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.

主要项目将使用Robinson实验室最近开发的dna条形码技术（称为“抗体库捕获”（ARC））来表征自身免疫性疾病患者的B细胞反应。该方法将DNA条形码与下一代测序相结合，能够大规模表征单个浆母细胞或抗原特异性B细胞表达的配对重链（HC）和轻链（LC）免疫球蛋白基因。虽然存在分析抗体的方法，但没有一种方法能够全面表征参与主动免疫反应的抗体，然后从生物信息学上识别那些最有可能起作用的抗体，即那些驱动疾病或作为触发致病性自身免疫反应的关键抗原标识符的抗体。ARC生成的测序数据集的规模使生物信息学能够生成该物种的“系统发育树”