Allergen cross-reactivity in allergic rhinitis and oral-allergy syndrome: a bioinformatic protein sequence analysis.

Abstract

Clinical allergy cross-reactivity that is seen with related inhalant allergens or between unrelated inhalant allergens and foods in oral allergy syndrome (OAS) remains poorly understood. The goal of this study is to determine whether clinical cross-reactivity can be identified from primary protein sequences in allergy epitopes and food proteins. High-throughput analysis was performed by assembling all known allergy epitopes within the Immune Epitope Database (IEDB; http://www.iedb.org) for 5 common species from 5 inhalant allergen subclasses and comparing their protein sequences to each other, as well as to sequences of intact proteins from known cross-reactive foods in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) protein database (http://www.uniprot.org) that have been implicated in OAS. Computational methods were employed to allow for exact matching, gaps, and similar amino acids using multiple algorithms. A phylogenetic tree was created to determine evolutionary relationships between cross-reactive epitopes in OAS. Twenty-three common inhalant allergens had 4429 unique epitopes; the 19 foods implicated in OAS had 9497 protein sequences. The Basic Local Alignment Search Tool (BLAST) algorithm identified interclass and intraclass sequence similarities for the 5 inhalant allergy classes with high similarity for mites, grasses, and trees. Analysis of OAS proteins identified 104 matches to inhalant allergy epitopes that are known to cross-react. The phylogenetic tree displayed relationships that mostly followed organism phylogeny. Use of primary protein sequences was successful in explaining clinical allergy cross-reactivity. Clinical correlation is needed for use of these epitopes as diagnostic or therapeutic entities for patients with cross-reactive allergic disease.