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Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait.

Harrigan GG, Venkatesh TV, Leibman M, Blankenship J, Perez T, Halls S, Chassy AW, Fiehn O, Xu Y, Goodacre R - Metabolomics (2016)

Bottom Line: Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester.The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

View Article: PubMed Central - PubMed

Affiliation: Compositional Biology Center, Monsanto Company, St. Louis, MO USA.

ABSTRACT

Introduction: Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

Objectives: Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

Methods: Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

Results: Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

Conclusion: The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

No MeSH data available.


Related in: MedlinePlus

a (Upper table) shows the average percent difference (for all metabolites) from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional control hybrids. b (Lower panels) presents the average percent difference from the combined-site conventional control mean in histogram form where the x-axis is magnitude of difference and the y-axis is frequency of observations (expressed as percent)
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Fig4: a (Upper table) shows the average percent difference (for all metabolites) from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional control hybrids. b (Lower panels) presents the average percent difference from the combined-site conventional control mean in histogram form where the x-axis is magnitude of difference and the y-axis is frequency of observations (expressed as percent)

Mentions: Figure 4a shows, for all metabolites, the average percent difference from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional hybrids (see Supplementary File S5 for results for individual metabolites). Figure 4b provides information regarding the magnitudes of differences in histogram form. As is readily observed, the distribution and magnitudes of differences for metabolites assessed across each hybrid grouping are remarkably similar (see Supplementary File S6 for individual metabolites). This approach allows the distribution of magnitudes of differences observed for the trait-positive, trait-negative, and conventional hybrids to be visually compared to each other and shows that, broadly, the GM and “near-isogenic” effects assessed in this study have no major impact on metabolite variation.Fig. 4


Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait.

Harrigan GG, Venkatesh TV, Leibman M, Blankenship J, Perez T, Halls S, Chassy AW, Fiehn O, Xu Y, Goodacre R - Metabolomics (2016)

a (Upper table) shows the average percent difference (for all metabolites) from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional control hybrids. b (Lower panels) presents the average percent difference from the combined-site conventional control mean in histogram form where the x-axis is magnitude of difference and the y-axis is frequency of observations (expressed as percent)
© Copyright Policy
Related In: Results  -  Collection

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Fig4: a (Upper table) shows the average percent difference (for all metabolites) from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional control hybrids. b (Lower panels) presents the average percent difference from the combined-site conventional control mean in histogram form where the x-axis is magnitude of difference and the y-axis is frequency of observations (expressed as percent)
Mentions: Figure 4a shows, for all metabolites, the average percent difference from the combined-site conventional control mean, standard deviation, first percentile, and 99th percentile for the trait-positive, trait-negative and conventional hybrids (see Supplementary File S5 for results for individual metabolites). Figure 4b provides information regarding the magnitudes of differences in histogram form. As is readily observed, the distribution and magnitudes of differences for metabolites assessed across each hybrid grouping are remarkably similar (see Supplementary File S6 for individual metabolites). This approach allows the distribution of magnitudes of differences observed for the trait-positive, trait-negative, and conventional hybrids to be visually compared to each other and shows that, broadly, the GM and “near-isogenic” effects assessed in this study have no major impact on metabolite variation.Fig. 4

Bottom Line: Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester.The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

View Article: PubMed Central - PubMed

Affiliation: Compositional Biology Center, Monsanto Company, St. Louis, MO USA.

ABSTRACT

Introduction: Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

Objectives: Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

Methods: Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

Results: Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

Conclusion: The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

No MeSH data available.


Related in: MedlinePlus