Rapid identification of an Arabidopsis NLR gene as a candidate conferring susceptibility to Sclerotinia sclerotiorum using time‐resolved automated phenotyping

Abstract

SUMMARY The broad host range necrotrophic fungus Sclerotinia sclerotiorum is a devastating pathogen of many oil and vegetable crops. Plant genes conferring complete resistance against S. sclerotiorum have not been reported. Instead, plant populations challenged by S. sclerotiorum exhibit a continuum of partial resistance designated as quantitative disease resistance (QDR). Because of their complex interplay and their small phenotypic effect, the functional characterization of QDR genes remains limited. How broad host range necrotrophic fungi manipulate plant programmed cell death is for instance largely unknown. Here, we designed a time‐resolved automated disease phenotyping pipeline enabling high‐throughput disease lesion measurement with high resolution, low footprint at low cost. We could accurately recover contrasted disease responses in several pathosystems using this system. We used our phenotyping pipeline to assess the kinetics of disease symptoms caused by seven S. sclerotiorum isolates on six A. thaliana natural accessions with unprecedented resolution. Large effect polymorphisms common to the most resistant A. thaliana accessions identified highly divergent alleles of the nucleotide‐binding site leucine‐rich repeat gene LAZ5 in the resistant accessions Rubezhnoe and Lip‐0. We show that impaired LAZ5 expression in laz5.1 mutant lines and in A. thaliana Rub natural accession correlate with enhanced QDR to S. sclerotiorum. These findings illustrate the value of time‐resolved image‐based phenotyping for unravelling the genetic bases of complex traits such as QDR. Our results suggest that S. sclerotiorum manipulates plant sphingolipid pathways guarded by LAZ5 to trigger programmed cell death and cause disease.

Pathogen-derived mechanical cues regulate the spatio-temporal implementation of plant defense

“…For quantitative RT-PCR analyses, inoculated plants were placed in mini-greenhouses during 24 hours to keep a high humidity level promoting infections. For phenotypic analysis, 3 leaves per plant were cut and put in Navautron devices to monitor the kinematic of lesion development (24).…”

“…3B). Finally, we compared resistance to S. sclerotiorum in Col-0 / pfd6-1 and WS-4 / bot1-7 plants using imagebased time-resolved phenotyping (17). bot1-7 is a katanin mutant allele in the Ws-4 background affected in the CMTs severing process (9).…”

Comparative transcriptome profiling identifies maize line specificity of fungal effectors in the maize–Ustilago maydis interaction

“…Other genes identified in QDR correspond to previously unidentified defence genes, such as the soybean ( Glycine max ) wound‐inducible domain protein WI12, the soybean serine hydroxymethyltransferase RHG4 and the rice ( Oryza sativa ) proline‐containing protein Pi21 (Cook et al ., 2012; Fukuoka et al ., 2009). In rare cases, NLR genes can also underlie QDR (Barbacci et al ., 2020; Poland et al ., 2009), which led to the hypothesis that allelic variants, i.e. weak alleles, of R genes can cause incomplete resistance.…”

Tackling microbial threats in agriculture with integrative imaging and computational approaches

“…However, applications in the agricultural context are largely missing for now as such approaches are experimentally demanding and costly. Improved pathogen inoculation techniques such as detached leaf assays [157] , [158] , [159] or head assays [160] combined with automated image analysis [161] will reduce the experimental burden. Consequently, collecting precise phenotypic data from a large infection matrix of hosts and pathogens has become increasingly accessible [157] .…”