Prospective Multi-Omic Analysis of At-Risk infants to Model Celiac Disease Pathogenesis

项目摘要 在全球范围内，包括乳糜泻在内的自身免疫性疾病的发病率大幅上升 （CD）。CD在自身免疫性疾病中是独特的，因为其遗传易感性、特定的人类 已知白细胞抗原（HLA）、产生自身抗体和触发物谷蛋白。然而，超过30%的 的人携带易感基因并接触麸质，但只有2-3%的人 即使在麸质暴露后几十年也会发生CD，这表明额外的环境因素对CD的影响至关重要。 因素这一发现强调了CD发病机制最早阶段知识的关键差距， 在暴露于麸质后导致耐受性丧失和随后的自身免疫性发展。 为了了解疾病发展中涉及的复杂相互作用，详细的数据收集和 多组学分析必须在疾病发生之前开始，通过疾病的发展，并进入 缓解。我有一个独特的前瞻性纵向出生队列来实现这一点。 我的工作表明，环境因素改变了肠道微生物组的组成和功能， 对高危婴儿CD易感性增加的潜在影响。我的初步数据显示 微生物组在物种水平上的改变在对麸质的耐受性丧失和CD发作之前存在。 因此，我建议研究肠道微生物组的作用，作为一个因素，可能发挥关键作用，在早期 在疾病的发病过程中涉及的步骤。我假设HLA遗传学结合环境因素 因素（分娩方式、饮食和抗生素暴露）可影响微生物组的组成和功能 最终导致免疫细胞的表观遗传变化，导致从耐受性到免疫性的转变。 对麸质的反应。在我的指导团队的指导下，在这5年中， 今年K23辅导职业发展奖，我的目标是获得微生物组分析的专业知识， 免疫学和计算分析，以创建可以识别生物途径的综合模型， 导致遗传上有自身免疫风险的儿童耐受性丧失的临床因素，目的是 CD的个性化预防。拟议的项目有三个主要目标。目的1我将确定宏基因组 在对麸质失去耐受性之前和之后的变化以及与婴儿环境因素的关系 CD和控制。在目标2中，我将使用以下方法确定循环单核细胞基因表达的改变： CD发展前后的单细胞RNA测序，并与对照进行比较。目标3将 利用多组学数据建立综合模型，以确定有助于和 可以预测高危儿童的CD发展。这项工作将为我的职业生涯奠定科学基础 作为NIH资助的独立临床研究者，他可以将翻译研究的专业知识与 CD，免疫学和生物信息学的临床专业知识，以开发计算模型，并最终 为有自身免疫性疾病风险的患者提供个性化药物的计划。

Project Summary Globally there is a tremendous rise in the incidence of autoimmune disease including celiac disease (CD). CD is unique among autoimmune disorders in that the genetic predisposition, specific human leukocyte antigen (HLA), auto antibodies produced, and trigger, gluten, are known. However, more than 30% of the population carry the predisposing genes and are exposed to gluten, yet only 2-3% of these individuals develop CD even decades after gluten exposure, suggesting a critical role for additional environmental factors. This finding highlights the crucial gap in knowledge of the earliest steps in CD pathogenesis that occur following the exposure to gluten leading to the loss of tolerance and subsequent development of autoimmunity. To understand the complex interactions involved in the development of disease, detailed data collection and multi-omic analysis must begin before the onset of disease, through the development of disease, and into remission. I have access to a unique prospective longitudinal birth cohort to accomplish this. My work has shown that environmental factors alter the gut microbiome composition and function with potential implications for increasing susceptibility to CD in at-risk infants. My preliminary data suggest that gut microbiome alterations at the species level are present prior to the loss of tolerance to gluten and onset of CD. Therefore, I propose to investigate the role of the gut microbiome as a factor that may play a key role in early steps involved in the onset of the disease. I hypothesize that HLA genetics in combination with environmental factors (delivery mode, diet, and antibiotic exposure) can affect the microbiome composition and function ultimately causing epigenetic changes in immune cells leading to the switch from tolerance to immune response to gluten in genetically predisposed individuals. With guidance from my mentoring team, during this 5 year K23 mentored career development award, my objective is gain expertise in microbiome analysis, immunology, and computational analysis to create integrative models that can identify biologic pathways and clinical factors that contribute to loss of tolerance in children genetically at risk of autoimmunity with the goal of personalized prevention of CD. The proposed project has three major aims. Aim 1 I will identify metagenomic alterations before and after the loss of tolerance to gluten and in relation to environmental factors in infants with CD and controls. In aim 2 I will determine alterations in gene expression of circulating monocytes using single cell RNA sequencing before and after the development of CD and compared to controls. Aim 3 will utilize the multi-omic data to build a integrative models to identify biological pathways that contribute to and may predict CD development in at-risk children. This work will lay the scientific framework to launch my career as an NIH-funded independent clinical investigator who can blend expertise in translational investigation, with clinical expertise in CD, immunology, and bioinformatics, to develop computational models and eventually programs for personalized medicine for patients at risk for autoimmune disease.