Root Metabolism: Integration of Natural Genetic Variation and Systems Biology
Moriyama*, Schnable*, Adamec, Doyle, Helikar, Walia, Zhang
Plant roots continuously monitor and respond to their environments in complex and dynamic ways. Even within individual species, different accessions can vary significantly in their:
- response to and preference for various soil types
- ability to defend against soil borne pests and pathogens, and
- ability to promote the growth of different microbial taxa.
Root metabolites and functionally relevant, taxa-specific metabolites could also have been lost in the process of domestication or crop improvement. To understand this agronomically-important variation, models will be constructed describing how metabolic composition and pathway configuration varies across diverse genotypes. These models will examine gene expression and metabolite abundance in the roots of maize varieties and related wild species. Using genome-wide association studies (GWAS), allelic variation controlling changes in root metabolism will be identified. By combining gene expression and metabolomics data, predictive metabolic models will be developed that can simulate the metabolic impact of individual or combinations of engineered changes to plant root metabolism. Exudate production and soil microbial population composition data obtained in Aim 2 will be layered onto these metabolic networks, and the integrated model will guide the development of engineered plant varieties in Aim 3. Engineered plants developed in Aim 3 will be characterized and the results will be used to refine the predictive model. The final predictive model will enable testing of engineered changes and their combinations. It will also guide the identification of synthetic biology targets for design of root metabolism while minimizing deleterious secondary effects on metabolism.
- Characterization of genetically controlled variation in root metabolism
- In-depth characterization of root metabolism across development and tissue types
- Develop and validate predictive models of root metabolism
- Develop computational tools and databases
Scully, E., T. Donze-Reiner, H. Wang, T. Eickhoff, F. Baxendale, P. Twigg, F. Kovacs, T. Heng-Moss, S. Sattler, and G. Sarath.. 2016. Identification of an orthologous clade of peroxidases that respond to feeding by greenbugs (Schizaphis graminum) in C4 grasses. Functional Plant Bio. 43: 1134-1148