Development of serologic test for early risk stratification of islet autoimmunity in genetically predisposed T1D individuals

项目总结/摘要 1型糖尿病（T1 D）是一种严重的多因素慢性自身免疫性疾病， 构成重大公共卫生挑战和财政负担的发病率。T1 D涉及遗传 易感性、免疫系统反应和导致疾病发生的环境因素， 进展由于缺乏T1 D的治愈性疗法，迄今为止最有希望的选择仍然是早期治疗。 干预的目的是减缓或预防易感个体进展为T1 D。电流 临床前T1 D阶段的诊断是基于针对两种或更多种 特异性抗原，即β细胞的破坏已经开始并且难以逆转。诊断 在疾病进展的早期预测胰岛自身抗体的发展的工具有可能避免 通过使用初级预防策略完全破坏β细胞。在这里，假设在 在IA的发展中，存在针对免疫原性病原体特异性和/或免疫原性免疫应答的独特的体液免疫应答。 相关的非胰岛自身免疫靶点，可用作进展为IA的早期风险分层。的 所提出的方法依赖于代表供体循环抗体库的整个结合空间 使用基于抗体结合特征的机器学习模型， 平均长度为9个氨基酸的肽，这是所有可能氨基酸的稀疏表示 组合。然后使用所得模型来鉴定具有高预测能力的病原体表位， 组合成具有诊断功效的面板。这项研究的总体目标是开发一个小组， 生物标志物，由潜在的病毒抗原和自身免疫靶点组成，用于早期预测胰岛 遗传易感个体的自身免疫。一个广泛的分析循环抗体库， 随着时间的推移，患者血清与机器学习模型相结合将用于发现免疫原性靶点 在病原体和人类蛋白质组中，可以用作IA和T1 D进展的预测因子。的 血清学（自身抗体检测）、遗传学（HLA基因型、点突变）和临床数据将用于 结合免疫应答分析数据，研究体液免疫应答的时间变化。 在进展至IA的不同时间点的反应。这项工作预计将产生数据，证明 一种新的免疫测定法用于胰岛自身免疫发展的早期风险分层的可行性 易患T1 D的人此外，它将作为抗原发现方法的演示， 一种鉴定难诊断病原体的诊断抗原的方法。

PROJECT SUMMARY/ABSTRACT Type 1 diabetes (T1D) is a serious multi-factorial chronic autoimmune disease with an annual 3% increase in the incidence rate that constitutes a major public health challenge and financial burden. T1D involves genetic predisposition, immune system response, and environmental factors that lead to disease initiation and progression. Due to the lack of curative therapies for T1D, the most promising option to date remains early intervention with the goal of slowing or preventing progression to T1D in predisposed individuals. Current diagnosis of the pre-clinical T1D stage is based on the detection of islet autoimmunity (IA) against two or more specific antigens, i.e. when the destruction of β-cells has already started and is difficult to reverse. A diagnostic tool predicting the development of islet autoantibodies early in the progression has the potential to avoid the destruction of β-cells altogether by using primary prevention strategies. Here, it is hypothesized that prior to the development of IA there is a distinct humoral immune response against immunogenic pathogen-specific and/or associated non-islet autoimmune targets that can be utilized as early risk stratification for progression to IA. The proposed approach relies on representing an entire binding space of a donor’s circulating antibody repertoire using machine learning models based on the antibody binding profile to a diverse, random library of 126,050 peptides with an average length of 9 amino acids, which is a sparse representation of all possible amino acid combinations. Resulting models are then used to identify pathogen epitopes with high predictive power that are combined into a panel with diagnostic efficacy. The overarching goal of this study is to develop a panel of biomarkers, consisting of potential viral antigens and autoimmune targets for early prediction of islet autoimmunity in genetically susceptible individuals. A broad profiling of the circulating antibody repertoire in patient’s serum combined with machine learning models over time will be used to discover immunogenic targets in both pathogen and human proteomes that can be used as predictors of progression to IA and T1D. The serologic (autoantibody detection), genetic (HLA genotype, point mutations) and clinical data will be used in combination with the immune response profiling data to investigate temporal alterations in humoral immune response at different timepoints of progression to IA. This work is expected to yield data demonstrating the feasibility of a novel immunoassay for early risk stratification of islet autoimmunity development in genetically predisposed T1D individuals. Additionally, it will serve as a demonstration of the antigen discovery approach as a means to identify diagnostic antigens for difficult pathogens.