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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/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，发病率每年增加3% T1D 的发病率构成了重大的公共卫生挑战和经济负担。易感性、免疫系统反应以及导致疾病发生和发生的环境因素由于缺乏针对 T1D 的治愈性进展疗法，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预防易感个体进展为 T1D。临床前 T1D 阶段的诊断基于针对两种或多种胰岛自身免疫 (IA) 的检测特定抗原，即当 β 细胞的破坏已经开始并且难以逆转时诊断。在进展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破坏 β 细胞。 IA 的发展存在针对免疫原性病原体特异性和/或的独特体液免疫反应相关的非胰岛自身免疫靶点可用作 IA 进展的早期风险分层。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到包含 126,050 个的多样化随机库平均长度为 9 个氨基酸的肽，这是所有可能的氨基酸的稀疏表示然后使用所得模型来识别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。本研究的总体目标是开发一个面板。生物标志物，包括潜在的病毒抗原和自身免疫靶标，用于早期预测胰岛遗传易感个体的自身免疫性的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性靶标在病原体和人类蛋白质组中，可用作 IA 和 T1D 进展的预测因子。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反应分析数据来研究体液免疫的时间变化这项工作预计会产生证明 IA 进展的不同时间点的反应。一种新型免疫分析方法对胰岛自身免疫发展的早期风险分层的可行性此外，它还将作为抗原发现方法的示范。一种识别疑难病原体诊断抗原的方法。