Metabolite Profiling of Tomato (Lycopersicon esculentum) Using ¹H NMR Spectroscopy as a Tool To Detect Potential Unintended Effects Following a Genetic Modification

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.

Flavonoid Profiling and Biosynthetic Gene Expression in Flesh and Peel of Two Tomato Genotypes Grown under UV-B-Depleted Conditions during Ripening

“…Several authors have already reported the occurrence of conjugation products of rutin with arabinose or xylose (30,31), but unfortunately the identification of the pentose residue was not possible by ESI triple-quadrupole fragmentation (32). Nonetheless, the putative identification of tomato metabolites by mass spectrometry was recently proposed by Moco et al (33) as an alternative to isolation and NMR characterization.…”

Metabolomics for metabolically manipulated plants: effects of tryptophan overproduction

“…From a practical point of view, monitoring of unexpected effects of metabolic engineering is clearly of great importance and metabolic profiling analyses that cover the widest range of metabolites may prove particularly informative. For this purpose, non-targeted methods based on GC-MS (Fiehn et al 2000;Garrat et al 2005;Roessner-Tunali et al 2003), HPLC-UV (photodiode array detection) (Fraser et al2000), HPLC-MS (Huhman and Sumner 2002;Roepenack-Lahaye et al 2004), 1 H-NMR (Baker et al 2006;Choi et al 2004;Gall et al 2003;Manetti et al 2004; see also the review by Ward et al 2007), and high-resolution MS (Hirai et al 2004;Oikawa et al 2006) have been applied. Given the wide chemical and quantitative diversity of plant metabolites, however, no single analytical methodology can provide a definitive and absolute metabolite profile.…”

“…Among the various multi-variate analysis techniques, principal component analysis (PCA) appears to be used most frequently in order to evaluate metabolic changes in GM crops (Choi et al 2004;Fiehn et al 2000;Gall et al 2003). Evaluation of loading plots can be used to find those metabolites most affected by genetic modification and which most contribute to the genotype difference (Baker et al 2006;Defernez et al 2004;Taylor et al 2002).…”

Challenges for Metabolomics as a Tool in Safety Assessments

“…Although there are complexities in the interpretation of data generated through modern profiling technologies (Broadhurst and Kell, 2006;Lay et al, 2006) including the fact that the data is not quantitative and there is no standardized framework for comparisons, the lack of variation between GM crops and their conventional comparators at the transcriptomic, proteomic, and metabolomic level has, nonetheless, been independently corroborated. These profiling evaluations extend to a wide range of plants including wheat (Baker et al, 2006;Gregersen et al, 2005;Ioset et al, 2007), potato (Catchpole et al, 2005;Defernez et al, 2004;Lehesranta et al, 2005), soybean (Cheng et al, 2008), rice (Dubouzet et al, 2007;Wakasa et al, 2006), tomato (Le Gall et al, 2003), tobacco, Arabidopsis (Kristensen et al, 2005), and Gerbera (Ainasoja et al, 2008).…”