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Discrimination Model of Cultivation Area of Alismatis Rhizoma using a GC-MS-Based Metabolomics Approach

GC-MS 기반 대사체학 기법을 이용한 택사의 산지판별모델

  • Received : 2015.12.30
  • Accepted : 2016.01.12
  • Published : 2016.02.29

Abstract

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.

Keywords

References

  1. Lee, H. W., Choi, J. H., Park, S. Y., Choo, B. K., Chun, J. M., Lee, A. Y. and Kim, H. K. : Constituents comparison of components in native and cultivated species of Angelica tenuissima Nakai. Kor. J. Med. Crop. Sci. 16, 168 (2008).
  2. Zhang, A., Sun, H., Wang, Z., Sun, W., Wang, P. and Wang, X. : Metabolomics: Towards understanding traditional Chinese medicine. Planta Medica. 76, 2026 (2010). https://doi.org/10.1055/s-0030-1250542
  3. Fiehn, O. : Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155 (2002). https://doi.org/10.1023/A:1013713905833
  4. Lindon, J. C., Holmes, E. and Nicholson, J. K. : Metabonomics in pharmaceutical R&D. FEBS J. 274, 1140 (2007). https://doi.org/10.1111/j.1742-4658.2007.05673.x
  5. Arbona, V., Iglesias, D. J., Talon, M. and Gomez-Cadenas, A. : Plant phenotype demarcation using nontargeted LC-MS and GC-MS metabolite profiling. J. Agric. Food Chem. 57, 7338 (2009). https://doi.org/10.1021/jf9009137
  6. Claudino, W. M., Goncalves, P. H., di Leo, A., Philip, P. A. and Sarkar, F. H. : Metabolomics in cancer: A bench-to-bedside intersection. Crit. Rev. Oncol/Hematol. 84, 1 (2012). https://doi.org/10.1016/j.critrevonc.2012.02.009
  7. Yang, S. O., Lee, S. W., Kim, Y. O., Lee, S. W., Kim, N. H., Choi, H. K., Jung, J. Y., Lee, D. H. and Shin, Y. S. : Comparative analysis of metabolites in roots of Panax ginseng Obtained from Different Sowing Methods. Kor. J. Med. Crop. Sci. 22, 17 (2014). https://doi.org/10.7783/KJMCS.2014.22.1.17
  8. De Vos, R. C. H., Moco, S., Lommen, A., Keurentjes, J. J. B., Bino, R. J. and Hall, R. D. : Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Prot. 2, 778 (2007). https://doi.org/10.1038/nprot.2007.95
  9. Kanani, H. : Standardizing GC-MS metabolomics. J. Chrom. B. 871, 191 (2008). https://doi.org/10.1016/j.jchromb.2008.04.049
  10. Kende, A., Portwood, D., Senior, A., Earll, M., Bolygo, E. and Seymour, M. : Target list building for volatile metabolite profiling of fruit. J. Chrom. A. 1217, 6718 (2010). https://doi.org/10.1016/j.chroma.2010.05.030
  11. Fukusaki, E. and Kobayashi, A. : Plant metabolomics: Potential for practical operation. J. Biosci. Bioengi. 100, 347 (2005). https://doi.org/10.1263/jbb.100.347
  12. Ozek, G., Demirci, F., Ozek, T., Tabanca, N., Wedge, D. E., Khan, S. I., Hüsnü, K., Baser, C., Duran, A. and Hamzaoglu, E. : Gas chromatographic - mass spectrometric analysis of volatiles obtained by four different techniques from Salvia rosifolia Sm. and evaluation for biological activity. J. Chrom. A. 1217, 741 (2010). https://doi.org/10.1016/j.chroma.2009.11.086
  13. Bylesjo, M., Rantalainen, M., Cloarec, O., Nicholson, J. K., Holmes, E. and Trygg, J. : OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. J. Chemomet. 20, 341 (2006). https://doi.org/10.1002/cem.1006
  14. Westerhuis, J. A., van Velzen, E. J. J., Hoefsloot, H. C. J. and Smilde, A. K. : Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabol. 6, 119 (2010). https://doi.org/10.1007/s11306-009-0185-z
  15. Hong Y. S. : Nutritional metabolomics. J. Kor. Soc. Food Sci. Nutr. 43, 179 (2014). https://doi.org/10.3746/jkfn.2014.43.2.179
  16. Miyazawa, M., Yoshinaga, S., Kashima, Y., Nakahashi, H., Hara, N., Nakagawa, H. and Usami, A. : Chemical composition and characteristic odor compounds in essential oil from Alismatis Rhizoma (Tubers of Alisma orientale). J. Oleo. Sci. [Epub ahead of print] (2015).
  17. Kang, J., Choi, M. Y., Kang, S., Kwon, H. N., Wen, H., Lee, C. H., Park, M., Wiklund, S., Kim, H. J., Kwon, S. W. and Park, S. : Application of a 1H nuclear magnetic resonance (NMR) metabolomics approach combined with orthogonal projections to latent structure-discriminant analysis as an efficient tool for discriminating between Korean and Chinese herbal medicines. J. Agric. Food Chem. 56, 11589 (2008). https://doi.org/10.1021/jf802088a
  18. Lee, D. Y., Kim, S. H., Kim, H. J. and Sung, S. H. : Discrimination of Alismatis rhizoma according to geographical origins using near infrared spectroscopy. Kor. J. Pharmacogn. 44, 344 (2013).
  19. Wiklund, S., Johansson, E., Sjostrom, L., Mellerowicz, E. J., Edlund, U., Shockcor, J. P., Gottfries, J., Moritz, T. and Trygg, J. : Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal. Chem. 80, 115 (2008). https://doi.org/10.1021/ac0713510

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