Abstract
The present study determines the potential antioxidants in Moutan
Cortex (MC) and predicts its targets of anti-oxidative activities. The
quantitative analysis and the free radical scavenging assays were
conducted to detect the main components in MC and assess its
anti-oxidant activities. The grey relational analysis and the
[40]network pharmacology approach were employed to predict its key
components and targets of anti-oxidant activities. Six main constitutes
in MCs were quantified by high performance liquid chromatography (HPLC)
and its anti-oxidant activities were evaluated by DPPH and ABTS free
radical scavenging methods. Then grey relational analysis was employed
to predict the key components acting on anti-oxidative activity based
on the chem-bio results. The predicted components and its mechanisms on
anti-oxidation were uncovered by [41]network pharmacology approach and
cell test, respectively. The content of paeonol and paeoniflorin
accounts for more than 80% the whole content of detected components.
However, the two main ingredients showed a great variety among MCs. The
antioxidant capacities of MCs also showed a great discrepancy based on
DPPH and ABTS methods. The key components acting on anti-oxidation were
identified to be paeonol, gallic acid and benzoylpaeoniflorin, and
their potential therapeutic targets were predicted and verified,
respectively. The present results reveal that MC has a significant
antioxidant activity and the compounds of paeonol, gallic acid and
benzoylpaeoniflorin could be considered as the promising antioxidant
candidates with the property of suppressing oxidative stress and
apoptosis.
Keywords: antioxidant, grey relational analysis, network pharmacology,
paeonol, paeonia suffruticosa andr
Introduction
Free radicals play an important role in maintaining homeostasis at the
cellular level in the normal healthy tissues. However, when the body
are exposed to the different physicochemical conditions, more free
radicals such as reactive oxygen species (ROS) and reactive nitrogen
species (RNS) will be generated and thus disturb the balance of ROS
generation and antioxidant defense systems, generally resulting in the
oxidative stress ([42]Devasagayam et al., 2004) and subsequently
causing the damage of cell membrane, protein and DNA ([43]Nijhawana et
al., 2019). The current investigations have found the close
relationship between oxidative stress and human disease. More and more
evidence reveals that the oxidative stress caused by ROS participates
in the development of aging, cancer ([44]Kudryavtseva et al., 2016),
neurodegenerative diseases ([45]Chauhan et al., 2006), cardiovascular
and metabolic diseases ([46]Incalza et al., 2017), and psychiatric
disorders ([47]Newton et al., 2015). Antioxidants from nature such as
vitamins, flavonoids and phenolic acid can effectively counter
oxidative stress by scavenging free radicals ([48]Halliwell et al.,
1992). Therefore, the exogenous antioxidants from food or supplements
are required to balance the ROS to normal levels in biological systems
under oxidative stress status. In recent years, the increasing numbers
of natural antioxidants are continuously found in medicinal plants
([49]Akwu et al., 2019; [50]Hamed et al., 2019; [51]Makinde et al.,
2019). Thus, the medicinal plants with potent anti-oxidative activity
play an important role in prevention and treatment of diseases related
to oxidative stress.
Paeonia suffruticosa Andr, belonging to Paeoniaceae family, is a
deciduous shrub with nearly worldwide distribution. The dried bark of
Paeonia suffruticosa Andr, commonly called Moutan Cortex (MC), has been
used in China for a long history. Many studies have identified and
reported more than one hundred of ingredients from MC, including
phenols, monoterpenes, monoterpene glycosides, flavonoids, tannins, and
triterpenoids ([52]Wang et al., 2019). Recent studies have revealed
that MC has strong pharmacological effects of anti-inflammatory and
anti-oxidation. Total glycoside of paeony could prevent
diabetes-associated renal damage against oxidative stress via NF-kB p65
and p38 MAPK pathway (Jing et al., 2010). MC was found significantly
increasing glutathione content and remarkedly decreasing induced nitric
oxide synthase activity in hippocampus tissue.
Grey relational analysis (GRA) is an analytical method based on the
development trend of the curve shape on each factor ([53]Zhu et al.,
2017). This method is generally employed to reveal the quantitative
comparison of the development trend in a dynamic variation system
([54]Abudukeremu et al., 2015). In this paper, GRA was carried out to
study the relationship between the variation trend of chemical
properties and anti-oxidative effects of MC. The compounds that most
relative with anti-oxidation were screened out based on the results
obtained from GRA, and could be considered as potent antioxidant
candidates.
Network pharmacology is a new discipline based on the basic theories of
systems biology. It conducts a comprehensive analysis of biological
systems and further find the specific node with multiple targets
([55]Zhu et al., 2018). This paradigm is capable of describing complex
interactions among biological systems, drugs, and diseases from a
network perspective and in this sense shares the holistic perspective
of TCM ([56]Ge et al., 2018). Network pharmacology has been
increasingly applied to exploring the pharmacological mechanisms of
medicinal plants and crude drugs ([57]Chen et al., 2019; [58]Zhang et
al., 2019).
In previous study, we had established a quantitative HPLC-MS method and
identified main constituents in MC ([59]Hou et al., 2018). In the
present study, we collected MCs from herbal market and quantified the
main compounds and evaluated its antioxidant activities at first, at
then the grey relational analysis was employed to predict the key
components that acting on anti-oxidative activities based on the
chemical contents and antioxidant activities. The uncovering
antioxidant targets and pathways that the selected components involved
were then revealed by network pharmacology and cell test verification.
Materials and Methods
Chemical Reagents
The standards (>98%, purity) of paeonol, paeoniflorin,
benzoylpaeoniflorin, paeonolide, gallic acid and oxypaeoniflorin were
purchased from Chengdu Herbpurify co, LTD. ABTS (2,
2′-azino-bis-(3-ethylbenzothiozoline-6-sulfonic acid) and DPPH (2,
2-diphenyl-1- picrylhydrazyl) were obtained from Sigma Aldrich, USA.
HPLC grade acetonitrile was got from Tedia Company, Inc, USA
(Fairfield, OH, United States). Formic acid with HPLC grade was
obtained from Chongqing Chuandong Chemical co, Ltd. Methanol for
extraction was purchased from Chendu Jinshan Chemical Reagent co, Ltd.
Folin & Clocalteu’s phenol reagents were purchased from Shanghai
Macklin Biochemical Co., Ltd.
Sample Collection and Preparation
45 batches of MC samples were purchased from Chongqing herbal medicine
market. The samples were from eight different production areas, in
which 3 samples were collected from Guangxi province (GX1-3); 10
batches were from Chongqing (CQ1-10); 13 batches were from Anhui
province (AH1-13); 10 batches were from Sichuan province (SC1-10); the
other nine samples were from Henan province (HN1-3), Hubei (HB1-3),
Shanxi (SXI) and Shandong province (SD1-2), respectively. The voucher
samples were deposited at college of pharmaceutical sciences and
Chinese medicine, Southwest University.
Duramens were removed from MC samples before be used. Then samples were
pulverized and filtered through an 80-mesh sieve. 0.50 g dried powder
sample was extracted with 50 ml 70% methanol aqueous solution for
30 min in an ultrasonic water bath (KQ5200E, 40 kHz). After extraction,
add solvent to bring the volume to 50 ml. Then the extract was
centrifuged at 10,000 rpm for 10 min, and the supernatant was collected
and filtered through a 0.22 μm syringe filter before analysis. All
samples were analyzed in triplicates.
Quantification of Six Main Components in Moutan Cortex
In previous study, we found paeonol, paeoniflorin, benzoylpaeoniflorin,
paeonolide, gallic acid and oxypaeoniflorin ([60]Supplementary Figure
S1) which were the predominant components in MC, and the quantitative
analytical method with simultaneously quantifying the six ingredients
in MC was established ([61]Ge et al., 2019). Briefly, The HPLC analyses
were performed using a LC-20A liquid chromatography system (Shimadzu
Co., Japan) and samples were separated on an Ecosil C[18] (250 mm ×
4.6 mm, 5 μM, Lubex Co., China). The eluent solvents consist of the
deionized water with 0.2% formic acid (A) and acetonitrile (B) using a
gradient program of 0–3 min, 90–89.2% A; 3–5 min, 89.2–88% A; 5–9 min,
88–87.8% A; 9–10 min, 87.8–87.7% A; 10–15 min, 87.7–87% A; 15–18 min,
87–85% A; 18–21 min, 85–84.2% A; 21–26 min, 84.2–26% A; 26–30 min,
26–10% A; 30–35 min, 10% A. The elution was performed with the eluent
solvent at a flow rate of 1.0 ml/min, and 10 µl of sample solution was
injected into the LC system for analysis. Two ultraviolet spectra were
monitored for acquiring chromatograms of six components, at 230 nm for
paeoniflorin and benzoylpaeoniflorin and 274 nm for gallic acid,
paeonol, paeonolide and oxypaeoniflorin, respectively. In this study,
the established method was applied to analyze the contents of paeonol,
paeoniflorin, benzoylpaeoniflorin, paeoniflorin, gallic acid and
oxypaeoniflorin of MCs to reflect its chemical properties.
Measurement of Antioxidant Activity by DPPH/ABTS^+
The antioxidant activity of MC extracts was determined by using DPPH
free radical scavenging assay with minor modification ([62]Dai et al.,
2013). Briefly, DPPH powder was dissolved in 100 ml methanol to freshly
prepare the 0.05 mM DPPH solution for DPPH method. The prepared
solution was stored at 4°C in dark. For each reaction, 0.1 ml of sample
solution was mixed with 3.9 ml DPPH stock solution and incubated for
30 min in dark. As a negative control, 3.9 ml of DPPH solution and
0.1 ml of 70% methanol aqueous solution were used. Then the absorbance
of reaction was measured at 517 nm using a UV spectrophotometer
(METASH, UV-6100) with an ascorbic acid (Vc) comparison. Additional
dilution was needed if the DPPH value measured was over the linear
range of the standard curve. All above samples were run in six
replicates. The scavenging activity (SC) of samples was expressed
through the following formula:
[MATH:
SC (100%) =100% × (A0 - A1)/A0
:MATH]
Where A[0] and A[1] represent the absorbance of negative control and
sample, respectively.
The scavenging activity of ABTS radical was determined according to the
reported method with slight modifications ([63]Re et al., 1999). The
ABTS radical cation was prepared by the reacting ABTS with potassium
persulphate. The mixture was incubated in dark at room temperature for
12 h. Then the ABTS radical cation solution was diluted with methanol
to give an absorbance of 0.70 ± 0.05 at 734 nm. After adding 0.2 ml of
the MC extract to 2.0 ml of diluted ABTS radical cation solution, the
absorbance was recorded after 15 min incubation. The above mentioned
samples were analyzed in six replicates.
Grey Relational Analysis
The grey relational analysis (GRA) was utilized as an evaluation system
to assess the effects of the diverse existing compounds on antioxidant
activity in this study ([64]Deng, 1989). The content of paeonol,
paeoniflorin, benzoylpaeoniflorin, paeonolide, gallic acid and
oxypaeoniflorin together with the scavenging activities from DPPH and
ABTS method was selected to be a grey system. The specific GRA
procedure is as follows:
Firstly, the raw data of associated factors are normalized and then the
deviation sequences are determined. Finally, the grey relational
coefficients are calculated by the following equations:
[MATH: Set x0=(x0<
/mn>(1),x0(
2),…, x0(n)) :MATH]
[MATH: x1= <
mrow>( x1(1), x1(2), ..., x1(n)); :MATH]
[MATH: x2= <
mrow>( x2(1), x2(2), ..., x2(n)); :MATH]
[MATH: ... :MATH]
[MATH: xi =
(x i
(1), xi (2), ..., xi <
mo>(n))
:MATH]
as the sequence of associated factors.
The correlation coefficient is defined as the following equation:
[MATH: γ(x0<
/mn>(k),<
mi>xi(k))=minimink|x0(k<
mo>)−xi(k<
mo>)|+ξmaximaxk|x0(k<
mo>)−xi(k<
mo>)||x0(
k)−xi(
k)|+ξmaximaxk|x0(k<
mo>)−xi(k<
mo>)|
:MATH]
Where ξ is the distinctive coefficient lying between 0 and 1, which is
set as to be 0.1.
The grey relation grade (GRG) is formulated as follows:
[MATH: γ(x0<
/mn>,xi
)=1n∑k=1nγ(x0<
/mn>(κ),xi(
κ)) :MATH]
Where n is the number of performance characteristics.
The influence degree of the factors including the contents of
components and antioxidant activities on the research object was
estimated by comparing their GRG value. The higher GRG value between
two associated factors is, the closer sequence of the two factors would
be.
Network Pharmacology Study
In order to further recognize the mechanisms of the underlying the
antioxidant effects on the targeted compounds from GRA, the network
pharmacological approach was used, including the evaluation of the
targeted compounds’ Absorption, Distribution, Metabolism, and Excretion
(ADME) properties, prediction of compounds-related targets, and
recognition of core functions and modules via the protein-protein
interaction (PPI) network approach. We identified a core modulatory
network and found the main pathway that involved in the antioxidant
activities of the targeted compounds.
Firstly, the structurally similar drugs of the selected components of
CM were screened by using MedChem studio, and then the antioxidant
targets of these drugs were obtained from the DrugBank database and
considered as the putative targets of the selected components of CM.
Next, we collected all the known therapeutic targets of drugs
contributed to antioxidant effects from DrugBank20
([65]http://www.drugbank.ca/, version 4.3) and the Online Mendelian
Inheritance in Man (OMIM) database21 ([66]http://www.omim.org/). After
overlap analysis of these collected targets, the putative CM
target-known therapeutic targets of the antioxidant network were
constructed using the links between putative targets of CM and known
antioxidant targets.
To further clarify the pathways involved in the putative CM targets,
the pathway enrichment analysis was performed by using the database
Visualization and Integrated Discovery software32 (DAVID,
[67]http://david.abcc.ncifcrf.gov/home.jsp, version 6.7) and based on
the pathway data obtained from the Kyoto Encyclopedia of Genes and
Genomes database (KEGG, [68]http://www.genome.jp/kegg/).
Cell Viability Assay
RAW 264.7 cells were incubated in DMEM medium supplemented with 10% FBS
and 1% penicillin/streptomycin. The cells were cultured in 96-well
plates and cultivated for 48 h while exposed to different treatments.
Cell viability was measured by MTT method. Briefly, incubated with
different compounds for 12 h, the cell culture was washed twice with
warm PBS buffer, and then fresh medium with 200 μM t-BHP was added to
the cell culture. After incubation for 1 h, the culture medium was
replaced with fresh medium containing 5 mg/ml MTT and incubated another
4 h, and then the medium were removed and 100 µl of dimethyl sulfoxide
(DMSO) was immediately added to the wells. The reagents were thoroughly
mixed and assayed at 540 nm on a SYNERGY H1 microplate reader.
ROS Levels in the Cells
The detection of intracellular ROS levels was conducted according to
the reported methods ([69]Hui et al., 2021). RAW 264.7 cells were
seeded in plates with a density of 5,000 cells per well. After 48 h
incubation, the cells were treated with the three components at 5, 10,
20, and 40 μM for 12 h, respectively and then the treated cells were
washed and incubated with 10 μM of H2DCF-DA for 30 min. Extracellular
H2DCF-DA was removed by washing the cultures twice with warm PBS. The
cellular oxidative stress was induced by incubating the cells with
200 μM t-BHP in PBS for 1 h. The cellular fluorescence intensities of
each well were measured and recorded with a SYNERGY H1 microplate
reader. The excitation and emission filters were set at 485 and 535 nm,
respectively. The results are expressed as the percentages between the
inhibition of the fluorescence relative and the untreated controls.
Values of fluorescence intensity were obtained from at least six
independent samples for each compound tested.
Gene Expression of TNF, ALB, VEGFA and Caspase3
Total RNA was extracted using a Qiagen RNeasy Mini kit (Qiagen, Inc,
USA). and cDNA was synthesized using a PrimeScript™ RT reagent Kit
(TaKaRa Inc, Japan). The reverse transcription-polymerase chain
reaction (RT-PCR) was applied to evaluate the mRNA expression of TNF,
ALB, VEGFA and Caspase3. RT-PCR primers for these genes were followed:
sense (5′-CCCTCACACTCACAAACCAC-3′) and antisense (5′-
CACCACAGGGCAAAGGAGAT-3′) for TNF; sense
(5′-AA-GACGTGTGTTGCCGATGA-3′) and antisense
(5′-GGCCTTTCAAATGGTGG-CAG-3′) for ALB; sense (5′-
GGGAGTCTGTGCTCTGGGAT-3′) and antisense
(5′-GGTGTCTGTCTGTCTGTCCG-3′) for VEGFA; sense
(5′-GGGGAGCTTGGAACGCTAAG-3′) and antisense (5′-
CCGTACCAGAGCGAGATGAC-3′) for Caspase3; sense (5′-
TGCTCCTCCCTGTTCCAGAG-3′) and antisense (5′-
CTCGTGGTTCACACCCATCA-3′) for GAPDH. The following PCR conditions
were applied at 95°C for 2 min, 40 cycles of 95°C for 15 s and 60°C for
1 min.
Statistical Analysis
The experimental data were presented as means of six replicates
determination ±standard deviation. All statistical analyses were
carried out using a SPSS20 software, Graphpad Prism software or online
software.
Results
Quantitative Analysis of Six Main Compounds in MC
As shown in [70]Table 1, paeonol and paeoniflorin were the main
ingredients of MC among the six detected components due to their
contents accounting for more than 80% the whole detected components,
furthermore paeonolide had the lowest content. The content of paeonol
ranged from 13.85 to 26.08 mg/g and paeoniflorin also showed a great
variety ranging from 3.95 to 14.31 mg/g. The results of quantitative
analysis indicated that the contents of the main components had great
variation among MCs from the herbal market. Furthermore, Hierarchical
Cluster Analysis (HCA) was applied to discriminate MCs based on the
contents of the six quantified components. The results showed that most
samples could gather together except the samples of CQ1, AH12 and AH13
that formed a small branch ([71]Supplementary Figure S2). The HCA
results indicated the holistic chemical properties of MCs were relative
stable.
TABLE 1.
Contents of the six constituents in 45 batches of MC (mg^.g^−1, x±SD, n
=6).
Samples Gallic acid Oxypaeoniflorin Paeonolide Paeoniflorin
Benzoyl-paeoniflorin Paeonol
GX1 1.23 ± 0.10 1.58 ± 0.08 0.38 ± 0.07 7.10 ± 0.08 0.92 ± 0.05 18.81 ±
1.21
GX2 1.26 ± 0.06 1.64 ± 0.06 0.25 ± 0.06 6.44 ± 0.07 1.19 ± 0.04 21.76 ±
0.38
GX3 1.10 ± 0.08 1.49 ± 0.05 0.18 ± 0.04 7.81 ± 0.08 0.94 ± 0.04 15.27 ±
0.42
CQ1 0.36 ± 0.04 3.43 ± 0.08 0.24 ± 0.05 14.24 ± 0.09 0.64 ± 0.05 18.17
± 0.53
CQ2 1.76 ± 0.07 0.78 ± 0.04 0.31 ± 0.04 3.95 ± 0.06 0.68 ± 0.06 18.63 ±
0.17
CQ3 0.82 ± 0.03 2.56 ± 0.09 0.23 ± 0.05 10.36 ± 0.07 1.07 ± 0.08 19.88
± 0.12
CQ4 0.48 ± 0.06 2.63 ± 0.15 0.75 ± 0.07 14.31 ± 0.15 0.93 ± 0.07 17.74
± 0.20
CQ5 1.04 ± 0.08 1.65 ± 0.07 0.32 ± 0.06 7.25 ± 0.07 0.99 ± 0.06 19.16 ±
1.25
CQ6 0.73 ± 0.05 1.45 ± 0.07 0.13 ± 0.08 7.48 ± 0.06 0.75 ± 0.07 16.07 ±
0.47
CQ7 1.25 ± 0.06 1.19 ± 0.06 0.22 ± 0.05 5.45 ± 0.08 0.73 ± 0.06 19.17 ±
0.26
CQ8 1.37 ± 0.05 1.64 ± 0.08 0.43 ± 0.07 8.03 ± 0.06 1.05 ± 0.05 21.67 ±
0.19
CQ9 0.67 ± 0.08 2.49 ± 0.04 0.57 ± 0.06 11.56 ± 0.07 0.76 ± 0.08 20.01
± 0.27
CQ10 1.25 ± 0.06 1.41 ± 0.05 0.17 ± 0.08 7.54 ± 0.06 0.89 ± 0.07 16.56
± 0.32
AH1 1.08 ± 0.06 1.43 ± 0.07 0.14 ± 0.05 5.83 ± 0.07 0.77 ± 0.06 17.67 ±
0.13
AH2 1.09 ± 0.05 1.54 ± 0.09 0.28 ± 0.05 6.85 ± 0.06 1.21 ± 0.05 18.39 ±
0.23
AH3 1.15 ± 0.07 1.59 ± 0.08 0.18 ± 0.06 7.06 ± 0.07 1.07 ± 0.08 15.79 ±
0.14
AH4 1.08 ± 0.08 1.76 ± 0.05 0.26 ± 0.06 8.54 ± 0.06 1.05 ± 0.08 17.55 ±
0.09
AH5 0.52 ± 0.05 1.38 ± 0.05 0.13 ± 0.08 5.92 ± 0.07 0.83 ± 0.05 17.22 ±
0.12
AH6 0.83 ± 0.06 1.56 ± 0.07 0.10 ± 0.06 7.95 ± 0.06 0.84 ± 0.04 17.34 ±
0.09
AH7 0.80 ± 0.04 1.43 ± 0.10 0.22 ± 0.07 6.20 ± 0.06 0.74 ± 0.06 16.59 ±
0.08
AH8 1.07 ± 0.08 1.24 ± 0.07 0.27 ± 0.08 6.53 ± 0.04 0.80 ± 0.05 17.96 ±
0.10
AH9 1.06 ± 0.07 1.79 ± 0.06 0.16 ± 0.05 8.05 ± 0.06 0.77 ± 0.07 21.74 ±
0.13
AH10 0.75 ± 0.05 1.38 ± 0.06 0.26 ± 0.07 6.93 ± 0.05 0.76 ± 0.07 17.38
± 0.11
AH11 0.50 ± 0.03 1.87 ± 0.03 0.54 ± 0.07 10.96 ± 0.06 1.87 ± 0.09 19.98
± 0.16
AH12 0.84 ± 0.06 2.15 ± 0.07 0.14 ± 0.04 10.71 ± 0.05 1.50 ± 0.05 26.08
± 1.02
AH13 0.33 ± 0.06 3.09 ± 0.06 0.41 ± 0.09 11.47 ± 0.07 0.85 ± 0.08 18.56
± 0.11
SC1 1.15 ± 0.07 1.48 ± 0.09 0.19 ± 0.04 7.78 ± 0.09 0.99 ± 0.07 14.55 ±
0.10
SC2 1.12 ± 0.04 1.46 ± 0.08 0.13 ± 0.06 7.64 ± 0.07 1.00 ± 0.06 15.69 ±
0.09
SC3 0.77 ± 0.07 1.40 ± 0.06 0.16 ± 0.08 7.50 ± 0.06 0.78 ± 0.04 16.75 ±
0.14
SC4 1.13 ± 0.08 1.70 ± 0.06 0.16 ± 0.05 7.38 ± 0.09 0.71 ± 0.05 17.74 ±
0.09
SC5 1.14 ± 0.07 1.13 ± 0.05 0.34 ± 0.07 6.33 ± 0.05 0.73 ± 0.09 17.49 ±
0.13
SC6 1.05 ± 0.04 1.70 ± 0.04 0.22 ± 0.01 8.49 ± 0.05 0.88 ± 0.07 18.59 ±
0.09
SC7 1.16 ± 0.04 1.51 ± 0.07 0.17 ± 0.07 8.03 ± 0.06 0.84 ± 0.06 16.63 ±
0.12
SC8 0.95 ± 0.06 1.46 ± 0.08 0.17 ± 0.08 7.25 ± 0.07 0.77 ± 0.06 15.30 ±
0.09
SC9 1.02 ± 0.07 1.92 ± 0.06 0.08 ± 0.04 8.10 ± 0.08 1.16 ± 0.08 22.83 ±
0.14
SC10 1.26 ± 0.08 1.40 ± 0.07 0.23 ± 0.08 7.55 ± 0.08 0.99 ± 0.04 19.92
± 0.13
HN1 0.76 ± 0.06 1.36 ± 0.07 0.19 ± 0.09 7.27 ± 0.06 0.80 ± 0.07 15.75 ±
0.09
HN2 0.95 ± 0.05 2.25 ± 0.08 0.16 ± 0.08 9.71 ± 0.05 1.15 ± 0.06 21.11 ±
0.23
HN3 1.13 ± 0.07 2.14 ± 0.06 0.13 ± 0.05 9.65 ± 0.06 1.20 ± 0.04 23.45 ±
0.34
HB1 0.86 ± 0.04 1.74 ± 0.05 0.20 ± 0.05 7.64 ± 0.01 0.90 ± 0.07 14.86 ±
0.09
HB2 1.75 ± 0.07 1.73 ± 0.07 0.14 ± 0.07 7.35 ± 0.06 1.05 ± 0.08 13.85 ±
0.31
HB3 0.91 ± 0.05 1.13 ± 0.06 0.66 ± 0.08 5.78 ± 0.05 0.71 ± 0.06 16.28 ±
0.12
SX1 0.92 ± 0.07 1.57 ± 0.05 0.28 ± 0.07 7.75 ± 0.08 0.82 ± 0.05 18.99 ±
0.09
SD1 1.18 ± 0.07 1.52 ± 0.04 0.45 ± 0.05 7.36 ± 0.07 1.11 ± 0.06 14.96 ±
0.08
SD2 0.72 ± 0.05 1.31 ± 0.06 0.77 ± 0.06 7.55 ± 0.08 0.86 ± 0.07 14.38 ±
0.09
[72]Open in a new tab
Antioxidant Properties of MC Based on DPPH and ABTS Assays
The antioxidant activity assays based on DPPH and ABTS free radical
scavenging activities were applied in the present study ([73]Figure 1).
The results from antioxidant property assays showed that all MCs were
capable of directly reacting with and quenching DPPH and ABTS radicals.
The MC samples of HN3, AH12 and CQ9 had exhibited the potent
antioxidant effects, while the samples of HB3, HB2 and SD2 showed the
lower antioxidant activities. The data concluded from the two methods
displayed a similar tendency of antioxidant activities of MCs. Besides,
the MC samples were gathered into two groups by Hierarchical Cluster
Analysis (HCA), in which AH12 and HN3 were clustered into one small
group, while other samples were gathered into another big branch
([74]Supplementary Figure S3). The HCA results from anti-oxidative
activity assay were different from with that of chemical analysis.
FIGURE 1.
[75]FIGURE 1
[76]Open in a new tab
Effect of various MC extracts on the activity of DPPH and ABTS.
The experimental data was combined from the quantitative analysis and
anti-oxidative activity assay and then was imported it into a SPSS
statistics 20 software for further analysis. Firstly, the raw data was
normalized and then was used for HCA. As shown in [77]Figure 2, most
samples were classified into one big group, while the sample AH12 and
HN3 gathered into a small branch, indicating that the chemical and the
anti-oxidative bioactivity properties of the 2 MC samples showed a
great discrepancy from other MC samples. However, the samples planted
from the same province always scattered among other samples and could
not gather into a group, possibly resulting from the different
processing procedure or species.
FIGURE 2.
[78]FIGURE 2
[79]Open in a new tab
HCA dendrogram of the 45 batches of MC samples based on the combined
data of contents of detected six components and anti-oxidative
activities.
Paeonol, Gallic Acid and Benzoylpaeoniflorin Were Evaluated as Potent
Antioxidants by Grey Relational Analysis
The GRA was employed to predict the main antioxidant compounds of MC by
comparing the tendency of anti-oxidative activity and contents of
quantified ingredients among MCs. The average GRG were obtained and
summarized in [80]Table 2. The values of the GRG ranged from 0.6219 to
0.9584, implying that all the detected compounds had high influence on
the anti-oxidative activity. According to GRG values from high to low,
the orders of the components most related to the anti-oxidative
activity were paeonol, gallic acid, benzoylpaeoniflorin, paeoniflorin,
oxypaeoniflorin and paeonolide, respectively. So the components of
paeonol, gallic acid and benzoylpaeoniflorin were thought as main
factors contributing to anti-oxidative properties with high GRG above
0.62. The phenolic compound paeonol was eventually found possessing the
highest anti-oxidative potentity with the GRG of 0.9584. Several
relative literatures had reported that paeonol induced many
pharmacological effects through inhibiting oxidative stress ([81]Qin et
al., 2010; [82]Bao et al., 2013; [83]Gong et al., 2017). As a member of
a polyphenol family, gallic acid is considered to be one of the most
abundant sources of nature antioxidants ([84]Roidoung et al., 2016;
[85]Rajan and Muraleedharan, 2017; [86]Ola-Davies and Olukole, 2018).
Benzoylpaeoniflorin, as a [87]monoterpene glycoside, also has been
proved as a strong antioxidant from related studies ([88]Fang et al.,
2008; [89]Xu et al., 2017). Therefore, the GRA results suggested that
the components of paeonol, gallic acid, and benzoylpaeoniflorin could
be considered as potential antioxidants of MC.
TABLE 2.
Grey relational grade with rank order of six compounds detected in MCs.
Compounds Average grey relational grade (n = 6) Order
DPPH ABTS
Gallic acid 0.8452 ± 0.092 0.8406 ± 0.061 2
Oxypaeoniflorin 0.7292 ± 0.032 0.7415 ± 0.055 5
Paeonolide 0.6219 ± 0.046 0.6287 ± 0.070 6
Paeoniflorin 0.7384 ± 0.078 0.7428 ± 0.042 4
Benzoylpaeoniflorin 0.7806 ± 0.084 0.7783 ± 0.076 3
Paeonol 0.9584 ± 0.036 0.9429 ± 0.069 1
[90]Open in a new tab
Network Pharmacology Analysis Predicted the Targets of Antioxidants
To further uncover possible mechanism of the compounds (paeonol, gallic
acid and benzoylpaeoniflorin) acting on anti-oxidative properties, we
used network pharmacology strategy to predict the putative targets of
these ingredients. First, 535 genes were selected and predicted as the
putative targets of the three compounds, including 177 genes of
paeonol, 258 genes of gallic acid and 100 genes of benzoylpaeoniflorin,
and 408 targets were yielded after deletion of duplicates. Detailed
information about the putative targets of the compounds was provided in
[91]Supplementary Table S2 and the compound-target network is shown in
[92]Figure 3. The analysis of component-target network included a total
of 411 nodes and 533 edges, including three component nodes and 408
target nodes. The connecting components or the nodes with more target
points play a pivotal role in the entire interaction network, which may
be the key component or target gene that plays an antioxidant role in
CMs.
FIGURE 3.
[93]FIGURE 3
[94]Open in a new tab
Compound-putative target network of gallic acid, benzoylpaeoniflorin
and paeonol. Light blue circles, light green circles and light red
circles represent the targets of compounds gallic acid,
benzoylpaeoniflorin and paeonol, representatively. The yellow square
frames represent the compounds.
Then the known therapeutic targets of drugs in the treatment of
oxidation related diseases were collected. After removing redundant
entries, 1,034 known therapeutic targets for the antioxidant activities
were used for the further data analysis.
The known therapeutic targets of the antioxidant activity networks and
putative compounds-target networks were then constructed. The
interaction between the target proteins was shown in [95]Figure 4,
which included a total of 110 hubs, 1,184 edges, of which hubs
represented the target protein and each edge represented the
protein-protein interaction. In this network interaction, it has a
network density of 0.197 with characteristics path length 1.966 and
21.527 average number of neighbours. The size and color of nodes was
proportional with the degree. We found that albumin (ALB) (degree =
76), Tumor Necrosis Factor (TNF) (degree = 65), Vascular Endothelial
Growth Factor A (VEGFA) (degree = 65), Caspase 3 (degree = 61), and
Mitogen-Activated Protein Kinase 1 (MAPK1) (degree = 61) had more than
60° value and were centrally located in the protein-protein interaction
network (PPI), indicating that these proteins were involved in the
pathogenesis of oxidation.
FIGURE 4.
[96]FIGURE 4
[97]Open in a new tab
Clusters of the compound-disease target’ PPI network. The size and the
color of the node represents the value of the degree, the thickness of
the side indicates the value of the Combine score.
The predicted targets from PPI network mainly responded to many
biological process, such as intrinsic apoptotic signaling regulation,
DNA damage, peptidyl-tyrosine autophosphorylation, proteolysis, and
nitric oxide biosynthetic process ([98]Figure 5A). The cellular
component analysis showed that the genes mainly related to
extracellular exosome, cytosol, extrinsic component of cytoplasmic side
of plasma membrane, mitochondrial outer membrane, cell surface,
extracellular matrix, Golgi apparatus, mitochondrion and external side
of plasma membrane ([99]Figure 5B). There targets also involved in many
protein receptor binding and protein activities, including ATP binding,
drug binding, heparin binding, small molecule binding and non-membrane
spanning protein tyrosine kinase activity, protein heterodimerization
activity, oxidoreductase activity, MAP kinase activity ([100]Figure
5C). To investigate the biological functions of these major hubs, a
pathway enrichment analysis was performed. 107 KEGG pathways were
obtained based on the PPI targets. As shown in [101]Figure 5D, the
major 10 KEGG pathways were significantly associated with various
physiological processes, including NOD-like receptor signaling pathway,
Toxoplasmosis, TNF signaling pathway, Proteoglycans in cancer, Ras
signaling pathway, NF-kappa B signaling pathway and PI3K-Akt signaling
pathway.
FIGURE 5.
[102]FIGURE 5
[103]Open in a new tab
Target protein GO enrichment analysis and KEGG pathway analysis. (A):
The top ten significantly enriched terms in biological process (BP);
(B): The top ten significantly enriched terms in cellular component
(CC); (C): The top 10 significantly enriched terms in molecular
function (MF); (D): The top 10 significantly enriched terms in KEGG.
Effects of Predicted Antioxidants on Cell Viability, ROS Levels and Gene
Expressions of TNF, ALB, VEGFA, Caspase3 in t-BHP-stimulated RAW 264.7 Cells
A MTT assay was performed to evaluate the effects of the components on
RAW264.7 cell viability. As shown in [104]Figure 6, the cell viability
of RAW 264.7 cells decreased to 40.5% after t-BHP stimulation, while
the components of paeonol, gallic acid and benzoylpaeoniflorin could
partly recover the t-BHP-stimulated cell viability and showed
significantly difference at the concentration of 20 μM (p < 0.05). The
t-BHP-induced intracellular ROS accumulation was monitored within cells
using a DCFH2-DA fluorescence intensity analysis. The results from
cellular ROS level further indicated that these components could
clearly inhibit the generation of cellular ROS induced by t-BHP. Among
the components, the treatment of gallic acid and benzoylpaeoniflorin
showed a dose-effect relationship, and paeonol treatment presented the
most ROS inhibition at the concentration of 20 μM ([105]Figure 7).
FIGURE 6.
[106]FIGURE 6
[107]Open in a new tab
Evaluation of cell viability in RAW264.7 with the MTT assay. The
components of gallic acid (A), paeonol (B), and benzoylpaeoniflorin (C)
show protective effects in the t-BHP stimulated RAW264.7 cells. Data
are given as mean ± standard deviation (n = 6). Compared with t-BHP
treatment cell group, ###p < 0.001; ##p < 0.01; ##p < 0.01. Compared
with Control, *** p < 0.001; ** p < 0.01; * p < 0.05.
FIGURE 7.
FIGURE 7
[108]Open in a new tab
Modulation of excess ROS by gallic acid, paeonol and
benzoylpaeoniflorin. Intracellular ROS was detected in t-BHP - induced
RAW264.7 cells treated with indicated concentrations of gallic acid,
paeonol and benzoylpaeoniflorin and quantified by DCFH2-DA fluorescence
intensity assay. Compared with t-BHP treatment cell group, *** p <
0.001; ** p < 0.01; * p < 0.05.
The genes of TNF, ALB, VEGFA and Caspase3 were predicted as the
oxidative relative targets of the three components through network
pharmacology analysis. Thus, we considered that the protective effects
of the components against t-BHP-stimulated oxidative stress related to
those target genes. The results from gene expression assay demonstrated
that these ingredients could lead to the most significant decrease in
the expression of the TNF, ALB, VEGFA and Caspase3 genes ([109]Figure
8). This result is in accordance with the reduction of intracellular
ROS in RAW264.7 cells. To sum up, these findings prove that the three
bioactive compounds from MC could significantly prevent t-BHP-induced
intracellular ROS generation in macrophages and protect the cells from
apoptosis induced by oxidative stress (p < 0.001). Therefore, it can be
suggested that the enrichment of paeonol, gallic acid and
benzoylpaeoniflorin in MC could present as potential antioxidants.
FIGURE 8.
[110]FIGURE 8
[111]Open in a new tab
The expression patterns of target genes in RAW264.7 macrophages treated
with gallic acid, paeonol and benzoylpaeoniflorin. The relative gene
expressions were evaluated by qRT-PCR. Compared with t-BHP treatment
cell group, *** p < 0.001; ** p < 0.01; * p < 0.05.
Discussion
Previous study had revealed that more than 100 compounds were isolated
and identified from Paeonia suffruticosa Andr, in which monoterpene
glycosides and phenols were the predominant constituents ([112]Wang et
al., 2019). In addition, peaonol, paeoniflorin and their derivatives
were the representative components of monoterpene glycosides and
phenols ([113]Zhou and Lv, 2008; [114]Wang P. et al., 2017; [115]Wang
Z. Q. et al., 2017). In the present study, we found peaonol and
paeoniflorin were the main components and account for 80% contents of
the total detected components, which were in accordance with our
previous study ([116]Ge et al., 2019). However, the chemical properties
of MCs from herbal market showed great diversities, in which the
contents of peaonol and paeoniflorin ranged from 13.85 to 26.08 mg/g
and 3.95–14.31 mg/g, respectively. Many factors may result in this
variation. Several studies had demonstrated that the different origins
of MCs showed great diversities on chemical properties ([117]He et al.,
2014). Besides, different cultivated areas and processing procedures
also results in chemical diversities in MCs.
The components of paeonol, gallic acid and benzoylpaeoniflorin obtained
high grades based on the chemical and bioactive evaluation by grey
relational analysis and thus these compounds were predicted as key
components of MC that acting on anti-oxidative activities. It is well
known phenolic acids present strong antioxidant capacities ([118]Akram
et al., 2019), so there is no doubt that paeonol and gallic acid
belonging to phenolic acid are selected as potent antioxidants.
Benzoylpaeoniflorin is a derivative of paeoniflorin, and compounds with
the same parent structure have shown therapeutic effect in experimental
diabetic nephropathy by preventing diabetes-associated renal damage
against oxidative stress (Fang et al., 2019). Besides, paeoniflorin and
its derivatives had been found significant protection effects by
ameliorating oxidative stress or involving a decrease in ROS production
in vivo ([119]Picerno et al., 2011; [120]Zhao et al., 2013; [121]Song
et al., 2017).
Network pharmacology strategy is a potent tool to reveal the putative
mechanisms of drugs like herbal medicine, which always are a complex
chemical composition. In the present study, we had selected three key
anti-oxidative components based on grey relational analysis. Thus,
clarifying the mechanism of antioxidant action of these compounds by
network pharmacology approach is a key imperative. Consequently, the
compound-putative target network, PPI network with common targets for
antioxidant, compound-disease were built to systematically analyze the
mechanism of antioxidant action of the selected compounds. This network
pharmacology study predicted the following four potential targets: ALB,
TNF, VEGFA and Caspase3. Among these, ALB functions in the regulation
of blood plasma colloid osmotic pressure and acts as a carrier protein
for a wide range of endogenous molecules. Human serum albumin appears
to reduce oxidative stress via NADPH oxidase inhibition in the human
vascular smooth muscle, indicating that the serum level may be a
critical determinant of vascular oxidative stress in some human
diseases ([122]Kinoshita et al., 2017). TNF is a cytokine secreted by
macrophages, and involve in the regulation of a wide spectrum of
biological functions. It has been reported that TNF-α/TNFR1 pathway
involved in LPS alleviated APAP-induced oxidative stress ([123]Zhao et
al., 2019). Oxidative stress also had been found highly correlated with
the presence of TNFA subgroup in patients with diabetes, diabetic
nephropathy and chronic lymphocytic leukemia ([124]Dabhi and Mistry,
2015; [125]Jevtovic-Stoimenov et al., 2017). Caspases are a family of
proteases involved in many important biological processes including
apoptosis and inflammation. Antioxidants like sulfated corn bran
polysaccharides could significantly inhibit the proliferation of A549
and HepG2 cell lines by the up-regulation at the mRNA expression level
of pro-apoptotic genes Caspase3, Caspase8, Caspase9 ([126]Xu et al.,
2016). Panina et al. found hyperbaric oxygenation induced oxidative
stress could significantly upregulate caspase-3-like activity and
expression of Caspase3 mRNA in the cerebral cortex of rat, while
addition of antioxidant led to the normalization of caspase3-like
activity ([127]Panina et al., 2018). The cell test in this study also
further proved that the candidate genes closely related to reduction of
oxidative stress, which was due to the treatment of the three
components.
By KEGG enrichment analysis, the top 10 pathways were mainly related to
cancer and immune system. As we know, one theory of tumorigenesis is
from oxidative stress that activates inflammatory pathways leading to
transformation of a normal cell to tumor cell. Oxidative stress can
activate a variety of transcription factors including NF-κB, AP-1, p53,
HIF-1α, PPAR-γ, β-catenin/Wnt, and Nrf2. Activation of these
transcription factors can lead to the expression of over 500 different
genes including inflammatory cytokines ([128]Reuter et al., 2010).
Extensive research has revealed the mechanism by which continued
oxidative stress can lead to chronic inflammation, which in turn could
mediate most chronic diseases including cancer ([129]Bartsch and Nair,
2006; [130]Grivennikov et al., 2010; [131]Grivennikov and Karin, 2010).
Conclusion
Taken together, the present results revealed that MC had significant
antioxidant activity, and the compounds of paeonol, gallic acid and
benzoylpaeoniflorin could be considered as promising antioxidant
candidates of MC and markers for quality control of MC. Furthermore, a
comprehensive method based on chemical analysis, bioactivity activity
assays coupled with grey relational analysis was established to
identify antioxidant candidates from MC, and network pharmacology
strategy was proved to be an efficient tool for uncovering
pharmacological mechanism of active ingredients.
Data Availability Statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found in the article/[132]Supplementary Material.
Author Contributions
YZ and XYW contributed to conception and design; YZ and XHW performed
the network pharmacology analysis, YL and RZ analyzed the data, FZ and
ZZ supervised, and wrote the article. All authors reviewed the
manuscript and approved the final manuscript.
Funding
This work was financially supported by the National Natural Science
Foundation of China (81703644); Capacity Building Project on
Sustainable Utilization of precious Chinese medicine resources
(2060302) and Fundamental Research Funds for the Central Universities
(XDJK 2019C054). Public service Platform for the industrialization of
technological innovation achievements in the field of Robot and
Intelligent Manufacturing in Chongqing 2019-00900-1-1), the Science and
Technology Research Program of Chongqing Municipal Education Commission
Grant (KJQN201901348, KJCX2020048).
Conflict of Interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary Material
The Supplementary Material for this article can be found online at:
[133]https://www.frontiersin.org/articles/10.3389/fphar.2021.748501/ful
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References