The Origins of Human Anti-Insulin B Lymphocytes in Type 1 Diabetes

项目摘要 B淋巴细胞通过将自身抗原呈递给 T细胞杀死β细胞。循环胰岛素自身抗体（IAAs）不是直接致病的，但有助于预测 T1 D通过发出这种危险的B/T淋巴细胞串扰信号。产生胰岛素的β细胞的免疫靶向 在症状性糖尿病发作之前发生数月甚至数十年。糖尿病进展中的异质结 临床试验反应减缓了T1 D治愈的研究。更好的生物标志物来识别和机械地 解释响应者需要克服这一瓶颈。保护性免疫反应通常由T 细胞选择和成熟的B淋巴细胞的亲和力，导致记忆B淋巴细胞 和抗体分泌细胞分化。然而，自身免疫反应并不总是遵循这一途径。 例如，我们发现抗胰岛素B细胞（AIBCs）加速T1 D易感小鼠的糖尿病，但很少有AIBCs 分化成IAA分泌细胞，尽管进入生发中心。填补知识空白 在T1 D早期扩展，我们建立了一个独特的，精心策划的症状前T1 D TrialNet生物库 参与者我们在IAA阴性的症状前T1 D供体的外周血中发现AIBCs， 证明胰岛素的B淋巴细胞自身免疫逃避了通过循环中的IAAs的常规检测， 处于危险中的个体的子集。每次T1 D TrialNet访视时的纵向采样和临床检测提供 随着T1 D从第1阶段（正常葡萄糖耐量）进展到第2阶段， 2期（葡萄糖耐量受损）和3期（糖尿病）。我们发现AIBCs偏向记忆B 细胞表型克隆扩增的记忆B淋巴细胞表达种系和最小突变的B细胞 1期T1 D供体中的受体。这些数据支持我们的假设，即AIBCs扩展并进入记忆 在葡萄糖耐量受损之前的隔室中，有时不产生IAA。我们将整合AIBC 表型，B细胞受体（免疫球蛋白）序列同一性，克隆相关性，B细胞受体 对胰岛素的亲和力/亲合力，以及胰岛素表位作图以鉴定控制胰岛素识别的特征。高- 与细胞表型配对的B细胞受体库的通量单细胞分析将鉴定 同一供体随时间推移和T1 D分期进展，以确定B淋巴细胞库和亚群 葡萄糖耐量丧失时发生的变化。除了记忆偏斜之外，AIBCs还表现出有偏见的V和J 免疫球蛋白基因使用。我们将跟踪这些特征的组合，并使用LIBRAseq来识别候选 自身反应性BCR，以重组表达和测试胰岛自身抗原识别，包括其他已知的 胰岛自身抗原（GAD 65、IA-2、ICA 512和ZNT 8）。抗胰岛素BCR人单克隆抗体 我们将开发的序列数据库和分析管道/代码将公开提供。这些研究 是在临床试验中使用AIBCs作为生物标志物以确定有利变化的必要步骤。 细胞自身免疫性和针对AIBCs经历的特异性变化的零解卷积应答异质性。

PROJECT SUMMARY B lymphocytes orchestrate autoimmune beta cell attack in type 1 diabetes (T1D) by presenting autoantigen to T cells which kill beta cells. Circulating insulin autoantibodies (IAAs) are not directly pathogenic but help predict T1D by signaling this dangerous B/T lymphocyte crosstalk. Immune targeting of insulin-producing beta cells occurs for months or even decades before symptomatic diabetes onset. Heterogeneity in diabetes progression and clinical trial responses slow the search for a T1D cure. Better biomarkers to identify and mechanistically explain responders are needed to overcome this bottleneck. Protective immune responses typically arise by T cell selection and affinity maturation of B lymphocytes in germinal centers, resulting in memory B lymphocyte and antibody-secreting cell differentiation. However, autoimmune responses do not always follow this pathway. For example, we find anti-insulin B cells (AIBCs) accelerate diabetes in T1D-prone mice, yet few AIBCs differentiate into IAA-secreting cells, despite entering germinal centers. To fill gaps in knowledge AIBC expansion early in T1D, we built a unique, carefully-curated biobank of pre-symptomatic T1D TrialNet participants. We find AIBCs in the peripheral blood of IAA-negative pre-symptomatic T1D donors, demonstrating B lymphocyte autoimmunity for insulin evades conventional detection via circulating IAAs in a subset of at-risk individuals. Longitudinal sampling and clinical testing at each T1D TrialNet visit provide opportunities to identify donor-specific changes as T1D progresses from stage 1 (normal glucose tolerance) to stage 2 (impaired glucose tolerance) and stage 3 (diabetes). We find AIBCs are skewed towards a memory B cell phenotype. Clonally-expanded, memory B lymphocytes express germline and minimally-mutated B cell receptors in stage 1 T1D donors. These data support our hypothesis that AIBCs expand and enter the memory compartment prior to glucose tolerance impairment, sometimes without IAA production. We will integrate AIBC phenotype, B cell receptor (immunoglobulin) sequence identity, clonal relatedness, B cell receptor affinity/avidity for insulin, and insulin epitope mapping to identify features that govern insulin recognition. High- throughput single cell analysis of B cell receptor repertoire paired with cell phenotype will identify changes in the same donor over time and with T1D stage progression to determine B lymphocyte repertoire and subset shifts that occur as glucose tolerance is lost. In addition to memory skewing, AIBCs show biased V and J immunoglobulin gene use. We will track this combination of features and use LIBRAseq to identify candidate autoreactive BCRs to recombinantly express and test for islet autoantigen recognition, including other known islet autoantigens (GAD65, IA-2, ICA512, and ZNT8). The human monoclonal antibodies, anti-insulin BCR sequence database, and analysis pipeline/code we will develop will be made publicly available. These studies are a necessary step towards using AIBCs as biomarkers in clinical trials to identify favorable changes in cellular autoimmunity and zero in on specific changes AIBCs undergo to deconvolute response heterogeneity.