Journal of Plant Biotechnology

  • 제37권1호
  • /
  • Pages.12-24
  • /
  • 2010
  • /
  • 1229-2818(pISSN)
  • /
  • 2384-1397(eISSN)
한국식물생명공학회 (The Korean Society of Plant Biotechnology)
권호 표지
DOI QR코드

DOI QR Code

식물대사체 연구의 현황과 전망

Present and prospect of plant metabolomics

  • 김석원 (한국생명공학연구원 생물자원센터) ;
  • 권용국 (한국생명공학연구원 생물자원센터) ;
  • 김종현 (한국생명공학연구원 식물시스템연구센터) ;
  • 유장렬 (한국생명공학연구원 식물시스템연구센터)
  • Kim, Suk-Weon (Biological Resources Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)) ;
  • Kwon, Yong-Kook (Biological Resources Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)) ;
  • Kim, Jong-Hyun (Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB)) ;
  • Liu, Jang-R. (Plant Systems Engineering Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB))
  • 투고 : 2010.02.17
  • 심사 : 2010.02.26
  • 발행 : 2010.03.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

식물 대사체 (plant metabolomics) 연구는 식물 세포 및 조직에 존재하는 모든 대사산물의 시간적, 공간적 변화를 추적 조사함으로써 식물의 복잡한 생리 현상을 총체적으로 이해하는 연구이다. 이와 같은 식물 대사체 연구는 최근 개발이 이루어지고 있는 여러 오믹스 연구 분야의 하나로 시스템생물학의 한 분야이다. 식물 대사체 연구는 시료로부터 순수 화합물 또는 복합물을 정제하거나 또는 정제가 이루어지지 않은 혼합액으로부터 대사체 스펙트럼 정보를 확보하여 분석이 이루어지므로 추출액 제조 및 얻어진 대사체 데이터의 분석과정의 표준화가 필수적으로 이루어져야 한다. 이는 대사체 분석 결과의 해상도 및 재현성의 확보의 핵심 요소 이다. 식물 대사체 연구는 기능유전체학의 연구 수단은 물론 식물의 종, 품종, 더 나아가 GM 식물의 식별, 대사조절 기작 규명, 유용물질 생산, 식물의 외부 환경 스트레스 요인에 대한 다양한 생리적 반응 이해 등 다양한 연구 분야에서 활용이 이루어지고 있다. 최근 식물 대사체 연구는 모델식물(벼, 애기장대)의 유전체 정보와 연계하여 돌연변이주의 분석을 통해 유전자의 기능 정의 수단으로 활용되고 있다. 따라서 향후 유전체 정보와 대사체 정보의 연계를 통해 복잡한 대사경로 규명이나 다양한 생리 현상 해석 연구가 더욱 활발하게 진행될 것으로 전망된다.

Plant metabolomics is a research 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. Metabolomics or metabolite fingerprinting techniques usually involves collecting spectra of crude solvent extracts without purification and separation of pure compounds or not in standardized conditions. Therefore, that requires a high degree of reproducibility, which can be achieved by using a standardized method for sample preparation and data acquisition and analysis. In plant biology, metabolomics is applied for various research fields including rapid discrimination between plant species, cultivar and GM plants, metabolic evaluation of commercial food stocks and medicinal herbs, understanding various physiological, stress responses, and determination of gene functions. Recently, plant metabolomics is applied for characterization of gene function often in combination with transcriptomics by analyzing tagged mutants of the model plants of Arabidopsis and rice. The use of plant metabolomics combined by transcriptomics in functional genomics will be the challenge for the coming year. This review paper attempted to introduce current status and prospects of plant metabolomics research.

키워드

참고문헌

  1. Ali K, Maltese F, Zyprian E, Rex M, Choi YH, Verpoorte R (2009) NMR metabolic fingerprinting based identification of grapevine metabolites associated with downy mildew resistance. J Agric Food Chem 57:9599-9606 https://doi.org/10.1021/jf902069f
  2. Almstetter MF, Appel IJ, Gruber MA, Lottaz C, Timischl B, Spang R, Dettmer K, Oefner PJ (2009) Integrative normalization and comparative analysis for metabolic fingerprinting by comprehensive two-Dimensional gas chromatography-time-offlight mass spectrometry. Anal Chem 81:5731-5739 https://doi.org/10.1021/ac900528b
  3. Anastasiadi M, Zira A, Magiatis P, Haroutounian SA, Skaltsounis AL, Mikros E (2009) 1H NMR-based metabonomics for the classification of greek wines according to variety, region, and vintage. comparison with HPLC data. J Agric Food Chem 57:11067-11074 https://doi.org/10.1021/jf902137e
  4. Arbona V, Iglesias DJ, Talo'n M, Cadenas AG (2009) Plant phenotype demarcation using nontargeted LC-MS and GCMS metabolite profiling. J Agric Food Chem 57:7338-7347 https://doi.org/10.1021/jf9009137
  5. Baker J, Hawkins N, Ward J, Lovegrove A, Napier J, Shewry P, Beale M (2006) A metabolomic study of substantial equivalence offield-grown genetically modified wheat. Plant Biotechnol J 4:381-392 https://doi.org/10.1111/j.1467-7652.2006.00197.x
  6. Beckmann M, Enot DP, Overy DP, Draper J (2007) Representation, comparison, and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars. J Agric Food Chem 55:3444-3451 https://doi.org/10.1021/jf0701842
  7. Beckmann M, Parker D, Enot DP, Duval E, Draper J (2008) High-throughput, non targeted metabolite fingerprinting using nominal mass flow injection electro spray mass spectrometry. Nat Protocols 3:435-445 https://doi.org/10.1038/nprot.2007.499
  8. Biais B, Beauvoit B, Allwood JW, Deborde C, Maucourt M, Goodacre R, Rolin D, Moing A (2010) Metabolic acclimation to hypoxia revealed by metabolite gradients in melon fruit. J Plant Physiology 167:242-245 https://doi.org/10.1016/j.jplph.2009.08.010
  9. Boccard J, Grata E, Thiocone A, Gauvrit J, Lant’ri P, Carrupt P, Wolfender J, Rudaz S (2007) Multivariate data analysis of rapid LC-TOF/MS experiments from Arabidopsis thaliana stressed by wounding. Chemometr Intel Lab Syst 86:189-197 https://doi.org/10.1016/j.chemolab.2006.06.004
  10. Charlton A, Allnutt T, Holmes S, Chisholm J, Bean S, Ellis N, Mullineaux P, Oehlschlager S (2004) NMR profiling of transgenic peas. Plant Biotechnol J 2:27-35 https://doi.org/10.1046/j.1467-7652.2003.00045.x
  11. Choi YH, Tapias EC, Kim HK, Lefeber AWM, Erkelens C, Verhoeven JTJ, Brzin J, Zel J, Verpoorte R (2004) Metabolic discrimination of Catharanthus roseus leaves infected by phytoplasma using 1H-NMR spectroscopy and multivariate data analysis. Plant Physiol 135(4): 2398-2410 https://doi.org/10.1104/pp.104.041012
  12. Davey MP, Woodward FI, Quick WP (2009) Intraspecfic variation in cold-temperature metabolic phenotypes of Arabidopsis lyrata ssp. Petraea. Metabolomics 5:138-149 https://doi.org/10.1007/s11306-008-0127-1
  13. Deborde C, Maucourt M, Baldet P, Bernillon S, Biais B, Talon G, Ferrand C, Jacob D, Dumazet HF, Daruvar A, Rolin D, Moing A (2009) Proton NMR quantitative profiling for quality assessment of greenhouse-grown tomato fruit. Metabolomics 5:183-198 https://doi.org/10.1007/s11306-008-0134-2
  14. Dunn WB, Bailey NJ C, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130:606-625 https://doi.org/10.1039/b418288j
  15. Enot D, Beckmann M, Overy D, Draper J (2006) Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals. Proc Natl Acad Sci 103:14865-14870 https://doi.org/10.1073/pnas.0605152103
  16. Enot DP, Lin W, Beckmann M, Parker D, Overy DP, Draper J (2008) Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data. Nat Protocols 3: 446 - 470 https://doi.org/10.1038/nprot.2007.511
  17. Fellenberg C, B’ttcher C, Vogt T (2009) Phenylpropanoid polyamine conjugate biosynthesis in Arabidopsis thaliana flower buds. Phytochemistry 70:1392-1400 https://doi.org/10.1016/j.phytochem.2009.08.010
  18. Fiehn O, Kopka J, Drmann P, Altmann T, Trethewey R ,Willmitzer L (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18:1157-1161 https://doi.org/10.1038/81137
  19. Garratt L, Linforth R, Taylor A, Lowe K, Power J, Davey M (2005) Metabolite fingerprinting in transgenic lettuce. Plant Biotechnol J 3:165-174 https://doi.org/10.1111/j.1467-7652.2004.00110.x
  20. Gidman E, Goodacre R, Emmett B, Smith A , Jones D (2003) Investigating plant-plant interference by metabolic fingerprinting. Phytochemistry 63:705-710 https://doi.org/10.1016/S0031-9422(03)00288-7
  21. Glauser G, Boccard J, Rudaz S, Wolfender JL (2010) Mass spectrometry-based metabolomics oriented by correlation analysis for wound induced molecule discovery: identification of a novel jasmonate glucoside. Phytochem Anal 21:95-101 https://doi.org/10.1002/pca.1155
  22. Grata E, Boccard J, Guillarme D, Glauser G, Carrupt PA, Farmer EE, Lucwolfender J, Rudaza S (2008) UPLC/TOF-MS for plant metabolomics: A sequential approach for wound marker analysis in Arabidopsis thaliana. J Chromato B 871:261-270 https://doi.org/10.1016/j.jchromb.2008.04.021
  23. Gray G, Heath D (2005) A global reorganization of the metabolome in Arabidopsis during cold acclimation is revealed by metabolic fingerprinting. Physiologia Plantarum 124:236-248 https://doi.org/10.1111/j.1399-3054.2005.00507.x
  24. Han J, Danell RM, Patel JR, Gumerov DR, Scarlett CO, Speir JP, Parker CE, Rusyn I, Zeisel S, Borchers CH (2008) Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry. Metabolomics 4:128-140 https://doi.org/10.1007/s11306-008-0104-8
  25. Hirai MY, Klein M, Fujikawa Y, Yano M, Goodenowe DB, Yamazaki Y, Kanaya S, Nakamura Y, Kitayama M, uzuki H, Sakurai N, Shibata D, Tokuhisa J, Reichelt M, Gershenzon J, Papenbrock J, Saito K (2005) Elucidation of gene-to-gene and metabolite-to-gene networks in Arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem 280:25590-25595 https://doi.org/10.1074/jbc.M502332200
  26. 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 understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci 101:10205-10210 https://doi.org/10.1073/pnas.0403218101
  27. Hou CC, Chen CH, Yang NS, Chen YP, Lo CP, Wang SY, Tien YJ, Tsai PW, Shyur LF (2010) Comparative metabolomics approach coupled with cell- and gene-based assays for species classification and anti-inflammatory bioactivity validation of Echinacea plants. J Nutr Biochem 1873-4847
  28. Huang J, Bhinu VS, Li Xiang, Bashi ZD, Zhou R, Hannoufa A (2009) Pleiotropic changes in Arabidopsis f5h and sct mutants revealed by large-scale gene expression and metabolite analysis. Planta 230:1057-1069 https://doi.org/10.1007/s00425-009-1007-2
  29. Jacob SS, Smith NW, Quigley CL (2007) Assessment of Chinese medicinal herb metabolite profiles by UPLC-MS-based methodology for the detection of aristolochic acids. J Sep Sci 30:1200-1206 https://doi.org/10.1002/jssc.200600517
  30. Johnson HE, Broadhurst D, Goodacre R, Smith AR (2003) Metabolic fingerprinting of salt-stressed tomatoes. Phytochemistry 62:919-928 https://doi.org/10.1016/S0031-9422(02)00722-7
  31. Jousse C, Vu TD, Tran TLM, Balkhi MHA, Molini R, Conti MB, Pilard S, Mathiron D, Hehn A, Bourgaudb F, Gontier E (2010) Tropane alkaloid profiling of hydroponic Datura innoxia Mill. plants inoculated with Agrobacterium rhizogenes. Phytochem Anal 21:118-127 https://doi.org/10.1002/pca.1180
  32. Jung SM, Kim SW, Ban SH, In DS, Jung JD, Chung HJ, Liu JR, Lim YP, Choi DW (2006) Glutamine accumulation inhibits root growth and lateral root formation in ginseng hairy root. Plant Science 170:801-807 https://doi.org/10.1016/j.plantsci.2005.11.013
  33. Kim HK , Choi YH, Erkelens C, Lefeber A, Verpoorte R (2005) Metabolic fingerprinting of Ephedra species using 1H-NMR spectroscopy and principal component analysis. Chem Pharm Bull 53:105-109 https://doi.org/10.1248/cpb.53.105
  34. Kim HS, Kim SW, Park YS, Kwon SY, Liu JR, Joung H, Jeon JH (2009a) Metabolic profiling of genetically modified potatoes using a combination of metabolite fingerprinting and multivariate analysis. Biotech Bioprocess Eng 14:738-747 https://doi.org/10.1007/s12257-009-0168-y
  35. Kim SW , Min SR, Kim JH, Park SK, Kim TI, Liu JR (2009b) Rapid discrimination of commercial strawberry cultivars using Fourier transform infrared spectroscopy data combined by multivariate analysis. Plant Biotechnology Reports 3:87-93 https://doi.org/10.1007/s11816-008-0078-z
  36. 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
  37. Kim SW, Ban SH, Jeong SC, Chung HJ, Ko SM, Yoo OJ, Liu JR (2007a) Genetic discrimination between Catharanthus roseus cultivars by metabolic fingerprinting using 1H NMR spectra of aromatic compounds. Biotech Bioprocess Eng 12:646-652 https://doi.org/10.1007/BF02931081
  38. Kim SW, Koo BC, Kim JH, Liu JR (2007b) Metabolic discrimination of sucrose starvation from Arabidopsis cell suspension by 1H NMR spectral analysis. Biotech Bioprocess Eng 12:653-661 https://doi.org/10.1007/BF02931082
  39. Korn M, Ga¨rtner T, Erban A, Kopka J, Selbig J, Hincha DK (2010) Predicting Arabidopsis freezing tolerance and heterosis in freezing tolerance from metabolite composition. Molecular Plant 3:224-235 https://doi.org/10.1093/mp/ssp105
  40. Kruger N, Troncoso-Ponce MA, Ratcliffe RG (2008) 1H NMR metabolite fingerprinting and metabolomic analysis of perchloric acid extracts from plant tissues. Nat Protocols 3:1001-1012 https://doi.org/10.1038/nprot.2008.64
  41. Kwak CW, Choung DH, Min SR, Kim SW, Liu JR, Chung H (2007) Fast determination of the ripeness stage of strawberries using infrared spectroscopy combined with principal component analysis. Analytical Sciences 23:895-899 https://doi.org/10.2116/analsci.23.895
  42. Lee EJ, Shaykhutdinov R, Weljie AM, Vogel HJ, Facchini PJ (2009) Quality assessment of ginseng by 1H NMR metabolite fingerprinting and profiling analysis. J Agric Food Chem 57:7513-7522 https://doi.org/10.1021/jf901675y
  43. Luo ZB, Janz D, Jiang X, Go¨ bel C, Wildhagen H, Tan Y, Rennenberg H, Feussner I, Polle A (2009) Upgrading root physiology for stress tolerance by Ectomycorrhizas: insights from metabolite and transcriptional profiling into reprogramming for stress anticipation. Plant Physiology 151:1902-1917 https://doi.org/10.1104/pp.109.143735
  44. Luthria DL, Lin LZ, Robbins RJ, Finley JW, Banuelos GS, Hnrnly JM (2008a) Discriminating between cultivars and treatments of broccoli using mass spectral fingerprinting and analysis of variance-principal component analysis. J. Agric. Food Chem. 56:9819-9827 https://doi.org/10.1021/jf801606x
  45. Luthria DL, Mukhopadhyay S, Robbins RJ, Finley JW, Banuelos GS, Hanrnly JM (2008b) UV spectral fingerprinting and analysis of variance-principal component analysis: a useful tool for characterizing sources of variance in plant materials. J Agric Food Chem 56:5457-5462 https://doi.org/10.1021/jf0734572
  46. Ma C, Wang H, Lua X, Xu G, Liu B (2008) Metabolic fingerprinting investigation of Artemisia annua L. in different stages of development by gas chromatography and gas chromatography mass spectrometry. J Chromato A 1186:412-419 https://doi.org/10.1016/j.chroma.2007.09.023
  47. Messerli G, Nia VP, Trevisan M, Kolbe A, Schauer N, Geigenberger P, Chen J, Davison AC, Fernie AR, Zeeman SC (2007) Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting. Plant Physiology 143:1484-1492 https://doi.org/10.1104/pp.106.090795
  48. Montoro P, Maldini M, Piacente S, Macchia M, Pizza C (2010) Metabolite fingerprinting of Camptotheca acuminata and the HPLC/ESI-MS/MS analysis of camptothecin and related alkaloids. J Pharm Biomed Anal 51:405-415 https://doi.org/10.1016/j.jpba.2009.05.013
  49. Mounet F, Chamley ML,Maucourt M, Cabasson C, Giraudel JL , Deborde C, Lessire R,Gallusci P,Bertrand A,Gaudille` re M,Rothan C, Rolin D, Moinga A (2007) Quantitative metabolic profiles of tomato flesh and seeds during fruit development: complementary analysis with ANN and PCA. Metabolomics 3
  50. Mungur R, Glass A, Goodenow D, Lightfoot D (2005) Metabolite fingerprinting in transgenic Nicotiana tabacum altered by the Escherichia coli glutamate dehydrogenase gene. J Biomed Biotechnol 2:198-214
  51. Parker D, Beckmann M, Enot DP, Overy DP, Rios ZC, Gilbert M, Talbot N, Draper J (2008) Rice blast infection of Brachypodium distachyon as a model system to study dynamic host/pathogen interactions. Nat plotocols 3:435-445 https://doi.org/10.1038/nprot.2007.499
  52. Parker David, Beckmann M, Zubair H, Enot DP, Rios ZC, Overy DP, Snowdon S, Talbot NJ, Draper J (2009) Metabolomic analysis reveals a common pattern of metabolic re-programming during invasion of three host plant species by Magnaporthe grisea. Plant Journal 59:723-737 https://doi.org/10.1111/j.1365-313X.2009.03912.x
  53. Peluffo L, Lia V, Troglia C, Maringolo C, Norma P, Escande A, Hopp HE, Lytovchenko A, Fernie AR, Heinz R, Carrari F (2010) Metabolic profiles of sunflower genotypes with contrasting response to Sclerotinia sclerotiorum infection. Phytochemistry 71:70-80 https://doi.org/10.1016/j.phytochem.2009.09.018
  54. Pereira G, Gaudillere J, Leeuwena C , Hilbert G, Maucourt M, Deborde C, Moing A, Rolin D (2006) 1H NMR metabolite fingerprints of grape berry: Comparison of vintage and soil effects in bordeaux grapevine growing areas. Analytica Chimica Acta 563:346-352 https://doi.org/10.1016/j.aca.2005.11.007
  55. Pongsuwan W, Banba T, Harada K, Yonetani T, Kobayashi A, Fukusaki E (2008) High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-offlight mass spectrometry. J Agric Food Chem 56:10705-10708 https://doi.org/10.1021/jf8018003
  56. Pongsuwan W, Fukusaki E, Bamba T, Yonetani T, Yamahara T, Kobayashi A (2007) Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting. J Agric Food Chem 55:231-236 https://doi.org/10.1021/jf062330u
  57. 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. Nat Biotech 19:45-50 https://doi.org/10.1038/83496
  58. Ren Y, Wang T, Peng Y, Xia B, Qu LJ (2009) Distinguishing transgenic from non-transgenic Arabidopsis plants by 1H NMR-based metabolic fingerprinting. J Genet Genomics 36:621-628 https://doi.org/10.1016/S1673-8527(08)60154-X
  59. Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer 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
  60. Sarembauda J, Pinto R, Rutledge D, Feinberg M (2007) Application of the ANOVA-PCA method to stability studies of reference materials. analytica chimica acta 603:147-154 https://doi.org/10.1016/j.aca.2007.09.046
  61. Scholz M, Gatzek S, Sterling A, Fiehn O, Selbig J (2004) Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 20:2447-2454 https://doi.org/10.1093/bioinformatics/bth270
  62. Shin MH, Lee DY Kirsten, Skogerson GW, Choi IG, Fiehn O, Kim KH (2010) Global metabolic profiling of plant cell wall polysaccharide degradation by Saccharophagus degradans. Biotechnol Bioeng 105:477-488 https://doi.org/10.1002/bit.22557
  63. Shin YS, Bang KH, In DS, Kim OT, Hyun DY, Ahn IO, Ku BC, Kim SW, Seong NS, Cha SW, Lee DH, Choi HK (2007) Fingerprinting analysis of fresh ginseng roots of different ages using 1H-NMR spectroscopy and principal components analysis. Arch Pharm Res 30:1625-1628 https://doi.org/10.1007/BF02977333
  64. Shin YS, Bang KH, In DS, Sung JS, Kim SY, Ku BC, Kim SW, Lee DH, Choi HK (2009) Fingerprinting differentiation of Astragalus membranaceus roots according to ages using 1HNMR spectroscopy and multivariate statistical analysis. Biomol Ther 17:133-137 https://doi.org/10.4062/biomolther.2009.17.2.133
  65. Silva MAD, Shanaiah N, Gowda GAN, Rosa-P´erez K, Hanson BA, Raftery D (2009) Application of $^{31}PNMR$ spectroscopy and chemical derivatization for metabolite profiling of lipophilic compounds in human serum. Magn Reson Chem 47:74-80 https://doi.org/10.1002/mrc.2480
  66. Smilde A, Jansen J, Hoefsloot H, Lamers R, Greef J, Timmerman M (2005) ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics 21:3043-3048 https://doi.org/10.1093/bioinformatics/bti476
  67. Son HS, Hwang GS, Kim KM, Kim EY, van den Berg F, Park WM, Lee CH, Hong YS (2009) 1H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains. Anal Chem 81:1137-1145 https://doi.org/10.1021/ac802305c
  68. Sun X, Zhang J, Zhang H, Ni Y, Zhang Q, Chen J, Guan Y (2010) The responses of Arabidopsis thaliana to cadmium exposure explored via metabolite profiling. Chemosphere 78:840-845 https://doi.org/10.1016/j.chemosphere.2009.11.045
  69. Takahashi H, Kai K, Shinbo Y, Tanaka K, Ohta D, Oshima T, Altaf-Ul-Amin Md, Kurokawa K, Ogasawara N, Kanaya S (2008) Metabolomics approach for determining growthspecific metabolites based on Fourier transform ion cyclotron resonance mass spectrometry. Anal Bioanal Chem 391:2769-2782 https://doi.org/10.1007/s00216-008-2195-5
  70. Tarachiwin L, Katoh A, Ute K, Fukusaki E (2008) Quality evaluation of Angelica acutiloba Kitagawa roots by 1H NMRbased metabolic fingerprinting. J Pharma Biomed Anal 48:42-48 https://doi.org/10.1016/j.jpba.2008.04.025
  71. Tian C, Chikayama E, Tsuboi Y, Kuromori T, Shinozaki K, Kikuchi J, Hirayama T (2007) Top-down Phenomics of Arabidopsis thaliana metabolic profiling by one- and twodimensional nuclear magnetic resonance spectroscopy and transcriptome analysis of albino mutants. J Biological Chemistry 282:18532-18541 https://doi.org/10.1074/jbc.M700549200
  72. Tianniam S, Bamba T, Fukusaki E (2009) Non-targeted metabolite fingerprinting of oriental folk medicine Angelica acutiloba roots by ultra performance liquid chromatography time-of-flight mass spectrometry. J Sep Sci 32:2233-2244 https://doi.org/10.1002/jssc.200900121
  73. Tianniam S, Bamba T, Fukusaki E (2010) Pyrolysis GC-MS-based metabolite fingerprinting for quality evaluation of commercial Angelica acutiloba roots. J Biosci Bioeng 109:89-93 https://doi.org/10.1016/j.jbiosc.2009.06.025
  74. t'Kindt R, Morreel K, Deforce D, Boerjan W, Bocxlaer JV (2009) Joint GC-MS and LC-MS platforms for comprehensive plant metabolomics: Repeatability and sample pre-treatment. J Chromato B 877:3572-3580 https://doi.org/10.1016/j.jchromb.2009.08.041
  75. Tohge T, Nishiyama Y, Hirai MY, Yano M, Nakajima JI, Awazuhara M, Inoue E, Takahashi H, Goodenowe DB, Kitayama MK, Noji M, Yamazaki M, Saito K (2005) Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. Plant J 42:218-235 https://doi.org/10.1111/j.1365-313X.2005.02371.x
  76. Trenkamp S, Eckes P, Busch M, Fernie AR (2009) Temporally resolved GC-MS-based metabolic profiling of herbicide treated plants treated reveals that changes in polar primary metabolites alone can distinguish herbicides of differing mode of action. Metabolomics 5:277-291 https://doi.org/10.1007/s11306-008-0149-8
  77. Ward J, Harris C, Lewis J, Beale M (2003) Assessment of 1H NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana. Phytochemistry 62:949-957 https://doi.org/10.1016/S0031-9422(02)00705-7
  78. Yamamoto H, Yamaji H, Fukusaki E, Ohno H, Fukuda H (2008) Canonical correlation analysis for multivariate regression and its application to metabolic fingerprinting. Biochem Eng J 40:199-204 https://doi.org/10.1016/j.bej.2007.12.009
  79. Yi LZ, Yuan DL, Liang YZ, Xie PS, Zhao Yu (2009) Fingerprinting alterations of secondary metabolites of tangerine peels during growth by HPLC-DAD and chemometric methods. Analytica Chimica Acta 649:43-51 https://doi.org/10.1016/j.aca.2009.07.009
  80. Zhou M, McDonald JF, Fern’ndez FM (2010) Optimization of a direct analysis in real time/ time-of-flight mass spectrometry method for rapid serum metabolomic fingerprinting. J Am Soc Mass 21:68-75 https://doi.org/10.1016/j.jasms.2009.09.004
  81. Zulak KG, Weljie AM, Vogel HJ, Facchini PJ (2008) Quantitative 1H NMR metabolomics reveals extensive metabolic reprogramming of primary and secondary metabolism in elicitortreated opium poppy cell cultures. BMC Plant Biology 8:5 https://doi.org/10.1186/1471-2229-8-5