Neutralization Fingerprinting Analysis of Polyclonal Antibody Responses against HIV-1

HIV-1 多克隆抗体反应的中和指纹图谱分析

• 批准号: 9407909
• 负责人: Ivelin Georgiev
• 金额: $ 65.07万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-06-06 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9407909
• 关键词: Address; Algorithmic Analysis; Algorithms; Antibodies; Antibody Response; Antibody Specificity; Antigens; Area; Binding; Biological; Characteristics; Collaborations; Collection; Complex; Computer Analysis; Computing Methodologies; Data; Derivation procedure; Development; Donor Selection; Economic Burden; Epitope Mapping; Epitopes; Fingerprint; Generations; Genetic; Goals; HIV; HIV Infections; HIV-1; HIV-1 vaccine; Hepatitis C; Immune system; Individual; Infection; Influenza C Virus; Knock-out; Laboratories; Least-Squares Analysis; Letters; Machine Learning; Methods; Monoclonal Antibodies; Mutation; Pattern; Phenotype; Population; Public Health; Sampling; Serum; Signal Transduction; Specificity; Techniques; Technology; United States National Institutes of Health; Vaccine Design; Validation; Variant; Virus; Work; base; cohort; health economics; improved; neutralizing antibody; next generation; novel; polyclonal antibody; prospective; response; sample collection; tool

Project Summary HIV-1 poses a substantial health and economic burden, with more than 30 million people currently infected worldwide. The search for an effective HIV-1 vaccine remains a top priority, and a deeper understanding of how the immune system recognizes HIV-1 can help inform vaccine design. Lately, much effort has focused on understanding the antibody responses to HIV-1 infection. However, the polyclonal neutralizing antibody responses in an individual are very complex. Standard methods for mapping such responses include various experimental techniques, but more recently, computational methods were also developed. These computational methods, which we call NFP (neutralization fingerprinting), are based on analysis of serum neutralization data that is typically obtained in the very first stages of donor sample characterization, and are therefore an efficient technology for accurately mapping antibody specificities in polyclonal responses. The NFP algorithms have already become an important tool in the HIV field and are being used extensively by laboratories throughout the world, including Duke CHAVI-ID, CAPRISA, NIH VRC, and MHRP. Here, we propose to develop next-generation NFP algorithms and apply them to address biological questions with important implications for understanding the interactions between HIV-neutralizing antibodies and the virus. Specifically, we will develop and apply novel algorithms for: (1) Antibody specificity prediction with significantly improved accuracy and reliability. These algorithms will immensely improve the utility of the NFP approach for prospective identification of antibody specificities in polyclonal sera. (2) Mapping broadly neutralizing antibody responses against novel epitopes on HIV-1 Env. We will use epitope- structural analysis and computational search algorithms to identify novel Env epitopes, and will screen donor samples for the presence of related NFP signals. Promising signals for novel antibody specificities will be characterized further through collaborations. (3) Population-level analysis of broadly neutralizing antibody responses to HIV-1. We will analyze large collections of samples from diverse HIV infection cohorts in order to determine common antibody specificities elicited in response to HIV-1, as well as patterns of potential association between features of the infecting virus sequence and the elicited epitope specificities. The proposed NFP algorithms will be made available to the public, and will be useful in a number of high-impact areas in the HIV field, including mapping of antibody specificities in previously uncharacterized samples, identification of novel Env epitopes, and large-scale analysis of broadly neutralizing antibody responses within a cohort, or at a population level. Overall, this work will lead to a better understanding of the neutralizing antibody responses against HIV-1 and will build a more complete picture of the epitopes on Env. The proposed algorithmic framework should be generalizable to other important viruses, such as influenza and hepatitis C, and therefore has the potential for a far-reaching impact on public health.

项目摘要 艾滋病毒-1造成了巨大的健康和经济负担，目前有3000多万人感染 全世界。寻找有效的艾滋病毒-1疫苗仍然是当务之急，并加深对 免疫系统如何识别HIV-1有助于指导疫苗设计。最近，很多努力都集中在 了解对HIV-1感染的抗体反应。然而，多克隆中和抗体 个体的反应是非常复杂的。用于映射这种响应的标准方法包括各种 实验技术，但最近，计算方法也被开发出来。这些 我们称之为NFP(中和指纹)的计算方法是基于对血清的分析 通常在供体样品表征的第一阶段获得的中和数据，并且是 因此，这是一种在多克隆反应中准确定位抗体特异性的有效技术。这个 NFP算法已经成为艾滋病毒领域的重要工具，并被 世界各地的实验室，包括杜克大学CHAVI-ID、CAPRISA、NIH VRC和MHRP。 在这里，我们建议开发下一代NFP算法，并将其应用于解决生物 对理解HIV中和抗体之间相互作用具有重要意义的问题 还有病毒。具体地说，我们将开发和应用新的算法来：(1)抗体特异性预测 具有显著提高的准确性和可靠性。这些算法将极大地提高 NFP方法用于多克隆血清中抗体特异性的前瞻性鉴定。(2)地图绘制 广泛中和针对HIV-1 env新表位的抗体反应。我们将使用表位- 结构分析和计算搜索算法识别新的环境抗原表位，并将筛选供体 对相关NFP信号的存在进行采样。研究新抗体特异性的有希望的信号将是 通过合作进一步表现出其特点。(3)广谱中和抗体的群体水平分析 对HIV-1的反应。我们将分析从不同的HIV感染队列收集的大量样本，以 以确定针对HIV-1的常见抗体特异性，以及潜在的模式 感染性病毒序列的特征与所激发的表位特异性之间的关系。 建议的NFP算法将向公众提供，并将在一些 艾滋病毒领域的高影响领域，包括绘制以前未确定的抗体特异性图 样本、新的env表位的鉴定和广谱中和抗体的大规模分析 在一个队列内，或在人口层面上的反应。总体而言，这项工作将有助于更好地理解 中和针对HIV-1的抗体反应，将建立一个更完整的环境抗原表位图景。 建议的算法框架应可推广到其他重要病毒，如流感和 丙型肝炎，因此有可能对公共卫生产生深远影响。