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.

项目总结/摘要 广泛中和抗体（BNAb）的开发仍然是有效疫苗的雄心勃勃的目标 应答可靠地引出BNAb的方法尚不清楚，它们自然发育的机制也不清楚 完全理解先前的工作，包括我们实验室的研究，已经确定了一些生殖系免疫球蛋白 (Ig)可变（V）基因等位基因更有可能成为HIV、流感和 自身免疫不幸的是，这只是粗略地触及了500多个记录在案的等位基因的表面。 分布在人群中。我们将与实验合作者密切合作， 方法加速IG表征与BNAb发现的长期目标。成功在这些 这些努力将有助于个性化医疗保健和疫苗接种策略的发展， 我们对适应性免疫反应的关键组成部分的理解。 该提案将利用正在产生的越来越多的单细胞（SC）数据， RNA表达使联合收割机与IG库结合，以提供许多有用的生物学背景进行分析。尽管 SC数据的优势及其对免疫学分析的潜力，它仍然是缺失的， 表示公共存储库中适应性免疫受体库（AIRR）数据集的百分比 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IG表达和免疫学结果相关的种系等位基因。这是一 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 在IG和等位基因序列内未捕获的因子之间，并将AIRR分析与多组学数据相关联。 结果还将指导与实验合作者的未来研究并推进新方法 适用于疾病的IG基因和B细胞生物学。