Towards Integrative Omics To Predict Celiac Disease Onset in At-risk Infants

在工业化国家，自身免疫性疾病的发病率急剧上升。乳糜泻 (CD)这是一种慢性自身免疫性肠病，由遗传易感人群摄入麸质引发。 个人，也不例外。CD在自身免疫性疾病中是独特的， 人白细胞抗原（HLA）、触发因子（谷蛋白）和产生的自身抗体是已知的。然而，在这方面， 肠粘膜暴露于麸质后的早期步骤导致耐受性丧失， 自身免疫过程的发展在很大程度上仍然是未知的。此外，对麸质的耐受性丧失 并不总是发生在面筋引入的时候，因为曾经被认为，但可能发生在任何时候，由于 未知的环境诱因因此，遗传易感性和暴露于麸质是必要的，但 足以发展CD。这一发现，沿着在过去四年中， 与其他自身免疫性疾病一致的数十年，必须由环境因素解释。最近 我的导师发表的工作表明，微生物群和宿主之间独特的相互作用可能导致 微生物组的改变导致在发病前产生特定的代谢物， 自身免疫性疾病肠道微生物组在婴儿期发育，对发育至关重要。 免疫系统，并在出生后的头三年组装成成人样的模式，这表明， 婴儿期的环境影响可能具有重要的持久影响。因此，我提议调查 肠道微生物组作为一个因素的作用，可能在参与发病的早期步骤中发挥关键作用， 疾病我假设婴儿在遗传上有患CD的风险， 与其他环境因素的影响相结合，可能导致 麸质耐受性和自身免疫的发病。我的目标是识别和验证特定的微生物组 可以预测遗传上有自身免疫风险的受试者的耐受性丧失的特征。拟建项目 有三个主要目标。在目标1中，我将根据以下内容确定发育中的微生物组的潜在差异： 婴儿的遗传风险。在目标2中，我将研究环境因素，如分娩方式， 感染和抗生素的使用改变了发育中的微生物组。在目标3中，我将研究饮食养生如何 改变发育中的微生物组，并检查微生物组和血清中的变化， 在发展出指示CD的自身抗体之后。通过这些目标，我可以提供 了解早期因素如何影响发育中的微生物组。这项试点工作将提供 K23应用程序的基础，将样本量和分析扩展到基因组学，代谢组学， 表观遗传学这项工作将奠定基础，以达到我的最终目标，成为一名翻译研究者 将基础科学的专业知识与CD的临床专业知识相结合，以开发预测模型， 为有自身免疫性疾病风险的患者提供个性化药物的计划。

Industrialized nations have seen a tremendous rise in the incidence of autoimmune disease. Celiac disease (CD), a chronic autoimmune enteropathy triggered by the ingestion of gluten in genetically predisposed individuals, is no exception. CD is unique among autoimmune disorders in that the genetic predisposition, human leukocyte antigen (HLA), triggering factor (gluten), and auto antibodies produced are known. However, the early steps following the intestinal mucosal exposure to gluten leading to the loss of tolerance and development of the autoimmune process are still largely unknown. Additionally, the loss of tolerance to gluten does not always occur at the time of gluten introduction as was once thought, but may occur at any point due to unknown environmental triggers. Thus, genetic predisposition and exposure to gluten are necessary but not sufficient to develop CD. This discovery, along with the profound increase in diagnosis over the last four decades consistent with other autoimmune disorders, must be explained by environmental factors. Recent work published by my mentor has shown that a unique interplay between the microbiota and host may lead to alterations in the microbiome resulting in the production of specific metabolites prior to the onset of autoimmune disease. The intestinal microbiome develops during infancy, is essential to the development of the immune system, and assembles into an adult-like pattern in the first three years after birth, suggesting that the environmental influences in infancy may have important lasting effects. 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 infants genetically at risk for CD will have a particular intestinal microbiota composition that, in combination with influences from other environmental factors, can contribute to the loss of gluten tolerance and to the onset of autoimmunity. My objective is to identify and validate specific microbiome profiles that can predict loss of tolerance in subjects genetically at risk of autoimmunity. The proposed project has three major aims. In Aim 1 I will determine underlying differences in the developing microbiome according to infants’ genetic risk. In Aim 2 I will examine how environmental factors such as birthing delivery mode, infection, and antibiotic use alter the developing microbiome. In Aim 3 I will investigate how dietary regimen alters the developing microbiome and examine changes that occur in the microbiome and serum before and after the development of autoantibodies that are indicative of CD. Through these aims I can provide the foundation to understand how early factors impact the developing microbiome. This pilot work will provide the basis for a K23 application which will expand the sample size and analysis to genomics, metabolomics, and epigenetics. This work will lay the foundation to reach my ultimate goal of becoming a translational investigator blending expertise in basic science, with clinical expertise in CD, to develop predictive models and eventually programs for personalized medicine for patients at risk for autoimmune disease.