Dissecting Phytophthora Resistance In Soybean Using Expression Profiling and Analysis of Quantitative Trait Loci.

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

Some plant disease resistance genes (major resistance genes) confer high levels of resistance against particular pathogens. These genes have been well studied over the past decade. Major resistance genes, however, are generally effective only against specific strains of a pathogen, and so become ineffective in the field when new pathogens strains appear. In contrast, partial or quantitative resistance is effective against all strains of a pathogen, albeit providing a lower level of protection. Multiple genes confer partial resistance, each making minor contributions, complicating the study of these genes. As a result, less is known about how they operate. The goal of this project is to understand the mechanisms of quantitative resistance of soybean against the oomycete pathogen Phytophthora sojae, which is one of the most damaging soybean diseases. Oomycetes are fungus-like organisms that are actually most closely related to brown algae such as kelp and diatoms. Phytophthora pathogens attack thousands of plant species, including many important crops. Two approaches will be combined to identify and characterize quantitative resistance genes. First, genetic crosses between soybean cultivars differing in their quantitative resistance to Phytophthora will be analyzed to identify genetic loci (called quantitative trait loci or QTL) in soybean that contribute to resistance. Second, the expression levels of thousands of soybean and Phytophthora genes during infection will be assayed using hybridization microarrays to determine the mechanisms of resistance contributed by different quantitative resistance loci. Pathogen gene expression patterns will be analyzed to determine the impact of plant defense mechanisms on the pathogen. Overall, it is anticipated that this research will result in new insights into plant defense mechanisms, new genetic and genomic resources for soybean researchers and breeders, and new statistical tools for analysis of gene expression data.