Serum miRNA Biomarkers of Islet Autoimmunity

 描述（由申请人提供）：1型糖尿病（T1 D）是由针对胰腺β细胞的慢性自身免疫反应引起的，导致其几乎完全根除并因此丧失胰岛素分泌。发现更好地标记疾病活动和β细胞破坏的新型生物标志物的需求尚未得到满足。新的生物标志物可以提高我们在自身抗体转换前识别高危个体的能力，进行早期干预，并可以帮助更精确地分期疾病进展，以促进风险分层并更好地指导临床试验的招募。我们的总体目标是发现和验证循环microRNAs（miRNAs），可用作胰岛自身免疫的新生物标志物。miRNA是一种小的非编码RNA分子，可调节细胞中的基因表达。miRNAs几乎调节所有途径，包括与T1 D相关的途径，如免疫系统和胰腺β细胞的关键功能，它们的发育和再生。miRNA也存在于血清和血浆中并且非常稳定。循环miRNA生物标志物在许多急性和慢性疾病中均有报道，但在T1 D中仍基本未探索。这是底特律亨利福特卫生系统（HFHS）和迈阿密大学（UM）研究小组的联合应用。每个小组都独立地发现了选定的miRNA与T1 D的关联。我们的初步数据涉及来自1型糖尿病试验网和1型糖尿病预防试验（DPT-1）的大型且特征良好的临床队列。我们的数据比较显示，我们独立发现的许多与胰岛自身免疫和/或T1 D相关的miRNA在两项研究中均被观察到。这种一致性为进一步研究T1 D中的循环miRNA提供了令人信服的理由，并推动我们提出这一应用，结合我们互补的专业知识和数据，相信这种联合努力将具有很强的协同作用，并将有力地影响T1 D miRNA生物标志物的发现和验证。我们提出以下目标：2）在TrialNet队列的纵向研究中，检查miRNA生物标志物与自身抗体的关系，以确定miRNA是否识别注定获得自身抗体的组或亲属，并且可以作为自身抗体转换之前的早期生物标志物; 3）检查具有高度易感性和保护性HLA单倍型的亲属是否具有与糖尿病进展相关的不同的循环miRNA模式; 4）构建复合预测模型，其结合了来自母研究的多种预后miRNA和其他生物标志物（遗传学、免疫学、代谢）。该多PI提案由T1 D、统计学、DPT-1/TrialNet队列中的生物标志物发现和预测研究、生物标志物数据挖掘以及临床前和临床水平的miRNA研究方面的专家领导。

 DESCRIPTION (provided by applicant): Type 1 diabetes (T1D) results from chronic autoimmune responses against pancreatic beta cells, leading to their virtually complete eradication and consequently loss of insulin secretion. There is an unmet need to discover novel biomarkers that better mark disease activity and beta cell destruction. Novel biomarkers could improve our ability to identify at-risk individuals before autoantibody conversion, for early intervention, and could help staging disease progression more precisely to facilitate risk stratification and better guide enrollment into trials. Our overall goal is to discover and validat circulating microRNAs (miRNAs) that can be exploited as novel biomarkers of islet autoimmunity. MiRNAs are small, non-coding RNA molecules that regulate gene expression in cells. MiRNAs regulate virtually all pathways, including those relevant to T1D, such as key functions of the immune system and pancreatic beta cells, their development and regeneration. MiRNAs are also present and remarkably stable in serum and plasma. Circulating miRNA biomarkers are reported in many conditions, both acute and chronic, but remain largely unexplored in T1D. This is a joint application from research groups at the Henry Ford Health System (HFHS) in Detroit and the University of Miami (UM). Each group has independently discovered associations of selected miRNAs with T1D. Our preliminary data involve large and well-characterized clinical cohorts from the Type 1 Diabetes TrialNet and the Diabetes Prevention Trial-Type 1 (DPT-1). Comparison of our data reveals that many of the miRNAs we independently found associated with islet autoimmunity and/or T1D were observed in both studies. This concordance provides compelling rationale to conduct further studies of circulating miRNAs in T1D, and gave us impetus to present this application, combining our complementary expertise and data, believing that this joint effort will be strongly synergistic and will powerfuly impact the discovery and validation of miRNA biomarkers for T1D. We propose the following aims: 1) Examine serum miRNAs as biomarkers of diabetes progression in two large data sets of progressors versus slow/non-progressors, both through cross-sectional and longitudinal analyses; 2) Examine the relationship of miRNA biomarkers to autoantibodies, in longitudinal studies of the TrialNet cohort, to determine if miRNAs identify a group or relatives destined to acquire autoantibodies and could serve as early biomarkers preceding autoantibody conversion; 3) Examine whether relatives with highly susceptible and protective HLA haplotypes have different patterns of circulating miRNAs, in relation to diabetes progression; 4) Construct composite predictive models incorporating multiple prognostic miRNAs and other biomarkers (genetic, immunological, metabolic) available from the parent studies. This multiple-PI proposal is led by experts in T1D, statistics, biomarker discovery and prediction studies in the DPT-1/TrialNet cohorts, biomarker data mining, and miRNA studies both at the preclinical and clinical levels.