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Listen to this siteWednesday 16 October 2002
This research project will contribute to a database containing full information on the metabolic profiles of a series of wild type and GM plants.
Study Duration : September 2001 to January 2005
Contractor : University of Wales, Aberystwyth
As part of a continuing collaboration between biologists and computer scientists, the project will produce an industrial strength, internet-accessible database containing full information on the metabolic profiles (metabolomes) of a series of wild type and GM plants.
The project focusses on Arabidopsis thaliana and Solanum tuberosum (potato). The former will be grown under a series of carefully controlled conditions and the latter at a field scale. State of the art statistical and machine learning methods will be applied to these data to develop a methodology that will further refine the safety assessment procedure.
The aim of the project was to determine which combination of metabolomic procedures, coupled with a purpose built data storage and analysis programme (ArMET), was the most appropriate for routine use to reproducibly analyse potato crops.
A hierarchical metabolomics approach was used successfully to separate GM potatoes (carrying genes for fructan and inulin synthesis) from non-GM potatoes. Single transgenic lines (fructan synthesis only) were found to be very similar to the parental Desiree cultivar. The transgenic potato lines were found to be no more different from the parental Desiree cultivar than the different cultivars were from each other.
Different methods of generating potato metabolomics data (electrospray MS fingerprinting; Gas Chromatography Mass Spectrometry (GC-MS) profiling) and different methods of data analysis (Principal Components Analysis, Discriminant Function Analysis, etc) gave essentially the same results.
The ArMET programme has a core section, covering all phases of the metabolomic process that can be tailored by research groups to their individual needs. In this project, a particular achievement was the refinement of a module to store GC-MS metabolite profile data for comparison with data from alternative sources. A major publication describing ArMET has been endorsed by key laboratories in the international science community and is expected to form a future focus for development of standards in metabolomics.
The final report is available from the FSA Library and Information centre.
To obtain a copy, please contact the Enquiry Desk, Dr Elsie Widdowson Library and Information Services, Food Standards Agency (tel: 020 7276 8181/8182 or email:
library&info@foodstandards.gsi.gov.uk
)
The following paper has also been published from this project:
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 (in press Nov. 2004)
Contact
:
Email
: science@foodstandards.gsi.gov.uk
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