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Advances in Plant Metabolomics

식물 대사체 연구의 진보

  • Kim, Suk-Won (Biological Resource Center, Korea Research Institute of Bioscience and Biotechnology) ;
  • Chung, Hoe-Il (Department of Chemistry, Hanyang University) ;
  • Liu, Jang-R. (Plant Genome Research Center, Korea Research Institute of Bioscience and Biotechnology)
  • 김석원 (한국생명공학연구원 생물자원센터) ;
  • 정회일 (한양대학교 화학과) ;
  • 유장렬 (한국생명공학연구원 식물유전체연구센터)
  • Published : 2006.09.30

Abstract

Plant metabolomics is a plant biology field for identifying all of the metabolites found in a certain plant cell, tissue, organ, or whole plant in a given time and conditions and for studying changes in metabolic profiling as time goes or conditions change. Metabolomics is one of the most recently developed omics for holistic approach to biology and is a kind of systems biology. For holistic approach, metabolomics frequently uses chemometrics or multivariate statistical analysis of metabolic profillings. In plant biology, metabolomics is useful to determine functions of genes often in combination with DHA microarrays by analyzing tagged mutants of the model plants Arabidopsis and rice. This review paper attempted to introduce basic concepts of metabolomics and practical uses of multivariate statistical analysis of metabolic profiling obtained by $^1$H HMR and Fourier transform infrared spectrometry.

식물대사체학은 식물세포, 조직, 기관, 혹은 개체수준에서 주어진 시간과 조건에서 발견되는 모든 대사물질을 밝히고, 시간의 경과 혹은 조건의 변화에 따른 metabolic profiling의 변화를 연구하는 식물학 분야이다. 식물대사체학은 생물에 대한 전체론적 접근을 위한 가장 최근에 발달된 omics분야의 하나로서 일종의 시스템 생물학이다. 전체론적 접근과 이해를 위해서 대사체학은 metabolic profiling의 계량화학 혹은 다변량분석 방법을 자주 사용한다. 식물학분야에서 대사체학은 애기장대나 벼와 같은 모델식물에 tag를 도입하여 형질전환시킨 돌연변이체에 대해 DNA microarray와 함께 사용하여 유전자의 기능을 밝히는데 유용하게 사용된다. 본 총설에서는 식물대사체학의 기본 개념과 1H NMR 혹은 FTIR으로 얻은 metabolic profiling의 다변량분석에 대한 실용적인 사용법을 소개하고자 하였다.

Keywords

References

  1. Dunn W B, Bailey NJ C, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130: 606-625 https://doi.org/10.1039/b418288j
  2. 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
  3. Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K (2004) Integration of transcriptomics and metabolomics for undertanding of global responses to nutritional stresses in Arsbidopsls thaliana. Proc Nat! Acad Sci USA 101: 10205-10210
  4. Kell DB, Darby RM, Draper J (2001) Genomic computing: explanatory analysis of plant expression profiling data using machine learning. Plant Physiology 126: 943-949 https://doi.org/10.1104/pp.126.3.943
  5. Kim SW, Ban SH, Chung H, Cho SH, Chung HJ, Choi PS, Yoo OJ, Liu JR (2004) Taxonomic discrimination of higher plants by multivariate analysis of Fourier transform infrared spectroscopy data. Plant Cell Rep 23: 246-250 https://doi.org/10.1007/s00299-004-0811-1
  6. 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 HNMR spectra of aromatic compounds. Plant Cell Rep (accepted)
  7. Raamsdonk LM, Teusink B, Broadhurst O, Zhang N, Hayes A. Walsh Me. Berden JA. Brindle KM. Kell DB. Rowland JJ, Westerhoff HV, van Dam K, Oliver SO (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
  8. Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Wilimitzer L, Fernie A (2001) Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13: 11-29 https://doi.org/10.1105/tpc.13.1.11

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