Published in

American Phytopathological Society, Molecular Plant-Microbe Interactions, 5(21), p. 646-657, 2008

DOI: 10.1094/mpmi-21-5-0646

Links

Tools

Export citation

Search in Google Scholar

Discovery of ADP-Ribosylation and Other Plant Defense Pathway Elements Through Expression Profiling of Four Different Arabidopsis–Pseudomonas R-avr Interactions

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Red circle
Preprint: archiving forbidden
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

A dissection of plant defense pathways was initiated through gene expression profiling of the responses of a single Arabidopsis thaliana genotype to isogenic Pseudomonas syringae strains expressing one of four different cloned avirulence (avr) genes. Differences in the expression profiles elicited by different resistance (R)-avr interactions were observed. A role for poly(ADP-ribosyl)ation in plant defense responses was suggested initially by the upregulated expression of genes encoding NUDT7 and poly(ADP-ribose) glycohydrolase in multiple R-avr interactions. Gene knockout plant lines were tested for 20 candidate genes identified by the expression profiling, and Arabidopsis NUDT7 mutants allowed less growth of virulent P. syringae (as previously reported) but also exhibited a reduced hypersensitive-response phenotype. Inhibitors of poly(ADP-ribose) polymerase (PARP) disrupted FLS2-mediated basal defense responses such as callose deposition. EIN2 (ethylene response) and IXR1 and IXR2 (cellulose synthase) mutants impacted the FLS2-mediated responses that occur during PARP inhibition, whereas no impacts were observed for NPR1, PAD4, or NDR1 mutants. In the expression profiling work, false-positive selection and grouping of genes was reduced by requiring simultaneous satisfaction of statistical significance criteria for each of three separate analysis methods, and by clustering genes based on statistical confidence values for each gene rather than on average fold-change of transcript abundance.