DOI QR코드

DOI QR Code

1H NMR 스펙트럼 데이터의 다변량 통계분석에 의한 벼 품종의 구분 및 주요 당 화합물의 정량분석

Metabolic Discrimination of Rice Cultivars and Relative Quantification of Major Sugar Compounds Using 1H NMR Spectroscopy Combined by Multivariate Statistical Analysis

  • 김석원 (한국생명공학연구원 생물자원센터) ;
  • 구본초 (한국생명공학연구원 생물자원센터) ;
  • 김종현 (한국생명공학연구원 식물전체연구센터) ;
  • 유장렬 (한국생명공학연구원 식물전체연구센터)
  • Kim, Suk-Weon (Biological Resources Center, Korea Research Institute of Bioscience and Biotechnology(KRIBB)) ;
  • Koo, Bon-Cho (Biological Resources Center, Korea Research Institute of Bioscience and Biotechnology(KRIBB)) ;
  • Kim, Jong-Hyun (Plant Genome Research Center, Korea Research Institute of Bioscience and Biotechnology(KRIBB)) ;
  • Liu, Jang-Ryol (Plant Genome Research Center, Korea Research Institute of Bioscience and Biotechnology(KRIBB))
  • 발행 : 2006.12.30

초록

건조된 벼 5 품종의 whole cell extracts로부터 $^1H$ NMR 스펙트럼 조사를 통해 다변량 통계분석법을 활용하여 벼 종자의 품종 구분이 가능함을 조사하였다. $^1H$ NMR스펙트럼 데이터에 기초한 PCA분석 결과 크게 3개의 그룹으로 구분이 이루어졌다. 즉, 상주벼가 나머지 4 품종의 벼와 크게 다르게 구분이 이루어졌으며 동진벼와 심백벼, 그리고 화만벼와 심백hetero 품종이 각각 하나의 소그룹으로 구분이 이루어졌다. 스펙트럼 영역에 있어서는 carbohydrate region이 품종에 따라 크게 달라지는 것으로 보아 탄수화물의 정량정성적 차이가 metabolic profiting에 의한 품종 구분에 중요한 역할을 하는 것으로 추론된다. 또한 $^1H$ NMR 스펙트럼 데이터에 기초하여 주요 당 화합물 (sucrose, glucose, maltose 등)의 상대적인 정량분석을 조사한 결과 상주벼의 경우 다른 벼 품종에 비해 sucrose 및 glucose 함량은 큰 차이가 없었으나 maltose 함량이 타 품종에 비해 약 2-4배 높음을 알 수 있었다. 따라서 본 연구에서 확립한 벼 종자의 whole cell extracts로부터 $^1H$ NMR 스펙트럼을 이용한 metabolic profiling 방법은 다양한 벼 종자의 신속한 품종구분은 물론 주요 carbohydrates의 간편한 정량분석 체계로 활용이 가능할 것으로 예상된다.

Discrimination of 5 rice cultivars (Sangjubyeo , Dongjinbyeo Simbaekbyeo , Hwamanbyeo , and Simbaek-hetero ) using metabolic profiling was carried out. Whole cell extracts from each cultivar were subjected to $^1H$ NMR spectroscopy. When spectral data were analyzed by principal component analysis, 5 cultivars were clustered into 3 groups: SJ, DJ + SB, and HM + SH. Thecultivars showed great difference in carbohydrate region of $^1H$ NMR spectra, suggesting that qualitative and quantitative differences in carbohydrate compounds play a major role in discrimination of the cultivars. In addition, it was readily possible to determine relative quantification of major carbohydrates including sucrose, glucose, maltose from spectral data of the cultivars. SJ showed 2 to 4 times higher content of maltose than the other rice cultivars. Overall results indicate that metabolic discrimination of rice cultivars using $^1H$ NMR spectroscopy combined by multivariate statistical analysis can be used for rapid discrimination of numerous rice cultivars and simple quantitative analysis system of major carbohydrate compounds in rice grains.

키워드

참고문헌

  1. Dunn WB, Bailey NJC, and Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130: 606-625 https://doi.org/10.1039/b418288j
  2. Eun MY, Kim YK, Cho YG, Kim YW, Chung TY, Choi HC (1990) Classification of Korean native rice cultivars by isozyme variations. Korean J Breed 21: 293-299
  3. Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18: 1157-1161 https://doi.org/10.1038/81137
  4. Fukuoka S, Hosaka K, Kamijima O (1992) Use of random amplified polymorphic DNAs (RAPDs) for identifcation of rice accessions. Japan J Genet 67: 247-252
  5. Gavaghan CL, Holmes E, Lenz E, Wilson (D, Nicholson JK (2000) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BLl OJ and Alpk:ApfCD mouse. FEBS Lett 484: 169-174 https://doi.org/10.1016/S0014-5793(00)02147-5
  6. Ghareyazie B, Huang N, Second G, Bennett J, Khush GS (1995) Classifcation of rice germplasm. I. Analysis using ALP and PCR-based RFLP. Theor Appl Genet 91: 218-227
  7. Glaszmann JC (1987) lsozymes and classifcation of Asian rice varieties. Theor Appl Genet 74: 21-30 https://doi.org/10.1007/BF00290078
  8. Kawase M (1994) Application of the restriction landmark genomic scanning (RLGS) methods to rice cultivars as a new fingerprinting technique. Theor Appl Genet 89: 861-864
  9. Kim SW, Ban SH, Jeong SC, Chung HJ, Ko S, Yoo OJ, Liu JR (2006) Genetic discrimination between Catharanthus roseus cultivars by metabolite fingerprinting using 1HNMR spectra of aromatic compounds. Plant Cell Rep (accepted)
  10. Mackill DJ (1995) Classifying japonica rice cultivars with RAPD markers. Crop Sci 35: 889-894 https://doi.org/10.2135/cropsci1995.0011183X003500030043x
  11. Maharjan RP, Ferenci T (2003) Global metabolite analysis: the influence of extraction methodology on metabolome profiles of Escherichia coli. Anal Biochem 313: 145-154 https://doi.org/10.1016/S0003-2697(02)00536-5
  12. Raamsdonk LM, Teusink B, Broadhurst D, Zhang N, Hayes A, Walsh MC, Berden JA, Brindle KM, Kell DB, Rowland JJ,Westerhoff HV, van Dam K, Oliver SG (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nature Biotech 19: 45-50 https://doi.org/10.1038/83496
  13. Subudhi PK. Nandi S. Casal C, Virmani SS. Huang N (1998) Classification of rice germplasm: Ill. High-resolution fingerprinting of cytoplasmic genetic male-sterile (CMS) lines with AFLP. Theor Appl Genet 96: 941-949 https://doi.org/10.1007/s001220050824
  14. Villas-Boas SG, Hojer-Pedersen J. Akesson M Smedsgaard J, Nielsen J (2005) Global metabolite analysis of yeast: evaluation of sample preparation methods. Yeast 22: 1155-1169 https://doi.org/10.1002/yea.1308
  15. Ward JL. Harris CH. Lewis J. Beale MH (2003) Assessment of $^1H$ NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaJiana. Phytochemistry 62: 949-957 https://doi.org/10.1016/S0031-9422(02)00705-7
  16. Wold H (1966) Estimation of principal components and related models by iterative least squares. In: Krishnaiah KR (ed), Multivariate Analysis, Academic Press, New York, pp. 391-420
  17. Yang GP, Saghai-Maroof MA, Xu CG, Zhang Q, Biyashev RM (1994) Comparative analysis of microsatellite DNA polymorphism in landrace and cultivars of rice. Mol Gen Genet 245: 187-1194
  18. Yu LX, Nguyen HT (1994). Genetic variation detected with RAPD markers among upland and lowland rice cultivars (Oryza sativa L) Theor Appl Genet 87: 668-672

피인용 문헌

  1. Multivariate Analysis on1H-NMR Spectroscopy of Olive Flounder Paralichthys olivaceus Serum vol.45, pp.4, 2012, https://doi.org/10.5657/KFAS.2012.0367