SummaryPhytic acid, myo-inositol-1,2,3,4,5,6-hexakis phosphate or Ins P 6 , is the most abundant myo-inositol phosphate in plant cells, but its biosynthesis is poorly understood. Also uncertain is the role of myo-inositol as a precursor of phytic acid biosynthesis. We identified a low-phytic acid mutant, lpa3, in maize. The Mu-insertion mutant has a phenotype of reduced phytic acid, increased myo-inositol and lacks significant amounts of myo-inositol phosphate intermediates in seeds. The gene responsible for the mutation encodes a myo-inositol kinase (MIK). Maize MIK protein contains conserved amino acid residues found in pfkB carbohydrate kinases. The maize lpa3 gene is expressed in developing embryos, where phytic acid is actively synthesized and accumulates to a large amount. Characterization of the lpa3 mutant provides direct evidence for the role of myo-inositol and MIK in phytic acid biosynthesis in developing seeds. Recombinant maize MIK phosphorylates myo-inositol to produce multiple myo-inositol monophosphates, Ins(1/3)P, Ins(4/6)P and possibly Ins(5)P. The characteristics of the lpa3 mutant and MIK suggest that MIK is not a salvage enzyme for myo-inositol recycling and that there are multiple phosphorylation routes to phytic acid in developing seeds. Analysis of the lpa2/lpa3 double mutant implies interactions between the phosphorylation routes.

This study was designed to elucidate the biological variation in expression of many metabolites due to environment, genotype, or both, and to investigate the potential utility of metabolomics to supplement compositional analysis for substantial equivalence assessments of genetically modified (GM) crops. A total of 654 grain and 695 forage samples from 50 genetically diverse non-GM DuPont Pioneer maize hybrids grown at six locations in the U.S. and Canada were analyzed by coupled gas chromatography time-of-flight-mass spectrometry (GC/TOF-MS). A total of 156 and 185 metabolites were measured in grain and forage samples, respectively. Univariate and multivariate statistical analyses were employed extensively to compare and correlate the metabolite profiles. We show that the environment had far more impact on the forage metabolome compared to the grain metabolome, and the environment affected up to 50% of the metabolites compared to less than 2% by the genetic background. The findings from this study demonstrate that the combination of GC/TOF-MS metabolomics and comprehensive multivariate statistical analysis is a powerful approach to identify the sources of natural variation contributed by the environment and genotype.

We evaluated the variability of metabolites in various maize hybrids due to the effect of environment, genotype, phenotype as well as the interaction of the first two factors. We analyzed 480 forage and the same number of grain samples from 21 genetically diverse non-GM Pioneer brand maize hybrids, including some with drought tolerance and viral resistance phenotypes, grown at eight North American locations. As complementary platforms, both GC/MS and LC/MS were utilized to detect a wide diversity of metabolites. GC/MS revealed 166 and 137 metabolites in forage and grain samples, respectively, while LC/MS captured 1341 and 635 metabolites in forage and grain samples, respectively. Univariate and multivariate analyses were utilized to investigate the response of the maize metabolome to the environment, genotype, phenotype, and their interaction. Based on combined percentages from GC/MS and LC/MS datasets, the environment affected 36% to 84% of forage metabolites, while less than 7% were affected by genotype. The environment affected 12% to 90% of grain metabolites, whereas less than 27% were affected by genotype. Less than 10% and 11% of the metabolites were affected by phenotype in forage and grain, respectively. Unsupervised PCA and HCA analyses revealed similar trends, i.e., environmental effect was much stronger than genotype or phenotype effects. On the basis of comparisons of disease tolerant and disease susceptible hybrids, neither forage nor grain samples originating from different locations showed obvious phenotype effects. Our findings demonstrate that the combination of GC/MS and LC/MS based metabolite profiling followed by broad statistical analysis is an effective approach to identify the relative impact of environmental, genetic and phenotypic effects on the forage and grain composition of maize hybrids.

Soybean (Glycine max (L.) Merr.) seeds with elevated or reduced percentages of palmitate and elevated percentages of stearate were compared with seeds of typical composition in tests for germination, seedling growth rate and leachate conductivity. In general, seeds with altered compositions did well in these physiological tests, but their vigour tended to be negatively correlated with the percentages of stearate and palmitate in various lipid classes.

No abstract