Plant disease resistance ( R ) genes control the recognition of specific pathogens and activate subsequent defense responses. We show that the Arabidopsis thaliana locus RESISTANCE TO POWDERY MILDEW8 ( RPW8 ) contains two naturally polymorphic, dominant R genes, RPW8.1 and RPW8.2 , which individually control resistance to a broad range of powdery mildew pathogens. Although the predicted RPW8.1 and RPW8.2 proteins are different from the previously characterized R proteins, they induce localized, salicylic acid–dependent defenses similar to those induced by R genes that control specific resistance. Apparently, broad-spectrum resistance mediated by RPW8 uses the same mechanisms as specific resistance.

The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against whole genome Sc. pombe and S. cerevisiae annotations further substantiate a 4-5% improvement in the number of correctly predicted genes. We demonstrate the success of a novel method of incorporating RNA-seq data into GHMM fungal gene prediction. This shows that a high quality annotation can be achieved without relying on protein homology or a training set of genes. CodingQuarry is freely available ( https://sourceforge.net/projects/codingquarry/ ), and suitable for incorporation into genome annotation pipelines.

Significance: Gene-for-gene immunity is frequently found in interactions between plants and host-adapted pathogens and reflects population-level diversification of immune receptors detecting matching pathogen effectors. We identified effector genes of a pathogenic powdery mildew fungus that are recognized by allelic variants of barley intracellular nucleotide-binding domain and leucine-rich repeat protein-type receptors. These pathogen effectors are phylogenetically unrelated, demonstrating that allelic immune receptors can evolve to recognize sequence-unrelated proteins. Conserved effector recognition in distantly related Arabidopsis indicates that the underlying mechanism is not restricted to monocotyledonous plants. Furthermore, our study reveals that the expression of a fungal avirulence effector alone is necessary and sufficient for allele-specific mildew resistance locus A receptor activation in planta .

The development of genetic markers is complex and costly in species with little pre-existing genomic information. Faba bean possesses one of the largest and least studied genomes among cultivated crop plants and no gene-based genetic maps exist. Gene-based orthologous markers allow chromosomal regions and levels of synteny to be characterised between species, reveal phylogenetic relationships and chromosomal evolution, and enable targeted identification of markers for crop breeding. In this study orthologous codominant cross-species markers have been deployed to produce the first exclusively gene-based genetic linkage map of faba bean (Vicia faba), using an F6 population developed from a cross between the lines Vf6 (equina type) and Vf27 (paucijuga type).