High-throughput proteomics to support wheat quality prediction

Wheat is one of the most important food crops worldwide and the consumption of wheat products contributes a significant share of carbohydrates, proteins, dietary fiber, Bvitamins and minerals to human nutrition. Predicting dough and breadmaking properties early in the value chain is important to reduce waste and products of inferior quality. The protein content of wheat flour largely determines its market price, because it is associated with bread volume. However, predictions of bread volume based on protein content alone fail to capture the complexity of gluten proteins, the key determinants of baking quality.
Gluten proteins are composed of the types ω-, α- and γ-gliadins as well as high- and lowmolecular-weight glutenin subunits (HMW-GS and LMW-GS). Their quantities are usually analyzed via reversed-phase high-performance liquid chromatography with UV detection, but this approach only captures gluten protein types and is not sufficient to capture single proteins. This is why high-throughput untargeted and targeted proteomics methods were developed to provide molecular-level insights into gluten protein composition. A targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) was established to quantify eleven gluten protein groups based on isotope-labeled internal standards corresponding to selected marker peptides [1]. The comparison of targeted and untargeted proteomics results revealed differences in protein composition, likely arising from different MS/MS acquisition strategies and protein assignment. The content of the analyzed proteins was correlated with baking quality traits in a multiple advanced generation intercross wheat population comprising 394 inbred lines. None of the individual groups correlated strongly with any baking quality trait. Six groups showed weak to moderate associations, mainly with grain protein content, sedimentation value, and wet gluten content. Bread volume was only weakly to moderately correlated, primarily with HMW-GS 1 (r = 0.41) and HMW-GS 3 (r = 0.40), supporting the superior effect of Dy10. In contrast, HMW-GS 5 (Dx2, Dx5) showed only small effects, consistent with a stronger influence of y-type glutenin subunits. Therefore, these results suggest that a more detailed understanding of individual glutenin subunits, particularly the y-type subunits, is required to accurately model and predict bread volume in diverse wheat populations. Further work will integrate additional data from the same wheat population grown under different environmental conditions to get a first insight into protein expression variability.