COMPUTATIONAL TOOLS FOR THE ANALYSIS OF HIGH-THROUGHPUT IMMUNOGLOBULIN SEQUENCING EXPERIMENTS

用于分析高通量免疫球蛋白测序实验的计算工具

• 批准号: 10322108
• 负责人: Steven H. Kleinstein
• 金额: $ 41.59万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322108
• 关键词: Affinity; Aging; Alleles; Amino Acid Motifs; Antibodies; Antigens; Autoantigens; Autoimmunity; B-Cell Antigen Receptor; B-Cell Receptor Binding; B-Lymphocytes; Bacteria; Binding; Biological; Blood; Cell Differentiation process; Cells; Clonal Expansion; Clone Cells; Collection; Computing Methodologies; Coupled; DNA receptor; Data; Data Set; Dengue; Development; Disease; Disease remission; Ecosystem; Endogenous Retroviruses; Epitopes; Event; Genes; Genetic Recombination; Graph; Haplotypes; Health; Human; Hypersensitivity; Immune response; Immune system; Immunoglobulin Somatic Hypermutation; Immunoglobulins; Immunologic Receptors; Immunotherapy; Individual; Infection; Influenza; Invaded; Laplacian; Learning; Lymphocyte; Malignant Neoplasms; Maps; Measures; Memory; Methods; Modeling; Multiple Sclerosis; Mus; Mutate; Mutation; Myasthenia Gravis; Nucleotides; Outcome; Pathogenicity; Patients; Pattern; Phylogenetic Analysis; Plasma Cells; Population; Process; Production; Property; Reading; Recording of previous events; Retroviridae; Sampling; Sequence Analysis; Series; Site; Specificity; Spottings; Structure; Surface; System; T-Cell Receptor; T-Lymphocyte; Techniques; Technology; Testing; Thymectomy; Tissue Differentiation; Tissues; Toxin; V(D)J Recombination; Vaccination; Vaccines; Virus; adaptive immune response; adaptive immunity; antigen binding; base; cell motility; cell type; clinically relevant; computerized tools; deep learning; experimental study; high throughput analysis; immunoglobulin receptor; immunological status; improved; insight; large datasets; method development; migration; multiple sclerosis patient; next generation sequencing; novel; open source; pathogen; pathogenic autoantibodies; predictive modeling; receptor; reconstruction; relapse prediction; response; simulation

PROJECT SUMMARY/ABSTRACT The ability of our immune system to respond effectively to pathogenic challenge or vaccination depends on a diverse repertoire of Immunoglobulin (Ig) receptors expressed by B lymphocytes. Each B cell receptor (BCR) is unique, having been assembled during lymphocyte development by recombination of germline encoded V(D)J genes. During the course of an immune response, B cells that initially bind antigen with low affinity through their BCR are modified through cycles of somatic hypermutation (SHM) and affinity-dependent selection to produce high-affinity memory and plasma cells. This affinity maturation is a critical component of T cell dependent adaptive immune responses. It helps guard against rapidly mutating pathogens and underlies the basis for many vaccines, but dysregulation can result in autoimmunity and other diseases. Next-generation sequencing (NGS) technologies have revolutionized our ability to carry out large-scale adaptive immune receptor repertoire sequencing (AIRR-Seq) experiments. AIRR-Seq is increasingly being applied to profile BCR repertoires and gain insights into immune responses in healthy individuals and those with a range of diseases, including autoimmunity, infection, allergy, cancer and aging. As NGS technologies improve, these experiments are producing ever larger datasets, with tens- to hundreds-of-millions of BCR sequences. Although promising, repertoire-scale data present fundamental challenges for analysis requiring the development of new techniques and the rethinking of existing methods that are not scalable to the large number of sequences being generated. This proposal describes the development of a series of novel computational methods to explore the central hypothesis that: B cell clonal relationships and lineage structures can be computationally derived from repertoire sequencing data and used to define B cell migration and differentiation networks in health and disease. Specifically, computational methods will be developed to: (Aim 1) identify clonally-related sequences and improve V(D)J gene assignment through determining the Ig locus haplotype, (Aim 2) reconstruct clonal lineages, and use these to learn B cell migration and differentiation networks, and (Aim 3) analyze sequences to predict repertoire properties and sequence motifs that are associated with antigen binding or clinically-relevant outcomes. These through human a combination of simulation-based studies, as (myasthenia gravis) and murine (endogenous methods will be validated well as testing on new experimental data from both retrovirus emergence) systems. Allmethods will be integrated and made available through our widely-used, open-source Immcantation framework, which provides a start-to-finish analytical ecosystem for AIRR-Seq analysis. Together, these methods provide a window into the micro-evolutionary dynamics that drive adaptive immunity and the dysregulation that occurs in disease.

项目摘要/摘要 我们的免疫系统有效应对病原挑战或疫苗接种的能力取决于 B淋巴细胞表达的免疫球蛋白受体的不同谱系。每个B细胞受体(BCR)都是 独一无二的，在淋巴细胞发育过程中通过生殖系编码的V(D)J重组而组装 基因。在免疫反应过程中，最初与低亲和力抗原结合的B细胞通过 它们的BCR通过体细胞超突变(SHM)和亲和力依赖的选择来修改 产生高亲和力的记忆和浆细胞。这种亲和力的成熟是T细胞的关键组成部分 依赖性适应性免疫反应。它有助于防止快速变异的病原体，并奠定了 作为许多疫苗的基础，但调节失调会导致自身免疫和其他疾病。下一代 测序(NGS)技术彻底改变了我们进行大规模适应性免疫的能力 受体谱系测序(AIRR-SEQ)实验。AIRR-Seq越来越多地被应用于配置文件 BCR曲目，并深入了解健康个体和患有各种疾病的人的免疫反应 疾病，包括自身免疫、感染、过敏、癌症和衰老。随着NGS技术的改进，这些 实验正在产生越来越大的数据集，具有数千万到数亿的bcr序列。 尽管很有希望，但曲目规模的数据对分析提出了根本的挑战，需要 新技术的开发和对不能大规模扩展的现有方法的重新思考 正在生成的序列数。这一提案描述了一系列小说的发展 探索中心假设的计算方法：B细胞克隆关系和谱系 结构可以通过计算从指令表测序数据中得到，并用于定义B细胞 健康和疾病中的迁移和分化网络。具体来说，计算方法将是 发展到：(目标1)通过以下方式识别克隆相关序列和改进V(D)J基因分配 确定Ig基因座单倍型，(目标2)重建克隆谱系，并用这些克隆谱系来了解B细胞的迁移 和分化网络，以及(目标3)分析序列以预测曲目属性和序列 与抗原结合或临床相关结果相关的基序。这些 穿过 人类 基于模拟的研究的组合，例如 (重症肌无力)和小鼠(内源性 将对方法进行验证 以及对来自两个国家的新的实验数据进行测试 逆转录病毒出现)系统。所有方法都将是 集成并通过我们广泛使用的开源移植框架提供，该框架提供 AIRR-SEQ分析的自始至终的分析生态系统。这些方法加在一起，提供了一个了解 驱动适应性免疫和疾病中发生的失调的微观进化动力。

项目成果

Spatiotemporal segregation of human marginal zone and memory B cell populations in lymphoid tissue.

• DOI: 10.1038/s41467-018-06089-1
• 发表时间: 2018-09-21
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Zhao Y;Uduman M;Siu JHY;Tull TJ;Sanderson JD;Wu YB;Zhou JQ;Petrov N;Ellis R;Todd K;Chavele KM;Guesdon W;Vossenkamper A;Jassem W;D'Cruz DP;Fear DJ;John S;Scheel-Toellner D;Hopkins C;Moreno E;Woodman NL;Ciccarelli F;Heck S;Kleinstein SH;Bemark M;Spencer J
• 通讯作者: Spencer J

High-throughput single-cell profiling of B cell responses following inactivated influenza vaccination in young and older adults.

• DOI: 10.18632/aging.204778
• 发表时间: 2023-06-26
• 期刊: AGING-US
• 影响因子: 5.2
• 作者: Wang, Meng;Jiang, Ruoyi;Mohanty, Subhasis;Meng, Hailong;Shaw, Albert C.;Kleinstein, Steven H.
• 通讯作者: Kleinstein, Steven H.

Mechanisms underlying B cell immune dysregulation and autoantibody production in MuSK myasthenia gravis.

• DOI: 10.1111/nyas.13535
• 发表时间: 2018-01
• 期刊: Annals of the New York Academy of Sciences
• 影响因子: 5.2
• 作者: Stathopoulos P;Kumar A;Heiden JAV;Pascual-Goñi E;Nowak RJ;O'Connor KC
• 通讯作者: O'Connor KC

Local Clonal Diversification and Dissemination of B Lymphocytes in the Human Bronchial Mucosa.

• DOI: 10.3389/fimmu.2018.01976
• 发表时间: 2018
• 期刊: Frontiers in immunology
• 影响因子: 7.3
• 作者: Ohm-Laursen L;Meng H;Chen J;Zhou JQ;Corrigan CJ;Gould HJ;Kleinstein SH
• 通讯作者: Kleinstein SH

Sex-Biased Aging Effects on Ig Somatic Hypermutation Targeting.

• DOI: 10.4049/jimmunol.2000576
• 发表时间: 2021-01-01
• 期刊: Journal of immunology (Baltimore, Md. : 1950)
• 作者: Cui A;Chawla DG;Kleinstein SH
• 通讯作者: Kleinstein SH