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

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.

Data visualization in yield component analysis: an expert study

“…Both agronomists and statisticians reported many additional problems with it. Although commonly used for multidimensional data sets (Ge et al, 2009;Kozak 2010b;Kusano et al, 2011;Eastham et al, 2012;Winderbaum et al, 2012), it is relatively new. Its unusual coordinate system can be problematic for a new user (Wegman, 1990;Wegman and Carr, 1993;Kozak, 2010b).…”

Challenges for Metabolomics as a Tool in Safety Assessments

“…Goodacre et al, 2004;Rischer and Oksman-Caldentey, 2006;Kusano et al, 2011) the large physico-chemical diversity of small molecule metabolites renders comprehensive metabolomic profiling through a single data acquisition technology impossible. A range of technologies associated with different detection capabilities (metabolite coverage and sensitivity), precision, resolution, throughput and reproducibility are now extensively deployed by the research community.…”

“…Recognizing inherent limitations for any given data acquisition technology Kusano et al (2011) applied a multi-platform approach to an evaluation of transgenic tomato. These authors used a combination of GC-MS, LC-MS, and CE-MS with each technology covering distinct metabolite classes.…”

“…Kusano et al (2011) compared a GM-tomato (a miraculin protein expressor) to not only to the parental line but to a panel of conventional reference varieties. The statistical design (described by the authors as a proof-of-safety test) involved comparing the difference between test and control and the determining whether these differences fell within equivalence limits established by the reference varieties.…”

Metabolomics applied in bioenergy

“…In comparison to other technologies, metabolomics is cheaper per sample than transcriptomics and is not reliant on the availability of genome sequences [8,9]. Therefore, it is considered a powerful tool for the unbiased characterization of genotypes and phenotypes with application in multiple areas, such as evaluation of genetically modified organisms [10][11][12], functional genomics [13][14][15][16][17], responses to environmental factors [18][19][20][21][22][23], metabolic engineering [24,25], and quantitative genetics [26][27][28][29][30]. Furthermore, metabolomics, together with multivariate and correlation analyses, is an excellent tool for the study of systems biology, being widely recognized as the cornerstone of this emerging area [22,[31][32][33][34].…”