Biological systems are exceedingly complex. The unraveling of the genome in plants and humans revealed fewer than the anticipated number of genes. Therefore, other processes such as the regulation of gene expression, the action of gene products, and the metabolic networks resulting from catalytic proteins must make fundamental contributions to the remarkable diversity inherent in living systems. Metabolomics is a relatively new approach aimed at improved understanding of these metabolic networks and the subsequent biochemical composition of plants and other biological organisms. Analytical tools within metabolomics including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy can profile the impact of time, stress, nutritional status, and environmental perturbation on hundreds of metabolites simultaneously resulting in massive, complex data sets. This information, in combination with transcriptomics and proteomics, has the potential to generate a more complete picture of the composition of food and feed products, to optimize crop trait development, and to enhance diet and health. Selected presentations from an American Chemical Society symposium held in March 2005 have been assembled to highlight the emerging application of metabolomics in agriculture.

The Organization for Economic Co-operation and Development (OECD) recommends the measurement of specific plant components for compositional assessments of new biotechnology-derived crops. These components include proximates, nutrients, antinutrients, and certain crop-specific secondary metabolites. A considerable literature on the natural variability of these components in conventional and biotechnology-derived crops now exists. Yet the OECD consensus also suggests measurements of any metabolites that may be directly associated with a newly introduced trait. Therefore, steps have been initiated to assess natural variation in metabolites not typically included in the OECD consensus but which might reasonably be expected to be affected by new traits addressing, for example, nutritional enhancement or improved stress tolerance. The compositional study reported here extended across a diverse genetic range of maize hybrids derived from 48 inbreds crossed against two different testers. These were grown at three different, but geographically similar, locations in the United States. In addition to OECD analytes such as proximates, total amino acids and free fatty acids, the levels of free amino acids, sugars, organic acids, and selected stress metabolites in harvested grain were assessed. The major free amino acids identified were asparagine, aspartate, glutamate, and proline. The major sugars were sucrose, glucose, and fructose. The most predominant organic acid was citric acid, with only minor amounts of other organic acids detected. The impact of genetic background and location was assessed for all components. Overall, natural variation in free amino acids, sugars, and organic acids appeared to be markedly higher than that observed for the OECD analytes.

Genetically engineered crops were first commercialized in 1994 and since then have been rapidly adopted, enabling growers to more effectively manage pests and increase crop productivity while ensuring food, feed, and environmental safety. The development of these crops is complex and based on rigorous science that must be well coordinated to create a plant with desired beneficial phenotypes. This article describes the general process by which a genetically engineered crop is developed from an initial concept to a commercialized product.

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.

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.