Barley mlo mutants are well known for their profound resistance against powdery mildew disease. Recently, mlo mutant plants were generated in hexaploid bread wheat (Triticum aestivum) with the help of transgenic (transcription-activator-like nuclease, TALEN) and non-transgenic (targeted induced local lesions in genomes, TILLING) biotechnological approaches. While full gene knockouts in the three wheat Mlo (TaMlo) homoeologs, created via TALEN, confer full resistance to the wheat powdery mildew pathogen (Blumeria graminis f.sp. tritici), the currently available TILLING-derived Tamlo missense mutants provide only partial protection against powdery mildew attack. Here we studied the infection phenotypes of TALEN- and TILLING-derived Tamlo plants to the two hemibiotrophic pathogens Zymoseptoria tritici, causing Septoria leaf blotch in wheat, and Magnaporthe oryzae pv. Triticum (MoT), the causal agent of wheat blast disease. While Tamlo plants showed unaltered outcomes upon challenge with Z. tritici, we found allele-specific levels of enhanced susceptibility to MoT, with stronger powdery mildew resistance correlated with more invasive growth by the blast pathogen. Surprisingly, unlike barley mlo mutants, young wheat mlo mutant plants do not show undesired pleiotropic phenotypes such as spontaneous callose deposits in leaf mesophyll cells or signs of early leaf senescence. In conclusion, our study provides evidence for allele-specific levels of enhanced susceptibility of Tamlo plants to the hemibiotrophic wheat pathogen MoT.

If genetic gains in wheat yield are to be achieved in today’s breeding, increasing genetic variability of cultivated genotypes is an essential requisite to meet. To this aim, alien gene transfer through chromosome engineering (CE) is a validated and sound strategy. Attempts to incorporate more than one alien segment into cultivated wheat have been rare, particularly for tetraploid durum wheat. Here we present the agronomic and quality performance of the first successful CE-mediated multiple introgression into the latter species. By assembling into 7AL, 3BS and 1AS arms of a single genotype homoeologous segments of Thinopyrum ponticum 7el1L, Aegilops longissima 3SlS, and Triticum aestivum 1DS arms, respectively, we have stacked several valuable alien genes, comprising Lr19+Sr25+Yp (leaf and stem rust resistance and a gene increasing semolina yellowness), Pm13 (powdery mildew resistance) and Gli-D1/Glu-D3 (genes affecting gluten properties), respectively. Advanced progenies of single, double and triple recombinants were field-tested across three years in a typical durum wheat growing area of Central Italy. The results showed that not only all recombinants had normal phenotype and fertility, but also that one of the triple recombinants had the highest yield through all seasons compared with all other recombinants and control cultivars. Moreover, the multiple introgressions enhanced quality traits, including gluten characteristics and semolina yellow index. Presence of effective disease resistance genes confers additional breeding value to the novel and functional CE products, which can greatly contribute to crop security and safety.

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale, thus one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images with the possibility of obtaining them in field conditions using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in combination, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. WFD2020 data are available freely at http://wfd.sysbio.ru/. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented by the authors as a bot on the Telegram platform, which allows assessing plants by lesions in the field conditions.