A bioinformatics approach to identify patients with symptomatic peanut allergy using peptide microarray immunoassay

Abstract

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.

Fig 1

Comparison of IgE (A) and IgG₄(B) binding diversity to peanut allergens Ara h 1, Ara h 2, and Ara h 3, and binding diversity to individual allergen Ara h 1, Ara h 2, and Ara h 3 between peanut-allergic and peanut-tolerant groups. Antibody binding diversity was measured as the number of positive peptides (robust Z score > 3) determined by using peptide microarray immunoassay.

Fig 2

Comparison of IgE (above x-axis line) and IgG₄ binding (below x-axis line) to peptides of Ara h 1, Ara h 2, and Ara h 3 between peanut-allergic (red lines) and peanut-tolerant (blue lines) groups. The bottom x-axis shows the overlapping peptides, and the top x-axis shows the corresponding amino acid number of the peptide. The y-axis shows the percentage of patients within each group showing positive binding to each peptide. IgE-binding regions/epitopes identified by using TileMap and the key peptide biomarkers identified by using machine learning methods are indicated with red circles and asterisks, respectively. The previously identified epitopes and immunodominant epitopes are indicated with gray and blue diamonds, respectively.

Fig 3

Decision tree built for classifying peanut-allergic and peanut-tolerant individuals. Sixty-two individuals were sorted from root (top circle) to leaf nodes (rectangles), based on their IgE reaction to a panel of peanut peptide biomarkers defined at each node (circle), which represent the splitting points. The peptides selected for each splitting point are listed under the circles, and the splitting threshold (Z score) appears in the circle. At each splitting point, individuals with IgE reactions to the selected peptide at or above the threshold are assigned to the right branch and below the threshold to the left. The value on the branch shows the number of individuals passing through. The percentage value under the leaf nodes represents the calculated accuracy.

Fig 4

Comparison of the diagnostic performance of different allergy tests and analysis methods in predicting the outcome of DBPCFC. The area under the ROC curve indicates how well a test method can distinguish between 2 diagnostic groups (peanut allergic vs peanut tolerant). The diagonal line indicates a completely random guess. Both IgE binding diversity (expressed as the number of positive peptides) and intensity (express as Z score) were measured by using peptide microarray.