Computational tools for analysis of B cell somatic hypermutation

描述（由申请人提供）：在免疫应答过程中，最初通过免疫球蛋白（Ig）受体结合低亲和力抗原的B细胞经过体细胞超突变和亲和力依赖选择的循环修饰，产生高亲和力记忆细胞和浆细胞。这种亲和成熟是T细胞依赖性适应性免疫反应的关键组成部分。它有助于预防迅速变异的病原体，是许多疫苗的基础。在实验衍生的Ig序列中检测选择的能力是许多研究的关键部分。这些技术不仅有助于了解对病原体的反应，而且还有助于确定抗原驱动选择在自身免疫、B细胞癌和某些物种免疫前反应多样化中的作用。尽管它的重要性，定量选择在B细胞Ig序列充满了困难。统计测试的必要参数（例如在没有选择的情况下替换突变的预期频率）的计算是非常重要的，并且在分析多个序列时，结果不容易解释。在本提案中，我们汇集了一个多学科的小组来开发和提供新的计算方法，用于量化和可视化体细胞超突变B细胞中的免疫选择。现有的体细胞超突变靶向模型将在新的实验基础上得到改进，以显著扩大未选择突变的可用数量。此外，我们将通过确定最有信息的邻近位置来扩展每个核苷酸的易变性模型。这将识别超出两个相邻碱基的热点/冷点基序，并允许间隙基序。接下来，在这些改进的突变靶向模型的基础上，我们将实施并验证新的统计测试，通过分析体细胞超突变模式来检测选择。目前考虑替换突变频率的方法将扩展到考虑氨基酸性状。此外，基于谱系树分析的新方法将用于克隆相关序列。最后，我们将开发从新兴技术中可视化大规模数据集的方法，从而可以对整个B细胞谱进行全面分析。所有这些方法都将通过我们现有的Ig序列分析网站提供，该网站也将大大扩展，以实现分析管道几乎所有步骤的自动化。

DESCRIPTION (provided by applicant): During the course of an immune response, B cell that initially bind antigen with low affinity through their Immunoglobulin (Ig) receptor are modified through cycles of somatic hypermutation 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. The ability to detect selection in experimentally-derived Ig sequences is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers, and the diversification of pre-immune repertoires in certain species. Despite its importance, quantifying selection in B cell Ig sequences is fraught with difficulties. The necessary parameters for statistical tests (such as the expected frequency of replacement mutations in the absence of selection) are non-trivial to calculate, and results are not easily interpretable when analyzing more than a handful of sequences. In this proposal, we bring together a multi- disciplinary group to develop and make available new computational methods for quantifying and visualizing immune selection in somatically hypermutated B cells. Existing models of somatic hypermutation targeting will be improved based on new experiments to significantly expand the available number of unselected mutations. In addition, we will extend models for the mutability of each nucleotide by determining the most informative neighboring positions. This will identify hot/cold-spot motifs that extend beyond two neighboring bases and allow for gapped motifs. Next, building on these improved models for mutation targeting, we will implement and validate new statistical tests for detecting selection by analyzing somatic hypermutation patterns. Current methods that consider the frequency of replacement mutations will be extended to account for amino acid traits. Furthermore, new methods based on lineage tree analysis will be developed for clonally-related sequences. Finally, we will develop methods to visualize large-scale datasets from emerging technologies that allow comprehensive analysis of entire B cell repertoires. All of these methods will be made available through our existing Ig sequence analysis website, which will also be significantly expanded to automate virtually all steps of the analysis pipeline. PUBLIC HEALTH RELEVANCE: The analysis of somatic mutation patterns in B cell Ig sequences to detect selection is a critical part of many studies. Such techniques are useful not only for understanding the response to pathogens, but also to determine the role of antigen-driven selection in autoimmunity, B cell cancers, and the diversification of pre-immune repertoires in certain species. In this proposal, we develop several computational methods to detect section with higher sensitivity and specificity, as well as visualization methods that will be helpful to analyze large-scale sequence data sets that are possible with new sequencing techniques.