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

项目摘要/摘要 1型糖尿病(T1D)是一种严重的多因素慢性自身免疫性疾病，每年 构成重大公共卫生挑战和财政负担的发病率。T1D涉及基因 易感性、免疫系统反应和环境因素导致疾病的发生和发展 进步。由于T1D缺乏根治疗法，迄今为止最有希望的选择仍然是早期 干预的目的是减缓或防止易患T1D的个体进展到T1D。当前 临床前T1D阶段的诊断基于对两个或两个以上胰岛自身免疫(IA)的检测 特定抗原，即β细胞的破坏已经开始，难以逆转。一份诊断报告 早期预测胰岛自身抗体发展的工具有可能避免 通过使用初级预防策略完全摧毁β-细胞。在这里，假设在此之前 免疫球蛋白的发展对免疫原性病原体特异性和/或免疫原性有明显的体液免疫反应 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。这个 所提出的方法依赖于表示供体循环抗体库的整个结合空间 使用基于抗体结合图谱的机器学习模型来构建126,050个不同的随机文库 平均长度为9个氨基酸的多肽，是所有可能氨基酸的稀疏表示 组合。然后，使用所得到的模型来识别具有高预测能力的病原体表位 组合成一个具有诊断功效的面板。这项研究的总体目标是开发一组 由潜在病毒抗原和自身免疫靶点组成的生物标记物可用于胰岛早期预测 遗传易感个体的自身免疫力。对循环抗体谱系的广泛分析 随着时间的推移，患者的血清与机器学习模型相结合将被用来发现免疫原靶 在病原体和人类蛋白质组中，可用作向IA和T1D进展的预测因子。这个 血清学(自身抗体检测)、遗传学(人类白细胞抗原基因型别、点突变)和临床数据将用于 结合免疫反应谱数据研究体液免疫的时间变化 在不同的进展时间点对IA作出反应。这项工作预计将产生数据，证明 一种新的免疫分析方法在胰岛自身免疫发生早期风险分层中的可行性 易患T1D的个体。此外，它还将作为抗原发现方法的演示，如 一种识别疑难病原体诊断抗原的方法。

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.