Leveraing 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: 8825760
• 负责人: Frederick Albert Matsen
• 金额: $ 37.98万
• 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/8825760
• 关键词: Address; Affinity; Algorithms; Antibodies; Antibody Formation; Appointment; Area; Attention; B cell repertoire; B-Lymphocytes; Bayesian Method; Bayesian Modeling; Biological Process; Cell Lineage; Cell Maturation; Cells; Clinic; Collaborations; Collection; Communicable Diseases; Computer software; Computing Methodologies; DNA Sequence Rearrangement; 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; Immunology; Individual; Infection; Influenza; Intervention; 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; Phylogenetic Analysis; Physics; Population; Populations at Risk; Predisposition; Process; Qualifying; Receptor Cell; Research; Research Personnel; Robin bird; 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; 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：每个人的健康很大程度上取决于其特定的免疫系统。免疫系统的适应性部分是身体学习识别病原体的手段。适应性免疫的缺陷使个体和人群面临感染性疾病和癌症的风险。目前可用的数学和计算工具尚未准备好描述抗体介导的适应性免疫系统因暴露于新的致病实体而发生的全部变化。特别是，最先进的方法受到阻碍，因为一次仅关注免疫细胞的一小部分，使用并非从数据导出的免疫细胞成熟的简单模型，并且仅给出参数的点估计。那些模型。研究人员建议通过开发一种新的方法来解决这些局限性，方法是通过开发一种新的方法来从抗体基因中获取高通量测序数据：1）第一个针对免疫细胞成熟的完全贝叶斯推理方法； 2）第一个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，用于获得项目集合联合分配给离散参数的后验分布 - 这些模型和推理框架的可扩展计算实现导致其广泛应用。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：免疫细胞受体高通量测序的综合统计模型和推断将为合理的疫苗设计、预测感染易感性以及了解免疫细胞癌症的发病机制提供所需的信息。 B 细胞谱系重建将使科学家能够追踪抗体响应病原体进化而发生的变化，从而使疫苗能够领先病原体一步。这种方法的延伸将是使用这些工具不仅可以表征个人的免疫力，还可以表征群体的免疫力，例如抵抗流行病的能力。我们的形式化将激发对具有挑战性统计方面的新型推理问题的研究。我们的方法将在开源软件中实施，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能找到免疫学之外的其他应用，例如宏基因组学。