Computational Methods for Analyzing lmmunoglobulin Allelic Diversity in B cells

分析 B 细胞中免疫球蛋白等位基因多样性的计算方法

• 批准号: 10751541
• 负责人: Noah Yann Lee
• 金额: $ 4.77万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10751541
• 关键词: Acceleration; Acute; Adaptive Immune System; Address; Algorithmic Analysis; Algorithms; Alleles; Antibodies; Antigen Receptors; Antigens; Autoimmunity; B-Lymphocytes; B-cell receptor repertoire sequencing; Biological; Cell surface; Cells; Cellular biology; Communities; Complex; Computing Methodologies; DNA Sequence Alteration; Data; Data Set; Development; Disease; Drops; Future; Genes; Genetic; Genetic Markers; Genetic Recombination; Genetic Transcription; Genomics; Genotype; Goals; Grouping; HIV Antibodies; Haplotypes; Immune response; Immune system; Immunogenetics; Immunoglobulin Genes; Immunoglobulin Variable Region; Immunoglobulins; Immunologic Memory; Immunologic Receptors; Immunologics; Immunology; Individual; Infection; Influenza; Link; Methodology; Methods; Modeling; Multiomic Data; Mutation; Nature; Outcome; Pathogenicity; Patients; Pattern; Phenotype; Population; Population Heterogeneity; Process; RNA; Receptor Cell; Research; Somatic Cell; Sorting; Specificity; Stereotyping; Surface; Testing; Training; Vaccination; Variant; Work; adaptive immune response; analysis pipeline; cell type; cohort; design; genetic analysis; genome wide association study; heuristics; immunoglobulin B; improved; interest; machine learning model; neutralizing antibody; novel; pathogen; personalized health care; public repository; response; success; tool; transcriptomics; trend; vaccination strategy; vaccine efficacy; vaccine response

PROJECT SUMMARY/ABSTRACT The development of broadly neutralizing antibodies (BNAbs) remains an ambitious objective for effective vaccine responses. Methods to reliably elicit BNAbs are not known, nor are the mechanisms for their natural development fully understood. Prior work, including research from our lab, has identified a handful of germline immunoglobulin (Ig) variable (V) gene alleles more likely to become broadly neutralizing antibodies for HIV, influenza, and autoimmunity. Unfortunately, this barely scratches the surface of the over 500 documented alleles broadly distributed across populations. We will work closely with experimental collaborators to develop computational methods accelerating Ig characterization with the long-term goal of BNAbs discovery. Success in these endeavors will contribute to the development of personalized healthcare and vaccination strategies, and increase our understanding of this critical component of the adaptive immune response. This proposal will take advantage of the growing quantity of single cell (SC) data being produced, which allows RNA expression to combine with Ig repertoires to confer a host of useful biological context to analyze. Despite the advantages of SC data and its potential to immunology analysis forward, it remains either missing or represents a fractional percentage of adaptive immune receptor repertoire (AIRR) datasets in public repositories due to a lack of analysis tools appropriate to the challenges of SC. To address this problem, we will develop Ig analysis methods leveraging SC data. Our goals are to improve coverage and accuracy of Ig V gene analysis and identify germline alleles associated with differential Ig expression and immunological outcomes. This is an early critical step in promoting broad use of SC data in AIRR analyses, understanding the complex interplay between Ig and factors not captured within the allelic sequence, and linking AIRR analysis to multi-omics data. Results will additionally guide future studies with experimental collaborators and advance novel methodology suited for Ig genes and B cell biology of diseases.

项目概要/摘要 开发广泛中和抗体（BNAb）仍然是有效疫苗的一个雄心勃勃的目标 回应。可靠地引发 BNAb 的方法尚不清楚，其自然发育的机制也不清楚 完全明白了。之前的工作，包括我们实验室的研究，已经鉴定出一些种系免疫球蛋白 (Ig) 可变 (V) 基因等位基因更有可能成为艾滋病毒、流感和流感病毒的广泛中和抗体 自身免疫。不幸的是，这仅仅触及了 500 多个有记录的等位基因的表面。 分布于人群中。我们将与实验合作者密切合作，开发计算 方法加速 Ig 表征，以实现 BNAb 发现的长期目标。在这些方面取得成功 我们的努力将有助于制定个性化医疗保健和疫苗接种策略，并增加 我们对适应性免疫反应这一关键组成部分的理解。 该提案将利用正在产生的单细胞（SC）数据数量不断增长的优势，这使得 RNA 表达与 Ig 库相结合，提供大量有用的生物学背景进行分析。尽管 尽管 SC 数据的优势及其在免疫学分析方面的潜力，但它仍然缺失或 表示公共存储库中适应性免疫受体库 (AIRR) 数据集的一小部分百分比 由于缺乏适合 SC 挑战的分析工具。为了解决这个问题，我们将开发Ig 利用 SC 数据的分析方法。我们的目标是提高 Ig V 基因分析的覆盖率和准确性 并鉴定与差异性 Ig 表达和免疫学结果相关的种系等位基因。这是一个 促进在 AIRR 分析中广泛使用 SC 数据的早期关键步骤，了解复杂的相互作用 比较 Ig 和等位基因序列中未捕获的因子，并将 AIRR 分析与多组学数据联系起来。 结果还将指导实验合作者的未来研究并推进新颖的方法 适用于疾病的 Ig 基因和 B 细胞生物学。