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 HIV-1造成了巨大的健康和经济负担，目前有3000多万人感染 国际吧寻找有效的HIV-1疫苗仍然是当务之急， 免疫系统如何识别HIV-1可以帮助疫苗设计。最近，许多努力都集中在 了解HIV-1感染的抗体反应。然而，多克隆中和抗体 个体的反应非常复杂。用于映射此类响应的标准方法包括各种 实验技术，但最近，计算方法也得到了发展。这些 计算方法，我们称之为NFP（中和指纹），是基于血清分析 中和数据通常在供体样品表征的最初阶段获得， 因此是一种在多克隆反应中准确定位抗体特异性的有效技术。的 NFP算法已经成为HIV领域的重要工具，并被广泛使用。 世界各地的实验室，包括杜克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中和抗体之间的相互作用具有重要意义的问题 和病毒具体而言，我们将开发和应用新的算法：（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 用于前瞻性鉴定多克隆血清中抗体特异性的NFP方法。(2)映射 针对HIV-1 Env上新表位的广泛中和抗体应答我们将使用抗原决定基- 结构分析和计算搜索算法，以确定新的Env表位，并将筛选供体 样本中是否存在相关NFP信号。新的抗体特异性的有希望的信号将是 通过合作进一步发展。(3)人群水平的广泛中和抗体分析 对HIV-1的反应。我们将分析来自不同HIV感染队列的大量样本， 以确定响应HIV-1引起的常见抗体特异性，以及潜在的 感染病毒序列的特征与引发的表位特异性之间的关联。 所提出的NFP算法将向公众提供，并且将在许多领域中有用。 艾滋病毒领域的高影响领域，包括绘制以前未表征的 样品，新Env表位的鉴定，以及广泛中和抗体的大规模分析 在一个群体中，或在人口水平上。总的来说，这项工作将有助于更好地了解 中和抗HIV-1的抗体应答，并将构建Env上表位的更完整图片。 所提出的算法框架应可推广到其他重要的病毒，如流感病毒和 丙型肝炎，因此有可能对公共卫生产生深远的影响。