Computational tools for analysis of B cell somatic hypermutation

描述（由申请方提供）：在免疫应答过程中，最初通过其免疫球蛋白（IG）受体以低亲和力结合抗原的B细胞通过体细胞超突变和亲和力依赖性选择循环进行修饰，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应答的关键组成部分。它有助于防止快速变异的病原体，并成为许多疫苗的基础。在实验获得的IG序列中检测选择的能力是许多研究的关键部分。这些技术不仅可用于理解对病原体的反应，而且可用于确定抗原驱动的选择在自身免疫、B细胞癌症和某些物种中免疫前谱系的多样化中的作用。尽管其重要性，量化选择在B细胞IG序列充满了困难。统计检验的必要参数（例如在没有选择的情况下预期的替换突变频率）是不容易计算的，并且当分析超过少数序列时，结果不容易解释。在这个提议中，我们汇集了一个多学科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基于新的实验进行改进，以显着扩大可获得的突变数量。此外，我们将通过确定信息量最大的相邻位置来扩展每个核苷酸的突变性模型。这将识别延伸超过两个相邻碱基的热点/冷点基序，并允许有间隙的基序。接下来，在这些改进的突变靶向模型的基础上，我们将通过分析体细胞超突变模式来实现和验证用于检测选择的新统计测试。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被开发用于克隆相关序列。最后，我们将开发方法来可视化来自新兴技术的大规模数据集，这些技术允许对整个B细胞库进行全面分析。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，以自动化分析管道的几乎所有步骤。 公共卫生相关性：分析B细胞IG序列中的体细胞突变模式以检测选择是许多研究的关键部分。这些技术不仅可用于理解对病原体的反应，而且可用于确定抗原驱动的选择在自身免疫、B细胞癌症和某些物种中免疫前谱系的多样化中的作用。在这个提议中，我们开发了几种计算方法来检测具有更高灵敏度和特异性的部分，以及可视化方法，这将有助于分析大规模的序列数据集，这是可能的新的测序技术。

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.