Document Detail

A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay.
MedLine Citation:
PMID:  22444503     Owner:  NLM     Status:  MEDLINE    
BACKGROUND: Peanut allergy is relatively common, typically permanent, and often severe. Double-blind, placebo-controlled food challenge is considered the gold standard for the diagnosis of food allergy-related disorders. However, the complexity and potential of double-blind, placebo-controlled food challenge to cause life-threatening allergic reactions affects its clinical application. A laboratory test that could accurately diagnose symptomatic peanut allergy would greatly facilitate clinical practice.
OBJECTIVE: We sought to develop an allergy diagnostic method that could correctly predict symptomatic peanut allergy by using peptide microarray immunoassays and bioinformatic methods.
METHODS: Microarray immunoassays were performed by using the sera from 62 patients (31 with symptomatic peanut allergy and 31 who had outgrown their peanut allergy or were sensitized but were clinically tolerant to peanut). Specific IgE and IgG(4) binding to 419 overlapping peptides (15 mers, 3 offset) covering the amino acid sequences of Ara h 1, Ara h 2, and Ara h 3 were measured by using a peptide microarray immunoassay. Bioinformatic methods were applied for data analysis.
RESULTS: Individuals with peanut allergy showed significantly greater IgE binding and broader epitope diversity than did peanut-tolerant individuals. No significant difference in IgG(4) binding was found between groups. By using machine learning methods, 4 peptide biomarkers were identified and prediction models that can predict the outcome of double-blind, placebo-controlled food challenges with high accuracy were developed by using a combination of the biomarkers.
CONCLUSIONS: In this study, we developed a novel diagnostic approach that can predict peanut allergy with high accuracy by combining the results of a peptide microarray immunoassay and bioinformatic methods. Further studies are needed to validate the efficacy of this assay in clinical practice.
Jing Lin; Francesca M Bruni; Zhiyan Fu; Jennifer Maloney; Ludmilla Bardina; Attilio L Boner; Gustavo Gimenez; Hugh A Sampson
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2012-03-23
Journal Detail:
Title:  The Journal of allergy and clinical immunology     Volume:  129     ISSN:  1097-6825     ISO Abbreviation:  J. Allergy Clin. Immunol.     Publication Date:  2012 May 
Date Detail:
Created Date:  2012-04-30     Completed Date:  2012-07-27     Revised Date:  2013-06-26    
Medline Journal Info:
Nlm Unique ID:  1275002     Medline TA:  J Allergy Clin Immunol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1321-1328.e5     Citation Subset:  AIM; IM    
Copyright Information:
Copyright © 2012 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.
Pediatric Allergy and Immunology, Mount Sinai School of Medicine, New York, NY 10029, USA.
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MeSH Terms
2S Albumins, Plant / immunology,  metabolism
Antigens, Plant / immunology,  metabolism
Child, Preschool
Computational Biology / methods
Epitopes / immunology,  metabolism
Glycoproteins / immunology,  metabolism
Immunoglobulin E / metabolism
Immunoglobulin G / metabolism
Models, Biological
Peanut Hypersensitivity / diagnosis*,  immunology*
Plant Proteins / immunology,  metabolism
Predictive Value of Tests
Protein Array Analysis
Grant Support
Reg. No./Substance:
0/2S Albumins, Plant; 0/Antigens, Plant; 0/Ara h 1 protein, Arachis hypogaea; 0/Ara h 2 allergen, Arachis hypogaea; 0/Ara h 3 allergen, Arachis hypogea; 0/Epitopes; 0/Glycoproteins; 0/Immunoglobulin G; 0/Plant Proteins; 37341-29-0/Immunoglobulin E

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