Search Results (188)

Puccinia striiformis f. sp. elymi (Pse) is a specialized forma specialis of stripe rust infecting Elymus dahuricus, yet its mitochondrial evolution remains poorly understood. In this study, we assembled the complete mitogenome of Pse using PacBio HiFi sequencing, yielding a circular mitogenome of 72,952 bp. This reveals a striking asymmetric evolutionary pattern with a 28.34% genomic contraction compared to the wheat stripe rust P. striiformis f. sp. tritici (Pst-CYR32). Our analysis demonstrates that this streamlining is strictly driven by a massive and systematic loss of mitochondrial introns. The Pse mitogenome exhibits negative GC-skew (−0.0184) consistent with strand-asymmetric mutational pressure, while maintaining a strictly conserved and syntenic complement of all 14 core protein-coding genes (PCGs), alongside 24 tRNAs and 2 rRNAs. Phylogenomic analysis positions Pse as sister to the Pst clade with strong support (100% bootstrap). A 748-bp SNP cluster within nad4 (14.2% sequence divergence versus 3.1% genome-wide average) provides a candidate molecular marker for lineage differentiation, pending population-level validation. This study establishes a genomic foundation for investigating mitochondrial reductive evolution in host-specialized rust lineages, highlighting the dynamic role of introns in driving organellar genome size variation. Full article

Stripe rust (Puccinia striiformis) and Fusarium head blight (FHB; caused by Fusarium graminearum) are fungal diseases that endanger wheat productivity; however, by pyramiding disease-resistant genes, the long-term resistance of wheat can be strengthened. In this study, a multi-parent pyramiding hybrid population was constructed using marker-assisted selection (MAS). After multiple generations of breeding, 168 F₆ lines were obtained. By combining molecular marker genotyping, field resistance identification, and agronomic trait evaluation, 19 lines with excellent agronomic traits were selected, which not only showed high resistance to stripe rust but also carried Fhb1 genes, some of which have the potential to be developed into new germplasms and offer important genetic resources for the breeding of wheat with long-lasting and broad-spectrum resistance. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is an important disease in wheat production. Breeding disease-resistant breeds is the most effective measure for preventing and controlling this disease. In this study, hybrid combinations were developed using wheat varieties Mianmai367 and Zhoumai22; 40059, 40047, and Zhoumai8425B. Mixed seed harvesting and artificial selection were conducted from the F₁ to F₄ generations, followed by manual screening of superior disease-resistant single plants in the F₅ generation to obtain 271 F₆ families. These F₆ families underwent molecular marker detection, disease resistance identification, and agronomic trait evaluation. The molecular markers included markers linked to YrZH84 (Xcfa2040, Xbarc32), YrZH22 (WGGB119, WGGB124), Yr30 (Xgwm533, We173), and Yr26 (Xbarc181). Through a comprehensive selection, wheat families with either single or multiple pyramided genes that exhibited both disease resistance and excellent agronomic traits were identified. Ultimately, 63 wheat families with excellent agronomic traits and disease resistance were selected. Among 63 pedigrees, there are three pedigrees containing four genes YrZH84, Yr30, YrZH22, and Yr26, four pedigrees containing three genes, 13 families containing two genes, 22 families containing one gene, and 21 families containing none of the genes. These families exhibit strong stripe rust resistance and superior agronomic characteristics, making them suitable for developing new wheat lines with durable resistance and high-yield potential. They thus provide effective materials for wheat breeding. Full article

Wheat is among the most important staple crops worldwide; however, its yield and quality are severely threatened by stripe rust caused by Puccinia striiformis f. sp. tritici (Pst). CH806 is a Thinopyrum intermedium-derived resistant breeding line developed in our laboratory that is highly resistant to the prevalent Chinese Pst races CYR32, CYR33, and CYR34 in field trials. A genetic population was developed by crossing CH806 with the susceptible cultivar Chuanmai 24. Phenotypic evaluation of the progeny under field conditions revealed segregation for stripe rust resistance in the F₂ generation. On the basis of the resistance phenotypes of the F₂ and F_2:3 populations, homozygous resistant and homozygous susceptible F₂ individuals were selected to construct resistant and susceptible DNA bulks, respectively, for genotyping using the Wheat 120K SNP array. Bulked segregant analysis indicated that the most significant SNPs were predominantly clustered on chromosome 4A. Subsequently, publicly available simple sequence repeat (SSR) markers on chromosome 4A and newly developed SSR markers within the candidate region that were enriched for polymorphic SNPs were used for linkage analysis. The resistance locus, temporarily designated YrCH806, was mapped to an interval flanked by markers Xwmc48/Xwmc89 and SSR4A-60, with genetic distances of 4.4 cM and 2.5 cM, respectively, corresponding to a physical position of 515.8–574.7 Mb on the wheat reference genome. The closest flanking marker, SSR4A-60, was successfully converted into a Kompetitive Allele-Specific PCR (KASP) marker. This high-throughput marker was subsequently utilized to screen a panel of wheat germplasms for the distribution of YrCH806. This study provides a novel resistance source and associated molecular markers for improving stripe rust resistance in wheat breeding programs. Full article

Stripe rust of wheat, caused by the obligate biotrophic fungus Puccinia striiformis f. sp. tritici (Pst), is a devastating disease. The natural degeneration and viability loss of Pst urediniospores directly impact its dispersal and epidemic potential, yet the underlying molecular mechanisms remain unclear. This study aimed to systematically decipher the key molecular changes during the natural degeneration of Pst urediniospores using a multi-omics approach. We performed integrated transcriptomic (RNA-seq) and metabolomic (LC-MS) analyses on relatively purified fresh urediniospores (CC group) and those undergoing room-temperature-induced degeneration (CM group) of the prevalent Pst race CYR34. A total of 1622 differentially expressed genes (DEGs) and 382 differentially accumulated metabolites (DAMs) were identified. Transcriptomic analysis revealed significant downregulation of core energy and biosynthetic pathways, including ribosome biogenesis and oxidative phosphorylation. Metabolomic profiling showed that lipids and lipid-like molecules, along with organic acids and derivatives, constituted the major classes of altered metabolites. DAMs were primarily enriched in pathways such as “Metabolic pathways” and “ABC transporters.” Integrated analysis indicated a prevalent negative correlation pattern between gene expression levels and metabolite abundance. This study provides a systematic molecular landscape associated with Pst urediniospore degeneration, revealing characteristics concomitant with the suppression of energy metabolism and translation functions, thereby offering novel insights and a data foundation for understanding the mechanisms of viability maintenance and loss. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici, threatens global wheat production, with climate change intensifying its spread. This meta-analysis, following PRISMA protocol, evaluated chemical and biological control methods through a systematic review of literature (2005–2025), identifying 12 peer-reviewed studies with 156 experimental comparisons under various conditions. Random effects models assessed treatment impacts on disease severity and grain productivity using standardized mean differences (SMDs). Chemical control significantly reduced stripe rust severity (SMD = −1.04) and improved productivity (SMD = 1.30), with low to moderate variability and consistent yield responses. Effectiveness varied by active ingredients and wheat types, with the greatest benefits in highly susceptible varieties. Biological control agents, particularly Bacillus, Pseudomonas, and Trichoderma species, also reduced disease severity (SMD = −2.19) and increased yield (SMD = 2.39), though with greater heterogeneity reflecting strain-specific and environmental effects. Chemical fungicides provided more predictable disease control, while biological agents offered significant yield increases with agroecological benefits. This meta-analysis demonstrates complementary roles for both approaches, strongly supporting integrated disease management combining plant resistance, optimal fungicide use, and strategic biological control to enhance resilience and sustainability of global cereal production systems. Full article

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), poses a severe threat to global wheat production. The adaptor protein complex AP-1 plays a crucial role in vesicular trafficking, yet its function in rust fungi remains poorly understood. In this study, a gene encoding an AP-1 σ subunit, designated PsAP1, was identified in Pst. The expression of PsAP1 was highly induced during the early infection stage. Heterologous expression of PsAP1 in a Fusarium graminearum mutant partially restored its pathogenic defects. Subcellular localization analysis revealed that PsAP1 localizes to the plasma membrane, cytoplasm, and nucleus. Silencing PsAP1 in wheat using Barley stripe mosaic virus-mediated host-induced gene silencing (BSMV-HIGS) significantly attenuated Pst pathogenicity, reducing hyphal growth by 6.7% (colony diameter), sporulation by 61.6% (lesion length), and pathogen biomass by 66%, along with enhanced accumulation of host reactive oxygen species. Transcriptomic analysis further demonstrated that silencing PsAP1 disrupted multiple pathways, including MAPK signaling, glutathione metabolism, and carbohydrate metabolism. These findings indicate that PsAP1 facilitates Pst infection by modulating vesicular trafficking, suppressing host immunity, and reprogramming host metabolism. This study provides novel insights into the pathogenic mechanisms of rust fungi and suggests a potential target for disease control. Full article

Background: Ammonium nitrogen (NH₄⁺) serves as a vital nitrogen source, playing pivotal regulatory roles in plant growth, development, and high-yield formation. Ammonium transporters (AMTs), encoded by the AMT gene family, are central to NH₄⁺ transport. However, the functional roles of AMT genes in wheat remain poorly understood. Methods: A comprehensive genome-wide analysis of the TaAMT gene family numbers was conducted, encompassing investigations into gene structure, protein motif composition, gene duplication events, collinearity relationships, and cis-acting regulatory elements. Furthermore, the expression patterns of distinct TaAMT members were examined under varying ammonium supply conditions and pathogen stress. Results: In this study, a total of 21 TaAMT members were identified. Additionally, all TaAMT proteins were localized to the plasma membrane. Phylogenetic analysis clustered these genes into four distinct subgroups. Comparative analyses of gene structure and conserved motifs revealed conserved domain composition and motif organization within each subgroup. Interspecific synteny analysis highlighted evolutionary conservation across species. Promoter region analysis identified multiple cis-regulatory elements associated with hormone signaling, light responsiveness, and abiotic stress adaptation. Expression profiling demonstrated that TaAMT members exhibit both tissue-specific and constitutive expression patterns across developmental stages. RT-qPCR further revealed that the expression of TaAMT members responds to varying concentrations of ammonium nitrogen supply, as well as infection stresses caused by stripe rust and powdery mildew. Conclusions: Collectively, this study uncovered the functional diversity of TaAMT members, offering novel molecular targets and theoretical foundations for breeding wheat varieties with enhanced nitrogen use efficiency and disease resistance. Full article

Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSI_O) index exhibited the best monitoring performance, with a field determination coefficient R² of 0.5713 (experimental field R² = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring. Full article

Magnesium (Mg) release (MGR) proteins play a crucial role in maintaining Mg²⁺ homeostasis in plant cells. However, MGR family genes have not yet been explored in crops. This study identified the wheat MGR (TaMGR) family members via BlastP alignment. A total of 15 MGR genes were mapped to 12 chromosomes. Cis-element prediction in the promoter region revealed that the ABA-responsive element (ABRE) was 100% conserved among all family members. Collinearity analysis indicates that MGR genes in monocot plants may have higher conservation compared to dicot plants. Expression profiling analyses uncovered the expression patterns of TaMGR genes across diverse tissues and under various stresses. Our results demonstrated that TaMGR5D and TaMGR5A.2 were significantly induced by both powdery mildew and stripe rust pathogen infections, whereas TaMGR4A transcript levels were upregulated in response to drought, heat and their combined stress. These findings indicate that TaMGRs may contribute coordinately to the regulation of wheat growth and development as well as adaptive responses to adverse conditions through member-specific expression patterns. This study systematically identified and analyzed the evolution and expression regulation characteristics of TaMGRs, providing a theoretical basis for in-depth research on the functional mechanisms of the TaMGRs and for improving the Mg use efficiency and stress adaptability of wheat via molecular approaches. Full article

Wheat, a globally vital food crop, faces severe threats from numerous foliar diseases, which often infect agricultural fields, significantly compromising yield and quality. Rapid and accurate identification of the specific disease is crucial for ensuring food security. Although progress has been made in wheat foliar disease detection using RGB imaging and spectroscopy, most prior studies have focused on identifying the presence of a single disease, without considering the need to operationalize such methods, and it will be necessary to differentiate between multiple diseases. In this study, we systematically investigate the differentiation of three wheat foliar diseases (e.g., powdery mildew, stripe rust, and leaf rust) and evaluate feature selection strategies and machine learning models for disease identification. Based on field experiments conducted from 2017 to 2024 employing artificial inoculation, we established a standardized hyperspectral database of wheat foliar diseases classified by disease severity. Four feature selection methods were employed to extract spectral features prior to classification: continuous wavelet projection algorithm (CWPA), continuous wavelet analysis (CWA), successive projections algorithm (SPA), and Relief-F. The selected features (which are derived by CWPA, CWA, SPA, and Relief-F algorithm) were then used as predictors for three disease-identification machine learning models: random forest (RF), k-nearest neighbors (KNN), and naïve Bayes (BAYES). Results showed that CWPA outperformed other feature selection methods. The combination of CWPA and KNN for discriminating disease-infected (powdery mildew, stripe rust, leaf rust) and healthy leaves by using only two key features (i.e., 668 nm at wavelet scale 5 and 894 nm at wavelet scale 7), achieved an overall accuracy (OA) of 77% and a map-level image classification efficacy (MICE) of 0.63. This combination of feature selection and machine learning model provides an efficient and precise procedure for discriminating between multiple foliar diseases in agricultural fields, thus offering technical support for precision agriculture. Full article

Von Willebrand factor A (vWA) genes play important roles in regulating plant growth and development, as well as biotic stresses. However, limited data are available on the contributions of vWA genes to wheat (Triticum aestivum L.). In this study, 114 TavWA genes were identified in the wheat genome, which were unevenly distributed on 21 chromosomes. According to the phylogenetic analysis, the 114 TavWAs were classified into six groups, two of which (G3 and G6) were unique to wheat. Fifty-five homoeologous gene sets among A, B, and D sub-genomes were detected, which play a crucial role in the expansion of the wheat vWA gene family. Analysis of specific spatiotemporal expression patterns showed that more than 50% of TavWAs (61 out of 114) exhibited tissue-specific expression. These included 71 TavWAs that responded to one or more of the four biotic stress treatments (flg22, chitin, powdery mildew, and stripe rust). Notably, these included TavWA1-7D, a recently reported key growth regulator in wheat, suggesting its additional role in biotic stress responses. RT-qPCR analysis indicated that eight genes (TavWA1-7D, TavWA24-2B, TavWA36-1D, TavWA37-7D, TavWA40, TavWA47, TavWA51, and TavWA53) may play important roles in wheat’s powdery mildew resistance. Collectively, the results of this study provide significant insights for future research on the involvement of vWA genes in the development and stress responses of wheat. Full article

In this study, the reactions of 70 bread wheat varieties released in Türkiye to five prevalent Pst races, including the Yr5-virulent PSTr-27, were evaluated. Reaction tests of wheat varieties to all races revealed PSTr-27 as the most aggressive race, followed by PSTr-31, PSTr-28, PSTr-29, and PSTr-30. Notably, only seven varieties (Kıraç 66, İkizce 96, Dinç, Altındane, Ziyabey 98, Bayraktar 2000, and Shiro) exhibited moderately resistant reactions to PSTr-27, while the remaining varieties were susceptible. The presence of nine important resistance (Yr) genes in these varieties was also screened at the molecular level. Yr5, Yr15, and Yr26 genes were not detected in any of the varieties and Yr10 and YrSP genes were each detected in only one variety, while the other genes were detected in different ratios. Molecular screening showed that 19 varieties with no resistance genes used in this study displayed susceptible reactions; however, ten varieties that did not carry any resistance genes showed resistant reactions to one or more races, suggesting the presence of unknown or novel resistance sources. Furthermore, gene combinations, particularly Yr10 + Yr18, significantly provided resistance to all Pst races studied. These findings highlight that continual monitoring of PSTr-27, and other Pst races is needed, since it can be a serious threat to wheat production in Türkiye and neighboring countries. Full article

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), represents a major global threat to wheat (Triticum aestivum. L). Planting varieties with adult-plant resistance (APR) is an effective approach for long-term management of this disease. The Chinese winter wheat variety Lantian 25 exhibits moderate-to-high APR against stripe rust under field conditions. To investigate the genetic basis of APR in Lantian 25, a set of 219 F₆ recombinant inbred lines (RILs) was created from a cross between Lantian 25 (resistant parent) and Huixianhong (susceptible parent). These RILs were assessed for maximum disease severity (MDS) in Pixian of Sichuan and Qingshui of Gansu over the 2020–2021 and 2021–2022 growing seasons, resulting in data from four different environments. Genotyping was performed on these lines and their parents using the wheat Illumina 50K single-nucleotide polymorphism (SNP) arrays. Composite interval mapping (CIM) identified six quantitative trait loci (QTL), named QYr.gaas-2BS, QYr.gaas-2BL, QYr.gaas-2DS, QYr.gaas-2DL, QYr.gaas-3BS and QYr.gaas-4BL, which were consistently found across two or more environments and explained 4.8–12.0% of the phenotypic variation. Of these, QYr.gaas-2BL, QYr.gaas-2DS, and QYr.gaas-3BS overlapped with previous studies, whereas QYr.gaas-2BS, QYr.gaas-2DS, and QYr.gaas-4BL might be novel. All the resistance alleles for these QTL originated from Lantian 25. Furthermore, four kompetitive allele-specific PCR (KASP) markers, Kasp_2BS_YR (QYr.gaas-2BS), Kasp_2BL_YR (QYr.gaas-2BL), Kasp_2DS_YR (QYr.gaas-2DS) and Kasp_2DL_YR (QYr.gaas-2DL), were developed and validated in 110 wheat diverse accessions. Additionally, we identified seven candidate genes linked to stripe rust resistance, including disease resistance protein RGA2, serine/threonine-protein kinase, F-box family proteins, leucine-rich repeat family proteins, and E3 ubiquitin-protein ligases. These QTL, along with their associated KASP markers, hold promise for enhancing stripe rust resistance in wheat breeding programs. Full article