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Metabolomics. 2016;12:82. Epub 2016 Mar 15.

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.

Author information

1
Compositional Biology Center, Monsanto Company, St. Louis, MO USA.
2
Regulatory Affairs, Monsanto Company, St. Louis, MO USA.
3
Statistics Technology Center, Monsanto Company, St. Louis, MO USA.
4
Chemistry Technology, Monsanto Company, St. Louis, MO USA.
5
Genome Center - Metabolomics, University of California at Davis, Davis, CA USA.
6
Genome Center - Metabolomics, University of California at Davis, Davis, CA USA ; Biochemistry Department, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.
7
School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7ND UK.

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.

KEYWORDS:

Genetic modification; Genetically modified (GM) traits; Maize (Zea mays); Marker-assisted back crossing; Marker-assisted breeding; Metabolomics; Natural variability

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