Gut Microbiome Dynamics in Peanut Allergy

项目总结/摘要 花生过敏影响2-5%的美国儿童和1%的美国总人口。越来越多的证据表明， 肠道微生物群在食物过敏病理学中的作用。我们的研究小组确定了不同的肠道微生物群， 鸡蛋过敏的儿童与对照组相比，我们确定了与后来的解决方案相关的肠道微生物群。 牛奶过敏肠道微生物组可能因食物过敏原而异。目前还没有关于肠道微生物组的研究， 对花生过敏的人我们的核心假设是肠道微生物群塑造了 花生过敏症也解决了我们将利用来自两个国家的纵向样本和补充数据， 由我们领导的NIAID资助的队列，以表征肠道微生物组在开发和解决 花生过敏先前的横断面研究不清楚生态失调是在食物过敏之前还是之后 发病在目标1中，我们将使用下一代测序技术来分析肠道微生物组随时间的变化，并评估其对肠道微生物的影响。 与花生过敏症的发展有关系。CoFAR观察性研究的参与者被招募为 患有高特应性过敏风险的婴儿。在入学时没有人对花生过敏，而现在有40.1%。使用倾斜的凳子 从婴儿期和儿童中期开始，我们将确定婴儿期肠道微生物组的特征是危险因素 花生过敏的发展，以及花生特有的肠道微生物组的纵向变化 过敏发展。在目标2中，我们将确定肠道微生物组与解决 花生过敏我们将研究参加脱敏研究的花生过敏儿童， 预计32.5%的患者的过敏症状会消退。我们将使用下一代测序技术对收集的粪便进行分析 纵向确定基线时的肠道微生物组特征，以及通过脱敏的变化， 与花生过敏的解决有关。肠道微生物群通过代谢物发挥免疫影响 (e.g.短链脂肪酸（SCFA）。在目标3中，我们将测量目标SCFA和全球SCFA。 代谢物组，以确定与花生过敏相关的横截面和纵向代谢物 分辨率然后，我们将应用系统生物学方法来（1）破译之间的因果关系 微生物组、代谢组和宿主，以及（2）确定花生过敏解决的因果关键驱动因素。双 本研究在表型良好的花生过敏受试者中产生的肠道微生物组和肠道代谢组数据， 沿着我们将从U19 AI 136053获得的平行宿主转录组数据，提供了前所未有的 有机会开发花生过敏的数据驱动机制模型。这项研究将使直接 通过一种创新的、综合的方法来确定肠道微生物组在花生过敏中的作用， 微生物组、代谢组和宿主转录组的检查。结果将包括第一个人类数据， 花生过敏症发展和解决中的肠道微生物组，以及新的致病菌鉴定 花生过敏解决的关键驱动因素（微生物、代谢物和宿主转录物）。这些结果将 阐明花生过敏的潜在机制，突出治疗和生物标志物的目标。

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.