Septoria tritici blotch (STB) of wheat, caused by the ascomycete Zymoseptoria tritici (formerly Mycosphaerella graminicola), is one of the most important foliar diseases of wheat. In Morocco, STB is a devastating disease in temperate wheat-growing regions, and the yield losses can exceed up to 50% under favorable conditions. The aims of this study were to identify sources of resistance to STB in Septoria Association Mapping Panel (SAMP), which is composed of 377 advanced breeding lines (ABLs) from spring bread wheat breeding program of ICARDA, and to identify loci associated with resistance to STB at seedling (SRT) as well as at the adult plant (APS) stages using genome-wide association mapping (GWAM). Seedling resistance was evaluated under controlled conditions with two virulent isolates of STB (SAT-2 and 71-R3) from Morocco, whereas adult plant resistance was assessed at two hot spot locations in Morocco (Sidi Allal Tazi, Marchouch) under artificial inoculation with a mixture of STB isolates. At seedling stage, 45 and 32 ABLs were found to be resistant to 71-R3 and SAT-2 isolates of STB, respectively. At adult plant stage, 50 ABLs were found to be resistant at hot spot locations in Morocco. Furthermore, 10 genotypes showed resistance in both locations during two cropping seasons. GWAM was conducted with 9,988 SNP markers using phenotypic data for seedling and the adult plant stage. MLM model was employed in TASSEL 5 (v 5.2.53) using principal component analysis and Kinship Matrix as covariates. The GWAM analysis indicated 14 quantitative trait loci (QTL) at the seedling stage (8 for isolate SAT-2 and 6 for isolate 71-R3), while 23 QTL were detected at the adult plant stage resistance (4 at MCH-17, 16 at SAT-17, and 3 at SAT-18). SRT QTL explained together 33.3% of the phenotypic variance for seedling resistance to STB isolate SAT-2 and 28.3% for 71-R3, respectively. QTL for adult plant stage resistance explained together 13.1, 68.6, and 11.9% of the phenotypic variance for MCH-17, SAT-17, and SAT-18, respectively. Identification of STB-resistant spring bread wheat germplasm in combination with QTL detected both at SRT and APS stage will serve as an important resource in STB resistance breeding efforts.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst) is one of the most destructive diseases of wheat (Triticum aestivum L.) worldwide causing huge yield losses every year. Development and deployment of resistant varieties is the most economical and environment friendly approach for controlling this disease. However, because of the continuous evolution of the pathogen, resistant genes are easily overcome by new virulent Pst races, which necessitates a continuous identification and introgression of resistance genes to develop resistant wheat varieties. To identify effective source of resistance, a genome-wide association study was performed using 426 elite bread wheat genotypes based on 5176 polymorphic Diversity Arrays Technology (DArT) markers. Adult-plant-resistance was evaluated under field conditions for yellow rust resistance for two consecutive years (2014 and 2015) at ICARDA Merchouch station, Morocco. Out of the 426 genotypes, 51.17% were highly resistant with 5–10% level of severity to yellow rust. Genome wide association studies (GWAS) using a mixed linear model (MLM) identified three DArT markers on chromosomes 1B, 2B and 7B which are significantly associated with stripe rust resistance at false discovery rate p ≤ 0.05. BLAST analysis confirmed that the marker 412,394 in chromosome 2B overlapped with two previously reported QTLs (QYrlu.cau-2BS1 Luke and QYrid.ui-2B.1_IDO444). However, the two other markers 542,318 (1B) and 583,038 (5B) were not mapped within any of the previously reported gene/QTL regions; therefore, these markers may represent novel resistance loci for yellow rust. The highly resistant elite genotypes and linked molecular markers are recommended for further gene introgression and pyramiding purposes in the wheat breeding programs after validation.

Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley ( Hordeum vulgare) plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection on seedling leaves using Cascade R-CNN (region-based convolutional neural network) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for Cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4 days postinfection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease.

Efficient early pathogen detection, before symptom apparition, is crucial for optimizing disease management. In barley, the fungal pathogen Pyrenophora teres is the causative agent of net blotch disease, which exists in two forms: P. teres f. sp. teres (Ptt), causing net-form of net blotch (NTNB), and P. teres f. sp. maculata (Ptm), responsible for spot-form of net blotch (STNB). In this study, we developed primers and a TaqMan probe to detect both Ptt and Ptm. A comprehensive k-mer based analysis was performed across a collection of P. teres genomes to identify the conserved regions that had potential as universal genetic markers. These regions were then analyzed for their prevalence and copy number across diverse Moroccan P. teres strains, using both a k-mer analysis for sequence identification and a phylogenetic assessment to establish genetic relatedness. The designed primer-probe set was successfully validated through qPCR, and early disease detection, prior to symptom development, was achieved using ddPCR. The k-mer analysis performed across the available P. teres genomes suggests the potential for these sequences to serve as universal markers for P. teres, transcending environmental variations.

Ce projet s'inscrit dans une démarche interdisciplinaire visant à résoudre les problématiques liées à l’helminthosporiose, une maladie cryptogamique causée par Pyrenophora teres, réduisant considérablement les rendements de l'orge (Hordeum vulgare L.).Dans ce travail, un modèle d'apprentissage profond combinant les architectures Cascade R-CNN et U-Net a été développé pour quantifier les symptômes de la maladie sur les feuilles d’orge. Le modèle a atteint une précision de 95 % dans la détection des lésions et s’est avéré particulièrement efficace pour identifier les petites lésions quatre jours après l'infection. Ce modèle a été validé par des mesures classiques et des méthodes de détection par PCR, fournissant ainsi un outil robuste pour la gestion automatisée des maladies en conditions réelles.En parallèle, une méthode de détection précoce basée sur l'analyse des k-mers des génomes de P. teres a permis de concevoir des amorces et une sonde TaqMan spécifiques, capables d’identifier P. teres f. sp. teres et P. teres f. sp. maculata avant l’apparition des symptômes. Cette détection sur plantes asymptomatiques, validée par qPCR et ddPCR, est une première à notre connaissance. Cette approche constitue donc une avancéemajeure pour la gestion de l’helminthosporiose chez l’orge, et plus généralement des maladies en milieu agricole.Dans le cadre des changements climatiques, les projections montrent que la température annuelle moyenne et les précipitations du trimestre le plus froid influencent la répartition future de P. teres. Les modèles prévoient une expansion de la présence du pathogène vers l'Amérique du Nord, l’Océanie et l’Europe d'ici 2040. L’analyse des souches hybrides a révélé une diversification accrue des effecteurs et métabolites secondaires,renforçant l’adaptabilité et la virulence du pathogène. Parmi les effecteurs identifiés figurent Kex2, RCLR-like motifs, MoCDIP11, ainsi que ToxB, PIN_09941, MoCDIP8, ToxB et Nip1, soulignant le rôle crucial des recombinaisons génétiques dans la pathogénicité des souches hybrides.Ce travail a permis de mettre en évidence l’importance de l’intégration des outils technologiques en biocontrôle pour élaborer des stratégies durables de gestion des maladies phytopathogènes dans un environnement en constante évolution.

Septoria tritici blotch (STB) caused by Zymoseptoria tritici is a major biotic stress in temperate wheat-growing regions of Morocco with yield losses reaching up to 40% under favourable conditions. Owing to the development of fungicide resistance, the use of resistant cultivars seems to be the best option for managing this disease. Therefore, knowledge of the pathogen population structure is indispensable for deploying an efficient and effective resistance breeding approach to combat STB. In this study, the response of 33 bread wheat genotypes (representing 16 resistance genes and QTL) was evaluated against 21 Z. tritici isolates under controlled conditions. Among 693 genotype-isolate interactions, 163 isolate-specific resistances (24%) were observed by comparing the means of disease severity data using LSD _0.01 and LSD _0.05 . In addition to resistant cultivar Murga (G25), two cultivars, Cs synthetic (G4) and KK 4500 (G9), exhibited resistance response to almost all isolates with mean disease severity of 4% and 14–15 isolate-specific resistances. This suggests that these genotypes can be utilized in STB resistance breeding programs for Morocco and durable resistance can be achieved by pyramiding identified R genes against Moroccan Z. tritici isolates.