RESUMEN

BACKGROUND: Yellow rust (Puccinia striiformis f. sp. tritici) is a devastating hazard to wheat production, which poses a serious threat to yield and food security in the main wheat-producing areas in eastern China. It is necessary to monitor yellow rust progression during spring critical wheat growth periods to support its prediction by providing timely calibrations for disease prediction models and timely green prevention and control. RESULTS: Three Sentinel-2 images for the disease during the three wheat growth periods (jointing, heading, and filling) were acquired. Spectral, texture, and color features were all extracted for each growth period disease. Then three period-specific feature sets were obtained. Given the differences in field disease epidemic status in the three periods, three period-targeted monitoring models were established to map yellow rust damage progression in spring and track its spatiotemporal change. The models' performance was then validated based on the disease field truth data during the three periods (87 for the jointing period, 183 for the heading period, and 155 for the filling period). The validation results revealed that the representation of the wheat yellow rust damage progression based on our monitoring model group was realistic and credible. The overall accuracy of the healthy and diseased pixel classification monitoring model at the jointing period reached 87.4%, and the coefficient of determination (R2) of the disease index regression monitoring models at the heading and filling periods was 0.77 (heading period) and 0.76 (filling period). The model-group-result-based spatiotemporal change detection of the yellow rust progression across the entire study area revealed that the area proportions conforming to the expected disease spatiotemporal development pattern during the jointing-to-heading period and the heading-to-filling period reached 98.2% and 84.4% respectively. CONCLUSIONS: Our jointing, heading, and filling period-targeted monitoring model group overcomes the limitations of most existing monitoring models only based on single-phase remote sensing information. It performs well in revealing the wheat yellow rust spatiotemporal epidemic in spring, can timely update disease trends to optimize disease management, and provide a basis for disease prediction to timely correct model. © 2024 Society of Chemical Industry.

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

BACKGROUND: Transcriptomics is being increasingly applied to generate new insight into the interactions between plants and their pathogens. For the wheat yellow (stripe) rust pathogen (Puccinia striiformis f. sp. tritici, Pst) RNA-based sequencing (RNA-Seq) has proved particularly valuable, overcoming the barriers associated with its obligate biotrophic nature. This includes the application of RNA-Seq approaches to study Pst and wheat gene expression dynamics over time and the Pst population composition through the use of a novel RNA-Seq based surveillance approach called "field pathogenomics". As a dual RNA-Seq approach, the field pathogenomics technique also provides gene expression data from the host, giving new insight into host responses. However, this has created a wealth of data for interrogation. RESULTS: Here, we used the field pathogenomics approach to generate 538 new RNA-Seq datasets from Pst-infected field wheat samples, doubling the amount of transcriptomics data available for this important pathosystem. We then analysed these datasets alongside 66 RNA-Seq datasets from four Pst infection time-courses and 420 Pst-infected plant field and laboratory samples that were publicly available. A database of gene expression values for Pst and wheat was generated for each of these 1024 RNA-Seq datasets and incorporated into the development of the rust expression browser ( http://www.rust-expression.com ). This enables for the first time simultaneous 'point-and-click' access to gene expression profiles for Pst and its wheat host and represents the largest database of processed RNA-Seq datasets available for any of the three Puccinia wheat rust pathogens. We also demonstrated the utility of the browser through investigation of expression of putative Pst virulence genes over time and examined the host plants response to Pst infection. CONCLUSIONS: The rust expression browser offers immense value to the wider community, facilitating data sharing and transparency and the underlying database can be continually expanded as more datasets become publicly available.

Asunto(s)

RESUMEN

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20-30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

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.

Asunto(s)

RESUMEN

Investigating the origin and dispersal pathways is instrumental to mitigate threats and economic and environmental consequences of invasive crop pathogens. In the case of Puccinia striiformis causing yellow rust on wheat, a number of economically important invasions have been reported, e.g., the spreading of two aggressive and high temperature adapted strains to three continents since 2000. The combination of sequence-characterized amplified region (SCAR) markers, which were developed from two specific AFLP fragments, differentiated the two invasive strains, PstS1 and PstS2 from all other P. striiformis strains investigated at a worldwide level. The application of the SCAR markers on 566 isolates showed that PstS1 was present in East Africa in the early 1980s and then detected in the Americas in 2000 and in Australia in 2002. PstS2 which evolved from PstS1 became widespread in the Middle East and Central Asia. In 2000, PstS2 was detected in Europe, where it never became prevalent. Additional SSR genotyping and virulence phenotyping revealed 10 and six variants, respectively, within PstS1 and PstS2, demonstrating the evolutionary potential of the pathogen. Overall, the results suggested East Africa as the most plausible origin of the two invasive strains. The SCAR markers developed in the present study provide a rapid, inexpensive, and efficient tool to track the distribution of P. striiformis invasive strains, PstS1 and PstS2.

RESUMEN

Understanding the mode of temporal maintenance of plant pathogens is an important domain of microbial ecology research. Due to the inconspicuous nature of microbes, their temporal maintenance cannot be studied directly through tracking individuals and their progeny. Here, we suggest a series of population genetic analyses on molecular marker variation in temporally spaced samples to infer about the relative contribution of sexual reproduction, off-season survival and migration to the temporal maintenance of pathogen populations. We used the proposed approach to investigate the temporal maintenance of wheat yellow rust pathogen, Puccinia striiformis f.sp. tritici (PST), in the Himalayan region of Pakistan. Multilocus microsatellite genotyping of PST isolates revealed high genotypic diversity and recombinant population structure across all locations, confirming the existence of sexual reproduction in this region. The genotypes were assigned to four genetic groups, revealing a clear differentiation between zones with and without Berberis spp., the alternate host of PST, with an additional subdivision within the Berberis zone. The lack of any differentiation between samples across two sampling years, and the very infrequent resampling of multilocus genotypes over years at a given location was consistent with limited over-year clonal survival, and a limited genetic drift. The off-season oversummering population in the Berberis zone, likely to be maintained locally, served as a source of migrants contributing to the temporal maintenance in the non-Berberis zone. Our study hence demonstrated the contribution of both sexual recombination and off-season oversummering survival to the temporal maintenance of the pathogen. These new insights into the population biology of PST highlight the general usefulness of the analytical approach proposed.

Asunto(s)