Transcriptome analysis in Aegilops tauschii unravels further insights into genetic control of stripe rust resistance

MAIN CONCLUSION

The Aegilops tauschii resistant accession prevented the pathogen colonization by controlling the sugar flow and triggering the hypersensitive reaction. This study suggested that NBS-LRRs probably induce resistance through bHLH by controlling JA- and SA-dependent pathways. Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst) is one of wheat's most destructive fungal diseases that causes a severe yield reduction worldwide. The most effective and economically-friendly strategy to manage this disease is genetic resistance which can be achieved through deploying new and effective resistance genes. Aegilops tauschii, due to its small genome and co-evolution with Pst, can provide detailed information about underlying resistance mechanisms. Hence, we used RNA-sequencing approach to identify the transcriptome variations of two contrasting resistant and susceptible Ae. tauschii accessions in interaction with Pst and differentially expressed genes (DEGs) for resistance to stripe rust. Gene ontology, pathway analysis, and search for functional domains, transcription regulators, resistance genes, and protein-protein interactions were used to interpret the results. The genes encoding NBS-LRR, CC-NBS-kinase, and phenylalanine ammonia-lyase, basic helix-loop-helix (bHLH)-, basic-leucine zipper (bZIP)-, APETALA2 (AP2)-, auxin response factor (ARF)-, GATA-, and LSD-like transcription factors were up-regulated exclusively in the resistant accession. The key genes involved in response to salicylic acid, amino sugar and nucleotide sugar metabolism, and hypersensitive response contributed to plant defense against stripe rust. The activation of jasmonic acid biosynthesis and starch and sucrose metabolism pathways under Pst infection in the susceptible accession explained the colonization of the host. Overall, this study can fill the gaps in the literature on host-pathogen interaction and enrich the Ae. tauschii transcriptome sequence information. It also suggests candidate genes that could guide future breeding programs attempting to develop rust-resistant cultivars.

The classification of wheat yellow rust disease based on a combination of textural and deep features

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.

New wheat variety INIA 440 - K'ANCHAREQ: Selection and agronomic and commercial characterization in Cusco, Peru

The objective of this study was to select and characterize agronomically the advanced bread wheat line H - 1246 which gave origin to the INIA wheat variety 440 - K'ANCHAREQ. The research included yield trials in farmers' fields during 4 production seasons (2012-2016), adaptation and agronomic efficiency trials in two production seasons (2016-2018). In addition, the reaction to Yellow Rust and distinctness, uniformity and stability characteristics of the new wheat variety and commercial controls were evaluated. The plots for each of the trials were conducted under a Completely Randomized Block design with three replications. At the end of the trials, desirable characteristics in the baking industry such as hectoliter weight, protein, ash, gluten and flour moisture were evaluated. The results showed that the new INIA 440 - K'ANCHAREQ variety has ten clear differences in qualitative characteristics, which distinguish it from other varieties and remained constant during the trials. The yield trials between locations showed the adaptation of the INIA 440 - K'ANCHAREQ variety to the different locations due to its high yield and hectoliter weight values. At the locality level, Andenes obtained the highest values in most of the production seasons. Adaptation trials during the second season showed the superiority of the new INIA 440 - K'ANCHAREQ variety for variables such as yield, plant height, ear size and thousand grain weight. The new variety showed no signs of stripe rust during the trials. Industrial quality trials indicated that it has good characteristics for the baking industry.

Chitosan-Salicylic acid and Zinc sulphate nano-formulations defend against yellow rust in wheat by activating pathogenesis-related genes and enzymes

Stripe rust instigated by Puccinia striiformis f. sp. tritici causes major yield loss in wheat. In this study, disease resistance was induced in wheat by pre-activation of pathogenesis related (PR) genes using two different nano-formulations (NFs) i.e. Chitosan- Salicylic acid (SA) NFs (CH-NFs) and Zinc sulphate NFs (Zn-NFs). These NFs were synthesized using green approach and were characterized using various techniques. Both NFs effectively controlled stripe rust in wheat genotypes (WH 711 and WH 1123) by significantly increasing activities of phenylalanine ammonia lyase, tyrosine ammonia lyase and polyphenol oxidase enzymes when compared with disease free-control and diseased plants. Total soluble sugar (TSS) level was highest in CH-NF treated plants. TSS was also relatively higher in diseased plants than disease free-control as well as Zn-NF treated plants. Both CH-NFs and Zn-NFs induced the expression of PR genes. In CH-NF treated plants, the relative expression of PR genes was higher on the 3rd day after spraying (DAS) of NFs as compared to diseased and Zn-NF treated plants in both the genotypes. While in case of Zn-NF treated plants, relative expression of PR genes was higher on 5th DAS as compared to diseased and disease free-control plants. Early rise in expression of PR genes due to NF treatments was responsible for disease resistance in both the wheat genotypes as evidenced by a lower average coefficient of infection. These NFs can be synthesized easily with low cost input, are eco-friendly and can be effectively used against yellow rust as well as other wheat diseases.

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

BACKGROUND

High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial.

RESULTS

The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r_s = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI).

CONCLUSIONS

The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust.

Structural characterization of stripe rust progress in wheat crops sown at different planting dates

An advanced insight into characterizing stripe rust progress curves is required to improve accuracy and efficiency of future research for disease measurement and estimation purposes. The rate of stripe rust increase in wheat crops is highly variable, resulting in variations and uncertainties in evaluating disease progress over time. This variability was described by fitting standard curves to disease severity data collected over a four-season experiment to identify effective disease curve elements in Iranian wheat cultivars planted at different dates. Gaussian curves appeared to be the best fitted models for all four growing seasons. Three Gaussian parameters in combination with the area under the disease progress curve (AUDPC), disease onset time, maximum disease incidence and severity were then considered to describe the rate of disease increase. Based on H-tests of Kruskal-Wallis one-way analysis of variance, cultivar and planting date significantly affected AUDPC, maximum disease incidence and severity. There were significant correlations between continuous disease descriptors. Then, significant associations were determined between AUDPC, disease onset time, Gaussian parameters, maximum disease incidence and severity according to factor analysis. With these novel findings, we should be aware of descriptive value of wheat-stripe-rust progress variables. Such information will assist with stripe rust measurements for wheat breeding programs, yield loss estimation, development of disease control strategy, and epidemiological studies.

Genome-wide association analysis of stripe rust resistance in modern Chinese wheat

BACKGROUND

Stripe rust (yellow rust) is a significant disease for bread wheat (Triticum aestivum L.) worldwide. A genome-wide association study was conducted on 240 Chinese wheat cultivars and elite lines genotyped with the wheat 90 K single nucleotide polymorphism (SNP) arrays to decipher the genetic architecture of stripe rust resistance in Chinese germplasm.

RESULTS

Stripe rust resistance was evaluated at the adult plant stage in Pixian and Xindu in Sichuan province in the 2015-2016 cropping season, and in Wuhan in Hubei province in the 2013-2014, 2016-2017 and 2018-2019 cropping seasons. Twelve stable loci for stripe rust resistance were identified by GWAS using TASSEL and GAPIT software. These loci were distributed on chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 4B (3), 4D, 6D, and 7B and explained 3.6 to 10.3% of the phenotypic variation. Six of the loci corresponded with previously reported genes/QTLs, including Sr2/Yr30/Lr27, while the other six (QYr.hbaas-1BS, QYr.hbaas-2BL, QYr.hbaas-3AL, QYr.hbaas-4BL.3, QYr.hbaas-4DL, and QYr.hbaas-6DS) are probably novel. The results suggest high genetic diversity for stripe rust resistance in this population. The resistance alleles of QYr.hbaas-2AS, QYr.hbaas-3BS, QYr.hbaas-4DL, and QYr.hbaas-7BL were rare in the present panel, indicating their potential use in breeding for stripe rust resistance in China. Eleven penta-primer amplification refractory mutation system (PARMS) markers were developed from SNPs significantly associated with seven mapped QTLs. Twenty-seven genes were predicted for mapped QTLs. Six of them were considered as candidates for their high relative expression levels post-inoculation.

CONCLUSION

The resistant germplasm, mapped QTLs, and PARMS markers developed in this study are resources for enhancing stripe rust resistance in wheat breeding.

Genome-wide association mapping for resistance to leaf, stem, and yellow rusts of common wheat under field conditions of South Kazakhstan

Common or bread wheat (Triticum aestivum L.) is the most important cereal crop in the world, including Kazakhstan, where it is a major agricultural commodity. Fungal pathogens producing leaf, stem, and yellow (stripe) rusts of wheat may cause yield losses of up to 50-60%. One of the most effective methods for preventing these losses is to develop resistant cultivars with high yield potential. This goal can be achieved using complex breeding studies, including the identification of key genetic factors controlling rust disease resistance. In this study, a panel consisting of 215 common wheat cultivars and breeding lines from Kazakhstan, Russia, Europe, USA, Canada, Mexico, and Australia, with a wide range of resistance to leaf rust (LR), stem rust (SR), and yellow rust (YR) diseases, was analyzed under field conditions in Southern Kazakhstan. The collection was genotyped using the 20K Illumina iSelect DNA array, where 11,510 informative single-nucleotide polymorphism markers were selected for further genome-wide association study (GWAS). Evaluation of the phenotypic diversity over 2 years showed a mostly mixed reaction to LR, mixed reaction/moderate susceptibility to SR, and moderate resistance to YR among wheat accessions from Kazakhstan. GWAS revealed 45 marker-trait associations (MTAs), including 23 for LR, 14 for SR, and eight for YR resistances. Three MTAs for LR resistance and one for SR resistance appeared to be novel. The MTAs identified in this work can be used for marker-assisted selection of common wheat in Kazakhstan in breeding new cultivars resistant to LR, SR, and YR diseases. These findings can be helpful for pyramiding genes with favorable alleles in promising cultivars and lines.

Whole-genome sequencing of Puccinia striiformis f. sp. tritici mutant isolates identifies avirulence gene candidates

Background

The stripe rust pathogen, Puccinia striiformis f. sp. tritici (Pst), threats world wheat production. Resistance to Pst is often overcome by pathogen virulence changes, but the mechanisms of variation are not clearly understood. To determine the role of mutation in Pst virulence changes, in previous studies 30 mutant isolates were developed from a least virulent isolate using ethyl methanesulfonate (EMS) mutagenesis and phenotyped for virulence changes. The progenitor isolate was sequenced, assembled and annotated for establishing a high-quality reference genome. In the present study, the 30 mutant isolates were sequenced and compared to the wide-type isolate to determine the genomic variation and identify candidates for avirulence (Avr) genes.

Results

The sequence reads of the 30 mutant isolates were mapped to the wild-type reference genome to identify genomic changes. After selecting EMS preferred mutations, 264,630 and 118,913 single nucleotide polymorphism (SNP) sites and 89,078 and 72,513 Indels (Insertion/deletion) were detected among the 30 mutant isolates compared to the primary scaffolds and haplotigs of the wild-type isolate, respectively. Deleterious variants including SNPs and Indels occurred in 1866 genes. Genome wide association analysis identified 754 genes associated with avirulence phenotypes. A total of 62 genes were found significantly associated to 16 avirulence genes after selection through six criteria for putative effectors and degree of association, including 48 genes encoding secreted proteins (SPs) and 14 non-SP genes but with high levels of association (P ≤ 0.001) to avirulence phenotypes. Eight of the SP genes were identified as avirulence-associated effectors with high-confidence as they met five or six criteria used to determine effectors.

Conclusions

Genome sequence comparison of the mutant isolates with the progenitor isolate unraveled a large number of mutation sites along the genome and identified high-confidence effector genes as candidates for avirulence genes in Pst. Since the avirulence gene candidates were identified from associated SNPs and Indels caused by artificial mutagenesis, these avirulence gene candidates are valuable resources for elucidating the mechanisms of the pathogen pathogenicity, and will be studied to determine their functions in the interactions between the wheat host and the Pst pathogen.

Evaluating stripe rust resistance in Indian wheat genotypes and breeding lines using molecular markers

Stripe rust (yellow rust), caused by Puccinia striiformis f. sp. tritici (Pst), is a serious disease of wheat worldwide, including India. Growing resistant cultivars is the most cost-effective and eco-friendly approach to manage the disease. In this study, 70 publically available molecular markers were used to identify the distribution of 35 Yr genes in 68 wheat genotypes. Out of 35 Yr genes, 25 genes amplified the loci associated with Yr genes. Of the 35, 18 were all-stage resistance ASR (All-stage resistance) genes and 7 (Yr16, Yr18, Yr29, Yr30, Yr36, Yr46 &Yr59) were APR (Adult-plant resistance) genes. In the field tests, evaluation for stripe rust was carried out under artificial inoculation of Pst. Fifty-three wheat genotypes were found resistant to yellow rust (ITs 0), accounting for 77.94% of total entries. Coefficients of infection ranged from 0 to 60 among all wheat genotypes. Two genotypes (VL 1099 & VL 3002) were identified with maximum 15 Yr genes followed by 14 genes in VL 3010 and HI8759, respectively. Maximum number of all-stage resistance genes were identified in RKD 292 (11) followed by ten genes in DBW 216, WH 1184 and VL 3002. Maximum number of adult-plant resistance gene was identified in VL 3009 (6), HI 8759 (5) and Lassik (4) respectively. Genes Yr26 (69.2%), Yr2 (69.1%), Yr64 (61.7%), Yr24 (58.9%), Yr7 (52.9%), Yr10 (50%) and Yr 48 (48.5%) showed high frequency among selected wheat genotypes, while Yr9 (2.94%), Yr36 (2.94%), Yr60 (1.47%) and Yr32 (8.8%) were least frequent in wheat genotypes. In future breeding programs, race specific genes and non-race specific genes should be utilised to pyramid with other effective genes to develop improved wheat cultivars with high-level and durable resistance to stripe rust. Proper deployment of Yr genes and utilizing the positive interactions will be helpful for resistance breeding in wheat.

Ethyl-methanesulfonate mutagenesis generated diverse isolates of Puccinia striiformis f. sp. tritici, the wheat stripe rust pathogen

Puccinia striiformis f. sp. tritici (Pst) is an obligate biotrophic fungal pathogen causing stripe rust, one of the most important diseases of wheat worldwide. Mutation is considered as one of the major mechanisms causing virulence changes in the pathogen population, but experimental evidence is limited. To study the effect of mutation on pathogen variation, we developed 33 mutant isolates by treating urediniospores of Pst race PSTv-18, avirulent to all of the 18 Yr single-gene lines used to differentiate Pst races, with ethyl methanesulfonate (EMS). These isolates were characterized as 24 races, including 19 new races, through virulence testing on the set of 18 wheat Yr single-gene differential lines; and as 21 multi-locus genotypes with 19 simple sequence repeat and 48 single-nucleotide polymorphism markers. Most of the mutant isolates had more than one avirulence gene and more than one marker locus changed compared to the wild type isolate, indicating that EMS is able to cause mutations at multiple genome sites. The results showed that mutation can cause substantial changes in both avirulence and other genomic regions. The different frequencies of virulence among the mutant isolates suggested homozygous or heterozygous avirulence loci in the parental isolate, or relative ease of mutation at some avirulence loci. The results are useful for understanding evolutionary mechanisms of the important fungal pathogen.