Diagnostic qPCR Assay to Detect Fusarium brasiliense, a Causal Agent of Soybean Sudden Death Syndrome and Root Rot of Dry Bean

Diagnosis of Soybean Diseases Caused by Fungal and Oomycete Pathogens: Existing Methods and New Developments

Soybean (Glycine max) acreage is increasing dramatically, together with the use of soybean as a source of vegetable protein and oil. However, soybean production is affected by several diseases, especially diseases caused by fungal seed-borne pathogens. As infected seeds often appear symptomless, diagnosis by applying accurate detection techniques is essential to prevent propagation of pathogens. Seed incubation on culture media is the traditional method to detect such pathogens. This method is simple, but fungi have to develop axenically and expert mycologists are required for species identification. Even experts may not be able to provide reliable type level identification because of close similarities between species. Other pathogens are soil-borne. Here, traditional methods for detection and identification pose even greater problems. Recently, molecular methods, based on analyzing DNA, have been developed for sensitive and specific identification. Here, we provide an overview of available molecular assays to identify species of the genera Diaporthe, Sclerotinia, Colletotrichum, Fusarium, Cercospora, Septoria, Macrophomina, Phialophora, Rhizoctonia, Phakopsora, Phytophthora, and Pythium, causing soybean diseases. We also describe the basic steps in establishing PCR-based detection methods, and we discuss potentials and challenges in using such assays.

Biocontrol of the causal brown patch pathogen Rhizoctonia solani by Bacillus velezensis GH1-13 and development of a bacterial strain specific detection method

Brown patch caused by the basidiomycete fungus Rhizoctonia solani is an economically important disease of cool-season turfgrasses. In order to manage the disease, different types of fungicides have been applied, but the negative impact of fungicides on the environment continues to rise. In this study, the beneficial bacteria Bacillus velezensis GH1-13 was characterized as a potential biocontrol agent to manage brown patch disease. The strain GH1-13 strongly inhibited the mycelial growth of turf pathogens including different anastomosis groups of R. solani causing brown patch and large patch. R. solani AG2-2(IIIB) hyphae were morphologically changed, and fungal cell death resulted from exposure to the strain GH1-13. In addition, the compatibility of fungicides with the bacterial strain, and the combined application of fungicide azoxystrobin and the strain in brown patch control on creeping bentgrass indicated that the strain could serve as a biocontrol agent. To develop strain-specific detection method, two unique genes from chromosome and plasmid of GH1-13 were found using pan-genome analysis of 364 Bacillus strains. The unique gene from chromosome was successfully detected using both SYBR Green and TaqMan qPCR methods in bacterial DNA or soil DNA samples. This study suggests that application of GH1-13 offers an environmentally friendly approach via reducing fungicide application rates. Furthermore, the developed pipeline of strain-specific detection method could be a useful tool for detecting and studying the dynamics of specific biocontrol agents.

An image-based technique for automated root disease severity assessment using PlantCV

Premise

Plant disease severity assessments are used to quantify plant-pathogen interactions and identify disease-resistant lines. One common method for disease assessment involves scoring tissue manually using a semi-quantitative scale. Automating assessments would provide fast, unbiased, and quantitative measurements of root disease severity, allowing for improved consistency within and across large data sets. However, using traditional Root System Markup Language (RSML) software in the study of root responses to pathogens presents additional challenges; these include the removal of necrotic tissue during the thresholding process, which results in inaccurate image analysis.

Methods

Using PlantCV, we developed a Python-based pipeline, herein called RootDS, with two main objectives: (1) improving disease severity phenotyping and (2) generating binary images as inputs for RSML software. We tested the pipeline in common bean inoculated with Fusarium root rot.

Results

Quantitative disease scores and root area generated by this pipeline had a strong correlation with manually curated values (R 2 = 0.92 and 0.90, respectively) and provided a broader capture of variation than manual disease scores. Compared to traditional manual thresholding, images generated using our pipeline did not affect RSML output.

Discussion

Overall, the RootDS pipeline provides greater functionality in disease score data sets and provides an alternative method for generating image sets for use in available RSML software.