Identification of Seasonal Variations of Antibodies against PR-10-Specific Epitopes Can Be Improved Using Peptide-Phage Display

Abstract

<b><i>Background:</i></b> In pollinosis patients, allergen-specific antibody titers show seasonal variations. Little is known about these variations at the epitope level. <b><i>Objectives:</i></b> We aimed at investigating seasonal variations on the level of allergen epitope recognition in patients with Bet v 1-related food allergy using a peptide phage display approach. <b><i>Methods:</i></b> Serum samples collected over 1 year from 4 patients of the placebo arm of the birch-associated soya allergy immunotherapy trial were included. To identify epitopes from Bet v 1-related food allergens, patient sera were used in peptide phage display experiments. In silico analysis of enriched allergen-related motifs was performed. <b><i>Results:</i></b> We identified epitope motifs related to Bet v 1 and its homologs in soya and hazelnut (Gly m 4 and Cor a 1, respectively) that were enriched in accordance with birch and hazel pollen exposure. Within several weeks after the birch pollen season peak, the pattern of identified epitope motifs differed considerably among patients. Data for amino acid preferences in homologous Bet v 1 and Cor a 1 epitope motifs identified for one of the investigated patients suggest changes in concentration or specificity of serum antibodies for the Cor a 1 epitope motif. <b><i>Conclusions:</i></b> Peptide phage display data suggest an impact of birch and hazel pollen exposure on the recognition pattern of Bet v 1-like allergen epitopes. Epitope-oriented analyses could provide deeper, personalized details regarding the allergen epitope recognition influenced by pollen exposure beyond the capability of current methods.

Peptide epitopes as biomarkers of soya sensitization in rBet v 1 immunotherapy of birch‐related soya allergy

“…Mean sequence frequencies of each enriched allergen epitope motif were compared between patients' sub‐groups. These frequencies were calculated by dividing the total number of sequences retrieved from a dataset containing a given motif by the number of unique sequences, as previously reported 23 . When sub‐grouping was not feasible, mean frequencies were compared between patients from the upper and lower tertiles of the recorded clinical measures.…”