The Velvet transcription factor PnVeA regulates necrotrophic effectors and secondary metabolism in the wheat pathogen Parastagonospora nodorum.

The fungus Parastagonospora nodorum causes septoria nodorum blotch on wheat. The role of the fungal Velvet-family transcription factor VeA in P. nodorum development and virulence was investigated here. Deletion of the P. nodorum VeA ortholog, PnVeA, resulted in growth abnormalities including pigmentation, abolished asexual sporulation and highly reduced virulence on wheat. Comparative RNA-Seq and RT-PCR analyses revealed that the deletion of PnVeA also decoupled the expression of major necrotrophic effector genes. In addition, the deletion of PnVeA resulted in an up-regulation of four predicted secondary metabolite (SM) gene clusters. Using liquid-chromatography mass-spectrometry, it was observed that one of the SM gene clusters led to an accumulation of the mycotoxin alternariol. PnVeA is essential for asexual sporulation, full virulence, secondary metabolism and necrotrophic effector regulation.

A reference genome for pea provides insight into legume genome evolution.

We report the first annotated chromosome-level reference genome assembly for pea, Gregor Mendel's original genetic model. Phylogenetics and paleogenomics show genomic rearrangements across legumes and suggest a major role for repetitive elements in pea genome evolution. Compared to other sequenced Leguminosae genomes, the pea genome shows intense gene dynamics, most likely associated with genome size expansion when the Fabeae diverged from its sister tribes. During Pisum evolution, translocation and transposition differentially occurred across lineages. This reference sequence will accelerate our understanding of the molecular basis of agronomically important traits and support crop improvement.

Real-Time PCR for Diagnosing and Quantifying Co-infection by Two Globally Distributed Fungal Pathogens of Wheat.

Co-infections - invasions of a host-plant by multiple pathogen species or strains - are common, and are thought to have consequences for pathogen ecology and evolution. Despite their apparent significance, co-infections have received limited attention; in part due to lack of suitable quantitative tools for monitoring of co-infecting pathogens. Here, we report on a duplex real-time PCR assay that simultaneously distinguishes and quantifies co-infections by two globally important fungal pathogens of wheat: Pyrenophora tritici-repentis and Parastagonospora nodorum. These fungi share common characteristics and host species, creating a challenge for conventional disease diagnosis and subsequent management strategies. The assay uses uniquely assigned fluorogenic probes to quantify fungal biomass as nucleic acid equivalents. The probes provide highly specific target quantification with accurate discrimination against non-target closely related fungal species and host genes. Quantification of the fungal targets is linear over a wide range (5000-0.5 pg DNA µl-1) with high reproducibility (RSD ≤ 10%). In the presence of host DNA in the assay matrix, fungal biomass can be quantified up to a fungal to wheat DNA ratio of 1 to 200. The utility of the method was demonstrated using field samples of a cultivar sensitive to both pathogens. While visual and culture diagnosis suggested the presence of only one of the pathogen species, the assay revealed not only presence of both co-infecting pathogens (hence enabling asymptomatic detection) but also allowed quantification of relative abundances of the pathogens as a function of disease severity. Thus, the assay provides for accurate diagnosis; it is suitable for high-throughput screening of co-infections in epidemiological studies, and for exploring pathogen-pathogen interactions and dynamics, none of which would be possible with conventional approaches.

Bioinformatic prediction of plant-pathogenicity effector proteins of fungi.

Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering.