Host-Induced Gene Silencing of a Sclerotinia sclerotiorum oxaloacetate acetylhydrolase Using Bean Pod Mottle Virus as a Vehicle Reduces Disease on Soybean.

A lack of complete resistance in the current germplasm complicates the management of Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum in soybean. In this study, we used bean pod mottle virus (BPMV) as a vehicle to down-regulate expression of a key enzyme in the production of an important virulence factor in S. sclerotiorum, oxalic acid (OA). Specifically, we targeted a gene encoding oxaloacetate acetylhydrolase (Ssoah1), because Ssoah1 deletion mutants are OA deficient and non-pathogenic on soybean. We first established that S. sclerotiorum can uptake environmental RNAs by monitoring the translocation of Cy3-labeled double-stranded and small interfering RNA (ds/siRNAs) into fungal hyphae using fluorescent confocal microscopy. This translocation led to a significant decrease in Ssoah1 transcript levels in vitro. Inoculation of soybean plants with BPMV vectors targeting Ssoah1 (pBPMV-OA) also led to decreased expression of Ssoah1. Importantly, pBPMV-OA inoculated plants showed enhanced resistance to S. sclerotiorum compared to empty-vector control plants. Our combined results provide evidence supporting the use of HIGS and exogenous applications of ds/siRNAs targeting virulence factors such as OA as viable strategies for the control of SSR in soybean and as discovery tools that can be used to identify previously unknown virulence factors.

Identification of Soybean (Glycine max) Check Lines for Evaluating Genetic Resistance to Sclerotinia Stem Rot.

Soybean production in the upper midwestern United States is affected by Sclerotinia stem rot (SSR) caused by the fungal pathogen Sclerotinia sclerotiorum. Genetic resistance is an important management strategy for this disease; however, assessing genetic resistance to S. sclerotiorum is challenging because a standardized method of examining resistance across genotypes is lacking. Using a panel of nine diverse S. sclerotiorum isolates, four soybean lines were assessed for reproducible responses to S. sclerotiorum infection. Significant differences in SSR severity were found across isolates (P < 0.01) and soybean lines (P < 0.01), including one susceptible, two moderately resistant, and one highly resistant line. These four validated lines were used to screen 11 other soybean genotypes to evaluate their resistance levels, and significant differences were found across genotypes (P < 0.01). Among these 11 genotypes, five commercial and public cultivars displayed high resistance and were assessed during field studies across the upper midwestern United States growing region to determine their response to SSR and yield. These five cultivars resulted in low disease levels (P < 0.01) in the field that were consistent with greenhouse experiment results. The yields were significantly different in fields with disease present (P < 0.01) and disease absent (P < 0.01), and the order of cultivar performance was consistent between environments where disease was present or absent, suggesting that resistance prevented yield loss to disease. This study suggests that the use of a soybean check panel can accurately assess SSR resistance in soybean germplasm and aid in breeding and commercial soybean development.

Validating Sclerotinia sclerotiorum Apothecial Models to Predict Sclerotinia Stem Rot in Soybean (Glycine max) Fields.

In soybean, Sclerotinia sclerotiorum apothecia are the sources of primary inoculum (ascospores) critical for Sclerotinia stem rot (SSR) development. We recently developed logistic regression models to predict the presence of apothecia in irrigated and nonirrigated soybean fields. In 2017, small-plot trials were established to validate two weather-based models (one for irrigated fields and one for nonirrigated fields) to predict SSR development. Additionally, apothecial scouting and disease monitoring were conducted in 60 commercial fields in three states between 2016 and 2017 to evaluate model accuracy across the growing region. Site-specific air temperature, relative humidity, and wind speed data were obtained through the Integrated Pest Information Platform for Extension and Education (iPiPE) and Dark Sky weather networks. Across all locations, iPiPE-driven model predictions during the soybean flowering period (R1 to R4 growth stages) explained end-of-season disease observations with an accuracy of 81.8% using a probability action threshold of 35%. Dark Sky data, incorporating bias corrections for weather variables, explained end-of-season disease observations with 87.9% accuracy (in 2017 commercial locations in Wisconsin) using a 40% probability threshold. Overall, these validations indicate that these two weather-based apothecial models, using either weather data source, provide disease risk predictions that both reduce unnecessary chemical application and accurately advise applications at critical times.

Weather-Based Models for Assessing the Risk of Sclerotinia sclerotiorum Apothecial Presence in Soybean (Glycine max) Fields.

Sclerotinia stem rot (SSR) epidemics in soybean, caused by Sclerotinia sclerotiorum, are currently responsible for annual yield reductions in the United States of up to 1 million metric tons. In-season disease management is largely dependent on chemical control but its efficiency and cost-effectiveness depends on both the chemistry used and the risk of apothecia formation, germination, and further dispersal of ascospores during susceptible soybean growth stages. Hence, accurate prediction of the S. sclerotiorum apothecial risk during the soybean flowering period could enable farmers to improve in-season SSR management. From 2014 to 2016, apothecial presence or absence was monitored in three irrigated (n = 1,505 plot-level observations) and six nonirrigated (n = 2,361 plot-level observations) field trials located in Iowa (n = 156), Michigan (n = 1,400), and Wisconsin (n = 2,310), for a total of 3,866 plot-level observations. Hourly air temperature, relative humidity, dew point, wind speed, leaf wetness, and rainfall were also monitored continuously, throughout the season, at each location using high-resolution gridded weather data. Logistic regression models were developed for irrigated and nonirrigated conditions using apothecial presence as a binary response variable. Agronomic variables (row width) and weather-related variables (defined as 30-day moving averages, prior to apothecial presence) were tested for their predictive ability. In irrigated soybean fields, apothecial presence was best explained by row width (r = -0.41, P < 0.0001), 30-day moving averages of daily maximum air temperature (r = 0.27, P < 0.0001), and daily maximum relative humidity (r = 0.16, P < 0.05). In nonirrigated fields, apothecial presence was best explained by using moving averages of daily maximum air temperature (r = -0.30, P < 0.0001) and wind speed (r = -0.27, P < 0.0001). These models correctly predicted (overall accuracy of 67 to 70%) apothecial presence during the soybean flowering period for four independent datasets (n = 1,102 plot-level observations or 30 daily mean observations).

Development and Evaluation of Glycine max Germplasm Lines with Quantitative Resistance to Sclerotinia sclerotiorum.

Sclerotinia sclerotiorum, the causal agent of Sclerotinia stem rot, is a devastating fungal pathogen of soybean that can cause significant yield losses to growers when environmental conditions are favorable for the disease. The development of resistant varieties has proven difficult. However, poor resistance in commercial cultivars can be improved through additional breeding efforts and understanding the genetic basis of resistance. The objective of this project was to develop soybean germplasm lines that have a high level of Sclerotinia stem rot resistance to be used directly as cultivars or in breeding programs as a source of improved Sclerotinia stem rot resistance. Sclerotinia stem rot-resistant soybean germplasm was developed by crossing two sources of resistance, W04-1002 and AxN-1-55, with lines exhibiting resistance to Heterodera glycines and Cadophora gregata in addition to favorable agronomic traits. Following greenhouse evaluations of 1,076 inbred lines derived from these crosses, 31 lines were evaluated for resistance in field tests during the 2014 field season. Subsequently, 11 Sclerotinia stem rot resistant breeding lines were moved forward for field evaluation in 2015, and seven elite breeding lines were selected and evaluated in the 2016 field season. To better understand resistance mechanisms, a marker analysis was conducted to identify quantitative trait loci linked to resistance. Thirteen markers associated with Sclerotinia stem rot resistance were identified on chromosomes 15, 16, 17, 18, and 19. Our markers confirm previously reported chromosomal regions associated with Sclerotinia stem rot resistance as well as a novel region of chromosome 16. The seven elite germplasm lines were also re-evaluated within a greenhouse setting using a cut petiole technique with multiple S. sclerotiorum isolates to test the durability of physiological resistance of the lines in a controlled environment. This work presents a novel and comprehensive classical breeding method for selecting lines with physiological resistance to Sclerotinia stem rot and a range of agronomic traits. In these studies, we identify four germplasm lines; 91-38, 51-23, SSR51-70, and 52-82B exhibiting a high level of Sclerotinia stem rot resistance combined with desirable agronomic traits, including high protein and oil contents. The germplasm identified in this study will serve as a valuable source of physiological resistance to Sclerotinia stem rot that could be improved through further breeding to generate high-yielding commercial soybean cultivars.