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Record information and status
Record ID
Date of creation
2012-05-09 21:04 UTC (dina.abdelhakim@cbd.int)
Date of publication
2012-05-09 21:04 UTC (dina.abdelhakim@cbd.int)

General Information
Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops.
Gareth S. Catchpole, Manfred Beckmann, David P. Enot, Madhav Mondhe, Britta Zywicki, Janet Taylor, Nigel Hardy, Aileen Smith, Ross D. King, Douglas B. Kell
Author’s contact information
John Draper
E-mail: jhd@aber.ac.uk

Institute of Biological Sciences,
University of Wales,
Aberystwyth SY23 3DA,
United Kingdom
  • English
Publication date
Summary, abstract or table of contents

There is current debate whether genetically modified (GM) plants might contain unexpected, potentially undesirable changes in overall metabolite composition. However, appropriate analytical technology and acceptable metrics of compositional similarity require development. We describe a comprehensive comparison of total metabolites in field-grown GM and conventional potato tubers using a hierarchical approach initiating with rapid metabolome "fingerprinting" to guide more detailed profiling of metabolites where significant differences are suspected. Central to this strategy are data analysis procedures able to generate validated, reproducible metrics of comparison from complex metabolome data. We show that, apart from targeted changes, these GM potatoes in this study appear substantially equivalent to traditional cultivars.
Thematic areas
  • Scientific and technical issues
    • Risk assessment
Background material to the “Guidance on risk assessment of living modified organisms”
Is this document is recommend as background material for the “Guidance on Risk Assessment of Living Modified Organisms”
Section(s) of the “Guidance on Risk Assessment of Living Modified Organisms” this background material is relevant
Additional Information
Type of resource
  • Article (journal / magazine / newspaper)
doi: 10.1073/pnas.0503955102
Publisher and its location
National Academy of Sciences of the United States of America
New York
© 2005 by The National Academy of Sciences of the USA
5 page PDF
Proclamations of the National academy of Sciences (PNAS)
Keywords and any other relevant information
Keywords: genetically modified substantial equivalence, machine learning

Citation: PNAS October 4, 2005 vol. 102 no. 40 14458-14462