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Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment

Author

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  • Miyako Kusano
  • Henning Redestig
  • Tadayoshi Hirai
  • Akira Oikawa
  • Fumio Matsuda
  • Atsushi Fukushima
  • Masanori Arita
  • Shin Watanabe
  • Megumu Yano
  • Kyoko Hiwasa-Tanase
  • Hiroshi Ezura
  • Kazuki Saito

Abstract

As metabolomics can provide a biochemical snapshot of an organism's phenotype it is a promising approach for charting the unintended effects of genetic modification. A critical obstacle for this application is the inherently limited metabolomic coverage of any single analytical platform. We propose using multiple analytical platforms for the direct acquisition of an interpretable data set of estimable chemical diversity. As an example, we report an application of our multi-platform approach that assesses the substantial equivalence of tomatoes over-expressing the taste-modifying protein miraculin. In combination, the chosen platforms detected compounds that represent 86% of the estimated chemical diversity of the metabolites listed in the LycoCyc database. Following a proof-of-safety approach, we show that % had an acceptable range of variation while simultaneously indicating a reproducible transformation-related metabolic signature. We conclude that multi-platform metabolomics is an approach that is both sensitive and robust and that it constitutes a good starting point for characterizing genetically modified organisms.

Suggested Citation

  • Miyako Kusano & Henning Redestig & Tadayoshi Hirai & Akira Oikawa & Fumio Matsuda & Atsushi Fukushima & Masanori Arita & Shin Watanabe & Megumu Yano & Kyoko Hiwasa-Tanase & Hiroshi Ezura & Kazuki Sait, 2011. "Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0016989
    DOI: 10.1371/journal.pone.0016989
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Jean Molinier & Gerhard Ries & Cyril Zipfel & Barbara Hohn, 2006. "Transgeneration memory of stress in plants," Nature, Nature, vol. 442(7106), pages 1046-1049, August.
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