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.

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.

In previous study, we found paeonol, paeoniflorin, benzoylpaeoniflorin, paeonolide, gallic acid and oxypaeoniflorin (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 (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 C₁₈ (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.

The antioxidant activity of MC extracts was determined by using DPPH free radical scavenging assay with minor modification (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: SC  ( 100% )   = 100% ×  ( A0  -  A1 ) /A0 Where A₀ and A₁ 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 (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.

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 (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: Set   x 0 = ( x 0 ( 1 ) , x 0 ( 2 ) , … ,   x 0 ( n ) )   x 1 =   (   x 1 ( 1 ) ,   x 1 ( 2 ) ,  ... ,   x 1 ( n ) ) ;   x 2 =   (   x 2 ( 1 ) ,   x 2 ( 2 ) ,  ... ,   x 2 ( n ) ) ; ...   x i   =   ( x  i  ( 1 ) ,   x i   ( 2 ) ,  ... ,   x i  ( n ) ) as the sequence of associated factors.

The correlation coefficient is defined as the following equation: γ ( x 0 ( k ) , x i ( k ) ) = min i min k | x 0 ( k ) − x i ( k ) |+ ξ max i max k | x 0 ( k ) − x i ( k ) | | x 0 ( k ) − x i ( k ) |+ ξ max i max k | x 0 ( k ) − x i ( k ) | 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: γ ( x 0 , x i ) = 1 n ∑ k = 1 n γ ( x 0 ( κ ) , x i ( κ ) ) 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.

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 (http://www.drugbank.ca/, version 4.3) and the Online Mendelian Inheritance in Man (OMIM) database21 (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, 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, http://www.genome.jp/kegg/).

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.

The detection of intracellular ROS levels was conducted according to the reported methods (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.

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′-CCC​TCA​CAC​TCA​CAA​ACC​AC-3′) and antisense (5′- CAC​CAC​AGG​GCA​AAG​GAG​AT-3′) for TNF; sense (5′-AA-GAC​GTG​TGT​TGC​CGA​TGA-3′) and antisense (5′-GGC​CTT​TCA​AAT​GGT​GG-CAG-3′) for ALB; sense (5′- GGG​AGT​CTG​TGC​TCT​GGG​AT-3′) and antisense (5′-GGT​GTC​TGT​CTG​TCT​GTC​CG-3′) for VEGFA; sense (5′-GGG​GAG​CTT​GGA​ACG​CTA​AG-3′) and antisense (5′- CCG​TAC​CAG​AGC​GAG​ATG​AC-3′) for Caspase3; sense (5′- TGC​TCC​TCC​CTG​TTC​CAG​AG-3′) and antisense (5′- CTC​GTG​GTT​CAC​ACC​CAT​CA-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.

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.

As shown in 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 (Supplementary Figure S2). The HCA results indicated the holistic chemical properties of MCs were relative stable.

The antioxidant activity assays based on DPPH and ABTS free radical scavenging activities were applied in the present study (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 (Supplementary Figure S3). The HCA results from anti-oxidative activity assay were different from with that of chemical analysis.

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 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.

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 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 (Qin et al., 2010; Bao et al., 2013; 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 (Roidoung et al., 2016; Rajan and Muraleedharan, 2017; Ola-Davies and Olukole, 2018). Benzoylpaeoniflorin, as a monoterpene glycoside, also has been proved as a strong antioxidant from related studies (Fang et al., 2008; 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.

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 Supplementary Table S2 and the compound-target network is shown in 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.

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 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.

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 (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 (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 (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 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.

A MTT assay was performed to evaluate the effects of the components on RAW264.7 cell viability. As shown in 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 (Figure 7).

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 (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.