Gut Microbiome Dynamics in Peanut Allergy

项目摘要/摘要 花生过敏影响2-5%的美国儿童和1%的美国总人口。越来越多的证据支持 肠道微生物区系在食物过敏病理生物学中的作用。我们的团队发现了不同的肠道微生物区系 鸡蛋过敏儿童与对照组的比较，我们发现肠道微生物区系与后来的解决有关 牛奶过敏。肠道微生物群可能因食物过敏原而不同。目前还没有关于肠道微生物群的研究。 表型良好的花生过敏受试者。我们的中心假设是肠道微生物区系塑造了发育 和花生过敏的解决办法。我们将利用纵向样本和来自两个 由NIAID资助的由我们领导的队列研究肠道微生物组动力学在肠道疾病的发展和解决中的作用 花生过敏。先前的横断面研究表明，不清楚生物失调是在食物过敏之前还是之后发生 开始了。在目标1中，我们将使用下一代测序来描述随时间推移的肠道微生物组，并评估其 与花生过敏发生发展的关系。COFAR观察性研究的参与者登记为 有高特应性风险的婴儿。注册时没有人对花生过敏，而现在有40.1%。使用倾斜的凳子 从婴儿期和儿童期中期，我们将确定婴儿期肠道微生物组特征是危险因素。 花生过敏的发展，以及花生肠道微生物群的纵向变化 过敏的发展。在目标2中，我们将确定肠道微生物群和拆分之间的关系 花生过敏。我们将研究参加脱敏研究的花生过敏儿童，其中花生 变态反应有望消退32.5%。我们将使用下一代测序来分析收集到的粪便 纵向确定肠道微生物组的基线特征，并通过脱敏改变， 与花生过敏的解决有关。肠道微生物区系通过代谢产物发挥免疫作用 (例如，短链脂肪酸(SCFA))。在目标3中，我们将衡量目标SCFA和全球 确定与花生过敏有关的横向和纵向代谢物的代谢组 决议。然后，我们将应用系统生物学方法来(1)破译 微生物组、代谢组和宿主，以及(2)确定花生过敏解决的原因关键驱动因素。双重奏 这项研究对表型良好的花生过敏受试者产生的肠道微生物组和肠道代谢组数据， 连同我们将从U19 AI136053获得的并行宿主转录组数据，提供了前所未有的 开发花生过敏的数据驱动机制模型的机会。这项研究将使直接 通过创新、集成的方法确定肠道微生物组在花生过敏中的作用的进展 微生物组、代谢组和寄主转录组的检查。结果将包括第一批人类数据 肠道微生物群在花生过敏发生和消退中的作用以及病因的新鉴定 花生过敏解决的关键驱动因素(微生物、代谢物和宿主转录本)。这些结果将 阐明花生过敏的潜在机制，并强调治疗和生物标记物的靶点。

PROJECT SUMMARY/ABSTRACT Peanut allergy affects 2-5% of US children and 1% of the overall US population. Growing evidence supports a role for gut microbiota in the pathobiology of food allergy. Our group identified differential gut microbiota in children with egg allergy vs. controls, and we identified gut microbiota associated with the later resolution of milk allergy. Gut microbiomes may differ by food allergen. There has been no study of the gut microbiome in well-phenotyped peanut allergic subjects. Our central hypothesis is that gut microbiota shape the development and resolution of peanut allergy. We will leverage longitudinal samples and complementary data from two NIAID-funded cohorts led by us to characterize gut microbiome dynamics in the development and resolution of peanut allergy. Prior cross-sectional studies leave unclear whether dysbiosis precedes or follows food allergy onset. In Aim 1, we will use next-generation sequencing to profile the gut microbiome over time and assess its relationship to the development of peanut allergy. Participants of the CoFAR Observational Study enrolled as infants with high atopic risk. None had peanut allergy at enrollment, while 40.1% now do. Using banked stool from infancy and mid-childhood, we will identify gut microbiome characteristics at infancy that are risk factors for the development of peanut allergy, and longitudinal changes to the gut microbiome that characterize peanut allergy development. In Aim 2, we will identify the relationship between gut microbiome and the resolution of peanut allergy. We will study peanut allergic children enrolled in a desensitization study, of whom peanut allergy is expected to resolve in 32.5%. We will use next-generation sequencing to profile stool collected longitudinally to identify gut microbiome characteristics at baseline, and changes through desensitization, that are associated with peanut allergy resolution. Gut microbiota exert immunologic influence through metabolites (e.g. short chain fatty acids (SCFAs)) they produce. In Aim 3, we will measure targeted SCFA and global metabolome to identify metabolites that are cross-sectionally and longitudinally associated with peanut allergy resolution. We will then apply systems biology methods to (1) decipher causal relationships between microbiome, metabolome, and host, and (2) identify causal key drivers of peanut allergy resolution. The dual gut microbiome and gut metabolome data generated on well-phenotyped peanut allergic subjects by this study, along with parallel host transcriptome data that we will have from U19 AI136053, offer an unprecedented opportunity to develop data-driven mechanistic models for peanut allergy. This study will enable direct progress toward defining the role of the gut microbiome in peanut allergy through an innovative, integrated examination of microbiome, metabolome, and host transcriptome. Results will include the first human data on the gut microbiome in peanut allergy development and resolution, as well as the novel identification of causal key drivers (microbes, metabolites, and host transcripts) of peanut allergy resolution. These results will elucidate mechanisms underlying peanut allergy and highlight therapeutic and biomarker targets.