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: 8631840
• 负责人: Steven H. Kleinstein
• 金额: $ 55.94万
• 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/8631840
• 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; 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; Metric; 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; Simulate; 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; public health relevance; response; simulation; statistics; tool; web based interface; web interface

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 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.

项目总结/摘要 我们的免疫系统对病原体攻击或疫苗接种有效反应的能力取决于 由B淋巴细胞表达的免疫球蛋白（IG）受体的多样性库。每个IG受体都是独特的， 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起来的。期间 在免疫应答过程中，最初通过其IG受体以低亲和力结合抗原的B细胞被 通过体细胞超突变（SHM）和亲和力依赖性选择的周期进行修饰，以产生高- 亲和记忆和浆细胞。这种亲和力成熟是T细胞依赖性适应性免疫应答的关键组成部分。 免疫反应。它有助于防止快速变异的病原体，并成为许多疾病的基础。 疫苗。大规模表征B细胞IG库现在在人类中是可行的。驱动 随着高通量测序技术的巨大进步，这些数据正在打开令人兴奋的 调查的途径。B细胞库的特征，包括多态性、偏向片段使用和 多样性，可以与临床相关的结果，如感染或疫苗接种的易感性 反应这些数据也有助于对B细胞和适应性免疫的基本理解。在 特别地，从IG突变模式估计正选择和负选择的能力具有广泛的应用前景。 这些应用不仅用于了解对病原体的免疫反应，而且对于确定 体细胞超突变在自身免疫和B细胞癌中的作用。虽然很有前途， 数据也对分析提出了根本性的挑战，需要开发新技术， 重新考虑现有的方法，这些方法不能扩展到生成的数百万个序列。这 一项提案描述了用于分析高通量IG测序数据集的新方法， 通过生物信息学和统计学方法的发展，计算建模和 序列数据挖掘将开发新的表征库属性的方法， 作为疾病风险、诊断和预后的生物标志物的潜力。具体而言，方法将是 开发的目的是：（目的1）将序列分组为克隆并改善V（D）J片段分配，从而允许 鉴定体细胞突变，（目标2）模拟SHM突变性和取代模式，以便它们可以 量化并比较各组，从而深入了解潜在的突变机制， (Aim 3）量化选择和表征克隆多样性，提供亲和力成熟的信息， 反应动力学这些方法将通过结合基于模拟的研究进行验证， 以及对来自人类和小鼠系统的新实验金标准数据集进行测试。所有 将通过网络界面和分发开放源码广泛提供各种方法。