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Wheat (Triticum spp.) is one of the most important cereal crops in the world. Several diseases affect wheat production and can cause 20-80% yield loss annually. Out of these diseases, stripe rust, also known as yellow rust (Puccinia striiformis f. sp. tritici), stem rust (Puccinia graminis f. sp. tritici), leaf rust (Puccinia recondita), and powdery mildew (Blumeria graminis f. sp. tritici) are the most important fungal diseases that infect the foliar part of the plant. Many efforts were made to improve wheat resistance to these diseases. Due to the continuous advancement in sequencing methods and genomic tools, genome-wide association study has become available worldwide. This analysis enabled wheat breeders to detect genomic regions controlling the resistance in specific countries. In this review, molecular markers significantly associated with the resistance of the mentioned foliar diseases in the last five years were reviewed. Common markers that control broad-spectrum resistance in different countries were identified. Furthermore, common genes controlling the resistance of more than one of these foliar diseases were identified. The importance of these genes, their functional annotation, and the potential for gene enrichment are discussed. This review will be valuable to wheat breeders in producing genotypes with broad-spectrum resistance by applying genomic selection for the target common markers and associated genes. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by a biotrophic, obligate fungus Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat fungal disease that exists worldwide and caused huge yield reductions during pandemic years. Low temperatures favor the development of the disease, but the global average temperature has been increasing since 1850, especially in China, which has a higher rising rate than the global average. In the last two decades, Pst isolates have shown increased aggressiveness under high temperatures. However, the effect of rising temperatures on the aggressiveness of Pst has remained unknown in China. Therefore, this study assessed the aggressiveness of 15 representative Pst isolates (6 new isolates collected before 2016 and 9 old isolates collected after 2016) in Gansu under high temperatures by measuring and comparing disease severity, spore germination, and latent period on wheat seedlings at 16 °C, 18 °C, and 22 °C. The results indicated that the six new isolates showed greater disease severity, higher spore germination ratio, and shorter latent period than the nine old isolates, indicating that the new isolates were more aggressive under high temperatures than the old isolates. Some new isolates, such as CYR34, CYR33, and CYR32, which are predominant, were inferred to be associated with high-temperature adaptation in addition to having more susceptible hosts. Our results provided an insight into changes in Pst isolates at warmer temperatures and increasing incidence of wheat stripe rust in China, especially in eastern sporadic epidemiological areas in recent years. Thus, the new isolates are likely to be a potential risk for causing increasing stripe rust incidence. [ABSTRACT FROM AUTHOR]

Xinjiang is an important region due to its unique epidemic characteristics of wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici. Some previous studies on race identification were conducted in this region, but it is still unclear how temporal changes affect the dynamics, diversity, and virulence characteristics of Pst races in Xinjiang. To gain a better understanding, we compared the race data from spring and winter wheat crops of 2022 with that of 2021. Our results showed a significant change in virulence frequency in 2022. Vr10, Vr13, and Vr19 exhibited an increasing trend, with a frequency of ≥18%, while the maximum decline was observed in Vr1, Vr3, and Vr9, with a frequency of ≤−25%. It was found that Yr5 and Yr15 remained effective against Xinjiang Pst races. The race diversity increased from 0.92 (70 races out of 345 isolates) to 0.94 (90 races out of 354 isolates) in 2022, with G22G being the dominant race group. Race CYR34 became prevalent in the region in 2022, while the LvG grouped was wiped out in 2022, from both summer and winter crop seasons. HyG and SuG groups showed an overall declining trend. Overall prevalent races showed over-summering and over-wintering behaviors in Xinjiang. The number of new races occurrence frequency increased by 34% in 2022, indicating a potential change in the population structure of Pst. It is crucial to introduce newly resistant gene cultivars in this region and to establish rust-monitoring protocols to prepare for any future epidemics. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a globally significant fungal disease that seriously threatens wheat yield, particularly in China. This study investigates the genetic structure and reproductive patterns of Pst populations in Ili, Xinjiang, using 12 pairs of Simple Sequence Repeat (SSR) molecular markers. Analyses of 79 Pst isolates from either spring or winter wheat areas in Ili revealed three primary genetic clusters, indicating notable differences between populations associated with spring and winter wheat. The STRUCTURE results, complemented by UPGMA and PCoA analyses, highlight significant genetic diversity within these populations, with evidence of genetic recombination and sexual reproduction in certain areas. Pst populations in Ili exhibit a mixed mode of reproduction, predominantly sexual in Qapqal and Xinyuan D and primarily asexual within the spring wheat populations. The gene flow analysis underscores extensive inter-population communication, which facilitates the spread and adaptation of the pathogen across diverse wheat-growing environments. This study marks the first documentation of sexual reproduction in Pst within Xinjiang, providing new insights into its spread and genetic variation. These findings suggest that sexual reproduction may play a role in the regional adaptation and evolution of Pst, impacting future management strategies for wheat stripe rust in Xinjiang and potentially in broader Central Asian contexts. [ABSTRACT FROM AUTHOR]

Wheat stripe rust is a fungal disease severely affecting wheat production. Breeding resistant cultivars is the most cost-effective and efficient way to control wheat stripe rust. YrZH84 is an all-stage resistance gene with good resistance to stripe rust. In this study, YrZH84 was introgressed from germplasm Lantian36 (LT36) into the two southwestern Chinese elite wheat cultivars Mianmai367 (MM367, carrying Yr10, Yr26), and Chuanmai104 (CM104, carrying Yr26), using marker-assisted selection. F1 seeds of the two cross-combinations were planted and self-crossed to develop the advanced generations in the field. A total of 397 introgression lines (ILs) were obtained in F6 and genotyped using molecular markers Xcfa2040, Xbarc32 (linked to YrZH84), Yr10 (linked to Yr10), We173, and Xbarc181 (linked to Yr26). The 397 ILs were also evaluated for resistance to stripe rust and agronomic traits, including plant height, number of tillers, spike length, thousand-grain weight, and spikelet number. Finally, 61 lines with appreciative agronomic traits and disease resistance were selected. Among these lines, 31 lines had stripe rust resistance gene YrZH84. These selected lines are expected to become new wheat varieties for their high resistance to stripe rust and excellent agronomic traits that will make important contributions to the control of stripe rust and wheat production. [ABSTRACT FROM AUTHOR]

[Objective] The study aims to explore the role of TaRING1 in the interaction between wheat and stripe rust, analyze its mechanism and provide theoretical basis for the green prevention and control of wheat stripe rust. [Methods] The interaction target proteins of TaRING1 were screened by yeast two-hybrid assays and verified by bimolecular fluorescence complementation (BiFC) and luciferase complementation (LCA) assays. The ubiquitylation function of TaRING1 was verified by ubiquitination analysis. The subcellular localization of target TaRIP92 was observed by transient expression in Nicotiana benthamiana and wheat protoplasts. [Results] Using TaRING1 as bait, the target protein TaRIP92 was screened by yeast two-hybrid, and the interaction between TaRING1 and TaRIP92 was verified by LCA and BiFC. In vitro ubiquitination assays proved that TaRING1 ubiquitinated TaRIP92. Transient expression in N . benthamiana and wheat protoplasts showed that TaRIP92 was localized in mitochondria. [Conclusion] TaRING1 interacts with and ubiquitinates the mitochondrial protein TaRIP92.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive fungal diseases of wheat, and seriously threatens safe production of the crop worldwide. In China, new races historically appeared and rapidly developed to be predominant races and have resulted in ineffectiveness and replacement of wheat resistance cultivars as well as massive reduction in yield. In the present study, the relative parasitic fitness of the two newly-emerged Yr5-virulent races (TSA-6 and TSA-9) were compared with those of four currently predominant Chinese races (CYR31, CYR32, CYR33, and CYR34) based on evaluation on 10 Chinese wheat cultivars. As a result, there were significant differences in the relative parasitic fitness parameters among overall tested races based on multiple comparison (LSD) analysis (PTSA-9 (0.95)>TSA-6 (0.92)>CYR34 (0.29)>CYR31 (–1.54)>CYR33 (–1.77). The results indicated that two Yr5-virulent races TSA-9 and TSA-6 possessed relative parasitic fitness higher than races CYR34, CYR31, and CYR33, but lower than race CYR32, and have potential risks in developing to be predominant races. Therefore, continual monitoring of both Yr5-virulent races, and their variants is needed. The use of wheat cultivars (lines) with Yr5 resistance gene singly in wheat breeding is essential for being avoided, and is suggested to combine with other effective stripe rust resistance genes.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), significantly impacts global wheat production, including China. Monitoring Pst virulence is crucial for disease management. This study used 18 Yr single‐gene isogenic lines of wheat to characterize the virulence of 2023 Pst collections in China. A total of 103 isolates were collected from 11 provinces, and 61 races were identified. Frequencies of virulence to the 18 Yr genes varied from 0% to 95.1%, with low frequencies (defined as less than 10%) to Yr85 (7.8%) and Yr10 (8.7%); moderate frequencies (10%–40%) to Yr24 (15.5%), Yr32 (16.5%), YrSP (37.9%) and Yr76 (37.9%); and high frequencies (>40%) to Yr7 (47.6%), Yr1 (68.0%), Yr9 (69.9%), Yr27 (69.9%), Yr17 (79.6%), Yr6 (89.3%), YrExp2 (92.2%), Yr43 (93.2%), Yr8 (95.1%) and Yr44 (95.1%). Yr6, Yr8, Yr17, Yr27, Yr43, Yr44 and YrExp2 have almost lost their effectiveness in all provinces, while Yr10, Yr24, Yr32 and Yr85 are effective in Hebei, Shandong, Shaanxi, Gansu and Anhui. No isolate was virulent to Yr5 or Yr15, indicating their effectiveness against Pst populations in China. The Pst isolates had a wide virulence spectrum ranging from 3 to 16 virulence factors, with an average of 9.5. Comparing 2023 to 2021, the PstCN‐040 and races sharing virulence to Yr1, Yr6, Yr7, Yr8, Yr9, Yr17, Yr43, Yr44 and YrExp2 predominated, with frequencies 16.4% and 25.2%. This study provides valuable information on the virulence composition of Pst populations in China, helping to guide the development of genetic resistance for wheat in China. [ABSTRACT FROM AUTHOR]

The virulence analysis of Puccinia stiiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is essential for predicting and managing the disease epidemic in Southwest China, where the wheat cultivation has significantly reduced in the past few decades due to the impact of this disease. From 2020 to 2021, 196 Pst isolates were collected from Guizhou, Yunnan, and Sichuan. The virulence and race assessments were conducted using Chinese differential genotypes. Additionally, the resistance expression of 102 wheat lines was evaluated in 2021 in two disease nurseries located in Ningnan and Jiangyou. All the 45 Pst isolates from Guizhou and Yunnan belonged to pathogroup Hybrid 46, with 36 identified as race CYR32. Among the 69 isolates from the Liangshan Prefecture, 67 belonged to the Hybrid 46 group, while the remaining two were identified as race CYR34 in the G-22 group. Furthermore, all 79 isolates from the western Sichuan Basin belonged to the G-22 group, with 54 identified as race CYR34. The diversity indices of the Pst populations from Guizhou, Sichuan, and Yunnan exhibited a sequential decline. Virulence variation among the Pst populations from Yunnan, Guizhou, and the Ganzi-Liangshan region was minimal; however, significant virulence differences were observed when these populations were compared to those from the western Sichuan Basin. Results from disease nurseries indicated that Pst virulence was notably stronger in Ningnan compared to that in Jiangyou. The Sichuan Basin exhibits a notable diversity in Pst virulence, coupled with a more frequent genetic exchange occurring between the Liangshan Prefecture and the Yunnan-Guizhou Plateau. This information is essential for developing effective management strategies to mitigate the impact of wheat stripe rust in this region. [ABSTRACT FROM AUTHOR]

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases. [ABSTRACT FROM AUTHOR]

Wheat stripe rust (WSR), a fungal disease capable of inflicting severe crop loss, threatens most of global wheat production. Breeding for genetic resistance is the primary defense against stripe rust infection. Further development of rustresistant wheat varieties depends on the ability to accurately and rapidly quantify rust resilience. In this study we demonstrate the ability of visible through shortwave infrared reflectance spectroscopy to effectively provide high-throughput classification of wheat stripe rust severity and identify important spectral regions for classification accuracy. Random forest models were developed using both leaf-level and canopy-level hyperspectral reflectance observations collected across a breeding population that was scored for WSR severity using 10 and 5 severity classes, respectively. The models were able to accurately diagnose scored disease severity class across these fine scoring scales between 45-52% of the time, which improved to 79-96% accuracy when allowing scores to be off-by-one. The canopy-level model demonstrated higher accuracy and distinct spectral characteristics relative to the leaf-level models, pointing to the use of this technology for field-scale monitoring. Leaf-level model performance was strong despite clear variation in scoring conducted between wheat growth stages. Two approaches to reduce predictor and model complexity, principal component dimensionality reduction and backward feature elimination, were applied here. Both approaches demonstrated that model classification skill could remain high while simplifying high-dimensional hyperspectral reflectance predictors, with parsimonious models having approximately 10 unique components or wavebands. Through the use of a high-resolution infection severity scoring methodology this study provides one of the most rigorous tests of the use of hyperspectral reflectance observations for WSR classification. We demonstrate that machine learning in combination with a few carefully-selected wavebands can be leveraged for precision remote monitoring and management of WSR to limit crop damage and to aid in the selection of resilient germplasm in breeding programs. [ABSTRACT FROM AUTHOR]

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), stands out as one of the most devastating epidemics impacting wheat production worldwide. Resistant wheat varieties had swiftly been overcome due to the emergence of new virulent Pst strains. Effectors secreted by Pst interfere with plant immunity, and verification of their biological function is extremely important for controlling wheat stripe rust. In this study, we identified an effector, Pst-18220, from Puccinia striiformis f. sp. tritici (Pst), which was induced during the early infection stage of Pst. Silencing the expression of Pst-18220 through virus-mediated host-induced gene silencing (HIGS) resulted in a decreased number of rust pustules. In Nicotiana benthamiana, it significantly suppressed cell death induced by Pseudomonas syringae pv. tomato (Pto) DC3000. In Arabidopsis, plants with stable overexpression of Pst-18220 showed increased susceptibility to Pto DC3000, accompanied by a decrease in the expression level of pattern-triggered immunity (PTI)/effector-triggered immunity (ETI)-related genes, namely, AtPCRK1, AtPCRK2, and AtBIK1. These results emphasize the significant role of the Pst candidate effector, Pst-18220, in rust pathogenicity and the suppression of plant defense mechanisms. This broadens our understanding of effectors without any known motif. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis, is one of the most serious diseases on wheat yield. It is of great significance to timely and accurately detect the disease, in order to monitor and prevent the wheat stripe rust. The stripe rust can infect the internal physical and chemical characteristics and external morphological structure of wheat. Solar-induced chlorophyll fluorescence (SIF) can be expected for the remote sensing detection of crop stress. The red-band sunlight-induced chlorophyll fluorescence (RSIF) has more information about photosystem II (PSII), thus sensitively representing the photosynthetic physiological state of plants. The SIF escape rate is closely related to the canopy geometry, leaf optical properties, and light energy utilization efficiency of vegetation. In this study, field-measured data was used to invert and calculate the SIF and its escape rate (εCP) at different scales (canopy scale SIFCanopy and photosystem scale SIFPS) in the red and far-red band. The contents of four wheat pigments were obtained to combine the leaf area index (LAI) closely related to vegetation growth. The physiological basis of RSIF escape rate (RεCP) was determined to monitor the wheat stripe rust. Subsequently, the response characteristics of RεCP under stripe rust stress were explored to compare with the SIF and its derived parameters (fluorescence yield ФF, apparent SIF yield SIFy) in the red and far-red light bands, the normalized difference vegetation index (NDVI), the MERIS terrestrial chlorophyll index (MTCI) and the simple ratio vegetation index (SR). In addition, a systematic analysis was performed on the response characteristics to SL under different disease severity (SL) and different chlorophyll contents (Chl). The results indicate that the correlations between nitrogen balance index (NBI), Chl, flavonoids (Flav), anthocyanins (Anth), and LAI and SL all reached the P<0.01 level, among which the correlation between Chl and SL was the highest. The correlations of RεCP with NBI, Chl, Flav, and Anth increased by 29.06% and 31.52% on average, respectively, compared with photosystem-scale RSIF and photosystem-scale far-red band SIF (far-red solarinduced chlorophyll fluorescence, FRSIF). The correlation with LAI increased by 15.63%, compared with the canopy-scale FRSIF. RεCP better reflected the variation in the crop physiology and canopy structure caused by disease stress. RεCP shared the highest correlation with SL, which was 60.87%, 42.31%, 17.46%, 39.62%, 34.55%, 5.71%, 13.85%, and 21.31% higher than those of canopy-scale FRSIF (FRSIFCanopy), photosystem-scale FRSIF (FRSIFPS), photosystem-scale RSIF (RSIFPS), apparent SIF yield in the red light band (RSIFy), fluorescence yield in the red light band (RФF), NDVI, MTCI and SR, respectively. In the mild to moderate (0%45%) disease conditions, the correlation between RεCP and SL increased by an average of 56.34% and 53.97%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP was sensitively responded to the variation in SL, which was better than the rest parameters. RεCP was most sensitive to the wheat stripe rust stress under the low (Chl≤30) and medium to high chlorophyll content (Chl>30). The correlation with SL increased by an average of 42.77% and 43.25%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP can serve as a suitable factor for remote sensing monitoring of wheat stripe rust. RεCP can greatly contribute to disease prevention for better yields. The finding can also provide a strong reference and powerful tool for remote sensing monitoring of crops in agricultural production. RSIF and escape rate can be incorporated into the remote sensing monitoring, in order to greatly improve the detection and monitoring of plant health status. [ABSTRACT FROM AUTHOR]

Stripe rust is a serious wheat disease occurring worldwide. At present, the most effective way to control it is to grow resistant cultivars. In this study, a population of 221 recombinant inbred lines (RILs) derived via single-seed descent from a hybrid of a susceptible wheat line, SY95-71, and a resistant line, XK502, was tested in three crop seasons from 2022 to 2024 in five environments. A genetic linkage map was constructed using 12,577 single-nucleotide polymorphisms (SNPs). Based on the phenotypic data of infection severity and the linkage map, five quantitative trait loci (QTL) for adult plant resistance (APR) were detected using the inclusive composite interval mapping (ICIM) method. These five loci are QYrxk502.swust-1BL, QYrxk502.swust-2BL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, explaining 5.67–19.64%, 9.63–36.74%, 9.58–11.30%, 9.76–23.98%, and 8.02–12.41% of the phenotypic variation, respectively. All these QTL originated from the resistant parent XK502. By comparison with the locations of known stripe rust resistance genes, three of the detected QTL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, may harbor new, unidentified genes. From among the tested RILs, 16 lines were selected with good field stripe rust resistance and acceptable agronomic traits for inclusion in breeding programs. [ABSTRACT FROM AUTHOR]

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information. [ABSTRACT FROM AUTHOR]

In this study, the compound 1-(4-(tert-butyl) phenyl)-3-hydroxy-2-methylpyridine-4 (1H) - one (hereinafter referred to as HAINU-19) was used as the active ingredient to prepare the microemulsion and determine its antifungal activity. First, through the screening solvents, co-solvents, and transparent, the formulation of 2.5% HAINU-19 microemulsion (mass fraction) was determined as follows: 2.5% HAINU-19, 15% ethanol, 10% cyclohexanone, 20% transparent (1601# : 500# : AC1810 = 3 : 3 : 1), with distilled water making up to 100%. All indicators meet national standards. The bioassay result of pot experiments showed that the protective and curative effects of 2.5% HAINU-19 microemulsion on wheat stripe rust were 100% at 1000, 500, and 250-fold dilutions, and curative effects were 76.67%, 81.00%, and 82.33%, respectively. The field trial results showed that the 2.5% HAINU19 microemulsion had a control efficacy of 86.13% on rice sheath blight at a 100-fold dilution and 71.24% at a 200-fold dilution. This study can provide an experimental basis and technical support for the development and application of the antifungal compound HAINU-19. [ABSTRACT FROM AUTHOR]

Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens. [ABSTRACT FROM AUTHOR]

Wheat (Triticum spp.) is one of the most important cereal crops in the world. Several diseases affect wheat production and can cause 20-80% yield loss annually. Out of these diseases, stripe rust, also known as yellow rust (Puccinia striiformis f. sp. tritici), stem rust (Puccinia graminis f. sp. tritici), leaf rust (Puccinia recondita), and powdery mildew (Blumeria graminis f. sp. tritici) are the most important fungal diseases that infect the foliar part of the plant. Many efforts were made to improve wheat resistance to these diseases. Due to the continuous advancement in sequencing methods and genomic tools, genome-wide association study has become available worldwide. This analysis enabled wheat breeders to detect genomic regions controlling the resistance in specific countries. In this review, molecular markers significantly associated with the resistance of the mentioned foliar diseases in the last five years were reviewed. Common markers that control broad-spectrum resistance in different countries were identified. Furthermore, common genes controlling the resistance of more than one of these foliar diseases were identified. The importance of these genes, their functional annotation, and the potential for gene enrichment are discussed. This review will be valuable to wheat breeders in producing genotypes with broad-spectrum resistance by applying genomic selection for the target common markers and associated genes.

Wheat stripe rust is a significant disease affecting wheat growth, often referred to as the “cancer of wheat”. Early and accurate detection of stripe rust is crucial for enabling crop managers to implement effective control measures. Hyperspectral remote sensing methods for crop disease detection have gained significant attention. However, commonly used spectral bands or spectral indices (SIs) from hyperspectral data often fail to capture the subtle changes associated with the early stages of crop diseases accurately. In this study, we propose a method for early detection of wheat stripe rust by combining pigments and SIs retrieved from UAV hyperspectral imagery. We acquired hyperspectral images of wheat stripe rust at 7, 16, and 23 days post-inoculation (DPI) using a UHD 185 hyperspectral sensor (450–950 nm) mounted on an S1000 hexacopter UAV. Pigments, including chlorophylls (Cab), carotenoids (Car), anthocyanins, Cab/Car, and 11 pigment-related SIs, were extracted from UAV hyperspectral images using radiative transfer modeling. The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. Models that combine pigments and SIs (PSIMs) perform better than those relying solely on SIs (SIMs) or pigments (PMs). Notably, the RF-based PSIM achieved overall accuracies of 78.1 % and 81.3 % during the asymptomatic (7 DPI) and minimally symptomatic (16 DPI) phases of disease, respectively. Additionally, the pigments in the PSIM contributed more significantly than the SIs, highlighting the importance of pigments in the early detection of stripe rust. Overall, the method combining pigments and spectral indices proposed in this study effectively enhances the early detection of wheat stripe rust and offers valuable insights into the early detection of other crop diseases.

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10−7 cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10−1 cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust. [ABSTRACT FROM AUTHOR]

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns. [ABSTRACT FROM AUTHOR]

Wheat stripe rust (WSR), a fungal disease capable of inflicting severe crop loss, threatens most of global wheat production. Breeding for genetic resistance is the primary defense against stripe rust infection. Further development of rust-resistant wheat varieties depends on the ability to accurately and rapidly quantify rust resilience. In this study we demonstrate the ability of visible through shortwave infrared reflectance spectroscopy to effectively provide high-throughput classification of wheat stripe rust severity and identify important spectral regions for classification accuracy. Random forest models were developed using both leaf-level and canopy-level hyperspectral reflectance observations collected across a breeding population that was scored for WSR severity using 10 and 5 severity classes, respectively. The models were able to accurately diagnose scored disease severity class across these fine scoring scales between 45-52% of the time, which improved to 79-96% accuracy when allowing scores to be off-by-one. The canopy-level model demonstrated higher accuracy and distinct spectral characteristics relative to the leaf-level models, pointing to the use of this technology for field-scale monitoring. Leaf-level model performance was strong despite clear variation in scoring conducted between wheat growth stages. Two approaches to reduce predictor and model complexity, principal component dimensionality reduction and backward feature elimination, were applied here. Both approaches demonstrated that model classification skill could remain high while simplifying high-dimensional hyperspectral reflectance predictors, with parsimonious models having approximately 10 unique components or wavebands. Through the use of a high-resolution infection severity scoring methodology this study provides one of the most rigorous tests of the use of hyperspectral reflectance observations for WSR classification. We demonstrate that machine learning in combination with a few carefully-selected wavebands can be leveraged for precision remote monitoring and management of WSR to limit crop damage and to aid in the selection of resilient germplasm in breeding programs.

ABSTRACT Stripe rust of wheat is caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst). Breeding durably resistant wheat varieties by disrupting the susceptibility (S) gene has an important impact on the control of wheat stripe rust. Mingxian169 (MX169) showed strong stripe rust susceptibility to all the races of Pst. However, molecular mechanisms and responsive genes underlying susceptibility of the wheat variety MX169 to Pst have not been elucidated. Here, we utilized next-generation sequencing technology to analyze transcriptomics data of “MX169” and high-resistance wheat “Zhong4” at 24, 48, and 120 h post-inoculation (hpi) with Pst. Comparative transcriptome analysis revealed 3,494, 2,831, and 2,700 differentially expressed genes (DEGs) at different time points. We observed an upregulation of DEGs involved in photosynthesis, flavonoid biosynthesis, pyruvate metabolism, thiamine metabolism, and other biological processes, suggesting their involvement in MX169’s response to Pst. DEGs encoding transcription factors were also identified. Our study suggested the potential susceptibility gene resources in MX169 related to stripe rust response could be valuable for understanding the mechanisms involved in stripe rust susceptibility and for improving wheat resistance to Pst.IMPORTANCEOur study suggests the potential susceptibility gene resources in MX169 related to stripe rust response could be valuable for understanding the mechanisms involved in stripe rust susceptibility and for improving wheat resistance to Pst.

Abstract Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens.

Xinjiang is an important region due to its unique epidemic characteristics of wheat stripe rust disease caused by Puccinia striiformis f. sp. tritici. Some previous studies on race identification were conducted in this region, but it is still unclear how temporal changes affect the dynamics, diversity, and virulence characteristics of Pst races in Xinjiang. To gain a better understanding, we compared the race data from spring and winter wheat crops of 2022 with that of 2021. Our results showed a significant change in virulence frequency in 2022. Vr10, Vr13, and Vr19 exhibited an increasing trend, with a frequency of ≥18%, while the maximum decline was observed in Vr1, Vr3, and Vr9, with a frequency of ≤−25%. It was found that Yr5 and Yr15 remained effective against Xinjiang Pst races. The race diversity increased from 0.92 (70 races out of 345 isolates) to 0.94 (90 races out of 354 isolates) in 2022, with G22G being the dominant race group. Race CYR34 became prevalent in the region in 2022, while the LvG grouped was wiped out in 2022, from both summer and winter crop seasons. HyG and SuG groups showed an overall declining trend. Overall prevalent races showed over-summering and over-wintering behaviors in Xinjiang. The number of new races occurrence frequency increased by 34% in 2022, indicating a potential change in the population structure of Pst. It is crucial to introduce newly resistant gene cultivars in this region and to establish rust-monitoring protocols to prepare for any future epidemics.

Wheat stripe rust, caused by a biotrophic, obligate fungus Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat fungal disease that exists worldwide and caused huge yield reductions during pandemic years. Low temperatures favor the development of the disease, but the global average temperature has been increasing since 1850, especially in China, which has a higher rising rate than the global average. In the last two decades, Pst isolates have shown increased aggressiveness under high temperatures. However, the effect of rising temperatures on the aggressiveness of Pst has remained unknown in China. Therefore, this study assessed the aggressiveness of 15 representative Pst isolates (6 new isolates collected before 2016 and 9 old isolates collected after 2016) in Gansu under high temperatures by measuring and comparing disease severity, spore germination, and latent period on wheat seedlings at 16 °C, 18 °C, and 22 °C. The results indicated that the six new isolates showed greater disease severity, higher spore germination ratio, and shorter latent period than the nine old isolates, indicating that the new isolates were more aggressive under high temperatures than the old isolates. Some new isolates, such as CYR34, CYR33, and CYR32, which are predominant, were inferred to be associated with high-temperature adaptation in addition to having more susceptible hosts. Our results provided an insight into changes in Pst isolates at warmer temperatures and increasing incidence of wheat stripe rust in China, especially in eastern sporadic epidemiological areas in recent years. Thus, the new isolates are likely to be a potential risk for causing increasing stripe rust incidence.

Wheat stripe rust is a fungal disease severely affecting wheat production. Breeding resistant cultivars is the most cost-effective and efficient way to control wheat stripe rust. YrZH84 is an all-stage resistance gene with good resistance to stripe rust. In this study, YrZH84 was introgressed from germplasm Lantian36 (LT36) into the two southwestern Chinese elite wheat cultivars Mianmai367 (MM367, carrying Yr10, Yr26), and Chuanmai104 (CM104, carrying Yr26), using marker-assisted selection. F1 seeds of the two cross-combinations were planted and self-crossed to develop the advanced generations in the field. A total of 397 introgression lines (ILs) were obtained in F6 and genotyped using molecular markers Xcfa2040, Xbarc32 (linked to YrZH84), Yr10 (linked to Yr10), We173, and Xbarc181 (linked to Yr26). The 397 ILs were also evaluated for resistance to stripe rust and agronomic traits, including plant height, number of tillers, spike length, thousand-grain weight, and spikelet number. Finally, 61 lines with appreciative agronomic traits and disease resistance were selected. Among these lines, 31 lines had stripe rust resistance gene YrZH84. These selected lines are expected to become new wheat varieties for their high resistance to stripe rust and excellent agronomic traits that will make important contributions to the control of stripe rust and wheat production.

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: Stripe rust has posed a serious threat to the wheat yield in recent years. It is crucial to breed the wheat varieties resistant to stripe rust. However, the identification of resistance is single and inefficient in traditional breeding. In this study, an efficient identification was proposed to determine the different resistance grades to the stripe rust using the time series of vegetation index response to wheat canopy. An unmanned aerial vehicle (UAV) was utilized to collect multi-temporal spectral images of the canopy in the naturally occurring breeding populations of colony wheat (600 samples in total, 516 genotypes). Six sensitive features were screened for the severity of stripe rust disease using Random Forest and ReliefF algorithms: normalized pigment chlorophyll index (NPCI), woebbecke index (WI), chlorophyll index rededge (CIrededge), (green atmospherically resistant index GARI), normalized difference vi (NDVI), and chlorophyll index green (CIgreen). These indices were verified as sensitive features. The severity of stripe rust disease incidence was dynamically characterized using the time series of these indices in the test population. The support vector machine (SVM) was used to establish a classification model for the severity grade of stripe rust disease, according to the sensitive features. There was a very small difference in the performance of the test set and the unscreened original features, indicating the effectiveness of the screened sensitive features. The time series of six sensitive traits was observed in the samples of different resistance grades. It was found that there were no significant differences in the CIgreen and CIrededge among the samples with the different resistance grades. This indicated that the saamples were not applicable to categorize the resistance grades to stripe rust. The differences exhibited by GARI, NDVI, NPCI and WI were used to classify the resistance grades to stripe rust. General machine learning cannot capture the smaller differences of feature variation in the samples with the different resistance grades. Therefore, an improved mode was proposed to extract the features from two-dimensional images that transformed vegetation index time series, in order to realize the classification of stripe rust resistance grade. Four time-series vegetation indices (NPCI, GARI, NDVI, and WI) were better distinguished the different disease resistance grades among the sensitive features, and then used to generate the Gramian Angular Summation Field (GASF) images by the Gramian Angular Field. Data augmentation was performed on the dataset to equalize the number of samples in each resistance grade. Each dataset had a total of 1 040 samples, and was then divided into four grades of stripe rust resistance, where each grade contained 260 sample images, while each dataset was divided into training, validation, and testing sets in the ratio of 6:2:2. DenseNet121 model was separately trained using each dataset, in order to classify the various stripe rust resistance. A better performance was achieved in the classification models with the GASF_NPCI and GASF_WI as the input features, compared with the GASF_GARI and GASF_NDVI. The model with the GASF_NPCI as a feature was slightly less effective in distinguishing the samples with the resistance grades R and MR, where the precision and recall were relatively low. There was no difference in the models with the GASF_WI for the precision and recall of the samples that predicted each stripe rust resistance grade. In the F1 scores of the test set, the different vegetation indices on the resistance grades of stripe rust in colony wheat were ranked in the order of NPCI, WI, GARI, NDVI. The classification model with the GASF_NPCI was the most effective in the test set, with an F1 score of up to 0.833. There was a better distinction of differences in the stripe rust resistance grades among different varieties (lines) of population wheat. The grades of wheat stripe rust resistance were fully identified using time series of spectral vegetation index. Meanwhile, the finding can also provide a strong reference for the disease resistance breeding of crops. [ABSTRACT FROM AUTHOR]

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

The virulence analysis of Puccinia stiiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is essential for predicting and managing the disease epidemic in Southwest China, where the wheat cultivation has significantly reduced in the past few decades due to the impact of this disease. From 2020 to 2021, 196 Pst isolates were collected from Guizhou, Yunnan, and Sichuan. The virulence and race assessments were conducted using Chinese differential genotypes. Additionally, the resistance expression of 102 wheat lines was evaluated in 2021 in two disease nurseries located in Ningnan and Jiangyou. All the 45 Pst isolates from Guizhou and Yunnan belonged to pathogroup Hybrid 46, with 36 identified as race CYR32. Among the 69 isolates from the Liangshan Prefecture, 67 belonged to the Hybrid 46 group, while the remaining two were identified as race CYR34 in the G-22 group. Furthermore, all 79 isolates from the western Sichuan Basin belonged to the G-22 group, with 54 identified as race CYR34. The diversity indices of the Pst populations from Guizhou, Sichuan, and Yunnan exhibited a sequential decline. Virulence variation among the Pst populations from Yunnan, Guizhou, and the Ganzi-Liangshan region was minimal; however, significant virulence differences were observed when these populations were compared to those from the western Sichuan Basin. Results from disease nurseries indicated that Pst virulence was notably stronger in Ningnan compared to that in Jiangyou. The Sichuan Basin exhibits a notable diversity in Pst virulence, coupled with a more frequent genetic exchange occurring between the Liangshan Prefecture and the Yunnan-Guizhou Plateau. This information is essential for developing effective management strategies to mitigate the impact of wheat stripe rust in this region.

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.

Stripe rust is a serious wheat disease occurring worldwide. At present, the most effective way to control it is to grow resistant cultivars. In this study, a population of 221 recombinant inbred lines (RILs) derived via single-seed descent from a hybrid of a susceptible wheat line, SY95-71, and a resistant line, XK502, was tested in three crop seasons from 2022 to 2024 in five environments. A genetic linkage map was constructed using 12,577 single-nucleotide polymorphisms (SNPs). Based on the phenotypic data of infection severity and the linkage map, five quantitative trait loci (QTL) for adult plant resistance (APR) were detected using the inclusive composite interval mapping (ICIM) method. These five loci are QYrxk502.swust-1BL, QYrxk502.swust-2BL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, explaining 5.67–19.64%, 9.63–36.74%, 9.58–11.30%, 9.76–23.98%, and 8.02–12.41% of the phenotypic variation, respectively. All these QTL originated from the resistant parent XK502. By comparison with the locations of known stripe rust resistance genes, three of the detected QTL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, may harbor new, unidentified genes. From among the tested RILs, 16 lines were selected with good field stripe rust resistance and acceptable agronomic traits for inclusion in breeding programs.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), stands out as one of the most devastating epidemics impacting wheat production worldwide. Resistant wheat varieties had swiftly been overcome due to the emergence of new virulent Pst strains. Effectors secreted by Pst interfere with plant immunity, and verification of their biological function is extremely important for controlling wheat stripe rust. In this study, we identified an effector, Pst-18220, from Puccinia striiformis f. sp. tritici (Pst), which was induced during the early infection stage of Pst. Silencing the expression of Pst-18220 through virus-mediated host-induced gene silencing (HIGS) resulted in a decreased number of rust pustules. In Nicotiana benthamiana, it significantly suppressed cell death induced by Pseudomonas syringae pv. tomato (Pto) DC3000. In Arabidopsis, plants with stable overexpression of Pst-18220 showed increased susceptibility to Pto DC3000, accompanied by a decrease in the expression level of pattern-triggered immunity (PTI)/effector-triggered immunity (ETI)-related genes, namely, AtPCRK1, AtPCRK2, and AtBIK1. These results emphasize the significant role of the Pst candidate effector, Pst-18220, in rust pathogenicity and the suppression of plant defense mechanisms. This broadens our understanding of effectors without any known motif.

Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease that seriously threatens the production of crops worldwide. Triadimefon is the widely-used fungicide for controlling the disease in China; however, as the fungicide targets a single site (position 401 in the 134th codon of the Cyp51 gene), the extensive application imposes a strong selection pressure on the pathogens, which may potentially lose the effect over time. In this study, 176 Pst field isolates sampled from different regions of Xinjiang were determined for their sensitivity to triadimefon because it is the few frequent Pst outbreak and representative area in China. The results showed that the Pst isolates collected from Yili, Xinjiang, exhibited a strong resistance to triadimefon with an average EC50 of 0.263 µg/mL, despite the rest of the isolates maintaining high sensitivity to triadimefon. The triadimefon-resistant and triadimefon-sensitive isolates did not display significant differences in sporulation, but the triadimefon-resistant isolates exhibited weaker adaptive traits in their latent period and urediniospore germination rate than the triadimefon-sensitive isolates. No cross-resistance was found for the other two fungicides, flubeneteram or pyraclostrobin; however, cross-resistance for the demethylation inhibitor (DMI) fungicides, tebuconazole and hexaconazole, was found. Genome sequencing revealed that the Tyrosine (Y) at 134 residue was mutated to Phenylalanine (F) in the Xinjiang isolates. Our study revealed that a natural mutation in Pst led to the efficacy loss of triadimefon to control the disease.

Stripe rust, a fungal disease caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive diseases affecting wheat production areas worldwide. In recent years in China, wheat stripe rust has caused huge yield losses throughout the vast Huang-Huai-Hai region, including the eastern coast regions, especially Shandong province. The aim of the present study was to explore the population structure and potential inoculum sources of the pathogen in this region. A total of 234 Pst isolates in 2021 were collected and isolated from seven provinces and identified for virulence phenotypes using 19 Chinese differentials and for genotypes using 17 single-nucleotide polymorphism-based Kompetitive allele-specific PCR markers. The virulence phenotype tests identified predominant races CYR34 (18.0%) and CYR32 (16.0%) in Shandong, which were similar to the results in Henan province, also with the predominant races CYR34 (21.9%) and CYR32 (18.8%). Based on the virulence data of phenolyping, the Pst populations in Shandong, Hubei, and Henan were similar. The genotypic analysis revealed remarkable gene flows among the Shandong, Hubei, Henan, Yunnan, and Guizhou populations, showing a migration of Pst from the southwestern oversummering regions to Shandong through the winter spore production regions. Genetic structure analysis also indicated an additional migration route from the northwestern oversummering regions through winter spore production regions to Shandong. The results are useful for understanding stripe rust epidemiology in the eastern coast region and improving control of the disease throughout the country. [ABSTRACT FROM AUTHOR]

Zhou 8425B is a widely used dwarf large panicle and disease-resistant and stress-resistant Wheat Founder Parent in the wheat production areas of the Yellow-Huaihe-Haihe Rivers regions. The analysis of stripe rust resistance of derived varieties and the genetic transmission information of stripe rust resistance gene carried by Zhou 8425B is of great value for wheat new variety breeding. In this study, we used a highly toxic race of stripe rust, Tiaozhong 34 (CYR34), to identify the resistance of 222 collected Zhou 8425B derived varieties to stripe rust at seedling stage. The hybrid strain mainly CYR34 was used to identify the resistance to stripe rust at adult stage of the derived varieties. Subsequently, molecular markers closely linked to the stripe rust resistance genes (YrZH84, YrZH84.2, Yr30, YrZH22, and Yr9 carried by Zhou 8425B) were used for genotype detection of the derived varieties. The results showed that Zhou 8425B was highly resistant to stripe rust stripe rust at seedling stage and adult stage to the current virulent dominant race CYR34. Among the 222 derivative varieties of Zhou 8425B, 14 of them, including Changmai 9, Jiyanmai 10, Bainong 4199, Saidemai 7, and Zhengmai 103, etc., showed stable disease resistance in two years, accounting for 6.3%; 52 derivative varieties, include Zhoumai 11, Zhoumai 22, Zhoumai 26, Zhoumai 36, Lantian 36, Cunmai 16 and Zhengpinmai 8, etc., had stable disease resistance during the whole growth period, accounting for 23.4%. Many derivative varieties of Zhou 8425B were mainly derived from six offspring, including Zhoumai 11, Zhoumai 12, Zhoumai 13, Zhoumai 15, Zhoumai 16, and Zhoumai 17. In the first generation, Zhoumai 16 and Zhoumai 13 directly derived more varieties because of their good agronomic characters, while Zhoumai 15 and Zhoumai 17 derived fewer varieties. Zhoumai 12 and Zhoumai 13 bred the second generation, Zhoumai 22, and then derived 45 sub-generations, and Zhoumai 11 bred Aikang 58, and then derived 54 sub-generations. The excellent stripe rust resistance gene of Zhou 8425B was continuously separated and polymerized in the process of genetic breeding. The frequencies of YrZH84, YrZH84.2, YrZH22, Yr30, and Yr9 in the derived offspring were 34.7%, 14.9%, 41.9%, 66.2%, and 67.1%, respectively. Among the derivative varieties carrying only one disease resistance gene, the average severity of YrZH84 was the lowest (14.4%). Among the derived varieties that aggregate two disease resistance genes, the average severity of carrying YrZH84+YrZH22 was the lowest (20.0%); among the derived varieties that aggregate three disease resistance genes, the average severity of carrying YrZH84+YrZH22+Yr9 was the lowest (17.2%). Among the derived varieties that aggregate four disease resistance genes, the average severity of carrying YrZH84+YrZH22+Yr30+Yr9 was 16.9%, and the average severity of carrying YrZH84.2+YrZH22+Yr30+Yr9 was 38.4%. At seedling stage, derived varieties that carried the YrZH84 resistance gene or a combination of genes containing YrZH84 during the entire growth period had better disease resistance. These results provide the information of stripe rust gene for the continuous improvement and utilization of the Founder Parent Zhou 8425B in China, identify the new derived germplasm with high resistance and high yield to the highly virulent physiological race CYR34, which providing a reference for the genetic breeding of wheat resistance to stripe rust in China. [ABSTRACT FROM AUTHOR]

Wheat cultivar Xiaoyan 6 (XY6) has high‐temperature seedling‐plant (HTSP) resistance to Puccinia striiformis f. sp. tritici (Pst). However, the molecular mechanism of Pst effectors involved in HTSP resistance remains unclear. In this study, we determined the interaction between two Pst effectors, PstCEP1 and PSTG_11208, through yeast two‐hybrid (Y2H), bimolecular fluorescence complementation (BiFC), and pull‐down assays. Transient overexpression of PSTG_11208 enhanced HTSP resistance in different temperature treatments. The interaction between PstCEP1 and PSTG_11208 inhibited the resistance enhancement by PSTG_11208. Furthermore, the wheat apoplastic thaumatin‐like protein 1 (TaTLP1) appeared to recognize Pst invasion by interacting with PSTG_11208 and initiate the downstream defence response by the pathogenesis‐related protein TaPR1. Silencing of TaTLP1 and TaPR1 separately or simultaneously reduced HTSP resistance to Pst in XY6. Moreover, we found that PstCEP1 targeted wheat ferredoxin 1 (TaFd1), a homologous protein of rice OsFd1. Silencing of TaFd1 affected the stability of photosynthesis in wheat plants, resulting in chlorosis on the leaves and reducing HTSP resistance. Our findings revealed the synergistic mechanism of effector proteins in the process of pathogen infection. [ABSTRACT FROM AUTHOR]

Wheat yellow (stripe) rust (Puccinia striiformis f. sp. tritici) is a dominant type of winter wheat disease. Developing new, highly productive varieties with increased immunological indicators helps to minimize the threat of rust spread. The progressive study searched the sources of resistance to the Pst populations and determined the effectiveness of Yr genes in Southeast Kazakhstan. Immunological studies ensued during 2018-2022 at the Kazakh Research Institute of Agriculture and Plant growing, Almaty, Kazakhstan. Wheat's 23 isogenic lines and 193 winter wheat genotypes attained evaluation for their reactions against an artificially infectious background of infection mixed with Pst pathotypes. Determining the intensity of virulence, the effectiveness of Yr genes, and the resistance of genotypes to the Pst population transpired in the said region. During the vegetation period, based on weather conditions, the accumulated flow of the source, and the period of infection, wheat genotypes responded differently to the rust disease manifestation. The wheat genotypes found resistant to P. striiformis and promising for selection with immunity reached nomination. Their practical use centered on increasing the immunological potential of the new winter wheat cultivars for creation and further reducing the large-scale use of fungicides and the negative environmental consequences. [ABSTRACT FROM AUTHOR]

Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. Additionally, the irregular edges of wheat stripe rust lesions make it challenging to draw samples. In this study, we propose a method for wheat stripe rust severity identification that combines SLIC superpixel segmentation and a random forest algorithm. This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. Then, a random forest model is used to classify the segmented subregion images, achieving fine-grained extraction of wheat stripe rust lesions. By merging the extracted subregion images and using pixel statistics, the percentage of lesion area is calculated, ultimately enabling the identification of the severity of wheat stripe rust. The results show that our method outperforms unsupervised classification algorithms such as watershed segmentation and K-Means clustering in terms of lesion extraction when using the segmented subregion dataset of wheat stripe rust. Compared to the K-Means segmentation method, the mean squared error is reduced by 1.2815, and compared to the watershed segmentation method, it is reduced by 2.0421. When compared to human visual inspection as the ground truth, the perceptual loss for lesion area extraction is 0.064. This method provides a new approach for the intelligent extraction of wheat stripe rust lesion areas and fading green areas, offering important theoretical reference for the precise prevention and control of wheat stripe rust. [ABSTRACT FROM AUTHOR]

The fungus Puccinia striiformis f. sp. tritici (Pst) is an important threat to wheat production because it can cause wheat stripe rust. The present study aimed to identify new stripe rust resistance genes and to provide a theoretical and practical basis for breeding wheat varieties with broad spectrum, stable, and durable resistance. Wheat leaves inoculated with wheat stripe rust fungus Chinese yellow rust 34 were collected at different time points for transcriptomic analysis based on the wheat stripe rust susceptible varieties AVOCET S (AVS) and AVSYr15NIL [near-isogenic line (NIL) derived from AVS]. The results showed that the number of upregulated genes in the two varieties was 294, 364, 398, and 604, and the number of downregulated genes was 520, 178, 570, and 345 on the 1st, 3rd, 5th, and 7th days post inoculation, respectively. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analyses found enrichment of differentially expressed genes in the peroxisome proliferators-activated receptor signaling pathways, plant-pathogen interaction, and styrene acrylic acid biosynthesis that encoded protein kinases, signal transduction, transcription factors, and functional protein components. Differentially expressed genes were randomly selected for quantitative reverse transcription PCR analysis, and the change trend was the same as in the transcriptome data. The results of this study suggest that genes in AVSYr15NIL related to the stripe rust response could be valuable for understanding the mechanisms involved in stripe rust resistance.

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10−7 cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10−1 cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust.

In the race to overcome the severe effects of climate change on crop production, yield and disease resistance are often prioritised while neglecting the nutritional quality. Wheat stripe rust constrains the photosynthetic functioning of plant leaves, which affects the supply and movement of assimilation products. Bread wheat quality is influenced by genetic and environmental effects and their interactions. The end-use quality of wheat is largely determined by specific gluten protein composition and molecular weight distribution. In this study, the effect of the presence and absence of stripe rust on gluten protein fractions in Avocet near-isogenic lines with resistance genes Yr9, Yr10, and Yr15 was determined, compared to the stripe rust susceptible Avocet S. The results highlights a relationship between the presence of the stripe rust disease, disease resistance genes and protein quality, therefore, breeding programs should consider this relationship when aiming to enhance resistance breeding as this can affect the end-use quality of wheat. Although effects of the resistance genes were not consistent, significant variations in the different protein fractions were noted.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a foliar disease that affects both winter and spring wheat crops in Xinjiang, China, which is linked to Central Asia. Race identification of Pst from spring wheat in Xinjiang was not done before. In this study, a total of 216 isolates were recovered from stripe rust samples of spring wheat in the region in 2021 and multiplied using the susceptible cultivar Mingxian 169. These isolates were tested on the Chinese set of 19 wheat differential lines for identifying Pst races. A total of 46 races were identified. Races Suwon-11-1, Suwon11-12, and CYR32 had high frequencies in the spring wheat region. The frequencies of virulence factors on differentials “Fulhard” and “Early Premium” were high (>95%), whereas the virulence factor to differential “Triticum spelta var. Album” (Yr5) was not detected, while virulence to other differentials showed variable frequency within different counties. The predominant races in winter wheat in the same season were also detected from spring wheat cultivars, indicating Pst spreading from winter wheat to spring wheat crops. Deploying resistance genes in spring and winter wheat cultivars is critical for control stripe rust. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease that seriously threatens the production of crops worldwide. Triadimefon is the widely-used fungicide for controlling the disease in China; however, as the fungicide targets a single site (position 401 in the 134th codon of the Cyp51 gene), the extensive application imposes a strong selection pressure on the pathogens, which may potentially lose the effect over time. In this study, 176 Pst field isolates sampled from different regions of Xinjiang were determined for their sensitivity to triadimefon because it is the few frequent Pst outbreak and representative area in China. The results showed that the Pst isolates collected from Yili, Xinjiang, exhibited a strong resistance to triadimefon with an average EC50 of 0.263 µg/mL, despite the rest of the isolates maintaining high sensitivity to triadimefon. The triadimefon-resistant and triadimefon-sensitive isolates did not display significant differences in sporulation, but the triadimefon-resistant isolates exhibited weaker adaptive traits in their latent period and urediniospore germination rate than the triadimefon-sensitive isolates. No cross-resistance was found for the other two fungicides, flubeneteram or pyraclostrobin; however, cross-resistance for the demethylation inhibitor (DMI) fungicides, tebuconazole and hexaconazole, was found. Genome sequencing revealed that the Tyrosine (Y) at 134 residue was mutated to Phenylalanine (F) in the Xinjiang isolates. Our study revealed that a natural mutation in Pst led to the efficacy loss of triadimefon to control the disease. [ABSTRACT FROM AUTHOR]

Wheat leaf rust and stripe rust are important diseases worldwide. Breeding resistant cultivars is an effective means to control wheat leaf and stripe rust. Wheat line L224-3 currently has high resistance to wheat leaf and stripe rust at the field. In this study, 166 recombinant inbred lines (RILs) derived from the L224-3 × Zhengzhou 5389 cross were used to map quantitative trait locus (QTL) for leaf and stripe rust resistance. The RILs and two parents were phenotyped for leaf rust severity at Baoding in Hebei province and Zhoukou in Henan province in the 2015/2016 and 2016/2017 cropping seasons, and for stripe rust severity at Baoding in Hebei Province and Mianyang in Sichuan Province in the 2015/2016 and 2016/2017 growth seasons. All the RILs and parents were also genotyped with the 660 K SNP array and simple sequence repeat (SSR) markers to screen for potential polymorphic markers associated with rust resistance. Four QTLs on chromosomes 1A, 2A, 4B and 7B, were detected using inclusive composite interval mapping (IciMapping). QLr.hbau-1A/QYr.hbau-1A, derived from susceptible parent Zhengzhou 5389, was pleiotropic for both leaf rust and stripe rust resistance and maybe a novel QTL. The second QTL, QLr.hbau-2A/QYr.hbau-2A derived from L224-3 for leaf rust and stripe rust resistance is possibly Lr37/Yr17. QLr.hbau-4B/QYr.hbau-4B might be a new locus for leaf rust and stripe rust resistance. The fourth QTL, QYr.hbau-7B is possibly a new QTL. The QTL identified in the present study with their flanking markers might be used for candidate gene mining and marker-assisted selection (MAS) in wheat breeding programs for rust resistance. [ABSTRACT FROM AUTHOR]

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions. [ABSTRACT FROM AUTHOR]

[Objectives] The urediniospores of wheat stripe rust pathogen is an important reason for the outbreak of wheat stripe rust. Aiming at the problems of low detection precision and low segmentation precision in the identification of microscopic image of urediniospores, an automatic detection method for urediniospores of wheat stripe rust pathogen based on spin Unet++ network was proposed. [Methods] According to the characteristics of dense and conglutination of the urediniospores in the microscopic image, a spinning connection structure was proposed by improving the feature extraction network of Unet ++, which optimized the segmentation precision of Unet ++ network and improved the segmentation rate of detection. According to the morphological characteristics of spores, a weighted mapping rectangle calculation formula was proposed to calculate its rectangular thermogram, so as to improve the precision of spore detection. [Results] The algorithm test results showed that the average precision of the improved algorithm reached 99.03%, and the segmentation rate was 86.45%, which met the requirements of accurate detection. Compared with the original CenterNet model, the overlap rate increased by 10.35 percentage points, the precision increased by 0.46 percentage points, and the memory usage reduced by 66.09%. [Conclusions] The model in this paper not only ensured a high precision of detection of urediniospores, but also ensured a high spore segmentation rate, which provided an effective method for the early warning of wheat stripe rust. [ABSTRACT FROM AUTHOR]