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Metabolic discrimination of Swertia mussotii and Swertia chirayita known as “Zangyinchen” in traditional Tibetan medicine by 1H NMR-based metabolomics
Journal of Pharmaceutical and Biomedical Analysis 98 (2014) 364–370
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Metabolic discrimination of Swertia mussotii and Swertia chirayita known as “Zangyinchen” in traditional Tibetan medicine by 1 H NMR-based metabolomics Gang Fan a , Wei-Zao Luo c , Shang-Hua Luo a , Yan Li a , Xian-Li Meng a , Xiang-Dong Zhou b,∗ , Yi Zhang a,∗ a
College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China Department of Medicinal Chemistry, College of Pharmacy, Third Military Medical University, Chongqing 400038, China c Chongqing Academy of Chinese Materia Medica, Chongqing 400065, China b
a r t i c l e
i n f o
Article history: Received 18 February 2014 Received in revised form 8 June 2014 Accepted 9 June 2014 Available online 16 June 2014 Keywords: 1 H NMR Metabolomics Swertia chirayita Swertia mussotii Multivariate statistical analysis
a b s t r a c t Swertia mussotii Franch. and Swertia chirayita Buch.–Ham. have been commonly used under the same name “Zangyinchen” for the treatment of liver and gallbladder diseases in traditional Tibetan medicine. Detailed characterization and comparison of the complete set of metabolites of these two species are critical for their objective identification and quality control. In this study, a rapid, simple and comprehensive 1 H NMR-based metabolomics method was first developed to differentiate the two species. A broad range of metabolites, including iridoid glycosides, xanthones, triterpenoids, flavonoids, carbohydrates, and amino acids, were identified. Statistical analysis showed evident differences between the two species, and the major markers responsible for the differences were screened. In addition, quantitative 1 H NMR method (qHNMR) was used for the target analysis of the discriminating metabolites. The results showed that S. mussotii had significantly higher contents of gentiopicrin, isoorientin, glucose, loganic acid, and choline, whereas S. chirayita exhibited higher levels of swertiamarin, oleanolic acid, valine, and fatty acids. These findings indicate that 1 H NMR-based metabolomics is a reliable and effective method for the metabolic profiling and discrimination of the two Swertia species, and can be used to verify the genuine origin of Zangyinchen. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Swertia mussotii Franch., referred to as “Zangyinchen” in Chinese, is a well-known Tibetan medicinal plant. It has been commonly used for the treatment of liver and gallbladder diseases, such as hepatitis, cholecystitis and fatty liver [1]. S. mussotii is an important and frequently used crude drug in traditional Tibetan medicine because of its reliable therapeutic efficacy. Modern investigations have shown that S. mussotii can repair the fibrillation of the liver [2] and alleviate the damage of immunological liver injury [3]. Swertia chirayita Buch.–Ham., also known as “Zangyinchen” in Chinese, is a widely used herbal medicine in India, Nepal and China. S. chirayita possesses various pharmacological activities, such as
∗ Corresponding authors at: College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Road, Weijiang, Chengdu 611137, Sichuan, China. Tel.: +86 23 68753701/+86 28 61800274/+86 28 61800160. E-mail addresses: [email protected] (X.-D. Zhou), [email protected] (Y. Zhang). http://dx.doi.org/10.1016/j.jpba.2014.06.014 0731-7085/© 2014 Elsevier B.V. All rights reserved.
antioxidant, anti-inflammatory and hepatoprotective [4–6] effects. In China, S. chirayita is frequently used to treat damp-heat and quench the fire of the liver and gallbladder in traditional Tibetan medicine system [1]. S. mussotii and S. chirayita have different morphological characteristics from a taxonomy perspective. However, herbal medicines are usually chopped to sell in markets or powdered to use in medical institutions. Therefore, these two crude drugs are easily confused with each other, and identifying them based on their morphological features is difficult. Suitable metabolic markers are crucial for their objective discrimination. At present, S. mussotii and S. chirayita are commonly used either as mixtures or substitutes for each other in traditional Tibetan medicine system because they have similar phytochemicals and pharmacological activities. However, whether the two herbal medicines can be used interchangeably remains uncertain, and evidence for their equivalent application is still lacking. Therefore, a rapid and effective method should be developed to evaluate the metabolites differences between S. chirayita and S. mussotii for their objective identification and safe use.
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Metabolomics has recently attracted increasing attention because of its holistic characteristics, and has been developed as an important method for the modern research of herbal medicines. HPLC–MS, GC–MS and 1 H NMR are the most commonly used analysis techniques in plant metabolomics. Compared with the two chromatographic approaches, 1 H NMR technique is regarded as the most promising tool because of its simple sample preparation, simultaneous detection of primary and secondary metabolites in a single run, and ease of quantitative analysis of the most abundant metabolites [7,8]. Therefore, in the past decade, 1 H NMR-based metabolomics approach has been widely used for comprehensive qualitative and/or quantitative analysis of the complete set of metabolites present in herbal medicines for species differentiation [9] or geographical origin discrimination [10]. However, no 1 H NMR metabolomics analysis has been performed on Swertia. In this study, we present the first report on the metabolite profiling analysis of two Swertia species, and develop a simple, rapid and reliable method for metabolic discrimination of S. chirayita and S. mussotii using 1 H NMR spectroscopy. 2. Materials and methods 2.1. Plant materials A total of 23 batches of samples were collected from different Tibetan medicine companies and herbal markets or harvested from various locations. Detailed information of the plant materials is shown in Supplementary Table S1. The botanical origins of the harvested samples were authenticated by Professor Wei-Zao Luo, and voucher specimens were deposited in the Chongqing Academy of Chinese Materia Medica, Chongqing, China. The botanical origins of the commercial samples were identified by microscopic examination based on their different microscopic characteristics as found in our previous study [11]. 2.2. Chemicals and reagents Methanol-d4 (CD3 OD, 99.8%), deuterium oxide (D2 O, 99.9%), chloroform-d (CDCl3 , 99.8%) and dimethyl sulfoxide-d6 (DMSO-d6 , 99.9%) were purchased from Cambridge Isotope Laboratories (Miami, FL, USA). KH2 PO4 was supplied by Chongqing Beibei Chemical Reagent Co., Ltd. (Chongqing, China). 3-(Trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP, 99%) was purchased from Sigma-Aldrich (St. Louis, Mo, USA). The standard compounds of oleanolic acid, sucrose, and alanine were purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). Swertiamarin, sweroside, loganic acid and gentiopicrin were purchased from Chengdu Herbpurify Co., Ltd. (Chengdu, China). 1,8-Dihydroxy-3,5-dimethoxy xanthone, 1,8-dihydroxy-3,7-dimethoxy xanthone, 1-hydroxy-3,7,8trimethoxy xanthone, 1,5,8-trihydroxy-3-methoxy xanthone, 7-O-[␣-l-rhamnopyranosyl-(1 → 2)--d-xylopyranosyl]-1,8dihydroxy-3-methoxy xanthone, mangiferin, and isoorientin were isolated and purified from S. mussotii, and their structures were unambiguously identified based on their 1 H NMR and 13 C NMR spectral data. Their purities were all determined to be over 98% by 1 H NMR and HPLC/UV analyses. 2.3. Sample preparation All the samples were oven-dried at 50 ◦ C until a constant weight was obtained. The dried powders (200 mg) were accurately weighed into a clean Erlenmeyer flask, and 1.0 mL of CD3 OD and 0.3 mL of KH2 PO4 buffer (pH 6.0) in D2 O containing 0.04% (wt/wt) TSP were added. The Erlenmeyer flask was then sealed and sonicated at room temperature for 30 min. The extracting solution was
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filtered through a 0.45 m membrane filter. Exactly 0.6 mL of filtrate was transferred into a standard 5 mm NMR tube for 1 H NMR analysis. 2.4.
1H
NMR analysis and data processing
All 1 H NMR spectra were acquired using a Varian 400 MHz NMR spectrometer (Agilent Technologies Inc., USA). The following parameters were used in all 1D 1 H NMR experiments: 128 transients were collected into 32 K data points with a spectral width of 6410.3 Hz, an acquisition time of 2.556 s and a relaxation delay of 4.0 s. The resulting spectra were phase-adjusted, baseline-corrected and referenced to an internal standard (TSP) at 0.00 ppm using MestReNova software (version 6.2, Mestrelabs Research SL). The NMR spectral data were reduced into 0.04 ppm spectral buckets corresponding to the region of ı 8.50–0.50. The regions of ı 3.34–3.26 and ı 4.90–4.74 were removed because of the residual signal of methanol and water, respectively. The spectra were then normalized to the total signal area and converted to a text file for statistical analysis. For quantitative 1 H NMR analysis, peak areas of the quantified signals of the analytes and internal standard were obtained by manual integration. Moreover, 2D NMR spectra were acquired using standard pulse programs. 2D J-resolved spectra were acquired using 64 scans per 128 increments for F1 and 8K for F2 using spectral widths of 6410.3 Hz in F2 and 50 Hz in F1. A 1.0 s relaxation delay was employed. The J-resolved spectra were tilted by 45◦ , and then symmetrized about F1. 2D 1 H–1 H COSY spectra were acquired with 1.0 s relaxation delay, 6410.3 Hz spectral width in both dimensions. The COSY spectra were transformed with a sine-bell weighting function (SSB = 0). 2.5. Quantification of the selected metabolites by 1 H NMR method Since the signal intensity is absolutely proportional to the molar concentration of metabolites in a 1 H NMR spectrum [7,8], the quantification of the selected metabolites can be performed by using ratio method. A known internal standard (in this case TSP) is used to determine the concentration of the targeted metabolites by using the following equation: mX = mST ×
A MW N X X ST AST
×
MWST
×
NX
,
where mX is the unknown mass of the targeted analyte and mST is the mass of the TSP; AX and AST are the integral areas for the selected signals, NX and NST are the number of protons generating the integral signals, MWX and MWST are the molecular weights of the targeted analyte and TSP, all respectively. 2.6. Statistical analysis Principal component analysis (PCA) was used to examine the intrinsic variation in the dataset and sort samples into groups. Partial least squares discriminant analysis (PLS-DA) was also performed to maximize separation between groups. PCA and PLS-DA were conducted with unit variance scaling (UV) using SIMCA-P software (version 11.5, Umetrics, Umeå, Sweden). Two-tailed t-test and analysis of variance (ANOVA) were done with GraphPad Prism software version 5.0 (GraphPad Software Inc., San Diego, CA). Statistical significance was determined on a 95% probability level (p < 0.05). Heatmap was generated using MultiExperiment Viewer software (MeV, version 4.8.1) based on unsupervised hierarchical clustering. Pearson’s correlation was used as distance measure and average linkage for agglomeration.
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Fig. 1. Representative 1 H NMR spectra of S. chirayita (sample no. 1) (A) and S. mussotii extracts (sample no. 12) (B). Expanded spectral regions of S. mussotii (C) low field region; (D) middle field region; (E) high field region. Signal assignments: (1) swertiamarin; (2) sweroside; (3) 1-hydroxy-3,7,8-trimethoxy xanthone; (4) 7-O[␣-l-rhamnopyranosyl-(1 → 2)--d-xylopyranosyl]-1,8-dihydroxy-3-methoxy xanthone; (5) gentiopicrin; (6) mangiferin; (7) 1,8-dihydroxy-3,7-dimethoxy xanthone; (8) isoorientin; (9) 1,8-dihydroxy-3,5-dimethoxy xanthone; (10) 1,5,8-trihydroxy-3-methoxy xanthone; (11) gallic acid; (12) oleanolic acid; (13) sucrose; (14) ␣-glucose; (15) -glucose; (16) choline; (17) loganic acid; (18) formic acid; (19) acetic acid; (20) succinic acid; (21) alanine; (22) valine; (23) saturated fatty acids; (24) unsaturated fatty acids; (25) sterols.
3. Results and discussion 3.1. Optimization of extraction solvents Five solvent systems, involving CD3 OD–D2 O (1:0.3, v/v), CD3 OD, CDCl3 , D2 O, and DMSO-d6 , were designed and optimized. By comparing the 1 H NMR spectra of the same sample under different extraction conditions (Supplementary Fig. S1) based on ANOVA followed by Tukey’s multiple comparison tests, we found that CDCl3 and D2 O could extract more fatty acids and sugars, respectively. However, the intensity of signals at ı 7.70–5.20 corresponding
to secondary metabolites in CDCl3 and D2 O was significantly lower than that in other solvents. DMSO-d6 could simultaneously extract primary and secondary metabolites, but most signals in the 1 H NMR spectra were severely overlapped. CD3 OD and CD3 OD–D2 O had similar metabolic profiling, and both could extract global metabolites. However, sugars and amino acids were more abundant in CD3 OD–D2 O compared with those in CD3 OD. Finally, CD3 OD–D2 O was selected as the preferred solvent, because it could simultaneously extract xanthones, iridoid glycosides, triterpenoids, flavonoids, carbohydrates, and amino acids present in the Zangyinchen in a single run.
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Fig. 2. Chemical structures of major secondary metabolites detected in Swertia extracts by means of 1 H NMR spectroscopy ((A) xanthones; (B) iridoid glycosides; (C) flavonoids; (D) triterpenoids).
3.2. Identification of metabolites Representative one-dimensional 1 H NMR spectra of S. mussotii and S. chirayita are shown in Fig. 1. The metabolites were assigned and identified by carefully comparing the 1 H NMR spectra of the standard compounds, spiking experiments and comparisons to the literature [8,12,13]. Moreover, the 2D-NMR spectra, including 1 H–1 H COSY and J-resolved spectra, were used to provide supporting information for metabolite assignments (Supplementary Figs. S2 and S3). Eventually, 25 metabolites (Table 1) were identified
in the 1 H NMR spectra. Chemical structures of representative secondary metabolites detected in Swertia extracts are shown in Fig. 2. 3.3. Multivariate statistical analysis The 1 H NMR data sets of all samples were subjected to PCA analysis. The PCA score plot of 23 samples (triplicates for each sample) is shown in Fig. 3A. Smaller between-extraction errors were observed according to the close proximity of the observations, thereby indicating the sample homogeneity and good repeatability
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Table 1 Identification and quantification of metabolites in two Swertia extracts by 1 H NMR spectroscopy. Peak no.
1
Metabolites
Swertiamarin
2
Sweroside
3 4
1-Hydroxy-3,7,8-trimethoxy xanthone 7-O-[␣-l-rhamnopyranosyl-(1 → 2)-d-xylopyranosyl]-1,8-dihydroxy-3methoxy xanthone Gentiopicrin
5
10 11 12
Mangiferin 1,8-Dihydroxy-3,7-dimethoxy xanthone Isoorientin 1,8-Dihydroxy-3,5-dimethoxy xanthone 1,5,8-Trihydroxy-3-methoxy xanthone Gallic acid Oleanolic acid
13 14 15 16 17 18 19 20 21 22 23 24
Sucrose ␣-Glucose -Glucose Choline Loganic acid Formic acid Acetic acid Succinic acid Alanine Valine Saturated fatty acids Unsaturated fatty acids
25
Sterols
6 7 8 9
Chemical shifts, multiplicitya and coupling constants of characteristic signals
7.67 (s), 5.71 (d, J = 1.6 Hz), 5.39 (m), 5.30 (m), 4.69 (d, J = 8.0 Hz), 4.39 (m) 7.61 (d, J = 2.5 Hz), 5.51 (m), 5.30 (m), 4.72 (d, J = 7.9 Hz), 4.47 (m), 2.75 (m) 7.54 (d, J = 9.2 Hz), 7.24 (d, J = 9.2 Hz) 7.51 (d, J = 9.2 Hz), 6.87 (d, J = 9.2 Hz), 1.20 (d, J = 6.4 Hz)
Contents (mg/g) of the discriminating metabolites (mean ± standard deviation) S. mussotii
S. chirayita
2.04 ± 0.30
4.27 ± 0.71
b
n.q.
n.q.
n.q. n.q.
n.q. n.q.
7.46 (d, J = 1.2 Hz), 5.75 (m), 5.66 (d, J = 2.8 Hz), 5.08 (m), 4.72 (d, J = 7.8 Hz) 7.40 (s), 6.82 (s), 6.39 (s) 6.85 (d, J = 9.2 Hz), 6.25 (d, J = 2.0 Hz)
14.88 ± 1.84
2.97 ± 0.45
n.q. n.q.
n.q. n.q.
7.36 (m), 6.93 (d, J = 8.8 Hz), 6.65 (s) 7.33 (d, J = 9.0 Hz), 6.61 (d, J = 9.0 Hz)
3.48 ± 0.63 n.q.
2.31 ± 0.28 n.q.
7.24 (d, J = 8.8 Hz), 6.79 (d, J = 8.8 Hz) 7.02 (s) 5.25 (s), 1.10 (s), 0.95 (s), 0.94 (s), 0.91 (s), 0.90 (s), 0.76 (s), 0.75 (s) 5.39 (d, J = 3.8 Hz), 4.14 (d, J = 8.4 Hz) 5.15 (d, J = 3.6 Hz) 4.53 (d, J = 8.0 Hz) 3.21 (s) 5.27 (d, J = 3.2 Hz), 1.08 (d, J = 6.8 Hz) 8.43 (s) 2.01 (s) 2.56 (s) 1.50 (d, J = 7.2 Hz) 1.05 (d, J = 6.8 Hz), 1.01 (d, J = 6.8 Hz) 0.88 (m), 1.24–1.35 (m), 1.57 (m), 2.27 (t, J = 7.4 Hz) 0.88 (m), 1.24–1.35 (m), 1.57 (m), 2.04 (m), 2.27 (t, J = 7.4 Hz), 2.74(m), 5.34 (m) 0.69 (s)
n.q. n.q. 3.28 ± 0.50
n.q. n.q. 5.47 ± 0.60
n.q. 23.79 ± 9.43
n.q. 11.59 ± 2.23
0.70 ± 0.10 5.03 ± 0.93 n.q. n.q. n.q. n.q. 0.67 ± 0.13 AX /AST c = 9.30 ± 0.98
0.28 ± 0.07 1.29 ± 0.23 n.q. n.q. n.q. n.q. 1.49 ± 0.21 AX /AST = 12.95 ± 2.17
n.q.
n.q.
a
Multiplicity: s, singlet; d, doublet; t, triplet; and m, multiplet. n.q., not quantified, because no significant differences were observed between the two species for these metabolites based on previous PLS-DA results. The content of fatty acids is not available because its molecular weight is unknown, and so AX /AST value is used for the comparison between species. AX and AST represent the integral areas of the quantified signals of the fatty acids and the TSP, respectively. b c
for the developed method. In addition, a clear separation between S. chirayita and S. mussotii was obtained by PC1, which indicated that their metabolic profiles were significantly different. In the PCA score plot, all S. mussotii samples were projected in a larger region compared with S. chirayita, indicating that the intraspecific variations in S. mussotii were larger than those in S. chirayita. This may be due to the fact that S. mussotii was collected from a broader area. Different geographical origins resulted in the between-sample metabolic differences. However, the biggest source of variance highlighted by PCA mainly resulted from the differences between S. chirayita and S. mussotii (i.e., interspecific variations), although their intraspecific variations were also observed. These findings indicated that the species was the most important factor in terms of the effect on metabolite variation compared with other factors. PLS-DA, a supervised analysis method, was applied to maximize separation between groups. This model was validated with 200 permutation tests. As shown in Supplementary Fig. S4, the PLS-DA model had a proper R2 Y intercept value of 0.127 and Q2 Y intercept value of −0.631. The score plot of PLS-DA (Fig. 3B) showed that this model could reliably differentiate S. chirayita samples from S. mussotii ones, which was in good agreement with the results of PCA. The corresponding loading plot of PLS-DA (Fig. 3C) showed that the separation mainly arose from the signals of gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, choline, fatty acids, and valine. Moreover, several metabolites corresponding to sugars were also found to play an important role in separating the two species.
However, it was very difficult to unambiguously identify them because the sugars regions (ı 3.85–3.32) were highly overlapped. Based on the positive or negative effects of these metabolites in PLS1, we can conclude that gentiopicrin, isoorientin, loganic acid, choline, ␣-glucose, -glucose, and several unidentified sugars were more abundant in S. mussotii, whereas the levels of swertiamarin, oleanolic acid, valine, and fatty acids were higher in S. chirayita. 3.4. Quantification of metabolites To give a clearer idea of the differences of individual metabolite levels between the two species, selected metabolites were quantitatively analyzed using qHNMR method. The characteristic signals of swertiamarin at ı 7.67 (s), gentiopicrin at ı 7.46 (d), isoorientin at ı 6.65 (s), loganic acid at ı 5.27 (d), choline at ı 3.21 (s), ␣-glucose at ı 5.15 (d), -glucose at ı 4.53 (d), oleanolic acid at ı 0.75(s), valine at ı 1.01 (d), and fatty acids at ı 2.27 (t) were selected for quantification. The intra-day precision of the qHNMR method was evaluated by six replicate measurements of the same sample (no. 12) in one day. The relative standard deviation (RSD) values were in the range of 1.1–2.5%. The method repeatability was evaluated by analyzing six different working solutions independently prepared from the same sample (no. 12). The RSD values ranged from 1.4% to 2.9%. The stability was assessed by analyzing the same sample solution (no. 12) within 24 h. ANOVA indicated that there were no significant differences between time of incubations (p > 0.05).
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Fig. 3. Score plots of PCA (A) and PLS-DA (B). All the samples are clearly classified into two groups corresponding to the two species, respectively. Abbreviations: SM, S. mussotii; SC, S. chirayita. Several metabolites responsible for the differentiation of the two species are indicated in the loading plot (C) of PLS-DA.
Recovery tests were performed by using standard addition method to evaluate the accuracy of the method. Three different concentrations of each standard solution (low, medium and high levels) were spiked into a known amount of sample (no. 12). The spiked samples were then extracted, processed, and quantified in accordance with the established methods. The average recoveries were between 98.7% and 101.2% with RSD values of less than 2.8% for swertiamarin, gentiopicrin, isoorientin, oleanolic acid and loganic acid. These discriminating metabolites were simultaneously determined using the developed method, and the results are shown in Table 1. To confirm the reliability of the qHNMR assay, we compared the present results with literature data using paired t-test. As shown in Supplementary Table S2, the p values were all higher than 0.05, indicating that the results determined by the qHNMR method were comparable to those of previously reported data. A two-tailed t-test was performed on the contents of the discriminating metabolites selected by PLS-DA to reveal paired differences between the two species. Supplementary Fig. S5 shows that the concentrations of the nine metabolites significantly differed (p < 0.001) between S. chirayita and S. mussotii. S. mussotii contained more gentiopicrin, isoorientin, glucose, loganic acid, and choline, whereas the levels of swertiamarin, oleanolic acid, valine, and fatty acids were obviously lower than those in S. chirayita. These findings were in good agreement with the PLS-DA results. In addition, relative expression values of the nine metabolites in the two species were submitted to hierarchical cluster analysis to obtain information for their discriminating power and to visualize their expression levels. The result was visually displayed on the heatmap (Fig. 4). Again, all samples were clearly divided into two
groups: group A (S. mussotii) for samples with higher level of glucose, isoorientin, gentiopicrin, loganic acid, and choline and group B (S. chirayita) for samples with higher level of fatty acids, swertiamarin, valine, and oleanolic acid. In this study, by combining PCA, PLS-DA, t-test, and heatmap results, we found that gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, glucose, choline, fatty acids, and valine were the metabolic markers for discrimination of S. chirayita and S. mussotii. Recent investigations have revealed that gentiopicrin, swertiamarin, isoorientin, loganic acid, and oleanolic acid are the main active constituents of Swertia plants [6]. They have been proven to possess various pharmacological activities, such as hepatoprotective, anti-inflammatory, and antioxidant effects [6,14–16]. The results of this study confirm that these metabolites play an important role in controlling the quality of S. mussotii and S. chirayita because of their strong discriminating power. Glucose, fatty acids, and valine are known to exist in many plants. They are essential primary metabolites for plant growth. Supplementary Fig. S5 shows that the quantities of these three metabolites dramatically differed (p < 0.001) between S. chirayita and S. mussotii. For example, the glucose level was on average two times higher in S. mussotii than that in S. chirayita. The differences may be indicative of adaptation to the specific environmental conditions of growth locations. Several studies show that climatic factors may affect the accumulation of reducing sugars in plants [10,17], and the concentration of reducing sugars (e.g., glucose) is significantly higher in plants grown at low temperatures [18]. Therefore, growth environments may have important contributions to the differences in the primary metabolites between S. chirayita and S. mussotii. More in-depth studies are necessary to
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Acknowledgment The authors gratefully acknowledge the financial support from the Innovation Team Building Program of Sichuan Provincial Department of Education (No. 11TD004). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jpba.2014.06.014. References
Fig. 4. Heatmap visualization of key metabolite expression of S. mussotii and S. chirayita based on unsupervised hierarchical cluster analysis ((1) glucose; (2) isoorientin; (3) gentiopicrin; (4) loganic acid; (5) choline; (6) fatty acids; (7) swertiamarin; (8) valine; (9) oleanolic acid). Expression percentage (%) of metabolite (=content of the metabolite/mean value of the content in the two species × 100) is used as input data. Each column represents a metabolite and each row represents a sample. The intensity of color indicates metabolite expression value, green: lowest, red: highest. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
reveal the correlation between their metabolic patterns and ecological factors. 4. Conclusion In summary, a 1 H NMR-based metabolomics approach was successfully developed for the metabolic profiling and discrimination of two Swertia species. Gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, glucose, choline, fatty acids, and valine were first found to be responsible for the discrimination of S. chirayita and S. mussotii. Our studies demonstrated that S. chirayita and S. mussotii exhibited significant differences in their metabolite profiles, particularly with regard to pharmacologically active secondary metabolites. Therefore, their application with the same dose under the same name of Zangyinchen in traditional Tibetan medicine is inappropriate. Further studies are necessary to determine the influence of the interspecific metabolites variations on the therapeutic efficacy of Zangyinchen.
[1] Y.C. Yang, Book of Tibetan Medicines, Qing-hai People s Press, Xining, 1991. [2] Y.H. Cao, Y. Wang, J.N. Ye, Differentiation of Swertia Mussotii Franch from Artemisiae capillaris Herba by capillary electrophoresis with electrochemical detection, J. Pharm. Biomed. Anal. 39 (2005) 60–65. [3] M. Xu, Resistance of Swertia mussotii Franch against immunological liver injury of mice, J. Northwest A&F Univ. (Nat. Sci. Ed.) 41 (2013) 32–36. [4] Y. Chen, B. Huang, J.S. He, L. Han, Y.C. Zhan, Y.W. Wang, In vitro and in vivo antioxidant effects of the ethanolic extract of Swertia chirayita, J. Ethnopharmacol. 136 (2011) 309–315. [5] R. Nagalekshmi, A. Menon, D.K. Chandrasekharan, C.K.K. Nair, Hepatoprotective activity of Andrographis paniculata and Swertia chirayita, Food Chem. Toxicol. 49 (2011) 3367–3373. [6] G. Brahmachari, S. Mondal, A. Gangopadhyay, D. Gorai, B. Mukhopadhyay, S. Saha, A.K. Brahmachari, Swertia (Gentianaceae): chemical and pharmacological aspects, Chem. Biodivers. 1 (2004) 1627–1651. [7] G. Fan, M.Y. Zhang, X.D. Zhou, X.R. Lai, Q.H. Yue, C. Tang, W.Z. Luo, Y. Zhang, Quality evaluation and species differentiation of Rhizoma coptidis by using proton nuclear magnetic resonance spectroscopy, Anal. Chim. Acta 747 (2012) 76–83. [8] H.K. Kim, Y.H. Choi, R. Verpoorte, NMR-based metabolomic analysis of plants, Nat. Protoc. 5 (2010) 536–549. [9] G. Fan, L.H. Tao, Q.H. Yue, T.T. Kuang, C. Tang, Y.D. Yang, W.Z. Luo, X.D. Zhou, Y. Zhang, Metabolic discrimination of Rhizoma coptidis from different species using 1 H NMR spectroscopy and principal component analysis, Planta Med. 78 (2012) 641–648. [10] E.J. Kim, J. Kwon, S.H. Park, C. Park, Y.B. Seo, H.K. Shin, H.K. Kim, K.S. Lee, S.Y. Choi, D.H. Ryu, G.S. Hwang, Metabolite profiling of Angelica gigas from different geographical origins using 1 H NMR and UPLC–MS analyses, J. Agric. Food Chem. 59 (2011) 8806–8815. [11] J.F. Zhao, Resources survey and pharmacognosy investigation of Zangyinchen, in: Master Thesis, Beijing University of Chinese Medicine, Beijing, China, 2008. [12] R. Verpoorte, Y.H. Choi, H.K. Kim, NMR-based metabolomics at work in phytochemistry, Phytochem. Rev. 6 (2007) 3–14. [13] S. Chatterjee, S. Srivastava, A. Khalid, N. Singh, R.S. Sangwan, O.P. Sidhu, R. Roy, C.L. Khetrapal, R. Tuli, Comprehensive metabolic fingerprinting of Withania somnifera leaf and root extracts, Phytochemistry 71 (2010) 1085–1094. [14] Z.W. Liu, C.X. Chen, R.M. Jin, G.Q. Shi, C.Q. Song, Z.B. Hu, Studies on liverprotection and promoting bile secretion of gentiopicroside, Chin. Tradit. Herb. Drugs 33 (2002) 47–50. [15] V. Jaishree, S. Badami, Antioxidant and hepatoprotective effect of swertiamarin from Enicostemma axillare against d-galactosamine induced acute liver damage in rats, J. Ethnopharmacol. 130 (2010) 103–106. [16] J. Liu, Oleanolic acid and ursolic acid: research perspectives, J. Ethnopharmacol. 100 (2005) 92–94. [17] Y. Jung, J. Lee, H.K. Kim, B.C. Moon, Y. Ji, D.H. Ryu, G.S. Hwang, Metabolite profiling of Curcuma species grown in different regions using 1 H NMR spectroscopy and multivariate analysis, Analyst 137 (2012) 5597–5606. [18] L. Lunackova, E. Masarovicova, A. Lux, Respiration rate and chemical composition of Karwinskia roots as affected by temperature, Biol. Plant. 43 (2000) 611–613.
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Short communication
Metabolic discrimination of Swertia mussotii and Swertia chirayita known as “Zangyinchen” in traditional Tibetan medicine by 1 H NMR-based metabolomics Gang Fan a , Wei-Zao Luo c , Shang-Hua Luo a , Yan Li a , Xian-Li Meng a , Xiang-Dong Zhou b,∗ , Yi Zhang a,∗ a
College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China Department of Medicinal Chemistry, College of Pharmacy, Third Military Medical University, Chongqing 400038, China c Chongqing Academy of Chinese Materia Medica, Chongqing 400065, China b
a r t i c l e
i n f o
Article history: Received 18 February 2014 Received in revised form 8 June 2014 Accepted 9 June 2014 Available online 16 June 2014 Keywords: 1 H NMR Metabolomics Swertia chirayita Swertia mussotii Multivariate statistical analysis
a b s t r a c t Swertia mussotii Franch. and Swertia chirayita Buch.–Ham. have been commonly used under the same name “Zangyinchen” for the treatment of liver and gallbladder diseases in traditional Tibetan medicine. Detailed characterization and comparison of the complete set of metabolites of these two species are critical for their objective identification and quality control. In this study, a rapid, simple and comprehensive 1 H NMR-based metabolomics method was first developed to differentiate the two species. A broad range of metabolites, including iridoid glycosides, xanthones, triterpenoids, flavonoids, carbohydrates, and amino acids, were identified. Statistical analysis showed evident differences between the two species, and the major markers responsible for the differences were screened. In addition, quantitative 1 H NMR method (qHNMR) was used for the target analysis of the discriminating metabolites. The results showed that S. mussotii had significantly higher contents of gentiopicrin, isoorientin, glucose, loganic acid, and choline, whereas S. chirayita exhibited higher levels of swertiamarin, oleanolic acid, valine, and fatty acids. These findings indicate that 1 H NMR-based metabolomics is a reliable and effective method for the metabolic profiling and discrimination of the two Swertia species, and can be used to verify the genuine origin of Zangyinchen. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Swertia mussotii Franch., referred to as “Zangyinchen” in Chinese, is a well-known Tibetan medicinal plant. It has been commonly used for the treatment of liver and gallbladder diseases, such as hepatitis, cholecystitis and fatty liver [1]. S. mussotii is an important and frequently used crude drug in traditional Tibetan medicine because of its reliable therapeutic efficacy. Modern investigations have shown that S. mussotii can repair the fibrillation of the liver [2] and alleviate the damage of immunological liver injury [3]. Swertia chirayita Buch.–Ham., also known as “Zangyinchen” in Chinese, is a widely used herbal medicine in India, Nepal and China. S. chirayita possesses various pharmacological activities, such as
∗ Corresponding authors at: College of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Road, Weijiang, Chengdu 611137, Sichuan, China. Tel.: +86 23 68753701/+86 28 61800274/+86 28 61800160. E-mail addresses: [email protected] (X.-D. Zhou), [email protected] (Y. Zhang). http://dx.doi.org/10.1016/j.jpba.2014.06.014 0731-7085/© 2014 Elsevier B.V. All rights reserved.
antioxidant, anti-inflammatory and hepatoprotective [4–6] effects. In China, S. chirayita is frequently used to treat damp-heat and quench the fire of the liver and gallbladder in traditional Tibetan medicine system [1]. S. mussotii and S. chirayita have different morphological characteristics from a taxonomy perspective. However, herbal medicines are usually chopped to sell in markets or powdered to use in medical institutions. Therefore, these two crude drugs are easily confused with each other, and identifying them based on their morphological features is difficult. Suitable metabolic markers are crucial for their objective discrimination. At present, S. mussotii and S. chirayita are commonly used either as mixtures or substitutes for each other in traditional Tibetan medicine system because they have similar phytochemicals and pharmacological activities. However, whether the two herbal medicines can be used interchangeably remains uncertain, and evidence for their equivalent application is still lacking. Therefore, a rapid and effective method should be developed to evaluate the metabolites differences between S. chirayita and S. mussotii for their objective identification and safe use.
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Metabolomics has recently attracted increasing attention because of its holistic characteristics, and has been developed as an important method for the modern research of herbal medicines. HPLC–MS, GC–MS and 1 H NMR are the most commonly used analysis techniques in plant metabolomics. Compared with the two chromatographic approaches, 1 H NMR technique is regarded as the most promising tool because of its simple sample preparation, simultaneous detection of primary and secondary metabolites in a single run, and ease of quantitative analysis of the most abundant metabolites [7,8]. Therefore, in the past decade, 1 H NMR-based metabolomics approach has been widely used for comprehensive qualitative and/or quantitative analysis of the complete set of metabolites present in herbal medicines for species differentiation [9] or geographical origin discrimination [10]. However, no 1 H NMR metabolomics analysis has been performed on Swertia. In this study, we present the first report on the metabolite profiling analysis of two Swertia species, and develop a simple, rapid and reliable method for metabolic discrimination of S. chirayita and S. mussotii using 1 H NMR spectroscopy. 2. Materials and methods 2.1. Plant materials A total of 23 batches of samples were collected from different Tibetan medicine companies and herbal markets or harvested from various locations. Detailed information of the plant materials is shown in Supplementary Table S1. The botanical origins of the harvested samples were authenticated by Professor Wei-Zao Luo, and voucher specimens were deposited in the Chongqing Academy of Chinese Materia Medica, Chongqing, China. The botanical origins of the commercial samples were identified by microscopic examination based on their different microscopic characteristics as found in our previous study [11]. 2.2. Chemicals and reagents Methanol-d4 (CD3 OD, 99.8%), deuterium oxide (D2 O, 99.9%), chloroform-d (CDCl3 , 99.8%) and dimethyl sulfoxide-d6 (DMSO-d6 , 99.9%) were purchased from Cambridge Isotope Laboratories (Miami, FL, USA). KH2 PO4 was supplied by Chongqing Beibei Chemical Reagent Co., Ltd. (Chongqing, China). 3-(Trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP, 99%) was purchased from Sigma-Aldrich (St. Louis, Mo, USA). The standard compounds of oleanolic acid, sucrose, and alanine were purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). Swertiamarin, sweroside, loganic acid and gentiopicrin were purchased from Chengdu Herbpurify Co., Ltd. (Chengdu, China). 1,8-Dihydroxy-3,5-dimethoxy xanthone, 1,8-dihydroxy-3,7-dimethoxy xanthone, 1-hydroxy-3,7,8trimethoxy xanthone, 1,5,8-trihydroxy-3-methoxy xanthone, 7-O-[␣-l-rhamnopyranosyl-(1 → 2)--d-xylopyranosyl]-1,8dihydroxy-3-methoxy xanthone, mangiferin, and isoorientin were isolated and purified from S. mussotii, and their structures were unambiguously identified based on their 1 H NMR and 13 C NMR spectral data. Their purities were all determined to be over 98% by 1 H NMR and HPLC/UV analyses. 2.3. Sample preparation All the samples were oven-dried at 50 ◦ C until a constant weight was obtained. The dried powders (200 mg) were accurately weighed into a clean Erlenmeyer flask, and 1.0 mL of CD3 OD and 0.3 mL of KH2 PO4 buffer (pH 6.0) in D2 O containing 0.04% (wt/wt) TSP were added. The Erlenmeyer flask was then sealed and sonicated at room temperature for 30 min. The extracting solution was
365
filtered through a 0.45 m membrane filter. Exactly 0.6 mL of filtrate was transferred into a standard 5 mm NMR tube for 1 H NMR analysis. 2.4.
1H
NMR analysis and data processing
All 1 H NMR spectra were acquired using a Varian 400 MHz NMR spectrometer (Agilent Technologies Inc., USA). The following parameters were used in all 1D 1 H NMR experiments: 128 transients were collected into 32 K data points with a spectral width of 6410.3 Hz, an acquisition time of 2.556 s and a relaxation delay of 4.0 s. The resulting spectra were phase-adjusted, baseline-corrected and referenced to an internal standard (TSP) at 0.00 ppm using MestReNova software (version 6.2, Mestrelabs Research SL). The NMR spectral data were reduced into 0.04 ppm spectral buckets corresponding to the region of ı 8.50–0.50. The regions of ı 3.34–3.26 and ı 4.90–4.74 were removed because of the residual signal of methanol and water, respectively. The spectra were then normalized to the total signal area and converted to a text file for statistical analysis. For quantitative 1 H NMR analysis, peak areas of the quantified signals of the analytes and internal standard were obtained by manual integration. Moreover, 2D NMR spectra were acquired using standard pulse programs. 2D J-resolved spectra were acquired using 64 scans per 128 increments for F1 and 8K for F2 using spectral widths of 6410.3 Hz in F2 and 50 Hz in F1. A 1.0 s relaxation delay was employed. The J-resolved spectra were tilted by 45◦ , and then symmetrized about F1. 2D 1 H–1 H COSY spectra were acquired with 1.0 s relaxation delay, 6410.3 Hz spectral width in both dimensions. The COSY spectra were transformed with a sine-bell weighting function (SSB = 0). 2.5. Quantification of the selected metabolites by 1 H NMR method Since the signal intensity is absolutely proportional to the molar concentration of metabolites in a 1 H NMR spectrum [7,8], the quantification of the selected metabolites can be performed by using ratio method. A known internal standard (in this case TSP) is used to determine the concentration of the targeted metabolites by using the following equation: mX = mST ×
A MW N X X ST AST
×
MWST
×
NX
,
where mX is the unknown mass of the targeted analyte and mST is the mass of the TSP; AX and AST are the integral areas for the selected signals, NX and NST are the number of protons generating the integral signals, MWX and MWST are the molecular weights of the targeted analyte and TSP, all respectively. 2.6. Statistical analysis Principal component analysis (PCA) was used to examine the intrinsic variation in the dataset and sort samples into groups. Partial least squares discriminant analysis (PLS-DA) was also performed to maximize separation between groups. PCA and PLS-DA were conducted with unit variance scaling (UV) using SIMCA-P software (version 11.5, Umetrics, Umeå, Sweden). Two-tailed t-test and analysis of variance (ANOVA) were done with GraphPad Prism software version 5.0 (GraphPad Software Inc., San Diego, CA). Statistical significance was determined on a 95% probability level (p < 0.05). Heatmap was generated using MultiExperiment Viewer software (MeV, version 4.8.1) based on unsupervised hierarchical clustering. Pearson’s correlation was used as distance measure and average linkage for agglomeration.
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Fig. 1. Representative 1 H NMR spectra of S. chirayita (sample no. 1) (A) and S. mussotii extracts (sample no. 12) (B). Expanded spectral regions of S. mussotii (C) low field region; (D) middle field region; (E) high field region. Signal assignments: (1) swertiamarin; (2) sweroside; (3) 1-hydroxy-3,7,8-trimethoxy xanthone; (4) 7-O[␣-l-rhamnopyranosyl-(1 → 2)--d-xylopyranosyl]-1,8-dihydroxy-3-methoxy xanthone; (5) gentiopicrin; (6) mangiferin; (7) 1,8-dihydroxy-3,7-dimethoxy xanthone; (8) isoorientin; (9) 1,8-dihydroxy-3,5-dimethoxy xanthone; (10) 1,5,8-trihydroxy-3-methoxy xanthone; (11) gallic acid; (12) oleanolic acid; (13) sucrose; (14) ␣-glucose; (15) -glucose; (16) choline; (17) loganic acid; (18) formic acid; (19) acetic acid; (20) succinic acid; (21) alanine; (22) valine; (23) saturated fatty acids; (24) unsaturated fatty acids; (25) sterols.
3. Results and discussion 3.1. Optimization of extraction solvents Five solvent systems, involving CD3 OD–D2 O (1:0.3, v/v), CD3 OD, CDCl3 , D2 O, and DMSO-d6 , were designed and optimized. By comparing the 1 H NMR spectra of the same sample under different extraction conditions (Supplementary Fig. S1) based on ANOVA followed by Tukey’s multiple comparison tests, we found that CDCl3 and D2 O could extract more fatty acids and sugars, respectively. However, the intensity of signals at ı 7.70–5.20 corresponding
to secondary metabolites in CDCl3 and D2 O was significantly lower than that in other solvents. DMSO-d6 could simultaneously extract primary and secondary metabolites, but most signals in the 1 H NMR spectra were severely overlapped. CD3 OD and CD3 OD–D2 O had similar metabolic profiling, and both could extract global metabolites. However, sugars and amino acids were more abundant in CD3 OD–D2 O compared with those in CD3 OD. Finally, CD3 OD–D2 O was selected as the preferred solvent, because it could simultaneously extract xanthones, iridoid glycosides, triterpenoids, flavonoids, carbohydrates, and amino acids present in the Zangyinchen in a single run.
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Fig. 2. Chemical structures of major secondary metabolites detected in Swertia extracts by means of 1 H NMR spectroscopy ((A) xanthones; (B) iridoid glycosides; (C) flavonoids; (D) triterpenoids).
3.2. Identification of metabolites Representative one-dimensional 1 H NMR spectra of S. mussotii and S. chirayita are shown in Fig. 1. The metabolites were assigned and identified by carefully comparing the 1 H NMR spectra of the standard compounds, spiking experiments and comparisons to the literature [8,12,13]. Moreover, the 2D-NMR spectra, including 1 H–1 H COSY and J-resolved spectra, were used to provide supporting information for metabolite assignments (Supplementary Figs. S2 and S3). Eventually, 25 metabolites (Table 1) were identified
in the 1 H NMR spectra. Chemical structures of representative secondary metabolites detected in Swertia extracts are shown in Fig. 2. 3.3. Multivariate statistical analysis The 1 H NMR data sets of all samples were subjected to PCA analysis. The PCA score plot of 23 samples (triplicates for each sample) is shown in Fig. 3A. Smaller between-extraction errors were observed according to the close proximity of the observations, thereby indicating the sample homogeneity and good repeatability
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Table 1 Identification and quantification of metabolites in two Swertia extracts by 1 H NMR spectroscopy. Peak no.
1
Metabolites
Swertiamarin
2
Sweroside
3 4
1-Hydroxy-3,7,8-trimethoxy xanthone 7-O-[␣-l-rhamnopyranosyl-(1 → 2)-d-xylopyranosyl]-1,8-dihydroxy-3methoxy xanthone Gentiopicrin
5
10 11 12
Mangiferin 1,8-Dihydroxy-3,7-dimethoxy xanthone Isoorientin 1,8-Dihydroxy-3,5-dimethoxy xanthone 1,5,8-Trihydroxy-3-methoxy xanthone Gallic acid Oleanolic acid
13 14 15 16 17 18 19 20 21 22 23 24
Sucrose ␣-Glucose -Glucose Choline Loganic acid Formic acid Acetic acid Succinic acid Alanine Valine Saturated fatty acids Unsaturated fatty acids
25
Sterols
6 7 8 9
Chemical shifts, multiplicitya and coupling constants of characteristic signals
7.67 (s), 5.71 (d, J = 1.6 Hz), 5.39 (m), 5.30 (m), 4.69 (d, J = 8.0 Hz), 4.39 (m) 7.61 (d, J = 2.5 Hz), 5.51 (m), 5.30 (m), 4.72 (d, J = 7.9 Hz), 4.47 (m), 2.75 (m) 7.54 (d, J = 9.2 Hz), 7.24 (d, J = 9.2 Hz) 7.51 (d, J = 9.2 Hz), 6.87 (d, J = 9.2 Hz), 1.20 (d, J = 6.4 Hz)
Contents (mg/g) of the discriminating metabolites (mean ± standard deviation) S. mussotii
S. chirayita
2.04 ± 0.30
4.27 ± 0.71
b
n.q.
n.q.
n.q. n.q.
n.q. n.q.
7.46 (d, J = 1.2 Hz), 5.75 (m), 5.66 (d, J = 2.8 Hz), 5.08 (m), 4.72 (d, J = 7.8 Hz) 7.40 (s), 6.82 (s), 6.39 (s) 6.85 (d, J = 9.2 Hz), 6.25 (d, J = 2.0 Hz)
14.88 ± 1.84
2.97 ± 0.45
n.q. n.q.
n.q. n.q.
7.36 (m), 6.93 (d, J = 8.8 Hz), 6.65 (s) 7.33 (d, J = 9.0 Hz), 6.61 (d, J = 9.0 Hz)
3.48 ± 0.63 n.q.
2.31 ± 0.28 n.q.
7.24 (d, J = 8.8 Hz), 6.79 (d, J = 8.8 Hz) 7.02 (s) 5.25 (s), 1.10 (s), 0.95 (s), 0.94 (s), 0.91 (s), 0.90 (s), 0.76 (s), 0.75 (s) 5.39 (d, J = 3.8 Hz), 4.14 (d, J = 8.4 Hz) 5.15 (d, J = 3.6 Hz) 4.53 (d, J = 8.0 Hz) 3.21 (s) 5.27 (d, J = 3.2 Hz), 1.08 (d, J = 6.8 Hz) 8.43 (s) 2.01 (s) 2.56 (s) 1.50 (d, J = 7.2 Hz) 1.05 (d, J = 6.8 Hz), 1.01 (d, J = 6.8 Hz) 0.88 (m), 1.24–1.35 (m), 1.57 (m), 2.27 (t, J = 7.4 Hz) 0.88 (m), 1.24–1.35 (m), 1.57 (m), 2.04 (m), 2.27 (t, J = 7.4 Hz), 2.74(m), 5.34 (m) 0.69 (s)
n.q. n.q. 3.28 ± 0.50
n.q. n.q. 5.47 ± 0.60
n.q. 23.79 ± 9.43
n.q. 11.59 ± 2.23
0.70 ± 0.10 5.03 ± 0.93 n.q. n.q. n.q. n.q. 0.67 ± 0.13 AX /AST c = 9.30 ± 0.98
0.28 ± 0.07 1.29 ± 0.23 n.q. n.q. n.q. n.q. 1.49 ± 0.21 AX /AST = 12.95 ± 2.17
n.q.
n.q.
a
Multiplicity: s, singlet; d, doublet; t, triplet; and m, multiplet. n.q., not quantified, because no significant differences were observed between the two species for these metabolites based on previous PLS-DA results. The content of fatty acids is not available because its molecular weight is unknown, and so AX /AST value is used for the comparison between species. AX and AST represent the integral areas of the quantified signals of the fatty acids and the TSP, respectively. b c
for the developed method. In addition, a clear separation between S. chirayita and S. mussotii was obtained by PC1, which indicated that their metabolic profiles were significantly different. In the PCA score plot, all S. mussotii samples were projected in a larger region compared with S. chirayita, indicating that the intraspecific variations in S. mussotii were larger than those in S. chirayita. This may be due to the fact that S. mussotii was collected from a broader area. Different geographical origins resulted in the between-sample metabolic differences. However, the biggest source of variance highlighted by PCA mainly resulted from the differences between S. chirayita and S. mussotii (i.e., interspecific variations), although their intraspecific variations were also observed. These findings indicated that the species was the most important factor in terms of the effect on metabolite variation compared with other factors. PLS-DA, a supervised analysis method, was applied to maximize separation between groups. This model was validated with 200 permutation tests. As shown in Supplementary Fig. S4, the PLS-DA model had a proper R2 Y intercept value of 0.127 and Q2 Y intercept value of −0.631. The score plot of PLS-DA (Fig. 3B) showed that this model could reliably differentiate S. chirayita samples from S. mussotii ones, which was in good agreement with the results of PCA. The corresponding loading plot of PLS-DA (Fig. 3C) showed that the separation mainly arose from the signals of gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, choline, fatty acids, and valine. Moreover, several metabolites corresponding to sugars were also found to play an important role in separating the two species.
However, it was very difficult to unambiguously identify them because the sugars regions (ı 3.85–3.32) were highly overlapped. Based on the positive or negative effects of these metabolites in PLS1, we can conclude that gentiopicrin, isoorientin, loganic acid, choline, ␣-glucose, -glucose, and several unidentified sugars were more abundant in S. mussotii, whereas the levels of swertiamarin, oleanolic acid, valine, and fatty acids were higher in S. chirayita. 3.4. Quantification of metabolites To give a clearer idea of the differences of individual metabolite levels between the two species, selected metabolites were quantitatively analyzed using qHNMR method. The characteristic signals of swertiamarin at ı 7.67 (s), gentiopicrin at ı 7.46 (d), isoorientin at ı 6.65 (s), loganic acid at ı 5.27 (d), choline at ı 3.21 (s), ␣-glucose at ı 5.15 (d), -glucose at ı 4.53 (d), oleanolic acid at ı 0.75(s), valine at ı 1.01 (d), and fatty acids at ı 2.27 (t) were selected for quantification. The intra-day precision of the qHNMR method was evaluated by six replicate measurements of the same sample (no. 12) in one day. The relative standard deviation (RSD) values were in the range of 1.1–2.5%. The method repeatability was evaluated by analyzing six different working solutions independently prepared from the same sample (no. 12). The RSD values ranged from 1.4% to 2.9%. The stability was assessed by analyzing the same sample solution (no. 12) within 24 h. ANOVA indicated that there were no significant differences between time of incubations (p > 0.05).
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Fig. 3. Score plots of PCA (A) and PLS-DA (B). All the samples are clearly classified into two groups corresponding to the two species, respectively. Abbreviations: SM, S. mussotii; SC, S. chirayita. Several metabolites responsible for the differentiation of the two species are indicated in the loading plot (C) of PLS-DA.
Recovery tests were performed by using standard addition method to evaluate the accuracy of the method. Three different concentrations of each standard solution (low, medium and high levels) were spiked into a known amount of sample (no. 12). The spiked samples were then extracted, processed, and quantified in accordance with the established methods. The average recoveries were between 98.7% and 101.2% with RSD values of less than 2.8% for swertiamarin, gentiopicrin, isoorientin, oleanolic acid and loganic acid. These discriminating metabolites were simultaneously determined using the developed method, and the results are shown in Table 1. To confirm the reliability of the qHNMR assay, we compared the present results with literature data using paired t-test. As shown in Supplementary Table S2, the p values were all higher than 0.05, indicating that the results determined by the qHNMR method were comparable to those of previously reported data. A two-tailed t-test was performed on the contents of the discriminating metabolites selected by PLS-DA to reveal paired differences between the two species. Supplementary Fig. S5 shows that the concentrations of the nine metabolites significantly differed (p < 0.001) between S. chirayita and S. mussotii. S. mussotii contained more gentiopicrin, isoorientin, glucose, loganic acid, and choline, whereas the levels of swertiamarin, oleanolic acid, valine, and fatty acids were obviously lower than those in S. chirayita. These findings were in good agreement with the PLS-DA results. In addition, relative expression values of the nine metabolites in the two species were submitted to hierarchical cluster analysis to obtain information for their discriminating power and to visualize their expression levels. The result was visually displayed on the heatmap (Fig. 4). Again, all samples were clearly divided into two
groups: group A (S. mussotii) for samples with higher level of glucose, isoorientin, gentiopicrin, loganic acid, and choline and group B (S. chirayita) for samples with higher level of fatty acids, swertiamarin, valine, and oleanolic acid. In this study, by combining PCA, PLS-DA, t-test, and heatmap results, we found that gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, glucose, choline, fatty acids, and valine were the metabolic markers for discrimination of S. chirayita and S. mussotii. Recent investigations have revealed that gentiopicrin, swertiamarin, isoorientin, loganic acid, and oleanolic acid are the main active constituents of Swertia plants [6]. They have been proven to possess various pharmacological activities, such as hepatoprotective, anti-inflammatory, and antioxidant effects [6,14–16]. The results of this study confirm that these metabolites play an important role in controlling the quality of S. mussotii and S. chirayita because of their strong discriminating power. Glucose, fatty acids, and valine are known to exist in many plants. They are essential primary metabolites for plant growth. Supplementary Fig. S5 shows that the quantities of these three metabolites dramatically differed (p < 0.001) between S. chirayita and S. mussotii. For example, the glucose level was on average two times higher in S. mussotii than that in S. chirayita. The differences may be indicative of adaptation to the specific environmental conditions of growth locations. Several studies show that climatic factors may affect the accumulation of reducing sugars in plants [10,17], and the concentration of reducing sugars (e.g., glucose) is significantly higher in plants grown at low temperatures [18]. Therefore, growth environments may have important contributions to the differences in the primary metabolites between S. chirayita and S. mussotii. More in-depth studies are necessary to
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Acknowledgment The authors gratefully acknowledge the financial support from the Innovation Team Building Program of Sichuan Provincial Department of Education (No. 11TD004). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jpba.2014.06.014. References
Fig. 4. Heatmap visualization of key metabolite expression of S. mussotii and S. chirayita based on unsupervised hierarchical cluster analysis ((1) glucose; (2) isoorientin; (3) gentiopicrin; (4) loganic acid; (5) choline; (6) fatty acids; (7) swertiamarin; (8) valine; (9) oleanolic acid). Expression percentage (%) of metabolite (=content of the metabolite/mean value of the content in the two species × 100) is used as input data. Each column represents a metabolite and each row represents a sample. The intensity of color indicates metabolite expression value, green: lowest, red: highest. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
reveal the correlation between their metabolic patterns and ecological factors. 4. Conclusion In summary, a 1 H NMR-based metabolomics approach was successfully developed for the metabolic profiling and discrimination of two Swertia species. Gentiopicrin, swertiamarin, oleanolic acid, isoorientin, loganic acid, glucose, choline, fatty acids, and valine were first found to be responsible for the discrimination of S. chirayita and S. mussotii. Our studies demonstrated that S. chirayita and S. mussotii exhibited significant differences in their metabolite profiles, particularly with regard to pharmacologically active secondary metabolites. Therefore, their application with the same dose under the same name of Zangyinchen in traditional Tibetan medicine is inappropriate. Further studies are necessary to determine the influence of the interspecific metabolites variations on the therapeutic efficacy of Zangyinchen.
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