IgE-Mediated Peanut Allergy: Current and Novel Predictive Biomarkers for Clinical Phenotypes Using Multi-Omics Approaches

Rebecca Czolk, Julia Klueber, Martin Sørensen, Paul Wilmes, Françoise Codreanu-Morel, Per Stahl Skov, Christiane Hilger, Carsten Bindslev-Jensen, Markus Ollert, Annette Kuehn*

*Kontaktforfatter for dette arbejde

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Food allergy is a collective term for several immune-mediated responses to food. IgE-mediated food allergy is the best-known subtype. The patients present with a marked diversity of clinical profiles including symptomatic manifestations, threshold reactivity and reaction kinetics. In-vitro predictors of these clinical phenotypes are evasive and considered as knowledge gaps in food allergy diagnosis and risk management. Peanut allergy is a relevant disease model where pioneer discoveries were made in diagnosis, immunotherapy and prevention. This review provides an overview on the immune basis for phenotype variations in peanut-allergic individuals, in the light of future patient stratification along emerging omic-areas. Beyond specific IgE-signatures and basophil reactivity profiles with established correlation to clinical outcome, allergenomics, mass spectrometric resolution of peripheral allergen tracing, might be a fundamental approach to understand disease pathophysiology underlying biomarker discovery. Deep immune phenotyping is thought to reveal differential cell responses but also, gene expression and gene methylation profiles (eg, peanut severity genes) are promising areas for biomarker research. Finally, the study of microbiome-host interactions with a focus on the immune system modulation might hold the key to understand tissue-specific responses and symptoms. The immune mechanism underlying acute food-allergic events remains elusive until today. Deciphering this immunological response shall enable to identify novel biomarker for stratification of patients into reaction endotypes. The availability of powerful multi-omics technologies, together with integrated data analysis, network-based approaches and unbiased machine learning holds out the prospect of providing clinically useful biomarkers or biomarker signatures being predictive for reaction phenotypes.

TidsskriftFrontiers in Immunology
Antal sider11
StatusUdgivet - 28. jan. 2021

Bibliografisk note

Funding Information:
Supported by the Luxembourg National Research Fund on PRIDE program grants PRIDE/11012546/NEXTIMMUNE and PRIDE17/11823097/MICROH; supported by the Personalized Medicine Consortium grant APSIS, PMC/2017/02 and by the Ministry of Research, Luxembourg.

Publisher Copyright:
© Copyright © 2021 Czolk, Klueber, Sørensen, Wilmes, Codreanu-Morel, Skov, Hilger, Bindslev-Jensen, Ollert and Kuehn.

Copyright 2021 Elsevier B.V., All rights reserved.

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