Effect of Sugar Beet Variety and Nonhost Plant on Rhizoctonia solani AG2-2IIIB Soil Inoculum Potential Measured in Soil DNA Extracts

Development of a Large-Scale Soil DNA Extraction Method for Molecular Quantification of Fusarium oxysporum f. sp. fragariae in Soil

The most common soilborne diseases affecting the strawberry industry in California include Verticillium wilt due to Verticillium dahliae, charcoal root rot due to Macrophomina phaseolina, and Fusarium wilt due to Fusarium oxysporum f. sp. fragariae. Detection of these pathogens in soil is an important facet of disease management and fumigation recommendations. Whereas the soil populations of both M. phaseolina and V. dahliae can be readily quantified with quantitative PCR (qPCR) assays using DNA extractions with 500 mg of soil, the single-cell nature of the F. oxysporum chlamydospore does not provide enough pathogen DNA from 500-mg extractions to be reliably quantified. Here, we describe an improved DNA extraction protocol from 10 to 15 g of soil that allows for the quantification of F. oxysporum f. sp. fragariae populations below 10 CFU/g. The relationship between results from the TaqMan qPCR assay and pathogen population density in soil was determined by using this extraction method in pathogen-free soils artificially infested with a hygromycin-resistant strain of F. oxysporum f. sp. fragariae to facilitate accurate colony counts when plated on a selective medium. Although the protocol was developed for F. oxysporum f. sp. fragariae, it is applicable for detection and quantification of other soilborne pathogens.

Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

Major latex protein-like encoding genes contribute to Rhizoctonia solani defense responses in sugar beet

Sugar beets are attacked by several pathogens that cause root damages. Rhizoctonia (Greek for “root killer”) is one of them. Rhizoctonia root rot has become an increasing problem for sugar beet production and to decrease yield losses agronomical measures are adopted. Here, two partially resistant and two susceptible sugar beet genotypes were used for transcriptome analysis to discover new defense genes to this fungal disease, information to be implemented in molecular resistance breeding. Among 217 transcripts with increased expression at 2 days post-infection (dpi), three resistance-like genes were found. These genes were not significantly elevated at 5 dpi, a time point when increased expression of three Bet v I/Major latex protein (MLP) homologous genes BvMLP1, BvMLP2 and BvML3 was observed in the partially resistant genotypes. Quantitative RT-PCR analysis on diseased sugar beet seedlings validated the activity of BvMLP1 and BvMLP3 observed in the transcriptome during challenge by R. solani. The three BvMLP genes were cloned and overexpressed in Arabidopsis thaliana to further dissect their individual contribution. Transgenic plants were also compared to T-DNA mutants of orthologous MLP genes. Plants overexpressing BvMLP1 and BvMLP3 showed significantly less infection whereas additive effects were seen on Atmlp1/Atmlp3 double mutants. The data suggest that BvMLP1 and BvMLP3 may contribute to the reduction of the Rhizoctonia root rot disease in sugar beet. Impact on the defense reaction from other differential expressed genes observed in the study is discussed.

A LysM effector protein from the basidiomycete Rhizoctonia solani contributes to virulence through suppression of chitin-triggered immunity

Rhizoctonia solani is a fungal species that belongs to the fungal division Basidiomycota. It is a soil-borne pathogen that attacks a broad range of plant species and crops. Disease symptoms are commonly seen as damping off of seedlings and root rot, although it can infect plants at any developmental stage. Despite the severity of this disease, many aspects in R. solani infection biology remain unclear. Here we investigated the role of a LysM effector, previously predicted from the genome of a R. solani AG2-2IIIB strain that has sugar beet as a host. Gene expression analysis showed that RsLysM was highly induced upon sugar beet infection. When RsLysM was heterologously expressed in Cercospora beticola, necrotic lesion size and fungal colonization ability were increased, indicating a role in virulence. RsLysM displayed chitin-binding affinity and suppression of chitin-triggered immunity but could not protect hyphae from hydrolysis. Overall, this study is the first characterization of a LysM effector from Basidiomycota, suggesting that this necrotrophic fungal species relies on perturbation of chitin-triggered immunity to establish a successful infection.

Control of Rhizoctonia solani in Sugar Beet and Effect of Fungicide Application and Plant Cultivar on Inoculum Potential in the Soil

Rhizoctonia solani (AG 2-2 IIIB) is the causal agent of Rhizoctonia root and crown rot, a disease that causes severe economic problems in sugar beet growing areas worldwide. In the United States, azoxystrobin is the most important active ingredient for fungicidal control of R. solani in sugar beet, showing efficacy superior to other substances. First reports on resistance development in R. solani, however, underline the importance of a careful fungicide resistance management. For this reason, the efficacy of a new fungicide mixture of azoxystrobin and difenoconazole was compared with a fungicide containing only azoxystrobin. Field trials were carried out under natural infection conditions as well as with inoculation in the years 2012, 2013, and 2014. Evaluation of the disease severity and the obtained white sugar yield of different sugar beet cultivars demonstrated that both fungicide treatments possess a similar efficacy, reducing the diseased beet surface by up to 78% and preventing yield losses. Additionally, a real-time PCR assay, based on DNA extracts from representative soil samples (250 g), was used to directly determine the effect of chemical treatment and plant cultivar on the soil-borne inoculum. Fungicide application significantly reduced the concentration of soil-borne inoculum by up to 97%. Furthermore, the results demonstrated that the cultivation of a susceptible cultivar significantly increases the concentration of R. solani in the soil by a factor of 200. In conclusion, the study implies that only a combination of resistant cultivar and fungicide application can prevent an accumulation of R. solani inoculum under conducive conditions in infested fields.