Literature DB >> 16366695

Natural variability of metabolites in maize grain: differences due to genetic background.

Tracey L Reynolds1, Margaret A Nemeth, Kevin C Glenn, William P Ridley, James D Astwood.   

Abstract

Understanding the impact of genetic diversity on crop biochemical composition is a prerequisite to the interpretation and potential relevance of biochemical differences experimentally observed between genotypes. This is particularly important in the context of comparative safety assessments for crops developed by new technologies such as genetic engineering. To interrogate the natural variability of biochemical composition, grain from seven maize hybrids grown at four geographically distinct sites in Europe was analyzed for levels of proximates (fat, protein, moisture, ash, and carbohydrates), fiber, amino acids, fatty acids, four vitamins, nine minerals, and secondary metabolites. Statistical evaluation of the compositional data at the p < 0.05 level compared each hybrid against every other hybrid (head-to-head) for all analytes at each site and then across all sites to understand the factors contributing to variability. Of the 4935 statistical comparisons made in this study, 40% (1986) were found to be significant. The magnitude of differences observed, as a percent, ranged between 0.84 and 149% when all individual sites and the combined sites were considered. The large number of statistically significant differences in the levels of these analytes between seven commercial hybrids emphasizes the importance of genetic background and environment as determinants of the biochemical composition of maize grain, reflects the inherent natural variability in those analytes across a representative sampling of maize hybrids, and provides a baseline of the natural range of these nutritional and antinutritional components in maize for comparative compositional assessments.

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Year:  2005        PMID: 16366695     DOI: 10.1021/jf051635q

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


  9 in total

1.  Metabolic profiling based on LC/MS to evaluate unintended effects of transgenic rice with cry1Ac and sck genes.

Authors:  Yuwei Chang; Chunxia Zhao; Zhen Zhu; Zeming Wu; Jia Zhou; Yanni Zhao; Xin Lu; Guowang Xu
Journal:  Plant Mol Biol       Date:  2012-01-22       Impact factor: 4.076

2.  Natural variation in crop composition and the impact of transgenesis.

Authors:  George G Harrigan; Denise Lundry; Suzanne Drury; Kristina Berman; Susan G Riordan; Margaret A Nemeth; William P Ridley; Kevin C Glenn
Journal:  Nat Biotechnol       Date:  2010-05       Impact factor: 54.908

3.  A statistical assessment of differences and equivalences between genetically modified and reference plant varieties.

Authors:  Hilko van der Voet; Joe N Perry; Billy Amzal; Claudia Paoletti
Journal:  BMC Biotechnol       Date:  2011-02-16       Impact factor: 2.563

4.  Expression of fungal diacylglycerol acyltransferase2 genes to increase kernel oil in maize.

Authors:  Janette Oakes; Doug Brackenridge; Ron Colletti; Maureen Daley; Deborah J Hawkins; Hui Xiong; Jennifer Mai; Steve E Screen; Dale Val; Kathryn Lardizabal; Ken Gruys; Jill Deikman
Journal:  Plant Physiol       Date:  2011-01-18       Impact factor: 8.340

5.  Evolution of risk assessment strategies for food and feed uses of stacked GM events.

Authors:  Catherine Kramer; Phil Brune; Justin McDonald; Monique Nesbitt; Alaina Sauve; Sabine Storck-Weyhermueller
Journal:  Plant Biotechnol J       Date:  2016-03-28       Impact factor: 9.803

6.  Nutritional Diversity in Native Germplasm of Maize Collected From Three Different Fragile Ecosystems of India.

Authors:  Sapna Langyan; Rakesh Bhardwaj; Jyoti Kumari; Sherry Rachel Jacob; Ishwari Singh Bisht; Someswara Rao Pandravada; Archna Singh; Pratap Bhan Singh; Zahoor Ahmed Dar; Ashok Kumar; Jai Chand Rana
Journal:  Front Nutr       Date:  2022-04-11

7.  Safe composition levels of transgenic crops assessed via a clinical medicine model.

Authors:  Rod A Herman; Peter N Scherer; Amy M Phillips; Nicholas P Storer; Mark Krieger
Journal:  Biotechnol J       Date:  2010-02       Impact factor: 4.677

8.  Hypothesis Testing of Inclusion of the Tolerance Interval for the Assessment of Food Safety.

Authors:  Hungyen Chen; Hirohisa Kishino
Journal:  PLoS One       Date:  2015-10-28       Impact factor: 3.240

9.  Roles of metabolic regulation in developing Quercus variabilis acorns at contrasting geologically-derived phosphorus sites in subtropical China.

Authors:  Jun Yuan; Ningxiao Sun; Hongmei Du; Shan Yin; Hongzhang Kang; Muhammad Umair; Chunjiang Liu
Journal:  BMC Plant Biol       Date:  2020-08-25       Impact factor: 4.215

  9 in total

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