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Phytophthora species cause some of the most serious diseases of trees and threaten forests in many parts of the world. Despite the generation of genome sequence assemblies for over 10 tree-pathogenic Phytophthora species and improved detection methods, there are many gaps in our knowledge of how these pathogens interact with their hosts. To facilitate cell biology studies of the infection cycle we examined whether the tree pathogen Phytophthora kernoviae could infect the model plant Nicotiana benthamiana. We transformed P. kernoviae to express green fluorescent protein (GFP) and demonstrated that it forms haustoria within infected N. benthamiana cells. Haustoria were also formed in infected cells of natural hosts, Rhododendron ponticum and European beech (Fagus sylvatica). We analysed the transcriptome of P. kernoviae in cultured mycelia, spores, and during infection of N. benthamiana, and detected 12,559 transcripts. Of these, 1,052 were predicted to encode secreted proteins, some of which may function as effectors to facilitate disease development. From these, we identified 87 expressed candidate RXLR (Arg-any amino acid-Leu-Arg) effectors. We transiently expressed 12 of these as GFP fusions in N. benthamiana leaves and demonstrated that nine significantly enhanced P. kernoviae disease progression and diversely localized to the cytoplasm, nucleus, nucleolus, and plasma membrane. Our results show that N. benthamiana can be used as a model host plant for studying this tree pathogen, and that the interaction likely involves suppression of host immune responses by RXLR effectors. These results establish a platform to expand the understanding of Phytophthora tree diseases.

Drought stress is one of the key plant stresses reducing grain yield in cereal crops worldwide. Although it is not a breeding target in Northern Europe, the changing climate and the drought of 2018 have increased its significance in the region. A key challenge, therefore, is to identify novel germplasm with higher drought tolerance, a task that will require continuous characterization of a large number of genotypes. The aim of this work was to assess if phenotyping systems with low-cost consumer-grade digital cameras can be used to characterize germplasm for drought tolerance. To achieve this goal, we built a proximal phenotyping cart mounted with digital cameras and evaluated it by characterizing 142 winter wheat genotypes for drought tolerance under field conditions. The same genotypes were additionally characterized for seedling stage traits by imaging under controlled growth conditions. The analysis revealed that under field conditions, plant biomass, relative growth rates, and Normalized Difference Vegetation Index (NDVI) from different growth stages estimated by imaging were significantly correlated to drought tolerance. Under controlled growth conditions, root count at the seedling stage evaluated by imaging was significantly correlated to adult plant drought tolerance observed in the field. Random forest models were trained by integrating measurements from field and controlled conditions and revealed that plant biomass and relative growth rates at key plant growth stages are important predictors of drought tolerance. Thus, based on the results, it can be concluded that the consumer-grade cameras can be key components of affordable automated phenotyping systems to accelerate pre-breeding for drought tolerance.