Large-Scale Sequencing and Characterizing of Autoantibody Responses

自身抗体反应的大规模测序和表征

• 批准号: 8732967
• 负责人: William H Robinson
• 金额: $ 35.91万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8732967
• 关键词: Affect; Affinity; Antibodies; Antibody Repertoire; Antigen-Antibody Complex; Antigens; Autoantibodies; Autoantigens; Autoimmune Diseases; Autoimmune Process; Autoimmune Responses; Autoimmunity; B-Lymphocytes; Binding; Bioinformatics; Cells; Clinical; Couples; DNA; Data Set; Dendritic Cells; Development; Diagnostic tests; Disease; Family; Fc Receptor; Generations; Immune; Immune response; Immunoglobulin Light Chain Genes; Individual; Inflammation; Interferons; Large-Scale Sequencing; Lead; Mediating; Methods; Nuclear; Nuclear Antigens; Pathogenesis; Patients; Pattern recognition receptor; Phylogenetic Analysis; Plasmablast; Population; Property; Proteins; Recombinant Antibody; Resources; Rheumatoid Arthritis; Role; Systemic Lupus Erythematosus; TNF gene; Technology; Testing; Therapeutic; Trees; insight; macrophage; next generation sequencing; novel diagnostics; novel therapeutic intervention; response; success

The Principal Project will characterize B cell responses in patients with autoimmune disease by using a DNA-barcoding technology recently developed in the Robinson lab, termed 'antibody repertoire capture' (ARC). The approach couples DNA barcoding with next-generation sequencing to enable large-scale characterization of the paired heavy-chain (HC) and light-chain (LC) immunoglobulin genes expressed by single plasmablasts or antigen-specific B cells. Although methods exist for profiling antibodies, none are able to comprehensively characterize the, antibodies involved in an active immune response and to then bioinformatically identify those most likely to be functional i.e., those that either drive the disease or serve as identifiers of the key antigens that trigger pathogenic autoimmune responses. The scale of the sequencing datasets generated by ARC enables bioinformatic generation of "phylogenetic trees" of the antibody repertoire. These phylogenetic trees guide identification of clonal families of affinity-matured antibodies and thereby rational selection of key antibodies, which can then be expressed for direct analysis of their binding and functional properties. We propose to use ARC to sequence and comprehensively dissect the autoantibody responses in rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), and, leveraging resources from the other ACE Projects and Cores, to test the overarching hypothesis that monoclonal autoantibodies contribute to the pathogenesis of RA and SLE by forming proinflammatory immune complexes (ICs) that dual-stimulate immune cells (by simultaneously engaging a pattern recognition receptor and either the B-cell or the Fc receptor). For instance, we hypothesize that RA-associated anti-citrullinated proteins antibodies (ACPAs) form ICs that dual-stimulate macrophages to produce TNF, and B cells to produce ACPAs; and that SLE-associated anti-nuclear antibodies (ANAs) bind nuclear antigens and thereby form ICs that dual-stimulate dendritic cells to produce IFN, and B cells to produce ANAs. In Aim 1, we will use ARC to sequence the antibody repertoires in patients with RA or SLE and identify antibody profiles that are associated with specific clinical subtypes or response to therapy. In Aim 2, we will clone and express rationally selected, affinity-matured antibodies from individuals with RA or SLE, and elucidate their autoantigen targets. In Aim 3, will characterize key RA and SLE recombinant antibodies identified in Aim 2 and uncover mechanisnis by which they contribute to autoimmune inflammation. Success would provide insights into the role of autoantibodies and the mechanisms by which they contribute to the pathogenesis of RA and SLE, and could lead to development of novel diagnostic tests and therapeutic approaches.

主要项目将通过使用罗宾逊实验室最近开发的DNA条形码技术来表征自身免疫性疾病患者的B细胞反应，该技术被称为“抗体谱系捕获”(ARC)。该方法将DNA条形码与下一代测序相结合，以实现对单个浆母细胞或抗原特异性B细胞表达的成对重链(HC)和轻链(LC)免疫球蛋白基因的大规模表征。虽然已经有了分析抗体的方法，但没有一种方法能够全面表征主动免疫反应中涉及的抗体，然后通过生物信息识别那些最有可能起作用的抗体，即那些要么驱动疾病，要么作为触发致病自身免疫反应的关键抗原的识别符的抗体。由ARC生成的测序数据集的规模使得能够以生物信息的方式生成 抗体库。这些系统发育树指导亲和力成熟的抗体克隆家族的鉴定，从而合理地选择关键抗体，然后表达这些抗体以直接分析其结合和功能特性。我们建议使用ARC对类风湿性关节炎(RA)和系统性红斑狼疮(SLE)的自身抗体反应进行排序和全面剖析，并利用来自其他ACE项目和核心的资源，通过形成双重刺激免疫细胞的促炎免疫复合体(IC)(通过同时进行模式识别)来检验主要假设，即单抗在RA和SLE的发病机制中起作用 受体和B细胞或Fc受体)。例如，我们假设RA相关的抗瓜氨酸 蛋白抗体(ACPA)形成IC，双重刺激巨噬细胞产生肿瘤坏死因子和B细胞 细胞产生ACPA；SLE相关抗核抗体(ANA)结合核抗原和 从而形成IC，双重刺激树突状细胞产生干扰素，B细胞产生ANA。 在目标1中，我们将使用ARC对RA或SLE患者的抗体库进行测序，并确定与特定临床亚型或治疗反应相关的抗体谱。 在目标2中，我们将克隆和 表达来自RA或SLE患者的合理选择的亲和力成熟的抗体，并阐明其 自身抗原靶标。 在目标3中，将鉴定在目标2中确定的关键RA和SLE重组抗体 并揭示它们促进自身免疫性炎症的机制。成功将提供 对自身抗体的作用和它们在慢性粒细胞白血病发病机制中的作用的见解 RA和SLE，并可能导致新的诊断测试和治疗方法的开发。