Puccinia Triticina Research Articles

Use of effective sources of winter wheat resistance in breeding for immunity

Winter wheat is one of the most important crops in Ukraine, and modern varieties have the potential to produce high yields under favourable conditions. The realisation of the genetic yield potential of a variety is the result of a complex interaction of genotype and environmental variables. In order to find effective sources of resistance, a collection of winter wheat accessions of different ecological and geographical origin was studied. The research was conducted in 2017-2019 in the Right-Bank Forest-Steppe, and the analysis of plant resistance of the winter wheat collection of the National Centre of Plant Genetic Resources of Ukraine was tested against a synthetic infectious background of septoria, brown rust and smut as opposed to the natural background of powdery mildew, leaf yellowing (pyrenophorosis) and other root rot. The collection of 32 varieties was represented by 9 countries, most of them of Ukrainian origin. In the field, on artificial and natural infectious backgrounds, we identified varieties characterised by group resistance to pathogens of major diseases. The studies of adult plant resistance under conditions of infectious natural background and the use of the method of artificial complex infectious background have established that it is possible to investigate and identify sources of resistance to both individual pathogens and a complex of pathogens. As a result, no accessions were found to possess group resistance against the six pathogens presented. The selected varieties, Zdoba Kyivska, Vykhovanka Odeska and Feonia, can be used as sources of group resistance, which allows for excellent breeding efforts leading to the development of resistant wheat varieties. The selected sources are suitable for inclusion in breeding programmes as starting material for the development of modern high-yielding winter durum wheat varieties resistant to diseases and other environmental factors

Influence of biotic and abiotic factors on the virulence of Puccinia triticina population in southern Russia

AbstractWheat leaf rust, caused by Puccinia triticina, is reported annually in southern Russia, the main wheat production region in Russia. The effect of biotic (types of cultivar resistance) and abiotic (northern, central and southern agro‐ecological zones) factors on virulence variability of the pathogen was analysed with 43 Lr differentials. Virulence frequencies to Lr genes 1, 2c, 3, 3bg, 11, 14a, 14b, 16, 17, 18, 23, 25, 26, 33, 34, 40 and Kanr exceeded 50% in all populations. A total of 81 virulence phenotypes (based on the standard set of 20 differentials) were identified among 108 isolates. The most common phenotypes were PHSTQ, THTTR, PHTTR and TKTTR. The minimum genetic distance (the highest similarity) was established between the P. triticina populations from susceptible cv. Krasnodarskaya 99 and cv. Brigada with race‐specific resistance. P. triticina populations from the susceptible cultivar in the three agro‐ecological zones were less variable and more similar than the cultivar‐specific and natural pathogen populations. Resistance types of host cultivars seem to have a greater effect on the pathogen population than abiotic factors. Nevertheless, abiotic factors need to be considered when making decisions about deployment of wheat varieties. Our results support the paradigm of a shift in the population of an obligate parasite towards increased virulence in response to the selection pressure of cultivars with race‐specific resistance.

Modeling the Impact of Proportion, Sowing Date, and Architectural Traits of a Companion Crop on Foliar Fungal Pathogens of Wheat in Crop Mixtures.

Diversification of cropping systems is a lever for the management of epidemics. However, most research to date has focused on cultivar mixtures, especially for cereals, even though crop mixtures can also improve disease management. To investigate the benefits of crop mixtures, we studied the effect of different crop mixture characteristics (i.e., companion proportion, sowing date, and traits) on the protective effect of the mixture. We developed a SEIR (Susceptible, Exposed, Infectious, Removed) model of two damaging wheat diseases (Zymoseptoria tritici and Puccinia triticina), which were applied to different canopy components, ascribable to wheat and a theoretical companion crop. We used the model to study the sensitivity of disease intensity to the following parameters: wheat-versus-companion proportion, companion sowing date and growth, and architectural traits. For both pathogens, the companion proportion had the strongest effect, with 25% of companion reducing disease severity by 50%. However, changing companion growth and architectural traits also significantly improved the protective effect. The effect of companion characteristics was consistent across different weather conditions. After decomposing the dilution and barrier effects, the model suggested that the barrier effect is maximized for an intermediate proportion of companion crop. Our study thus supports crop mixtures as a promising strategy to improve disease management. Future studies should identify real species and determine the combination of host and companion traits to maximize the protective effect of the mixture. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Classification of yellow rust of wheat from Sentinel-2 satellite imagery using deep learning artificial neural network

Classification of yellow rust of wheat from Sentinel-2 satellite imagery using deep learning artificial neural network

The analysis of Puccinia triticina field populations in Canada between 2018 and 2020 using restriction site‐associated DNA genotyping‐by‐sequencing

AbstractPuccinia triticina, the causal agent of wheat leaf rust, is a dynamic pathogen causing significant yield losses worldwide. In this study, we analysed the virulence and genetic structure of 98 P. triticina isolates collected in Canada between 2018 and 2020. Isolates from Manitoba and Saskatchewan were found to be highly related for virulence, as were isolates from Quebec and Ontario. Isolates from Alberta had a virulence profile more similar to those from Manitoba and Saskatchewan than to Ontario and Quebec. To study the genetic structure of P. triticina populations, we used the restriction site‐associated DNA (RAD) genotyping‐by‐sequencing method to identify single‐nucleotide polymorphisms (SNPs). The Puccinia DNA sequences were aligned against the reference genome of P. triticina race BBBD and 1898 SNPs were identified. The phylogenetic analysis using SNP markers grouped these P. triticina into three genetic clades. The separation of P. triticina into three genetic clades was also supported by principal component analysis. Isolates from Clade 1 and Clade 2 were found throughout Canada, whereas Clade 3 isolates were only found in Ontario and Quebec. There were differences in virulence profiles among P. triticina isolates from the three genetic clades and a general correlation between virulence phenotypes and SNP genotypes was observed. These results indicate that the SNPs derived from RAD genotyping‐by‐sequencing could be useful in tracking the genetic and virulence dynamics of this pathogen in Canadian wheat.

Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning

Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning

Nuclear exchange generates population diversity in the wheat leaf rust pathogen Puccinia triticina

In clonally reproducing dikaryotic rust fungi, non-sexual processes such as somatic nuclear exchange are postulated to play a role in diversity but have been difficult to detect due to the lack of genome resolution between the two haploid nuclei. We examined three nuclear-phased genome assemblies of Puccinia triticina, which causes wheat leaf rust disease. We found that the most recently emerged Australian lineage was derived by nuclear exchange between two pre-existing lineages, which originated in Europe and North America. Haplotype-specific phylogenetic analysis reveals that repeated somatic exchange events have shuffled haploid nuclei between long-term clonal lineages, leading to a global P. triticina population representing different combinations of a limited number of haploid genomes. Thus, nuclear exchange seems to be the predominant mechanism generating diversity and the emergence of new strains in this otherwise clonal pathogen. Such genomics-accelerated surveillance of pathogen evolution paves the way for more accurate global disease monitoring.

Identification and introgression of a novel leaf rust resistance gene from Thinopyrum intermedium chromosome 7Js into wheat.

A novel leaf rust resistance locus located on a terminal segment (0-69.29 Mb) of Thinopyrum intermedium chromosome arm 7JsS has been introduced into wheat genome for disease resistance breeding. Xiaoyan 78829, a wheat-Thinopyrum intermedium partial amphiploid, exhibits excellent resistance to fungal diseases in wheat. To transfer its disease resistance to common wheat (Triticum aestivum), we previously developed a translocation line WTT26 using chromosome engineering. Disease evaluation showed that WTT26 was nearly immune to 14 common races of leaf rust pathogen (Puccinia triticina) and highly resistant to Ug99 race PTKST of stem rust pathogen (P. graminis f. sp. tritici) at the seedling stage. It also displayed high adult plant resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Cytogenetic and molecular marker analysis revealed that WTT26 carried a T4BS·7JsS chromosome translocation. Once transferred into the susceptible wheat genetic background, chromosome 7JsS exhibited its resistance to leaf rust, indicating that the resistance locus was located on this alien chromosome. To enhance the usefulness of this locus in wheat breeding, we further developed several new translocation lines with small Th. intermedium segments using irradiation and developed 124 specific markers using specific-locus amplified fragment sequencing, which increased the marker density of chromosome 7JsS. Furthermore, a refined physical map of chromosome 7JsS was constructed with 74 specific markers, and six bins were thus arranged according to the co-occurrence of markers and alien chromosome segments. Combining data from specific marker amplification and resistance evaluation, we mapped a new leaf rust resistance locus in the 0-69.29Mb region on chromosome 7JsS. The translocation lines carrying the new leaf rust resistance locus and its linked markers will contribute to wheat disease-resistance breeding.

Mapping of prehaustorial resistance against wheat leaf rust in einkorn (Triticum monococcum), a progenitor of wheat

Wheat leaf rust (Puccinia triticina) is one of the most significant fungal diseases of wheat, causing substantial yield losses worldwide. Infestation is currently being reduced by fungicide treatments and mostly vertical resistance. However, these measures often break down when the fungal virulence pattern changes, resulting in a breakdown of vertical resistances. In contrast, the prehaustorial resistance (phr) that occurs in the einkorn–wheat leaf rust interaction is race-independent, characterized by an early defense response of plants during the prehaustorial phase of infestation. Einkorn (Triticum monococcum) is closely related to Triticum urartu as a progenitor of wheat and generally shows a high level of resistance against leaf rust of wheat. Hence, einkorn can serve as a valuable source to improve the level of resistance to the pathogen in future wheat lines. In particular, einkorn accession PI272560 is known to exhibit a hypersensitive prehaustorial effector triggered immune reaction, preventing the infection of P. triticina. Remarkably, this effector-triggered immune reaction turned out to be atypical as it is non-race-specific (horizontal). To genetically dissect the prehaustorial resistance (phr) in PI272560, a biparental F2 population of 182 plants was established after crossing PI272560 with the susceptible T. boeoticum accession 36554. Three genetic maps comprising 2,465 DArT-seq markers were constructed, and a major QTL was detected on chromosome 5A. To locate underlying candidate genes, marker sequences flanking the respective QTL were aligned to the T. urartu reference genome and transcriptome data available from the parental accessions were used. Within the QTL interval of approximately 16.13 million base pairs, the expression of genes under inoculated and non-inoculated conditions was analyzed via a massive analysis of cDNA (MACE). Remarkably, a single gene located 3.4 Mbp from the peak marker within the major QTL was upregulated (20- to 95-fold) after the inoculation in the resistant accession in comparison to the susceptible T. boeoticum accession. This gene belongs to a berberine bridge enzyme-like protein that is suspected to interact on the plant surface with glycoside hydrolases (GH) secreted by the fungus and to induce a hypersensitive defense reaction in the plant after fungal infections.

Selected emerging and reemerging plant pathogens affecting the food basket: A threat to food security

The introduction and spread of plant diseases and insect pests, mostly in horticulture and forestry, among food crops and other plant species have significant global repercussions for the farmers, seed industry, policy-makers, and the general public. It is important to increase the risk analysis, technical aspects of quarantine, and other control strategies. Programs to improve various protective measures against plant diseases and insect pests have been introduced by several agricultural ministries in various countries. It also offers an opportunity to raise public awareness of the need to deal with more serious issues connected to the threat posed by emerging and reemerging phytopathogens to food crops, forest trees, and other ecosystem services provided by the environment. Selected and significant developing diseases, their consequences, and potential sources of novel pathogens are addressed in this overview. The diseases reviewed include late blight of potatoes, Banana Xanthomonas Wilt (BXW), and black stem rust of wheat, which form a major food basket across the globe. Pathogen distribution and climate interactions, relationships between pathogenic virulence and climate, and the effect of climate on emerging pathogens are also discussed. Discussed also include changes in the ecological settings and atmospheric composition that favor infections but are unfavorable to host plants, this aggravating disease epidemics, as well as static management strategies that promote the creation of novel diseases and pests. Environmental change, particularly when coupled with introduction of new pathogen/host, may cause unprecedented outcomes. New pathogens affect the measures farmers apply to effectively manage the new pathogen in addition to the feasibility of cropping systems in various regions.

Brown rust resistance screening and molecular characterization of wheat cultivars by molecular markers

Brown rust resistance screening and molecular characterization of wheat cultivars by molecular markers

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Genome-wide association study of Asian and European common wheat accessions for yield-related traits and stripe rust resistance.

Identifying novel loci of yield-related traits and resistance to stripe rust (caused by Puccinia striiformis f. sp. tritici) in wheat will help in breeding wheat that can meet projected demands in diverse environmental and agricultural practices. We performed a genome-wide association study with 24,767 SNPs in 180 wheat accessions that originated in 16 Asian or European countries between latitudes 30°N and 45°N. We detected seven accessions with desirable yield-related traits and 42 accessions that showed stable, high degrees of stripe rust resistance in multi-environment field assessments. A marker-trait association analysis of yield-related traits detected 18 quantitative trait loci (QTLs) in at least two test environments and two QTLs related to stripe rust resistance in at least three test environments. Five of these QTLs were identified as potentially novel QTLs by comparing their physical locations with those of known QTLs in the Chinese Spring (CS) reference genome RefSeq v1.1 published by the International Wheat Genome Sequencing Consortium; two were for spike length, one was for grain number per spike, one was for spike number, and one was for stripe rust resistance at adult plant stage. We also identified 14 candidate genes associated with the five novel QTLs. These QTLs and candidate genes will provide breeders with new germplasm and can be used to conduct marker-assisted selection in breeding wheat with improved yield and stripe rust resistance.

Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease

In recent years, signal processing and deep learning convergence has sparked transformative synergies across various domains, including image and speech recognition, natural language processing, autonomous systems, and healthcare diagnostics. This fusion capitalizes on the strength of signal processing in extracting meaningful features from raw data and the prowess of deep learning in unraveling intricate patterns, driving innovation and research into uncharted territories. This paper explores literature spanning the past three years to illuminate the dynamic landscape of scholarly endeavors that leverage the integration of signal processing techniques within deep learning architectures. The resulting paradigm shift magnifies the precision and efficiency of applications in computer vision, speech and audio processing, natural language comprehension, and interdisciplinary domains like healthcare. Notable advances include synergizing wavelet transformations with convolutional neural networks (CNNs) for enhanced image classification accuracy, integrating spectrogram-based features with deep learning architectures for improved speech-to-text accuracy, and pioneering the fusion of wavelet packet decomposition into recurrent architectures for sentiment analysis. Moreover, the paper delves into developing and evaluating a U-Net neural network model for image segmentation, investigating its performance under varying training conditions using metrics such as confusion matrices, heat maps, and precision-recall curves. The comprehensive survey identifies research gaps, notably within the context of wheat rust detection, and emphasizes the need for tailored innovations to enhance accuracy and efficiency. Overall, the synthesis of signal processing techniques with deep learning architectures propels innovation, poised to address complex challenges across diverse domains

Cloning of the wheat leaf rust resistance gene Lr47 introgressed from Aegilops speltoides

Leaf rust, caused by Puccinia triticina Eriksson (Pt), is one of the most severe foliar diseases of wheat. Breeding for leaf rust resistance is a practical and sustainable method to control this devastating disease. Here, we report the identification of Lr47, a broadly effective leaf rust resistance gene introgressed into wheat from Aegilops speltoides. Lr47 encodes a coiled-coil nucleotide-binding leucine-rich repeat protein that is both necessary and sufficient to confer Pt resistance, as demonstrated by loss-of-function mutations and transgenic complementation. Lr47 introgression lines with no or reduced linkage drag are generated using the Pairing homoeologous1 mutation, and a diagnostic molecular marker for Lr47 is developed. The coiled-coil domain of the Lr47 protein is unable to induce cell death, nor does it have self-protein interaction. The cloning of Lr47 expands the number of leaf rust resistance genes that can be incorporated into multigene transgenic cassettes to control this devastating disease.

Wheat leaf rust control through biofabricated zinc oxide nanoparticles

Wheat leaf rust control through biofabricated zinc oxide nanoparticles

Postulation of leaf rust resistance genes in Indian and exotic wheat germplasm using near-isogenic lines (NILs) and molecular markers

Leaf rust caused by Puccinia triticina Eriks. (Pt) is one of the most devastating fungal diseases affecting wheat (Triticum aestivum L.) production worldwide. Deployment of resistant cultivars is a prominent eco-friendly approach to manage this disease. But major constraint in this case is emergence of new pathotypes of pathogen due to faster evolution. Widening the gene pool by gene mining from diverse panel is the key strategy to keep pace with the ever-evolving pathogen. So, in this study attempts were made to identify leaf rust resistance genes in a selected set of 149 germplasm lines by seedling reaction test against two most prevalent Pt pathotypes, 77–9 (MHTKL) and 77–5 (THTTM) and for field resistance at Ludhiana and Gurdaspur during 2018–2021. Based on seedling infection type (IT) data, 88 lines were found resistant (low IT; 0–2) and 39 lines were susceptible (high IT; ≥3). Under field conditions, 77 lines showed highly resistance response across the locations and years. Gene postulation in the test germplasm was done by gene matching technique and further confirmed by linked molecular markers; which revealed the presence of 11 Lr genes singly or in combinations. Leaf rust resistance gens; Lr9, Lr19, Lr24, Lr34, Lr37, Lr42, Lr45, Lr52, Lr57, Lr58 and Lr76 were detected in 4, 7, 10, 1, 7, 33, 41, 44, 2, 24 and 6 lines respectively. The present study's resistant germplasm lines can be utilised as resistant parents in crosses intended for pyramiding and the deployment of leaf rust resistance genes.

Candidate effectors for leaf rust resistance gene Lr28 identified through transcriptome and in-silico analysis.

Puccinia spp. causing rust diseases in wheat and other cereals secrete several specialized effector proteins into host cells. Characterization of these proteins and their interaction with host's R proteins could greatly help to limit crop losses due to diseases. Prediction of effector proteins by combining the transcriptome analysis and multiple in-silico approaches is gaining importance in revealing the pathogenic mechanism. The present study involved identification of 13 Puccinia triticina (Pt) coding sequences (CDSs), through transcriptome analysis, that were differentially expressed during wheat-leaf rust interaction; and prediction of their effector like features using different in-silico tools. NCBI-BLAST and pathogen-host interaction BLAST (PHI-BLAST) tools were used to annotate and classify these sequences based on their most closely matched counterpart in both the databases. Homology between CDSs and the annotated sequences in the NCBI database ranged from 79 to 94% and with putative effectors of other plant pathogens in PHI-BLAST from 24.46 to 54.35%. Nine of the 13 CDSs had effector-like features according to EffectorP 3.0 (≥0.546 probability of these sequences to be effector). The qRT-PCR expression analysis revealed that the relative expression of all CDSs in compatible interaction (HD2329) was maximum at 11 days post inoculation (dpi) and that in incompatible interactions (HD2329 + Lr28) was maximum at 3 dpi in seven and 9 dpi in five CDSs. These results suggest that six CDSs (>0.8 effector probability as per EffectorP 3.0) could be considered as putative Pt effectors. The molecular docking and MD simulation analysis of these six CDSs suggested that candidate Lr28 protein binds more strongly to candidate effector c14094_g1_i1 to form more stable complex than the remaining five. Further functional characterization of these six candidate effectors should prove useful for a better understanding of wheat-leaf rust interaction. In turn, this should facilitate effector-based leaf rust resistance breeding in wheat.

Effect of abiotic factors on pathotypes causing yellow and brown rust in wheat

An attempt was made to determine the most favourable abiotic factors influencing germination of urediniospores of different pathotypes of Puccinia species. The causal organism of rusts in wheat is Puccinia spp. Five pathotypes of Puccinia striiformis (46S119, 78S84, 110S84, 110S119, 238S119) causal organism of yellow rust and two pathotypes of Puccinia triticina (77-5 and 77-9) causal organism of brown rust in wheat were obtained from Department of Plant Pathology, Punjab Agricultural University, Ludhiana. The data related to spore germination on agar slides was analysed and the levels of urediniospores germination at different temperatures (5,10,15 and 20oC) and pH (5,6,7 and 8) for each pathotype was compared using analysis of variance. The most appropriate temperature and pH were later used to conduct an experiment to study effect of different light intensities (500, 750,1000 and 1250 lux) on spore germination of all the pathotypes under study. The data showed that on agar, 15°C was proved as most suitable for urediniospore germination for Puccinia striiformis. Mean per cent spore germination was highest over the temperature range 15°C (43.55%) for Pst pathoypes and dropped significantly at 10°C (37.97%), 20°C (29.66%) and 5°C (21.04%). Mean urediniospore germination for Puccinia triticina was highest at 20°C (43.89%) followed by 15°C (39.44%), 10°C (30.43) and 5°C (27.39% ). Experimental results revealed that per cent spore germination was better under pH 7 followed by 6, 5 and 8 for all the pathotypes considered for study. The highest urediniospore germination was observed for 1250 lux (46.54%) followed by 1000 lux (41.29%), 750 lux (38.42%) and 500 lux (27.60%).

In-silico prediction, characterization, molecular docking and dynamic simulation studies for screening potential fungicides against leaf rust of Triticum aestivum

Triticum aestivum is an important crop worldwide, which is a large source of food grain. T.aestivum demands on developed countries will grow every year, this increase in the demand is profoundly serious especially in the light climate change which would lead to a 29% reduction in final productivity. Rust fungus attacks the T.aestivum, specifically newly planted T.aestivum plants, which block the vascular system, stun, and finally damage grain and tillers. In present study we predict the 3D structure then find the binding pocket and conserved domains for MAPkinase-1 of Puccinia triticina. After that, screen the FungiPAD, PubChem, NPAtlas databases by physicochemical properties, docking, clustering, ADME (Absorption, distribution, metabolism, and excretion) and PAINS (pan assay interference compounds) filter analysis. Through this screening process screen the nine compounds, which are benzovindiflupyr, furametpyr, isopyrazam, fenaminstrobin, and flumorph from Fungicide database: zoxamide, vinclozolin, pentachloronitrobenzene, and dithianon from PubChem database, based on the binding energy, clustering, ADME and PAINS analysis. All these nine compounds bind in the same pocket and show the same pattern of interaction. Among these nine compounds, select the two compounds (PubChem:122087 (-6.96 kcal/mol) and FDBD02904 (-8.62 kcal/mol)) based on binding energy for 100 ns MD simulation and free energy calculation. MD simulation shows stability throughout the simulation, and it shows the sable interaction when compounds bind to the MAPKinase 1 protein which may help to protein kinase pathways in plant defense response. This result helps to design alternative fungicide against the wheat rust disease. Communicated by Ramaswamy H. Sarma