Quantitative Methods to Assess Differential Susceptibility of Arabidopsis thaliana Natural Accessions to Dickeya dadantii.

Martine Rigault,Fabien Chardon,Amélie Buellet,Mathilde Fagard,Alia Dellagi,Céline Masclaux-Daubresse

doi:10.3389/fpls.2017.00394

Abstract

Among the most devastating bacterial diseases of plants, soft rot provoked by Dickeya spp. cause crop yield losses on a large range of species with potato being the most economically important. The use of antibiotics being prohibited in most countries in the field, identifying tolerance genes is expected to be one of the most effective alternate disease control approaches. A prerequisite for the identification of tolerance genes is to develop robust disease quantification methods and to identify tolerant plant genotypes. In this work, we investigate the feasibility of the exploitation of Arabidopsis thaliana natural variation to find tolerant genotypes and to develop robust quantification methods. We compared different quantification methods that score either symptom development or bacterial populations in planta. An easy to set up and reliable bacterial quantification method based on qPCR amplification of bacterial DNA was validated. This study demonstrates that it is possible to conduct a robust phenotyping of soft rot disease, and that Arabidopsis natural accessions are a relevant source of tolerance genes.

Highlights

Plants are exposed to biotic and abiotic stresses that lead to important crop yield losses
The first method consists in attributing a disease severity index (DSI) to each symptom based on the scale indicated in Figure 1A as previously described (Fagard et al, 2007)
To assess whether differential symptom severity on the eight accessions analyzed in Figure 2 was associated with a differential pathogen growth, bacterial populations were quantified by qPCR in these accessions

Summary

Introduction

Plants are exposed to biotic and abiotic stresses that lead to important crop yield losses. Characterizing the main processes involved in virulence on the pathogen side and involved in defense on the plant side requires a reliable system to score disease severity parameters (Brouwer et al, 2003; Trontin et al, 2011). Quantification of disease can rely on different parameters that vary according to the pathogen considered. Symptom severity is considered as a good indicator of the pathogen’s impact on its host during disease. Another parameter that can be considered, is the in planta pathogen growth or pathogen burden (Brouwer et al, 2003)

Methods

Results

Conclusion