Evaluation of the susceptibility of modern, wild, ancestral, and mutational wheat lines to Septoria tritici blotch disease

Ajaz, Sobia; Benbow, Harriet R.; Christodoulou, T.; Uauy, Cristóbal; Doohan, Fiona M.

doi:10.1111/ppa.13369

Cited by 3 publications

(1 citation statement)

References 66 publications

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“…Additional techniques like genome editing ( Zhang et al., 2017 ) and Eco-Tilling ( Ajaz et al., 2021 ) have been increasingly utilized in wheat, having great potential to contribute to SB resistance breeding in near future.…”

Section: Biotechnological Approachesmentioning

confidence: 99%

Managing spot blotch disease in wheat: Conventional to molecular aspects

Roy

He²,

Gahtyari³

et al. 2023

Front. Plant Sci.

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Spot blotch (SB) caused by Bipolaris sorokiniana (teleomorph Cochliobolus sativus) is one of the devastating diseases of wheat in the warm and humid growing areas around the world. B. sorokiniana can infect leaves, stem, roots, rachis and seeds, and is able to produce toxins like helminthosporol and sorokinianin. No wheat variety is immune to SB; hence, an integrated disease management strategy is indispensable in disease prone areas. A range of fungicides, especially the triazole group, have shown good effects in reducing the disease, and crop-rotation, tillage and early sowing are among the favorable cultural management methods. Resistance is mostly quantitative, being governed by QTLs with minor effects, mapped on all the wheat chromosomes. Only four QTLs with major effects have been designated as Sb1 through Sb4. Despite, marker assisted breeding for SB resistance in wheat is scarce. Better understanding of wheat genome assemblies, functional genomics and cloning of resistance genes will further accelerate breeding for SB resistance in wheat.

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Section: Biotechnological Approachesmentioning

confidence: 99%

Managing spot blotch disease in wheat: Conventional to molecular aspects

Roy

He²,

Gahtyari³

et al. 2023

Front. Plant Sci.

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SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images

Mathieu,

Reder,

Siah

et al. 2024

Plant Methods

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Background Investigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici—a frequently occurring and damaging disease on wheat crops. Results We developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions. Conclusions SeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.

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Identification of Wheat Septoria tritici Resistance Genes in Wheat Germplasm Using Molecular Markers

Kokhmetova,

Bolatbekova,

Zeleneva

et al. 2024

Plants

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Zymoseptoria tritici (Z. tritici) is the main threat to global food security; it is a fungal disease that presents one of the most serious threats to wheat crops, causing severe yield losses worldwide, including in Kazakhstan. The pathogen leads to crop losses reaching from 15 to 50%. The objectives of this study were to (1) evaluate a wheat collection for Z. tritici resistance during the adult plant and seedling growth stages, (2) identify the sources of resistance genes that provide resistance to Z. tritici using molecular markers linked to Stb genes, and (3) identify potentially useful resistant wheat genotypes among cultivars and advanced breeding lines. This study evaluated 60 winter and spring wheat genotypes for Z. tritici resistance. According to the field reactions, 22 entries (35.7%) showed ≤10% disease severity in both years. The resistant reaction to a mix of Z. tritici isolates in the seedling stage was associated with adult plant resistance to disease in four wheat entries. The resistance of Rosinka 3 was due to the presence of Stb8; Omskaya 18 showed an immune reaction in the field and a moderately susceptible reaction in the seedling stage, possibly provided by a combination of the Stb7 and Stb2 genes. The high resistance in both the adult and seedling stages of Omskaya 29 and KR11-03 was due to the Stb4 and Stb2 genes and, possibly, due to the presence of unknown genes. A linked marker analysis revealed the presence of several Stb genes. The proportion of wheat entries with Stb genes was quite high at twenty-seven of the genotypes tested (45.0%), including four from Kazakhstan, nine from Russia, nine from the CIMMYT-ICARDA-IWWIP program, and five from the CIMMYT-SEPTMON nursery. Among the sixty entries, ten (16.7%) carried the resistance genes Stb2 and Stb8, and the gene Stb4 was found in seven cultivars (11.6%). Marker-assisted selection can be efficiently applied to develop wheat cultivars with effective Stb gene combinations that would directly assist in developing durable resistance in Kazakhstan. Resistant genotypes could also be used as improved parents in crossing programs to develop new wheat cultivars.

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Evaluation of the susceptibility of modern, wild, ancestral, and mutational wheat lines to Septoria tritici blotch disease

Cited by 3 publications

References 66 publications

Managing spot blotch disease in wheat: Conventional to molecular aspects

Managing spot blotch disease in wheat: Conventional to molecular aspects

SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images

Identification of Wheat Septoria tritici Resistance Genes in Wheat Germplasm Using Molecular Markers

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