Phyllosphere microbial diversity and specific taxa mediate within-cultivar resistance to Phytophthora palmivora in cacao

The oomycete pathogen Phytophthora palmivora, which causes black pod rot (BPR) on cacao (Theobroma cacao L.), is responsible for devastating yield losses worldwide. Genetic variation in resistance to Phytophthora spp. is well documented among cacao cultivars, but variation has also been observed in the incidence of BPR even among trees of the same cultivar. In light of evidence that the naturally occurring phyllosphere microbiome can influence foliar disease resistance in other host-pathogen systems, it was hypothesized that differences in the phyllosphere microbiome between two field accessions of the cultivar Gainesville II 164 could be responsible for their contrasting resistance to P. palmivora. Bacterial alpha diversity was higher but fungal alpha diversity was lower in the more resistant accession MITC-331, and the accessions harbored phyllosphere microbiomes with distinct community compositions. Six bacterial and 82 fungal amplicon sequence variants (ASVs) differed in relative abundance between MITC-333 and MITC-331, including bacterial putative biocontrol agents and a high proportion of fungal pathogens, and nine fungal ASVs were correlated with increased lesion development. The roles of contrasting light availability and host mineral nutrition, particularly potassium, are also discussed. Results of this preliminary study can be used to guide research into microbiome-informed integrated pest management strategies effective against Phytophthora spp. in cacao. IMPORTANCE Up to 40% of the world's cacao is lost each year to diseases, the most devastating of which is black pod rot, caused by Phytophthora palmivora. Though disease resistance is often attributed to cacao genotypes (i.e., disease-resistant rootstocks), this study highlights the role of the microbiome in contributing to differences in resistance even among accessions of the same cacao cultivar. Future studies of plant-pathogen interactions may need to account for variation in the host microbiome, and optimizing the cacao phyllosphere microbiome could be a promising new direction for P. palmivora resistance research.

EffectorO: Motif-Independent Prediction of Effectors in Oomycete Genomes Using Machine Learning and Lineage Specificity

Oomycete plant pathogens cause a wide variety of diseases, including late blight of potato, sudden oak death, and downy mildews of plants. These pathogens are major contributors to loss in numerous food crops. Oomycetes secrete effector proteins to manipulate their hosts to the advantage of the pathogen. Plants have evolved to recognize effectors, resulting in an evolutionary cycle of defense and counter-defense in plant-microbe interactions. This selective pressure results in highly diverse effector sequences that can be difficult to computationally identify using only sequence similarity. We developed a novel effector prediction tool, EffectorO, that uses two complementary approaches to predict effectors in oomycete pathogen genomes: i) a machine learning-based pipeline that predicts effector probability based on the biochemical properties of the N-terminal amino-acid sequence of a protein and ii) a pipeline based on lineage specificity to find proteins that are unique to one species or genus, a sign of evolutionary divergence due to adaptation to the host. We tested EffectorO on Bremia lactucae, which causes lettuce downy mildew, and Phytophthora infestans, which causes late blight of potato and tomato, and predicted many novel effector candidates while recovering the majority of known effector candidates. EffectorO will be useful for discovering novel families of oomycete effectors without relying on sequence similarity to known effectors. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Extracellular Recognition of Oomycetes during Biotrophic Infection of Plants

Extracellular recognition of pathogens by plants constitutes an important early detection system in plant immunity. Microbe-derived molecules, also named patterns, can be recognized by pattern recognition receptors (PRRs) on the host cell membrane that trigger plant immune responses. Most knowledge on extracellular pathogen detection by plants comes from research on bacterial and fungal pathogens. For oomycetes, that comprise some of the most destructive plant pathogens, mechanisms of extracellular pattern recognition have only emerged recently. These include newly recognized patterns, e.g., cellulose-binding elicitor lectin, necrosis and ethylene-inducing peptide 1-like proteins (NLPs), and glycoside hydrolase 12, as well as their receptors, e.g., the putative elicitin PRR elicitin response and the NLP PRR receptor-like protein 23. Immunity can also be triggered by the release of endogenous host-derived patterns, as a result of oomycete enzymes or damage. In this review we will describe the types of patterns, both pathogen-derived exogenous and plant-derived endogenous ones, and what is known about their extracellular detection during (hemi-)biotrophic oomycete infection of plants.

Spatial dissection of the Arabidopsis thaliana transcriptional response to downy mildew using Fluorescence Activated Cell Sorting

Changes in gene expression form a crucial part of the plant response to infection. In the last decade, whole-leaf expression profiling has played a valuable role in identifying genes and processes that contribute to the interactions between the model plant Arabidopsis thaliana and a diverse range of pathogens. However, with some pathogens such as downy mildew caused by the biotrophic oomycete pathogen Hyaloperonospora arabidopsidis (Hpa), whole-leaf profiling may fail to capture the complete Arabidopsis response encompassing responses of non-infected as well as infected cells within the leaf. Highly localized expression changes that occur in infected cells may be diluted by the comparative abundance of non-infected cells. Furthermore, local and systemic Hpa responses of a differing nature may become conflated. To address this we applied the technique of Fluorescence Activated Cell Sorting (FACS), typically used for analyzing plant abiotic responses, to the study of plant-pathogen interactions. We isolated haustoriated (Hpa-proximal) and non-haustoriated (Hpa-distal) cells from infected seedling samples using FACS, and measured global gene expression. When compared with an uninfected control, 278 transcripts were identified as significantly differentially expressed, the vast majority of which were differentially expressed specifically in Hpa-proximal cells. By comparing our data to previous, whole organ studies, we discovered many highly locally regulated genes that can be implicated as novel in the Hpa response, and that were uncovered for the first time using our sensitive FACS technique.