Biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy

花生过敏表型异质性的生物标志物和关键驱动因素

• 批准号: 10415894
• 负责人: Supinda Bunyavanich
• 金额: $ 35.95万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-06 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415894
• 关键词: 3-Dimensional; Acute; Address; Affect; Allergic; Allergic Reaction; Allergy to peanuts; Anaphylaxis; Anxiety; Area; Asthma; Bayesian Network; Binding; Biological Markers; Biology; Blood specimen; Caring; Child; Clinical; Computational Biology; Data; Decision Trees; Detection; Ensure; Epitopes; Failure; Food; Food Hypersensitivity; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic Variation; Genomics; Goals; Heterogeneity; IgE; IgG4; Immune; Impairment; Individual; Knowledge; Lead; Leukocytes; Link; Machine Learning; Methods; Network-based; Oral; Pathway Analysis; Pathway interactions; Phenotype; Predictive Value; Proteins; Provider; Quantitative Trait Loci; RNA Sequences; Reaction; Research Personnel; Resources; Rest; Sampling; Severities; Stress; Time; Tissue-Specific Gene Expression; Transcript; Uncertainty; Urticaria; Validation; Work; accurate diagnostics; base; biomarker development; biomarker panel; causal variant; cohort; decision tree learning; desensitization; diagnostic biomarker; differential expression; food challenge; genetic signature; learning network; peripheral blood; predictive marker; predictive test; prospective; random forest; response; success; tool; transcriptome; transcriptome sequencing; transcriptomics

Biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy PROJECT 3 SUMMARY / ABSTRACT Unexpected allergic reactions to peanut are the most common cause of fatal food-related anaphylaxis. There is currently no method to predict reaction thresholds for subjects with peanut allergy. Given two peanut allergic subjects with similar clinical profiles, one may react with anaphylaxis to minute exposure while the other may react with hives to larger amounts. The same individuals may have disparate responses to desensitization that cannot be predicted. This heterogeneity in reaction threshold, reaction severity, and desensitization success is crippling to peanut allergic individuals, whose lives are impaired by anxiety that small exposures could lead to anaphylaxis at any time. Additionally, providers cannot offer early guidance whether resource-intensive desensitization efforts will succeed. With peanut allergy now affecting 2-5% of US schoolchildren, these areas of uncertainty stress the need to identify biomarkers of reaction threshold, reaction severity, and desensitization potential. Based on our demonstrated work in biomarker development and integrative genomics, we hypothesize that biomarkers and causal key drivers of phenotypic heterogeneity in peanut allergy can be identified through integrated network-based examination of peripheral blood transcriptomes, epitope-binding, and clinical parameters from peanut allergic subjects. We will study peanut allergic subjects undergoing oral food challenges and desensitization to pursue three specific aims that address unmet needs in peanut allergy care and knowledge. In Aim 1, we will identify a resting-state peripheral blood biomarker of exquisitely sensitive (low threshold) peanut allergy by RNA-sequence profiling baseline peripheral blood from low and high threshold peanut allergic children, differential gene expression analysis, machine learning, and weighted gene coexpression network analysis. The predictive biomarker of reaction threshold identified will be prospectively validated. In Aim 2, we will identify causal key drivers of peanut allergy severity. We will use baseline and post-challenge RNAseq profiles, expression quantitative trait loci, epitope-binding scores, and clinical variables to build the first probabilistic causal network specific to food allergy. We will apply key driver analysis to this network to identify genes, epitopes, and clinical variables that causally modulate peanut reaction severity. This information-rich, data-driven network will be shared with other investigators seeking to elucidate mechanisms underlying peanut allergy. In Aim 3, we will identify an early-appearing peripheral blood biomarker of peanut desensitization potential using samples obtained from subjects during desensitization, leukocyte deconvolution, machine learning, and weighted gene coexpression network analysis. The results from this project will directly address unmet needs in the management of peanut allergy by identifying peripheral blood biomarkers that predict reaction threshold and desensitization potential in peanut allergic individuals. The project will also further our mechanistic understanding of peanut allergy severity. Although we focus on peanut allergy, we expect that many findings will be applicable to other food allergies.

花生过敏表型异质性的生物标志物和致病关键驱动因素 项目3摘要/摘要 对花生的意外过敏反应是致命性食物过敏反应的最常见原因。的确有 目前还没有方法预测花生过敏受试者的反应阈值。给了两颗花生过敏 临床症状相似的受试者，其中一人可能对微小的接触过敏反应，而另一人可能 与更大剂量的麻疹发生反应。同一个体可能对脱敏有不同的反应 无法预测。反应阈值、反应严重程度和脱敏成功率的异质性是 对花生过敏的人，他们的生活因小剂量暴露可能导致的焦虑而受损 任何时候都有过敏反应。此外，供应商无法提供早期指导，无论是资源密集型 脱敏的努力将会成功。由于花生过敏现在影响到2%-5%的美国学童，这些地区 不确定性强调需要确定反应阈值、反应严重程度和 脱敏潜力。基于我们在生物标记物开发和集成方面的示范工作 基因组学，我们假设花生表型异质性的生物标志物和原因关键驱动因素 过敏可以通过基于网络的外周血液转录本的综合检查来识别， 花生过敏受试者的表位结合和临床参数。我们将研究花生过敏的对象 正在接受口服食物挑战和脱敏，以追求三个具体目标，以解决未得到满足的需求 花生过敏护理和知识。在目标1中，我们将确定一个静息状态的外周血液生物标记物 用RNA序列分析基线外周血中花生过敏(低阈值) 低阈值和高阈值花生过敏儿童，差异基因表达分析，机器学习，以及 加权基因共表达网络分析。确定的反应阈值的预测生物标志物将是 预期会得到验证。在目标2中，我们将确定花生过敏严重程度的原因关键驱动因素。我们将使用 基线和挑战后的RNAseq图谱、表达数量性状基因座、表位结合分数以及 临床变量来建立食物过敏的第一个特定的概率因果网络。我们将应用关键驱动程序 对这个网络的分析，以确定因果调节花生的基因、表位和临床变量 反应严重。这个信息丰富、数据驱动的网络将与其他寻求 阐明花生过敏的潜在机制。在目标3中，我们将识别早期出现的外周血液 花生脱敏潜力的生物标记物使用脱敏过程中从受试者获得的样本， 白细胞去卷积、机器学习和加权基因共表达网络分析。结果是 将直接解决花生过敏管理中未得到满足的需求，方法是确定 预测花生过敏反应阈值和脱敏潜能的外周血液生物标志物 个人。该项目还将进一步加深我们对花生过敏严重程度的机械理解。虽然我们 关注花生过敏，我们预计许多发现将适用于其他食物过敏。