43-8 Sat Jan 2 Dynamic Bayesian network models of Arabidopsis thaliana transcriptome time series data reveals possible role for HyPRPs in systemic acquired resistance Filzen, RC*; Banday, Z; Greenberg, JT; University of Chicago; University of Chicago; University of Chicago rfilzen@uchicago.edu
As climate change progresses, plants suffer increasing levels of environmental stress, commonly pathogen stress. Understanding how plants combat pathogen stress thus represents a critical area of research. Systemic acquired resistance (SAR) is one plant defense mechanism and can be conceptualized into two stages: the priming stage and the resistant stage. The priming stage is induced by a primary pathogen infection in a lower leaf triggering defense chemical biosynthesis, particularly in the chloroplast. Defense chemical transport to aerial leaves produces the resistant state in which these leaves are more resistant to secondary pathogen infection. HyPRPs are a novel protein family thought to be involved in SAR due to their unique structure and chloroplast localization. The N-terminal hydrophobic domain, proline rich region, and lipid transport protein-like domain, are predicted to give rise to their novel bipartite localization mechanism. This project uses the model organism Arabidopsis thaliana to investigate how HyPRPs might be involved in both the priming and resistant stages of SAR. In silico methods using Arabidopsis transcriptome data were used to generate predictions for protein interactions and involvement in the priming stage of SAR. These models will be used to further guide experiments and prioritize specific HyPRPs for future wet-lab assays. In vivo assays such as transient expression, SAR challenge in hyprp knockouts, and fluorescence localization microscopy reveal how HyPRPs may act in SAR responses and the impact of their structural domains in subcellular localization. Together, these two approaches to protein characterization will further the understanding of pathogen stress response in plants.