UC San Diego

During pathogen infection, plants recognize microbial molecules known as pathogen associated molecular patterns (PAMPS) by surface localized pattern recognition receptors (PRRs), these results in physiological changes that limit pathogen growth in a process known as PAMP-triggered immunity (PTI). Recognition of PAMPs triggers the production of the phytohormone salicylic acid (SA), which is important for defense against biotrophic pathogens and requires redox regulated NONEXPRESSER OF PR GENES1 (NPR1), a master regulator of SA- mediated defense. Although there is significant research on the transcriptional changes related to defense signaling, there is limited information on the early defense related protein changes that occurs before major transcriptional changes. Here we present the mass spectrometry result of early changes in protein abundance, and phosphorylation of Arabidopsis thaliana plants treated with defense elicitor BTH. We observed that 48 proteins changed in abundance, and 43 in phosphorylation following BTH treatment. We also analyzed the changes in abundance in NPR1 mutant (npr1-1) and observed that 43 proteins changed in abundance. We characterized the roles of 9 of the observed proteins in defense against three pathogens Hyaloperonospora arabidopsidis (Hpa), Pseudomonas syringae pv. (Pto) DC3000, and Botrytis cinerea. Our results reveal the novel role of 2 proteins in defense against PtoDC3000, 5 proteins in defense against Hpa and 3 proteins in defense against botrytis. We further analyzed the BTH induced proteome changes observed in our mass spectrometry results of the total proteome, phosphoproteome and npr1-1 proteome with STRING, an online database of known and predicted protein interactions. The predicted networks created from STRING were used in combination with our infection bioassay results to predict the roles of our proteins in known and novel defense networks. Our study emphasizes the strength of mass spectrometry as a tool to discover proteins with observable phenotypes and to predict novel protein networks