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Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, are the key to preventing and controlling stripe rust. The current spore detection is time-consuming and requires auxiliary equipment, but many optical detection methods and sensors with high efficiency, sensitivity, and detection ability have been developed and widely used. Thus, the investigation of optical parameters is the prerequisite for optical sensitive detection and is detailed in this study. Firstly, the microscopic images and surface elements of wheat stripe rust urediospores were obtained using a high-resolution electron microscope and an energy spectrum analyzer. The reflectivity and absorptivity spectra in the near-infrared band of active/inactivate urediospores were measured. Further, the optical parameters, such as reflection phase shift, complex refractive index, and dielectric constant, of the urediospores were analyzed based on electromagnetic theory and Kramers–Kronig relation. The results revealed that the urediospores have a strong tolerance to near-infrared light, and the real part of the complex refractive index n was between 1.0 and 1.4. These observations indicate that whether the spores are active or not has little effect on the real part and dielectric constant of the complex refractive index but has a significant impact on the imaginary part and extinction coefficient of the complex refractive index, and the corresponding relationship between the optical properties of urediospores and biological components was established. These findings were verified with HPLC-MS and Near-Infrared Spectroscopy Analysis Technology and lay a theoretical foundation for detecting urediospores of wheat stripe rust by using optical sensors. The study provides a reference for the analysis of optical characteristics of other biological samples.

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici is a destructive disease. Its pathogen frequently adapts to newly invaded regions and overcomes resistance in wheat cultivars. This disease is especially important in China due to its favorable conditions for the stripe rust epidemic and the recombination population structure of pathogens. Xinjiang is a vast epidemic region in China, but very limited research on this disease has been performed in this region. In this study, we identified 25 races from 129 isolates collected from winter wheat fields from five different regions (Nileke, Xinyuan, Gongliu, Huocheng, and Qapqal) of Yili, Xinjiang, using the Chinese set of 19 differential wheat lines. All isolates were virulent on the differentials Fulhad and Early Premium, but no isolates were virulent on Yr5. Among the 25 races, Suwon11-1 was the most prevalent, followed by CYR34. Both races were found in four out of the five locations. It is important to continue monitoring stripe rust and its pathogen races in this region, as it forms a pathway between China and Central Asia. Collaborative research is essential for controlling stripe rust in this region, other regions in China, and neighboring countries.

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

Glucose-6-phosphate dehydrogenase (G6PDH), the rate-limiting enzyme of the pentose phosphate pathway (PPP), plays a pivotal role in plant stress responses. However, the function and mechanism of G6PDHs in crop plants challenged by fungal pathogens remain poorly understood. In this study, a wheat G6DPH gene responding to infection by Puccinia striiformis f. sp. tritici (Pst), designated TaG6PDH2, was cloned and functionally identified. TaG6PDH2 expression was significantly upregulated in wheat leaves inoculated with Pst or treated with abiotic stress factors. Heterologous mutant complementation and enzymatic properties indicate that TaG6PDH2 encodes a G6PDH protein. The transient expression of TaG6PDH2 in Nicotiana benthamiana leaves and wheat protoplasts revealed that TaG6PDH2 is a chloroplast-targeting protein. Silencing TaG6PDH2 via the barley stripe mosaic virus (BSMV)-induced gene silencing (VIGS) system led to compromised wheat resistance to the Pst avirulent pathotype CYR23, which is implicated in weakened H2O2 accumulation and cell death. In addition, TaG6PDH2 was confirmed to interact with the wheat glutaredoxin TaGrxS4. These results demonstrate that TaG6PDH2 endows wheat with increased resistance to stripe rust by regulating reactive oxygen species (ROS) production.

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset.

In silico study of biologically invading organisms provide a means to evaluate the complex and potentially cryptic factors that can influence invasion success in scenarios where empirical studies would be difficult, if not impossible, to conduct. I used a disease event simulation program to evaluate whether the two most frequently used types of plant pathogen dispersal kernels for epidemiological projections would provide complementary or divergent projections of epidemic severity when the hosts in a disease outbreak differed from the hosts in the at-risk population in the degree of susceptibility. Exponential dispersal kernel simulations of wheat stripe rust (Pucciniastriiformis var trittici) predicted a relatively strong and dominant influence of the at-risk population on the end epidemic severity regardless of outbreak disease levels. Simulations using a modified power law dispersal kernel gave projections that varied depending on the amount of disease in the outbreak and some interactions were counter-intuitive and opposite of the exponential dispersal kernel projections. Although relatively straightforward, the disease spread simulations in the present study strongly suggest that a more biologically accurate dispersal kernel generates complexity that would not be revealed by an exponential dispersal gradient and that selecting a less accurate dispersal kernel may obscure important interactions during biological invasions.

Climate change affects crops development, pathogens survival rates and pathogenicity, leading to more severe disease epidemics. There are few reports on early, simple, large-scale quantitative detection technology for wheat diseases against climate change. A new technique for detecting wheat stripe rust (WSR) during the latent period based on hyperspectral technology is proposed. Canopy hyperspectral data of WSR was obtained; meanwhile, duplex PCR was used to measure the content of Puccinia striiformis f.sp. tritici (Pst) in the same canopy section. The content of Pst corresponded to its spectrum as the classification label of the model, which is established by discriminant partial least squares (DPLS) and support vector machine (SVM) algorithm. In the spectral region of 325–1075 nm, the model’s average recognition accuracy was between 75% and 80%. In the sub-band of 325–1075 nm, the average recognition accuracy of the DPLS was 80% within the 325–474 nm. The average recognition accuracy of the SVM was 83% within the 475–624 nm. Correlation analysis showed that the disease index of WSR was positively correlated with soil nitrogen nutrition, indicating that the soil nitrogen nutrition would affect the severity of WSR during the latent period.

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a destructive disease that occurs throughout the major wheat-growing regions of the world. This pathogen is highly variable due to the capacity of virulent races to undergo rapid changes in order to circumvent resistance in wheat cultivars and genotypes and to adapt to different environments. Intensive efforts have been made to study the genetics of wheat resistance to this disease; however, no known avirulence genes have been molecularly identified in Pst so far. To identify molecular markers for avirulence genes, a Pst panel of 157 selected isolates representing 126 races with diverse virulence spectra was genotyped using 209 secreted protein gene-based single nucleotide polymorphism (SP-SNP) markers via association analysis. Nineteen SP-SNP markers were identified for significant associations with 12 avirulence genes: AvYr1, AvYr6, AvYr7, AvYr9, AvYr10, AvYr24, AvYr27, AvYr32, AvYr43, AvYr44, AvYrSP, and AvYr76. Some SP-SNPs were associated with two or more avirulence genes. These results further confirmed that association analysis in combination with SP-SNP markers is a powerful tool for identifying markers for avirulence genes. This study provides genomic resources for further studies on the cloning of avirulence genes, understanding the mechanisms of host–pathogen interactions, and developing functional markers for tagging specific virulence genes and race groups.

Wheat stripe rust poses a serious threat to wheat production. An effective prediction method is important for food security. In this study, we developed a prediction model for wheat stripe rust based on vegetation indices and meteorological features. First, based on time-series Sentinel-2 remote sensing images and meteorological data, wheat phenology (jointing date) was estimated using the harmonic analysis of time-series combined with average cumulative temperature. Then, vegetation indices were extracted based on phenological information. Meteorological features were screened using correlation analysis combined with independent t-test analysis. Finally, a random forest (RF) was used to construct a prediction model for wheat stripe rust. The results showed that the RF model using the input combination (phenological information-based vegetation indices and meteorological features) produced a higher prediction accuracy and a kappa coefficient of 88.7% and 0.772, respectively. The prediction model using phenological information-based vegetation indices outperformed the prediction model using single-date image-based vegetation indices, and the overall accuracy improved from 62.9% to 78.4%. These results indicated that the method combining phenological information-based vegetation indices and meteorological features can be used for wheat stripe rust prediction. The results of the prediction model can provide guidance and suggestions for disease prevention in the study area.

In this study, we successfully introgressed and validated Yr59 into four elite wheat cultivars, Jimai 22, Chuanmai 42, Zhengmai 9023 and Xinmai 26 through marker-assisted backcross selection. Used as female parents, these four cultivars were crossed with wheat line PI 660061 (Yr59). After two backcrosses and marker-assisted selection, the progenies were selfed and advanced to the BC2F4 generation. A total of 123 BC2F4 lines were selected based on agronomic traits and stripe rust resistance, and their BC2F5 and BC2F6 progenies were further evaluated for stripe rust resistance and agronomic traits. Seven markers linked with relevant genes, including Xbarc32, Xwgp5175, Xwmc557 and Xcfa2040 linked with Yr59; Xwmc658 with YrJ22; WE173 and Xbarc181 with Yr26, were used to genotype the breeding lines. A total of 109 introgression lines with positive markers for Yr59 were identified for further stripe rust and agronomic trait evaluation. Finally, 16 lines had higher levels resistance to stripe rust, and similar or superior agronomic traits compared to their parents were obtained. These lines can be released as new cultivars for various regions after regional tests and also can be used as resistance stocks for regional breeding programs to develop new cultivars with adequate and durable resistance to stripe rust.

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.

Puccinia striiformis f. sp. tritici Eriks., the cause of wheat yellow or stripe rust on wheat, undergoes sexual reproduction on barberry, but it is unclear if barberry plays any role in stripe rust epidemics under natural conditions. P. striiformis f. sp. tritici was isolated from its alternate host barberry (Berberis spp.) and primary host wheat in the vicinity of barberry by inoculation of aeciospores and urediniospores on Mingxian 169 cultivar in Qinghai province of China in 2018. The P. striiformis f. sp. tritici isolates from barberry and wheat were characterized to virulence patterns by inoculation on 24 differentials bearing Yr gene under control conditions and analyzed using 12 polymorphic simple sequence repeat (SSR) markers. The occurrence frequency of P. striiformis f. sp. tritici on barberry was 1.87% by inoculation aecia, collected from barberry on Mingxian 169 of wheat. A close virulence relationship was presented between P. striiformis f. sp. tritici isolates from both barberry and wheat based on virulence simple matching coefficient and principal coordinates analysis (PCoA). Additionally, the same genetic ancestry, based on structure analysis by STRUCTURE program and genetic relationship analyses using discriminant analysis of principal components and PCoA, was shared between P. striiformis f. sp. tritici isolates from barberry and those from wheat. Together, all the results indicated that the role of barberry in providing aeciospores as an inoculum source causing wheat stripe rust epidemic in Qinghai in spring is of considerable importance.

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a serious disease on wheat in the United States, especially after 2000. In the present study, 2,247 Pst isolates collected over all stripe rust epidemiological regions in the United States from 2010 to 2017 were genotyped at 14 simple sequence repeat (SSR) loci to investigate the population diversity, dynamics, and differentiation. A total of 1,454 multilocus genotypes (MLGs) were detected. In general, the populations in the west (regions 1–6) had more MLGs and higher diversities than the populations in the east (regions 7–12). The populations of 2010 and 2011 were more different from the other years. Genetic variation was higher among years than among regions, indicating the fast changes of the population. The divergence (Gst) was bigger between the west population and east population than among regions within either the west or east population. Gene flow was stronger among the regional populations in the east than in the west. Clustering analyses revealed 3 major molecular groups (MGs) and 10 sub-MGs by combining the genotypic data of 2010–2017 isolates with those of 1968–2009. MG1 contained both 1968–2009 isolates (23.1%) and 2010–2017 isolates (76.9%). MG2 had 99.4% of isolates from 1968–2009. MG3, which was the most recent and distinct group, had 99.1% of isolates from 2010–2017. Of the 10 sub-MGs, 5 (MG1-3, MG1-5, MG3-2, MG3-3, and MG3-4) were detected only from 2011 to 2017. The SSR genotypes had a moderate, but significant correlation (r = 0.325; p < 0.0001) with the virulence phenotype data. The standard index values of association (rbarD = 0.11) based on either regional or yearly populations suggest clonal reproduction. This study indicated high diversity, fast dynamics, and various levels of differentiation of the Pst population over the years and among epidemiological regions, and the results should be useful for managing wheat stripe rust.

Wheat stripe rust, powdery mildew, and Fusarium head blight (FHB) are the three most important diseases in wheat worldwide. Growing resistant cultivars is the most economic and effective method to control these diseases. To assess the disease resistance of commercial wheat cultivars and regional trial wheat lines in the Huang-Huai-Hai region of China, 146 wheat entries were inoculated with the Chinese prevalent Puccinia striiformis f. sp. tritici (Pst) races CYR32, CYR33, CYR34, and Blumeria graminis f. sp. tritici (Bgt) isolate E09 under controlled greenhouse conditions, respectively, these entries were also tested with the mixed Pst races, Bgt and FHB isolates at adult-plant stage in the field, respectively. The results showed that 108 (73.97%), 83 (56.85%), 99 (67.81%), and 22 (15.07%) entries were resistant to CYR32, CYR33, CYR34, and E09 at the seedling stage, respectively, 102 (69.86%), 24 (16.44%), and 2 (1.37%) entries were resistant to stripe rust, powdery mildew, and Fusarium head blight at the adult-plant stage, respectively. Additionally, the possible resistance gene(s) in these entries were postulated by the closely linked markers of stripe rust resistance genes Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, powdery mildew resistance gene Pm21, and Fusarium head blight resistance gene Fhb1. Combined with disease resistance and molecular markers tests, 62, nine, and three wheat entries were postulated to carry the Yr9, Yr17, Yr26 gene, respectively, and no entries contained Yr5, Yr10, Yr15, Yr18, Pm21, and Fhb1 gene. This study laid a theoretical foundation for rational utilization of these entries and gene in wheat breeding programs and disease control.

Puccinia striiformis f. sp. tritici (Pst) is an important pathogen of wheat (Triticum aestivum L.) stripe rust, and the effector protein secreted by haustoria is a very important component involved in the pathogenic process. Although the candidate effector proteins secreted by Pst haustoria have been predicted to be abundant, few have been functionally validated. Our study confirmed that chitin and flg22 could be used as elicitors of the pathogenic-associated molecular pattern-triggered immune (PTI) reaction in wheat leaves and that TaPr-1-14 could be used as a marker gene to detect the PTI reaction. In addition, the experimental results were consistent in wheat protoplasts. A rapid and efficient method for screening and identifying the effector proteins of Pst was established by using the wheat protoplast transient expression system. Thirty-nine Pst haustorial effector genes were successfully cloned and screened for expression in the protoplast. We identified three haustorial effector proteins, PSEC2, PSEC17, and PSEC45, that may inhibit the response of wheat to PTI. These proteins are localized in the somatic cytoplasm and nucleus of wheat protoplasts and are highly expressed during the infection and parasitism of wheat.

Wheat rusts are the key biological constraint to wheat production in Ethiopia—one of Africa’s largest wheat producing countries. The fungal diseases cause economic losses and threaten livelihoods of smallholder farmers. While it is known that wheat rust epidemics have occurred in Ethiopia, to date no systematic long-term analysis of past outbreaks has been available. We present results from one of the most comprehensive surveillance campaigns of wheat rusts in Africa. More than 13,000 fields have been surveyed during the last 13 years. Using a combination of spatial data-analysis and visualization, statistical tools, and empirical modelling, we identify trends in the distribution of wheat stem rust (Sr), stripe rust (Yr) and leaf rust (Lr). Results show very high infection levels (mean incidence for Yr: 44%; Sr: 34%; Lr: 18%). These recurrent rust outbreaks lead to substantial economic losses, which we estimate to be of the order of 10s of millions of US-D annually. On the widely adopted wheat variety, Digalu, there is a marked increase in disease prevalence following the incursion of new rust races into Ethiopia, which indicates a pronounced boom-and-bust cycle of major gene resistance. Using spatial analyses, we identify hotspots of disease risk for all three rusts, show a linear correlation between altitude and disease prevalence, and find a pronounced north-south trend in stem rust prevalence. Temporal analyses show a sigmoidal increase in disease levels during the wheat season and strong inter-annual variations. While a simple logistic curve performs satisfactorily in predicting stem rust in some years, it cannot account for the complex outbreak patterns in other years and fails to predict the occurrence of stripe and leaf rust. The empirical insights into wheat rust epidemiology in Ethiopia presented here provide a basis for improving future surveillance and to inform the development of mechanistic models to predict disease spread.

More virulent and aggressive races of Puccinia striiformis f. sp. tritici (Pst), the pathogen causing wheat stripe rust, have been spreading around the world since 2000 causing large grain yield losses. A better understanding of the genome and genetic diversity of these new Pst races will be useful to develop new strategies to ameliorate these losses. In this study, we generated an improved genome assembly of a post-2000 virulent race from the Western USA designated as PST-130. We implemented a haplotype phasing strategy using the diploid-aware assembler, Falcon-Unzip and new long-read technology from PacBio to phase the two genomes of this dikaryotic organism. The combination of these new technologies resulted in an improved PST-130 assembly with only 151 contigs (85.4 Mb, N50 of 1.44 Mb), and a complementary assembly (haplotigs) with 458 contigs (65.9 Mb, N50 of 0.235 Mb, PRJNA650506). This new assembly improved gene predictions resulting in 228 more predicted complete genes than in the initial Illumina assembly (29,178 contigs, N50 of 5 kb). The alignment of the non-repetitive primary and haplotig contigs revealed and average of 5.22 SNP/kb, with 39.1% showing 10 SNP/kb. This large divergent regions may represent introgressions of chromosome segments from more divergent Pst races in regions where a complete sexual cycle and recombination are possible. We hypothesize that some of the divergent regions in PST-130 may be related to the European “Warrior” race PST-DK0911 because this genome is more similar to PST-130 (3.18 SNP/kb) than to the older European race PST-104E (3.75 SNP/kb). Complete phasing of additional Pst genomes or sequencing individual nuclei will facilitate the tracing of the haploid genomes introduced by the new Pst races into local populations.

The emergence of new physiological races of Puccinia striiformis f. sp. tritici (Pst) causing wheat stripe rust can lead to the loss of resistance of wheat cultivars to stripe rust, thus resulting in severe losses in wheat yield. In this study, after the germination of urediospores of three Pst strains including the original strain (CYR32, a dominant physiological race of Pst in China) and two virulence-mutant strains (CYR32-5 and CYR32-61) acquired from CYR32 via UV-B radiation, proteomic analysis based on isobaric tags for relative and absolute quantification (iTRAQ) technology was performed on the strains. A total of 2,271 proteins were identified, and 59, 74, and 64 differentially expressed proteins (DEPs) were acquired in CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively. The acquired DEPs were mainly involved in energy metabolism, carbon metabolism, and cellular substance synthesis. Furthermore, quantitative reverse transcription PCR assays were used to determine the relative expression of the 6, 7, and 1 DEPs of CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively, at the transcriptional level. The relative expression levels of one, five, and one gene, respectively, encoding the DEPs, were consistent with the corresponding protein abundance determined by iTRAQ technology. Compared with CYR32, the DEPs associated with energy metabolism and stress-including E3JWK6, F4S0Z3, and A8N2Q4-were up-regulated in the mutant strains. The results indicated that the virulence-mutant strains CYR32-5 and CYR32-61 had more tolerance to stress than the original strain CYR32. The results obtained in this study are of great significance for exploring the virulence variation mechanisms of Pst, monitoring the changes in Pst populations, breeding new disease-resistant wheat cultivars, and managing wheat stripe rust sustainably.

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most serious threats to wheat production worldwide. Changes of Pst virulence may circumvent resistance in wheat varieties, and application of fungicides may cause environmental problems. Parasites of Pst can be used to develop biological agents for environmentally friendly control of this fungal disease. Here, we report a hyperparasitic fungus isolated from Pst and identified it as Simplicillium obclavatum through molecular and morphological characterizations. We demonstrated that inoculation of Pst-infected wheat leaves with S. obclavatum reduced the production and germination rate of Pst urediniospores. Therefore, S. obclavatum has the potential to be developed into a biological control agent for managing wheat stripe rust.

Stripe rust is a destructive disease of wheat worldwide. Growing resistant cultivars is the most effective, easy-to-use, economical, and environmentally friendly strategy for the control of the disease. However, P. striiformis f. sp. tritici can produce new virulent races that may circumvent race-specific resistance. Therefore, understanding the genetic basis of the interactions between wheat genes for resistance and P. striiformis f. sp. tritici genes for avirulence is useful for improving cultivar resistance for more effective control of the disease. This study developed a high-quality map that facilitates genomic and genetic studies of important traits related to pathogen pathogenicity and adaptation to different environments and crop cultivars carrying different resistance genes. The information on avirulence/virulence genes identified in this study can be used for guiding breeding programs to select combinations of genes for developing new cultivars with effective resistance to mitigate this devastating disease., Puccinia striiformis f. sp. tritici, the causal agent of wheat stripe (yellow) rust, is an obligate, biotrophic fungus. It was difficult to study the genetics of the pathogen due to the lack of sexual reproduction. The recent discovery of alternate hosts for P. striiformis f. sp. tritici makes it possible to study inheritance and map genes involved in its interaction with plant hosts. To identify avirulence (Avr) genes in P. striiformis f. sp. tritici, we developed a segregating population by selfing isolate 12-368 on barberry (Berberis vulgaris) plants under controlled conditions. The dikaryotic sexual population segregated for avirulent/virulent phenotypes on nine Yr single-gene lines. The parental and progeny isolates were whole-genome sequenced at >30× coverage using Illumina HiSeq PE150 technology. A total of 2,637 high-quality markers were discovered by mapping the whole-genome sequencing (WGS) reads to the reference genome of strain 93-210 and used to construct a genetic map, consisting of 41 linkage groups, spanning 7,715.0 centimorgans (cM) and covering 68 Mb of the reference genome. The recombination rate was estimated to be 1.81 ± 2.32 cM/10 kb. Quantitative trait locus analysis mapped six Avr gene loci to the genetic map, including an Avr cluster harboring four Avr genes, AvYr7, AvYr43, AvYr44, and AvYrExp2. Aligning the genetic map to the reference genome identified Avr candidates and narrowed them to a small genomic region (

Puccinia striiformis f. sp. tritici, the causal agent of wheat stripe (yellow) rust, is an obligate, biotrophic fungus. It was difficult to study the genetics of the pathogen due to the lack of sexual reproduction. The recent discovery of alternate hosts for P. striiformis f. sp. tritici makes it possible to study inheritance and map genes involved in its interaction with plant hosts. To identify avirulence (Avr) genes in P. striiformis f. sp. tritici, we developed a segregating population by selfing isolate 12-368 on barberry (Berberis vulgaris) plants under controlled conditions. The dikaryotic sexual population segregated for avirulent/virulent phenotypes on nine Yr single-gene lines. The parental and progeny isolates were whole-genome sequenced at >30× coverage using Illumina HiSeq PE150 technology. A total of 2,637 high-quality markers were discovered by mapping the whole-genome sequencing (WGS) reads to the reference genome of strain 93-210 and used to construct a genetic map, consisting of 41 linkage groups, spanning 7,715.0 centimorgans (cM) and covering 68 Mb of the reference genome. The recombination rate was estimated to be 1.81 ± 2.32 cM/10 kb. Quantitative trait locus analysis mapped six Avr gene loci to the genetic map, including an Avr cluster harboring four Avr genes, AvYr7, AvYr43, AvYr44, and AvYrExp2. Aligning the genetic map to the reference genome identified Avr candidates and narrowed them to a small genomic region (

Puccinia striiformis f. sp. tritici, the causal agent of wheat stripe (yellow) rust, is an obligate, biotrophic fungus. It was difficult to study the genetics of the pathogen due to the lack of sexual reproduction. The recent discovery of alternate hosts for P. striiformis f. sp. tritici makes it possible to study inheritance and map genes involved in its interaction with plant hosts. To identify avirulence (Avr) genes in P. striiformis f. sp. tritici, we developed a segregating population by selfing isolate 12-368 on barberry (Berberis vulgaris) plants under controlled conditions. The dikaryotic sexual population segregated for avirulent/virulent phenotypes on nine Yr single-gene lines. The parental and progeny isolates were whole-genome sequenced at >30× coverage using Illumina HiSeq PE150 technology. A total of 2,637 high-quality markers were discovered by mapping the whole-genome sequencing (WGS) reads to the reference genome of strain 93-210 and used to construct a genetic map, consisting of 41 linkage groups, spanning 7,715.0 centimorgans (cM) and covering 68 Mb of the reference genome. The recombination rate was estimated to be 1.81 ± 2.32 cM/10 kb. Quantitative trait locus analysis mapped six Avr gene loci to the genetic map, including an Avr cluster harboring four Avr genes, AvYr7, AvYr43, AvYr44, and AvYrExp2 Aligning the genetic map to the reference genome identified Avr candidates and narrowed them to a small genomic region (

Wheat stripe rust is a severe threat of almost all wheat-growing regions in the world. Being an obligate biotrophic fungus, Puccinia striiformis f. sp. tritici (PST) produces new virulent races that break the resistance of wheat varieties. In this study, 115 progeny isolates were generated through sexual reproduction on susceptible Himalayan Berberis pseudumbellata using a dominant Pakistani race (574232) of PST. The parental isolate and progeny isolates were characterized using 24 wheat Yr single-gene lines and ten simple sequence repeat (SSR) markers. From the one-hundred-and-fifteen progeny isolates, 25 virulence phenotypes (VPs) and 60 multilocus genotypes were identified. The parental and all progeny isolates were avirulent to Yr5, Yr10, Yr15, Yr24, Yr32, Yr32, YrSp, YrTr1, YrExp2, Yr26, and YrTye and virulent to Yr1, Yr2, Yr6, Yr7, Yr8, Yr9, Yr17, Yr25, Yr27, Yr28, YrA, Yr44, and Yr3. Based on the avirulence/virulence phenotypes, we found that VPs virulent to Yr1, Yr2, Yr9, Yr17, Yr47, and YrA were controlled by one dominant gene, those to YrSp, YrTr1, and Yr10 by two dominant genes, and those to YrExp2 by two complementary dominant genes. The results are useful in breeding stripe rust-resistant wheat varieties and understanding virulence diversity.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most serious threats to wheat production worldwide. Changes of Pst virulence may circumvent resistance in wheat varieties, and application of fungicides may cause environmental problems. Parasites of Pst can be used to develop biological agents for environmentally friendly control of this fungal disease. Here, we report a hyperparasitic fungus isolated from Pst and identified it as Simplicillium obclavatum through molecular and morphological characterizations. We demonstrated that inoculation of Pst-infected wheat leaves with S. obclavatum reduced the production and germination rate of Pst urediniospores. Therefore, S. obclavatum has the potential to be developed into a biological control agent for managing wheat stripe rust.

Stripe rust caused by Puccinia striiformis f. sp. tritici is an important wheat disease worldwide that is greatly influenced by environmental conditions. Ultraviolet B (UV-B) radiation is one important environmental factor affecting the occurrence and epidemiology of wheat stripe rust. Investigating UV-B radiation effects on the epidemiology of stripe rust may be conducive to monitoring and predicting this disease. In this study, wheat seedlings were exposed to UV-B radiation during different periods under laboratory conditions and radiation effects on epidemiological components of wheat stripe rust were investigated. Results showed that incubation period was shortened, and the infection efficiency, sporulation quantity and disease index increased when UV-B radiation was performed only pre-inoculation. When the UV-B radiation was performed only post-inoculation or both pre- and post-inoculation, the incubation period was prolonged, and the infection efficiency, sporulation quantity and disease index were reduced. When healthy wheat seedlings were inoculated using urediospores collected from wheat leaves irradiated by UV-B only post-inoculation or both pre- and post-inoculation, infection efficiency, sporulation quantity and disease index were also reduced. However, in the latter, the disease incubation period did not differ under varying UV-B radiation intensities compared to that when wheat leaves were not treated with UV-B radiation. Overall, the effects of direct exposure of wheat plants to UV-B radiation with different intensities in different periods on epidemiological components of wheat stripe rust were systematically explored, and the results suggest that the effects of UV-B radiation increased gradually with the increase of UV-B radiation intensity. This information provides a basis for monitoring and predicting this disease as well as for conducting further studies on pathogen virulence variation.

Wheat yellow rust is a common agricultural disease that affects the crop every year across the world. The disease not only negatively impacts the quality of the yield but the quantity as well, which results in adverse impact on economy and food supply. It is highly desired to develop methods for fast and accurate detection of yellow rust in wheat crop; however, high-resolution images are not always available which hinders the ability of trained models in detection tasks. The approach presented in this study harnesses the power of super-resolution generative adversarial networks (SRGAN) for upsampling the images before using them to train deep learning models for the detection of wheat yellow rust. After preprocessing the data for noise removal, SRGANs are used for upsampling the images to increase their resolution which helps convolutional neural network (CNN) in learning high-quality features during training. This study empirically shows that SRGANs can be used effectively to improve the quality of images and produce significantly better results when compared with models trained using low-resolution images. This is evident from the results obtained on upsampled images, i.e., 83% of overall test accuracy, which are substantially better than the overall test accuracy achieved for low-resolution images, i.e., 75%. The proposed approach can be used in other real-world scenarios where images are of low resolution due to the unavailability of high-resolution camera in edge devices.

RPM1 is a CC-NBS-LRR protein that was first shown to be required for resistance to Pseudomonas syringae pv. maculicola in Arabidopsis thaliana. Our previous study showed that TaRPM1 gene in wheat was upregulated about six times following infection by Puccinia striiformis f. sp. tritici (Pst) under high temperature, compared with normal temperature. To study the function of TaRPM1 in wheat high-temperature seedling-plant (HTSP) resistance to Pst, the full length of TaRPM1 was cloned, with three copies each located on chromosomes 1A, 1B, and 1D. Transient expression of the TaRPM1-GFP fusion protein in Nicotiana benthamiana indicated that TaRPM1 localizes in the cytoplasm and nucleus. Profiling TaRPM1 expression indicated that TaRPM1 transcription was rapidly upregulated upon Pst inoculation under high temperature. In addition, TaRPM1 was induced by exogenous salicylic acid hormone application. Silencing TaRPM1 in wheat cultivar Xiaoyan 6 (XY 6) resulted in reduced HTSP resistance to Pst in terms of reduced number of necrotic cells and increased uredinial length, whereas no obvious phenotypic changes were observed in TaRPM1-silenced leaves under normal temperature. Related defense genes TaPR1 and TaPR2 were downregulated in TaRPM1-silenced plants under high temperature. We conclude that TaRPM1 is involved in HTSP resistance to Pst in XY 6.

Wheat stripe rust is a severe threat of almost all wheat-growing regions in the world. Being an obligate biotrophic fungus, Puccinia striiformis f. sp. tritici (PST) produces new virulent races that break the resistance of wheat varieties. In this study, 115 progeny isolates were generated through sexual reproduction on susceptible Himalayan Berberis pseudumbellata using a dominant Pakistani race (574232) of PST. The parental isolate and progeny isolates were characterized using 24 wheat Yr single-gene lines and ten simple sequence repeat (SSR) markers. From the one-hundred-and-fifteen progeny isolates, 25 virulence phenotypes (VPs) and 60 multilocus genotypes were identified. The parental and all progeny isolates were avirulent to Yr5, Yr10, Yr15, Yr24, Yr32, Yr43, YrSp, YrTr1, YrExp2, Yr26, and YrTye and virulent to Yr1, Yr2, Yr6, Yr7, Yr8, Yr9, Yr17, Yr25, Yr27, Yr28, YrA, Yr44, and Yr3. Based on the avirulence/virulence phenotypes, we found that VPs virulent to Yr1, Yr2, Yr9, Yr17, Yr47, and YrA were controlled by one dominant gene; those to YrSp, YrTr1, and Yr10 by two dominant genes; and those to YrExp2 by two complementary dominant genes. The results are useful in breeding stripe rust-resistant wheat varieties and understanding virulence diversity.

Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings&ndash, BPNN model had the best performance, which modeling accuracy (RC2) and validation accuracy (RP2) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is an airborne fungal disease which always destructs leaf and leads to stagnation of grain filling, decreasing of kernel weight, thin seed and yield loss. Winter wheat Huixianhong is a special germplasm with special characteristics of tolerance or resistance against stripe rust. In order to understand the effect on Huixianhong from stripe rust, we designed two kinds of treatment, inoculation of stripe rust fungi (IH) and artificial immune by bactericide (CK) to study the dynamic of disease process, the grain filling and the thousand-kernel weight (TKW). Our results showed that the incubation period of Hongxinahui was 13.5 days. The prevalence increased from 32.9% at 15 days after jointing to 80.0% at 9 days after booting, and reached to 97.0% at the heading stage. The infection type (IT) was 7 to 9 at the beginning of anthesis. The severity, leaf withered area ratio and disease index at the 15th day after anthesis were 67.17%, 98.17% and 0.6717, respectively. The IH maximum increasing rate of leaf necrosis and chlorosis area was from heading beginning stage to anthesis beginning stage, which increased from 18.66 mm2·d-1 to 21.04 mm2·d-1. The maximum rate of grain filling was 1.25 mg·d-1 at the 18th day after anthesis, which was earlier than that of CK by 3.3 days. The IH thousand-kernel weight Loss (TKWL) was more than that of CK by 6.19%, the stage of heading and amature were 3.0 days and 4.5 days earlier than CK, respectively. The stripe rust infection seriously destructed the photosynthetic function of leaf at the earlier stage of grain filling, i.e. at the beginning of anthesis, which led to the most important biomass loss and the grain filling rate decrease. As far as stripe rust is concerned, Huixianhong is a high susceptible, easily been infected, seriously showing symptoms and most quickly epidemic type but can successfully complete grain filling in high quality. It is a very useful germplasm for creating and selecting special breeding materials against stripe rust.

Phytopathogens have a limited range of host plant species that they can successfully parasitise ie. that they are adapted for. Infection of plants by nonadapted pathogens often results in an active resistance response that is relatively poorly characterised because phenotypic variation in this response often does not exist within a plant species, or is too subtle for genetic dissection. In addition, complex polygenic inheritance often underlies these resistance phenotypes and mutagenesis often does not impact upon this resistance, presumably due to genetic or mechanistic redundancy. Here it is demonstrated that phenotypic differences in the resistance response of Brachypodium distachyon to the nonadapted wheat stripe rust pathogen Puccinia striiformis f. sp. tritici (Pst) are genetically tractable and simply inherited. Two dominant loci were identified on B. distachyon chromosome 4 that each reduce attempted Pst colonisation compared with sib and parent lines without these loci. One locus (Yrr1) is effective against diverse Australian Pst isolates and present in two B. distachyon mapping families as a conserved region that was reduced to 5 candidate genes by fine mapping. A second locus, Yrr2, shows Pst race-specificity and encodes a disease resistance gene family typically associated with host plant resistance. These data indicate that some components of resistance to nonadapted pathogens are genetically tractable in some instances and may mechanistically overlap with host plant resistance to avirulent adapted pathogens., Author summary Plant pathogens are specialists and can colonise only a limited number of plant species (hosts). Pathogen infection of a plant that is not a host of the disease often results in an active plant defense response. This poorly characterised defense response is durable as phytopathogens rarely successfully colonise new hosts. The ability to transfer this resistance to host plants would be highly beneficial in protecting crops from disease. However, this resistance has been difficult to genetically dissect as all members of a plant species are resistant. Here we show that some accessions of the model grass Brachypodium distachyon show some variation in their ability to suppress colonisation by the wheat stripe rust pathogen Puccinia striiformis. Brachypodium is not a host species of wheat stripe rust disease and no accessions are fully susceptible, however, some allow more pathogen growth than others. We have exploited these relatively subtle phenotypic differences to genetically dissect this difference in resistance and identified two Brachypodium loci that contribute increased resistance to the nonadapted wheat stripe rust pathogen.

Multilayered defense responses ensure that plants are hosts to only a few adapted pathogens in the environment. The host range of a plant pathogen depends on its ability to fully overcome plant defense barriers, with failure at any single step sufficient to prevent life cycle completion of the pathogen. Puccinia striiformis, the causal agent of stripe rust (=yellow rust), is an agronomically important obligate biotrophic fungal pathogen of wheat and barley. It is generally unable to complete its life cycle on the non-adapted wild grass species Brachypodium distachyon, but natural variation exists for the degree of hyphal colonization by Puccinia striiformis. Using three B. distachyon mapping populations, we identified genetic loci conferring colonization resistance to wheat-adapted and barley-adapted isolates of P. striiformis. We observed a genetic architecture composed of two major effect QTLs (Yrr1 and Yrr3) restricting the colonization of P. striiformis. Isolate specificity was observed for Yrr1, whereas Yrr3 was effective against all tested P. striiformis isolates. Plant immune receptors of the nucleotide binding, leucine-rich repeat (NB-LRR) encoding gene family are present at the Yrr3 locus, whereas genes of this family were not identified at the Yrr1 locus. While it has been proposed that resistance to adapted and non-adapted pathogens are inherently different, the observation of (1) a simple genetic architecture of colonization resistance, (2) isolate specificity of major and minor effect QTLs, and (3) NB-LRR encoding genes at the Yrr3 locus suggest that factors associated with resistance to adapted pathogens are also critical for non-adapted pathogens., Author summary Plants are constantly exposed to a multitude of potential pathogens but remain immune to most of these due to a multilayered immune system. Pathogens have specialized by adapting to certain host plants and their defense barriers. Most of our understanding of plant-pathogen interactions stems from these highly specialized interactions, because they are characterized by qualitative interactions (resistant or susceptible). It has generally been assumed that the genetic and molecular basis of resistance to non-adapted pathogens is fundamentally different, as either no variation exists in a species (complete immunity) or variation encompasses only early pathogen invasion (colonization), but not full susceptibility. We have studied the interaction between the agronomically important fungal stripe rust pathogen (Puccinia striiformis) of wheat and barley with the wild grass species Brachypodium distachyon. Rust infections consist of two stages: colonization of plant tissues followed by a reproductive phase. We identified natural variation for the degree of P. striiformis colonization in different B. distachyon accessions and dissected the genetic architecture controlling resistance at this infection stage. QTLs conferring resistance possessed several characteristics similar to adapted host systems, indicating that resistance to adapted and non-adapted pathogens are not intrinsically different.

Puccinia striiformis f.sp. tritici (Pst) is the causal agent of stripe (yellow) rust on wheat. It seriously threatens wheat production worldwide. The obligate biotrophic fungus is highly capable of producing new virulent races that can overcome resistance. Studying the inheritance of Pst virulence using the classical genetic approach was not possible until the recent discovery of its sexual stage on barberry plants. In the present study, 127 progeny isolates were obtained by selfing a representative Chinese Yellow Rust (CYR) race, CYR32, on Berberis aggregate. The parental isolate and progeny isolates were characterized by testing them on 25 wheat lines with different Yr genes for resistance and 10 simple sequence repeat (SSR) markers. The 127 progeny isolates were classified into 27 virulence phenotypes (VPs), and 65 multi-locus genotypes (MLGs). All progeny isolates and the parental isolate were avirulent to Yr5, Yr8, Yr10, Yr15, Yr24, Yr26, Yr32, and YrTr1; but virulent to Yr1, Yr2, Yr3, Yr4, Yr25, Yr44, and Yr76. The VPs of the parental isolate to nine Yr genes (Yr6, Yr7, Yr9, Yr17, Yr27, Yr28, Yr43, YrA, and YrExp2) and the avirulence phenotype to YrSP were found to be heterozygous. Based on the segregation of the virulence/avirulence phenotypes, we found that the VPs to Yr7, Yr28, Yr43, and YrExp2 were controlled by a dominant gene; those to Yr6, Yr9, and YrA (Yr73, Yr74) by two dominant genes; those to Yr17 and Yr27 by one dominant and one recessive gene; and the avirulence phenotype to YrSP by two complementary dominant genes. Molecular mapping revealed the linkage of 10 virulence/avirulence genes. Comparison of the inheritance modes of the virulence/avirulence genes in this study with previous studies indicated complex interactions between virulence genes in the pathogen and resistance genes in wheat lines. The results are useful for understanding the plant-pathogen interactions and developing wheat cultivars with effective and durable resistance.

Puccinia striiformis f.sp. tritici (Pst) is the causal agent of stripe (yellow) rust on wheat. It seriously threatens wheat production worldwide. The obligate biotrophic fungus is highly capable of producing new virulent races that can overcome resistance. Studying the inheritance of Pst virulence using the classical genetic approach was not possible until the recent discovery of its sexual stage on barberry plants. In the present study, 127 progeny isolates were obtained by selfing a representative Chinese Yellow Rust (CYR) race, CYR32, on Berberis aggregate. The parental isolate and progeny isolates were characterized by testing them on 25 wheat lines with different Yr genes for resistance and 10 simple sequence repeat (SSR) markers. The 127 progeny isolates were classified into 27 virulence phenotypes (VPs), and 65 multi-locus genotypes (MLGs). All progeny isolates and the parental isolate were avirulent to Yr5, Yr8, Yr10, Yr15, Yr24, Yr26, Yr32, and YrTr1; but virulent to Yr1, Yr2, Yr3, Yr4, Yr25, Yr44, and Yr76. The VPs of the parental isolate to nine Yr genes (Yr6, Yr7, Yr9, Yr17, Yr27, Yr28, Yr43, YrA, and YrExp2) and the avirulence phenotype to YrSP were found to be heterozygous. Based on the segregation of the virulence/avirulence phenotypes, we found that the VPs to Yr7, Yr28, Yr43, and YrExp2 were controlled by a dominant gene; those to Yr6, Yr9, and YrA (Yr73, Yr74) by two dominant genes; those to Yr17 and Yr27 by one dominant and one recessive gene; and the avirulence phenotype to YrSP by two complementary dominant genes. Molecular mapping revealed the linkage of 10 virulence/avirulence genes. Comparison of the inheritance modes of the virulence/avirulence genes in this study with previous studies indicated complex interactions between virulence genes in the pathogen and resistance genes in wheat lines. The results are useful for understanding the plant-pathogen interactions and developing wheat cultivars with effective and durable resistance.

Characterization of newly isolated mycoviruses may contribute to understanding of the evolution and diversity of viruses. Here, a deep sequencing approach was used to analyze the double-stranded RNA (dsRNA) mycoviruses isolated from field-collected P. striiformis samples in China. Database searches showed the presence of at least four totivirus-like sequences, termed Puccinia striiformis virus 1 to 4 (PsV1 to 4). All of these identified sequences contained two overlapping open reading frames (ORFs) which encode a putative coat protein (CP) and an RNA-dependent RNA polymerase (RdRp) showing similar structures to members of the genus Totivirus. Each PsV contained a -1 ribosomal frameshifting region with a slippery site and a pseudoknot structure in the overlapped regions of these ORFs, indicating that the RdRp is translated as a CP-RdRp fusion. Phylogenetic analyses based on RdRp and CP suggested that these novel viruses belong to the genus Totivirus in the family Totiviridae. The presences of these PsVs were further validated by transmission electron microscope (TEM) and RT-PCR. Taken together, our results demonstrate the presence of diverse, novel totiviruses in the P. striiformis field populations.

WRKY transcription factors (TFs) play crucial roles in plant resistance responses to pathogens. Wheat stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst), is a destructive disease of wheat (Triticum aestivum) worldwide. In this study, the two WRKY genes TaWRKY49 and TaWRKY62 were originally identified in association with high-temperature seedling-plant resistance to Pst (HTSP) resistance in wheat cultivar Xiaoyan 6 by RNA-seq. Interestingly, the expression levels of TaWRKY49 and TaWRKY62 were down- and up-regulated, respectively, during HTSP resistance in response to Pst. Silencing of TaWRKY49 enhanced whereas silencing TaWRKY62 reduced HTSP resistance. The enhanced resistance observed on leaves following the silencing of TaWRKY49 was coupled with increased expression of salicylic acid (SA)- and jasmonic acid (JA)-responsive genes TaPR1.1 and TaAOS, as well as reactive oxygen species (ROS)-associated genes TaCAT and TaPOD; whereas the ethylene (ET)-responsive gene TaPIE1 was suppressed. The decreased resistance observed on leaves following TaWRKY62 silencing was associated with increased expression of TaPR1.1 and TaPOD, and suppression of TaAOS and TaPIE1. Furthermore, SA, ET, MeJA (methyl jasmonate), hydrogen peroxide (H2O2) and abscisic acid (ABA) treatments increased TaWRKY62 expression. On the other hand, MeJA did not affect the expression of TaWRKY49, and H2O2 reduced TaWRKY49 expression. In conclusion, TaWRKY49 negatively regulates while TaWRKY62 positively regulates wheat HTSP resistance to Pst by differential regulation of SA-, JA-, ET and ROS-mediated signaling.

Characterization of newly isolated mycoviruses may contribute to understanding of the evolution and diversity of viruses. Here, a deep sequencing approach was used to analyze the double-stranded RNA (dsRNA) mycoviruses isolated from field-collected P. striiformis samples in China. Database searches showed the presence of at least four totivirus-like sequences, termed Puccinia striiformis virus 1 to 4 (PsV1 to 4). All of these identified sequences contained two overlapping open reading frames (ORFs) which encode a putative coat protein (CP) and an RNA-dependent RNA polymerase (RdRp) showing similar structures to members of the genus Totivirus. Each PsV contained a -1 ribosomal frameshifting region with a slippery site and a pseudoknot structure in the overlapped regions of these ORFs, indicating that the RdRp is translated as a CP-RdRp fusion. Phylogenetic analyses based on RdRp and CP suggested that these novel viruses belong to the genus Totivirus in the family Totiviridae. The presences of these PsVs were further validated by transmission electron microscope (TEM) and RT-PCR. Taken together, our results demonstrate the presence of diverse, novel totiviruses in the P. striiformis field populations.

Rice (Oryza sativa L.) is the only widely cultivated gramineous crops that cannot be infected by rust fungi. To decipher the molecular basis of rice nonhost resistance (NHR) to Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat stripe rust, proteomic analysis was performed using the two-dimensional electrophoresis (2-DE) technique. The expressed proteins from rice leaves 24 and 48 h post inoculation with Pst and from mock-inoculated leaves were identified. Quantitative analysis revealed a total of 27 differentially expressed proteins in response to Pst inoculation. Most of these proteins fall into the category “response to stimulus” and are involved in basic resistance processes, such as glycerol-3-phosphate and hydrogen peroxide signaling. A homologue of wheat leaf rust resistance protein Lr10 was also identified, implicating multiple layers of plant defense are implicated in rice NHR to Pst. These results demonstrate an intrinsic relationship between host and nonhost resistance. Changes in abundance of these proteins, together with their putative functions reveal a comprehensive profile of rice NHR to Pst and provide new insights into plant immunity.

The obligate bitrophic fungus Puccinia striiformis f. sp. tritici (Pst) causes stripe (yellow) rust on wheat worldwide. Here, we report a novel fungal strain able to hyperparasitize Pst. The strain was isolated from grey-colored rust pustules, and was identified as Alternaria alternata (Fr.: Fr.) keissler based on a combination of morphological characteristics and multi-locus (ITS, GAPDH, and RPB2) phylogeny. Upon artificial inoculation, the hyperparasite reduced the production and viability of urediniospores, and produced a typical grey-colored rust pustule symptom. Scanning electron microscopy demonstrated that the strain could efficiently penetrate and colonize Pst urediniospores. This study first demonstrates that A. alternata could parasitize Pst and indicates its potential application in the biological control of wheat stripe rust disease.

To combat the ever-increasing threat of wheat yellow rust worldwide, understanding of the pathogen (Puccinia striiformis) population biology is indispensable. Molecular markers, particularly microsatellites, have been reported to be important tools for deciphering pathogen population structure, invasion sources, and migration history. The utility of these DNA-based markers and sequencing has been increased by the direct DNA extraction from infected leaves with subsequent multiplex-based SSR genotyping. In this chapter we describe the protocol for direct DNA extraction and its genotyping with microsatellite markers in multiplex reactions. We describe the procedure for allele scoring, and various troubles faced during microsatellite scoring and potential solutions for them.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat in China. Rapid change to virulence following release of resistant cultivars necessitates ongoing discovery and exploitation of new resistance resources. Considerable effort has been directed at non-host resistance (NHR) which is believed to be durable. In the present study we identified rice mutant crr1 (compromised resistance to rust 1) that exhibited compromised NHR to Pst. Compared with wild type rice variety Nipponbare, crr1 mutant displayed a threefold increase in penetration rate by Pst, and enhanced hyphal growth. The pathogen also developed haustoria in crr1 mesophyll cells, but failed to sporulate. The response to the adapted rice pathogen Magnaporthe oryzae was unchanged in crr1 relative to the wild type. Several defense-related genes involved in the SA- and JA-mediated defense pathways response and in phytoalexin synthesis (such as OsPR1a, OsLOX1, and OsCPS4) were more rapidly and strongly induced in infected crr1 leaves than in the wild type, suggesting that other layers of defense are still in effect. Genetic analysis and mapping located the mutant loci at a region between markers ID14 and RM25792, which cover about 290 kb genome sequence on chromosome 10. Further fine mapping and cloning of the locus should provide further insights into NHR to rust fungi in rice, and may reveal new strategies for improving rust resistance in wheat.

The obligate bitrophic fungus Puccinia striiformis f. sp. tritici (Pst) causes stripe (yellow) rust on wheat worldwide. Here, we report a novel fungal strain able to hyperparasitize Pst. The strain was isolated from gray-colored rust pustules, and was identified as Alternaria alternata (Fr.: Fr.) keissler based on a combination of morphological characteristics and multi-locus (ITS, GAPDH, and RPB2) phylogeny. Upon artificial inoculation, the hyperparasite reduced the production and viability of urediniospores, and produced a typical gray-colored rust pustule symptom. Scanning electron microscopy demonstrated that the strain could efficiently penetrate and colonize Pst urediniospores. This study first demonstrates that A. alternata could parasitize Pst and indicates its potential application in the biological control of wheat stripe rust disease.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most devastating diseases of wheat in China. Rapid change to virulence following release of resistant cultivars necessitates ongoing discovery and exploitation of new resistance resources. Considerable effort has been directed at non-host resistance (NHR) which is believed to be durable. In the present study we identified rice mutant crr1 (compromised resistance to rust 1) that exhibited compromised NHR to Pst. Compared with wild type rice variety Nipponbare, crr1 mutant displayed a threefold increase in penetration rate by Pst, and enhanced hyphal growth. The pathogen also developed haustoria in crr1 mesophyll cells, but failed to sporulate. The response to the adapted rice pathogen Magnaporthe oryzae was unchanged in crr1 relative to the wild type. Several defense-related genes involved in the SA- and JA-mediated defense pathways response and in phytoalexin synthesis (such as OsPR1a, OsLOX1, and OsCPS4) were more rapidly and strongly induced in infected crr1 leaves than in the wild type, suggesting that other layers of defense are still in effect. Genetic analysis and mapping located the mutant loci at a region between markers ID14 and RM25792, which cover about 290 kb genome sequence on chromosome 10. Further fine mapping and cloning of the locus should provide further insights into NHR to rust fungi in rice, and may reveal new strategies for improving rust resistance in wheat.

Secale cereale L. has been used worldwide as a source of genes for agronomic and resistance improvement. In this study, a stable wheat-rye substitution line and 3 primary 1RS.1BL translocation lines were selected from the progeny of the crossing of the Chinese local rye Aigan variety and wheat cultivar Mianyang11. The substitution and translocation lines were identified by molecular cytogenetic analysis. PCR results, fluorescence in situ hybridization and acid polyacrylamide gel electrophoresis indicated that there were a pair of 1R chromosomes in the substitution line which have been named RS1200-3, and a pair of 1RS.1BL translocation chromosomes in the other 3 translocation lines, which have been named RT1163-4, RT1217-1, and RT1249. When inoculated with stripe rust isolates, these 4 lines expressed high resistance to several Puccinia striiformis f. sp Tritici pathotypes that are virulent on Yr9. Moreover, the different response pattern of resistance among them suggested that the diversity of resistance genes for wheat stripe rust exists in the rye. These 4 lines also showed better agronomic performances than their wheat parent. The GS indices also showed the genetic diversity of the 1RS which derived from same rye variety. The present study indicates that rye cultivars may carry untapped variations that could potentially be used for wheat improvement.

Background Emerging and re-emerging pathogens imperil public health and global food security. Responding to these threats requires improved surveillance and diagnostic systems. Despite their potential, genomic tools have not been readily applied to emerging or re-emerging plant pathogens such as the wheat yellow (stripe) rust pathogen Puccinia striiformis f. sp. tritici (PST). This is due largely to the obligate parasitic nature of PST, as culturing PST isolates for DNA extraction remains slow and tedious. Results To counteract the limitations associated with culturing PST, we developed and applied a field pathogenomics approach by transcriptome sequencing infected wheat leaves collected from the field in 2013. This enabled us to rapidly gain insights into this emerging pathogen population. We found that the PST population across the United Kingdom (UK) underwent a major shift in recent years. Population genetic structure analyses revealed four distinct lineages that correlated to the phenotypic groups determined through traditional pathology-based virulence assays. Furthermore, the genetic diversity between members of a single population cluster for all 2013 PST field samples was much higher than that displayed by historical UK isolates, revealing a more diverse population of PST. Conclusions Our field pathogenomics approach uncovered a dramatic shift in the PST population in the UK, likely due to a recent introduction of a diverse set of exotic PST lineages. The methodology described herein accelerates genetic analysis of pathogen populations and circumvents the difficulties associated with obligate plant pathogens. In principle, this strategy can be widely applied to a variety of plant pathogens. Electronic supplementary material The online version of this article (doi:10.1186/s13059-015-0590-8) contains supplementary material, which is available to authorized users.