Birch pollen allergy ranks among the most prevalent pollen allergies in Northern and Central Europe. Estimated 100 million individuals worldwide suffer from this IgE-mediated disease leading to clinical manifestations such as hayfever causing a major health and economic burden. Bet v 1 is the sole major birch pollen allergen. It sensitizes more than 90% of individuals allergic to birch pollen and mainly contributes to pollen-associated food allergy.
Representing such a clinically important allergen, various strategies to treat and/or cure birch pollen allergy have been elaborated in the last decades. Passive immunization with allergen-specific antibodies is one approach that recently came into the focus of researchers. Studies showed that a single injection of human monoclonal allergen-specific IgG antibodies significantly reduced allergic symptoms in birch pollen-allergic patients.
However, the production of full monoclonal antibodies in sufficient amounts is laborious and expensive.
This is the reason why we generated and isolated Bet v 1-specific nanobodies to find out if allergen-specific nanobodies have similar protective potential as monoclonal antibodies.
Therefore, a cDNA-VHH library was constructed from a camel that was immunized with Bet v 1 and screened for Bet v 1-specific binders by phage display. Our isolated nanobodies not only recognized Bet v 1 with high affinity but cross-react with birch pollen related allergens from alder and hazel. Both characteristics defined our allergen-specific nanobodies already as suitable candidates for further investigations. More importantly, it revealed that our selected nanobodies inhibited allergic patients' polyclonal IgE binding to Bet v 1 and Bet v 1-homologues and reduced allergen-induced basophil activation.
Our study reports for the first time on allergen-specific nanobodies as useful tools for future treatment of pollen allergy.
This work was supported by the Austrian Science Fund (FWF) grants I3946-B33, F4607, and P32953 and by the Russian Foundation for Basic Research (RFBR) grant 18-515-14003.