Microarray molecular mapping of horses with severe asthma

Author:

White Samuel J.1ORCID,Couetil Laurent2ORCID,Richard Eric A.34,Marti Eliane5,Wilson Philippe B.1ORCID

Affiliation:

1. Nottingham Trent University Nottinghamshire UK

2. Veterinary Clinical Sciences, College of Veterinary Medicine Purdue University West Lafayette Indiana USA

3. LABÉO Frank Duncombe Caen Cedex France

4. Normandie Univ, UniCaen, BIOTARGEN Saint‐Contest France

5. Department of Clinical Research and Veterinary Public Health University of Bern Bern Switzerland

Abstract

AbstractBackgroundSevere asthma (SA) in horses, resembling human asthma, is a prevalent, debilitating allergic respiratory condition marked by elevated allergen‐specific immunoglobulin E (IgE) against environmental proteins; however, research exploring the exposome's influence on IgE profiles is currently limited but holds paramount significance for diagnostic and therapeutic developments.AnimalsThirty‐five sports horses were analyzed, consisting of environmentally matched samples from France (5 SA; 6 control), the United States (6 SA; 6 control), and Canada (6 SEA; 6 control).MethodsThis intentional cross‐sectional study investigated the sensitization profiles of SA‐affected and healthy horses via serological antigen microarray profiling. Partial least square‐discriminant analysis (PLS‐DA) was used to identify and rank the importance of allergens for class separation (ie, affected/non‐affected) as variable influence of projection (VIP), and allergen with commonality internationally established via frequency analysis.ResultsPLS‐DA models showed high discriminatory power in predicting SA in horses from Canada (area under the curve [AUC] 0.995) and France (AUC 0.867) but poor discriminatory power in horses from the United States (AUC 0.38). Hev b 5.0101, Cyn D, Der p 2, and Rum cr were the only shared allergens across all geographical groups.Conclusions and Clinical ImportanceMicroarray profiling can identify specific allergenic components associated with SA in horses, while mathematical modeling of this data can be used for disease classification, highlighting the variability of sensitization profiles between geographical locations and emphasizing the importance of local exposure to the prevalence of different allergens. Frequency scoring analysis can identify important variables that contribute to the classification of SA across different geographical regions.

Publisher

Wiley

Subject

General Veterinary

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