Complete mapping of immune selection from antibodies to HIV

从抗体到 HIV 的免疫选择的完整图谱

• 批准号: 10305599
• 负责人: Jesse D Bloom
• 金额: $ 9.27万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-07 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10305599
• 关键词: Academy; Address; Affect; Algorithmic Software; Amino Acids; Animals; Antibodies; Bar Codes; Big Data; Biological Assay; Communities; Computer software; Critiques; Data; Data Set; Enzyme-Linked Immunosorbent Assay; Escape Mutant; Evolution; Goals; HIV; HIV Antibodies; HIV envelope protein; HIV vaccine; Human; Immune; Immunity; Intuition; Libraries; Maps; Medical; Medicine; Membrane Proteins; Methods; Modernization; Monoclonal Antibodies; Mutation; Plasma; Rabies Vaccines; Research; Research Personnel; Resolution; Serum; Site; Structure; Techniques; Therapeutic; Vaccinated; Vaccine Design; Vaccines; Viral; Viral Proteins; Virus; Visualization; Work; base; computerized tools; cost; deep sequencing; design; env Gene Products; experimental study; improved; mutant; neutralizing antibody; open source; pressure; programs; prophylactic; response; tool; vaccine development; vaccine evaluation; vaccine strategy; vaccine-induced antibodies; vaccinology; web platform

PROJECT SUMMARY This isolation of exceptionally broad anti-HIV antibodies has revealed sites of vulnerability on the virus’s surface protein, Env. Major efforts are now underway to elicit such antibodies with vaccines, or to use the antibodies directly as therapeutics. The design of such vaccines and therapies requires carefully characterizing how monoclonal antibodies and polyclonal sera recognize Env. We have recently developed a new deep-sequencing based approach to functionally characterize antibody-Env interactions on a large scale. Here we will greatly extend the utility of this approach by making it possible to easily and completely map the effects of all amino-acid mutations on viral recognition by both neutralizing and non- neutralizing antibodies or sera. Specifically, we will: 1) Create libraries of viruses carrying all single amino-acid mutations to Env, as well as many combinations of mutations. These libraries will be designed in a way that enables them to be easily and cheaply characterized by deep sequencing. 2) Develop methods to use the libraries to efficiently map how mutations to Env affect virus recognition by neutralizing and non-neutralizing antibodies and sera. 3) Create algorithms and software to analyze and visualize the “Big Data” sets generated by the mappings. We will use these tools to completely map how Env mutations affect recognition by important monoclonal antibodies, as well as natural and vaccine-induced plasma responses. We will also distribute the experimental and computational tools so that they can be easily used by the entire HIV research community. Overall, this work will develop powerful methods to functionally map the antigenic effects of mutations to HIV. Such maps will aid in the basic study of HIV and inform the design of vaccines and therapeutics.

项目总结 这种异常广泛的抗艾滋病毒抗体的分离揭示了脆弱的部位 在病毒的表面蛋白Env.目前正在进行重大的努力来诱导这种抗体。 使用疫苗，或直接将抗体用作疗法。这种疫苗的设计 而治疗方法需要仔细鉴定单抗和多克隆 血清识别环境。我们最近开发了一种新的基于深度测序的方法来 从功能上表征抗体与环境病毒之间的大规模相互作用。在这里我们将极大地 扩展此方法的实用程序，使其能够轻松而完整地映射 所有氨基酸突变对病毒中和和非识别的影响 中和抗体或血清。具体来说，我们会： 1)创建携带所有单一氨基酸突变到环境中的病毒库，以及 突变的多种组合。这些库的设计方式将是 使它们能够容易和廉价地进行深度测序。 2)开发使用这些库的方法来有效地将突变与环境的影响进行映射 通过中和和非中和抗体和血清识别病毒。 3)创建算法和软件来分析和可视化大数据集 由映射生成。 我们将使用这些工具通过以下方式完整地描绘环境突变如何影响识别 重要的单抗，以及自然和疫苗诱导的血浆反应。 我们还将分发实验和计算工具，以便它们可以容易地 被整个艾滋病毒研究界使用。总体而言，这项工作将得到有力的发展 方法从功能上定位突变对HIV的抗原性。这样的地图将有助于 艾滋病毒的基础研究，并为疫苗和疗法的设计提供信息。