Purification and identification of globulin-1 S allele as a novel allergen with N-glycans in wheat (Triticum aestivum)

Efficient experimental method for purifying allergens from aqueous extracts

Studying the molecular and immunological basis of allergic diseases often requires purified native allergens. The methodologies for protein purification are usually difficult and may not be completely successful. The objective of this work was to describe a methodology to purify allergens from their natural source, while maintaining their native form. The purification strategy consists of a three-step protocol and was used for purifying five specific allergens, Ole e 1, Amb a 1, Alt a 1, Bet v 1 and Cup a 1. Total proteins were extracted in PBS (pH 7.2). Then, the target allergens were pre-purified and enriched by salting-out using increasing concentrations of ammonium sulfate. The allergens were further purified by anion exchange chromatography. Purification of Amb a 1 required an extra step of cation exchange chromatography. The detection of the allergens in the fractions obtained were screened by SDS-PAGE, and Western blot when needed. Further characterization of purified Amb a 1 was performed by mass spectrometry. Ole e 1, Alt a 1, Bet v 1 and Cup a 1 were obtained at > 90 % purity. Amb a 1 was obtained at > 85 % purity. Overall, we propose an easy-to-perform purification approach that allows obtaining highly pure allergens. Since it does not involve neither chaotropic nor organic reagents, we anticipate that the structural and biological functions of the purified molecule remain intact. This method provides a basis for native allergen purification that can be tailored according to specific needs.

Determining the Impact of Genotype × Environment on Oat Protein Isolate Composition Using HPLC and LC-MS Techniques

The effect of genotype and environment on oat protein composition was analyzed through size exclusion-high-performance liquid chromatography (SE-HPLC) and liquid chromatography-mass spectrometry (LC-MS) to characterize oat protein isolate (OPI) extracted from three genotypes grown at three locations in the Canadian Prairies. SE-HPLC identified four fractions in OPI, including polymeric globulins, avenins, glutelins, and albumins, and smaller proteins. The protein composition was dependent on the environment, rather than the genotype. The proteins identified through LC-MS were grouped into eight categories, including globulins, prolamins/avenins, glutelins, enzymes/albumins, enzyme inhibitors, heat shock proteins, grain softness proteins, and allergenic proteins. Three main globulin protein types were also identified, including the P14812|SSG2-12S seed storage globulin, the Q6UJY8_TRITU-globulin, and the M7ZQM3_TRIUA-Globulin-1 S. Principal component analysis indicated that samples from Manitoba showed a positive association with the M7ZQM3_TRIUA-Globulin-1 S allele and Q6UJY8_TRITU-globulin, while samples from Alberta and Saskatchewan had a negative association with them. The results show that the influence of G × E on oat protein fractions and their relative composition is crucial to understanding genotypes' behavior in response to different environments.

A systematic review on the recent advances of wheat allergen detection by mass spectrometry: future prospects

Wheat is one of the three major staple foods in the world. Although wheat is highly nutritional, it has a variety of allergenic components that are potentially fatal to humans and pose a significant hazard to the growth and consumption of wheat. Wheat allergy is a serious health problem, which is becoming more and more prevalent all over the world. To address and prevent related health risks, it is crucial to establish precise and sensitive detection and analytical methods as well as an understanding of the structure and sensitization mechanism of wheat allergens. Among various analytical tools, mass spectrometry (MS) is known to have high specificity and sensitivity. It is a promising non immune method to evaluate and quantify wheat allergens. In this article, the current research on the detection of wheat allergens based on mass spectrometry is reviewed. This review provides guidance for the further research on wheat allergen detection using mass spectrometry, and speeds up the development of wheat allergen research in China.

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins

Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify allergens were based on homology or conventional machine learning methods, which were inefficient and still had room to be improved for the detection of allergens with low homology. In addition, few methods based on deep learning were reported, although deep learning has been successfully applied to several tasks in protein sequence analysis. In the present work, a deep neural network-based model, called DeepAlgPro, was proposed to identify allergens. We showed its great accuracy and applicability to large-scale forecasts by comparing it to other available tools. Additionally, we used ablation experiments to demonstrate the critical importance of the convolutional module in our model. Moreover, further analyses showed that epitope features contributed to model decision-making, thus improving the model's interpretability. Finally, we found that DeepAlgPro was capable of detecting potential new allergens. Overall, DeepAlgPro can serve as powerful software for identifying allergens.