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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

Variance component analysis averaged across all metabolites These results highlight the lack of any trait effect; the term Tester represents variation due to the two different female lines used in hybrid formation. Location*Entry effect which represents the effect of the interaction between location and each of the hybrid entries. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term Segregant (Variant Tester) represents variation due to differences between the trait-positive and trait-negative segregants
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Fig5: Variance component analysis averaged across all metabolites These results highlight the lack of any trait effect; the term Tester represents variation due to the two different female lines used in hybrid formation. Location*Entry effect which represents the effect of the interaction between location and each of the hybrid entries. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term Segregant (Variant Tester) represents variation due to differences between the trait-positive and trait-negative segregants

Mentions: Variance component analysis (VCA) was conducted to quantify the effect of the test factors on the maize grain metabolite profiles. The results of the VCA combined across all components is presented in Fig. 5 (see Supplementary File S7 for variation in levels of individual metabolites) and demonstrated that the contribution of near-isogenic and GM effects were extremely small relative to the much larger location and tester effects (Fig. 5). This is followed by a tester effect where the term “Tester” represents variation due to the two different females (testers) used in hybrid formation and the Rep (Location) effect. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term “Segregant (Variant Tester)” represents variation due to differences between the trait-positive and trait-negative segregants. These latter terms encompass variation that would be associated with any near-isogenic and GM trait effects. It should be note that although the term “Segregant (Variant Tester)” involves a direct comparison of the POS and NEG variants (for each tester set) some contribution from a near-isogenic effect can be assumed and this term cannot strictly be viewed as a GM effect. Regardless, it is apparent that, overall, term “Segregant (Variant Tester)” is associated with the smallest source of variation in this study (Fig. 5). Indeed, this term was associated with precisely 0.0 % variation for 113 of the 153 metabolites analyzed. Only a total of 6 metabolites (cholesterol, ethanolamine, lysine, melezitose, shikimic acid, and squalene) had a Segregant (Variant Tester) effect of >5 % (i.e. 147/153 of metabolites had values <5 %). Of these, most were associated with high residuals, and/or larger variation attributable to other factors such as location or tester, and iii) no consistent pattern of differences between the paired negative- and positive segregant-derived hybrids. As an illustrative example, lysine had a Segregant (Variant Tester) of 5.44 % but the variance component term for location was 58.37 %. Pairwise comparisons of lysine levels showed that, for both the S4062Y and T2502Z hybrids tester, only one of the POS entries showed a statistically significant difference (α = 0.05) when compared to the conventional control values. As an other example, differences in shikimic acid levels between POS and NEG segregants are associated with only one tester (S4062Y, Supplementary File S3) but not the other (T2502Z, Supplementary File S4).Fig. 5


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)

Variance component analysis averaged across all metabolites These results highlight the lack of any trait effect; the term Tester represents variation due to the two different female lines used in hybrid formation. Location*Entry effect which represents the effect of the interaction between location and each of the hybrid entries. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term Segregant (Variant Tester) represents variation due to differences between the trait-positive and trait-negative segregants
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4940444&req=5

Fig5: Variance component analysis averaged across all metabolites These results highlight the lack of any trait effect; the term Tester represents variation due to the two different female lines used in hybrid formation. Location*Entry effect which represents the effect of the interaction between location and each of the hybrid entries. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term Segregant (Variant Tester) represents variation due to differences between the trait-positive and trait-negative segregants
Mentions: Variance component analysis (VCA) was conducted to quantify the effect of the test factors on the maize grain metabolite profiles. The results of the VCA combined across all components is presented in Fig. 5 (see Supplementary File S7 for variation in levels of individual metabolites) and demonstrated that the contribution of near-isogenic and GM effects were extremely small relative to the much larger location and tester effects (Fig. 5). This is followed by a tester effect where the term “Tester” represents variation due to the two different females (testers) used in hybrid formation and the Rep (Location) effect. The term “Variant (Tester)” represents variation within the variants associated with a given tester and the term “Segregant (Variant Tester)” represents variation due to differences between the trait-positive and trait-negative segregants. These latter terms encompass variation that would be associated with any near-isogenic and GM trait effects. It should be note that although the term “Segregant (Variant Tester)” involves a direct comparison of the POS and NEG variants (for each tester set) some contribution from a near-isogenic effect can be assumed and this term cannot strictly be viewed as a GM effect. Regardless, it is apparent that, overall, term “Segregant (Variant Tester)” is associated with the smallest source of variation in this study (Fig. 5). Indeed, this term was associated with precisely 0.0 % variation for 113 of the 153 metabolites analyzed. Only a total of 6 metabolites (cholesterol, ethanolamine, lysine, melezitose, shikimic acid, and squalene) had a Segregant (Variant Tester) effect of >5 % (i.e. 147/153 of metabolites had values <5 %). Of these, most were associated with high residuals, and/or larger variation attributable to other factors such as location or tester, and iii) no consistent pattern of differences between the paired negative- and positive segregant-derived hybrids. As an illustrative example, lysine had a Segregant (Variant Tester) of 5.44 % but the variance component term for location was 58.37 %. Pairwise comparisons of lysine levels showed that, for both the S4062Y and T2502Z hybrids tester, only one of the POS entries showed a statistically significant difference (α = 0.05) when compared to the conventional control values. As an other example, differences in shikimic acid levels between POS and NEG segregants are associated with only one tester (S4062Y, Supplementary File S3) but not the other (T2502Z, Supplementary File S4).Fig. 5

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