Advertisement

Journal of Plant Research

, Volume 123, Issue 3, pp 291–298 | Cite as

Toward genome-wide metabolotyping and elucidation of metabolic system: metabolic profiling of large-scale bioresources

  • Masami Yokota Hirai
  • Yuji Sawada
  • Shigehiko Kanaya
  • Takashi Kuromori
  • Masatomo Kobayashi
  • Romy Klausnitzer
  • Kosuke Hanada
  • Kenji Akiyama
  • Tetsuya Sakurai
  • Kazuki Saito
  • Kazuo Shinozaki
JPR Symposium International Conference on Arabidopsis Research 2010

Abstract

An improvement in plant production is increasingly important for a sustainable human society. For this purpose, understanding the mechanism of plant production, that is, the plant metabolic system, is an immediate necessity. After the sequencing of the Arabidopsis genome, it has become possible to obtain a bird’s eye view of its metabolism by means of omics such as transcriptomics and proteomics. Availability of thousands of transcriptome data points in the public domain has resulted in great advances in the methodology of functional genomics. Metabolome data can be a “gold mine” of biological findings. However, as the total throughput of metabolomics is far lower than that of transcriptomics due to technical difficulties, there is currently no publicly available large-scale metabolome dataset that is comparable in size to the transcriptome dataset. Recently, we established a novel methodology, termed widely targeted metabolomics, which can generate thousands of metabolome data points in a high-throughput manner. We previously conducted a targeted metabolite analysis of large-scale Arabidopsis bioresources, namely transposon-tagged mutants and accessions, to make a smaller dataset of metabolite accumulation. In this paper, we release approximately 3,000 metabolic profiles obtained by targeted analysis for 36 metabolites and discuss the possible regulation of amino acid accumulation.

Keywords

Bioresources Co-accumulation High-throughput analysis Large-scale analysis Metabolome Mutant 

Notes

Acknowledgments

This work was supported in part by the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (Project name: “Elucidation of Amino Acid Metabolism in Plants Based on Integrated Omics Analyses”).

Supplementary material

10265_2010_337_MOESM1_ESM.pdf (151 kb)
Figure S1 (PDF 150 kb)
10265_2010_337_MOESM2_ESM.ppt (58 kb)
Figure S2 (PPT 57 kb)
10265_2010_337_MOESM3_ESM.ppt (70 kb)
Figure S3 (PPT 70 kb)
10265_2010_337_MOESM4_ESM.ppt (74 kb)
Figure S4 (PPT 74.5 kb)
10265_2010_337_MOESM5_ESM.xls (20 kb)
Table S1 (XLS 20 kb)
10265_2010_337_MOESM6_ESM.xls (1.8 mb)
Table S2 (XLS 1840 kb)
10265_2010_337_MOESM7_ESM.xls (73 kb)
Table S3 (XLS 73 kb)
10265_2010_337_MOESM8_ESM.xls (132 kb)
Table S4 (XLS 131 kb)
10265_2010_337_MOESM9_ESM.xls (80 kb)
Table S5 (XLS 80 kb)

References

  1. Akiyama K, Chikayama E, Yuasa H, Shimada Y, Tohge T, Shinozaki K, Hirai MY, Sakurai T, Kikuchi J, Saito K (2008) PRIMe: a web site that assembles tools for metabolomics and transcriptomics. In Silico Biol 8:339–345PubMedGoogle Scholar
  2. Arabidopsis Genome Initiative (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408:796–815CrossRefGoogle Scholar
  3. Bais P, Moon SM, He K, Leitao R, Dreher K, Walk T, Sucaet Y, Barkan L, Wohlgemuth G, Roth MR, Wurtele ES, Dixon P, Fiehn O, Lange BM, Shulaev V, Sumner LW, Welti R, Nikolau BJ, Rhee SY, Dickerson JA (2010) PlantMetabolomics.org: a web portal for plant metabolomics experiments. Plant Physiol, pp 109.151027Google Scholar
  4. Beekwilder J, van Leeuwen W, van Dam NM, Bertossi M, Grandi V, Mizzi L, Soloviev M, Szabados L, Molthoff JW, Schipper B, Verbocht H, de Vos RC, Morandini P, Aarts MG, Bovy A (2008) The impact of the absence of aliphatic glucosinolates on insect herbivory in Arabidopsis. PLoS ONE 3:e2068CrossRefPubMedGoogle Scholar
  5. Clough SJ, Bent AF (1998) Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant J 16:735–743CrossRefPubMedGoogle Scholar
  6. Craigon DJ, James N, Okyere J, Higgins J, Jotham J, May S (2004) NASCArrays: a repository for microarray data generated by NASC’s transcriptomics service. Nucleic Acids Res 32:D575–D577CrossRefPubMedGoogle Scholar
  7. Gigolashvili T, Engqvist M, Yatusevich R, Muller C, Flugge UI (2007a) HAG2/MYB76 and HAG3/MYB29 exert a specific and coordinated control on the regulation of aliphatic glucosinolate biosynthesis in Arabidopsis thaliana. New Phytol 177:627–642CrossRefPubMedGoogle Scholar
  8. Gigolashvili T, Yatusevich R, Berger B, Muller C, Flugge U-I (2007b) The R2R3-MYB transcription factor HAG1/MYB28 is a regulator of methionine-derived glucosinolate biosynthesis in Arabidopsis thaliana. Plant J 51:247–261CrossRefPubMedGoogle Scholar
  9. Glinski M, Weckwerth W (2006) The role of mass spectrometry in plant systems biology. Mass Spectrom Rev 25:173–214CrossRefPubMedGoogle Scholar
  10. Goda H, Sasaki E, Akiyama K, Maruyama-Nakashita A, Nakabayashi K, Li W, Ogawa M, Yamauchi Y, Preston J, Aoki K, Kiba T, Takatsuto S, Fujioka S, Asami T, Nakano T, Kato H, Mizuno T, Sakakibara H, Yamaguchi S, Nambara E, Kamiya Y, Takahashi H, Hirai MY, Sakurai T, Shinozaki K, Saito K, Yoshida S, Shimada Y (2008) The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access. Plant J 55:526–542CrossRefPubMedGoogle Scholar
  11. Grubb CD, Abel S (2006) Glucosinolate metabolism and its control. Trends Plant Sci 11:89–100CrossRefPubMedGoogle Scholar
  12. Halkier BA, Gershenzon J (2006) Biology and biochemistry of glucosinolates. Annu Rev Plant Biol 57:303–333CrossRefPubMedGoogle Scholar
  13. 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 USA 101:10205–10210CrossRefPubMedGoogle Scholar
  14. Hirai MY, Sugiyama K, Sawada Y, Tohge T, Obayashi T, Suzuki A, Araki R, Sakurai N, Suzuki H, Aoki K, Goda H, Nishizawa OI, Shibata D, Saito K (2007) Omics-based identification of Arabidopsis Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc Natl Acad Sci USA 104:6478–6483CrossRefPubMedGoogle Scholar
  15. Horan K, Jang C, Bailey-Serres J, Mittler R, Shelton C, Harper JF, Zhu J-K, Cushman JC, Gollery M, Girke T (2008) Annotating genes of known and unknown function by large-scale coexpression analysis. Plant Physiol 147:41–57CrossRefPubMedGoogle Scholar
  16. Jen CH, Manfield IW, Michalopoulos I, Pinney JW, Willats WG, Gilmartin PM, Westhead DR (2006) The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis. Plant J 46:336–348CrossRefPubMedGoogle Scholar
  17. Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, D’Angelo C, Bornberg-Bauer E, Kudla J, Harter K (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J 50:347–363CrossRefPubMedGoogle Scholar
  18. Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmuller E, Dormann P, Weckwerth W, Gibon Y, Stitt M, Willmitzer L, Fernie AR, Steinhauser D (2005) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21:1635–1638CrossRefPubMedGoogle Scholar
  19. Kuromori T, Hirayama T, Kiyosue Y, Takabe H, Mizukado S, Sakurai T, Akiyama K, Kamiya A, Ito T, Shinozaki K (2004) A collection of 11,800 single-copy Ds transposon insertion lines in Arabidopsis. Plant J 37:897–905CrossRefPubMedGoogle Scholar
  20. Kuromori T, Wada T, Kamiya A, Yuguchi M, Yokouchi T, Imura Y, Takabe H, Sakurai T, Akiyama K, Hirayama T, Okada K, Shinozaki K (2006) A trial of phenome analysis using 4000 Ds-insertional mutants in gene-coding regions of Arabidopsis. Plant J 47:640–651CrossRefPubMedGoogle Scholar
  21. Kusano M, Fukushima A, Arita M, Jonsson P, Moritz T, Kobayashi M, Hayashi N, Tohge T, Saito K (2007) Unbiased characterization of genotype-dependent metabolic regulations by metabolomic approach in Arabidopsis thaliana. BMC Syst Biol 1:53CrossRefPubMedGoogle Scholar
  22. Magnan F, Ranty B, Charpenteau M, Sotta B, Galaud J-P, Aldon D (2008) Mutations in AtCML9, a calmodulin-like protein from Arabidopsis thaliana, alter plant responses to abiotic stress and abscisic acid. Plant J 56:575–589CrossRefPubMedGoogle Scholar
  23. Malitsky S, Blum E, Less H, Venger I, Elbaz M, Morin S, Eshed Y, Aharoni A (2008) The transcript and metabolite networks affected by the two clades of Arabidopsis glucosinolate biosynthesis regulators. Plant Physiol 148:2021–2049CrossRefPubMedGoogle Scholar
  24. Manfield IW, Jen CH, Pinney JW, Michalopoulos I, Bradford JR, Gilmartin PM, Westhead DR (2006) Arabidopsis Co-expression Tool (ACT): web server tools for microarray-based gene expression analysis. Nucleic Acids Res 34:W504–W509CrossRefPubMedGoogle Scholar
  25. Matsuda F, Hirai MY, Sasaki E, Akiyama K, Yonekura-Sakakibara K, Provart NJ, Sakurai T, Shimada Y, Saito K (2010) AtMetExpress development: a phytochemical atlas of Arabidopsis thaliana development. Plant Physiol 152:566–578CrossRefPubMedGoogle Scholar
  26. Mutwil M, Obro J, Willats WGT, Persson S (2008) GeneCAT—novel webtools that combine BLAST and co-expression analyses. Nucl Acids Res 36:W320–W326CrossRefPubMedGoogle Scholar
  27. Obayashi T, Hayashi S, Saeki M, Ohta H, Kinoshita K (2009) ATTED-II provides coexpressed gene networks for Arabidopsis. Nucl Acids Res 37:D987–D991CrossRefPubMedGoogle Scholar
  28. Rawat A, Seifert G, Deng Y (2008) Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data. BMC Bioinformatics 9:S7CrossRefPubMedGoogle Scholar
  29. Saito K, Matsuda F (2010) Metabolomics for functional genomics, systems biology, and biotechnology. Annu Rev Plant Biol 61Google Scholar
  30. Saito K, Hirai MY, Yonekura-Sakakibara K (2008) Decoding genes with coexpression networks and metabolomics—‘majority report by precogs’. Trends Plant Sci 13:36–43CrossRefPubMedGoogle Scholar
  31. Sawada Y, Akiyama K, Sakata A, Kuwahara A, Otsuki H, Sakurai T, Saito K, Hirai MY (2009a) Widely targeted metabolomics based on large-scale MS/MS data for elucidating metabolite accumulation patterns in plants. Plant Cell Physiol 50:37–47CrossRefPubMedGoogle Scholar
  32. Sawada Y, Toyooka K, Kuwahara A, Sakata A, Nagano M, Saito K, Hirai MY (2009b) Arabidopsis bile acid: sodium symporter family protein 5 is involved in methionine-derived glucosinolate biosynthesis. Plant Cell Physiol 50:1579–1586CrossRefPubMedGoogle Scholar
  33. Sawada Y, Kuwahara A, Nagano M, Narisawa T, Sakata A, Saito K, Hirai MY (2009c) Omics-based approaches to methionine side chain elongation in Arabidopsis: characterization of the genes enconding methylthioalkylmalate isomerase and methylthioalkylmalate dehydrogenase. Plant Cell Physiol 50:1181–1190 CrossRefPubMedGoogle Scholar
  34. Schmid M, Davison TS, Henz SR, Pape UJ, Demar M, Vingron M, Scholkopf B, Weigel D, Lohmann JU (2005) A gene expression map of Arabidopsis thaliana development. Nat Genet 37:501–506CrossRefPubMedGoogle Scholar
  35. Sønderby IE, Hansen BG, Bjarnholt N, Ticconi C, Halkier BA, Kliebenstein DJ (2007) A systems biology approach identifies a R2R3 Myb gene subfamily with distinct and overlapping functions in regulation of aliphatic glucosinolates. PLoS ONE 2:e1322CrossRefPubMedGoogle Scholar
  36. Srinivasasainagendra V, Page GP, Mehta T, Coulibaly I, Loraine AE (2008) CressExpress: a tool for large-scale mining of expression data from Arabidopsis. Plant Physiol 147:1004–1016CrossRefPubMedGoogle Scholar
  37. Steinhauser D, Usadel B, Luedemann A, Thimm O, Kopka J (2004) CSB.DB: a comprehensive systems-biology database. Bioinformatics 20:3647–3651CrossRefPubMedGoogle Scholar
  38. Takahashi H, Kawazoe M, Wada M, Hirai A, Nakamura K, Altaf-Ul-Amin M, Sawada Y, Hirai MY, Kanaya S (2009) KNApSAcK gene classification system for Arabidopsis thaliana: comparative genomic analysis of unicellular to seed plants. Plant Biotechnol 26:509–516Google Scholar
  39. Tokimatsu T, Sakurai N, Suzuki H, Ohta H, Nishitani K, Koyama T, Umezawa T, Misawa N, Saito K, Shibata D (2005) KaPPA-view: a web-based analysis tool for integration of transcript and metabolite data on plant metabolic pathway maps. Plant Physiol 138:1289–1300CrossRefPubMedGoogle Scholar
  40. Toufighi K, Brady SM, Austin R, Ly E, Provart NJ (2005) The botany array resource: e-Northerns, expression angling, and promoter analyses. Plant J 43:153–163CrossRefPubMedGoogle Scholar
  41. Unwin RD, Griffiths JR, Leverentz MK, Grallert A, Hagan IM, Whetton AD (2005) Multiple reaction monitoring to identify sites of protein phosphorylation with high sensitivity. Mol Cell Proteomics 4:1134–1144CrossRefPubMedGoogle Scholar
  42. Weckwerth W, Loureiro ME, Wenzel K, Fiehn O (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA 101:7809–7814CrossRefPubMedGoogle Scholar
  43. Werner E, Croixmarie V, Umbdenstock T, Ezan E, Chaminade P, Tabet JC, Junot C (2008a) Mass spectrometry-based metabolomics: accelerating the characterization of discriminating signals by combining statistical correlations and ultrahigh resolution. Anal Chem 80:4918–4932CrossRefPubMedGoogle Scholar
  44. Werner E, Heilier JF, Ducruix C, Ezan E, Junot C, Tabet JC (2008b) Mass spectrometry for the identification of the discriminating signals from metabolomics: Current status and future trends. J Chromatogr B Analyt Technol Biomed Life Sci 871:143–163CrossRefPubMedGoogle Scholar
  45. Zimmermann P, Hirsch-Hoffmann M, Hennig L, Gruissem W (2004) GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol 136:2621–2632CrossRefPubMedGoogle Scholar

Copyright information

© The Botanical Society of Japan and Springer 2010

Authors and Affiliations

  • Masami Yokota Hirai
    • 1
    • 2
    • 3
  • Yuji Sawada
    • 1
    • 2
  • Shigehiko Kanaya
    • 4
  • Takashi Kuromori
    • 1
  • Masatomo Kobayashi
    • 5
  • Romy Klausnitzer
    • 1
  • Kosuke Hanada
    • 1
  • Kenji Akiyama
    • 1
  • Tetsuya Sakurai
    • 1
  • Kazuki Saito
    • 1
    • 6
  • Kazuo Shinozaki
    • 1
  1. 1.RIKEN Plant Science CenterYokohamaJapan
  2. 2.JST, CRESTKawaguchiJapan
  3. 3.Graduate School of Bioagricultural SciencesNagoya UniversityNagoyaJapan
  4. 4.Graduate School of Information ScienceNara Institute of Science and TechnologyNaraJapan
  5. 5.RIKEN BioResource CenterTsukubaJapan
  6. 6.Graduate School of Pharmaceutical SciencesChiba UniversityChibaJapan

Personalised recommendations