Background Sclerotinia sclerotiorum, the cause of Sclerotinia stem rot (SSR), is a host generalist necrotrophic fungus that can cause major yield losses in chickpea (Cicer arietinum) production. This study used RNA sequencing to conduct a time course transcriptional analysis of S. sclerotiorum gene expression during chickpea infection. It explores pathogenicity and developmental factors employed by S. sclerotiorum during interaction with chickpea. Results During infection of moderately resistant (PBA HatTrick) and highly susceptible chickpea (Kyabra) lines, 9491 and 10,487 S. sclerotiorum genes, respectively, were significantly differentially expressed relative to in vitro. Analysis of the upregulated genes revealed enrichment of Gene Ontology biological processes, such as oxidation-reduction process, metabolic process, carbohydrate metabolic process, response to stimulus, and signal transduction. Several gene functional categories were upregulated in planta, including carbohydrate-active enzymes, secondary metabolite biosynthesis clusters, transcription factors and candidate secreted effectors. Differences in expression of four S. sclerotiorum genes on varieties with different levels of susceptibility were also observed. Conclusion These findings provide a framework for a better understanding of S. sclerotiorum interactions with hosts of varying susceptibility levels. Here, we report for the first time on the S. sclerotiorum transcriptome during chickpea infection, which could be important for further studies on this pathogen’s molecular biology.

Sclerotinia sclerotiorum is an important fungal pathogen of chickpea (Cicer arietinum L.) and it can cause yield losses up to 100%. The wild progenitors are much more diverse than domesticated chickpea and this study describes how this relates to S. sclerotiorum resistance. Initially, the pathogenicity of nine Australian S. sclerotiorum isolates was examined on three Cicer lines to develop a robust phenotyping assay and significant differences in isolate aggressiveness were identified with 6 isolates being classed as highly aggressive and 3 as moderately aggressive. We identified two S. sclerotiorum isolates, CU8.20 and CU10.12, to be highly aggressive and moderately aggressive, respectively. A subsequent phenotyping assay was conducted using the two isolates to evaluate 86 wild Cicer accessions (Cicer reticulatum and Cicer echinospermum) and two C. arietinum varieties for resistance to S. sclerotiorum. A subset of 12 genotypes was further evaluated, and subsequently, two wild Cicer accessions with consistently high levels of resistance to S. sclerotiorum were examined using the initially characterised nine isolates. Wild Cicer accessions Karab_084 and Deste_063 demonstrated consistent partial resistance to S. sclerotiorum. There were significant differences in responses to S. sclerotiorum across wild Cicer collection sites. The Cermik, Karabahce and Destek sites’ responses to the aggressive isolate CU8.20 ranged from resistant to susceptible, highlighting an interaction between isolate genotype and chickpea collection site for sclerotinia stem rot resistance. This is the first evidence of partial stem resistance identified in wild Cicer germplasm, which can be adopted in chickpea breeding programs to enhance S. sclerotiorum resistance in future chickpea varieties.

The fungus Sclerotinia sclerotiorum infects hundreds of plant species including many crops. Resistance to this pathogen in canola (Brassica napus L. subsp. napus) is controlled by numerous quantitative trait loci (QTL). For such polygenic traits, genomic prediction may be useful for breeding as it can capture many QTL at once while also considering nonadditive genetic effects. Here, we test application of common regression models to genomic prediction of S. sclerotiorum resistance in canola in a diverse panel of 218 plants genotyped at 24,634 loci. Disease resistance was scored by infection with an aggressive isolate and monitoring over 3 wk. We found that including first-order additive × additive epistasis in linear mixed models (LMMs) improved accuracy of breeding value estimation between 3 and 40%, depending on method of assessment, and correlation between phenotypes and predicted total genetic values by 14%. Bayesian models performed similarly to or worse than genomic relationship matrix-based models for estimating breeding values or overall phenotypes from genetic values. Bayesian ridge regression, which is most similar to the genomic relationship matrix-based approach in the amount of shrinkage it applies to marker effects, was the most accurate of this family of models. This confirms several studies Abbreviations: AGG, Australian Grains Genebank; ASSYST, Associative expression and systems analysis of complex traits in oilseed rape/canola; AUDPC, area under the disease progress curve; BLUP, best linear unbiased predictor; DPI, days post inoculation; G-BLUP, genomic best linear unbiased predictor; GEBV, genomic estimated breeding value; h, square root of narrow-sense heritability; H 2 , broad-sense heritability; h 2 , narrow-sense heritability; LMM, linear mixed model; MCMC, Markov chain Monte Carlo; PSCL, proportion of soft or collapsed lesions; QTL, quantitative trait loci; RR-BLUP, ridge regression best linear unbiased predictor; SNP, single nucleotide polymorphism. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.