Organismal phenotypes are controlled by complex networks that buffer against mutation, environmental noise or error ensuring the proper phenotype is produced. In contrast, most phenotypes vary between individuals of a species allowing for evolution. This includes the plant metabolome that has complex enzymatic interactions and is controlled by intricate signaling interactions but also shows inter-specific variation. One poorly understood mechanism that can allow networks to show extensive phenotypic variation is multi-locus epistasis that simultaneously impacts multiple nodes of a network. Epistasis is frequently found in naturally variable systems such as crop yield and human disease susceptibility but the molecular mechanism is rarely identified. This project will use natural variation in the rice and Arabidopsis metabolomes as model systems to identify mechanisms by which metabolomic Quantitative Trait Loci form an epistatic network that constrains potential variation present within the metabolome. The project builds on previous studies in the model system Arabidopsis thaliana that identified eight naturally variable loci that epistatically interact in a genetic network to control swaths of Arabidopsis primary metabolism. Specific allele combinations at four of these loci lead to plants with 800% increases in steady state content of metabolites within part of the TCA cycle. Specific objectives include cloning the genes underlying these loci and manipulating the homologous genes in rice to test their ability to control primary metabolism in a monocot crop. In addition, precise measures of epistasis in the Arabidopsis and rice metabolomes will be made by analyzing a large Recombinant Inbred Line population in each species. Finally the data generated will be used to develop a metabolic network de novo using a logic based algorithm that has identified novel metabolic networks in other metabolomics data. In the future, this knowledge will allow for the development of models that integrate natural variation in plant metabolic networks to potentially predict phenotypic diversity.
Analysis of natural variation in most organisms focuses on single genes of large effect due to relative ease of identification and modeling. However, this is only one aspect of natural variation and organismal evolution. In contrast, most traits are under complex control including significant epistasis with large phenotypic consequences. This proposal will begin to provide insights into how epistasis and biological networks may control complex traits. Understanding complex epistatic interactions will provide insights into other complex traits such as crop yield and human disease that are under epistatic control. The proposed project will provide research opportunities for high school, undergraduate, and graduate students. Students will be trained in modern metabolic biochemistry and molecular genetics to prepare them for future careers in industry or academics. The undergraduate students will be highly encouraged and guided to develop and devise their own projects within the frame of this proposal. Any publication likely to result from this proposal will likely include at least one undergraduate student as a co-author who was integral in designing and interpreting the experiments. Established outreach programs will be used to recruit minority students from local high schools and colleges throughout the USA for summer internships. In addition, the principal investigator will be involved in teaching, both in a university classroom setting and in ongoing outreach efforts to educate community members about plant metabolism, quantitative genetics, biochemistry, molecular biology and their integration in factorial experiments. All data will be available through the project website and long-term through The Arabidopsis Information Resource (TAIR: www.arabidopsis.org) and Gramene (www.gramene.org).
