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Effectors encoded by avirulence genes (Avr) interact with the Phytophthora sojae resistance gene (Rps) products to generate incompatible interactions. The virulence profile of P. sojae is rapidly evolving as a result of the large‐scale deployment of Rps genes in soybean. For a successful exploitation of Rps genes, it is recommended that soybean growers use cultivars containing the Rps genes corresponding to Avr genes present in P. sojae populations present in their fields. Determination of the virulence profile of P. sojae isolates is critical for the selection of soybean cultivars. High‐resolution melting curve (HRM) analysis is a powerful tool, first applied in medicine, for detecting mutations with potential applications in different biological fields. Here, we report the development of an HRM protocol, as an original approach to discriminate effectors, to differentiate P. sojae haplotypes for six Avr genes. An HRM assay was performed on 24 P. sojae isolates with different haplotypes collected from soybean fields across Canada. The results clearly confirmed that the HRM assay discriminated different virulence genotypes. Moreover, the HRM assay was able to differentiate multiple haplotypes representing small allelic variations. HRM‐based prediction was validated by phenotyping assays. This HRM assay provides a unique, cost‐effective and efficient tool to predict virulence pathotypes associated with six different Avr (1b, 1c, 1d, 1k, 3a and 6) genes from P. sojae, which can be applied in the deployment of appropriate Rps genes in soybean fields. [ABSTRACT FROM AUTHOR]

Introduction: Cranberry (Vaccinium macrocarpon) is an important crop grown in the United States and Canada, with the province of Québec being the world's largest producer of organic cranberry. However, cranberry fruit rot (CFR), caused by 12 fungal species, has become a major issue affecting yield. Methods: A molecular detection tool was used to detect the presence of the 12 CFR fungi and evaluate CFR species across three farms with different fungicide strategies in Québec. The incidence and frequency of CFR fungi were evaluated for 2020 and compared with 2018 data from the same farms in Québec. Machine-learning models were used to determine the possibility of explaining CFR disease and species based on climate, and analyze the effects of weather variables on CFR presence andprimary fungal species. Results: The most predominant CFR species remained the same in both years, with Godronia cassandrae and Coleophoma empetri being the two most common, but some species showed changes in relative abundance. Furthermore, this study examined the diversity variations in 2018 and 2020, with data showing an overall increase in diversity over the period. The results showed that fungicide applications impacted the species composition of CFR among the farms. Five weather variables (daily snow on the ground (cm), total daily precipitation (mm), daily atmospheric pressure (kPa), daily relative humidity (%) and daily temperature (°C)) were selected and found to contribute differently to the model with atmospheric pressure being the most important. Surprisingly, temperature and precipitations did not influence much the incidence of fungal pathogen species and each CFR species behaved differently in response to environmental factors. Discussion: Overall, this study highlights the complexity of predicting CFR disease, as caused by 12 fungi, and of developing effective disease management strategies for CFR. [ABSTRACT FROM AUTHOR]

Phytophthora sojae, the agent responsible for stem and root rot, is one of the most damaging plant pathogens of soybean. To establish a compatible-interaction, P. sojae secretes a wide array of effector proteins into the host cell. These effectors have been shown to act either in the apoplastic area or the cytoplasm of the cell to manipulate the host cellular processes in favor of the development of the pathogen. Deciphering effector-plant interactions is important for understanding the role of P. sojae effectors in disease progression and developing approaches to prevent infection. Here, we review the subcellular localization, the host proteins, and the processes associated with P. sojae effectors. We also discuss the emerging topic of effectors in the context of effector-resistance genes interaction, as well as model systems and recent developments in resources and techniques that may provide a better understanding of the soybean-P. sojae interaction. [ABSTRACT FROM AUTHOR]