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      Metabolite profiling of tomato (Lycopersicon esculentum) using 1H NMR spectroscopy as a tool to detect potential unintended effects following a genetic modification.

      Journal of Agricultural and Food Chemistry
      Amino Acids, analysis, Carbohydrates, Glycosides, Lycopersicon esculentum, genetics, growth & development, metabolism, Magnetic Resonance Spectroscopy, methods, Nucleosides, Nucleotides, Plants, Genetically Modified, Zea mays

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          Abstract

          The maize transcription factors LC and C1 were simultaneously overexpressed in tomato with the aim of producing lines with increased amounts of flavonols. The metabolite composition of these genetically modified tomatoes has been compared with that of azygous (nonmodified) controls grown side-by-side under the same conditions. It has been possible to observe metabolic changes in both types at different stages of maturity. (1)H NMR spectra showed that the levels of glutamic acid, fructose, and some nucleosides and nucleotides gradually increase from the immature to the ripe stage, whereas some amino acids such as valine and gamma-aminobutyric acid were present in higher amounts in unripe tomatoes. Apart from the significantly increased content of six main flavonoid glycosides (mainly kaempferol-3-O-rutinoside, with additional increases in kaempferol-3,7-di-O-glucoside (1), kaempferol-3-O-rutinoside-7-O-glucoside (2), kaempferol-3-O-glucoside, a dihydrokaempferol-O-hexoside (3), and naringenin-7-O-glucoside), the levels of at least 15 other metabolites were found to be different between the two types of red tomato. Among them were citric acid, sucrose, phenylalanine, and trigonelline. However, although statistically significant, these changes in mean values were relatively minor (less than 3-fold) and within the natural variation that would be observed in a field-grown crop. Nevertheless, this study clearly showed that NMR combined with chemometrics and univariate statistics can successfully trace even small differences in metabolite levels between plants and therefore represents a powerful tool to detect potential unintended effects in genetically modified crops.

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