A catalogue of Triticum monococcum genes encoding toxic and immunogenic peptides for celiac disease patients

The celiac disease (CD) is an inflammatory condition characterized by injury to the lining of the small-intestine on exposure to the gluten of wheat, barley and rye. The involvement of gluten in the CD syndrome has been studied in detail in bread wheat, where a set of “toxic” and “immunogenic” peptides has been defined. For wheat diploid species, information on CD epitopes is poor. In the present paper, we have adopted a genomic approach in order to understand the potential CD danger represented by storage proteins in diploid wheat and sequenced a sufficiently large number of cDNA clones related to storage protein genes of Triticum monococcum. Four bona fide toxic peptides and 13 immunogenic peptides were found. All the classes of storage proteins were shown to contain harmful sequences. The major conclusion is that einkorn has the full potential to induce the CD syndrome, as already evident for polyploid wheats. In addition, a complete overview of the storage protein gene arsenal in T. monococcum is provided, including a full-length HMW x-type sequence and two partial HMW y-type sequences

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts