Literature DB >> 20158212

Impact of genetics and environment on the metabolite composition of maize grain.

Kirsten Skogerson1, George G Harrigan, Tracey L Reynolds, Steven C Halls, Martin Ruebelt, Alberto Iandolino, Anand Pandravada, Kevin C Glenn, Oliver Fiehn.   

Abstract

This study sought to assess genetic and environmental impacts on the metabolite composition of maize grain. Gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) measured 119 identified metabolites including free amino acids, free fatty acids, sugars, organic acids, and other small molecules in a range of hybrids derived from 48 inbred lines crossed against two different tester lines (from the C103 and Iodent heterotic groups) and grown at three locations in Iowa. It was reasoned that expanded metabolite coverage would contribute to a comprehensive evaluation of the grain metabolome, its degree of variability, and, in principle, its relationship to other compositional and agronomic features. The metabolic profiling results established that the small molecule metabolite pool is highly dependent on genotypic variation and that levels of certain metabolite classes may have an inverse genotypic relationship to each other. Different metabolic phenotypes were clearly associated with the two distinct tester populations. Overall, grain from the C103 lines contained higher levels of free fatty acids and organic acids, whereas grain from the Iodent lines were associated with higher levels of amino acids and carbohydrates. In addition, the fold-range of genotype mean values [composed of six samples each (two tester crosses per inbred x three field sites)] for identified metabolites ranged from approximately 1.5- to 93-fold. Interestingly, some grain metabolites showed a non-normal distribution over the entire corn population, which could, at least in part, be attributed to large differences in metabolite values within specific inbred crosses relative to other inbred sets. This study suggests a potential role for metabolic profiling in assisting the process of selecting elite germplasm in biotechnology development, or marker-assisted breeding.

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Year:  2010        PMID: 20158212     DOI: 10.1021/jf903705y

Source DB:  PubMed          Journal:  J Agric Food Chem        ISSN: 0021-8561            Impact factor:   5.279


  23 in total

1.  Metabolic changes in transgenic maize mature seeds over-expressing the Aspergillus niger phyA2.

Authors:  Jun Rao; Litao Yang; Jinchao Guo; Sheng Quan; Guihua Chen; Xiangxiang Zhao; Dabing Zhang; Jianxin Shi
Journal:  Plant Cell Rep       Date:  2015-11-18       Impact factor: 4.570

Review 2.  Mass spectrometry strategies in metabolomics.

Authors:  Zhentian Lei; David V Huhman; Lloyd W Sumner
Journal:  J Biol Chem       Date:  2011-06-01       Impact factor: 5.157

3.  The complex genetic architecture of the metabolome.

Authors:  Eva K F Chan; Heather C Rowe; Bjarne G Hansen; Daniel J Kliebenstein
Journal:  PLoS Genet       Date:  2010-11-04       Impact factor: 5.917

4.  Characterization of GMO or glyphosate effects on the composition of maize grain and maize-based diet for rat feeding.

Authors:  Stéphane Bernillon; Mickaël Maucourt; Catherine Deborde; Sylvain Chéreau; Daniel Jacob; Nathalie Priymenko; Bérengère Laporte; Xavier Coumoul; Bernard Salles; Peter M Rogowsky; Florence Richard-Forget; Annick Moing
Journal:  Metabolomics       Date:  2018-02-17       Impact factor: 4.290

5.  Rice bran fermented with saccharomyces boulardii generates novel metabolite profiles with bioactivity.

Authors:  Elizabeth P Ryan; Adam L Heuberger; Tiffany L Weir; Brittany Barnett; Corey D Broeckling; Jessica E Prenni
Journal:  J Agric Food Chem       Date:  2011-02-09       Impact factor: 5.279

6.  Metabolomic and functional genomic analyses reveal varietal differences in bioactive compounds of cooked rice.

Authors:  Adam L Heuberger; Matthew R Lewis; Ming-Hsuan Chen; Mark A Brick; Jan E Leach; Elizabeth P Ryan
Journal:  PLoS One       Date:  2010-09-23       Impact factor: 3.240

7.  Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Authors:  Samuel M D Seaver; Louis M T Bradbury; Océane Frelin; Raphy Zarecki; Eytan Ruppin; Andrew D Hanson; Christopher S Henry
Journal:  Front Plant Sci       Date:  2015-03-10       Impact factor: 5.753

8.  Accumulation of 5-hydroxynorvaline in maize (Zea mays) leaves is induced by insect feeding and abiotic stress.

Authors:  Jian Yan; Alexander E Lipka; Eric A Schmelz; Edward S Buckler; Georg Jander
Journal:  J Exp Bot       Date:  2014-09-30       Impact factor: 6.992

Review 9.  Metabolomics of genetically modified crops.

Authors:  Carolina Simó; Clara Ibáñez; Alberto Valdés; Alejandro Cifuentes; Virginia García-Cañas
Journal:  Int J Mol Sci       Date:  2014-10-20       Impact factor: 5.923

10.  Variability in nutrient composition of cereal grains from different origins.

Authors:  Jinyoung Lee; Doo Seok Nam; Changsu Kong
Journal:  Springerplus       Date:  2016-04-06
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