Metabolite profiling of maize kernels--genetic modification versus environmental influence.

A metabolite profiling approach based on gas chromatography-mass spectrometry (GC-MS) was applied to investigate the metabolite profiles of genetically modified (GM) Bt-maize (DKC78-15B, TXP 138F) and Roundup Ready-maize (DKC78-35R). For the comparative investigation of the impact of genetic modification versus environmental influence on the metabolite profiles, GM maize was grown together with the non-GM near-isogenic comparators under different environmental conditions, including several growing locations and seasons in Germany and South Africa. Analyses of variance (ANOVA) revealed significant differences between GM and non-GM maize grown in Germany and South Africa. For the factor genotype, 4 and 3%, respectively, of the total number of peaks detected by GC-MS showed statistically significant differences (p < 0.01) in peak heights as compared to the respective isogenic lines. However, ANOVA for the factor environment (growing location, season) revealed higher numbers of significant differences (p < 0.01) between the GM and the non-GM maize grown in Germany (42%) and South Africa (10%), respectively. This indicates that the majority of differences observed are related to natural variability rather than to the genetic modifications. In addition, multivariate data assessment by means of principal component analysis revealed that environmental factors, that is, growing locations and seasons, were dominant parameters driving the variability of the maize metabolite profiles.

Genetic and environmental influence on maize kernel proteome.

Comparative targeted compositional analysis is currently an important element in the safety assessment of genetically modified plants. Profiling methods have been suggested as nontargeted tools to improve the detection of possible unintended effects. In this study, the capability of 2-dimensional electrophoresis to detect significant differences among seven conventional maize (Zea mays) cultivars grown in six different locations in Germany during two consecutive seasons was evaluated. Besides maize genotype, both geographic location and season had a significant effect on protein profiles. Differences as high as 55- and 53-fold in the quantity of specific proteins were recorded, the median observed difference being around 6- and 5-fold between the genotypes and growing locations, respectively. Understanding the variation in the quantity of individual proteins should help to put the variation of endogenous proteins and the novel proteins in the genetically modified plants in perspective. This together with the targeted analyses the profiling methods, including proteomics, could also help to get a deeper insight into the unintended alterations that might have occurred during the genetic modification process.

Metabolome variability in crop plant species--when, where, how much and so what?

"Omics" technologies provide coverage of gene, protein and metabolite analysis that is unsurpassed compared with traditional targeted approaches. There are a growing number of examples indicating that profiling approaches can be used to expose significant sources of variation in the composition of crop and model plants caused by genetic background, breeding method, growing environment (site, season), genotype × environment interactions and crop cultural practices to name but a few. Whilst breeders have long been aware of such variation from tried and tested targeted analytical approaches, the broad-scale, so called "unbiased" analysis of the metabolome now possible, offers a major upside to our understanding of the true extent of variation in a plethora of metabolites relevant to human and animal health and nutrition. Metabolomics is helping to provide targets for plant breeding by linking gene expression, and allelic variation to variation in metabolite complement (functional genomics), and is also being deployed to better assess the potential impacts of climate change and reduced input agricultural systems on crop composition. This review will provide examples of the factors driving variation in the metabolomes of crop species.

Comparison of two GM maize varieties with a near-isogenic non-GM variety using transcriptomics, proteomics and metabolomics.

The aim of this study was to evaluate the use of four nontargeted analytical methodologies in the detection of unintended effects that could be derived during genetic manipulation of crops. Three profiling technologies were used to compare the transcriptome, proteome and metabolome of two transgenic maize lines with the respective control line. By comparing the profiles of the two transgenic lines grown in the same location over three growing seasons, we could determine the extent of environmental variation, while the comparison with the control maize line allowed the investigation of effects caused by a difference in genotype. The effect of growing conditions as an additional environmental effect was also evaluated by comparing the Bt-maize line with the control line from plants grown in three different locations in one growing season. The environment was shown to play an important effect in the protein, gene expression and metabolite levels of the maize samples tested where 5 proteins, 65 genes and 15 metabolites were found to be differentially expressed. A distinct separation between the three growing seasons was also found for all the samples grown in one location. Together, these environmental factors caused more variation in the different transcript/protein/metabolite profiles than the different genotypes.

Influence of the input system (conventional versus organic farming) on metabolite profiles of maize ( Zea mays ) kernels.

Maize ( Zea mays ) kernels grown conventionally and organically, respectively, were investigated using a gas chromatography/mass spectrometry (GC/MS)-based metabolite profiling methodology. By analysis of three cultivars grown at two locations with different input systems and at a third location where both organic and conventional farming were applied, the impact of the growing regime on the metabolite spectrum should be put into the context of natural variability. The applied analytical approach involved consecutive extraction of freeze-dried maize flour and subsequent subfractionation. Approximately 300 compounds from a broad spectrum of chemical classes were detected, of which 167 were identified. The metabolite profiling data were statistically assessed via principal component analysis (PCA) and analysis of variance (ANOVA). The PCA demonstrated that the observed separations were mainly due to genetic differences (cultivars) and environmental influences. The different input systems (conventional/organic) only led to minor differentiations. ANOVA and quantification of selected constituents confirmed these observations. Only three metabolites (malic acid, myo-inositol, and phosphate) were consistently different because of the employed input system if samples from all field trials were considered.