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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

PCA and MB-PCA of maize grain metabolome data. PCA showed the effect of location (Fig. 2a). MB-PCA showed that the female tester had a significant influence on the data at each location as can be seen in the super scores (Fig. 2b). The block scores (Fig. 2c–e) showed similar discrimination at all three locations [Illinois (ILMN)], Minnesota (MNOW), Nebraska, (NEST). No clear separation between the GM and non-GM hybrids was observed. TEV total explained variance
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Fig2: PCA and MB-PCA of maize grain metabolome data. PCA showed the effect of location (Fig. 2a). MB-PCA showed that the female tester had a significant influence on the data at each location as can be seen in the super scores (Fig. 2b). The block scores (Fig. 2c–e) showed similar discrimination at all three locations [Illinois (ILMN)], Minnesota (MNOW), Nebraska, (NEST). No clear separation between the GM and non-GM hybrids was observed. TEV total explained variance

Mentions: The entries for this study were based on the listing in Table. A total of four paired positive and negative segregants (e.g. POS A1 and NEG A1) were generated through backcrossing as shown in Fig 2. Each segregant as well as the recurrent parent (RP) was crossed with two different testers for a total of nine (4 POS, 4 NEG, one RP) hybrid entries per tester set


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)

PCA and MB-PCA of maize grain metabolome data. PCA showed the effect of location (Fig. 2a). MB-PCA showed that the female tester had a significant influence on the data at each location as can be seen in the super scores (Fig. 2b). The block scores (Fig. 2c–e) showed similar discrimination at all three locations [Illinois (ILMN)], Minnesota (MNOW), Nebraska, (NEST). No clear separation between the GM and non-GM hybrids was observed. TEV total explained variance
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Related In: Results  -  Collection

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Fig2: PCA and MB-PCA of maize grain metabolome data. PCA showed the effect of location (Fig. 2a). MB-PCA showed that the female tester had a significant influence on the data at each location as can be seen in the super scores (Fig. 2b). The block scores (Fig. 2c–e) showed similar discrimination at all three locations [Illinois (ILMN)], Minnesota (MNOW), Nebraska, (NEST). No clear separation between the GM and non-GM hybrids was observed. TEV total explained variance
Mentions: The entries for this study were based on the listing in Table. A total of four paired positive and negative segregants (e.g. POS A1 and NEG A1) were generated through backcrossing as shown in Fig 2. Each segregant as well as the recurrent parent (RP) was crossed with two different testers for a total of nine (4 POS, 4 NEG, one RP) hybrid entries per tester set

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