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Functional characterization of effector proteins of fungal obligate biotrophic pathogens, especially confirmation of avirulence (Avr) properties, has been notoriously difficult, due to the experimental intractability of many of these organisms. Previous studies in wheat have shown promising data suggesting the type III secretion system (T3SS) of bacteria may be a suitable surrogate for delivery and detection of Avr properties of fungal effectors. However, these delivery systems were tested in the absence of confirmed Avr effectors. Here, we tested two previously described T3SS-mediated delivery systems for their suitability when delivering two confirmed Avr effectors from two fungal pathogens of wheat, Puccinia graminis f. sp. tritici and Magnaporthe oryzae pathotype tritici. We showed that both effectors (AvrSr50 and AvrRmg8) were unable to elicit a hypersensitive response on wheat seedlings with the corresponding resistance gene when expressed by the Pseudomonas fluorescens “Effector to Host Analyser” (EtHAn) system. Furthermore, we found the utility of Burkholderia glumae for screening Avr phenotypes is severely limited, as the wild-type strain elicits nonhost cell death in multiple wheat accessions. These results provide valuable insight into the suitability of these systems for screening fungal effectors for Avr properties that may help guide further development of surrogate bacterial delivery systems in wheat. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Targeting host processes that allow pathogens to thrive can be invaluable in resistance breeding. Here, we generated a deep-sequencing transcriptome time course for Puccinia striiformis f. sp. tritici (Pst) infection on wheat and compared datasets from three wheat varieties with different levels of susceptibility to two tested pathogen isolates. We sought genes specifically altered in a susceptible host as candidates that might support colonisation. Host responses differed between Pst-varietal pairs most prominently early during infection. Notably, however, nuclear genes encoding chloroplast-localised proteins (NGCPs) exhibited temporal coordination of expression profiles that differed at later time points in relation to Pst susceptibility. Disrupting one such NGCP, encoding the chloroplast-localised RNA binding protein TaCSP41a, led to lower Pst susceptibility. These analyses thus highlight NGCPs as prime targets for Pst manipulation during infection and point to TaCSP41a disruption as a potential source of Pst resistance for breeding programmes.

Eastern Africa is a significant region of common bean (Phaseolus vulgaris L.) production and genetic diversity. Insect pests are a major biotic constraint in subsistence crop production systems. Bean fly (Ophiomyia spencerella) is a serious pest of beans in eastern Africa highlands. Breeding efforts focus on combining adaptability traits with user preferred seed types. However, lack of information on molecular markers linked to genes modulating bean fly resistance has slowed breeding progress. The objectives were to: (i) characterize genetic diversity and uncover putative bean fly resistant genotypes within diverse seed types and market classes and (ii) identify genomic regions controlling bean fly resistance using genome-wide association analysis (GWAS). A set of 276 diverse genotypes comprising local landraces and varieties from Kenya alongside introductions from International Centre for Tropical Agriculture (CIAT), were assembled. The germplasm represented varied bean production ecologies and seed types. Genetic diversity conforming to Andean and Mesoamerican genepools was established. Out of 276 genotypes evaluated, 150 were Andean, 74 were Mesoamerican and 52 were admixed. Twenty-two genotypes were resistant to bean fly. Association mapping results for stem damage score and plant mortality identified six significant single-nucleotide polymorphisms (SNPs) on chromosomes Pv01 and Pv09. The most significant SNP marker was 12 kilobases downstream of Phvul.001G074900 gene with LOD score > 4.0 hence in linkage disequilibrium with the postulated gene. The identified candidate gene is pleiotropic and modulates both flowering time and plant responses to stress. These findings are a key step towards marker-enabled breeding in common bean for sub-Saharan Africa.

Plant pathogens suppress defense responses to evade recognition and promote successful colonization. Although identifying the genes essential for pathogen ingress has traditionally relied on screening mutant populations, the post-genomic era provides an opportunity to develop novel approaches that accelerate identification. Here, RNA-seq analysis of 68 pathogen-infected bread wheat (Triticum aestivum) varieties, including three (Oakley, Solstice and Santiago) with variable levels of susceptibility, uncovered a branched-chain amino acid aminotransferase (termed TaBCAT1) as a positive regulator of wheat rust susceptibility. We show that TaBCAT1 is required for yellow and stem rust infection and likely functions in branched-chain amino acid (BCAA) metabolism, as TaBCAT1 disruption mutants had elevated BCAA levels. TaBCAT1 mutants also exhibited increased levels of salicylic acid (SA) and enhanced expression of associated defense genes, indicating that BCAA regulation, via TaBCAT1, has a key role in SA-dependent defense activation. We also identified an association between the levels of BCAAs and resistance to yellow rust infection in wheat. These findings provide insight into SA-mediated defense responses in wheat and highlight the role of BCAA metabolism in the defense response. Furthermore, TaBCAT1 could be manipulated to potentially provide resistance to two of the most economically damaging diseases of wheat worldwide.

Background Traditional Map based Cloning approaches, used for the identification of desirable alleles, are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism. High throughput sequencing based Mapping-by-sequencing approach requires an ordered genome assembly and cannot be used with fragmented, un-scaffolded draft genomes, limiting its application to model species and precluding many important organisms. Results We addressed this gap in resource and presented a computational method and software implementations called CHERIPIC (Computing Homozygosity Enriched Regions In genomes to Prioritise Identification of Candidate variants). We have successfully validated implementation of CHERIPIC using three different types of bulk segregant sequence data from Arabidopsis, maize and barley, respectively. Conclusions CHERIPIC allows users to rapidly analyse bulk segregant sequence data and we have made it available as a pre-packaged binary with all dependencies for Linux and MacOS and as Galaxy tool. Electronic supplementary material The online version of this article (10.1186/s12859-018-2515-5) contains supplementary material, which is available to authorized users.

Summary In recent years, the number of emergent plant pathogens (EPPs) has grown substantially, threatening agroecosystem stability and native biodiversity. Contributing factors include, among others, shifts in biogeography, with EPP spread facilitated by the global unification of monocultures in modern agriculture, high volumes of trade in plants and plant products and an increase in sexual recombination within pathogen populations. The unpredictable nature of EPPs as they move into new territories is a situation that has led to sudden and widespread epidemics. Understanding the underlying causes of pathogen emergence is key to managing the impact of EPPs. Here, we review some factors specifically influencing the emergence of oomycete and fungal EPPs, including new introductions through anthropogenic movement, natural dispersal and weather events, as well as genetic factors linked to shifts in host range.

Whole genome sequencing using high-throughput sequencing (HTS) technologies offers powerful opportunities to study genetic variation. Mapping the mutations responsible for different phenotypes is generally an involved and time-consuming process so researchers have developed user-friendly tools for mapping-by-sequencing, yet they are not applica- ble to organisms with non-sequenced genomes. We introduce SDM (SNP Distribution Method), a reference independent method for rapid discovery of mutagen-induced muta- tions in typical forward genetic screens. SDM aims to order a disordered collection of HTS reads or contigs such that the fragment carrying the causative mutation can be identified. SDM uses typical distributions of homozygous SNPs that are linked to a phenotype-altering SNP in a non-recombinant region as a model to order the fragments. To implement and test SDM, we created model genomes with an idealised SNP density based on Arabidop- sis thaliana chromosome 1 and analysed fragments with size distribution similar to reads or contigs assembled from HTS sequencing experiments. SDM groups the contigs by their normalised SNP density and arranges them to maximise the fit to the expected SNP distribution. We tested the procedure in existing datasets by examining SNP distributions in recent out-cross and back-cross experiments in Arabidopsis thaliana backgrounds. In all the examples we analysed, homozygous SNPs were normally distributed around the causal mutation. We used the real SNP densities obtained from these experiments to prove the efficiency and accuracy of SDM. The algorithm was able to successfully identify small sized (10-100 kb) genomic regions containing the causative mutation.