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Metabolic profiling of Angelica acutiloba roots utilizing gas chromatography–time-of-flight–mass spectrometry for quality assessment based on cultivation area and cultivar via multivariate pattern recognition
JOURNAL OF BIOSCIENCE AND BIOENGINEERING Vol. 105, No. 6, 655–659. 2008 DOI: 10.1263/jbb.105.655
© 2008, The Society for Biotechnology, Japan
Metabolic Profiling of Angelica acutiloba Roots Utilizing Gas Chromatography–Time-of-Flight–Mass Spectrometry for Quality Assessment Based on Cultivation Area and Cultivar via Multivariate Pattern Recognition Sukanda Tianniam,1 Lucksanaporn Tarachiwin,2 Takeshi Bamba,3 Akio Kobayashi,1 and Eiichiro Fukusaki1* Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan,1 Nara Prefectual Small & Medium Sized Enterprises Support Corporation, 129-1 Kashiwagi, Nara city, Nara 630-8031, Japan,2and Department of Applied Environmental Biology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan3 Received 28 December 2007/Accepted 24 March 2008
Gas chromatography time-of-flight mass spectrometry was applied to elucidate the profiling of primary metabolites and to evaluate the differences between quality differences in Angelica acutiloba (or Yamato-toki) roots through the utilization of multivariate pattern recognition-principal component analysis (PCA). Twenty-two metabolites consisting of sugars, amino and organic acids were identified. PCA analysis successfully discriminated the good, the moderate and the bad quality Yamato-toki roots in accordance to their cultivation areas. The results signified two reducing sugars, fructose and glucose being the most accumulated in the bad quality, whereas higher quantity of phosphoric acid, proline, malic acid and citric acid were found in the good and the moderate quality toki roots. PCA was also effective in discriminating samples derive from different cultivars. Yamato-toki roots with the moderate quality were compared by means of PCA, and the results illustrated good discrimination which was influenced most by malic acid. Overall, this study demonstrated that metabolomics technique is accurate and efficient in determining the quality differences in Yamato-toki roots, and has a potential to be a superior and suitable method to assess the quality of this medicinal plant. [Key words: metabolomics, Angelica acutiloba, multivariate pattern recognition, gas chromatography– time-of-flight–mass spectrometry, principal component analysis]
marker compounds ferulic acid and ligustilide (3–6). The drawback for this approach is the universality of these metabolites. Both ferulic acid and ligustilides are not unique chemical identities of toki roots, as they are also distributed in other roots of traditional Chinese medicinal plants, such as Ligusticum chuangxiong, Cnidium officinal, Rhizoma Chuanxiong and Cnidii Rhizoma (7–10). In addition, when quantifying ligustilides, the result was found to be unreliable and difficult to execute as it is extremely versatile; it could be oxidized, isomerized and dimerized into other pthalide groups (7–9, 11, 12). Some pthalides decompose rapidly at high temperatures (12). Therefore, as toki roots are pretreated (washed) in hot water and dried (10), quantifying pthalides is not an appropriate alternative method. Thus, it is crucial to find an alternative analytical method that is accurate, effective and effortless. If such procedure could be established, it could be standardized method which can be utilized as a quality control (QC) parameter for commercial application of toki roots. Metabolomics is an interdisciplinary tool that includes a quantitative exhaustive profiling of all metabolites contained
Dried roots of Angelica acutiloba (Yamato-toki in Japanese) are one of the popular varieties of Traditional Chinese Medicine (TCM) that have been used for its pharmacological benefits (1) in treating female gynecological illnesses, such as menstrual disorders, urgent premature birth, and menopause (2). For these reasons, Yamato-toki roots are widely commercialized in Japan. However, as Yamato-toki roots are traditional medicines sold primarily by professional herbalists, commercial values of them are determined by their subjective observations of smell, taste and appearances. This makes quality assessment in the markets extremely difficult and impractical when considering mass productions of this product. Therefore, the need for a practical and standardized quality assessment method for industrialized Yamato-toki roots is a critical topic. Currently, toki’s qualities, not only Yamato-toki, but also Chinese and Korean Angelica roots, are being assessed by determining the concentration of two pharmacological bio* Corresponding author. e-mail: [email protected] phone/fax: +81-(0)6-6879-7424 655
656
J. BIOSCI. BIOENG.,
TIANNIAM ET AL.
TABLE 1. Description of Angelica acutiloba root samples Toki 1 2 3 4
Seed origin (cultivars) Anonymous Fukuda Shoten Fukuda Shoten Fukuda Shoten
Area of cultivation Ofuka, Nara, Japan Niigata, Coast of Sea of Japan, Japan Aomori, Nara, Japan Sakurai, Nara, Japan
in a target organism through the use of high throughput machines, such as gas chromatography mass spectrometry (GC-MS) (13). Metabolomics enables sample classification of diverse biological status, origin or quality in samples, by means of chemometrics, such as principal component analysis (PCA). Therefore, it should be easier, faster and more accurate to analyze large amount of samples, especially with GC-MS as it provides high sensitivity, reproducibility and repeatability (14). Piao et al. (15) have authenticated Korean Angelica, Angelica gigas, from Chinese and Japanese Angelica sp. by applying metabolomics method. This research underlined two unique metabolites decursin and decursinol angelate. These metabolites are found exclusively in A. gigas thus they could be used as the influential factors for good PCA discrimination with the other two species. Their result demonstrated that metabolomics technique is a good candidate, and should be appropriate to distinguish differences in Japanese toki roots. In our study, a combination of gas chromatography–time-of-flight–mass spectrometry (GC-TOF-MS) based on pattern recognition analysis techniques was developed for the quality evaluation of Yamato-toki roots. In this new approach, chemical fingerprinting was used instead of specific marker compounds to differentiate the quality, which gave faster and more reliable results compared to that obtained from the sensory test. MATERIALS AND METHODS Solvents and reagents Analytical grade methanol and chloroform purchased from Wako (Osaka) were used as extraction solvents. The internal standard, ribitol and pyridine, were also purchased from Wako. Derivatizing reagents, methoxyamine hydrochloride and N-methyl-N-(trimethylsilyl) trifluroacetamide (MSTFA) were purchased from Sigma (St. Louis, MO, USA) and GL Science (Tokyo), respectively. Toki samples All Yamato-toki root samples were provided by a folk medicine company, Fukuda Shoten (Nara) and an anonymous provider. The qualities were determined by a veteran based on the sensory test as described in Table 1. The Toki roots were ground into powder with a Wonder Blender (Osaka Chemical, Osaka). The ground samples were vacuum sealed and kept under −30°C until analysis. Sample extraction Forty milligrams of ground sample was exposed to 1000 µl MeOH : H2O : CH3Cl (2.5: 1:1) and 60 µl ribitol (20 mg ml–1). The mixture was then shaken at 20 Hz for 5 min with a mixer mill, and centrifuged (model 5415R; Eppendorf, Hamburg, Germany) at 13,200 rpm under 4°C for 3 min. Nine hundred microliters of supernatant containing hydrophilic primary metabolites was mixed with 400 µl of milli-Q H2O, and re-centrifuged under the same conditions. Four hundred microliters supernatant was collected and dried in a centrifugal concentrator (model VC-36S; Taitec, Saitama) at room temperature for approximately 2 h, followed by a drying process in a freeze dryer over night before derivatization. Sample derivatization Fifty microliters of methoxyamine hydrochloride in pyridine (20 mg ml–1) was added to the dried
Quality Moderate Bad Moderate Good
hydrophilic crude extract, and incubated in thermomixer comfort (Eppendorf AG model 5355) at 30°C for 90 min in order to induce the methoxymation reaction. One hundred microliters of MSTFA was then added and incubated again at 37°C for 30 min, in order to induce the silylation reaction before injecting to the GC-TOF/MS machine. GC-TOF-MS parameters One microliter of each derivatized sample was injected into the Agilent 6890N Gas Chromatograph by an Agilent 7683B Autosampler (Agilent, Atlanta, GA, USA) with a split ratio of 25 and separated by a 30 m × 0.25 mm I.D. ×0.25 µm WCOT fused silica capillary column (Varian, Lakeforest, CA, USA). The injector temperature was set at 230°C, where the gas flow rate was set at 1 ml/min. The column temperature maintained at 80°C for 2 min, and then gradually increased to 330°C at an increasing rate of 15°C/min. The column effluent was later introduced into Pegasus III time-of-flight mass spectrometer (LECO, St. Joseph, MI, USA) through the transfer line set at 250°C and the ion source temperature was at 200°C. The detected mass range was 85–500 m/z, where the detector voltage was set at 1650 V. Chromatographic data processing and compound identification Raw chromatographic data acquired from GC-TOF-MS analysis were processed by an in-house algorithm, ChromaTOF (ver. 2.32, LECO), in which automatic peak detection and mass spectrum deconvolution (compound identification) were performed with references to an in-house library, Max-Planck Institute of molecular plant physiology library, and to the main library, hierarchically in this order. Then, the data was transferred and baselinecorrected by the LineUp software (Informatrix, Woodinville, WA, USA). The data was normalized with the peak area of the ribitol, and subjected to other prepossessing procedures using the in-house algorithm, Pirotrans. Multivariate analysis The processed matrix data was subjected into PCA using the commercial software Pirouette (Informatrix). It created the secondary dimension score plots to visualize the contrast between different quality samples. The reason for the clusters separation was indicated by the loading plots of the corresponding principal components.
RESULTS AND DISCUSSION GC-TOF-MS chromatographic data of various qualities of Yamato-toki was applied to the multivariate pattern recognition, specifically to PCA. PCA converts complex chromatographic data into comprehensible matrix data sets. Furthermore, the program can visualize the differences existing within the data, while identifying the significant variables that influences the discrimination in quality. This makes this analysis extremely efficient, as results can be acquired with high reliability and accuracy. Twenty-two primary metabolites were identified based on the in-house library, ChromaTOF (Fig. 1). The corresponding fragment patterns were in good agreement to those in the libraries consisting of alanine, propanedioic acid, valine, phosphoric acid, proline, glycine, succinic acid, propanoic acid, fumaric acid, serine, pipecolic acid, threonine, malic
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TABLE 2. The detected chromatographic and spectrometric data of the 22 identified compounds analyzed by GC-TOF-MS
FIG. 1. Metabolite identification of 22 chemical constituents found in Angelica acutiloba roots through GC-TOF-MS analysis: 1, alanine; 2, propanedioic acid; 3, valine; 4, phosphoric acid; 5, proline; 6, glycine; 7, succinic acid; 8, propanoic acid; 9, fumaric acid; 10, serine; 11, pipecolic acid; 12, threonine; 13, malic acid; 14, GABA; 15, ornithine; 16, asparagine; 17, citric acid; 18, quinic acid; 19, fructose; 20, glucose; 21, galacturonic acid; 22, inositol.
FIG. 2. PCA score plot with respect to PCs 1 and 2 discriminating all four Angelica acutiloba root samples (toki no. 1, 2, 3 and 4).
acid, 4-aminobutyric acid (GABA), ornithine, asparagine, citric acid, quinic acid, fructose, glucose, galacturonic acid and inositol. The corresponding retention times and their fragment patterns are illustrated in Table 2. All chromatographic data were peak-aligned to that of the best quality toki (toki no. 4), baseline-corrected, normalized by ribitol’s peak area, and spectra binning were carried out prior to PCA. The best quality discrimination results were obtained when standard normal variate (SNV) was applied in data transformation. PCA analysis of all four toki samples showed mutual discrimination (Fig. 2). The score plot indicates that the separation, with reference to the first principal component (PC1 = 51.3%) describes the variations that exist between good, moderate and bad quality toki. The influential factor that should determine these classifications is the cultivation areas. On the other hand, the second principal component (PC2= 25.2%) illustrates the differences influenced by their cultivar origin. Toki sample provided by Fukuda-shoten (toki no. 2, 3 and 4) were grouped altogether on the same level whereas as those from an anonymous provider (toki no. 1) clustered at the opposite end. Therefore, in order to study the effect of cultivation area on the quality of Yamato-toki roots, toki samples derived from the same cultivar (Fukuda Shoten, Nara) were compared. The geographical conditions influencing the toki
Identified tR Mass fragments metabolites (min) Alanine 5.27 116, 147 Propanedioic acid 6.38 73, 147, 305 Valine 6.48 100, 144, 218 Phosphoric acid 7.03 133, 299, 314 Proline 7.37 142 Glycine 7.42 85, 147, 100, 174, 248 Succinic acid 7.48 147, 247 Propanoic acid 7.58 73, 75, 1 Fumaric acid 7.83 147, 245 Serine 7.87 100, 204, 218, 248 Pipecolic acid 7.99 156 Threonine 8.10 117, 101, 147, 218, 219, 291 Malic acid 9.02 147, 233 4- aminobutyric acid 9.42 147, 86, 100, 174, 304 Ornithine 10.06 102, 147 Asparagine 10.50 116, 132, 147, 188, 231 Citric acid 11.53 147, 211, 273 Quinic acid 11.78 147, 255, 345 Fructose 1 11.87 103, 147, 217, 307 Fructose 2 11.91 103, 147, 217, 307 Glucose 1 12.05 103, 147, 160, 205, 319 Glucose 2 12.18 103, 147, 160, 205, 319 Galacturonic acid 12.73 103, 160, 333 Inositol 13.30 147, 191, 217, 305, 318 Bold numbers represent the highest intensity mass ion of referred compound.
roots quality was clearly observed with PC1 of the PCA score plot in the first and second PCs (Fig. 3). PC1 (81.3%) best disassociated the toki samples in accordance to their qualities, because the clustering was also plotted in an orderly fashion from the good to the bad qualities (left to right) of the matrix. Observation of loading plot corresponding to PC1 (Fig. 4) showed that the bad quality toki (toki no. 2) differed from other quality toki depending on the concentration of fructose and glucose. Samples from the good (toki no. 4) and moderate (toki no. 3) qualities had lower concentration of these two compounds, but greater amounts in phosphoric acid, proline, malic acid and citric acid were found. It has been reported that under a cold temperature, root respiration in some plants is low, thus reducing sugars will be accumulated while starch is consumed (16, 17). Bad quality toki (toki no. 2) was cultivated in the Northern part of Japan. Its cold climates should be one of the main factors that account for the high concentration of reducing sugars found. On the contrary, toki grown at an elevated temperature will stores less reducing sugars due to an exponential increase in root respiration because of nutrition uptake, root growth, maintenance, symbiotic processes and stress-related phenomena (18–22). According to the sensory evaluation results by the professional herbalists, Yamato-toki cultivated in Niigata (toki no. 2) was judged as bad, whereas toki from Nara (toki nos. 3 and 4) had the better qualities. From these premises, it can be presumed that the colder temperature is one of the most influential affect in producing bad quality toki roots, where as the warmer climate produces better quality toki. These results validate the fact that Angelica seeds cultivated in different areas and therefore exposed to the different environmental conditions does affect their qualities.
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FIG. 3. PCA score plot with respect to PCs 1 and 2 discriminating Angelica acutiloba roots provided by Fukuda-shoten, cultivated in different areas: Niigata prefecture (toki no. 2), Aomori, Nara (toki no. 3) and Sakurai, Nara (toki no. 4).
FIG. 4. Loading plot of PC 1 showing significant metabolites that influence the separation (in Fig. 2) between toki nos. 2, 3 and 4.
As mentioned previously, the quality of TCM is accompanied by several factors. One of these factors is how manufacturers are drying the toki roots. To explore the effect of differences in toki providers’ drying processes on the quality, Yamato-toki having the same quality but derived from different providers were compared. Hence, the difference of toki roots between Fukuda Shoten (toki no. 3) and an anonymous company (toki no. 1) were determined. The best discrimination was evidently observed by PC1 (69.9%) of the PCA score plot (Fig. 5). By an observation from the corresponding loading plot of PC1 shown in Fig. 6, toki roots derived from Fukuda Shoten’s seeds (toki no. 3) possessed higher amount of alanine, phosphoric acid, proline, citric acid, quinic acid, glucose and inositol. On the other hand, those roots from an anonymous company (toki no. 1) accumulated extremely high concentration of malic acid and fructose. These variations in metabolites come from the different cultivation conditions and different sample preparation procedures. A previous study has evidently show the effect of drying duration on qualities in A. acutiloba var. sugiyamae with respect to their concentration of sucrose and the extracted dilute ethanol-soluble contents (23). Therefore, the sample preparation procedures do have an affect on the chemical compositions and quality of Yamato-toki.
FIG. 5. PCA score plot with respect to PCs 1 and 2 discriminating Angelica acutiloba roots provided from two different cultivars: an anonymous provider (toki no. 1) and Fukuda shoten (toki no. 3).
FIG. 6. Loading plot of PC 1 describing significant metabolites, which influence the discrimination (in Fig. 4) between toki nos. 1 and 3.
In this research, the analysis of primary metabolites by GC-TOF-MS coupled with multivariate pattern recognition by PCA was successfully achieved, in order to study the climatic and industrial affects (cultivars) on the quality of Yamato-toki roots. It was found that the differences in quality with respect to their primary metabolite constituents was directly correlated to the physiological conditions of roots which in turn were mutually influenced by the climatic condition of the cultivation area. Furthermore, PCA was also successful in differentiating Yamato-toki roots derived from different cultivars. This finding suggests that the different drying methods between the two providers of the samples could have been resulting in the quality differences. This study affirms that metabolomics is an efficient and suitable technique for the quality assessment of Yamato-toki roots. ACKNOWLEDGMENTS The authors would like to express our gratitude to Fukuda Shoten for providing toki samples. This study was partly supported by Nara Prefecture in Collaboration with Regional Entities for the Advancement of Technological Excellence from Japan Science and Technology Corporation (JST-CREATE).
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REFERENCES 1. Wen, K. C., Huang, C. Y., and Lu, F. L.: Determination of baicalin and puerarin in traditional Chinese medicinal preparations by high-performance liquid chromatography. J. Chromatogr. A, 631, 241–250 (1993). 2. Sheng, Y. X., Li, L., Wang, Q., Guo, H. Z., and Guo, D. A.: Simultaneous determination of gallic acid, albiflorin, paeoniflorin, ferulic acid and benzoic acid in Si–Wu decoction by high-performance liquid chromatography DAD method. J. Pharm. Biomed. Anal., 37, 805–810 (2005). 3. Shi, Z., He, J., Zhao, M., and Chang, W.: Simultaneous determination of five marker constituents in traditional Chinese medicinal preparation Le-Mai granule by high performance liquid chromatography. J. Pharm. Biomed. Anal., 37, 469–473 (2005). 4. Dong, Z. B., Li, S.P., Hong, M., and Zhu, Q.: Hypothesis of potential active components in Angelica sinensis by using biomembrane extraction and high performance liquid chromatography. J. Pharm. Biomed. Anal., 38, 664–669 (2005). 5. Zhao, K. J., Dong, T. T. X., Tu, P. F., Song, Z. H., Lo, C. K., and Tsim, K. W. K.: Molecular genetic and chemical assessment of radix Angelica (Danggui) in China. J. Agric. Food Chem., 51, 2576–2583 (2003). 6. Lu, G. H., Chan, K., Chan, C. L., Leung, K., Jiang, Z. H., and Zhao, Z. Z.: Quantification of ligustilides in the roots of Angelica sinensis and related umbelliferous medicinal plants by high-performance liquid chromatography and liquid chromatography–mass spectrometry. J. Chromatogr. A, 1046, 101– 107 (2004). 7. Kobayashi, M., Fujita, M., and Mitsuhashi, H.: Studies on the constituents of umbelliferae plants. XV. Constituents of Cnidium officinale: occurrence of pregnenolone, coniferylferulate and hydroxyphthalides. Chem. Pharm. Bull., 35, 1427– 1433 (1987). 8. Kobayashi, M. and Mitsuhashi, H.: Studies on the constituents of umbelliferae plants. XVII. Structures of three new ligustilide derivatives from Ligusticum wallichii. Chem. Pharm. Bull., 35, 4789–4792 (1987). 9. Tsuchida, T., Kobayashi, M., Kaneko, K., and Mitsuhashi, H.: Studies of the constiuents of umbelliferae plants. XVI. Isolation and structures of three ligustilides derivatives from Angelica acutiloba. Chem. Pharm. Bull., 35, 4460–4464 (1987). 10. Society of Japanese Pharmacopoeia: The Japanese pharmacopoeia XIV Part II, official monographs, p. 899–956. Yakuji Nippon, Tokyo (2001). 11. Kaoudji, M., De Pachtere, F., Pouget, C., and Chulia, A. J.: Three additional phthalide derivatives, an epoxymonomer and two dimmers, from Ligusticum wallichii rhizomes. J.
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Nat. Prod., 49, 872–877 (1986). 12. Gijbels, M. J. M., Scheffer, J. J. C., and Bearheim Svendsen, A.: Occurrence and prevention of isomerization of ligustilides during gas-liquid chromatography on packed columns. Chromatographia, 15, 358–360 (1982). 13. Fukusaki, E. and Kobayashi, A.: Plant metabolomics: potential for practical operation. J. Biosci. Bioeng., 100, 347–354 (2005). 14. Jonsson, P., Johansson, E. S., Wuolikainen, A., Lindberg, J., Schuppe-Koistinen, I., Kusano, M., Sjostrom, M., Trygg, J., Moritz, T., and Antti, H.: Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data—a potential tool for multi-parametric diagnosis. J. Prot. Res., 5, 1407–1414 (2006). 15. Piao, X. L., Park, J. H., Cui, J., Kim, D. H., and Yoo, H. H.: Development of gas chromatography/mass spectrometry-pattern recognition method for the quality control of Korean Angelica. J. Pharm. Biomed. Anal., 44, 1163–1167 (2007). 16. Lunackova, L., Masarovicova, E., and Lux, A.: Respiration rate and chemical composition of Karwinskia roots as affected by temperature. Biol. Plant., 43, 611–613 (2000). 17. Covey-Crump, E. M., Attwood, R. G., and Atkin, O. K.: Regulation of root respiration in two species of Plantago that differ in relative growth: the effect of short- and long-term changes in temperature. Plant Cell Environ., 25, 1501–1513 (2002). 18. Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408, 184–187 (2000). 19. Ma, J. F. and Furukawa, J.: Recent progress in the research of external Al detoxification in higher plants: a minireview. J. Inorg. Biochem., 97, 46–51 (2003). 20. Chen, Y. X., Lin, Q., Luo, Y. M., He, Y. F., Zhen, S. J., Yu, Y. L., Tian, G. M., and Wong, M. H.: The role of citric acid on the phytoremediation of heavy metal contaminated soil. Chemosphere, 50, 807–811 (2003). 21. Rauer, W. E.: Structure and function of metal chelators produced by plants. The case for organic acids, amino acids, phytin and metallothioneins. Cell Biochem. Biophys., 31, 19–48 (1999). 22. Saber, N. E., Abdel-Moneim, A. M., and Baraket, S. Y.: Role of organic acids in sunflower tolerance to heavy metals. Biol. Plant., 42, 65–73 (1999). 23. Masaki, A., Mitsutoshi, A., Takaaki, H., and Yoshio, H.: Preparation and chemical evaluation of Angelicae radix produced in Hokkaido [Japan]. Part III. Time course study of dilute ethanol-soluble extract and sucrose contents. Rep. Hokkaido Inst. Public Health, 50, 6–10 (2000).
© 2008, The Society for Biotechnology, Japan
Metabolic Profiling of Angelica acutiloba Roots Utilizing Gas Chromatography–Time-of-Flight–Mass Spectrometry for Quality Assessment Based on Cultivation Area and Cultivar via Multivariate Pattern Recognition Sukanda Tianniam,1 Lucksanaporn Tarachiwin,2 Takeshi Bamba,3 Akio Kobayashi,1 and Eiichiro Fukusaki1* Department of Biotechnology, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan,1 Nara Prefectual Small & Medium Sized Enterprises Support Corporation, 129-1 Kashiwagi, Nara city, Nara 630-8031, Japan,2and Department of Applied Environmental Biology, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan3 Received 28 December 2007/Accepted 24 March 2008
Gas chromatography time-of-flight mass spectrometry was applied to elucidate the profiling of primary metabolites and to evaluate the differences between quality differences in Angelica acutiloba (or Yamato-toki) roots through the utilization of multivariate pattern recognition-principal component analysis (PCA). Twenty-two metabolites consisting of sugars, amino and organic acids were identified. PCA analysis successfully discriminated the good, the moderate and the bad quality Yamato-toki roots in accordance to their cultivation areas. The results signified two reducing sugars, fructose and glucose being the most accumulated in the bad quality, whereas higher quantity of phosphoric acid, proline, malic acid and citric acid were found in the good and the moderate quality toki roots. PCA was also effective in discriminating samples derive from different cultivars. Yamato-toki roots with the moderate quality were compared by means of PCA, and the results illustrated good discrimination which was influenced most by malic acid. Overall, this study demonstrated that metabolomics technique is accurate and efficient in determining the quality differences in Yamato-toki roots, and has a potential to be a superior and suitable method to assess the quality of this medicinal plant. [Key words: metabolomics, Angelica acutiloba, multivariate pattern recognition, gas chromatography– time-of-flight–mass spectrometry, principal component analysis]
marker compounds ferulic acid and ligustilide (3–6). The drawback for this approach is the universality of these metabolites. Both ferulic acid and ligustilides are not unique chemical identities of toki roots, as they are also distributed in other roots of traditional Chinese medicinal plants, such as Ligusticum chuangxiong, Cnidium officinal, Rhizoma Chuanxiong and Cnidii Rhizoma (7–10). In addition, when quantifying ligustilides, the result was found to be unreliable and difficult to execute as it is extremely versatile; it could be oxidized, isomerized and dimerized into other pthalide groups (7–9, 11, 12). Some pthalides decompose rapidly at high temperatures (12). Therefore, as toki roots are pretreated (washed) in hot water and dried (10), quantifying pthalides is not an appropriate alternative method. Thus, it is crucial to find an alternative analytical method that is accurate, effective and effortless. If such procedure could be established, it could be standardized method which can be utilized as a quality control (QC) parameter for commercial application of toki roots. Metabolomics is an interdisciplinary tool that includes a quantitative exhaustive profiling of all metabolites contained
Dried roots of Angelica acutiloba (Yamato-toki in Japanese) are one of the popular varieties of Traditional Chinese Medicine (TCM) that have been used for its pharmacological benefits (1) in treating female gynecological illnesses, such as menstrual disorders, urgent premature birth, and menopause (2). For these reasons, Yamato-toki roots are widely commercialized in Japan. However, as Yamato-toki roots are traditional medicines sold primarily by professional herbalists, commercial values of them are determined by their subjective observations of smell, taste and appearances. This makes quality assessment in the markets extremely difficult and impractical when considering mass productions of this product. Therefore, the need for a practical and standardized quality assessment method for industrialized Yamato-toki roots is a critical topic. Currently, toki’s qualities, not only Yamato-toki, but also Chinese and Korean Angelica roots, are being assessed by determining the concentration of two pharmacological bio* Corresponding author. e-mail: [email protected] phone/fax: +81-(0)6-6879-7424 655
656
J. BIOSCI. BIOENG.,
TIANNIAM ET AL.
TABLE 1. Description of Angelica acutiloba root samples Toki 1 2 3 4
Seed origin (cultivars) Anonymous Fukuda Shoten Fukuda Shoten Fukuda Shoten
Area of cultivation Ofuka, Nara, Japan Niigata, Coast of Sea of Japan, Japan Aomori, Nara, Japan Sakurai, Nara, Japan
in a target organism through the use of high throughput machines, such as gas chromatography mass spectrometry (GC-MS) (13). Metabolomics enables sample classification of diverse biological status, origin or quality in samples, by means of chemometrics, such as principal component analysis (PCA). Therefore, it should be easier, faster and more accurate to analyze large amount of samples, especially with GC-MS as it provides high sensitivity, reproducibility and repeatability (14). Piao et al. (15) have authenticated Korean Angelica, Angelica gigas, from Chinese and Japanese Angelica sp. by applying metabolomics method. This research underlined two unique metabolites decursin and decursinol angelate. These metabolites are found exclusively in A. gigas thus they could be used as the influential factors for good PCA discrimination with the other two species. Their result demonstrated that metabolomics technique is a good candidate, and should be appropriate to distinguish differences in Japanese toki roots. In our study, a combination of gas chromatography–time-of-flight–mass spectrometry (GC-TOF-MS) based on pattern recognition analysis techniques was developed for the quality evaluation of Yamato-toki roots. In this new approach, chemical fingerprinting was used instead of specific marker compounds to differentiate the quality, which gave faster and more reliable results compared to that obtained from the sensory test. MATERIALS AND METHODS Solvents and reagents Analytical grade methanol and chloroform purchased from Wako (Osaka) were used as extraction solvents. The internal standard, ribitol and pyridine, were also purchased from Wako. Derivatizing reagents, methoxyamine hydrochloride and N-methyl-N-(trimethylsilyl) trifluroacetamide (MSTFA) were purchased from Sigma (St. Louis, MO, USA) and GL Science (Tokyo), respectively. Toki samples All Yamato-toki root samples were provided by a folk medicine company, Fukuda Shoten (Nara) and an anonymous provider. The qualities were determined by a veteran based on the sensory test as described in Table 1. The Toki roots were ground into powder with a Wonder Blender (Osaka Chemical, Osaka). The ground samples were vacuum sealed and kept under −30°C until analysis. Sample extraction Forty milligrams of ground sample was exposed to 1000 µl MeOH : H2O : CH3Cl (2.5: 1:1) and 60 µl ribitol (20 mg ml–1). The mixture was then shaken at 20 Hz for 5 min with a mixer mill, and centrifuged (model 5415R; Eppendorf, Hamburg, Germany) at 13,200 rpm under 4°C for 3 min. Nine hundred microliters of supernatant containing hydrophilic primary metabolites was mixed with 400 µl of milli-Q H2O, and re-centrifuged under the same conditions. Four hundred microliters supernatant was collected and dried in a centrifugal concentrator (model VC-36S; Taitec, Saitama) at room temperature for approximately 2 h, followed by a drying process in a freeze dryer over night before derivatization. Sample derivatization Fifty microliters of methoxyamine hydrochloride in pyridine (20 mg ml–1) was added to the dried
Quality Moderate Bad Moderate Good
hydrophilic crude extract, and incubated in thermomixer comfort (Eppendorf AG model 5355) at 30°C for 90 min in order to induce the methoxymation reaction. One hundred microliters of MSTFA was then added and incubated again at 37°C for 30 min, in order to induce the silylation reaction before injecting to the GC-TOF/MS machine. GC-TOF-MS parameters One microliter of each derivatized sample was injected into the Agilent 6890N Gas Chromatograph by an Agilent 7683B Autosampler (Agilent, Atlanta, GA, USA) with a split ratio of 25 and separated by a 30 m × 0.25 mm I.D. ×0.25 µm WCOT fused silica capillary column (Varian, Lakeforest, CA, USA). The injector temperature was set at 230°C, where the gas flow rate was set at 1 ml/min. The column temperature maintained at 80°C for 2 min, and then gradually increased to 330°C at an increasing rate of 15°C/min. The column effluent was later introduced into Pegasus III time-of-flight mass spectrometer (LECO, St. Joseph, MI, USA) through the transfer line set at 250°C and the ion source temperature was at 200°C. The detected mass range was 85–500 m/z, where the detector voltage was set at 1650 V. Chromatographic data processing and compound identification Raw chromatographic data acquired from GC-TOF-MS analysis were processed by an in-house algorithm, ChromaTOF (ver. 2.32, LECO), in which automatic peak detection and mass spectrum deconvolution (compound identification) were performed with references to an in-house library, Max-Planck Institute of molecular plant physiology library, and to the main library, hierarchically in this order. Then, the data was transferred and baselinecorrected by the LineUp software (Informatrix, Woodinville, WA, USA). The data was normalized with the peak area of the ribitol, and subjected to other prepossessing procedures using the in-house algorithm, Pirotrans. Multivariate analysis The processed matrix data was subjected into PCA using the commercial software Pirouette (Informatrix). It created the secondary dimension score plots to visualize the contrast between different quality samples. The reason for the clusters separation was indicated by the loading plots of the corresponding principal components.
RESULTS AND DISCUSSION GC-TOF-MS chromatographic data of various qualities of Yamato-toki was applied to the multivariate pattern recognition, specifically to PCA. PCA converts complex chromatographic data into comprehensible matrix data sets. Furthermore, the program can visualize the differences existing within the data, while identifying the significant variables that influences the discrimination in quality. This makes this analysis extremely efficient, as results can be acquired with high reliability and accuracy. Twenty-two primary metabolites were identified based on the in-house library, ChromaTOF (Fig. 1). The corresponding fragment patterns were in good agreement to those in the libraries consisting of alanine, propanedioic acid, valine, phosphoric acid, proline, glycine, succinic acid, propanoic acid, fumaric acid, serine, pipecolic acid, threonine, malic
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TABLE 2. The detected chromatographic and spectrometric data of the 22 identified compounds analyzed by GC-TOF-MS
FIG. 1. Metabolite identification of 22 chemical constituents found in Angelica acutiloba roots through GC-TOF-MS analysis: 1, alanine; 2, propanedioic acid; 3, valine; 4, phosphoric acid; 5, proline; 6, glycine; 7, succinic acid; 8, propanoic acid; 9, fumaric acid; 10, serine; 11, pipecolic acid; 12, threonine; 13, malic acid; 14, GABA; 15, ornithine; 16, asparagine; 17, citric acid; 18, quinic acid; 19, fructose; 20, glucose; 21, galacturonic acid; 22, inositol.
FIG. 2. PCA score plot with respect to PCs 1 and 2 discriminating all four Angelica acutiloba root samples (toki no. 1, 2, 3 and 4).
acid, 4-aminobutyric acid (GABA), ornithine, asparagine, citric acid, quinic acid, fructose, glucose, galacturonic acid and inositol. The corresponding retention times and their fragment patterns are illustrated in Table 2. All chromatographic data were peak-aligned to that of the best quality toki (toki no. 4), baseline-corrected, normalized by ribitol’s peak area, and spectra binning were carried out prior to PCA. The best quality discrimination results were obtained when standard normal variate (SNV) was applied in data transformation. PCA analysis of all four toki samples showed mutual discrimination (Fig. 2). The score plot indicates that the separation, with reference to the first principal component (PC1 = 51.3%) describes the variations that exist between good, moderate and bad quality toki. The influential factor that should determine these classifications is the cultivation areas. On the other hand, the second principal component (PC2= 25.2%) illustrates the differences influenced by their cultivar origin. Toki sample provided by Fukuda-shoten (toki no. 2, 3 and 4) were grouped altogether on the same level whereas as those from an anonymous provider (toki no. 1) clustered at the opposite end. Therefore, in order to study the effect of cultivation area on the quality of Yamato-toki roots, toki samples derived from the same cultivar (Fukuda Shoten, Nara) were compared. The geographical conditions influencing the toki
Identified tR Mass fragments metabolites (min) Alanine 5.27 116, 147 Propanedioic acid 6.38 73, 147, 305 Valine 6.48 100, 144, 218 Phosphoric acid 7.03 133, 299, 314 Proline 7.37 142 Glycine 7.42 85, 147, 100, 174, 248 Succinic acid 7.48 147, 247 Propanoic acid 7.58 73, 75, 1 Fumaric acid 7.83 147, 245 Serine 7.87 100, 204, 218, 248 Pipecolic acid 7.99 156 Threonine 8.10 117, 101, 147, 218, 219, 291 Malic acid 9.02 147, 233 4- aminobutyric acid 9.42 147, 86, 100, 174, 304 Ornithine 10.06 102, 147 Asparagine 10.50 116, 132, 147, 188, 231 Citric acid 11.53 147, 211, 273 Quinic acid 11.78 147, 255, 345 Fructose 1 11.87 103, 147, 217, 307 Fructose 2 11.91 103, 147, 217, 307 Glucose 1 12.05 103, 147, 160, 205, 319 Glucose 2 12.18 103, 147, 160, 205, 319 Galacturonic acid 12.73 103, 160, 333 Inositol 13.30 147, 191, 217, 305, 318 Bold numbers represent the highest intensity mass ion of referred compound.
roots quality was clearly observed with PC1 of the PCA score plot in the first and second PCs (Fig. 3). PC1 (81.3%) best disassociated the toki samples in accordance to their qualities, because the clustering was also plotted in an orderly fashion from the good to the bad qualities (left to right) of the matrix. Observation of loading plot corresponding to PC1 (Fig. 4) showed that the bad quality toki (toki no. 2) differed from other quality toki depending on the concentration of fructose and glucose. Samples from the good (toki no. 4) and moderate (toki no. 3) qualities had lower concentration of these two compounds, but greater amounts in phosphoric acid, proline, malic acid and citric acid were found. It has been reported that under a cold temperature, root respiration in some plants is low, thus reducing sugars will be accumulated while starch is consumed (16, 17). Bad quality toki (toki no. 2) was cultivated in the Northern part of Japan. Its cold climates should be one of the main factors that account for the high concentration of reducing sugars found. On the contrary, toki grown at an elevated temperature will stores less reducing sugars due to an exponential increase in root respiration because of nutrition uptake, root growth, maintenance, symbiotic processes and stress-related phenomena (18–22). According to the sensory evaluation results by the professional herbalists, Yamato-toki cultivated in Niigata (toki no. 2) was judged as bad, whereas toki from Nara (toki nos. 3 and 4) had the better qualities. From these premises, it can be presumed that the colder temperature is one of the most influential affect in producing bad quality toki roots, where as the warmer climate produces better quality toki. These results validate the fact that Angelica seeds cultivated in different areas and therefore exposed to the different environmental conditions does affect their qualities.
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FIG. 3. PCA score plot with respect to PCs 1 and 2 discriminating Angelica acutiloba roots provided by Fukuda-shoten, cultivated in different areas: Niigata prefecture (toki no. 2), Aomori, Nara (toki no. 3) and Sakurai, Nara (toki no. 4).
FIG. 4. Loading plot of PC 1 showing significant metabolites that influence the separation (in Fig. 2) between toki nos. 2, 3 and 4.
As mentioned previously, the quality of TCM is accompanied by several factors. One of these factors is how manufacturers are drying the toki roots. To explore the effect of differences in toki providers’ drying processes on the quality, Yamato-toki having the same quality but derived from different providers were compared. Hence, the difference of toki roots between Fukuda Shoten (toki no. 3) and an anonymous company (toki no. 1) were determined. The best discrimination was evidently observed by PC1 (69.9%) of the PCA score plot (Fig. 5). By an observation from the corresponding loading plot of PC1 shown in Fig. 6, toki roots derived from Fukuda Shoten’s seeds (toki no. 3) possessed higher amount of alanine, phosphoric acid, proline, citric acid, quinic acid, glucose and inositol. On the other hand, those roots from an anonymous company (toki no. 1) accumulated extremely high concentration of malic acid and fructose. These variations in metabolites come from the different cultivation conditions and different sample preparation procedures. A previous study has evidently show the effect of drying duration on qualities in A. acutiloba var. sugiyamae with respect to their concentration of sucrose and the extracted dilute ethanol-soluble contents (23). Therefore, the sample preparation procedures do have an affect on the chemical compositions and quality of Yamato-toki.
FIG. 5. PCA score plot with respect to PCs 1 and 2 discriminating Angelica acutiloba roots provided from two different cultivars: an anonymous provider (toki no. 1) and Fukuda shoten (toki no. 3).
FIG. 6. Loading plot of PC 1 describing significant metabolites, which influence the discrimination (in Fig. 4) between toki nos. 1 and 3.
In this research, the analysis of primary metabolites by GC-TOF-MS coupled with multivariate pattern recognition by PCA was successfully achieved, in order to study the climatic and industrial affects (cultivars) on the quality of Yamato-toki roots. It was found that the differences in quality with respect to their primary metabolite constituents was directly correlated to the physiological conditions of roots which in turn were mutually influenced by the climatic condition of the cultivation area. Furthermore, PCA was also successful in differentiating Yamato-toki roots derived from different cultivars. This finding suggests that the different drying methods between the two providers of the samples could have been resulting in the quality differences. This study affirms that metabolomics is an efficient and suitable technique for the quality assessment of Yamato-toki roots. ACKNOWLEDGMENTS The authors would like to express our gratitude to Fukuda Shoten for providing toki samples. This study was partly supported by Nara Prefecture in Collaboration with Regional Entities for the Advancement of Technological Excellence from Japan Science and Technology Corporation (JST-CREATE).
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