Nuclear magnetic resonance based metabolomic differentiation of different Astragali Radix

Chinese Journal of Natural Medicines 2017, 15(5): 03630374

Chinese Journal of Natural Medicines

Nuclear magnetic resonance based metabolomic differentiation of different Astragali Radix LI Ai-Ping1, 2Δ, LI Zhen-Yu1Δ*, QU Ting-Li1, 3, QIN Xue-Mei1*, DU Guan-Hua1, 4 1

Modern Research Center for Traditional Chinese Medicine, Shanxi University, Shanxi 030006, China; College of Chemistry and Chemical Engineering, Shanxi University, Shanxi 030006, China; 3 School of Pharmaceutical Science, Shanxi Medical University, Shanxi 030006, China; 4 Institute of Materia Medica, Chinese Academy of Medical Sciences, Beijing 100050, China 2

Available online 20 May, 2017

[ABSTRACT] Astragali Radix (AR) is one of the most popular herbal medicines in traditional Chinese medicine (TCM). Wild AR is believed to be of high quality, and substitution with cultivated AR is frequently encountered in the market. In the present study, two types of ARs (wild and cultivated) from Astragalus membranaceus (Fisch.) Bge. and A. membranaceus var. mongholicus (Bge.) Hsiao, growing in different regions of China, were analyzed by NMR profiling coupled with multivariate analysis. Results showed that both could be differentiated successfully and cultivation patterns or growing years might have greater impact on the metabolite compositions than the variety; the metabolites responsible for the separation were identified. In addition, three extraction methods were compared and the method (M1) was used for further analysis. In M1, the extraction solvent composed of water, methanol, and chloroform in the ratio of 1 : 1 : 2 was used to obtain the aqueous methanol (upper layer) and chloroform (lower layer) fractions, respectively, showing the best separation. The differential metabolites among different methods were also revealed. Moreover, the sucrose/glucose ratio could be used as a simple index to differentiate wild and cultivated AR. Meanwhile, the changes of correlation pattern among the differential metabolites of the two varieties were found. The work demonstrated that NMR-based non-targeted profiling approach, combined with multivariate statistical analysis, can be used as a powerful tool for differentiating AR of different cultivation types or growing years. [KEY WORDS] Nuclear Magnetic Resonance; Chemical profiling; Astragali Radix; Cultivation patterns; Variety; Regions

[CLC Number] R917

[Document code] A

[Article ID] 2095-6975(2017)05-0363-12

Introduction Astragali Radix (AR), also known as Huangqi in China, is dried root of Astragalus membranaceus (Fisch.) Bge. or A. membranaceus var. Mongholicus (Bge.), belonging to the

[Received on] 27-Oct.-2016 [Research funding] This work was supported by the Ministry of Agriculture for providing New Application for Herbal Research Grant Scheme (NRGS) (No. NH1014D040), the National 12th 5-Year Science and Technology Support Program (No. 2011BA107B01), the Science and Technology Innovation Team of Shanxi Province (No. 2013131015), and the National Natural Science Foundation of China (No. 31570346). [*Corresponding authors] Tel (Fax): 86-351-7011501, E-mail: qinxm@ sxu.eud.cn (QIN Xue-Mei); Tel (Fax): 86-351-7018379, E-mail: [email protected] (LI Zhen-Yu). ∆ These authors contributed to this work equally. These authors have no conflict of interest to declare. Published by Elsevier B.V. All rights reserved

Leguminosae family [1]. It has been shown to have immunostimulant, hepatoprotective, tonic, diuretic, antidiabetic expectorant, analgesic, and sedative properties [2-4]. As a traditional folk medicine, it has been used for many therapeutic purposes in Asia, including in China, Korea, Japan, Mongolia and Siberia [5]. In addition to its medicinal use, it is also used in nutraceutical products, including herbal teas, soft drinks, soups, and trail mixes [6-8]. Extensive chemial studies in recent years have revealed that the AR possesses various compounds, including flavonoids, saponins, polysaccharides, and amino acids [9-12]. A. membranaceus (Mojia in Chinese) is mainly distributed in Heilongjiang (HLJ), Shandong (SD) and Sichuan (SC) of China, while A. membranaceus var. Mongholicus (Bge.) (Menggu in Chinese) is distributed mainly in the northern part of China, such as Shanxi (SX), Neimenggu (NM), Gansu (GS), and Shanxxi (SSX). Today, there are two types of growth pattern (cultivated and wild or semi-wild) for both Menggu and Mojia AR. The wild or semi-wild AR is often

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distributed in droughty mountainous areas, and grows more than 5 years before harvest, while the cultivated AR is often cultivated in wet and flat soil, and the growing years are only one year for Mojia AR and two years for Menggu AR. Usually, the wild or semi-wild AR has longer and thicker roots, because of the longer growing years. In the market, AR is graded by the root length, diameter, and physical appearance: the longer and thicker the roots, the higher the quality [13] . Due to the increasing demands of wild AR, the substitution with cultivated AR is frequently encountered in the drug market. According to the Chinese Pharmacopeia, astragaloside IV and calycosin-7-O-β-D-glucoside are used as chemical marker for quality control of AR. However, it is difficult to differentiate cultivated and wild AR by content determination of these marker compounds. Nowadays, metabolic fingerprinting has been widely used as a state-of-the-art technique in medicinal plant research [14]. Some recent studies have shown that the age of ginseng can be successfully discriminated by the metabolic fingerprinting coupled with the multivariate analysis. NMR has some unique advantages in metabolic fingerprinting studies, such as rapidity, non-selectiveness, reproducibility, and stability [15-16]. In addition, detailed structural information of metabolites, including chemical shifts and coupling constants, can be directly obtained. This makes NMR an ideal choice for the profiling of the medicinal plants, such as ginseng [17-18], Tussilago farfara [19], Angelica acutiloba [20], and Artemisia afra [21]. In the present study, 58 AR samples from different cultivation

regions of China were collected and analyzed using 1H NMRbased metabolic profiling approach with various solvents to develop a differentiation method for different ARs.

Materials and Methods Plant materials 58 AR samples with two varieties of Astragalus (A. membranaceus and A. membranaceus var. mongholicus, Mojia Huangqi and Menggu Huangqi in Chinese, respectively) were collected from different locations as shown on the map (Fig. 1) and detailed information are included Table 1. All the

Fig. 1 Geographical growing areas of Astragali Radix

Table 1 List of Astragali Radix plant materials No.

Youer no.

1

HQ-HLJ-1

A membranaceus (Fisch.) Bge.

Astragalus spp.

Growing years over 5

Heilongjiang

Growing locations

Cultivation patterns

2

HQ-HLJ-2

Mojia

over 5

Heilongjiang

wild

-

3

HQ-HLJ-3

1

Heilongjiang, Hulan County

cultivated

-

4

HQ-HLJ-4

over 5

Heilongjiang

wild

Commercial

5

HQ-HLJ-5

over 5

Jiagedaqi

wild

Field

6

HQ-HLJ-5

over 5

Jiagedaqi

wild

Field

7

HQ-SD-1

1

Shandong, Wendeng

cultivated

Commercial

8

HQ-SD-1

1

Shandong, Wendeng

cultivated

Commercial Commercial

wild

Source Field

9

HQ-SD-1

1

Shandong, Wendeng

cultivated

10

HQ-SD-1

1

Shandong, Wendeng

cultivated

Commercial

11

HQ-SD-1

1

Shandong, Wendeng

cultivated

Commercial Commercial

12

HQ-SD-1

1

Shandong, Wendeng

cultivated

13

HQ-SX-22

A.membranaceus var. Mongholicus (Bge.)

over 5

Shanxi, Hunyuan county

wild

Field

14

HQ-SX-23

Menggu

over 5

Shanxi, Hunyuan county

wild

Field

15

HQ-SX-24

over 5

Shanxi, Daixian county

wild

Field

16

HQ-SX-25

over 5

Shanxi, Daixian county

wild

Field

17

HQ-SX-26

over 5

Shanxi, Yingxian county

wild

Field

18

HQ-SX-27

over 5

Shanxi, Yingxian county

wild

Field

19

HQ-SX-28

over 5

Shanxi, Yingxian county

wild

Field

20

HQ-SX-29

over 5

Shanxi, Yingxian county

wild

Field

21

HQ-SX-30

over 5

Shanxi, Hunyuan county

wild

Field

22

HQ-SX-31

over 5

Shanxi, Hunyuan county

wild

Field

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Table 1, Continued No.

Youer no.

Astragalus spp.

Growing years

23

HQ-SX-32

over 5

Shanxi, Tianzhen county

wild

Field

24

HQ-SX-33

over 5

Shanxi, Hunyuan county

wild

Field,

25

HQ-SX-34

over 5

Shanxi, Hunyuan county

wild

Field,

26

HQ-SX-35

over 5

Shanxi, Hunyuan county

wild

Field,

27

HQ-SX-37

over 5

Shanxi, Hunyuan county

wild

Field,

28

HQ-SX-36

over 5

Shanxi, Yanggao county

wild

Field,

29

HQ-SX-39

over 5

Shanxi

wild

Field

30

HQ-SSX-1

over 5

Shanxi, Zizhou county

wild

Locally bought

31

HQ-SSX-2

over 5

Shanxi, Yulin

wild

Locally bought

32

HQ-SSX-4

over 5

Shanxi, Yulin

wild

Locally bought

33

HQ-SSX-5

over 5

Shanxi, Yulin

wild

Locally bought

34

HQ-SSX-6

over 5

Shanxi, Yulin

wild

Locally bought

35

HQ-SSX-7

over 5

Shanxi, Yulin

wild

Locally bought

36

HQ-SSX-8

over 5

Shanxi, Yulin

wild

Locally bought

37

HQ-NM-7

2

Neimeng, Chifeng

cultivated

Commercial,

38

HQ-NM-3

2

Neimeng, Guyang county

cultivated

Commercial

39

HQ-NM-8

2

Neimeng

cultivated

Commercial

40

HQ-NM-9

2

Neimeng, Chifeng

cultivated

Commercial

41

HQ-NM-2

2

Neimeng, Shangdu county

cultivated

Commercial

42

HQ-NM-6

2

Neimeng, Guyang county

cultivated

Commercial

43

HQ-NM-10

2

Neimeng, Guyang county

cultivated

Commercial

44

HQ-NM-11

2

Neimeng, Xinghe county

cultivated

Commercial

45

HQ-GS-1

2

Gansu

cultivated

Locally bought

46

HQ-GS-2

2

Gansu, Longxi county

cultivated

Locally bought

47

HQ-GS-3

2

Gansu, Dangchang county

cultivated

Locally bought

48

HQ-GS-4

2

Gansu, Dangchang county

cultivated

Locally bought

49

HQ-GS-8

2

Gansu, Weiyuan county

cultivated

Field

50

HQ-GS-9

2

Gansu, Longxi county

cultivated

Field

51

HQ-GS-10

2

Gansu, Minxian county

cultivated

Field

52

HQ-GS-11

2

Gansu

cultivated

Locally bought

53

HQ-GS-16

2

Gansu, Minxian county

cultivated

Locally bought

54

HQ-GS-16

2

Gansu, Minxian county

cultivated

Locally bought

55

HQ-GS-19

2

Gansu, Longxi county

cultivated

Locally bought

56

HQ-GS-19

2

Gansu, Minxian county

cultivated

Locally bought

57

HQ-GS-22

2

Gansu, Weiyuan county

cultivated

Locally bought

58

HQ-GS-22

2

Gansu, Weiyuan county

cultivated

Locally bought

plant materials were authenticated by Prof. QIN Xue-Mei, and the voucher specomens were deposited in the herbarium of Modern Research Center for Traditional Chinese Medicine of Shanxi University. These samples were freeze-dried and grinded to fine powders with a pestle and mortar and then stored at −80 °C until analysis. Solvents and chemicals Analytical grade chloroform, methanol, and acetone were purchased from Fengchuan Chemical Co. Ltd. (Tianjin,

Growing locations

Cultivation patterns

Source

China). Deuterated chloroform (CDCl3, 99.8% D) containing tetramethylsilane (TMS, 0.03%, m/V), methnol-d4 (99.8% D) and D2O were obtained from Merck (Darmstadt, Germany). Sodium 3-trimethlysilyl [2, 2, 3, 3-d4] propionate (TSP) was from Cambridge Isotope Laboratories Inc. (Andover, MA, USA), and NaOD was purchased from Armar (Dottingen, Switzerland). Sample preparation Three different extraction procedures were used in the

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present study. In the first procedure (M1), a sample of 200 mg of lyophilized powder were transferred into 10-mL glass centrifuge tube and mixed with 6 mL of extraction solvent composed of water, methanol, and chloroform in the ratio of 1 : 1 : 2. At room temperature, the contents of the tube were mixed thoroughly, sonicated for 25 min, and centrifuged at 3 500 rmin–1 for 25 min. The chloroform (lower layer) and aqueous methanol (upper layer) fractions were transferred separately into a 25-mL round-bottomed flask and dried with a rotary vacuum evaporator. Chloroform fractions were dissolved in 800 μL of CDCl3, and aqueous methanol fractions were dissolved in 800 μL of mixture (1 : 1) of CD3OD and KH2PO4 buffer in D2O (adjusted to pH 6.0 by 1 molL –1 NaOD) containing 0.05% TSP. The supernatants (600 μL) of all the samples were transferred into 5-mm NMR tube for NMR analysis after centrifugation for at 13 000 rmin–1 for or 15 min. In the second procedure (M2), 200 mg of lyophilized powder were transferred into 10-mL glass centrifuge tube and mixed with 6 mL of extraction solvent composed of acetone and water in the ratio of 3 : 1. At room temperature, the contents of the tube were mixed thoroughly, sonicated for 25 min, and centrifuged at 3 500 rmin–1 for 25 min. The supernatant was transferred into a 25-mL round-bottomed flask and dried with a rotary vacuum evaporator. The fractions were dissolved in 800 μL of CD3OD. The supernatants (600 μL) of all the samples were transferred into 5-mm NMR tube for NMR analysis after centrifugation at 13 000 rmin–1 for 15 min. The third method (M3) was similar to M2, except that the sample was extracted with 6 mL of extraction solvent composed of chloroform and methanol in the ratio of 1 : 2. NMR measurements 1 H NMR was recorded at 25 °C on a Bruker 600 MHz AVANCE Ш NMR spectrometer (600.13 MHz proton frequency). CD3OD and CDCl3 were used for internal lock purposes. Each 1H NMR spectrum was consisted of 64 scans requiring 5-min acquisition time with the following parameters: 0.18 Hz/point, pulse width (PW) = 30◦ (12.7 μs), and relaxation delay (RD) = 5.0 s. A presaturation sequence was used to suppress the residual H2O signal with low power selective irradiation at the H2O frequency during the recycle delay. FIDs were Fourier transformed with LB = 0.3 Hz. The resulting spectra were manually phased and baseline-corrected, and calibrated to TSP at 0.00 for water fractions and TMS at 0.00 for organic fractions and spectra were referenced to the residual signal of CD3OD at  3.31 for only CD3OD redissolved fraction. Data analysis The 1H NMR spectra were processed using MestReNova (version 8.0.1, Mestrelab Research, Santiago de Compostella, Spain). For aqueous methanol fraction of M1, spectral intensities were scaled to TSP and reduced to integrated regions of equal width (0.04) corresponding to the region of  0.20–  9.20.

The regions of  4.70–5.02 and  3.281–3.360 were excluded from the analysis due to the presence of the signal from residual signal of H2O and CD3OD, respectively. For spectral intensities from M2 and M3, they were reduced to integrated regions of equal width (0.04) corresponding to the region of  0.2–9.32 by MestReNova. The regions of  4.70–5.06 and  3.280–3.348 were excluded from the analysis because of the residual signal of H2O and CD3OD, respectively. For the chloroform fraction, spectral intensities were scaled to TMS and reduced to integrated regions 0.04 ppm corresponding to the region of  0.50–10.02. The region between  7.22 and  7.30 was removed from the analysis because of the residual signal of CHCl3. The remaining regions were normalized to the whole spectrum for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA), which were performed in SIMCA-P software (version 13.0, Umetrics, Umeå, Sweden). All imported data were pareto-scaled for the multivariate analysis. Pareto scaling, in which each variable is divided by the square root of the standard deviation, gives greater weight to the variables with larger intensity but is not as extreme as the use of unscaled data. Pareto scaling is typically used when a very large dynamic range exists in the dataset [22-23]. Principal components analysis (PCA), which is an unsupervised clustering method requiring no prior knowledge of the data set that condenses the multivariate data into a reduced number of variables called principal components, was initially performed to examine the intrinsic variation in the dataset and to obtain an overview of variation among the groups [24]. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) were employed to maximize the separation between the groups and limit the impact of NMR data variation that is unrelated to sample class [25-26]. The quality of the models was described by R2 and Q2 values. R2 is defined as the proportion of variance in the data explained by the models and indicates the goodness of fit. Q2 is defined as the proportion of variance in the data predictable by the model and indicates predictability [26]. Relative amount of metabolites was evaluated based on the integrated regions (buckets) of the NMR spectra and ANOVA was performed in Excel to test the significance of differences in the metabolite levels among the samples of different regions. The differences were tested on a 95% probability level (P < 0.05). Hierarchical cluster analysis (HCA) and Pearson’s correlation (to test pairwise linear correlations for the identified metabolites in Radix Astragali obtained from different regions) analysis were performed by MetaboAnalyst 2.0 (http://www.metaboanalyst.ca/, free of charge), a comprehensive tool suit for metabolomic data analysis.

Results and Discussion

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As shown in Fig. 2, the AR crude drug from six different

LI Ai-Ping, et al. / Chin J Nat Med, 2017, 15(5): 363374

regions of China showed different appearances in root length, diameter, and color. However, aqueous methanol fraction of these AR yielded similar 1H NMR spectra except SD samples. In addition, all the spectra were represented by high concentrations of primary metabolites such as amino acids, sugars, and organic acids, but low in phenolic compounds (partly amplificated).

5.28, 5.4, 4.59, and 5.19 were assigned as anomeric protons of fructose, maltose, sucrose, α-glucose, and β-glucose in the carbohydrate region, and fumaric acid ( 6.56, s), formic acid ( 8.47, s) were identified in the phenolic region. For the chloroform fractions, the dominant signals were from fatty acids or their esters, as revealed by the termial methyl ( 0.98), α-CH2 ( 2.3), β-CH2 ( 1.6), allylic CH2 ( 2.05), and bis-allylic CH2 ( 2.77), all the other protons of hydrocarbon chain ( 1.2–1.3), and olefinic protons ( 5.35). The chemical shifts and coupling constants of all the identfied metabolites are sumarized in Table 2.

Fig. 2 Astragali Radix roots from 6 different regions

Metabolite identification The signals were assigned based on comparisons with the chemical shift of standard compounds using the chenomx NMR suite software, Human Metabolomics Database, as well as the reported literature data [27-28]. The signal overlap was partly resovled by the use of J-resolved spectra. The 1H NMR spectrum of AR can be divided into three distinct regions (Fig. 3). Organic acids, such as GABA, succinic acid, acetic acid, and citric acid, and several amino acids, such as valine ( 1.01, 1.06), alanine ( 1.48), N-acetyl-aspartate ( 2.83, 2.95), threonine ( 3.97), glutamine/glutamate ( 2.15, 2.49), and taurine ( 3.51), were identifed in the corresponding organic acids and amino acids regions, respectively. The signals at  4.17,

Fig. 3 Representative 1H NMR spectrum of AR, the spectrum was subdivided into three spectral regions (A,  0.3–2.9; B,  3.0–4.6; and C,  5.0–9.2)

Table 2 Chemical shift assignments in aqueous extracts of Astragali Radix No.

Metabolite

Selected characteristic signals in NMR

1

Saponins

0.34 (s), 0.55 (s)

2

Valine

1.01 (d, 7), 1.06 (d, 7)

3

Threonine

1.34 (d, 6.6)

γ-CH3

4

Lysine

1.47 (m), 1.73 (m), 1.89 (m)

-CH2

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Assignment

γ, γ′-CH3, β-C

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Table 2, Continued No.

Metabolite

Selected characteristic signals in NMR

Assignment β-CH3

5

Alanine

1.48 (d, 7.2)

6

Arginine

1.6, 1.7, 1.9 (m), 3.24 (t, 7.0), 3.76 (t)

7

Acetic acid

1.94 (s)

CH3

8

Proline

2.00 (m), 2.02–2.33 (m), 3.35 (t), 4.12 (m)

α-CH, β-CH2, γ-CH2, δ-CH, δ′-CH

9

Glutamine/Glutamate

2.15 (m), 2.49 (m), 4.9 (s)

β-CH2, γ-CH2, α-CH, COOH

10

GABA

2.3 (t, 7.2), 3 (t),

α-CH, γ-CH2

11

Malate

2.42 (dd, 15.64, 9.33), 2.70 (dd, 15.53, 3.46), 4.28 (dd, 9.15, 3.45), 2.43 (dd, 15.31)

β'-CH, β-CH, α-CH, COOH

12

Succinic acid

2.45 (s)

CH2

13

α-ketoglutarate

2.45 (t, 6.9 Hz), 3.01 (t, 6.9 Hz)

β-CH2, γ-CH2 α, α′-CH2, γ, γ′-CH2

14

Citrate

2.54(d, 16.56), 2.71(d, 16.41)

15

N-Acetyl-Aspartate

2.83 (dd, 8.16, 16.94), 2.95 (dd, 3.97, 16.94)

16

malonate

3.13 (s)

CH2

17

Choline

3.22 (s)

N-CH3

18

Taurine

3.24 (t), 3.44 (t)

CH2-N

19

Betaine

3.27 (s), 3.9 (s)

N(CH3)+, CH2

20

Xylose

3.38 (t, 9.4), 4.54 (d, 6.7), 5.17 (d, 4.0)

21

Phenylalanine

3.44 (t, 9.5), 7.33(m)

Ar-CH, Ar-CH

22

Glycine

3.68 (s)

CH

23

β-Glucose

4.59 (d, 7.9)

1CH

24

α-Glucose

5.19 (d, 3.73)

1CH

25

Maltose

5.33 (d, 3.85)

β'-CH, β-CH, α-CH, COOH

Sucrose

5.4 (3.83), 3.44 (dd, 9.5, 9.5), 3.75 (dd, 9.7, 9.5), 4.04 (dd, 10.2, 10.3), 4.17 (d, 8.64), 3.66 (s)

1CH, 2CH, 3CH, 4CH, 5CH, 6CH,

27

Raffinose

4.97 (d, 3.72), 5.45 (d, 3.6)

1CH, 2CH, 3CH, 5CH

28

Fumaric acid

6.53 (s)

CH=CH

29

Adenine

8.21 (s), 8.26 (s)

1=CH, 4=CH

30

Formic acid

8.47 (s)

CH

31

Ononin-7-glucoside

7.27 (d, 2.4), 7.19 (dd, 2.4, 9), 8.14 (d, 9), 7.0 (d, 9), 7.49 (d, 9)

6Ar-H, 7Ar-H, 8Ar-H, 2, 6-Ar-H, 3, 5-Ar-H

32

Calycosin-7-glucoside

7.27 (d, 2.4), 7.19 (dd, 2.4, 9), 8.14 (d, 9), 7.07(s)

6Ar-H, 7Ar-H, 8Ar-H, 2Ar-H

26

Multivariate data analysis (MvDA) To get a preliminary overview of the general similarities and differences among the 58 collections, both aqueous methanol and chloroform fractions of AR samples were first analyzed by PCA, and a separation can be seen between the wild (including semi-wild) and cultivated samples in the score plot of first three PCs (PC1: 25.9%; PC2: 15.7%; PC3: 11%) for the aqueous methanol fracitons. However, for the chloroform fractions, no obvious separation was observed (Figs. 4A and 4B). This findings were futher confirmed by HCA, antother unsupervized clustering method, and the results (Figs. 4C and 4D) were consistent with the PCA, which also grouped the 58 AR samples into two clusters for aqueous methanol fractions. Both the PCA and HCA results suggested that cultivation patterns or growth years may have greater impact on the metabolite composition than the variety. The 8 NM samples are scattered in wild and cultivated groups

(5 in wild, and 3 in cultivated), due to the fact that both wild and cultivated AR are grown in NM. In addition, two GS samples from Dangchang County were located in the wild group. Most of the AR drugs from GS are cultivated in the central Gansu province of Longxi, Weiyuan County, and there were also some wild ARs distributed in southern part of GS, such as Dangchang and Minxian. For the Mojia AR from HLJ and SD, the PCA analysis of the aqueous methanol fractions (Fig. 5A) revealed that the first two components accounted for 62% of the total variance and the HLJ samples can be separated from the SD samples by PC1. Furthermore, permutation tests were also performed to validate the PLS-DA model. All Q2max and R2 values were higher in the permutation test than in the real model, revealing great predictability and goodness of fit (Fig. 5B). OPLS-DA was used to reveal the differential metabolites between the Mojia AR from the two locations, and the cross

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Fig. 4 Three-dimensional score plots of a PCA performed to discriminate Astragali Radix from different regions. (A) aqueous methanol (upper) fractions; (B) the chloroform (lower) fractions; Dendogram of HCA using Ward's minimum variance method of 58 samples of the two varieties of Astragali Radix. (C) aqueous methanol (upper) fractions; (D) the chloroform (lower) fractions

validated score plot and corresponding loading plots are displayed in Fig. 5D, which showed that metabolites such as betaine, α-glucose, arginine, and citrate were higher in HLJ samples, and the levels of raffinose, aspartate, succinate and glutamine/glutamate were higher in SD samples. For the Menggu AR, as both wild and cultivated ARs were distributed in NM, the NM samples were excluded in further analysis. The PCA analysis of the AR samples from SX, SSX, and GS (PC1: 27.5%; PC2: 13.1%) showed that GS samples (cultivated, 2 years) were on the negative side of PC2, and the SX samples and SSX samples (wild, over 5 years) were located in the positive side of PC2, which could be further separated by PC1. The separation between the GS samples and SX/SSX samples was more remarkable than those between the SX and SSX samples. As partial overlap was

observed between SX and SSX groups, other two extraction methods (M2 and M3) were applied to see whether better separations could be achieved. The three methods were compared by PCA using 6 individual samples. In M1 procedure, 18 AR samples could be divided into three groups: SX group , GS group and SSX group. In M2, the SX and SSX groups were merged into one group. In M3, three groups could be oberved, but partial overlap was shown. Thus, the M1 was better than the other two methods and used to find the differential metabolites of Menggu AR from different locations. For the SX and GS samples, the OPLS-DA results showed that SX samples contained more betaine, N-Acetyl-aspartate, malate, succinic acid, taurine, β-glucose, and citrate, while the GS samples contained more GABA, glycine, sucrose, arginine, and phenylalanine. For the

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Fig. 5 (A) PCA score plot showing the grouping of Mojia AR according to cultivated regions (HLJ and SD) for aqueous methanol (upper) fractions, (B) permutation test for 200 times, (C) OPLS-DA score plot, (D) S-plot

SSX and GS samples, the SSX samples contained more citrate, taurine, betaine, malate, α-glucose and β-glucose, but less phenylalanine, proline, GABA, lysine, N-Acetyl-aspartate, arginine, glycine, acetate, glutamine/glutamate and sucrose than the GS samples. The results suggested that betaine, N-Acetyl-aspartate, malate, succinic acid, taurine, β-glucose, α-glucose, and citrate were accumulated in wild AR, and GABA, glycine, sucrose, lysine, arginine, phenylalanine, acetate, and glutamine/glutamate were accumulated in cultivated AR. The differential metabolites between the two types of wild samples were also determined. Compared with the SSX group, the SX group contained more betaine, N-Acetyl-aspartate, proline, GABA, glutamine/glutamate, malate, glycine, lysine, and less arginine, sucrose, citrate, and taurine. PCA was further applied to reveal the difference of metabolic profiles between the different extraction methods. For the SX samples, the extracts were clustered according to the type of solvents used in the extraction methods, and M1 could be separated from M2 and M3 by PC1, while M2 and M3 could be further separated by PC2. For the SSX and GS samples, the extraction methods resulted in the same clustering pattern as those of SX. Interpretation of the corresponding loading plot of SX samples revealed that extracts from M1 were dominated by higher amount of phenylalanine, glycine, betaine, sucrose,

and some unidentified metabolites (3.64, 3.52, 4.08). In addition, M2 was characterized by higher levels of choline, xylose and some amino acids such as valine, proline and threonine and M3 was characterized by higher levels of arginine and taurine. Metabolite quantification The differential metabolites as revealed above were relatively quantified using bucket data of 1H NMR spectrometry. As shown in Fig. 6, the concentration of metabolites in different AR samples varied greatly. Compared with cultivated AR, wild AR contained more betaine, xylose, α-glucose, arginine, malate, N-Acetyl-Asparate, maltose, citrate, β-glucose, taurine, valine and calycosin, but less sucrose, phenylalanine, alanine, and adenine. In addition, the ratio of sucrose/glucose (both  and  form) were calclulated for different AR samples. As shown in Fig. 7, the cultivated AR samples from SD (Mojia AR) and GS (Menggu AR) exhibited extremly higher sucrose/glucose ratio than those of wild (including semi-wild) AR from HLJ (Mojia AR), SSX (Menggu AR) and SX (Menggu AR). Thus, the sucrose/ glucose ratio can be used as an simple method for differentiating ARs of different cultivation types or growing ages. Fig. 8 shows the correlation matrices [29] separately for 5 groups as built from the Pearson s correlations among the 16 metabolites detected in the AR samples and facilitated by using scaled colors. The correlations among the metabolites

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Fig. 6 Quantification of identified metabolites selected by ANOVA (P < 0.05) in AR extracts analyzed by 1H NMR for 2 different cultivation patterns (wild vs cultivated)

were generally positive for the Mojia AR, but many negative correlations were observed for Menggu AR by visual analysis. Changes in correlation pattern (from positive to negative or vice versa) were observed for α-glucose (correlated to betaine, phenylalanine and citrate), taurine (correlated to alanine and phenylalanine), citrate (correlated to α-glucose, sucrose and betaine) when comparing between the two varieties. In addition, for the wild AR, the correlations of α-glucose/β-glucose and alanine, sucrose/β-glucose were positive, and maltose/N- Ace-

tyl-Aspartate, β-glucose and alanine were negative, while for cultivated AR, all these correlations were negative. It is not clear how or why the metabolites correlations changed in such a way, due to cultivation pattern and genetic variations. In the present study, AR from one species with two varieties of Astragalus (A. Membranaceus and A. membranaceus var. mongolicus) grown in different regions of China, were compared and characterized based on multivariate statistical analysis of 1H NMR based metabolomic data. 29 Primary

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Fig. 7 The sucrose/glucose ratios for AR of 5 different regions based on their integrated area

metabolites, including amino acids, organic acids, sugars, and 3 secondary metabolites were identified without chromatographic separation. Multivariate analysis showed that the different AR samples could be differentiated successfully, and the cultivation pattern or growth years might have greater impact on the metabolite composition than the variety. Compared with the cultivated AR, the wild AR accumulated more betaine, xylose, α-glucose, arginine, malate, N-Acetyl-Asparate, maltose, citrate, β-glucose, taurine, valine and calycosin, but less sucrose, phenylalanine, alanine and adenine. And the sucrose/glucose ratio can be used to differentiate the wild and cultivated AR. The results obtained in the present study demonstrated that NMR-based non-targeted profiling approach, combined with multivariate statistical analyses, can be used as a powerful tool for differentiating the AR of different cultivation type or growth years.

Fig. 8 Pearson s correlation matrices of 16 metabolites of AR from 5 different regions. The color scale is relative to the Pearson s correlation coefficients. 1. sucrose, 2. betaine, 3. phenylalanine, 4. xylose, 5. α-glucose, 6. arginine, 7. malate, 8. N-Acetyl-Aspartate, 9. maltose, 10. citrate, 11. β-glucose, 12. alanine, 13. taurine, 14. adenine, 15. valine, 16. calycosin

Plants often accumulate specific secondary metabolites in response to abiotic and biotic stresses [30-31]. The accumulation of different metabolites in the ARs of different regions may reflect their adaptation to different environments. However, it is not possible at present to ascertain the relative contributions

of the environmental factors and how much such chemical composition differences have an impact on the bioactivities of AR. In the present study, most of the compounds identified were primary metabolites, and further studies should be

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Table 3 Parameters indicating the model quality OPLS-DA model 2

R2Y(cum) Q2 (cum)

N

R X(cum)

HLJ vs. SD (upper)

1P + 4O

0.984

1

0.994

SX vs. GS

1P + 3O

0.718

1

0.755

SXX vs. GS

1P + 3O

0.793

1

0.783

SX vs. SSX

1P + 3O

0.744

1

0.799

conducted on the other preparation methods, in which more secondary metabolites can be enriched. Primary metabolites are essential to the growth of plants, and seem to have no relationship with the bioactivities of the herbs. However, acorroding to recent research reports [32-33], these metabolites occurring in large amounts in cells may form a third type of liquid, also known as deep eutectic solvents. The natural deep eutectic solvents (NADES) have been proven to be excellent solvent for a wide range of metabolites that are non-soluble or poorly soluble in water, such as rutin, and may be involved in the biosynthesis and storage of various non-water soluble metabolites in cells. Thus, their role in the bioactivities of herbal drugs should be further investigated.

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Cite this article as: LI Ai-Ping, LI Zhen-Yu, QU Ting-Li, QIN Xue-Mei, DU Guan-Hua. Nuclear magnetic resonance based metabolomic differentiation of different Astragali Radix [J]. Chin J Nat Med, 2017, 15(5): 363-374

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