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

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B细胞受体（BCR）是 独特的，在淋巴细胞发育过程中通过生殖系编码的V（D）J 基因.在免疫应答过程中，最初以低亲和力结合抗原的B细胞通过免疫应答被激活。 它们的BCR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， 产生高亲和力记忆和浆细胞。这种亲和力成熟是T细胞增殖的关键组成部分。 依赖的适应性免疫反应它有助于防止快速变异的病原体， 这是许多疫苗的基础，但失调可导致自身免疫和其他疾病。下一代 测序（NGS）技术已经彻底改变了我们进行大规模适应性免疫的能力， 受体库测序（AIRR-Seq）实验。AIRR-Seq越来越多地应用于轮廓 BCR库，并获得对健康个体和具有一系列免疫反应的个体的免疫反应的见解。 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， 实验正在产生越来越大的数据集，具有数千万到数百万的BCR序列。 尽管很有希望，但库规模数据对分析提出了根本挑战，需要 开发新技术，重新考虑现有的方法，这些方法不能大规模推广。 生成的序列数。这一建议描述了一系列小说的发展 计算方法来探讨中心假设，即：B细胞克隆关系和谱系 结构可以通过计算从库测序数据中得到，并用于定义B细胞 健康和疾病的迁移和分化网络。具体而言，计算方法将 开发的目的是：（目的1）鉴定克隆相关序列，并通过以下方法改善V（D）J基因分配 确定IG基因座单倍型，（目的2）重建克隆谱系，并使用这些来了解B细胞迁移 和分化网络，和（目的3）分析序列，以预测剧目的性质和序列 与抗原结合或临床相关结果相关的基序。这些 通过 人类 基于模拟的研究的组合，如 （重症肌无力）和小鼠（内源性 方法将得到验证 以及对来自两个实验室的新实验数据进行测试， 逆转录病毒出现）系统。所有方法都将 集成并通过我们广泛使用的开源Immcantation框架提供，该框架提供 用于AIRR-Seq分析的从头到尾的分析生态系统。这些方法一起提供了一个窗口， 驱动适应性免疫和疾病中发生的失调的微观进化动力学。

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.