Immunologic basis of phenotypic heterogeneity in peanut allergy

项目2-摘要 花生过敏（PA）的许多方面都存在异质性，包括引发PA的花生剂量。 症状（阈值）、诱发症状的严重程度和症状类型（目标 （一）被触动的器官。一个重要的少数（高达20%）将超过他们的PA，而大多数人保留 临床反应性我们认为PA临床表型的这种天然异质性是一种有价值的研究方法。 有机会了解有助于疾病的免疫机制。我们假设不同的免疫系统 IgE、IgG 4、CD 4 + T细胞和嗜碱性粒细胞水平的应答是重要的临床异质性的基础， 此外，我们假设这些不同的免疫谱形成PA预后的基础。通过 临床核心，我们将获得200-250例接受PA口服食物挑战的受试者，这些受试者具有较低的 或对花生有高反应阈值的人，或不过敏的人。我们还将获得98个高门槛 受试者随机接受低于阈值的花生在饮食中（或继续避免），谁将有一个 在他们被研究的2.5年里，各种各样的临床结果。我们将执行高维 对这些受试者进行免疫分析，以确定连接对花生的免疫和临床反应的途径。我们 研究发现，对牛奶过敏原表位结合的IgE进行生物信息学分析不仅可以预测临床症状， 因此，我们可以预测牛奶过敏表型，但准确预测牛奶口服免疫治疗后的治疗成功。我们将 应用这种方法来测试表位结合是否可以预测对花生的临床反应性，并预测对 饮食干预我们还发现，花生特异性Th 2的大小和分化 PA受试者的反应高度异质。我们将确定Th 2的临床意义， 使用基于CD 154的检测和表型分析的组合， 通过流式细胞术和多重细胞因子测定分析花生应答性T细胞。我们已经开发 基于CyTOF的方法研究全血细胞对花生的反应，并已发表 初步数据表明，我们可以识别嗜碱性粒细胞和其他细胞类型中的信号传导和激活， 不仅在体外，而且在体内。我们假设，全血中细胞活化的广泛分布是由 CyTOF将识别新的应答细胞群落，这些细胞群落可区分免疫缺陷病毒的关键临床表型。 宾夕法尼亚州我们的项目的成功完成将建立一个临床免疫网络的PA，覆盖了详细的 花生特异性抗体、T细胞和效应细胞的高维网络上的临床表型 应答我们预计，这一临床免疫网络不仅将揭示机制，而且将是一个新的研究领域。 指导PA个性化管理和治疗策略的基本工具。

Project 2-Summary There is heterogeneity in many aspects of peanut allergy (PA), including the dose of peanut that triggers symptoms (the threshold), the severity of symptoms that are induced, and the type of symptoms (target organ(s)) that are triggered. A significant minority (up to 20%) will outgrow their PA, while the majority retains clinical reactivity. We propose that this natural heterogeneity in the clinical phenotype of PA is a valuable opportunity to understand immune mechanisms contributing to disease. We hypothesize that distinct immune responses at the level of IgE, IgG4, CD4+ T cells, and basophils underlie important clinical heterogeneity, and furthermore we hypothesize that these distinct immune profiles form a basis for prognosis of PA. Through the Clinical Core, we will have access to 200-250 subjects undergoing oral food challenge for PA who have a low or high threshold of reactivity to peanut, or who are not allergic. We will also have access to 98 high-threshold subjects randomized to receive sub-threshold peanut in the diet (or continue avoidance), and who will have a diverse set of clinical outcomes over the 2.5 years that they are studied. We will perform high dimensional immune profiling on these subjects to identify pathways linking immune and clinical responses to peanut. We have found that bioinformatics analysis of IgE binding to epitopes in milk allergens can predict not only clinical phenotype of milk allergy, but accurately predict treatment success after milk oral immunotherapy. We will apply this approach to test if epitope binding can predict clinical reactivity to peanut, and predict response to dietary intervention. We have also identified that the magnitude and differentiation of the peanut-specific Th2 response is highly heterogeneous in PA subjects. We will determine the clinical implications of Th2 heterogeneity within this diverse cohort, using a combination of CD154-based detection and phenotypic analysis of peanut-responsive T cells by flow cytometry, and multiplex cytokine assays. We have developed CyTOF-based approaches to study the response of cells in whole blood to peanut, and have published as well as preliminary data demonstrating that we can identify signaling and activation in basophils and other cell types not only in vitro but also in vivo. We hypothesize that broad profiling of the activation of cells in whole blood by CyTOF will identify novel communities of responding cells that discriminate between key clinical phenotypes of PA. The successful completion of our project will build a clinical-immune network of PA that overlays a detailed clinical phenotype over a high dimensional network of peanut-specific antibodies, T cells, and effector cell responses. We anticipate that this clinical-immune network will not only reveal mechanisms, but will be an essential tool for guiding personalized management and treatment strategies for PA.