor VII [241]P08709 F7 10 1
T82 3-hydroxy-3-methylglutaryl-coenzyme A reductase [242]P04035 HMGCR 9
1
T83 Glutathione S-transferase Mu 1 [243]P09488 GSTM1 10 0
T84 Beta-1 adrenergic receptor [244]P08588 ADRB1 8 0
T85 Sex hormone-binding globulin [245]P04278 SHBG 10 0
T86 Cyclin-dependent kinase 8 [246]P49336 CDK8 6 1
T87 Cytochrome P450 2C19 [247]P33261 CYP2C19 10 1
T88 Transthyretin [248]P02766 TTR 8 1
T89 Steroid 17-alpha-hydroxylase/17,20 lyase [249]P05093 CYP17A1 7 1
T90 Lysosomal acid glucosylceramidase [250]P04062 GBA 3 0
T91 Potassium voltage-gated channel subfamily H member 2 [251]Q12809
KCNH2 4 1
T92 Endothelin-converting enzyme 1 [252]P42892 ECE1 3 0
T93 Sodium channel protein type 5 subunit alpha [253]Q14524 SCN5A 3 1
T94 Gamma-aminobutyric acid type B receptor subunit 1 [254]Q9UBS5
GABBR1 2 0
T95 Aldehyde dehydrogenase, mitochondrial [255]P05091 ALDH2 3 0
T96 Alpha-ketoglutarate-dependent dioxygenase FTO [256]Q9C0B1 FTO 1 0
[257]Open in a new tab
3.4. C-T-D network construction and CI analysis
A C-T-D network was constructed by the Cytoscape 3.8.2 software to
facilitate the visualization and interpretation of the complex
relationships according to the corresponding information, including
effective compounds, potential targets and related diseases of SXD,
which consisted of 159 nodes (96 target nodes, 57 compound nodes, 5
herb nodes and 1 disease node) and 523 edges ([258]Fig. 3A). Most
targets were shared by candidate effective compounds of each herb in
SXD. The high interconnectedness of the C-T-P network was due to the
high interconnection degrees of these candidate effective compounds,
including quercetin (M56, degree = 51), luteolin (M51, degree = 23),
ferulic acid (M48, degree = 19), isoferulic acid (M47, degree = 19),
caffeic acid (M46, degree = 17), kaempferol (M55, degree = 13) and
anhydroicaritin (M20, degree = 13). Subsequently, we calculated the
parameters of the C-T-D network by using NetworkAnalyzer function and
analyze the RSR of parameters by the SPSS AU platform ([259]Table S1).
Quercetin, the compound with the largest degree in the C-T-D network,
only ranked 12 according to RSR. However, the compound anhydroicaritin,
the seventh-ranked degree in the network, ranked the highest RSR value.
So, a more systematic and accurate index was needed to screen effective
compounds in SXD. Based on the network parameters, the content and
pharmacokinetic parameter of the compounds in SXD, CI was proposed to
screen the core compounds ([260]Tables S2 and S3). Isoferulic acid
(M47), quercetin (M56), calycosin (M11), ferulic acid (M48), kaempferol
(M55), calycosin 7-O-glucoside (M17), formononetin (M9), astragaloside
IV (M15) and saikosaponin D (M39), were ranked as the top 9 compounds
according to the accumulative CI value more than 90 %, which could be
the core compounds of SXD in the treatment of CHD ([261]Fig. 3B). In
addition, among the top 15 effective compounds in the CI value ranking,
the emperor herb HQ contained 7 effective compounds, and CH and SM as
the minister herbs contained 4 and 3 effective compounds, respectively.
Fig. 3.
[262]Fig. 3
[263]Open in a new tab
Screening of core compounds for SXD against CHD. (A) C-T-D network for
SXD against CHD (The blue diamond represents a compound shared by two
or more herbs); (B) Contribution index of each effective compound (top
15) for SXD against CHD.
3.5. Enrichment analyses and construction of T-P network
The 96 potential targets were entered into the DAVID database for GO
and KEGG enrichment analysis. The GO enrichment analysis results were
reflected in three aspects: BP, CC and MF. Under the condition of
p < 0.05, 640, 63 and 101 items were obtained from these three aspects,
respectively. The top of 10 items were taken to make a visual bar chart
using the bioinformatics platform, as shown in [264]Fig. 4A, which
indicated that SXD may regulate ERK1 and ERK2 cascade, transcription
from RNA polymerase II promoter, MAPK cascade, RNA polymerase II
transcription factor activity, protein binding, and perinuclear region
of cytoplasm for treating CHD. Moreover, a total of 155 pathways were
identified by KEGG analysis. All the pathways involved in the S-value
of potential targets were ranked and analyzed. Based on the p < 0.05,
the top 20 pathways enriched by KEGG were selected according to the
P-weight, as shown in [265]Fig. 4B and C and [266]Table S4. Among them,
fluid shear stress and atherosclerosis, lipid and atherosclerosis,
PI3K-Akt signaling pathway, MAPK signaling pathway, HIF-1 signaling
pathway, and EGFR tyrosine kinase inhibitor resistance were probably
associated with SXD against CHD.
Fig. 4.
[267]Fig. 4
[268]Open in a new tab
Potential targets enrichment analysis and T-P network analysis of SXD
treatment for CHD. (A, B) GO and KEGG enrichment analysis for SXD
against CHD; (C) T-P network of SXD against CHD where the size of
pathway node is based on the P-weight, and the color of the target node
represents the degree value.
3.6. Core targets expression analysis
In [269]GSE66360, we analyzed the expression of key targets in MI
samples (n = 49) and control samples (n = 50). As shown in [270]Fig. 5,
the expression levels of TNF, IL-6, IL-1β, PTGS2 and VEGFA were
up-regulated than those of the control group (p < 0.05), while the
expression level of TP53 was down-regulated (p < 0.05). There was no
significant difference in the expression levels of other key targets
(p > 0.05). Therefore, we considered the targets with significant
expression differences as core targets. Subsequently, the core targets
were entered into the VaeElect database to obtain the correlation score
between the core targets and the disease ([271]Table S5). Obviously, we
found that the average disease's causing likelihood of IL-1β was as
high as 81.8 %, but that of IL-6 was relatively low, which was only
56.1 %. Therefore, these core targets were used as the main targets of
SXD against CHD and included in subsequent analysis.
Fig. 5.
[272]Fig. 5
[273]Open in a new tab
Analysis of core targets expression based on GEO dataset (The values of
p < 0.05 was considered to be significant expression differences).
3.7. Analysis of molecular docking results
Molecular docking is the docking of ligands and receptors in the active
pocket through one or more hydrogen bonds, which is a process
accompanied by a change in binding energy. In order to verify the
reliability of the network pharmacology research results, these core
targets, TNF (PDB ID: [274]7KP9), IL-6 (PDB ID: [275]4O9H), IL-1β (PDB
ID: [276]5I1B), VEGFA (PDB ID: [277]5DN2), TP53 (PDB ID: [278]5AOI) and
PTGS2 (PDB ID: [279]5F19) were selected as receptors, and they were
docked with the core compounds in [280]Fig. 6A. In general, when the
ligand-receptor binding energy is lower than 0, the binding energy is
about the greater the possibility of interaction. When the binding
energy is lower than −5.0 kcal/mol, it contains good affinity. For
these effective compounds, quercetin, calycosin, kaempferol, calycosin
7-O-glucoside, formononetin, astragaloside IV and saikosaponin D could
produce lower binding activity with most targets, especially with TNF
and PTGS2. Among them, calycosin 7-O-glucoside have the lowest docking
binding energy to PTGS2 (−11.3 kcal/mol), and isoferulic acid has the
highest binding energy to IL-6 (−5.2 kcal/mol). Subsequently, six
results with relatively low binding energy of core targets were
selected for visualization. As shown in [281]Fig. 6B,
calycosin7-O-glucoside could interact with Lys11 (B), Leu157 (A) and
Leu (B) via one hydrogen bonds in TNF, respectively. Astragaloside IV
was more capable of interacting with Gly42 (H) via two hydrogen bonds
and with Ley175 (H), Ala173 (H), Lys169 (L), Gly43 (L) and Gln39 (H)
via one hydrogen bond in IL-6. Calycosin 7-O-glucoside could interact
with Tyr90 (A), Pro87 (A), Glu64 (A), Ser5 (A), Lys65 (A) and Tyr68 (A)
via one or two hydrogen bonds in IL-1β. The results of the interaction
of VEGFA, TP53 and PTGS2 with the core compounds were also seen in
[282]Fig. 6B. Therefore, these results suggested a strong binding
between core compound-target pairs and demonstrated that the target
proteins were in a favorable conformation.
Fig. 6.
[283]Fig. 6
[284]Open in a new tab
Molecular docking of the core compound-target pair of SXD treatment for
CHD. (A) Heat map of estimated binding energy where colour change from
blue to red indicates binding energy from high to low. (B) Molecular
docking of core compounds of SXD and receptor proteins.
3.8. Experimental verification
3.8.1. SXD ameliorates the myocardial injury and inflammatory response in CHD
model rats
As shown in [285]Fig. 7A, HWI in the CHD model group was significantly
higher than that in the sham-operated group. Compared with the CHD
model group, the HWI levels of Bet and SXD groups were reduced to
different degrees. TTC staining was used to evaluate myocardial infarct
size. Compared with sham-operated group, myocardial infarction size was
significantly increased in CHD model group, while myocardial infarction
size decreased after SXD treatment ([286]Fig. 7B). In the CHD model
group, H&E and Masson staining showed severe necrosis of myocardial
fibers, inflammatory cell infiltration, and tissue structure disorder.
The degree of tissue fibrosis and cell damage were significantly
improved in Bet and SXD groups ([287]Fig. 7C). In addition, serum IL-1β
and IL-6 levels were increased in the CHD model group compared to the
Sham group (p < 0.01). Compared with the model group, the levels of
serum IL-1β and IL-6 in each dosing group were decreased to varying
degrees ([288]Fig. 7D). In addition, SXD could also change the levels
of serum ALD, Ang Ⅱ and NT-proBNP in CHD rats. The results showed that
SXD significantly reduces HWI, alleviates myocardial tissue injury and
reduces the level of serum inflammation in CHD rats.
Fig. 7.
[289]Fig. 7
[290]Open in a new tab
SXD significantly alleviates myocardial injury and inflammatory
response in CHD rats. Sham: Sham operation group; Mod: CHD model group;
Bet: Betaloc group (10 mg/kg); SXDL: SXD low-dose group (4.33 g/kg);
SXDH: SXD high-dose group (12.99 g/kg). (A) Heart weight index (n = 6);
(B) Infarct size of heart tissue (n = 3); (C) H&E and Masson staining
of the heart tissue; (D) ELISA was performed to examine the contents of
IL-6, IL-1β, ALD, Ang Ⅱ and NT-proBNP in the rat serum (n = 6).
*p < 0.05, **p < 0.01, vs. Control; ^#p < 0.05, ^##p < 0.01, vs. H/R.
3.8.2. SXD effectively enhances cell viability and decreases the production
of ROS
To identify the most effective concentration of SXD for myocardial
protection, the MTT method was used. Compared with the control group,
the H/R-induced cardiomyocytes showed lower cell viability ([291]Fig.
8A). In the H/R + Diazoxide groups comparing with the H/R group,
Diazoxide increased the viability. Further, a 24 h pretreatment with
SXD at various concentrations (250 μg/mL, 500 μg/mL and 1 mg/mL)
significantly increased cell viability, compared with the H/R group
(p < 0.05). Thus, the concentrations of 250 μg/mL, 500 μg/mL and
1 mg/mL of SXD were selected to explore the possible mechanism. To
confirm that SXD could protect H9c2 cells through anti-oxidative
activity, we analyzed the ROS production ([292]Fig. 8B and [293]S2). As
shown in [294]Fig. 8B, the ROS level was higher in the H/R group than
control, while SXD markedly decreased the production of ROS in H/R
stimulated H9c2 cells.
Fig. 8.
[295]Fig. 8
[296]Open in a new tab
SXD increases cell viability and attenuates H/R-induced H9c2 cell
injury. (A)The viability of cardiomyocyte cell line H9c2 after treated
with H/R or SXD was determined by MTT assays (n = 5); (B) ROS levels
were detected by flow cytometry (n = 3); (C) Effects of SXD on the
apoptosis ratio of H9c2 cells (n = 3); (D) ELISA was performed to
examine the contents of TNF, IL-6 and IL-1β in H9c2 cells culture
supernatant (n = 3). *p < 0.05, **p < 0.01, vs. Control; ^#p < 0.05,
^##p < 0.01, vs. H/R.
3.8.3. SXD protects H9c2 cells from H/R-induced myocardial damage
Meanwhile, to investigate the anti-apoptotic effect of SXD, flow
cytometry was used. As displayed in [297]Fig. 8C, compared with the
control group, the number of apoptotic cells increased in the H/R
group, whereas pretreatment with SXD reduced the apoptotic ratio
(p < 0.01). Collectively, these data show that SXD can reduce the
cellular apoptosis induced by H/R.
Moreover, three different proinflammatory cytokines (TNF, IL-6 and
IL-1β) were detected, and as shown in [298]Fig. 8D, ELISA proved that
the contents of TNF, IL-6 and IL-1β in H9c2 cells culture supernatant
were reduced in SXD-treated H9c2 cells, as compared to H/R-induced H9c2
cells (p < 0.01). The results showed that H/R-stimulated production of
TNF, IL-6 and IL-1β in H9c2 cells were abolished by SXD.
3.8.4. The expression of mRNA and protein during the H/R-induced inhibition
of apoptosis
It was found that ROS production and DNA damage activated the TP53
signaling pathway, and TP53 further up-regulated cytochrome C-mediated
caspase-3 activation, leading to apoptosis [[299]29]. TP53, caspase3
and cytochrome C mRNA expression levels were examined in order to
further clarify the molecular mechanism of the H/R-induced inhibition
of apoptosis. RT-qPCR results in [300]Fig. 9A showed that TP53
expression was significantly up-regulated in the H/R group (p < 0.01),
and this expression was dramatically reversed by SXD treatment at a
concentration of 500 μg/mL and 1 mg/mL for 24 h (p < 0.01). However, no
obvious difference in TP53 expression was observed between the groups
treated with SXD at 250 μg/mL, diazoxide and the H/R group, separately
(p > 0.05). RT-qPCR results showed that caspase3 expression was
significantly up-regulated in the H/R group (p < 0.01) and this
expression was dramatically reversed by treatment with SXD at
250 μg/mL, 500 μg/mL and 1 mg/mL for 24 h (p < 0.01). RT-qPCR results
showed that cytochrome C expression was significantly up-regulated in
the H/R group (p < 0.01) and this expression was dramatically reversed
by treatment with SXD at 250 μg/mL, 500 μg/mL and 1 mg/mL for 24 h
(p < 0.01). However, no significant difference in caspase3 and
cytochrome C expression was observed between the groups treated with
diazoxide and the H/R group.
Fig. 9.
[301]Fig. 9
[302]Open in a new tab
The expression of mRNA and protein during the H/R-mediated inhibition
of apoptosis. (A) The mRNA expressions of TP53, caspase3 and cytochrome
C in H/R-induced cardiomyocyte cell line H9c2 after treated with SXD
were determined (n = 3); (B) Western blotting analysis detected the
caspase3, caspase9 and cytochrome C protein expression levels in
H/R-induced H9c2 cells after treated with SXD (n = 3); (C) Activity
Assay Kit was performed to examine the activity of caspase3 in
H/R-induced cardiomyocyte cell line H9c2 after treated with SXD
(n = 3). *p < 0.05, **p < 0.01, vs. Control; ^#p < 0.05, ^##p < 0.01,
vs. H/R.
To further elucidate the mechanism by which SXD protect the H/R-induced
inhibition of apoptosis, we analyzed the expression of caspase3,
caspase9 and cytochrome C that regulate cellular apoptosis using
western blotting analysis. The results revealed that the expression of
caspase3, caspase9 and cytochrome C were significantly decreased by SXD
treatment for 24 h compared with the H/R-induced H9c2 cells model
group, especially when the concentration was 1 mg/mL in [303]Fig. 9B
(The uncropped versions of [304]Fig. 9B was provide as [305]Supplement
Fig. S3). Additionally, the activity of caspase3 in H/R-induced H9c2
cells culture supernatant were reduced in SXD-treated H9c2 cells, as
compared to H/R-induced H9c2 cells in [306]Fig. 9C (p < 0.05). In
summary, these data indicate that SXD significantly relieve myocardial
injury from the perspective of anti-apoptosis via down-regulating the
caspase3, caspase9 and cytochrome C protein expression levels.
4. Discussion
CHD is a complex cardiovascular disease that seriously threatens human
health. The pathological mechanism of CHD is in line with the
pathogenic characteristics of the “qi” stagnation. The Chinese medicine
understanding of the “qi” stagnation will cause the weak and slow blood
flow and abnormal hemorheology. Therefore, treating this disease from
the “qi” stagnation and blood stasis has important practical
significance for the diagnosis and treatment of modern CHD. Treating
this disease from the “qi” stagnation and blood stasis has important
practical significance for the diagnosis and treatment of modern CHD.
SXD is a classic TCM prescription and possesses the effect of
nourishing the “qi” and blood for the body. SXD, which is composed of
Astragalus membranaceus var. mongholicus (Bunge) Hsiao (HQ),
Anemarrhena asphodeloides Bge. (ZM), Bupleurum chinense DC. (CH),
Platycodon grandiflorum (Jacq.) A. DC. (JG) and Cimicifuga foetida L.
(SM), has been successfully applied in clinical treatment of CHD
[[307]30]. The combination of each herb in SXD has a synergistic effect
on the treatment. Research has indicated that JG can enhance the
distribution or absorption of SXD, which reflects the courier role of
PG [[308]10]. SXD has not been found to be used in other countries. But
it should be noted that those plants used for the preparation of SXD
was not unique for TCM. They also have been used in other countries,
including Japan, Korea, Russia and other regions [[309][31], [310][32],
[311][33], [312][34]]. For example, HQ is included in the Japanese
Pharmacopoeia, and the team from Japan investigated the different
protective effects of astragalus [[313]31,[314]32]. In addition, CH,
which is widespread in the eastern regions of Russia as well as in
Mongolia and Korea, have been widely applied as a choleretic and
hepatoprotective remedy in traditional Russian medicine [[315]33]. The
standardized DA-9805 consists of CH from Dong-A ST (Yongin, Korea) can
protect dopaminergic neurons against 6-hydroxydopamine in a Parkinson's
model of neurotoxic disease [[316]34]. At present, SXD is widely used
in the treatment of CHD, but its therapeutic mechanism is not clear.
The purpose of this study is to provide scientific basis for the
treatment of SXD with CHD.
In this study, through the combination of UPLC-Q-TOF-MS/MS technology
and an integrated network pharmacology, we directly identified the
potential active ingredients and targets of SXD in the treatment of CHD
from a large number of data, and understood the molecular mechanism and
important pathways of TCM. 57 effective compounds and 96 corresponding
potential targets were selected, which were largely involved in
multiple biological processes and pathways associated with CHD. To
better understand the contribution of compounds in SXD, we introduced a
parameter CI that simulated compatible combinations of effective
compounds in SXD in terms of intrinsic properties and network
importance. From the C-T-D network and CI calculation, it was revealed
that the core compounds, including isoferulic acid, quercetin,
calycosin, ferulic acid, kaempferol, calycosin 7-O-glycoside,
formononetin, astragaloside IV and saikosaponin D, were ranked as the
top 9 compounds according to the sum of their CI value more than 90 %.
The core compounds were the main compounds of SXD in the treatment of
CHD. Pharmacological studies have shown that the emperor herb HQ in SXD
and its compounds have various biological activities, such as
regulating immunity, anti-oxidation, anti-inflammatory, anti-apoptosis
and preventing cardiovascular diseases [[317][35], [318][36],
[319][37], [320][38]]. Through compounds collecting and targets
screening, it turned out that HQ contained the vast majority of 20
compounds and more targets than other herbs. The analysis of the top 15
compounds with CI calculation found that HQ involved 7 effective
compounds. Therefore, we could conclude that the effective compounds of
SXD in the treatment of CHD were mainly contained in HQ. HQ contains
astragaloside IV with significant anti-inflammatory and cardiovascular
protective effects [[321]39,[322]40]. For the quercetin with a high CI,
it has good antithrombotic and cardiac function improvement effects,
which is of great significance for the treatment of cardiovascular
diseases [[323]41]. And quercetin can reduce the levels of TNF and
IL-1β to show anti-inflammatory properties in the serum of patients
with coronary artery disease [[324]42]. Meanwhile, kaempferol can
upregulate miR-26a-5p by inhibiting the TLR4/NF-κB signaling pathway,
reduce the apoptosis of endothelial cells induced by oxidized
low-density lipoprotein, and play an important role in
anti-atherosclerosis [[325]43]. Formononetin, a methoxyisoflavone
abundant in many herbs, has been found to alleviate atherosclerosis by
modulating the interaction between KLF4 and SRA in Apo E-deficient mice
[[326]44]. Research has shown that calycosin exhibits anti-apoptotic
effects by activating ERα/β and enhancing Akt phosphorylation in
cardiomyocytes [[327]45]. SM is used as the minister in SXD, and the
compounds including isoferulic acid, ferulic acid and caffeic acid were
the main effective compounds that can play an antioxidant role
[[328]46]. In addition to this, some other compounds have good activity
in the prevention and treatment of CHD. A study by Zhang et al.
luteolin has a strong cardioprotective effect, and its mechanism may be
related to the down-regulation of TLR4-mediated NF-κB/NLRP3
inflammasome in vivo and in vitro [[329]47]. Therefore, it is
speculated that SXD can treat CHD through the synergistic effect of
multiple compounds.
PPI network and the published microarray data result showed that TNF,
IL-6, IL-1β, VEGFA, TP53 and PTGS2, were the core target proteins.
Through cluster analysis of protein interaction network MCODE, it was
found that the core target proteins were mainly concentrated in cluster
1. Cluster 1 mainly involved signaling pathways such as fluid shear
stress and atherosclerosis, lipid and atherosclerosis, HIF-1 signaling
pathway, IL-17 signaling pathway, TNF signaling pathway, PI3K-Akt
signaling pathway, MAPK signaling pathway, etc. Cluster 2 mainly
involved AGE-RAGE signaling pathway in diabetic complications,
phospholipase D signaling pathway, etc. And cluster 3 mainly involved
Ras signaling pathway, VEGF signaling pathway, etc. Meanwhile, these
core target proteins are involved in various signaling pathways related
to inflammation, apoptosis, immunity, metabolism, cell apoptosis and
proliferation in the treatment of CHD. For the target with the highest
degree value, TNF is an important inflammatory mediator in
post-ischemic myocardial dysfunction, and the level of TNF in plasma
can reflect the degree of cardiac damage in patients with CHD
[[330]48]. Similarly, IL-6 is a key cytokine, which can mediate a
variety of inflammatory responses and immune regulation pathways, and
it plays an indispensable role in the occurrence and development of
cardiovascular diseases [[331]49]. Besides, IL-6 is an inflammatory
marker of coronary microvascular disease in women [[332]50]. Compared
with normal subjects, IL-1β is significantly increased in patients with
atherosclerosis, which plays an important role in atherosclerosis
[[333]51]. VEGFA is a major factor for promoting angiogenesis, and
increased spontaneous production of VEGFA may induce angiogenesis after
acute myocardial infarction, which is due to initiating ROS-ER
stress-autophagy axis in the vascular endothelial cells
[[334]52,[335]53]. TP53 is an important tumor suppressor gene and plays
an important role in apoptosis, genome stability, inhibition of
angiogenesis, etc [[336]54]. The above studies showed that SXD had
multi-target effects on the regulation of CHD, and these targets
involved cell proliferation and apoptosis, signal transduction,
inflammatory response, oxidative stress, gene expression, angiogenesis,
etc. These targets were closely related to the occurrence and
development of CHD, which confirmed that SXD has the effect of treating
CHD.
In the process of GO enrichment analysis, BP mainly included
cytokine-mediated signaling pathway, positive regulation of gene
expression, negative regulation of gene expression; CC mainly involved
extracellular space, and extracellular region, cell surface; MF mainly
involved enzyme binding, identity protein binding, and RNA polymerase
II transcription factor activity, ligand-activated sequence-specific
DNA binding. From the analysis of KEGG enriched pathways, it could be
seen that AGE-RAGE signaling pathway in diabetic complications,
pathways in cancer, fluid shear stress and atherosclerosis, lipid and
atherosclerosis, PI3K-Akt signaling pathway, MAPK signaling pathway,
HIF-1 signaling pathway, EGFR tyrosine kinase inhibitor resistance. The
PI3K-Akt signaling pathway is an important signaling pathway in
organisms. Phosphorylated Akt can activate endothelial nitric oxide
synthase and heat shock proteins to protect the myocardium [[337]55].
Studies have found that ischemic preconditioning can activate the
PI3K-Akt signaling pathway in cardiomyocytes, thereby reducing
apoptosis, eliminating intracellular reactive oxygen species, and
protecting mitochondrial function [[338]56]. MAPK signaling pathway is
an important intracellular transduction pathway. Studies have shown
that inhibiting MAPK signaling pathway can reduce inflammation, improve
intimal thickening and inhibit plaque formation [[339]57]. HIF-1 is a
heterodimeric transcription factor composed of two subunits, HIF-1α and
HIF-1β. HIF-1α is highly expressed in atherosclerotic sites, and the
HIF-1 signaling pathway obstruction can exert myocardial protective
effect, reduce ischemia-reperfusion injury, improve cardiac function
and reduce infarct size [[340]58,[341]59]. In addition, by regulating
AGE-RAGE pathway, it can affect oxidative stress and inflammatory
response in IR-induced myocardial injury in diabetic rats [[342]60].
This is a complex process involving multiple compounds, multiple
targets and multiple pathways, which reflects the holistic and
systematic nature of SXD treatment of the CHD. The results of molecular
docking further showed that the core targets can be stably combined
with the core effective compounds in SXD, which further verified the
reliability of the prediction of the network pharmacology method. For
the core targets TNF, IL-6 and other core targets mentioned in this
paper, we found that the gene expression of the core genes was
significantly difference in circulating endothelial cells of patients
with myocardial infarction [[343]61]. Therefore, the network
pharmacological results indicate that SXD may improve myocardial
ischemia and reperfusion injury through inflammation and
anti-apoptosis. The results of in vivo experiments in rats showed that
SXD can significantly reduce HWI, myocardial tissue damage, serum
inflammation, ALD, Ang Ⅱ and NT-proBNP in rats with CHD. Therefore, the
results indicated that SXD can effectively treat CHD. Based on what
mentioned above, the rat cardiomyocytes H9c2 were oxygenated for 12 h
after 8 h of hypoxia to establish the cell injury model of H/R, and
screen whether SXD has the effect of treating myocardial damage and
other injuries. The results showed that H/R could significantly inhibit
the cell viability of H9c2, and different concentrations of SXD and
positive drugs could improve the cell damage caused by H/R and increase
the cell viability. In addition, we further explored the possible
mechanism of SXD therapy. H/R can induce apoptosis of H9c2 cells, and
this drug can inhibit apoptosis to a certain extent, which may be
because SXD can improve cell vitality to a certain extent. Similarly,
it has been reported that H/R can lead to inflammatory responses
resulting from myocardial cell damage [[344]62]. On the basis of this
study, we found that SXD can inhibit the secretion of inflammatory
factors in H9c2 cells and reduce the expression of inflammatory
factors, and then SXD has a certain anti-inflammatory effect, so as to
alleviate the secondary damage caused by the accumulation of
inflammatory factors in cardiomyocytes. As shown in the review, through
the predicted results of network pharmacology and experimental
verification, we revealed that SXD can relieve myocardial ischemia
reperfusion injury or myocardial injury caused by myocardial infarction
from the perspective of anti-inflammation and anti-apoptosis. The
compounds of SXD are expected to become potential drugs for the
treatment of CHD, and may become the focus of future research.
5. Conclusions
In conclusion, we performed the UPLC-Q-TOF-MS/MS technology, an
integrated network pharmacology and experimental verification to reveal
the pharmacological mechanism of SXD against CHD. Among all effective
compounds, isoferulic acid, quercetin, calycosin, ferulic acid,
kaempferol, calycosin 7-O-glycoside, formononetin, astragaloside IV and
saikosaponin D in SXD have high CI and good affinity with potential
targets, which may play an important role in the treatment of CHD.
Moreover, a further in vivo validation experiment that SXD alleviates
myocardial tissue injury and reduce the level of serum IL-6, IL-1β,
ALD, Ang Ⅱ and NT-proBNP in vitro validation experiment illustrated
that SXD may protect myocardial cell via reducing the expression of the
inflammatory factors and pro-apoptotic protein, thus playing a
protective role in myocardial injury caused by CHD. Although the
verification of the mechanism is not completely in depth, our results
provide a partial research basis for the application of SXD and a new
idea for the clinical prevention and treatment of myocardial injury
caused by CHD.
Ethics approval and consent to participate
All procedures were strictly approved by the Animal Experiment Ethics
Committee of the Shaanxi University of Chinese Medicine (Ethics
Approval No. SUCMDL20220310006).
Funding
This research was funded by the National Natural Science Foundation of
China (82004011 and 81903786), the Natural Science Foundation of
Shaanxi Province (2022SF-221), and Subject Innovation Team of Shaanxi
University of Chinese Medicine (2019-YL10).
Data availability statement
Data will be made available on request.
CRediT authorship contribution statement
Hao-ming Zhou: Writing – original draft, Software, Data curation,
Conceptualization. Shi-jun Yue: Writing – review & editing, Funding
acquisition, Conceptualization. Wen-xiao Wang: Methodology, Funding
acquisition. Qiao Zhang: Investigation, Formal analysis. Ding-qiao Xu:
Resources, Formal analysis. Jia-jia Li: Investigation. Yu-ping Tang:
Funding acquisition, Conceptualization. Xin-yu Yang: Writing – review &
editing, Funding acquisition.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Footnotes
^Appendix A
Supplementary data to this article can be found online at
[345]https://doi.org/10.1016/j.heliyon.2024.e29558.
Contributor Information
Shi-jun Yue, Email: shijun_yue@163.com.
Yu-ping Tang, Email: yupingtang@sntcm.edu.cn.
Xin-yu Yang, Email: yangxinyu@bjsjth.cn.
Abbreviation
ALD
Aldosterone
Ang Ⅱ
Angiotensin Ⅱ
BP
biological process
CC
cellular component
CH
roots of Bupleurum chinense DC
CHD
coronary heart disease
CI
contribution index
C-T-D:
compound-target-disease
DL:
drug-likeness
ECL:
chemiluminescence
GO
Gene ontology
HQ
roots of Astragalus membranaceus var. mongholicus (Bunge) Hsiao
H/R
hypoxia/reoxygenation
HWI
heart weight index
JG
roots of Platycodon grandiflorum (Jacq.) A. DC
KEGG
Kyoto Encyclopedia of Genes and Genomes
LAD
left anterior descending coronary artery
MF
molecular function
MI
myocardial infarction
MTT
3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di- phenytetrazoliumromide
assay
NT-proBNP
N-terminal pro-brain natriuretic peptide;
OB
oral bioavailability
PPI
protein-protein interaction
PVDF
polyvinylidene difluoride;
RIPA
radio immunoprecipitation assay
ROS
reactive oxygen species
RSR
rank-sum ratio
RT-qPCR
real time quantitative PCR
SDS-PAGE
sodium dodecyl sulfate-polyacrylamide gel electrophoresis
SM
rhizomes of Cimicifuga foetida L
SXD
Shengxian Decoction
TCM
traditional Chinese medicine;
T-P:
target-pathway
UPLC-Q-TOF-MS/MS
ultra-performance liquid chromatography-quadrupole-time of
flight-mass spectrometry
TTC
2,3,5-triphenyl-2H-tetrazolium chloride;
ZM
rhizomes of Anemarrhena asphodeloides Bge
Appendix A. Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
[346]mmc1.docx^ (3.4MB, docx)
References