Cell-surface receptors play pivotal roles in many biological processes, including immunity, development, and reproduction, across diverse organisms. How cell-surface receptors evolve to become specialised in different biological processes remains elusive. To shed light on the immune-specificity of cell-surface receptors, we analyzed more than 200,000 genes encoding cell-surface receptors from 350 genomes and traced the evolutionary origin of immune-specific leucine-rich repeat receptor-like proteins (LRR-RLPs) in plants. Surprisingly, we discovered that the motifs crucial for co-receptor interaction in LRR-RLPs are closely related to those of the LRR-receptor-like kinase (RLK) subgroup Xb, which perceives phytohormones and primarily governs growth and development. Functional characterisation further reveals that LRR-RLPs initiate immune responses through their juxtamembrane and transmembrane regions, while LRR-RLK-Xb members regulate development through their cytosolic kinase domains. Our data suggest that the cell-surface receptors involved in immunity and development share a common origin. After diversification, their ectodomains, juxtamembrane, transmembrane, and cytosolic regions have either diversified or stabilised to recognise diverse ligands and activate differential downstream responses. Our work reveals a mechanism by which plants evolve to perceive diverse signals to activate the appropriate responses in a rapidly changing environment.

Introgressions of chromosomal segments from related species into wheat are important sources of resistance against fungal diseases. The durability and effectiveness of introgressed resistance genes upon agricultural deployment is highly variable—a phenomenon that remains poorly understood, as the corresponding fungal avirulence genes are largely unknown. Until its breakdown, the Pm17 resistance gene introgressed from rye to wheat provided broad resistance against powdery mildew ( Blumeria graminis ). Here, we used quantitative trait locus (QTL) mapping to identify the corresponding wheat mildew avirulence effector AvrPm17 . It is encoded by two paralogous genes that exhibit signatures of reoccurring gene conversion events and are members of a mildew sublineage specific effector cluster. Extensive haplovariant mining in wheat mildew and related sublineages identified several ancient virulent AvrPm17 variants that were present as standing genetic variation in wheat powdery mildew prior to the Pm17 introgression, thereby paving the way for the rapid breakdown of the Pm17 resistance. QTL mapping in mildew identified a second genetic component likely corresponding to an additional resistance gene present on the 1AL.1RS translocation carrying Pm17. This gene remained previously undetected due to suppressed recombination within the introgressed rye chromosomal segment. We conclude that the initial effectiveness of 1AL.1RS was based on simultaneous introgression of two genetically linked resistance genes. Our results demonstrate the relevance of pathogen-based genetic approaches to disentangling complex resistance loci in wheat. We propose that identification and monitoring of avirulence gene diversity in pathogen populations become an integral part of introgression breeding to ensure effective and durable resistance in wheat.

Plant genomes encode hundreds of secreted peptides; however, relatively few have been characterised. We report here an uncharacterised, stress-induced family of plant signalling peptides, which we call CTNIPs. Based on the role of the common co-receptor BRASSINOSTEROID INSENSITIVE 1-ASSOCIATED KINASE 1 (BAK1) in CTNIP-induced responses, we identified in Arabidopsis thaliana the orphan receptor kinase HAESA-LIKE 3 (HSL3) as the CTNIP receptor via a proteomics approach. CTNIP-binding, ligand-triggered complex formation with BAK1, and induced downstream responses all involve HSL3. Notably, the HSL3-CTNIP signalling module is evolutionarily conserved amongst most extant angiosperms. The identification of this novel signalling module will further shed light on the diverse functions played by plant signalling peptides and will provide insights into receptor-ligand co-evolution.

Transposable elements (TEs) constitute a large fraction of plant genomes and are mostly present in a transcriptionally silent state through repressive epigenetic modifications, such as DNA methylation. TE silencing is believed to influence the regulation of adjacent genes, possibly as DNA methylation spreads away from the TE. Whether this is a general principle or a context-dependent phenomenon is still under debate, pressing for studying the relationship between TEs, DNA methylation, and nearby gene expression in additional plant species. Here, we used the grass Brachypodium distachyon as a model and produced DNA methylation and transcriptome profiles for 11 natural accessions. In contrast to what is observed in Arabidopsis thaliana, we found that TEs have a limited impact on methylation spreading and that only few TE families are associated with a low expression of their adjacent genes. Interestingly, we found that a subset of TE insertion polymorphisms is associated with differential gene expression across accessions. Thus, although not having a global impact on gene expression, distinct TE insertions may contribute to specific gene expression patterns in B. distachyon.

Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023, the first open-to-the-public Genomes to Fields initiative Genotype by Environment prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements, and field management notes gathered by the project over 9 years. The competition attracted registrants from around the world with representation from academic, government, industry, and nonprofit institutions as well as unaffiliated. These participants came from diverse disciplines, including plant science, animal science, breeding, statistics, computational biology, and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved 2 models combining machine learning and traditional breeding tools: 1 model emphasized environment using features extracted by random forest, ridge regression, and least squares, and 1 focused on genetics. Other high-performing teams’ methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics, weather, and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition.

Plants deploy a diverse array of pattern recognition receptors (PRRs), which perceive microbe-associated molecular patterns to activate immune responses. Leucine-rich repeat receptor-like kinase subgroup XII (LRR-RLK-XII) represents one of the largest PRR families owing to lineage-specific diversification. Through bioinformatics and synthetic biology approaches, we characterized LRR-RLK-XIIs from 285 plant species and identified a receptor, “SCORE,” that perceives cold shock protein (CSP) peptides. SCORE orthologs from multiple angiosperm lineages exhibit CSP recognition polymorphisms, indicating recurrent selection for pathogen recognition through substitutions at key amino acid residues. Through functional phylogenomics and protein structure predictions, we engineered SCORE variants capable of detecting multiple phytopathogen CSP peptides, thus revealing the diverse PRR recognition landscape in plants. Our strategy holds promise for engineering plant immune receptors, particularly for perennial crops.