Does Gene Expression Pattern Predict Pathogen Growth? Testing Inferences from a Proposed Model of the Arabidopsis thaliana Defense Signaling Network

Abstract

Agriculture is a globally significant and influential component of local economies as a fundamental principle of functional community is a reliable source of food. A primary challenge for plants in survival is the recognition and suppression of pathogen infection. Studies using the model organism, Arabidopsis thaliana indicate recognition of non-self, complex signal transduction and effector expression exist as primary defense mechanisms to ultimately suppress pathogen proliferation, (Jones and Dangl, 2006). Microarray experiments have provided quantitative gene expression profiles of plant defense responses and indicated rapid transcriptional activation of thousands of genes upon pathogen infection, (Katagiri, 2004). Furthermore, gene expression profiling of pathogen induced genes combined with reverse genetics have been used as high-dimensional data to construct a model of the Arabidopsis defense signaling network, (Sato et. al., unpublished). The network model successfully predicted known defense-gene interactions. Thus, we assume the network model provides a means to efficiently predict uncharacterized mutant phenotypes. Currently, it is not clear which transcriptionally induced genes actually contribute to defense against the pathogen. This project focused on evaluating whether or not eight transcriptionally induced genes in microarray studies actually contribute to the defense response against the pathogen Pseudomnas syringae p.v. maculicola.