2012 Annual Report
1a.Objectives (from AD-416):
1. Determine if unintended effects are produced in transgenic crops, using fruit ripening in tomato as a model system. 1A. Determine if unintended effects are produced in transgenic crops, using gene expression analysis as a monitoring tool. 1B. Determine if unintended effects are produced in the fruit of transgenic crops that affect fruit quality or composition, through metabolomic and proteomic profiling and an examination of agronomic trait performance.
2. Genetically identify the genes affecting iron levels and bioavailability in maize seed using maize quantitative genetics and Caco-2 cell culture in vitro digestion assay. Determine Fe levels and bioavailability in genetically engineered maize seed.
1b.Approach (from AD-416):
1) Utilize genomic, metabolomic, proteomic and agronomic approaches to evaluate phenotypic difference between tomatoes. 1A) Utilize natural diversity between tomato cultivars, together with conventional breeding techniques, to capture a reasonable phenotypic range from diverse tomato germplasm. 1B) Utilize RNAi and artificial microRNA gene silencing technologies to adjust RIN gene expression levels and alter fruit ripening. Compare phenotypic effects of transgenes to the range observed with conventional cultivars..
2)Leverage research on fruit specific or ripening stage specific promoter sequences to further tailor the modulation of RIN gene expression in the target tissue. Assess the efficacy of tailored gene modulation on reducing unintended effects via genomic, metabolomic, proteomic and agronomic monitoring.
Advances in maize genomics have allowed us to take a longer but more powerful road to our ultimate goal of describing factors that influence iron nutritional quality in maize grain. We used ultra performance liquid chromatography (UPLC) paired with a quadruple-time of flight (Q-TOF) tandem mass spectrometer to examine differences in maize grain composition within a panel of diverse inbred lines, current commercial varieties, and specialty germplasm created by scientists in Ithaca NY. We chose to profile cooked maize rather than raw maize, as our goal is to better understand the composition of food products rather than only consider the composition of raw maize (which is not consumed by people). We observed approximately 10,000 chemical markers with our UPLC/Q-TOF approach; typical chemical profiling experiments on raw maize observe approximately 1,500 chemical markers. Subsequent analyses have revealed that the cooked maize extracts allow us to simultaneously observe small chemicals and proteins, which allows us to more completely describe the composition of maize grain at far lower cost and effort. Statistical genetic approaches developed by colleagues at the Holley Center that have revolutionized gene discovery in maize were applied to our dataset. This allows us to leverage investments made in maize genome sequencing to help understand the genes responsible for determining hundreds of compounds in maize grain. Once this stage of analysis is complete, we will be able to examine the differences (genetic, chemical, and nutritional) in our specialty germplasm created to vary in the fraction of bioavailable iron. This information will also allow us to estimate iron nutritional quality and create new hypotheses.
In addition, next generation sequencing technologies are being used to better characterize the genes and patterns of expression found in our specialty and other diverse germplasm. Genotype-by-sequencing, a technology pioneered by colleagues at the Holley Center in Ithaca NY, has been applied to new varieties created to have high or low levels of bioavailable iron. RNA was isolated from roots of plants grown in natural soils (but planted in a greenhouse), to examine the patterns of gene expression found in diverse maize varieties and the high/low bioavailable iron maize. This latter project is being done in conjunction with ARS scientists in MO and university scientists in MN.
Finally, an in vitro assay to test the iron nutritional quality of food products such as corn meal was used to examine diverse maize varieties. This will permit us to use statistical genetics techniques to map genes important for iron nutritional quality. Evaluating diverse maize varieties in parallel with current commercial releases will allow us to estimate bioavailable iron in the current and potential future food supply.
Leveraging genomes to better understand chemistry. Identification of particular chemical compounds out of complex mixtures is a difficult problem for food chemists and safety regulators. ARS researchers at the Robert W. Holley Center for Agriculture and Health at Ithaca, New York, developed a procedure that combines analytical chemistry with multivariate statistics, building on previous investments in the sequencing of the maize genome. Thus, the annotated maize genome can help chemists to identify novel compounds found in maize grain. This discovery may help breeders to tailor grain chemistry and quality to better meet the needs of consumers and producers.
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Dileo, M., Strahan, G.D., Hoekenga, O. 2011. Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PLoS One. 6(10):e26683. DOI: 10.1371/journal.pone.0026683.