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Introduction: Molecular studies of hen's egg allergens help define allergic phenotypes, with IgE to sequential (linear) epitopes on the ovomucoid (OVM) protein associated with a persistent disease. Epitope profiles of other egg allergens are largely unknown. The objective of this study was to construct an epitope library spanning across 7 allergens and further evaluate sequential epitope-specific (ses-)IgE and ses-IgG4 among baked-egg reactive or tolerant children. Methods: A Bead-Based Epitope Assay was used to identify informative IgE epitopes from 15-mer overlapping peptides covering the entire OVM and ovalbumin (OVA) proteins in 38 egg allergic children. An amalgamation of 12 B-cell epitope prediction tools was developed using experimentally identified epitopes. This ensemble was used to predict epitopes from ovotransferrin, lysozyme, serum albumin, vitellogenin-II fragment, and vitellogenin-1 precursor. Ses-IgE and ses-IgG4 repertoires of 135 egg allergic children (82 reactive to baked-egg, the remaining 52 tolerant), 46 atopic controls, and 11 healthy subjects were compared. Results: 183 peptides from OVM and OVA were screened and used to create an aggregate algorithm, improving predictions of 12 individual tools. A final library of 65 sequential epitopes from 7 proteins was constructed. Egg allergic children had higher ses-IgE and lower ses-IgG4 to predominantly OVM epitopes than both atopic and healthy controls. Baked-egg reactive children had similar ses-IgG4 but greater ses-IgE than tolerant group. A combination of OVA-sIgE with ses-IgEs to OVM-023 and OVA-028 was the best predictor of reactive phenotype. Conclusion: We have created a comprehensive epitope library and showed that ses-IgE is a potential biomarker of baked-egg reactivity. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnosis of peanut allergy is a significant clinical challenge. Here, a novel diagnostic blood test using the peanut bead‐based epitope assay ("peanut BBEA") was developed utilizing the LEAP cohort and then validated using two independent cohorts. Methods: The development of the peanut BBEA diagnostic test followed the National Academy of Medicine's established guidelines with discovery performed on 133 subjects from the non‐interventional arm of the LEAP trial and an independent validation performed on 82 subjects from the CoFAR2 and 84 subjects from the POISED study. All samples were analyzed using the peanut BBEA methodology, which measures levels of IgE to two Ara h 2 sequential (linear) epitopes and compares their combination to a threshold pre‐specified in the model development phase. When a patient has an inconclusive outcome by skin prick testing (or sIgE), IgE antibody levels to this combination of two epitopes can distinguish whether the patient is "Allergic" or "Not Allergic." Diagnoses of peanut allergy in all subjects were confirmed by double‐blind placebo‐controlled food challenge and subjects' ages were 7–55 years. Results: In the validation using CoFAR2 and POISED cohorts, the peanut BBEA diagnostic test correctly diagnosed 93% of the subjects, with a sensitivity of 92%, specificity of 94%, a positive predictive value of 91%, and negative predictive value of 95%. Conclusions: In validation of the peanut BBEA diagnostic test, the overall accuracy was found to be superior to existing diagnostic tests for peanut allergy including skin prick testing, peanut sIgE, and peanut component sIgE testing. [ABSTRACT FROM AUTHOR]

Background: The majority of children with cow's milk allergy (CMA) tolerate baked milk. However, reactivity to fermented milk products such as yogurt/cheese has not been previously evaluated. We sought to determine whether children with CMA could tolerate yogurt/cheese and whether a patient's IgE and IgG4‐binding pattern to milk protein epitopes could distinguish clinical reactivity. Methods: Four groups of reactivity were identified by Oral food challenge: baked milk reactive, fermented milk reactive, whole milk reactive, and outgrown. sIgE and sIgG4 binding to milk protein epitopes were assessed with a novel Luminex‐based peptide assay (LPA). Using machine learning techniques, a model was developed to predict different degrees of CMA. Results: The baked milk reactive patients demonstrated the highest degree of IgE epitope binding, which was followed sequentially by fermented milk reactive, whole milk reactive, and outgrown. Data were randomly divided into two groups with 75% of the data utilized for model development (n = 68) and 25% for testing (n = 21). All 68 children used for training were correctly classified with models using IgE and IgG4 epitopes. The average cross‐validation accuracy was much higher for models using IgE plus IgG4 epitopes by LPA (84.8%), twice the performance of the serum component proteins assayed by UniCAP (41.9%). The performance of the model on "unseen data" was tested using the 21 withheld patients, and the accuracy of IgE was 86% (AUC = 0.89) while of IgE+IgG4 model was 81% (AUC = 0.94). Conclusion: Using a novel high‐throughput LPA, we were able to distinguish the diversity of IgE/IgG4 binding to epitopes in the varying CMA phenotypes. LPA is a promising tool to predict correctly different degrees of CMA. The new food challenge sequence and Luminex‐based peptide assay to identify the clinical phenotypes of reactivity to baked, fermented, and whole cow's milk. [ABSTRACT FROM AUTHOR]

Highlights: [•] A peptide microarray was used to explore the IgE binding epitopes of β-CN digests. [•] Similar peptide profiles were obtained with simulated and human digestive fluids. [•] The most immunoreactive areas of the protein were β-CN fragments (57–68) and (82–93). [•] Both systems provided a good estimation of the potential allergenicity of β-CN digests. [ABSTRACT FROM AUTHOR]