Assessing RNAi as a reverse genetic tool for global analysis of NBS-LRR gene function in Medicago truncatula

The majority of functionally-characterized disease resistance (R) genes encode NBS-LRR proteins. However, plants contain hundreds of NBS-LRR encoding genes, and it is still difficult to identify the genes that are responsible for resistance to particular pathogens. The objective of this proposal is to utilize RNAi (RNA interference) as a reverse genetic tool for global analysis of NBS-LRR gene function in Medicago truncatula. Our strategy is to inactivate multiple target genes in a single transgenic line, thus reducing the number of analyses required for global analysis of NBS-LRR gene function. Transgenic lines with successfully 'knockeddown' NBS-LRR genes will be made publicly available for comparing disease responses to a large number of pathogens between wild-type and transgenic plants. This research will elucidate the function ofNBS-LRR genes and it will facilitate the map-based cloning of functional resistance genes by narrowing down the number of candidate genes. In a broader sense, this research will facilitate the genetic improvement of disease resistance in alfalfa (M. sativa). Alfalfa is the fourth-most important crop in the US but has an intractable genetic system. However, alfalfa is closely related to the model legume M truncatula and both species share many common pathogens. The resistance genes identified in M. truncatula will provide new tools for the improvement of cultivated alfalfa. This project is risky and represents a preliminary and "exploratory phase" activity, because we do not currently know if RNAi can be used efficiently to knock down the expression of members of a multi gene family. The success of this project will provide a new technology for global analysis ofNBS-LRR gene function in plants. Initially, we will choose several highly expressed gene clusters and clusters containing functional R genes against Erysiphe pisi and Colletotrichum trifolii. Once we validate our approach in the small scale experiments, we will move on toward application of this technology for genome-wide analysis.