A new approach to detecting unintended changes in GM foods

Researchers from Cornell University have recently published a study in The Plant Genome which may help in understanding whether genetic modification causes unintended changes in food quality and composition. The research examines the relative effects of genetic and transgenic (genetically modified) variation on the metabolomes of field-grown tomatoes, using non-targeted liquid chromatography mass spectrometry (LC-MS) metabolic fingerprinting followed by a range of different statistical correlation techniques on the datasets.
A selection of diverse genotypes was examined – including modern breeding lines, commercially important varieties, obsolete commercial varieties and vintage/speciality varieties – to estimate the range of metabolic diversity achieved by conventional plant breeding. Tomato seeds were germinated in a climate controlled greenhouse and then transplanted for cultivation under standard horticultural practices. Two plants per genotype of the conventional varieties were planted in each of two replicated field blocks, and then one pool of 10 fruit was obtained from the two plants in each block, picked when field-ripe. A second field was planted that included all transgenic plants along with controls.
After picking, whole tomatoes were flash frozen in liquid nitrogen, pulverized and extracted in a methanolic solution. Extracts were then analysed by LC-MS with the tentative identification of specific metabolites accomplished by comparing pseudomolecular ions and their daughter fragments to the literature and available databases.
Using Principal Component Analysis, Hoekenga et al found clear trends related to genetic background and ripeness. No differences were found, however, between transgenic genotypes and their nontransgenic parent varieties. Using other statistical analysis methods such as Weighted Correlation Network Analysis, 15 metabolic features of potential interest were identified; however, only five showed significant differences between the nontransgenic parent and the transgenic lines.
The findings suggest that there was little biochemical change due to genetic modification in this case. The researchers propose that the use of metabolic fingerprinting coupled with statistical analysis is a valuable tool to examine both large and small genetic effects on phenotypes of interest or high value.


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