Leveraging deep sequencing data to understand antibody maturation

利用深度测序数据了解抗体成熟度

• 批准号: 9318527
• 负责人: Frederick Albert Matsen
• 金额: $ 37.68万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9318527
• 关键词: Adaptive Immune System; Address; Affinity; Algorithms; Antibodies; Antibody Formation; Appointment; Area; Attention; B cell repertoire; B-Lymphocytes; Bayesian Method; Bayesian Modeling; Biological Process; Cell Lineage; Cell Maturation; Cell model; Cells; Clinic; Collaborations; Collection; Communicable Diseases; Computer software; Computing Methodologies; Data; Epidemic; Event; Evolution; Exposure to; Floor; Genes; Goals; Grant; Health; High-Throughput Nucleotide Sequencing; Human; Human Resources; Immune; Immune system; Immunity; Immunoglobulin Somatic Hypermutation; Immunological Models; Immunology; Individual; Infection; Influenza; Joints; Lead; Learning; Legal patent; Light; Malignant Neoplasms; Markov Chains; Mathematics; Mediating; Metagenomics; Methodology; Methods; Modeling; Molecular Evolution; Mutation; Nucleotides; One-Step dentin bonding system; Pathogenesis; Pathogenicity; Phylogenetic Analysis; Physics; Population; Population Study; Populations at Risk; Predisposition; Process; Receptor Cell; Research; Research Personnel; Sampling; Scientist; Site; Statistical Methods; Statistical Models; Techniques; Trees; Uncertainty; V(D)J Recombination; Vaccine Design; Vaccines; Work; adaptive immunity; analytical method; cancer cell; clinical application; computerized tools; deep sequencing; immunological intervention; improved; innovation; novel strategies; open source; pathogen; public health relevance; reconstruction; response; theories; time use; tool

DESCRIPTION (provided by applicant): Intellectual Merit: The health of each human being is critically dependent on its particular immune system. The adaptive component of the immune system is the means by which the body learns to recognize pathogens. Deficiencies in adaptive immunity place the individual as well as the population at risk for infectious diseases and cancers. The currently available mathematical and computational tools are not yet ready to characterize the full collection of changes in the antibody-mediated adaptive immune system occurring in response to exposure to new pathogenic entities. In particular, state-of-the-art methods are hindered by only focusing on a small subset of the immune cells at a time, using simple models of immune cell maturation that are not derived from data, and only giving point estimates for parameters of those models. The investigators propose to address these limitations by developing a novel approach to high throughput sequencing data from antibody genes by developing: 1) the first fully Bayesian inferential approach to immune cell maturation; 2) the first comprehensive statistical model of antibody cell maturation and evolution, including sequence models of antibody somatic hypermutation inferred directly from data; 3) innovative inferential tools to obtain posterior distributions on the joint assignment of collections of itemsto discrete parameters - scalable computational implementations of these models and inferential frameworks leading to their widespread application. In short, our work will both develop much needed analytical methods for a recently developed type of data and open a new area of statistical research. Broader Impacts: Comprehensive statistical modeling and inference of high throughput sequencing of immune cell receptors will provide information needed for rational vaccine design, prediction of susceptibility to infections, and understanding of the pathogenesis of immune cell cancers. B cell lineage reconstructions will allow scientists to track the changes that happen to an antibody in response to pathogen evolution, enabling vaccines to stay one step ahead of pathogens. An extension of this approach will be to use these tools to characterize not only the immunity of individuals, but also of populations, for example in their ability to resist epidemics. Our formalization will motivate research on a new type of inference problem with challenging statistical aspects. Our methods will be implemented in open-source software, so that any immunology lab or clinic can use these new approaches. Moreover, the proposed statistical methodology should find other applications beyond immunology, for example, in metagenomics.

描述（由申请人提供）：智力优点：每个人的健康都严重依赖于其特定的免疫系统。免疫系统的适应性成分是身体学习识别病原体的手段。适应性免疫的缺陷使个人和群体处于感染性疾病和癌症的风险之中。目前可用的数学和计算工具尚未准备好表征抗体介导的适应性免疫系统中响应于暴露于新病原体而发生的变化的全部集合。特别地，现有技术的方法受到阻碍，因为一次仅关注免疫细胞的一小部分，使用不是从数据中导出的免疫细胞成熟的简单模型，并且仅给出这些模型的参数的点估计。研究人员建议通过开发一种新的方法来解决这些限制，该方法通过开发：1）第一种完全贝叶斯推理的免疫细胞成熟方法; 2）第一种抗体细胞成熟和进化的综合统计模型，包括直接从数据推断的抗体体细胞超突变的序列模型; 3）创新的推理工具，以获得后验分布的联合分配项目的集合离散参数-可扩展的计算实现这些模型和推理框架，导致其广泛的应用。简而言之，我们的工作将为最近开发的数据类型开发急需的分析方法，并开辟统计研究的新领域。更广泛的影响：免疫细胞受体的高通量测序的综合统计建模和推断将为合理的疫苗设计、对感染的易感性的预测以及对免疫细胞癌症的发病机制的理解提供所需的信息。B细胞谱系重建将使科学家能够跟踪抗体响应病原体进化而发生的变化，使疫苗能够领先病原体一步。这种方法的一个扩展将是使用这些工具不仅描述个人的免疫力，而且描述群体的免疫力，例如他们抵抗流行病的能力。我们的形式化将激励研究一种新的类型的推理问题具有挑战性的统计方面。我们的方法将在开源软件中实现，以便任何免疫学实验室或诊所都可以使用这些新方法。此外，所提出的统计方法应该在免疫学之外找到其他应用，例如在宏基因组学中。