Proteomics-Based Characterization of Food Allergens and Effects from Food Processing

Abstract

Food contains a lot of proteins, but only a small fraction of them are allergens either in their native forms or in products resulting from food processing. There is a need for sensitive and rapid methods for detecting the presence of allergens in foods, as well as analyse the modifications induced by food processing. This research is important if we realize the potential of new analitycal strategies andnovel processing techniques that may reduce the allergenicity of foods. Nowadays, mass Spectrometry and Liquid Chromatography (LC) are extensively used for the characterization of allergenic proteins and peptides: the application of proteomics for the analysis of allergenic proteins has been recently termed allergenomics. Unfortunately, the variability of food allergens makes it difficult to develop a generic and universal method for their characterization. A major challenge for MS techniques is sample preparation and its related issues, as in the case of Apiaceae allergens, for many of which there is a lack of reference materials and methodologies. This PhD thesis research project aimed to make a contribution for the determination of food allergens by LCMS and to evaluate the effect of some food processing on allergenicity of food through nanoliquid chromatography and electrospray ionization-ion trap mass spectrometry (nanoHPLC-ESI-IT-MSn). A specific bioinformatic approach was developed, in order to characterize the most important food allergens from soft cheese and plant samples from the Apiaceae family and to describe the effects to the allergenic potential of soft cheese samples during several conditioning systems. In this work, the peptides FVAPFPEVF from cow’s milk (CM) allergen Bos d 9 and the peptide QEPVLGPVRGPFPIIV from CM allergen Bos d 11 were identified and proposed as valid marker peptides for CM allergens detection and for future analysis in this field, e.g. for quantification purposes. In addition, the results demonstrated the effect of some packaging conditions on reducing the total allergenic power of soft cheese, and showed the condition treatment S1 (based on potassium sorbate) as an optimal solution for reducing the total potential of soft cheese, both in terms of allergenic intensity and in terms of allergen variability. Using a similar strategy, several experiments were also performed for detecting the presence of Apiaceae allergens from plant samples and to characterize one or more potential markers for their detection in food using LCMS. Among those, the hydrophilic peptide FYETKDTDILAAFR from the allergen Spi o Rubisco is proposed as a potential marker for routine detection of Apiaceae allergens in food through ESI-MSn. Other aspects regarding this allergen should be investigated, such as its behaviour under food processing and its role in the allergenic reactions (epitope mapping). In this last step, a particular attention was given to the sample preparation and to the selection of eco-compatible extraction buffers, and to the overall optimization of LCMS experimental conditions.