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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

ASCA results showed the effects of location, trait, and tester. It can be seen that separation between growing location and tester was observed and p values <0.001, for both factors, were obtained from the corresponding permutation tests. In other words, not a single case of 1000 permutations had obtained higher sum of squares (SSQs) than the observed one. By contrast, the scores plots obtained from the GM submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids, showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding  distribution
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Fig3: ASCA results showed the effects of location, trait, and tester. It can be seen that separation between growing location and tester was observed and p values <0.001, for both factors, were obtained from the corresponding permutation tests. In other words, not a single case of 1000 permutations had obtained higher sum of squares (SSQs) than the observed one. By contrast, the scores plots obtained from the GM submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids, showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding distribution

Mentions: The original PCA highlighted that growing location had the greatest impact on the maize metabolome when assessing all metabolite features while MB-PCA established that use of a different female tester to generate the maize hybrids also had an effect. The clustering from both of these algorithms indicated that there was no discernible effect of GM or of residual genetic variation. We tested this observation by employing an ASCA model. In ASCA, as described in materials and methods, the data matrix is modeled as the sum of a set of effect matrices. The scores plots from the three submatrices (location, tester, trait) of are given in Fig. 3. The presence of a factor effect (observed SSQs; represented by the red vertical line in Fig. 3) can be visualized by determining whether that red line is distinct from the distribution. It can be seen that separation between growing location and tester was observed and that this was statistically significant (p < 0.001). By contrast, the scores plots obtained from the “GM” submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding distribution. Indeed, the low value for the observed SSQs even in relation to the distribution (Fig. 3) reiterates how negligible metabolomic differences between the GM-trait positive, GM-trait-negative, and conventional hybrids are. In summary, ASCA confirmed the results derived through MB-PCA that growing location was the factor with the most significant impact on the grain metabolome, followed by tester, whereas there was no significant differences between the GM and non-GM hybrid comparators.Fig. 3


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)

ASCA results showed the effects of location, trait, and tester. It can be seen that separation between growing location and tester was observed and p values <0.001, for both factors, were obtained from the corresponding permutation tests. In other words, not a single case of 1000 permutations had obtained higher sum of squares (SSQs) than the observed one. By contrast, the scores plots obtained from the GM submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids, showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding  distribution
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4940444&req=5

Fig3: ASCA results showed the effects of location, trait, and tester. It can be seen that separation between growing location and tester was observed and p values <0.001, for both factors, were obtained from the corresponding permutation tests. In other words, not a single case of 1000 permutations had obtained higher sum of squares (SSQs) than the observed one. By contrast, the scores plots obtained from the GM submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids, showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding distribution
Mentions: The original PCA highlighted that growing location had the greatest impact on the maize metabolome when assessing all metabolite features while MB-PCA established that use of a different female tester to generate the maize hybrids also had an effect. The clustering from both of these algorithms indicated that there was no discernible effect of GM or of residual genetic variation. We tested this observation by employing an ASCA model. In ASCA, as described in materials and methods, the data matrix is modeled as the sum of a set of effect matrices. The scores plots from the three submatrices (location, tester, trait) of are given in Fig. 3. The presence of a factor effect (observed SSQs; represented by the red vertical line in Fig. 3) can be visualized by determining whether that red line is distinct from the distribution. It can be seen that separation between growing location and tester was observed and that this was statistically significant (p < 0.001). By contrast, the scores plots obtained from the “GM” submatrix involving a three-way comparison of the GM-trait positive, GM-trait-negative, and conventional hybrids showed no significant difference as seen in the figures of the observed SSQs superimposed on the corresponding distribution. Indeed, the low value for the observed SSQs even in relation to the distribution (Fig. 3) reiterates how negligible metabolomic differences between the GM-trait positive, GM-trait-negative, and conventional hybrids are. In summary, ASCA confirmed the results derived through MB-PCA that growing location was the factor with the most significant impact on the grain metabolome, followed by tester, whereas there was no significant differences between the GM and non-GM hybrid comparators.Fig. 3

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