Skip to main content
SpringerLink
Log in

Methods, applications and concepts of metabolite profiling: Primary metabolism

  • Chapter
Book cover Plant Systems Biology

Part of the book series: Experientia Supplementum ((EXS,volume 97))

Abstract

In the 1990s the concept of a comprehensive analysis of the metabolic complement in biological systems, termed metabolomics or alternately metabonomics, was established as the last of four cornerstones for phenotypic studies in the post-genomic era. With genomic, transcriptomic, and proteomic technologies in place and metabolomic phenotyping under rapid development all necessary tools appear to be available today for a fully functional assessment of biological phenomena at all major system levels of life. This chapter attempts to describe and discuss crucial steps of establishing and maintaining a gas chromatography/electron impact ionization/mass spectrometry (GC-EI-MS)-based metabolite profiling platform. GC-EI-MS can be perceived as the first and exemplary profiling technology aimed at simultaneous and non-biased analysis of primary metabolites from biological samples. The potential and constraints of this profiling technology are among the best understood. Most problems are solved as well as pitfalls identified. Thus GC-EI-MS serves as an ideal example for students and scientists who intend to enter the field of metabolomics. This chapter will be biased towards GC-EI-MS analyses but aims at discussing general topics, such as experimental design, metabolite identification, quantification and data mining.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tweeddale H, Notley-McRobb L, Ferenci T (1998) Effect of slow growth on metabolism of Escherichia coli, as revealed by global metabolite pool (‘metabolome’) analysis. J Bacteriol 180: 5109–5116

    PubMed  CAS  Google Scholar 

  2. Oliver SG, Winson MK, Kell DB, Baganz F (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol 16: 373–378

    Article  PubMed  CAS  Google Scholar 

  3. Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29: 1181–1189

    Article  PubMed  CAS  Google Scholar 

  4. Stoughton RB, Friend SH (2005) Innovation — How molecular profiling could revolutionize drug discovery. Nat Rev Drug Dis 4: 345–350

    Article  CAS  Google Scholar 

  5. Trethewey RN, Krotzky AJ, Willmitzer L (1999) Metabolic profiling: a Rosetta stone for genomics? Curr Opin Plant Biol 2: 83–85

    Article  PubMed  CAS  Google Scholar 

  6. Fiehn O (2002) Metabolomics — the link between genotypes and phenotypes. Plant Mol Biol 48: 155–171

    Article  PubMed  CAS  Google Scholar 

  7. Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochem 62: 817–836

    Article  CAS  Google Scholar 

  8. Fernie AR, Trethewey RN, Krotzky AJ, Willmitzer L (2004) Metabolite profiling: from diagnostics to systems biology. Nat Rev Mol Cell Biol 5: 763–769

    Article  PubMed  CAS  Google Scholar 

  9. Jellum E, Helland P, Eldjarn L, Markwardt U, Marhofer J (1975) Development of a computer-assisted search for anomalous compounds (CASAC). J Chromatogr 112: 573–580

    Article  PubMed  CAS  Google Scholar 

  10. Jellum E (1977) Profiling of human-body fluids in healthy and diseased states using gaschromatography and mass-spectrometry, with special reference to organic-acids. J Chromatrogr B 143: 427–462

    Article  CAS  Google Scholar 

  11. Jellum E (1979) Application of mass-spectrometry and metabolite profiling to the study of human-diseases. Philosophical Transactions of the Royal Society of London Series A-Mathematical Physical and Engineering Sciences, 293: 13–19

    Article  CAS  Google Scholar 

  12. Fiehn O, Kopka J, Dörmann P, Altmann T, Trethewey RN, Willmitzer L (2000) Metabolite profiling for plant functional genomics. Nat Biotechnol 18: 1157–1161

    Article  PubMed  CAS  Google Scholar 

  13. Roessner U, Wagner C, Kopka J, Trethewey RN, Willmitzer L (2000) Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J 23: 131–142

    Article  PubMed  CAS  Google Scholar 

  14. Kopka J, Fernie AF, Weckwerth W, Gibon Y, Stitt M (2004) Metabolite profiling in plant biology: platforms and destinations. Genome Biol 5(6): 109–117

    Article  PubMed  Google Scholar 

  15. Erban A, Schauer N, Fernie AR, Kopka J (2006) Non-supervised construction and application of mass spectral and retention time index libraries from time-of-flight GC-MS metabolite profiles. In: W Weckwerth (ed.): Methods in Molecular Biology Vol. 358. Humana Press Inc., Totowa, USA, pp 19–38

    Google Scholar 

  16. Kopka J (2006) Gas chromatography mass spectrometry, Chapter 1.1. In: K Saito, R Dixon, L Willmitzer (eds): Plant Metabolomics (Biotechnology in Agriculture and Forestry Vol. 57), Springer-Verlag, Heidelberg, pp 3–20

    Chapter  Google Scholar 

  17. Kaplan F, Kopka J, Haskell DW, Zhao W, Schiller KC, Gatzke N, Sung DY, Guy CL (2004) Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiol 136: 4159–4168

    Article  PubMed  CAS  Google Scholar 

  18. Weckwerth W, Loureiro ME, Wenzel K, Fiehn O (2004) Differential metabolic networks unravel the effects of silent plant phenotypes. Proc Natl Acad Sci USA 18: 7809–7814

    Article  Google Scholar 

  19. Catchpole GS, Beckmann M, Enot DP, Mondhe M, Zywicki B, Taylor J, Hardy N, Smith A, King RD, Kell DB et al. (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc Natl Acad Sci USA 102: 14458–14462

    Article  PubMed  CAS  Google Scholar 

  20. Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer L, Fernie AR (2001) Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13: 11–29

    Article  PubMed  CAS  Google Scholar 

  21. Junker BH, Wuttke R, Tiessen A, Geigenberger P, Sonnewald U, Willmitzer L, Fernie AR (2004) Temporally regulated expression of a yeast invertase in potato tubers allows dissection of the complex metabolic phenotype obtained following its constitutive expression. Plant Mol Biol 56: 91–110

    Article  PubMed  CAS  Google Scholar 

  22. Birkemeyer C, Luedemann A, Wagner C, Erban A, Kopka J (2005) Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling. Trends Biotechnol 23: 28–33

    Article  PubMed  CAS  Google Scholar 

  23. Bino RJ, deVos CHR, Lieberman M, Hall RD, Bovy A, Jonker HH, Tikunov Y, Lommen A, Moco S, Levin I (2005) The light-hyperresponsive high pigment-2(dg) mutation of tomato: alterations in the fruit metabolome. New Phytologist 166: 427–438

    Article  PubMed  CAS  Google Scholar 

  24. Kovàts ES (1958) Gas-chromatographische charakterisierung organischer verbindungen: teil 1. retentionsindices aliphatischer halogenide, alkohole, aldehyde und ketone. Helv Chim Acta 41: 1915–1932

    Article  Google Scholar 

  25. Wagner C, Sefkow M, Kopka J (2003) Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochem 62: 887–900

    Article  CAS  Google Scholar 

  26. Ausloos P, Clifton CL, Lias SG, Mikaya AI, Stein SE, Tchekhovskoi DV, Spark-man OD, Zaikin V, Zhu D (1999) The critical evaluation of a comprehensive mass spectral library. J Am Soc Mass Spectrom 10: 287–299

    Article  PubMed  CAS  Google Scholar 

  27. Stein SE (1999) An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. J Am Soc Mass Spectrom 10: 770–781

    Article  CAS  Google Scholar 

  28. Halket JM, Waterman D, Przyborowska AM, Patel RKP, Fraser PD, Bramley PM (2005) Chemical derivatization and mass spectral libraries in metabolic profiling by GC/MS and LC/MS/MS. J Exp Bot 56: 219–243

    Article  PubMed  CAS  Google Scholar 

  29. Gullberg J, Jonsson P, Nordström A, Sjöström M, Moritz T (2004) Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal Biochem 331: 283–295

    Article  PubMed  CAS  Google Scholar 

  30. Duran AL, Yang J, Wang L, Sumner LW (2003) Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics 19: 2283–2293

    Article  PubMed  CAS  Google Scholar 

  31. Jonsson P, Gullberg J, Nordström A, Kusano M, Kowalczyk M, Sjöström M, Moritz T (2004) A strategy for identifying differences in large series of metabolomic samples analyzed by GC/MS. Anal Chem 76: 1738–1745

    Article  PubMed  CAS  Google Scholar 

  32. Jonsson P, Johansson AI, Gullberg J, Trygg J, Grung B, Marklund S, Sjöström M, Antti H, Moritz T (2005) High-throughput data analysis for detecting and identifying differ ences between samples in GC/MS-based metabolomic analyses. Anal Chem 77: 5635–5642

    Article  PubMed  CAS  Google Scholar 

  33. Bino RJ, de Vos CHR, Lieberman M, Hall RD, Bovy A, Jonker HH, Tikunov Y, Lommen A, Moco S, Levin I (2005) The light-hyperresponsive high pigment-2dg mutation of tomato: alterations in the fruit metabolome. New Phytol 166: 427–438

    Article  PubMed  CAS  Google Scholar 

  34. Vorst O, de Vos CHR, Lommen A, Staps RV, Visser RGF, Bino RJ, Hall RD (2005) A nondirected approach to the differential analysis of multiple LC-MS derived metabolic pro-files. Metabolomics 1: 169–180

    Article  CAS  Google Scholar 

  35. Luedemann A, Erban A, Wagner C, Kopka J (2004) Method for analyzing metabolites. International patent application (PCT/EP2004/014450) published under the patent cooperation treaty (WO 2005/059556 A1)

    Google Scholar 

  36. Mashego MR, Wu L, Van Dam JC, Ras C, Vinke JL, Van Winden WA, Van Gulik WM, Heijnen JJ (2004) MIRACLE: mass isotopomer ratio analysis of U-13C-labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites. Biotech Bioeng 85: 620–628

    Article  CAS  Google Scholar 

  37. Wu L, Mashego MR, van Dam JC, Proell AM, Vinke JL, Ras C, van Winden WA, van Gulik WM, Heijnen JJ (2005) Quantitative analysis of the microbial metabolome by isotope dilution mass spectrometry using uniformly 13C-labeled cell extracts as internal standards. Anal Biochem 336: 164–171

    Article  PubMed  CAS  Google Scholar 

  38. Roessner-Tunali U, Hegemann B, Lytovchenko A, Carrari F, Bruedigam C, Granot D, Fernie AR (2003) Metabolic profiling of transgenic tomato plants overexpressing hexokinase reveals that the influence of hexose phosphoryla-tion diminishes during fruit development. Plant Physiol 133: 84–99

    Article  PubMed  CAS  Google Scholar 

  39. Schauer N, Zamir D, Fernie AR (2005) Metabolic profiling of leaves and fruit of wild species tomato: a survey of the Solanum lycopersicum complex. J Exp Bot 56: 297–307

    Article  PubMed  CAS  Google Scholar 

  40. Kopka J (2005) Current challenges and developments in GC-MS based metabolite profiling technology. J Biotechnol 124: 312–322

    Article  Google Scholar 

  41. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acid Res 32: D277–280

    Article  PubMed  CAS  Google Scholar 

  42. Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, Schomburg D (2004) BRENDA, the enzyme database: updates and major new developments. Nucleic Acid Res 32: D431–433

    Article  PubMed  CAS  Google Scholar 

  43. Krieger CJ, Zhang P, Mueller LA, Wang A, Paley S, Arnaud M, Pick J, Rhee SY, Karp PD (2004) MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acid Res 32: D438–442

    Article  PubMed  CAS  Google Scholar 

  44. Schauer N, Steinhauser D, Strelkov S, Schomburg D, Allison G, Moritz T, Lundgren K, Roessner-Tunali U, Forbes MG, Willmitzer L et al. (2005) GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Letters 579: 1332–1337

    Article  PubMed  CAS  Google Scholar 

  45. Oksman-Caldentey K-M, Inzé D, Orešič M (2004) Connecting genes to metabolites by a systems biology approach. Proc Natl Acad Sci USA 101: 9949–9950

    Article  PubMed  CAS  Google Scholar 

  46. Trethewey RN (2004) Metabolite profiling as an aid to metabolic engineering in plants. Curr Opin Plant Biol 7: 196–201

    Article  PubMed  CAS  Google Scholar 

  47. Fiehn O, Kopka J, Trethewey RN, Willmitzer L (2000a) Identification of uncommon plant metabolites based on calculation of elemental compositions using gas chromatography and quadrupole mass spectrometry. Anal Chem 72: 3573–3580

    Article  PubMed  CAS  Google Scholar 

  48. Kopka J, Schauer N, Krueger S, Birkemeyer C, Usadel B, Bergmüller E, Dörmann P, Gibon Y, Stitt M, Willmitzer L et al. (2005) GMD@CSBDB: The Golm Metabolome Database. Bioinformatics 21: 1635–1638

    Article  PubMed  CAS  Google Scholar 

  49. Sinha AE, Fraga CG, Prazen BJ, Synovec RE (2004a) Trilinear chemometric analysis of two dimensional comprehensive gas chromatography-time-of-flight mass spectrometry data. J Chromatogr A 1027: 269–277

    Article  PubMed  CAS  Google Scholar 

  50. Sinha AE, Hope JL, Prazen BJ, Nilsson EJ, Jack RM, Synovec RE (2004b) Algorithm for locating analytes of interest based on mass spectral similarity in GC × GC-TOF-MS data: analysis of metabolites in human infant urine. J Chromatogr A 1058: 209–215

    Article  PubMed  CAS  Google Scholar 

  51. Sinha AE, Prazen BJ, Synovec RE (2004c) Trends in chemometric analysis of comprehensive two-dimensional separations. Anal Bioanal Chem 378: 1948–1951

    Article  PubMed  CAS  Google Scholar 

  52. Kell DB, Brown M, Davey HM, Dunn WB, Spasic I, Oliver SG (2005) Metabolic foot-printing and systems biology: The medium is the message. Nat Rev Microbiol 3: 557–565

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476, Potsdam-Golm, Germany

    Dirk Steinhauser & Joachim Kopka

Authors
  1. Dirk Steinhauser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joachim Kopka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Plant Sciences, Swiss Federal Institute of Technology, ETH Zentrum, LFW E, 8092, Zürich, Switzerland

    Sacha Baginsky

  2. MPI for Molecular Plant Physiology, Am Mühlenberg 1, 14476, Golm, Germany

    Alisdair R. Fernie

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Birkhäuser Verlag/Switzerland

About this chapter

Cite this chapter

Steinhauser, D., Kopka, J. (2007). Methods, applications and concepts of metabolite profiling: Primary metabolism. In: Baginsky, S., Fernie, A.R. (eds) Plant Systems Biology. Experientia Supplementum, vol 97. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-7439-6_8

Download citation

Publish with us

Policies and ethics

Search

Navigation