Computational tools for the analysis of high-throughput immunoglobulin sequencing

用于分析高通量免疫球蛋白测序的计算工具

• 批准号: 8835027
• 负责人: Steven H. Kleinstein
• 金额: $ 40.76万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8835027
• 关键词: Address; Affinity; Appearance; Autoimmune Diseases; Autoimmunity; B cell repertoire; B-Lymphocytes; Base Composition; Base Pairing; Bayesian Modeling; Bioinformatics; Biological Markers; Biological Models; Cell division; Cells; Clinical; Clonal Expansion; Code; Codon Nucleotides; Collaborations; Communities; Complementarity Determining Regions; Computer Simulation; Computing Methodologies; DNA; DNA Repair Pathway; Data; Data Analyses; Data Set; Development; Diagnosis; Framework Regions; Genes; Genetic Polymorphism; Genetic Recombination; Genotype; Gold; Grouping; Health; High-Throughput Nucleotide Sequencing; Human; Immune; Immune response; Immune system; Immunoglobulin Somatic Hypermutation; Immunoglobulins; Immunology; Infection; Knowledge; Laboratory Research; Lymphocyte; Malignant Neoplasms; Measures; Mediating; Memory; Methods; Modeling; Mus; Mutate; Mutation; Nucleotides; Outcome; Pattern; Physiological; Plasma Cells; Point Mutation; Population Dynamics; Predisposition; Process; Property; Relative (related person); Role; Sequence Analysis; Silent Mutation; Somatic Mutation; Source Code; Spottings; System; T-Lymphocyte; Techniques; Technology; Testing; Trees; Vaccination; Vaccines; Work; adaptive immunity; antigen binding; base; clinically relevant; computer science; computerized tools; data mining; disorder risk; experience; flexibility; high throughput analysis; immunoglobulin receptor; improved; insight; method development; next generation sequencing; novel strategies; open source; outcome forecast; pathogen; response; simulation; statistics; tool; web based interface; web interface

DESCRIPTION (provided by applicant): 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 Ig receptor is unique, having been assembled during lymphocyte development by somatic recombination of gene segments. During the course of an immune response, B cell that initially bind antigen with low affinity through their Ig receptor 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. Large-scale characterization of B cell Ig repertoires is now feasible in humans. Driven by the dramatic improvements in high-throughput sequencing technologies, these data are opening up exciting avenues of inquiry. Features of the B cell repertoire, including polymorphisms, biased segment usage and diversity, can be correlated with clinically relevant outcomes, such as susceptibility to infection or vaccination response. These data can also contribute to basic understanding of B cells and adaptive immunity. In particular, the ability to estimate positive and negative selection from Ig mutation patterns has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B cell cancers. Although promising, repertoire-scale data also present fundamental challenges for analysis requiring the development of new techniques and the rethinking of existing methods that are not scalable to the millions of sequences being generated. This proposal describes novel approaches for the analysis of high-throughput Ig sequencing data sets enabled through a combination of bioinformatics and statistics method development, computational modeling and sequence data-mining. New ways to characterize repertoire properties will be developed that have the potential for use as biomarkers for disease risk, diagnosis and prognosis. Specifically, methods will be developed to: (Aim 1) group sequences into clones and improve V(D)J segment assignment, thus allowing identification of somatic mutations, (Aim 2) model SHM mutability and substitution patterns so they can be quantified and compared across groups, thus providing insights into underlying mutation mechanisms, and (Aim 3) quantify selection and characterize clonal diversity, providing information on affinity maturation and response dynamics. These methods will be validated through a combination of simulation-based studies, as well as testing on new experimental gold-standard data sets from both human and murine systems. All of the methods will be made widely available through web interfaces and distribution of open-source code.

描述（由申请人提供）：我们的免疫系统有效应对病原体攻击或疫苗接种的能力取决于 B 淋巴细胞表达的多种免疫球蛋白 (Ig) 受体。每个 Ig 受体都是独特的，是在淋巴细胞发育过程中通过基因片段的体细胞重组而组装的。在免疫反应过程中，最初通过其 Ig 受体以低亲和力结合抗原的 B 细胞通过体细胞超突变 (SHM) 和亲和力依赖性选择的循环进行修饰，以产生高亲和力记忆细胞和浆细胞。这种亲和力成熟是 T 细胞依赖性适应性免疫反应的关键组成部分。它有助于防止快速变异的病原体，并且是许多疫苗的基础。 B 细胞 Ig 库的大规模表征现在在人类中是可行的。在高通量测序技术显着改进的推动下，这些数据开辟了令人兴奋的探究途径。 B 细胞库的特征，包括多态性、片段使用偏倚和多样性，可以与临床相关结果相关，例如对 感染或疫苗接种反应。这些数据还有助于对 B 细胞和适应性免疫的基本了解。特别是，估计正向和负向选择的能力 从 Ig 突变模式中获得的结果具有广泛的应用，不仅可以了解对病原体的免疫反应，而且对于确定体细胞过度突变在自身免疫和 B 细胞癌症中的作用也至关重要。尽管很有希望，但曲目规模的数据也给分析带来了根本性挑战，需要开发新技术并重新思考现有方法，这些方法无法扩展到生成的数百万个序列。该提案描述了通过生物信息学和统计学方法开发、计算建模和序列数据挖掘相结合来分析高通量 Ig 测序数据集的新方法。将开发表征谱系特性的新方法，这些方法有可能用作疾病风险、诊断和预后的生物标志物。具体来说，将开发方法来：（目标 1）将序列分组为克隆并改进 V（D）J 片段分配，从而识别体细胞突变，（目标 2）建立 SHM 突变性和替代模式模型，以便对它们进行量化和比较跨群体，从而提供对潜在突变机制的见解，并且（目标 3）量化选择和表征克隆多样性，提供有关亲和力成熟和响应动态的信息。这些方法将通过基于模拟的研究以及对来自人类和小鼠系统的新实验金标准数据集的测试相结合来进行验证。所有方法都将通过网络界面和开源代码的分发来广泛使用。