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

Overview of attention for article published in PLOS ONE, February 2011
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Title
Covering Chemical Diversity of Genetically-Modified Tomatoes Using Metabolomics for Objective Substantial Equivalence Assessment
Published in
PLOS ONE, February 2011
DOI 10.1371/journal.pone.0016989
Pubmed ID
Authors

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.

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Mendeley readers

The data shown below were compiled from readership statistics for 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 3 2%
Spain 2 1%
France 1 <1%
Italy 1 <1%
South Africa 1 <1%
Portugal 1 <1%
Israel 1 <1%
United States 1 <1%
Unknown 133 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 19%
Researcher 25 17%
Student > Bachelor 17 12%
Professor > Associate Professor 10 7%
Student > Master 10 7%
Other 34 24%
Unknown 20 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 47%
Chemistry 14 10%
Biochemistry, Genetics and Molecular Biology 13 9%
Engineering 6 4%
Computer Science 2 1%
Other 14 10%
Unknown 28 19%