Non-Targeted Metabolomic Analysis of Methanolic Extracts of Wild-Simulated and Field-Grown American Ginseng

Aiming at revealing the structural diversity of secondary metabolites and the different patterns in wild-simulated American ginseng (WsAG) and field-grown American ginseng (FgAG), a comprehensive and unique phytochemical profile study was carried out. In the screening analysis, a total of 121 shared compounds were characterized in FgAG and WsAG, respectively. The results showed that both of these two kinds of American ginseng were rich in natural components, and were similar in terms of the kinds of compound they contained. Furthermore, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a difference between FgAG and WsAG, and 22 robust known biomarkers enabling the differentiation were discovered. For WsAG, there were 12 potential biomarkers including two ocotillol-type saponins, two steroids, six damarane-type saponins, one oleanane-type saponins and one other compound. On the other hand, for FgAG, there were 10 potential biomarkers including two organic acids, six damarane-type saponins, one oleanane-type saponin, and one ursane. In a word, this study illustrated the similarities and differences between FgAG and WsAG, and provides a basis for explaining the effect of different growth environments on secondary metabolites.


Introduction
American ginseng (Panax quinquefolius L.) is grouped into four categories: wild, wild-simulated, woods-grown, and field-grown [1,2]. The herb growing in its native habitat is called wild American ginseng. Wild-simulated American ginseng (WsAG) refers to a method of growing ginseng in a hardwood forest environment under natural conditions without any other human intervention [2][3][4]. As such, WsAG roots are indeed indistinguishable from the wild roots due to the similar characteristics. When the seeds are planted in hardwood forests, and are grown in prepared rows or beds, or with removed ground vegetation or fertilizer and pesticides being available [5][6][7], it is called as wood-grown ginseng. This variety of American ginseng requires 6 to 9 years to mature before harvesting [8]. Different from wild-simulated category, the quality of wood-grown ginseng is between that of the wild and field-grown categories. That means, wood-grown American ginseng cannot be considered a substitute of the wild one. Field-grown American ginseng (FgAG), also called cultivated American ginseng, is intensely cultivated under artificial shade structures using fertilizers and pesticides [4,9].  FgAG-ESI +   In FgAG and WsAG, these compounds were all shared constituents, including 47 protopanaxdiol-type saponins, 23 protopanaxtriol-type saponins, 15 oleanane-type saponins, 10 ocotillol-type saponins, one ursane, one flavonoid, one lignin, 12 organic acids and organic acid esters, three steroids, and eight other compounds.

Biomarker Discovery for FgAG and WsAG
The QC injections were clustered tightly in PCA indicating a satisfactory stability of the system. The PCA 2D plots of the samples from FgAG and WsAG groups were classified into two clusters according to their common spectral characteristics (Figure 3), with the FgAG samples of different years clustered into one group, while the WsAG samples were clustered into another group. The FgAG and WsAG samples were clearly separated, indicating that these two herb species could be easily differentiated.  In FgAG and WsAG, these compounds were all shared constituents, including 47 protopanaxdiol-type saponins, 23 protopanaxtriol-type saponins, 15 oleanane-type saponins, 10 ocotillol-type saponins, one ursane, one flavonoid, one lignin, 12 organic acids and organic acid esters, three steroids, and eight other compounds.

Biomarker Discovery for FgAG and WsAG
The QC injections were clustered tightly in PCA indicating a satisfactory stability of the system. The PCA 2D plots of the samples from FgAG and WsAG groups were classified into two clusters according to their common spectral characteristics (Figure 3), with the FgAG samples of different years clustered into one group, while the WsAG samples were clustered into another group. The FgAG and WsAG samples were clearly separated, indicating that these two herb species could be easily differentiated. In FgAG and WsAG, these compounds were all shared constituents, including 47 protopanaxdiol-type saponins, 23 protopanaxtriol-type saponins, 15 oleanane-type saponins, 10 ocotillol-type saponins, one ursane, one flavonoid, one lignin, 12 organic acids and organic acid esters, three steroids, and eight other compounds.

Biomarker Discovery for FgAG and WsAG
The QC injections were clustered tightly in PCA indicating a satisfactory stability of the system. The PCA 2D plots of the samples from FgAG and WsAG groups were classified into two clusters according to their common spectral characteristics (Figure 3), with the FgAG samples of different years clustered into one group, while the WsAG samples were clustered into another group. The FgAG and WsAG samples were clearly separated, indicating that these two herb species could be easily differentiated.  After OPLS-DA plots (Figures 4A and 5A) in both negative and positive modes were generated, the maximum separation between MsAG and FgAG groups was available. In the sufficient permutation test, the lines of grouping samples were significantly located underneath the random sampling lines ( Figures 4B and 5B), which indicated a fine validity for the following characteristic metabolites biomarkers identification [42]. S-plots were then created to explore the potential chemical markers that contributed to the differences. Based on p values (p < 0.05) and VIP values (VIP > 3) [30,61] from univariate statistical analysis, 22 robust known biomarkers enabling the differentiation between FgAG and WsAG, were marked and listed ( Figures 4C and 5C and Table 2). Additionally, a heatmap was generated from these biomarkers in order to systematically evaluate the biomarkers (Figure 6), which visually showed the intensities of potential biomarkers between two species.
After OPLS-DA plots (Figures 4A and 5A) in both negative and positive modes were generated, the maximum separation between MsAG and FgAG groups was available. In the sufficient permutation test, the lines of grouping samples were significantly located underneath the random sampling lines (Figures 4B and 5B), which indicated a fine validity for the following characteristic metabolites biomarkers identification [42]. S-plots were then created to explore the potential chemical markers that contributed to the differences. Based on p values (p < 0.05) and VIP values (VIP > 3) [30,61] from univariate statistical analysis, 22 robust known biomarkers enabling the differentiation between FgAG and WsAG, were marked and listed ( Figures 4C and 5C and Table 2). Additionally, a heatmap was generated from these biomarkers in order to systematically evaluate the biomarkers (Figure 6), which visually showed the intensities of potential biomarkers between two species.

Discussion
In the screening analysis, 121 compounds were characterized in FgAG and WsAG, respectively. The results showed that both of these kinds of American ginseng were rich in natural components. These 121 compounds were all shared constituents in FgAG and WsAG, which means that they were similar in terms of the kinds of compound they contained. It has been reported that there are high ginsenoside contents in American ginseng. In this study, ginsenosides were also the main chemical components. Besides the most common dammarane-type and oleanane-type saponins, the ocotilloltype saponins are also occupying a notable proportion. The ocotillol-type is the characteristic type of saponin enabling American ginseng to be differentiated from Asian ginseng. So far, the studies on the mechanism of biosynthesis were focused on dammarane-type and oleanane-type ginsenosides. For example, dammaranediol was obtained by DS (dammarenediol synthase), and then modified by CYP450 to obtain dammarane-type saponins. Another example, oleanane-type ginsenosides were obtained by modifing β-amyrin with CYP450 and UGT (UDP-glycosyltransferase). Actually, there were little literature about the mechanism of ocotillol-type ginsenoside biosynthesis. The

Discussion
In the screening analysis, 121 compounds were characterized in FgAG and WsAG, respectively. The results showed that both of these kinds of American ginseng were rich in natural components. These 121 compounds were all shared constituents in FgAG and WsAG, which means that they were similar in terms of the kinds of compound they contained. It has been reported that there are high ginsenoside contents in American ginseng. In this study, ginsenosides were also the main chemical components. Besides the most common dammarane-type and oleanane-type saponins, the ocotilloltype saponins are also occupying a notable proportion. The ocotillol-type is the characteristic type of saponin enabling American ginseng to be differentiated from Asian ginseng. So far, the studies on the mechanism of biosynthesis were focused on dammarane-type and oleanane-type ginsenosides. For example, dammaranediol was obtained by DS (dammarenediol synthase), and then modified by CYP450 to obtain dammarane-type saponins. Another example, oleanane-type ginsenosides were obtained by modifing β-amyrin with CYP450 and UGT (UDP-glycosyltransferase). Actually, there were little literature about the mechanism of ocotillol-type ginsenoside biosynthesis. The

Discussion
In the screening analysis, 121 compounds were characterized in FgAG and WsAG, respectively. The results showed that both of these kinds of American ginseng were rich in natural components. These 121 compounds were all shared constituents in FgAG and WsAG, which means that they were similar in terms of the kinds of compound they contained. It has been reported that there are high ginsenoside contents in American ginseng. In this study, ginsenosides were also the main chemical components. Besides the most common dammarane-type and oleanane-type saponins, the ocotillol-type saponins are also occupying a notable proportion. The ocotillol-type is the characteristic type of saponin enabling American ginseng to be differentiated from Asian ginseng. So far, the studies on the mechanism of biosynthesis were focused on dammarane-type and oleanane-type ginsenosides. For example, dammaranediol was obtained by DS (dammarenediol synthase), and then modified by CYP450 to obtain dammarane-type saponins. Another example, oleanane-type ginsenosides were obtained by modifing β-amyrin with CYP450 and UGT (UDP-glycosyltransferase). Actually, there were little literature about the mechanism of ocotillol-type ginsenoside biosynthesis. The phytochemicals in WsAG and FgAG might provide a material basis for mechanistic studies. In short, this comprehensive and unique phytochemical profile study revealed the structural diversity of secondary metabolites and the similar patterns in FgAG and WsAG.
Furthermore, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a few differences between FgAG and WsAG, and 22 robust known biomarkers enabling the differentiation were discovered. This study illustrated the differences between FgAG and WsAG, and provided a basis for explaining the effect of different growth environments on secondary metabolites. For WsAG, there are 12 potential biomarkers, including two ocotillol-type saponins (12,47), two steroids (22,117), six damarane-type saponins (21, 35, 37, 42, 44, 112), one oleanane-type saponin (102) and one other compound (6). The contents of these 12 components in WsAG were much greater than in FgAG. On the other hand, for FgAG, there are 10 potential biomarkers including two organic acids (105, 115), six damarane-type saponins (13, 14, 31, 46, 81, 91, 99), one oleanane-type saponin (46), and one ursane (70), which contents in FgAG were much greater than in WsAG. It has been reported that wild American ginseng has better biological activity than the FgAG. As is known, biological activity is caused by the high contents of phytochemicals. Correlation studies between potential markers and biological activities could be performed in the future.
Even so, there are still several unresolved issues. For example, as shown in BPI chromatograms, though 121 compounds were identified, there are still some unidentified components. Further research should be carried on based on the formula of these unknown compounds.

Materials and Reagents
Twenty eight batches of commercially available FgAGs and WsAGs root products were collected or purchased from different cultivation areas in China and American, including 20 batches of FgAGs and eight batches of WsAGs. A detailed sample list is provided in Table 2.
For FgAGs, six roots of each sample were selected for analysis, while for WsAGs, 2-3 roots of each sample were analyzed. All the herbs were authenticated by the authors and the corresponding voucher specimens have been deposited in the Research Center of Natural Drug, School of Pharmaceutical Sciences, Jilin University, China.
Acetonitrile and methanol suitable for UPLC-MS were purchased from Fisher Chemical Company (Geel, Belgium). Formic acid was purchased from Sigma-Aldrich Company (St. Louis, MO, USA). Deionized water was purified using a Millipore water purification system (Millipore, Billerica, MA, USA). All other chemicals were of analytical grade.

Sample Preparation and Extraction
All samples were respectively air-dried, grinded and sieved (Chinese National Standard Sieve No. 3, R40/3 series) to get a homogeneous powder. Then each fine powder was accurately weighed (0.2 g) and soaked with 10 mL of methanol overnight. On the second day, each powder was extracted in an ultrasonic bath (power of 250 W, frequency of 40 kHz) for half an hour. After cooling to room temperature, the lost weight was replenished with methanol. After filtering through a syringe filter (0.22 µm), the extracts were injected directly into the UPLC system. In addition, to ensure the stability and suitability consistency of MS analysis, a quality control (QC) sample was prepared by pooling the same volume (50 µL) from every sample and five QC injections were performed randomly through the whole worklist. The volumes injected for samples and QC were all 5 µL for each run.

UPLC-QTOF-MS
UPLC-QTOF-MS E analysis was performed on a Waters Xevo G2-XS QTOF mass spectrometer (Waters Co., Milford, MA, USA) equipped with a UPLC system through an electrospray ionization (ESI) interface. Chromatographic separation was performed on an ACQUITY UPLC BEH C 18 (100 mm × 2.1 mm, 1.7 µm) from Waters Corporation (Milford, MA, USA). The mobile phases were composed of eluent A (0.1% formic acid in water, v/v) and eluent B (0.1% formic acid in acetonitrile, v/v) with flow rate of 0.4 mL/min. The elution conditions applied were: 0→2 min, 10% B; 2→26 min, 10~100% B; 26→29 min, 100% B; 29→29.1 min, 100~10% B; 29.1→32 min, 10% B. The temperature of the autosampler and the UPLC column manager were set at 15 • C and 30 • C respectively. Mixtures of 90/10 and 10/90 water/acetonitrile were used as the weak wash solvent and the strong wash solvent respectively. The mass spectrum was acquired from 100 to 1500 Da in MS E mode. The positive mode conditions were: capillary voltage, 2.6 kV; cone voltage, 40 V; source temperature, 150 • C; desolvation temperature, 400 • C; cone gas flow, 50 L/h; desolvation gas flow, 800 L/h. Negative mode conditions were identical to the positive mode conditions except for capillary voltage (2.2 kV). In MS E mode, data acquisition was performed via the mass spectrometer by rapidly switching from a low-collision energy (CE) scan to a high-CE scan during a single LC run. The low energy function was set to 6 V, while ramp collision energy of high energy function was set to 20~40 V. Leucine enkephalin (m/z 556.2771 in positive mode and 554.2615 in negative mode) was used as external reference of Lock Spray™ infused at a constant flow of 10 µL/min. During acquisition, data were collected in continumn mode for the screening analysis, and in centroid mode for the metabolomics analysis.

Chemical Information Database for the Components of FgAG and WsAG
Besides the in-house Traditional Medicine Library in the UNIFI software, a systematic investigation of chemical components was conducted [34]. A self-built database of compounds that were isolated from FgAG and WsAG was established by searching online databases or internet search engines such as PubMed, Full-Text Database (CNKI), ChemSpider, Web of Science and Medline. Chemical information including the component name, molecular formula and structure of the components from the herbs were obtained from the database [56].

The Screening Analysis by the UNIFI Platform
To quickly identify the chemical compounds, the MS raw data, compressed with Waters Compression and Archival Tool v1.10, was automatically screened and identified by using the streamlined workflow of UNIFI 1.7.0 software (Waters, Manchester, UK) [30]. The parameters were as follows: the minimum peak area of 200 was set for 2D peak detection; the peak intensity of low energy over 1000 counts and the peak intensity of high energy over 200 counts were selected for 3D peak detection. Mass error up to ±5 ppm for identified compounds, retention time in the range of ±0.1 min was allowed to match the reference substance. The matching compounds would be generated predicted fragments from structure. The negative adducts containing +COOH and -H and positive adducts containing +H and +Na were selected in the analysis. Leucine-enkephalin was selected as the reference compound, and [M − H] − 554.2620 was used for negative ion and [M + H] + 556.2766 was used for positive ion [72].

The Metabolomics Analysis
The raw data were processed by MarkerLynx XS V4.1 software (Waters, Milford, CT, USA) for alignment, deconvolution, data reduction, etc. [73]. A MarkerLynx processing method was firstly created, and the main parameters were as follows: retention time range 0-26 min, mass range 100-1500 Da, mass tolerance 0.10, minimum intensity 5%, mass window 0.10, retention time window 0.20, marker intensity threshold 2000 counts and noise elimination level 6. After processing the data, the results could be showed in the Extended Statistics (XS) Viewer. m/z-RT pairs with corresponding intensities for all the detected peaks from each data file were listed. The same value of RT and m/z in different batched of samples were regarded as the same component. Then, multivariate statistical analysis was performed. Firstly, principal component analysis (PCA) was used to show the pattern recognition and maximum variation aiming to obtain the overview and classification. Secondly, orthogonal projections to latent structures discriminant analysis (OPLS-DA) in both positive and negative modes was performed in order to get the maximum separation between MsAG and FgAG group and to explore the potential chemical markers that contributed to the differences. Then, S-plots was created to provide visualization of the OPLS-DA predictive component loading to facilitate model interpretation. Meawhile, variable importance for the projection (VIP) was helpful to screen the different components, and the metabolites with VIP value (>3.0) were considered as potential markers [29]. In addition, the permutation test was performed to provide reference distributions of the R 2 /Q 2 -values that could indicate the statistical significance [30][31][32][33]. Simca 15.0 software (Umetrics, Malmö, Sweden) was used to show the analysis results [56,74].

Conclusions
In a comprehensive and unique phytochemical profile study, a total of 121 chemical compounds with different structural types were identified from WsAG and FgAG. The structural patterns included protopanaxdiol-type saponins, protopanaxtriol-type saponins, ocotillol-type saponins, oleanane-type saponins and other glyosides, organic acid and organic acid esters, steroids, etc. The results showed that WsAG and FgAG were rich in natural components. Furthermore, these 121 compounds were all shared constituents in them, meaning that they were similar in the kinds of compounds they contain. In metabolomic analysis, it was found that there indeed existed several differences in the contents of the constituents between FgAG and WsAG, and 22 robust known biomarkers enabling the differentiation were discovered. In a word, the results will fill the data gap in the study on the chemical constituents of WsAG and provide a reference for quantitative determinations in its quality control.