Leveraing deep sequencing data to understand antibody maturation

描述(由申请人提供)：智力价值：每个人的健康都严重依赖于其特定的免疫系统。免疫系统的适应性部分是身体学习识别病原体的手段。适应性免疫缺陷使个人和人群面临感染疾病和癌症的风险。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的特征。特别是，最先进的方法受到阻碍，因为一次只关注免疫细胞的一小部分，使用不是从数据得出的免疫细胞成熟的简单模型，并且只给出这些模型的参数的点估计。为了解决这些局限性，研究人员建议开发一种新的方法来高通量地对抗体基因进行测序，方法是开发：1)第一个完全贝叶斯推理的免疫细胞成熟和进化的方法；2)第一个抗体细胞成熟和进化的全面统计模型，包括直接从数据推断抗体体细胞超突变的序列模型；3)创新的推理工具，用于获得联合分配项目集合的后验分布；3)离散参数-这些模型和推理框架的可扩展计算实现和推理框架，导致它们的广泛应用。简而言之，我们的工作既将为最近开发的数据开发急需的分析方法，又将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全面统计建模和推断将提供合理的疫苗设计、感染易感性预测和了解免疫细胞癌症发病机制所需的信息。B细胞谱系的重建将使科学家能够跟踪抗体随着病原体进化而发生的变化，使疫苗能够领先病原体一步。这一方法的延伸将是使用这些工具不仅表征个人的免疫力，而且还表征群体的免疫力，例如他们抵抗流行病的能力。我们的形式化将推动对一种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现，这样任何免疫学实验室或临床都可以使用这些新方法。此外，拟议的统计方法应该在免疫学之外找到其他应用，例如，在元基因组学中。

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.