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

开发用于遗传易感性 T1D 个体胰岛自身免疫早期风险分层的血清学检测

• 批准号: 10760885
• 负责人: Laimonas Kelbauskas
• 金额: $ 30万
• 依托单位: BIOMORPH TECHNOLOGIES LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10760885
• 关键词: Amino Acids; Antibodies; Antibody Repertoire; Antigens; Autoantibodies; Autoimmune; Beta Cell; Binding; Biological Assay; Chemicals; Classification; Clinical; Clinical Data; Complex; Custom; DNA; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early Intervention; Ensure; Environmental Risk Factor; Epitopes; Etiology; Evaluation; Financial Hardship; Funding; Genetic; Genetic Predisposition to Disease; Genotype; Goals; Health; Healthcare Systems; Human; Immune response; Immunoassay; Incidence; Individual; Insulin-Dependent Diabetes Mellitus; Knowledge; Laboratories; Length; Libraries; Life Style; Maps; Measures; Modeling; National Institute of Allergy and Infectious Disease; Pathogenesis; Patients; Peptide Synthesis; Peptides; Performance; Point Mutation; Predisposition; Prevention strategy; Primary Prevention; Proteins; Proteome; Public Health; RNA; Recombinant Proteins; Research; Sampling; Serology; Serology test; Serum; Services; Small Business Innovation Research Grant; Specificity; Testing; Time; United States National Institutes of Health; Validation; Viral; Viral Antigens; Viral Proteins; Viral Vaccines; Work; antigen diagnostic; biomarker discovery; biomarker panel; biomarker validation; candidate marker; chronic autoimmune disease; computerized tools; cost; cross reactivity; curative treatments; density; diagnostic tool; dietary; disability; disease diagnostic; disorder prevention; early detection biomarkers; endocrine pancreas development; experience; high risk; immunogenic; insulin dependent diabetes mellitus onset; islet autoimmunity; islet cell antibody; machine learning method; machine learning model; machine learning prediction; member; novel; pathogen; pre-clinical; predictive modeling; predictive panel; predictive tools; prevent; programs; progression risk; protein biomarkers; research clinical testing; risk prediction; risk stratification; technological innovation; vector

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.

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，每年增加3％ 构成主要公共卫生挑战和金融伯恩的事件率。 T1D涉及遗传 易感性，免疫系统反应以及导致疾病开始和的环境因素 进展。由于缺乏T1D治疗疗法，迄今为止最有前途的选择仍然很早 干预措施的目的是放缓或防止易感个体的T1D进展。当前的 临床前T1D阶段的诊断是基于对两个或更多的胰岛自身免疫（IA）的检测 特定的抗原，即β细胞的破坏已经开始且难以逆转。诊断 预测胰岛自动抗体发展的工具在进展中有可能避免 通过使用初级预防策略，完全破坏β细胞。在这里，假设在 IA的发展具有针对免疫原性病原体特异性和/或 相关的非ISLET自身免疫靶标可以用作早期风险分层以进行IA。 建议的方法依赖于表示供体循环抗体库的整个结合空间 使用基于对潜水员的抗体结合曲线的机器学习模型，126,050的随机库 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。然后将结果模型用于识别具有高预测能力的病原体表位 合并成具有诊断效率的面板。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标组成，用于早期预测胰岛 一般易感人士的自身免疫性。循环抗体库的广泛分析 随着时间的推移，患者的血清与机器学习模型相结合，将用于发现免疫原靶 在病原体和人类蛋白质中，可以用作进展到IA和T1D的预测指标。这 血清学（自身抗体检测），遗传（HLA基因型，点突变）和临床数据将用于 结合免疫响应分析数据，研究体液免疫的暂时变化 在不同时间点向IA的响应。这项工作有望产生数据，证明 新型免疫测定法对胰岛自身免疫发展的早期风险分层的可行性一般 倾向的T1D个体。此外，它将作为抗原发现方法的演示 一种鉴定困难病原体诊断抗原的手段。