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Metabolome technology for the profiling of GM and conventionally bred plant materials
Project Code: G02006;
Taylor, J., King, R. D., Altmann, T. & Fiehn, O. Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics 18 S241-S248 (2002).
A proposed framework for the description of plant metabolomics experiments and their results. Helen Jenkins, Nigel Hardy, Manfred Beckmann, John Draper, Aileen R. Smith, Janet Taylor, Oliver Fiehn, Royston Goodacre, Raoul J. Bino, Robert Hall, Joachim Kopka, Geoffrey A. Lane, B. Markus Lange, Jang R. Liu, Pedro Mendes, Basil J. Nikolau, Stephen G. Oliver, Norman W. Paton, Sue Rhee, Ute Roessner-Tunali, Kazuki Saito, Jørn Smedsgaard, Lloyd W. Sumner, Trevor Wang, Sean Walsh, Eve Syrkin Wurtele, Douglas B. Kell. Nature Biotechnology 22,1601-1606.
Britta Zywicki, Gareth Catchpole, John Draper, and Oliver Fiehn. Comparison of rapid LC-ESI-MS/MS methods for determination of glycoalkaloids in transgenic field grown potatoes. Analytical Biochemistry , 2004
University of Wales, Aberystwyth
There is currently much public debate about the safety of genetically- engineered crops in the human diet.
Concerns have been voiced that the process of gene transfer itself or unexpected regulatory or enzymatic
activities of a transgene product in a new genetic background might cause crop plants to produce undesirable
metabolites. ‘Substantial equivalence’ guidelines recommend new varieties be compositionally similar to
varieties with a history of safe use, apart from the targeted bio-engineered changes. However, appropriate
analytical technology and acceptable metrics of compositional similarity require development. Looking for
’unanticipated’ changes in plant composition requires a comprehensive and reproducible assessment of ‘global’
metabolite content and although metabolomics technology was emerging at the start of the G02006 project it
lacked standardised operating procedures. Importantly, research at the time was limited to small scale ‘batch’
experiments (e.g. < 100 samples) performed on a single instrument and no attempt had been made to compare
data between laboratrories. Metabolomics data are inherently highly dimensional and contain signals derived
from many uncharacterised natural chemicals which causes problem for consistent annotation of variables
(metabolites). In larger, longer term experiments metabolomics data will also intrinsically contain further
substantial instrument- and biologically-derived variance which demands specialised strategies for data handing
and meaningful analysis. The overall project aim was therefore to determine which of any combination of
metabolomics analytical routines, coupled with bespoke data storage and advanced data analysis was the most
appropriate for routine use for comprehensive compositional comparisons of food raw materials in large scale
experiment s using field-grown crops. The project had access to field trials of GM and comparator non-GM
potatoes in Germany that provided a large number of tuber samples (> 12,000). Transgenic potato lines were
chosen that had been genetically engineered to produce novel inulin-type fructans.
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