Sclerotinia stem rot is an economically important disease of canola (Brassica napus) and is caused by the fungal pathogen Sclerotinia sclerotiorum. This study evaluated the differential gene expression patterns of S. sclerotiorum during disease development on two canola lines differing in susceptibility to this pathogen. Sequencing of the mRNA libraries derived from inoculated petioles and mycelium grown on liquid medium generated approximately 164 million Illumina reads, including 95 million 75-bp-single reads, and 69 million 50-bp-paired end reads. Overall, 36% of the quality filter-passed reads were mapped to the S. sclerotiorum reference genome. On the susceptible line, 1301 and 1214 S. sclerotiorum genes were differentially expressed at early (8–16 hours post inoculation (hpi)) and late (24–48 hpi) infection stages, respectively, while on the resistant line, 1311 and 1335 genes were differentially expressed at these stages, respectively. Gene ontology (GO) categories associated with cell wall degradation, detoxification of host metabolites, peroxisome related activities like fatty acid ß-oxidation, glyoxylate cycle, oxidoreductase activity were significantly enriched in the up-regulated gene sets on both susceptible and resistant lines. Quantitative RT-PCR of six selected DEGs further validated the RNA-seq differential gene expression analysis. The regulation of effector genes involved in host defense suppression or evasion during the early infection stage, and the expression of effectors involved in host cell death in the late stage of infection provide supporting evidence for a two-phase infection model involving a brief biotrophic phase during early stages of infection. The findings from this study emphasize the role of peroxisome related pathways along with cell wall degradation and detoxification of host metabolites as the key mechanisms underlying pathogenesis of S. sclerotiorum on B. napus.

Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five machine learning (ML) models were evaluated in classification and regression modes for predicting disease establishment under different air temperatures and leaf wetness duration conditions. Model algorithms were trained and tested using 20-fold cross validation. Correspondence between predicted and observed values were measured using Cohen’s Kappa (classification) and Lin’s concordance coefficients (regression). The artificial neural network (ANN) algorithms had average accuracies ≥ 89% (classification) and R² ≥ 88% (regression) on canola and dry bean and their correspondence agreements were ≥ 0.83, which is considered substantial to almost perfect. In contrast, logistic regression algorithms had accuracies of 88% for dry bean and 78% for canola; other models were similarly inconsistent. Implementation of ANN models in disease warning systems could help farmers with spraying decisions. At the same time, these models provide insights on temperature and leaf wetness requirements for development of S. sclerotiorum diseases in these crops. Results of this study show the potential of ML models as tools for epidemiological studies on other pathosystems.

White mold, caused by Sclerotinia sclerotiorum, is the most important disease affecting dry bean production in North Dakota. This disease currently is managed mainly through fungicides applied during the flowering stage. A disease-forecasting model was developed to help growers with their decision to apply these fungicides. The model was built using weather variables collected during eight consecutive half-month periods between 1 May and 31 August 2003 to 2005 and white mold incidence data obtained from 150 fields. The model was produced using logistic regression analysis, and includes total rainfall, average minimum temperature, and number of rainy days in the first half of June, July, and August, respectively, as predictors and explained 85% of the variability. The model was validated using an independent disease data set collected from 100 fields during the same years. The model exhibited high true positive ratio (0.79) and very high accuracy (0.91) between observed and predicted probabilities of white mold incidence. Results from this study suggest that in-season macro-weather variables could be used to predict the risk of white mold, which in-turn could help growers make better-informed decisions on whether or not to apply fungicides for white mold control.

The impact of wetness duration and incubation temperatures on Sclerotinia sclerotiorum ascospore germination and ascosporic infection efficiency were evaluated. Ascospore germination was optimal when incubated in continuous moisture (free water) at 21°C. Significantly lower germination was observed at 10 or 30°C. Interrupting ascospore wet incubation was detrimental for germination. In infection efficiency studies, dry bean and canola flowers were inoculated with dry ascospores and placed on leaves of dry bean and canola plants, respectively. Dry bean plants were incubated for 196 h at 18 to 20°C in alternating 8 to 16 h wet/12 to 24 h dry periods. Canola plants were incubated for 240 h at 10, 15, 20, 25, or 30°C in alternating 6 to 18 h wet/18 to 6 h dry periods. Interrupting wet incubation delayed symptom appearance and hindered development of the epidemics on both plant types. Logistic regression models estimated at 50% the probability of disease development on dry bean and canola plants when 68 and 48 h of wet incubation at 20°C accumulated in a period of 6 days, respectively. The canola model was validated using data from field trials. Results of these studies will contribute to develop more accurate warning models for diseases caused by S. sclerotiorum.

Growth chamber studies were conducted using rehydrated dry bean (Phaseolus vulgaris) flowers (RDBF) to assess the influence of temperature (18 and 22°C), relative humidity (RH; 25 and 90%), and ascospore concentrations (10², 10³, and 10⁴ ascospores/ml) on white mold incidence in dry bean. Additional studies were carried out to determine the influence of inoculum type (ascospore and mycelium) and to estimate the effect of duration of drying of colonized RDBF on viability of Sclerotinia sclerotiorum and white mold incidence. There was a linear increase in white mold incidence with increase in ascospore concentration but neither temperature nor RH levels significantly affected disease development. In the inoculum type study, both temperature and RH levels significantly affected white mold incidence; however, neither ascospore nor mycelial inocula had a significant effect on white mold incidence. Drying colonized RDBF for up to 96 h did not affect S. sclerotiorum viability; but the amount of white mold incidence depended more on post-inoculation RH and drying duration than on the temperatures tested. Colonized RDBF dried for 96 h took approximately three times longer to achieve 100% white mold incidence compared with colonized RDBF dried for 24 h. These results suggest the potential for greater white mold development with higher ascospore availability and the potential of dry S. sclerotiorum-colonized dry bean flowers as a viable inoculum source.