Title: "Mapping and Modeling the Arabidopsis Ionome"
Speaker: Ivan Baxter, Senior Research Associate, Bindley Bioscience Center, Purdue University
Place: Mechanical Engineering (ME) 161; October 16, 2007, Tuesday, 4:30pm

Abstract

As the demand for biofuels grows, both biofuel crops and food crops will be required to make more efficient use of fertilizers and micronutrients while increasing their yield. To achieve this, we will need to understand the plants' physiological responses to the soil environment and the genes that direct and carry out these responses. We have employed mineral nutrient and trace element profiling, using inductively coupled plasma-mass spectrometry (ICP-MS), as a tool to determine the biological significance of connections between a plant's genome and its elemental profile, or "ionome." Our focus is on genes that control uptake and accumulation of solutes, including Ca, K, Mg, P (macronutrients in fertilizer); Co, Cu, Fe, Li, Mn, Mo, Ni, Se, Zn (micronutrients of significance to plant and human health); and As, Cd, and Na (elements causing agricultural or environmental problems). To date we have analyzed the shoot ionome of over 60,000 Arabidopsis plants while screening forward genetics, reverse genetics, recombinant inbred, and natural populations. To maximize the value of this ionomics approach, we have developed a regularly updated, publicly searchable online database containing our ionomic data (www.purdue.edu/dp/ionomics). I will describe how we have used DNA microarray-based approaches and QTL mapping to identify genes responsible for interesting ionomic phenotypes from natural and induced populations. Additionally, I will discuss how, by varying the concentrations of iron in the soil, we have observed significant differences in the accumulation of micronutrients and used this data to build a model that predicts the iron status of a plant. Finally, I will describe some of our new projects: using the natural populations of Arabidopsis for association mapping to find genes and alleles more rapidly, analyzing field-grown rice grains, and analyzing the yeast knockout collection.



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