Czech J. Food Sci., 2013, 31(4):368-375 | DOI: 10.17221/177/2013-CJFS

Application of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomic fingerprinting to characterise GM and conventional maize varietiesOriginal Paper

Lukáš VÁCLAVÍK1, Jaroslava OVESNÁ2, Ladislav KUČERA2, Jan HODEK2, Kateřina DEMNEROVÁ3, Jana HAJŠLOVÁ1
1 Department of Food Analysis and Nutrition, Faculty of Food and Biochemical Technology, Institute of Chemical Technology Prague, Prague, Czech Republic,
2 Department of Molecular Biology, Division of Plant Genetics, Breeding and Product Quality, Crop Research Institute, Prague, Czech Republic,

The feasibility of metabolomic fingerprinting approach based on ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-QTOFMS) was studied to assess its ability to discriminate between maize varieties, and to show the associations between them on the metabolomic level. The non-targeted metabolomic analysis was applied to assess the variability within two varieties grown under different environmental conditions and to characterise the association within a sample set comprising both conventional and transgenic (MON-ØØ81Ø-6) maize varieties cultivated under the same environmental conditions (locality). Typical metabolomic fingerprints were established for individual plants. The plants representing two varieties formed well separated clusters. Metabolomic fingerprints of the second sample set enabled their unambiguous discrimination. The differences in metabolomic fingerprints between maize varieties were identified and documented by grouping in PCA and/or CA. The results indicate a similar genetic basis of transgenic maize varieties as they descend from a MON 810 event. The results explicitly showed that the variability of the metabolites in MON 810 did not exceed the ranges measured within the conventional varieties, thus supporting the concept of substantial equivalence.

Keywords: chemometric analysis; maize; metabolomics

Published: August 31, 2013  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
VÁCLAVÍK L, OVESNÁ J, KUČERA L, HODEK J, DEMNEROVÁ K, HAJŠLOVÁ J. Application of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) metabolomic fingerprinting to characterise GM and conventional maize varieties. Czech J. Food Sci. 2013;31(4):368-375. doi: 10.17221/177/2013-CJFS.
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Barros E., Lazar S., Anttonen M.J., Van Dijk J.P., Röhling R.M., Kok E.J., Engel K.H. (2010): Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics. Plant Biotechnology Journal, 8: 436-451. Go to original source... Go to PubMed...
    2. Beruetta L.A., Alonso-Salces R.M., Hérberger K. (2007): Supervised pattern recognition in food analysis. Journal of Chromatography A, 1158: 196-214. Go to original source... Go to PubMed...
    3. Böhm Ch., Kailing K., Koger P., Zimek A., (2004): Computing clusters of correlation connected objects. In: Proceeding ACM International Conference on Management of Data (SIGMOD), Paris, France, 2004. Go to original source...
    4. Cellini F., Chesson A., Colquhoun I., Constable A., Davies H.V., Engel K.H., Gatehouse A.M.R., Kärenlampi S., Kok E.J., Leguay J.J., Lehesrant A. S., Noteborn H.P.J.M., Pedersen A.J., Smith M. (2004): Unintended effects and their detection in genetically modified crops. Food and Chemical Toxicology, 42: 1089-1125. Go to original source... Go to PubMed...
    5. Cevallos-Cevallos J.M., Reyes-de-Corcuera J.I., Etxeberria E., Danyluk M.D., Rodrick G.E. (2009): Metabolomic analysis in food science: a review. Trends in Food Science & Technology, 20: 557-566. Go to original source...
    6. Clive J. (2012): Global Status of Commercialized Biotech/ GM Crops. ISAAA Brief No. 44. ISAAA, Ithaca.
    7. Dunn W.B., Ellis D.I. (2005): Metabolomics: current analytical platforms and methodologies. TrAC-Trends in Analytical Chemistry, 24: 285-294. Go to original source...
    8. EFSA Panel on Genetically Modified Organisms (GMO) (2011): Guidance for risk assessment of food and feed from genetically modified plants. EFSA Journal, 9(5): 2150: 37. doi:10.2903/j.efsa.2011.2150 Go to original source...
    9. EN ISO 21571 (2005): Foodstuffs - Methods of Analysis for the Detection of Genetically Modified Organisms and Derived Products - Qualitative Nucleic acid Based Methods. ISO Copyright Office, Geneva.
    10. FAO/WHO (2000): Safety Aspects of Genetically Modified Foods of Plant Origin. Food and Agriculture Organization/World Health Organization, Roma.
    11. Frank T., Röhling R.M., Davies H.V., Barros E., Engel K.H. (2012): Metabolite profiling of maize kernels - genetic modification versus environmental influence. Journal of Agricultural and Food Chemistry, 60: 3005-3012. Go to original source... Go to PubMed...
    12. Gao A.G., Hakimi S.M., Mittanck C.A., Wu Y., Woerner B.M., Stark D.M., SHAH D.M., Liang J.H., Rommens C.M.T. (2000): Fungal athogen protection in potato by expression of a plant defensing peptide. Nature Biotechnology, 18: 1307-1310. Go to original source... Go to PubMed...
    13. Heinemann J.A., Kurenbach B., Quist D. (2011): Molecular profiling - a tool for addressing emerging gaps in the comparative risk assessment of GMOs. Environment International, 37: 1285-1293. Go to original source... Go to PubMed...
    14. Ivosev G., Burton L., Bonner R. (2008): Dimensionality reduction and visualization in principal component analysis. Analytical Chemistry, 80: 4933-4944. Go to original source... Go to PubMed...
    15. Katajamaa M., Oresic M. (2007): Data processing for mass spectrometry-based metabolomics. Journal of Chromatography A, 1158: 318-328. Go to original source... Go to PubMed...
    16. Koziel M.G., Beland G.L., Bowman C., Carozzi N.B., Crenshaw R., Crossland L. Dawson J., Desai N., Hill M., Kadwell S., Launis K., Lewis K., Maddox D., McPherson K., Meghji M.R., Merlin E., Rhodes R., Warren G.W., Wright M., Evola S.V. (1993): Field performance of elite transgenic maize plants expressing insecticidal protein derived from Bacillus thuringiensis. Nature Biotechnology, 11: 194-200. Go to original source...
    17. Kuiper H.A., Kleter G.A., Notenorn H.O.J.M., Kok E.J. (2001): Assessment of the food safety issues related to genetically modified foods. The Plant Journal, 27: 503-528. Go to original source... Go to PubMed...
    18. Lancashire P.D., Bleiholder H., van den Boom T., Langeluddeke P., Stauss R., Weber E., Witzenberger A. (1991): An uniform decimal code for growth stages of crops and weeds. Annals of Applied Biology 119: 561-601. Go to original source...
    19. Leon C., Rodriguez-Meizoso I., Lucio M., GarciaCañas V., Ibañez E., Schmitt-Kopplin P., Cifuentes A. (2009): Metabolomics of transgenic maize combining Fourier transform-ion cyclotron resonance-mass spectrometry, capillary electrophoresis-mass spectrometry and pressurized liquid extraction. Journal of Chromatography A, 1216: 7314-7323. Go to original source... Go to PubMed...
    20. Manneti C., Bianchetti C., Casciani L., Castro C., Di Cocco M.E., Miccheli A., Motto M., Conti F. (2006): A metabolomic study if transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids. Journal of Experimental Botany, 57: 2613-2625. Go to original source... Go to PubMed...
    21. Marmiroli N., Meastri E., Gulli M., Malcevschi A., Peano C., Bordoni R., De Bellis G. (2008): Methods for detection of GMOs in food and feed. Analytical and Bioanalytical Chemistry, 392: 369-384. Go to original source... Go to PubMed...
    22. OECD (1993): Safety Evaluation of Foods Derived by Modern Biotechnology: Concepts and Principles. Organization of Economic Cooperation and Development.
    23. Ovesná J., Kučera L., Hodek J., Demnerová K. (2010). Reliability of PCR-based screening for identification and quantification of GMOs. Czech Journal of Food Sciences, 28, 133-138. Go to original source...
    24. Querci M., van den Bulcke M., Zel J., van den Eede G., Broll H. (2010): New approaches in GMO detection. Analytical and Bioanalytical Chemistry, 396: 1991-2002. Go to original source... Go to PubMed...
    25. Wishart D.S. (2008): Metabolomics: applications to food science and nutrition research. Trends in Food Science & Technology, 17: 482-493. Go to original source...
    26. Xie G.X., Ni Y., Su M.M., Zhang Y.Y., Zhao A.H., Gao X.F., Liu Z., Xiao P.G., Jia W. (2008): Application of ultraperformance LC-TOF MS metabolite profiling techniques to the analysis of medicinal Panax herbs. Metabolomics, 4: 248-260. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY NC 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content