Leaf rust resistance is of high importance for a sustainable European wheat production. The expression of known resistance genes starts at different developmental stages of wheat. Breeding for resistance can be supported by a fast, precise, and resource-saving phenotyping. The examination of detached leaf assays of juvenile plants inoculated under controlled conditions and phenotyped by a robotic- and computer-based, high-throughput system is a promising approach in this respect. Within this study, the validation of the phenotyping workflow was conducted based on a winter wheat set derived from Central Europe and examined at different plant developmental stages. Moderate Pearson correlations of 0.38–0.45 comparing leaf rust resistance of juvenile and adult plants were calculated and may be mainly due to different environmental conditions. Specially, the infection under controlled conditions was limited by the application of a single rust race at only one time point. Our results suggest that the diversification with respect to the applied rust race spectrum is promising to increase the consistency of detached leaf assays and the transferability of its results to the field.

Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M₃ generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of “a” resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of “b” alleles in Lr9 and Lr10—except for one line with allele “a” in Lr9 and three mutant lines with allele “a” in Lr10—showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with “b” alleles in Lr1, Lr2a, Lr10, and Lr17 and “a” alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the “a” alleles of Lr3 and Lr9.

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

The increasing threat posed by wheat rust diseases caused by Puccinia spp. necessitates the development of resistance strategies that extend beyond conventional race-specific mechanisms. Although recent reviews (2023–2025) have emphasized gene discovery and genomic approaches, comparatively less attention has been given to the potential roles of metabolic regulation and micronutrient homeostasis in host–pathogen interactions. Here, we present a narrative synthesis of current evidence and propose a conceptual framework in which induced mutagenesis (ethyl methanesulfonate, EMS, and γ-irradiation) serves as a tool for investigating interactions among redox regulation, iron (Fe) homeostasis, and disease resistance. A key component of this framework is the proposed interplay between reactive oxygen species (ROS) signaling and Fe partitioning. Vacuolar iron transporters (VITs), ferritins, and associated transport networks regulate intracellular Fe distribution and may influence Fe availability at the host–pathogen interface, potentially affecting fungal development and host defense responses. This concept of “iron-withholding immunity” may operate alongside ROS-mediated defense processes, linking metabolism with immune function. Observations from mutant wheat populations are broadly consistent with the hypothesis that these processes may contribute to durable adult-plant resistance (APR), which is characterized by reduced disease development, coordinated defense responses, and relative stability across environments. In some studies, Fe-enriched mutant lines have been associated with enhanced expression of pathogenesis-related genes and the occurrence of combined APR and seedling-resistance phenotypes, suggesting possible links between micronutrient homeostasis and immunity. Integration of high-throughput phenotyping with genotype × environment × time (G × E × T) frameworks may further improve our understanding of quantitative resistance and disease-associated traits. Overall, this review highlights the potential importance of nutrient homeostasis, redox regulation, and susceptibility modulation as components of future research aimed at developing climate-resilient and nutritionally improved wheat cultivars.

Background: In plant breeding, many studies currently investigate the application of machine learning (ML) to genomic prediction, hoping for an improvement in prediction accuracy compared to standard models like Genomic Best Linear Unbiased Prediction (GBLUP). However, ML algorithms require much higher computational resources. This study aims to reduce the computational requirements and speed up training time by developing a novel autoencoder architecture inspired by haplotype blocks. Our approach incorporates prior knowledge on genetic linkage, inspired by haplotype block building, into the autoencoder architecture, resulting in a new encoded variable per haplotype block. We further modified our model into a semi-supervised version by adding available yield information. We used features extracted from the autoencoder’s block layer as inputs for Random Forest and GBLUP models to predict the yield of hybrid and inbred crops. Results: Genomic prediction based on the extracted features maintained prediction accuracies equal to using the original marker data, even with a variable reduction of up to 98% and significantly reduced computation time. Prediction accuracy of the supervised component was in some cases equal to and in some lower than the prediction accuracy achieved using GBLUP. Effects estimated for haplotype block variants using our new method showed a high correlation to the blockwise sum of marker effects, which is the current standard approach for haplotype block effects. Correlation between the two block effect estimation approaches was very low for some blocks, which might indicate the incorporation of non-linear effects by the autoencoder. Conclusions: Our approach introduces a new perspective on processing haplotype blocks for genomic prediction, potentially providing more flexible modelling opportunities without the use of multiple binary dummy variables for each block variant. Additionally, training time for ML models may be significantly reduced by using the reduced feature sets generated using our method. By adding the semi-supervised component, the model is able to estimate values similar to marker effects for each block on yield. In future work, this may provide a new way of quantifying the importance of haplotype blocks for selection and breeding.

Each breeding program must find a balance between short-term genetic gain and the preservation of genetic variation. Different selection criteria, cross selection indices and cross-planning methods have been proposed in order to improve the trade-off between both goals compared to truncation selection and mating best with best. The relative advantage of each of these measures and methods may change depending on the duration of the breeding program because each has its particular strengths and weaknesses. We specifically focused on a short-term recurrent genomic selection program spanning five generations and used two datasets with 224 oat and 293 wheat lines, respectively, for a simulation study which evaluated genetic gain and genetic diversity for 19 different breeding strategies. Overall, we found that strategies that combined different selection criteria, cross selection indices, and/or cross-planning methods rather than relying on just one measure alone resulted in a higher genetic gain compared to truncation selection while maintaining at least the same amount of genetic diversity. We therefore conclude that combining different measures into breeding strategies can enhance breeding progress without any need for additional resources.