Project Number: 3602-22000-016-00-D
Project Type: In-House Appropriated
Start Date: May 14, 2008
End Date: May 13, 2013
The long-term objectives of this project are to facilitate development of durable and effective pest controls through host resistance either selected or genetically engineered and to minimize the risks to deployment of new genes for resistance. Over the next 5 years we will focus on the following specific objectives: (1) Better understand the molecular bases of resistance and susceptibility in wheat; (2) Reveal insight into the molecular basis of virulence in Hessian fly; (3) Elucidate Hessian fly population structure and risks to new genes for resistance. Despite their economic importance, little is known about the molecular interactions between Hessian fly and wheat that result in resistance or susceptibility, the molecular mechanism of resistance in wheat, or the effects of these interactions on the genetic structure of fly populations.
Objective 1: Gene expression in compatible and incompatible wheat-Hessian fly interactions will be assessed by microarray technology and 454FLX sequencing. Gene function will be assessed with BLAST. Enzyme and substrate binding activities will be verified by protein expression and biochemical analyses. Promoter regions will be identified by various bioinformatic softwares. Viral-induced gene silencing (VIGS) will assess the involvement of wheat genes during compatible and incompatible interactions. Objective 2: Microarray technology and 454FLX sequencing will reveal gene expression in the larval Hessian fly during compatible and incompatible interactions with wheat. The morphology of midgut and salivary gland tissues will be examined by transmission electron microscopy. Comparative transcriptomics will identify Hessian fly genes involved in parasitism of wheat. The role of Hessian fly genes in host susceptibility or resistance will be assessed through RNAi knockdown. Objective 3: Microsatellite markers will be used to assess heterogeneity and gene flow in Hessian fly populations. Changes in allelic variation will assist in assessing the risks to deployed resistance. Differentiation at different geographic scales will be assessed by Fst and Rst values. Estimation of effective population size (Ne) will be used to measure the strength of genetic drift in populations.