Direct Evidence Demonstrates that Puccinia striiformis f. sp. tritici Infects Susceptible Barberry to Complete Sexual Cycle in Autumn

Wheat stripe rust is an airborne and destructive disease caused by a heteroecious rust fungus Puccinia striiformis f. sp. tritici (Pst). Studies have demonstrated that the rust pathogen accomplishes sexual reproduction on susceptible barberry under natural conditions in spring, whereas Pst infection on barberry is still in blank in other seasons. In late October 2016, aecial production on barberry shrubs were observed in Linzhi, Tibet, China. Therefore, experimental tests were conducted to verify the existence of sexual cycles of Pst in this season. By inoculating 52 aecial clusters from 30 rusted barberry leaves, four Pst samples, T1 to T4, were successfully recovered from the rusted barberry shrubs. Sixty-five single uredinium (SU) isolates were derived from the four Pst samples. Based on virulence tests on the Chinese differential hosts, T1 to T4 samples were unknown races and showed mixed reactions on some differentials. Twenty-one known races and 44 unknown races belonging to five race groups were identified among the 65 SU isolates. Meanwhile, the 65 SU isolates produced 26 various virulence patterns (VPs; called VP1-VP26) on 25 single Yr gene lines and 15 multilocus genotypes (MLGs) at nine simple sequence repeat marker loci. Clustering analysis showed similar lineage among subpopulations and different lineage between subpopulations. Linkage disequilibrium analysis indicated that the SU population was produced sexually. This study first reported that Pst infects susceptible barberry to complete sexual reproduction in autumn. The results update the knowledge of disease cycle and management of wheat stripe rust and contribute to the understanding of rust genetic diversity in Tibet.

A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images

Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identifying wheat yellow rust from unmanned aerial vehicle (UAV) images. The method was based on the pyramid scene parsing network (PSPNet) semantic segmentation model to classify healthy wheat, yellow rust wheat, and bare soil in small-scale UAV images, and to investigate the spatial generalization of the model. In addition, it was proposed to use the high-accuracy classification results of traditional algorithms as weak samples for wheat yellow rust identification. The recognition accuracy of the PSPNet model in this study reached 98%. On this basis, this study used the trained semantic segmentation model to recognize another wheat field. The results showed that the method had certain generalization ability, and its accuracy reached 98%. In addition, the high-accuracy classification result of a support vector machine was used as a weak label by weak supervision, which better solved the labeling problem of large-size images, and the final recognition accuracy reached 94%. Therefore, the present study method facilitated timely control measures to reduce economic losses.

Distinct Life Histories Impact Dikaryotic Genome Evolution in the Rust Fungus Puccinia striiformis Causing Stripe Rust in Wheat

Stripe rust of wheat, caused by the obligate biotrophic fungus Puccinia striiformis f.sp. tritici, is a major threat to wheat production worldwide with an estimated yearly loss of US $1 billion. The recent advances in long-read sequencing technologies and tailored-assembly algorithms enabled us to disentangle the two haploid genomes of Pst. This provides us with haplotype-specific information at a whole-genome level. Exploiting this novel information, we perform whole-genome comparative genomics of two P. striiformis f.sp. tritici isolates with contrasting life histories. We compare one isolate of the old European lineage (PstS0), which has been asexual for over 50 years, and a Warrior isolate (PstS7 lineage) from a novel incursion into Europe in 2011 from a sexual population in the Himalayan region. This comparison provides evidence that long-term asexual evolution leads to genome expansion, accumulation of transposable elements, and increased heterozygosity at the single nucleotide, structural, and allele levels. At the whole-genome level, candidate effectors are not compartmentalized and do not exhibit reduced levels of synteny. Yet we were able to identify two subsets of candidate effector populations. About 70% of candidate effectors are invariant between the two isolates, whereas 30% are hypervariable. The latter might be involved in host adaptation on wheat and explain the different phenotypes of the two isolates. Overall, this detailed comparative analysis of two haplotype-aware assemblies of P. striiformis f.sp. tritici is the first step in understanding the evolution of dikaryotic rust fungi at a whole-genome level.

Distinct Life Histories Impact Dikaryotic Genome Evolution in the Rust Fungus Puccinia striiformis Causing Stripe Rust in Wheat

Stripe rust of wheat, caused by the obligate biotrophic fungus Puccinia striiformis f.sp. tritici, is a major threat to wheat production worldwide with an estimated yearly loss of US $1 billion. The recent advances in long-read sequencing technologies and tailored-assembly algorithms enabled us to disentangle the two haploid genomes of Pst. This provides us with haplotype-specific information at a whole-genome level. Exploiting this novel information, we perform whole-genome comparative genomics of two P. striiformis f.sp. tritici isolates with contrasting life histories. We compare one isolate of the old European lineage (PstS0), which has been asexual for over 50 years, and a Warrior isolate (PstS7 lineage) from a novel incursion into Europe in 2011 from a sexual population in the Himalayan region. This comparison provides evidence that long-term asexual evolution leads to genome expansion, accumulation of transposable elements, and increased heterozygosity at the single nucleotide, structural, and allele levels. At the whole-genome level, candidate effectors are not compartmentalized and do not exhibit reduced levels of synteny. Yet we were able to identify two subsets of candidate effector populations. About 70% of candidate effectors are invariant between the two isolates, whereas 30% are hypervariable. The latter might be involved in host adaptation on wheat and explain the different phenotypes of the two isolates. Overall, this detailed comparative analysis of two haplotype-aware assemblies of P. striiformis f.sp. tritici is the first step in understanding the evolution of dikaryotic rust fungi at a whole-genome level.

Pathogenomic Analysis of Wheat Yellow Rust Lineages Detects Seasonal Variation and Host Specificity

Recent disease outbreaks caused by (re-)emerging plant pathogens have been associated with expansions in pathogen geographic distribution and increased virulence. For example, in the past two decades' wheat yellow (stripe) rust, Puccinia striiformis f. sp. tritici, has seen the emergence of new races that are adapted to warmer temperatures, have expanded virulence profiles, and are more aggressive than previous races, leading to wide-scale epidemics. Here, we used field-based genotyping to generate high-resolution data on P. striiformis genetics and carried out global population analysis. We also undertook comparative analysis of the 2014 and 2013 UK populations and assessed the temporal dynamics and host specificity of distinct pathogen genotypes. Our analysis revealed that P. striiformis lineages recently detected in Europe are extremely diverse and in fact similar to globally dispersed populations. In addition, we identified a considerable shift in the UK P. striiformis population structure including the first identification of one infamous race known as Kranich. Next, by establishing the genotype of both the pathogen and host within a single infected field sample, we uncovered evidence for varietal specificity for genetic groups of P. striiformis. Finally, we found potential seasonal specificity for certain genotypes of the pathogen with several lineages identified only in samples collected in late spring and into the summer, whereas one lineage was identified throughout the wheat growing season. Our discovery of which wheat varieties are susceptible to which specific P. striiformis isolates, and when those isolates are prevalent throughout the year, represents a powerful tool for disease management.