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. 2021 Apr 26;12:659954. doi: 10.3389/fphar.2021.659954

Characterization of the Components and Pharmacological Effects of Mountain-Cultivated Ginseng and Garden Ginseng Based on the Integrative Pharmacology Strategy

Sen Li 1,2,†, Ping Wang 3,†, Wenzhi Yang 4, Chunhui Zhao 3, Luoqi Zhang 1,2, Jingbo Zhang 5, Yuewen Qin 3, Haiyu Xu 3,*, Luqi Huang 2,*
PMCID: PMC8108004  PMID: 33981239

Abstract

Panax ginseng C. A. Mey (PGCAM) is a herbaceous perennial belonging to the Araliaceae family, mainly including Mountain-Cultivated Ginseng (MCG) and Garden Ginseng (GG) on the market. We aimed to establish a rapid, accurate and effective method to distinguish 15-year-old MCG and GG using ultra-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry (UPLC-QTOF-MS/MS), and also explored the pharmacological mechanisms of the main components using the Integrative Pharmacology-based Network Computational Research Platform of Traditional Chinese Medicine (TCMIP V2.0; http://www.tcmip.cn/). Altogether, 23 potential quality markers were characterized to distinguish 15-year-old MCG and GG, including ginsenosides Ra2, Rg1, and Ra1, and malonyl-ginsenoside Ra3, etc. The contents of 19 constituents (mainly protopanaxadiol-type) were higher in MCG compared with that in GG, and four constituents (mainly carbohydrate compounds) were higher in GG. The 105 putative targets corresponding to 23 potential quality markers were mainly involved in 30 pathways, which could be divided into 10 models, such as immune regulation, systems (metabolic, nervous, cardiovascular, reproductive), blood-pressure regulation, as well as antitumor, antiaging, antibacterial and anti-inflammatory effects. Furthermore, the potential quality markers of MCG and GG could inhibit the proliferation of breast cancer by regulating the mRNA expression of PSA, S6K, MDM2, and P53 genes by acting on AR, MTOR, PI3K and other targets. The Integrative Pharmacology Strategy may provide an efficient way to identify chemical constituents and explore the pharmacological actions of TCM formulations.

Keywords: mountain-cultivated ginseng, garden ginseng, UHPLC/IM-QTOF-HDMSE, metabolomics analysis, TCMIP V2.0

Introduction

Panax ginseng C. A. Mey (PGCAM) is an herbaceous perennial belonging to the Araliaceae family. Its dried root and rhizome (Panax ginseng C. A. Mey. Radix et Rhizoma) have been used as a herbal medicine in China and some Asian countries for thousands of years (Mancuso and Santangelo, 2017), and have immunomodulation, antifatigue, antiaging, and anticancer effects (Shi et al., 2019). PGCAM was firstly recorded in Shennong’s Classic of Materia Medica (Liu et al., 2013), during which all PGCAM grew wild. However, with the development of society, wild-growing PGCAM could not meet human requirements. To solve the problem, farmers began to study and explore suitable cultivation methods for PGCAM. There are four types of PGCAM according to different cultivation methods: wild-growing ginseng, transplanted ginseng, mountain-cultivated ginseng (MCG) and garden ginseng (GG) (Yang et al., 2013). MCG and GG are recorded in the Pharmacopoeia of the People’s Republic of China from 2005 edition. MCG and GG have become the main varieties on the market.

MCG refers to the seeds of PGCAM germinated and grown in tall mountains and dense forests for 10–20 years (Zhu et al., 2018), so also called “Lin-Xia-Shan-Shen”. If MCG grows long enough, its quality and efficacy become almost the same as that of wild PGCAM. GG refers to PGCAM that planted in a garden and harvested after 4–6 years. In general, the quality of MCG is higher than that of GG, which has a stronger pharmacological effect (Kim et al., 2020). The price of PGCAM is directly proportional to the grown period: the longer the period, the higher the price. Therefore, adulteration or falsification of PGCAM has always been a serious problem in the commercial market.

To solve this problem, researchers have proposed different identification strategies for PGCAM. With the continuous development of new technologies, ultra-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry (UPLC-QTOF-MS/MS) has been widely used in the analysis of chemical components of PGCAM (Wu et al., 2018), multiple compounds have been identified, including ginsenosides, polysaccharides, fatty acids, volatile oils and amino acids (Liang et al., 2016). Ginsenosides, as the main components of PGCAM, show important pharmacological activities during the treatment of cardiovascular diseases (Kim, 2018). In addition, Prof. Xu and his colleague selected 12 chemical components to distinguish GG4–7 years and MCG15 years: ginsenoside Ra3/isomer, gypenoside XVII, quinquenoside R1, ginsenoside Ra7, notoginsenoside Fe, ginsenoside Ra2, ginsenoside Rs6/Rs7, malonyl ginsenoside Rc, malonyl ginsenoside Rb1, malonyl ginsenoside Rb2, palmitoleic acid, and ethyl linoleate (Xu et al., 2016). However, the characteristic components and pharmacological effects of MCG and GG grown for 15 years have not been reported. Therefore, we aimed to employ the Integrative Pharmacology Strategy to characterize the differential component between the two types of Ginseng and explore their pharmacological effects in vitro. Integrative Pharmacology Strategy (IPS) pays attention to the interactions between Chinese prescriptions and the organism from multiple levels and multiple aspects, its research content mainly includes component analysis, network pharmacology analysis and pharmacological experiment verification (Xu and Yang, 2014). The IPS systematically analyzes the interaction between TCM formulations and the organism from multiple levels and multiple aspects to form a new mode of research of TCM. To practice this strategy better, we established Integrative Pharmacology-based Network Computational Research Platform of Traditional Chinese Medicine (TCMIP V2.0; www.tcmip.cn/), which comprising five databases and seven functional modules (Xu et al., 2019).

Therefore, we aimed to identify potential quality markers to distinguish 15-year-old MCG and GG using the UPLC-QTOF-MS/MS. Then, we employed the TCMIP V2.0 to carry out the network pharmacology analysis of the differential components contained in MCG and GG. Finally, we verified the results of network pharmacology in vitro. Our research will be a good application of Integrative Pharmacology Strategy.

Materials and Methods

Chemicals and Reagents

Twelve batches of MCG and GG were collected from the cultivation areas in Huanren County (41.26°N, 125.36°E; Benxi City, Liaoning Province, China) in September 2017, and the voucher specimens were deposited in our lab. Sixty-six standards of PGCAM were purchased from Shanghai Standard Biotech (Shanghai, China) or isolated from the roots of PGCAM and Panax notoginseng C. A. Mey (Yang et al., 2016b). (Supplementary Table S1). HPLC-grade acetonitrile (CH3CN) and methanol were purchased from Thermo Fisher Scientific (Fair Lawn, NJ, United States). Formic acid (FA) was obtained from Sigma-Aldrich (Saint Louis, MO, United States). Ultra-pure water was prepared in-house using the Milli-Q™ system (Millipore, Bedford. MA, United States).

Sample Preparation

First, 50 mg of MCG powder and GG powder were soaked with 3 ml of 70% methanol (v/v), respectively, followed by ultrasound extraction for 60 min at 25°C. Then, the solution was centrifugated at 14,000 rpm for 10 min at room temperature after compensating with 70% methanol for the weight lost. The supernatant was transferred to a 5 ml volumetric flask and diluted to the scale mark. Finally, 1 mlwell-mixed liquid was centrifuged for 10 min, and supernatant were transferred to autosampler vials for analyses. Herbal samples were injected randomly. An equal volume of all test solutions was mixed to prepare the quality control (QC) sample, which was used to monitor the stability of the analytical system.

Chromatographic Separation and MS Conditions

UPLC-MS was performed on an Acquity™ UPLC I-Class/Vion Ion Mobility Spectrometry (IMS)-QTOF system (Waters, Milford, MA, United States). The chromatographic separation was carried out on a BEH Shield RP18 column (2.1 × 100 mm, 1.7 µm) hyphenated with a VanGuard™ pre-column (2.1 × 50 mm, 1.7 µm; Waters) maintained at 35°C. The mobile phase consisted of 0.1% FA in CH3CN (A) and 0.1% FA in H2O (B). The optimized gradient program was: 0–2 min, 15–20% A; 2–7 min, 20–30% A; 7–17 min, 30–33% A; 17–20 min, 33–60% A; 20–22 min, 60–98% A; 22–24 min, 98–98% A. The flow rate was 0.3 ml/min and the injection volume was 3 μL. A 3-min re-equilibration time was set between successive injections. A “purge–wash–purge” cycle was set on the autosampler, with 10% CH3CN-H2O (v/v) as the purge solvent and 50% CH3CN-H2O as the wash solvent, to minimize the carry-over between injections.

The MS experiment performed on a Vion IMS-QTOF mass spectrometer in the negative electrospray ionization mode (Waters, Corp., Milford, United States). The LockSpray™ ion source was equipped under the following parameters: capillary voltage, 1.0 kV; cone voltage, 20 V; source offset, 80 V; source temperature, 120°C; desolvation gas temperature, 500°C; desolvation gas flow (N2), 800 L/h; cone gas flow (N2), 50 L/h. Default parameters were defined for the traveling-wave IMS separation. HDMSE data covered a m/z of 300–1,500 at 0.3 s per scan. The low collision energy was set at 6 eV and the high-energy ramp was 40–80 eV. Data acquisition was controlled by the UNIFI 1.9.3.0 software (Waters, Corp., Milford, United States). The accuracy error threshold was fixed at 10 ppm.

Date Processing

Raw HDMSE data were corrected with reference to m/z 554.2620 in the 12 batches of samples and QC by UNIFI 1.9.3.0. Then, preliminary data were processed automatically by Progenesis QI 2.1 software (Waters, Corp., Milford, CT, United States) [M-H]− and [M + FA-H]− were the main adduct ions in the negative mode. Efficient menu-guided processing, peak alignment and peak selection could generate a data matrix, including retention time (tR), m/z, normalized peak area, and collisioncross-secions (CCS). All the ion signals detected in each sample were normalized to the obtained value of the total ion count. The data matrix was filtered based on “80% rule” and “30% variation” (Li J. et al., 2019).

The Untargeted Metabolomics Analysis Based on Multivariate Statistical Analysis

Processed data were subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) using SIMCA-P 14.1 (Umetrics, Umea, Sweden). Potential quality markers were filtered out according to the variable importance for projection (VIP) values (VIP>1.5) and Student’s t-test (p < 0.05), which could be used distinguish between MCG and GG.

Prediction of Putative Targets of Potential Quality Markers

The mol. formats of potential quality markers were uploaded to TCMIP to predict the putative targets using the TCM target prediction and function analysis module (TTFM) of TCMIP according to the similarity of chemical structures to known drugs on the market. The Tanimoto Score was set at 0.7 (moderate similarity) to select constitute–putative target pairs.

Pathway Enrichment Analysis

To further explore the biological functions of the putative targets, the pathway–enrichment analyses was undertaken using the database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (https://david.ncifcrf.gov). Pathways with p < 0.05 were selected for further analyses.

Cell Culture

MCF-7 cell line was kindly provided by Prof. Xiujie Wang (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences). The MCF-7 cell line was cultured in DMEM high glucose medium (Thermo, Fair Lawn, NJ, United States) with 1% penicillin/streptomycin (Invitrogen, Carlsbad, CA, United States)and 10% fetal bovine serum (FBS; Sijiqing, Huzhou, Zhejiang, China) at 37°C in humid atmosphere with 5% CO2.

Cell Proliferation Analysis

The extraction of MCG and GG was prepared according to Sample Preparation. Then, the methanol was volatilized and the residue was redissolved with sterile water. After filtered by 0.22 μm filter film, the herbal samples were stored in a refrigerator at 4°C. MCF-7 cell line was seeded in 96-well plates (5×103 cells/well) incubating at 37°C for 24 h. Various concentrations of MCG or GG were added to the wells and then incubated for 48 h. Cell proliferation was evaluated using a Cell Counting Kit-8 (CCK8) assay. The cells treated with various conditions were incubated with 10 μL CCK-8 solution (Dojindo, Kumamoto, Japan) for 90 min. Next the absorbance was measured by a Microplate Reader under 450 nm (Thermo, Fair Lawn, NJ, United States). All experiments were performed in triplicate and repeated four times. Data were presented as mean ± standard deviation (SD). A One-way ANOVA determined whether the results had statistical significance.

Quantitative Real-Time PCR Analysis

The total RNA was isolated from the cultured cells using Trizol (Tiangen, China). Then, cDNA was synthesized using a ReverTra Ace quantitative polymerase chain reaction (PCR) RT Master Mix Kit with gDNA Remover (TOYOBO Co., OSAKA, Japan) according to the manufacturer’s protocol. PCR product amplification was performed with SYBR Green Master Mix (Juhemei, China) on an Agilent Mx 3000P Real-Time PCR System (Applied Biosystems, CA). Primers were 5′-GAATCATCGGACTC AGGTACATC-3′ and 5′-TCT​GTC​TCA​CTA​ATT​GCT​CTC​CT-3′ for MDM2; 5′-CAG​CAC​ATG​ACG​GAG​GTT​GT-3′ and 5′-TCA​TCC​AAA​TAC​TCC​ACA​CGC-3′ for P53; 5′-GTG​AGG​CAG​GCG​ACT​AAT​CAG-3′ and 5′-GTTCCCGTACC TGTTTGCAG-3′ for PSA; 5′-TTT​GAG​CTA​CTT​CGG​GTA​CTT​GG-3′ and 5′-CGA​TGA​AGG​GAT​GCT​TTA​CTT​CC-3′ for S6K; 5′-CTGGAACGGTGAAGGT GACA-3′ and 5′-AAG​GGA​CTT​CCT​GTA​ACA​ATG​CA-3′ for BETA-ACTIN, and 40 PCR cycles (30 s denaturation at 95°C, 20 s annealing at 60°C, 30 s elongation at 72°C) were run. BETA-ACTIN was used as an internal standard. The relative mRNA levels were determined using the 2−ΔΔCt method.

Statistical Analyses

All data were analyzed by SPSS 22.0 software (SPSS Inc., Chicago,IL, United States). Figures in this research were drawn with GraphPad Prism 8.0 software (GraphPad Prism, San Diego, CA, United States). The results were shown as mean ± standard deviation (SD). Significant differences between normally distributed gene expression data were determined by one-way analysis of variance (ANOVA). The significant difference was set at p < 0.05.

Results

Differential Components Between MCG and GG Based on Untargeted Metabolomics

Multi-batch HDMSE data were processed by Progenesis QI, and generated a list comprising 1,023 ions (Supplementary Table S2). These ions were filtered based on “80% rule” and “30% variation” (Supplementary Table S3). Then, the remained 954 ions were subjected to PCA and OPLS-DA. MCG and GG groups were separated well in PCA score Plots (Figure1A), indicating the great difference in chemical profiles. To obtain better discrimination, they were subjected to OPLS-DA with R2X at 0.865 and Q2 at 0.897, especially in the component P1+3 direction (Figure 1B). The validity and predictability of OPLS-DA model was evaluated by 200-time permutation test (Figure 1C). The regression lines of R2 and Q2 decreased with decreased in the correlation coefficients between permuted and original response variables. The result indicated that the predictive OPLS-DA models did not overfit. S-plots were obtained to show responsibility of each ion for these variations more showed intuitively the contribution of differential ions on distinguishing between MCG and GG. Most of the ions clustered around the original point (Figure 1D), and only a few of them were scattered in the margin region. Just these few ions that representing chemical constituents contributed to the separation observed in the score plots. The ions that far away from the original point, always have higher value of VIP (Supplementary Figure S1). Therefore, 42 ions with VIP >1.5 and p < 0.05 (in the Student’s t-test) were selected as potential quality markers (Supplementary Table S4), 31 (73.8%) of which were higher in MG, and 11 (26.2%) were higher in GG. Nine representative compounds were illustrated in Figures 2A–I.

FIGURE 1.

FIGURE 1

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The untargeted metabolomics analysis based on multivariate statistical analysis of MCG and GG. (A): score plot of PCA; (B) score plot of OPLS-DA; (C) validation plots obtained from 200 permutation tests for the OPLS-DA models; (D) score plot of S-Plot.

FIGURE 2.

FIGURE 2

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Box charts illustrating the distribution difference of the nine marker compounds among MCG and GG. (A–F) can be the characteristic components of MCG. (G–I) can be the characteristic components of GG.

Identification of the Potential Quality Markers

Sixty-six standard compounds and our house library were helped to identify the 23 potential quality markers. The base peak intensity chromatograms of MCG and GG corresponding to negative ion mode were shown in Figure 3. The 23 identified components with numbers on the peaks were classified as protopanaxadiol (PPD)-type sapogenins (M1, M3, M5, M6, M8, M11, M13, M17, M19, M20, M23, M24, M32, M33, M34, M42), Protopanaxatriol (PPT)-type sapogenins (M9, M12, M27, M28, M31) and carbohydrate compounds (M7, M15). The detailed information of the 23 components was listed in Table 1.

FIGURE 3.

FIGURE 3

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UHPLC/IM-QTOF-HDMSE of MCG and GG in the negative ion modes. MCG: the root and rhizome of Mountain-Cultivated Panax ginseng C. A. Mey; GG: the root and rhizome of Garden Panax ginseng C. A. Mey. (CG).

TABLE 1.

Information of ginsenoside markers for differentiating among MCG and GG.

No VIP tR (min) Measured value (m/z) Theoretical value (m/z) Error (ppm) Formula Adducts ESI-MS2 Compound name References
M1s 7.60 11.25 1,255.6319 1,255.6328 −0.7167 C58H98O26 +HCOO 1,209.6271,1077.5858 Ginsenoside Ra2 Zuo et al. (2019)
945.5293,783.4898
621.4375,459.3847
375.2879
M3 5.53 13.92 1,295.6267 1,295.6278 −0.8490 C61H100O29 −H 1,251.6307,1209.6275 Malonyl-ginsenoside Ra1 Yang et al. (2016a)
1,077.5819,945.5435
783.4900,621.4377
459.382,375.2831
M5 4.23 12.00 1,295.6265 1,295.6278 −1.0034 C61H100O29 −H 1,251.6338,1209.6275 Malonyl-ginsenoside Ra2 Yang et al. (2016a)
1,077.5776,945.5433
783.4934,621.4313
459.3848,375.2904
M6s 4.11 18.07 991.5473 991.5483 −1.0085 C48H82O18 +HCOO 945.5399,783.4903 Notoginsenoside K Zuo et al. (2019)
621.4348,459.4850
375.2907
M7 4.11 0.78 341.1085 341.108 0.000 C12H22O11 −H — Sucrose or maltose —
M8 3.84 16.31 1,295.6282 1,295.6278 0.3087 C61H100O29 +HCOO 1,209.6230,1077.5819 PPD-20-GlcXylXyl-3-GlcGlc-malonyl Yang et al. (2012)
945.5395,783.4899
621.4408,459.3875
375.2977
M9s 3.19 9.54 845.4896 845.4904 −0.9462 C42H72O14 +HCOO 799.4859,637.4303 Ginsenoside Rg1 Li J. et al. (2019)
475.3788,391.2841
M11 3.01 9.91 1,325.6375 1,325.6378 −0.2263 C62H102O30 −H 1,107.5964,1077.5855 Malonyl-ginsenoside Ra3 or isomer Yang et al. (2016a), Li J. et al. (2019)
945.5390,783.4896
621.4342,459.3928
375.2854
M12s 3.00 9.62 716.3360 716.3367 −0.9772 C63H106O30 +2HCOO 1,341.7149,1209.6689,1077.6205,945.5751 Notoginsenoside S or isomer —
783.5146, 637.4337
M13 2.98 12.35 1,325.6376 1,325.6378 −0.1509 C62H102O30 −H 1,107.5927,1077.5818 Malonyl-ginsenoside Ra3 or isomer Yang et al. (2016a), Li J. et al. (2019)
945.5395,783.4899
621.4408,459.3821
375.2929
M15 2.97 1.35 341.1084 341.1085 −0.2932 C12H22O11 −H — Sucrose or maltose —
M17 2.76 18.66 1,031.5434 1,031.5433 0.0969 C51H84O21 −H 945.5440,783.4870 Malonyl-floralginsenoside Rd Zuo et al. (2019)
621.4381,459.3851
375.2907
M19 2.55 19.03 1,117.5429 1,117.5432 −0.2684 C54H86O24 −H 998.4552,945.5363 Malonyl-floralginsenoside Rd6 or isomer Zhang et al. (2019)
927.5302,783.4798
621.4394,459.3851
M20 2.34 19.17 1,033.5563 1,033.5583 −1.9351 C50H84O19 +HCOO 987.5576,945.5401 PPD-3GLc-ace Zhang et al. (2019)
783.4869,621.4349
459.3851,376.9835
M23 2.16 9.08 731.3414 731.3420 −0.8204 C64H108O31 +2HCOO 621.4376,459.3874 Notoginsenoside T or isomer —
375.2953
M24s 2.11 11.61 1,239.6367 1,239.6380 −1.0487 C59H100O27 −H 1,107.5968,1077.5858 Ginsenoside Ra3 Yang et al. (2013)
945.5432,783.4898
621.4376,459.3847
375.2,929
M27s 1.93 6.90 887.4990 887.5010 −2.2535 C44H74O15 +HCOO 841.4941,781.4711 Notoginsenoside Rt
637.4310,619.4188
475.3793,391.2845
M28 1.91 10.49 887.4999 887.5010 −1.2394 C44H74O15 +HCOO 841.4971,781.4917 Acetyl panajaponol A Wang et al. (2016)
637.4240,619.4183
475.3789,391.2841
M31s 1.87 6.81 1,031.5432 1,031.5433 −0.0969 C51H84O21 −H 799.4758,637.4341 Malonyl floralginsenoside Re1 Zuo et al. (2019)
475.3792,391.2869
M32 1.78 16.74 1,295.6266 1,295.6278 −0.9262 C61H100O29 −H 1,107.5887,1077.5786 PPD-20-GlcXylXyl-3-GlcGlc-malonyl Yang et al. (2012)
945.5435,783.4830
621.4377,459.3794
375.2905
M33s 1.77 13.12 1,255.6302 1,255.6328 −2.0707 C58H98O26 +HCOO 1,209.6275,1077.5862 Ginsenoside Ra1 Zuo et al. (2019)
945.5396,783.4865
621.4377
M34s 1.73 18.82 1,031.5434 1,031.5433 0.0969 C51H84O21 −H 1,077.5992,945.5440 Malonyl-floralginsenoside Rd5 Zuo et al. (2019)
783.4869,621.4381
459.3851,375.2907
M42 1.51 7.38 971.4854 971.4857 −0.3088 C48H76O20 −H 799.4721,637.4405 (US-5)-glc-glurA-glc Yang et al. (2012)
475.3847,391.2894
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s, Ginsenosides identified by comparing with reference standards.

Characteristic MS/MS features of the ginsenoside Ra2 were observed in the MS2 spectrum, including m/z 1209.6271 ([M-H]−), 1077.5858 ([M-H-Xyl]−), 945.5432 ([M-H-Xyl-Ara]−), 783.4898 ([M-H-Xyl-Ara-Glc]−), 621.4375 ([M-H-Xyl-Ara-2Glc]−), and 459.3874 ([PPD-H]−) (Figure 4). M1 (ginsenoside Ra2, tR, 11.25 min, chemical formula, C58H98O26), M6 (notoginsenoside K, tR, 18.07 min, chemical formula, C48H82O18), M24 (ginsenoside Ra3, tR, 11.61 min, chemical formula, C59H100O27), M33 (ginsenoside Ra1, tR, 13.12 min, chemical formula, C58H98O26), M34 (malonyl-floralginsenoside Rd5, tR, 18.82 min, chemical formula, C51H84O21) were identified as PPD-type sapogenins compared with the reference compounds (Supplementary Table S1). Taking ginsenoside Ra2 as an example, the fragmentation pattern of panaxadiol saponins was analyzed.

FIGURE 4.

FIGURE 4

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Structural elucidation of ginsenoside Ra2 and Rg1 based on the negative CID-MS2 data.

Taking ginsenoside Rg1 as an example, the fragmentation pattern of panaxatriol saponins was analyzed. Characteristic MS/MS features of ginsenoside Rg1 were observed in the MS2 spectrum, including m/z 799.4859 ([M-H]−), 637.4334 ([M-H-Glc]−), 475.3788 ([M-H-2Glc]−), and 391.2841 ([M-H-2Glc-C6H12]−) (Figure 4). M9 (ginsenoside Rg1, tR, 9.54 min, chemical formula, C42H72O14), M12 (notoginsenoside S, tR, 9.62 min, chemical formula, C63H106O30), M27 (notoginsenoside Rt, tR, 6.90 min, chemical formula, C44H74O15), M31 (malonyl-floralginsenoside Re1, tR, 6.81 min, chemical formula, C51H84O21) were identified as PPT-type sapogenins compared with the reference compounds (Supplementary Table S1).

Functional Analysis of Potential Quality Markers

A total of 105 putative targets were predicated based on 23 primarily identified compounds (Tanimoto score ≥0.7) using TTFM of TCMIP v2.0 (Supplementary Table S5). Which were mainly involved in 30 pathways after functional analysis DAVID 6.8 (Supplementary Table S6). The network of interactions between 23 components, the corresponding 105 putative targets and 30 pathways was visualized using Cytoscape v3.7.1 (Boston, MA, United States) as show in Figure 5. The 30 pathways were related to 10 function modules, such as metabolic system, nervous system, cardiovascular system, immune-regulation system and reproductive system. Among them, there were 12 immune pathways (fc epsilon RI signaling pathway; leukocyte transendothelial migration; non-small cell lung cancer; pathways in cancer; acute myeloid leukemia; toll-like receptor signaling pathway; small cell lung cancer; apoptosis; prostate cancer; natural killer cell mediated cytotoxicity; glioma; mammalian target of rapamycin signaling pathway (mTOR) signaling pathway), nine metabolic pathways (starch and sucrose metabolism; oxidative phosphorylation; type II diabetes mellitus (DM); insulin signaling pathway; galactose metabolism; tricarboxylic acid cycle (TCA cycle); glycolysis/gluconeogenesis; fructose and mannose metabolism; amino sugar and nucleotide sugar metabolism), four nervous system pathways (Alzheimer’s disease; Parkinson’s disease; Huntington’s disease; erbB signaling pathway), there cardiovascular system pathways (cardiac muscle contraction; contraction of vascular smooth muscle; aldosterone-regulated sodium reabsorption) and two reproductive-system pathways (progesterone-mediated oocyte maturation; Oocyte meiosis).

FIGURE 5.

FIGURE 5

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Illustration of the relevance among marker compounds for distinguishing MCG and GG, their target, pathways and therapeutic effects. Blue nodes represent characteristic constituents of MCG. Green nodes represent characteristic constituents of GG. Orange nodes refer to the targets; Purple nodes represent the pathways; Yellow nodes refer to therapeutic effects. Red nodes represent the effect of ginseng.

Inhibitory Effect of MCG and GG on MCF-7 Cell Line by Regulating the PSA, MDM2, P53, and S6K genes

The enrichment analysis above showed that 23 components played more importment roles in tumor pathway, which indicated that the anticancer effect of MCG was stronger than GG. Therefore, we selected MCF-7 cell line to verify the results. The CCK-8 assay showed that MCG or GG did not significantly inhibit the proliferation of MCF-7 cell line when the concentration was lower than 4 mg/ml. When the concentration of MCG or GG was 8 mg/ml, the survival rate of MCG group was only 11.8%, and GG group was 21.99%, which indicated that the concentration of 4–8 mg/ml was the range of MCG and GG to inhibit the proliferation of MCF-7 cell line (Supplementary Figure S2). Thus, the concentrations of 4, 5, 6, 7, and 8 mg/ml were set to explore the inhibitory effect of MCG and GG on MCF-7 cell line. When the extract concentration was 4 mg/ml, the survival rate of MCF-7 cell line was significantly inhibited by MCG compared with GG (Figure 6A).

FIGURE 6.

FIGURE 6

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In vitro pharmacodynamics experiments and RT-PCR detection of key effector genes in MCG and GG. (A): Results of in vitro pharmacodynamics experiments of MCG and GG. (B–F): Results of RT-PCR detection of key effector genes in MCG and GG. “*”: p < 0.05; “**”: p < 0.01; “***”: p < 0.001; “****”: p < 0.0001. (F):Mechanism of anti-breast cancer effect of quality markers of MCG.

Therefore, MG and GG at 4 mg/ml were selected to subject to RT-PCR detection. The mRNA expression of PSA, MDM2 and S6K genes in the group of MCG was significantly down-regulated compared with that of control group, and the degree of down regulation was stronger than that of GG. Conversely, the mRNA expression of P53 gene was significantly up-regulated, and the degree stronger than that of GG (Figures 6B–E).

Discussion

PGCAM as a herbal nutritional supplement is well known for its extensive pharmacological effects (Nguyen and Nguyen, 2019). MCG and GG are the main PGCAM varieties on the market to meet the huge demand of people (Yang et al., 2013) (Shen et al., 2019). At present, ginsenoside is the main marker for identification of different species of MCG and GG. For example, ginsenosides can be used to distinguish different planting methods and different years of ginseng (Cui et al., 2013; Ruan et al., 2018). As natural plants, the environment and duration of growth affect the quality and price of PGCAM directly. (Xu et al., 2016; Zhu et al., 2018). Many studies have been undertaken to characterize the chemical profiles of PGCAM cultivated in different environments for various durations, which could help distinguish diverse samples (Chang et al., 2016; Chang et al., 2017). Hence, we aimed to employ integrative pharmacology strategy to characterize the differential components between the two types of Ginseng and explore their pharmacological effects by network pharmacology and verified in vitro.

Altogether, 23 chemical components were identified to distinguish the two sets of samples, and nine of them were identified according to use of reference compounds. The characteristic fragments of PPD-saponins were m/z 459.38 and m/z 375.29. These data could be used to identify unknown marker compounds belonging to this type, including M14, M16, M18, M26, M29, M30, M36, M39, and M41. Ginsenosides were the main characteristic components of the MCG group. Carbohydrates were the main characteristic components of the GG group. The peak area of PPD-type saponins in the MCG group was higher than that of GG group, and the peak area of carbohydrate compounds was higher in the GG group. The results showed that ginsenoside Ra2 and sucrose could not only distinguish 15 years old MCG and GG, but also 10–20 years old MCG and 4–6 years old GG (Xu et al., 2016). Ginsenoside Rg1 can not only distinguish the 15-year-old forest ginseng and garden ginseng, but also distinguish the ginseng of different ages, different planting sites and different slope directions (Zhu et al., 2021). In conclusion, some of the quality markers in the same region can distinguish ginseng under different conditions. It shows that our research results have certain universality.

To explore further the pharmacological activities of these marker components, components–targets–pathways network was constructed. We found that the marker components could regulate immune, metabolic, nervous–system, cardiovascular–system and reproductive–system pathways. Among them, immunomodulatory and metabolic regulatory pathways were the main pathways regulated by marker components during the treatment of cancer and diabetes mellitus.

The pharmacological effects of marker components were investigated further. Ginsenoside Rg1 has been found to: 1) protect the heart from cardiovascular diseases (Xu et al., 2018); 2) impact the neuroendocrine system for treatment of depression (Mou et al., 2017); 3) inhibit inflammation and apoptosis (Guo et al., 2019); 4) possess neuroprotective properties (Li G. et al., 2019); 5) enhance gene expression and oxidative muscle metabolism in muscles (Jeong et al., 2019); 6) protect against diabetic nephropathy (DN) by reducing oxidative stress (Du et al., 2018). Hence, Rg1 can regulate the cardiovascular system, nervous system and immune system. Sucrose can reduce procedural pain from single events, including heel lancing, venipuncture and intramuscular injection, which shows that sucrose can regulate the nervous system without side effects. (Stevens et al., 2016). Ginsenoside Ra1 is the main active component of ginseng used for immune regulation (Liu et al., 2019). Hence, ginsenosides Ra1 can affect the cardiovascular system and regulate the immune system. Notoginsenoside K has been shown to exhibit immunologic-adjuvant activities on the humoral immune responses of ICR mice against ovalbumin (Sun et al., 2005; Qin et al., 2006). Ginsenosides Ra1, notoginsenoside K, and ginsenoside Rg1 can affect the cardiovascular system, immune regulation, nervous system, and have anti-inflammatory and antioxidant effects. Sucrose has a protective effect upon HepG2 cells and fibroblasts (Sim et al., 2020), which suggests that sucrose has an antioxidant effect. Maltose can enhance the anti-melanogenic activity (Bin et al., 2017). The results showed that the pharmacological activities of potential quality markers were consistent with those reported in the literature. Seven malonylginsenosides (malonyl-ginsenoside Ra1, malonyl-ginsenoside Ra2, malonyl-ginsenoside Ra3, malonyl-floralginsenoside Rd, malonyl-floralginsenoside Rd6, malonyl-floralginsenoside Re1 and malonyl-floralginsenoside Rd5) were identified as the PPD-type markers of MCG and GG. However, reports on the pharmacologic effects and mechanism of action of malonyl saponins are lacking, which needed further study.

Network pharmacology pathway enrichment showed that the cancer pathways enriched by quality markers were more abundant. Therefore, we used MCF-7 cell line to evaluate the anticancer effect of MCG and GG. The results showed that the anticancer effect of MCG was better than GG, which was consistent with the published data (Kim et al., 2020). The main genes involve in anticancer effect of MCG and GG are PIK3CA, PIK3CG, MTOR and AR, which are also the key genes of PI3K/Akt/mTOR pathway. Among them, the quality markers acting on PIK3CA, PIK3CG and AR were malonyl -ginsenoside Ra3, malonyl-floralginsenoside Rd, malonyl -ginsenoside Ra1, malonyl-floralginsenoside Rd6, malonyl -ginsenoside Ra2, malonyl-floralginsenoside Re1, malonyl-floralginsenoside Rd5, notoginsenoside Rt, notoginsenoside S, notoginsenoside T, notoginsenoside K, ginsenoside Ra1, ginsenoside Ra2, ginsenoside Ra3, Ginsenoside Rg1. The potential quality markers acting on MTOR were malonyl -ginsenoside Ra3, malonyl-floralginsenoside Rd, malonyl -ginsenoside Ra1, malonyl-floralginsenoside Rd6, malonyl -ginsenoside Ra2, malonyl-floralginsenoside Re1, malonyl-floralginsenoside Rd5, which were common quality markers of PIK3CA, PIK3CG, MTOR. Except Notoginsenoside Rt, the content of the remaining 14 quality markers in MCG was higher than GG. In conclusion, the 14 quality markers of MCG play a better role in anticancer by acting on PI3K/Akt/mTOR pathway while the data of mRNA expression of PIK3CA, PIK3CG, MTOR, AR in the MCF-7 cell samples treated by MCG and GG showed that there was no significant difference between ginseng treatment group and control group. However, we found that downstream genes of the PI3K pathway play an important role in cancer therapy through literature research. In addition, MDM2 has been shown to be abnormally upregulated due to gene amplification, increased transcription, and enhanced translation, leading to enhanced degradation and reduction of P53 activity in some human tumors (Konopleva et al., 2020)and therefore, many drugs/compounds have been developed for reactivation of P53 gene by inhibiting MDM2 interaction with P53 in order to treat cancer (Gupta et al., 2019). Moreover, MTOR pathway plays an important role in the treatment of human cancer. mTORC1 mediates its function via its downstream targets 40 S ribosomal S6 kinases (S6K) (Sridharan and Basu, 2020). Prostate specific antigen (PSA) is a serine protease produced by prostate epithelial cells and prostate cancer (PCA), which can be regulated by AR. The increase of PSA level can be used as a diagnostic marker of tumor or cancer (Balk et al., 2003). The RT-PCR data showed that mRNA expression of MDM2, S6K, PSA were significantly decreased, and the mRNA expression of P53 gene was increased in MCG compared with GG. In summary, the results show that the quality markers of MCG can reduce the survival rate of breast cancer cells better by acting on PIK3CG, PIK3CA, mTOR, AR genes and co-regulating the effector genes MDM2, P53, PSA, S6K (Figure 6F).

Conclusion

We characterized, for the first time, 23 chemical components to distinguish 15-year-old MCG and GG, which corresponding to 105 putative targets and 30 pathways. In addition, the 23 components inhibit the proliferation of breast cancer (MCF-7 cells) by regulating the mRNA expression of PSA, S6K, MDM2 and P53, in which MG exhibited stronger effects than that of GG. Integrative Pharmacology Strategy undoubtedly provide an efficient way to identify chemical constituents and explore the pharmacological actions of TCM formulations.

Acknowledgments

We thank JZ in Liaoning Shangyao Good Nurse Pharmaceutical Co., Ltd. for his help on the suppling the 15 years of MCG and GG samples.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Author Contributions

HX and LH designed the study. SL and PW drafted the manuscript. SL and WY analyzed the chemical constituents in PGCAM. SL analyzed of network pharmacology. SL, CZ, and LZ carried out anticancer pharmacodynamics experiment and RT-PCR detection. JZ and YQ established the UNIFI database of PGCAM. The other authors participated in the design of the study and performed the statistical analysis All authors read and approved the final version of the manuscript.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC1702303, 2017YFC1702104), National Natural Science Foundation of China (81830111, 81774201), National Science and Technology Major Project of China (2019ZX09201005–001–003), the Youth Innovation Team of Shaanxi Universities and Shaanxi Provincial Science and Technology Department Project (2016SF-378), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (ZXKT17058). These funding agencies had no role in the study design, collection, analyses, or interpretation of data, writing of the report, or the decision to submit the manuscript for publication.

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.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2021.659954/full#supplementary-material.

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Associated Data

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Supplementary Materials

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Data Availability Statement

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