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急剧上升。 (CD)。CD在自身免疫性疾病中是独一无二的，因为人类特有的遗传易感性 白细胞抗原(人类白细胞抗原)，产生的自身抗体，以及触发因素，面筋，都是已知的。然而，超过30%的人 的人携带易感基因并暴露在面筋中，但这些人中只有2%-3% 即使在面筋暴露几十年后也会出现CD，这表明额外的环境因素起到了关键作用 各种因素。这一发现突显了对CD发病机制最早步骤的认识上的关键差距 在接触面筋后，会导致耐受性丧失和随后的自身免疫力的发展。 为了了解疾病发展过程中涉及的复杂相互作用，详细的数据收集和 多组分析必须在疾病发生之前开始，通过疾病的发展，并进入 减刑。我有机会接触到一个独特的预期纵向出生队列来实现这一点。 我的工作表明，环境因素会改变肠道微生物群的组成和功能 增加高危婴儿对Cd的易感性的潜在意义。我的初步数据表明，直觉 在丧失对面筋的耐受性和Cd发病之前，在物种水平上就存在微生物组的改变。 因此，我建议研究肠道微生物群的作用，作为一个可能在早期发挥关键作用的因素。 在疾病的发病过程中涉及的步骤。我假设人类白细胞抗原遗传学与环境因素相结合 因素(给药方式、饮食和抗生素暴露)会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，导致从耐受到免疫的转变 遗传易感个体对面筋的反应。在我的指导团队的指导下，在这5年中 今年K23职业发展指导奖，我的目标是获得微生物组分析方面的专业知识， 免疫学和计算分析，以创建综合模型，可以识别生物途径和 导致自身免疫遗传风险儿童耐受性丧失的临床因素 个性化的CD预防。拟议的项目有三个主要目标。目标1我将确定元基因组学 婴幼儿面筋耐受性丧失前后的变化及其与环境因素的关系 带CD和控件。在目标2中，我将确定循环单核细胞基因表达的变化 CD发生前后的单细胞RNA测序，并与对照组进行比较。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的框架 作为一名由NIH资助的独立临床研究员，他可以将翻译研究方面的专业知识与 CD、免疫学和生物信息学方面的临床专业知识，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.