Abstract
BackgroundMolecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops. Holistic approaches for molecular characterization using omics platforms can be used to confirm the intended impact of the genetic engineering, but can also reveal the unintended changes at the omics level as a first assessment of potential risks. The potential of omics platforms for risk assessment of GM crops has rarely been used for this purpose because of the lack of a consensus reference and statistical methods to judge the significance or importance of the pleiotropic changes in GM plants. Here we propose a meta data analysis approach to the analysis of GM plants, by measuring the transcriptome distance to untransformed wild-types.ResultsIn the statistical analysis of the transcriptome distance between GM and wild-type plants, values are compared with naturally occurring transcriptome distances in non-GM counterparts obtained from a database. Using this approach we show that the pleiotropic effect of genes involved in indirect insect defence traits is substantially equivalent to the variation in gene expression occurring naturally in Arabidopsis.ConclusionTranscriptome distance is a useful screening method to obtain insight in the pleiotropic effects of genetic modification.
Highlights
Molecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops
For the analysis of the transcriptome in the GM and wild type plants, RNA of Col-3, COX + and COX++ transgenic lines was isolated for Arabidopsis ATH1 GeneChip hybridization
Pleiotropic transcriptional effects in the GM plants are smaller than pleiotropic variation in nature Here we have used different methods to detect, interpret and assess the unintended changes in the transcriptome of transgenic lines compared with Col-3
Summary
Molecular characterization is an essential step of risk/safety assessment of genetically modified (GM) crops. To judge whether an unintended change is significant, the magnitude of the changes should be evaluated and judged against a baseline representing the natural variation in the trait under evaluation (proteome, metabolome and/or transcriptome) in the natural parental lines [6,8], wild relatives [9,10], populations derived from the parental lines and populations exposed to naturally occurring biotic and/or abiotic stress factors [6,7,11] Such a comparison is not trivial, as for each sample thousands of data points are generated with each of these omics technologies. An example, where such statistical analysis was successfully used, is our earlier work [12] in which a method that determines the metabolic ‘hyper-plane distance’ between samples was presented
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