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 al., unpublished).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.
Additional contributors: Masanao Sato; Fumiaki Katagirip; Jane Glazebrook (faculty mentor).
This project was supported by a grant from the University of Minnesota
Undergraduate Research Opportunities Program (UROP).
Longlet, Michael J..
Does Gene Expression Pattern Predict Pathogen Growth? Testing Inferences from a Proposed Model of the Arabidopsis thaliana Defense Signaling Network.
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