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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要 HIV-1 造成巨大的健康和经济负担，目前有超过 3000 万人感染全世界。寻找有效的 HIV-1 疫苗仍然是当务之急，并且更深入地了解免疫系统如何识别 HIV-1 有助于为疫苗设计提供信息。最近，很多努力都集中在了解抗体对 HIV-1 感染的反应。然而，多克隆中和抗体个人的反应非常复杂。绘制此类响应的标准方法包括各种实验技术，但最近还开发了计算方法。这些我们称之为 NFP（中和指纹法）的计算方法基于血清分析中和数据通常在供体样品表征的第一阶段获得，并且是因此，这是一种在多克隆反应中准确绘制抗体特异性的有效技术。这 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 Duke 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问题对于理解 HIV 中和抗体之间的相互作用具有重要意义的问题和病毒。具体来说，我们将开发和应用新的算法：（1）抗体特异性预测精度和可靠性显着提高。这些算法将极大地提高实用性用于前瞻性鉴定多克隆血清中抗体特异性的 NFP 方法。 (2) 映射广泛中和针对 HIV-1 Env 上新表位的抗体反应。我们将使用表位- 结构分析和计算搜索算法来识别新的 Env 表位，并筛选供体是否存在相关 NFP 信号的样本。新型抗体特异性的有希望的信号将是通过合作进一步表征。 (3) 广泛中和抗体的群体水平分析对 HIV-1 的反应。我们将分析来自不同 HIV 感染群体的大量样本，以便按顺序进行分析确定针对 HIV-1 引起的常见抗体特异性，以及潜在的模式感染病毒序列特征与引发的表位特异性之间的关联。拟议的 NFP 算法将向公众开放，并将在许多领域发挥作用 HIV 领域的高影响领域，包括绘制以前未表征的抗体特异性样品、新型 Env 表位的鉴定以及广泛中和抗体的大规模分析群体内或人群水平上的反应。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，并将构建 Env 上表位的更完整图像。所提出的算法框架应该可以推广到其他重要病毒，例如流感病毒和丙型肝炎，因此有可能对公共健康产生深远的影响。