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/文摘