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Quick and Accurate Measurements of HIV Broadly Neutralizing Antibody Susceptibility

Quick and Accurate Measurements of HIV Broadly Neutralizing Antibody Susceptibility
快速准确地测量 HIV 广泛中和抗体敏感性
批准号:
10676422
负责人:
Zizhang Sheng
金额:
$100.24万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-04-01 至 2026-03-31

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中文摘要
翻译
项目总结 HIV感染者携带的病毒准种具有不同的基因序列和广泛的易感性 针对HIV包膜(Env)的中和抗体(BNAbs)。由于目前的检测方法存在困难, 证实了病毒对bNAbs的敏感性,大多数临床试验招募了参与者,而不知道他们的病毒 敏感度。因此,需要一种准确、灵敏、周转快(1-2周)的分析方法。我们建议 要通过两个方面来实现这一目标:目标1,散装环境中和的表型(环境功能)分析 评估和目标2,高通量中和预测的基因类型(环境序列)算法 敏感度。我们提出了10个针对5个不同gp120位点的HIV bNAb,其中2个是非HIV的。 以人IgG1抗体为阴性对照。目前的检测很困难,因为有两个劳动密集型和 耗时的程序:环境单基因组扩增(SGA)和个体环境克隆。目标1将 解决后一种需要的程序,因为目前的TZM-BL中和试验不能梳理出大块 不同bNAb敏感性的环境克隆。目标1将应用DNA条形码同时跟踪bNAb 以高通量方式检测数百种环境病毒变体的灵敏度。这将通过一种新颖的环境-- 表达慢病毒系统,其中每个环境都连接到唯一的条形码序列。目标2将针对环境 测序,SGA是必需的,因为Sanger测序不能对批量模板进行排序。我们 在目标2中建议应用第三代测序技术--纳米孔测序,以实现有效的散装 模板排序和开发机器学习模型,以实现快速可靠的预测算法。 结合文献中丰富的bNAb中和数据和本项目的目标1,训练 计算模型可以快速、准确和高通量地预测bNAb易感性。自.以来 从参与者的HIV DNA库中检测到低产量的功能性env序列和少数变异 ,我们在目标3中建议研究病毒粒子捕获试验是否可以应用于富集 功能环境。我们还将应用DNA分析来丰富病毒库中的微小变异。我们有 储存在现场的生物标本,并可从艾滋病临床试验小组获得广泛分布的艾滋病毒亚型 (ACTG)和HIV疫苗试验网络(HVTN)用于分析开发和验证的生物仓库。全 建议的R61目标有明确的里程碑,R61分析将以简化和 适应临床实验室的标准操作程序(SOP)。哥伦比亚大学临床试验支持 实验室设备齐全,人员齐全,可执行标准操作规程,并提供化验输入和验证 性能。快速、准确、灵敏的bNAb药敏试验方法的建立 扭亏为盈将改变bNAb试验的登记人数,并极大地改善bNAb产品的临床开发。
英文摘要
PROJECT SUMMARY HIV-infected individuals harbor viral quasispecies that differ in genetic sequences and susceptibility to broadly neutralizing antibodies (bNAbs) targeting the HIV envelope (Env). Due to difficulties with current assays in confirming viral sensitivity to bNAbs, most clinical trials enrolled participants without knowledge of their viral susceptibility. Thus, an accurate and sensitive assay with quick turnaround (1-2 weeks) is needed. We propose to approach this aim with two prongs: Aim 1, a phenotypic (Env function) assay for bulk Env neutralization assessment and Aim 2, a genotypic (Env sequence) algorithm for high-throughput prediction of neutralization susceptibility. We propose 10 HIV bNAbs targeting 5 distinct gp120 sites for assay development, with 2 non-HIV human IgG1 antibodies as negative controls. Current assays are difficult because of two labor-intensive and time-consuming procedures: Env single-genome amplification (SGA) and individual Env cloning. Aim 1 will address the latter procedure required because the current TZM-bl neutralization assay cannot tease out bulk Env clones that differ in bNAb susceptibility. Aim 1 will apply DNA barcoding to simultaneously track bNAb sensitivity of hundreds of Env variants in a high-throughput manner. This will be achieved by a novel Env- expressing lentiviral system in which each Env is linked to a unique barcode sequence. Aim 2 will address Env sequencing, for which SGA is necessary because Sanger sequencing cannot sequence bulk templates. We propose in Aim 2 to apply a third-generation sequencing technology, Nanopore sequencing, for effective bulk template sequencing and develop machine learning models for a fast and reliable prediction algorithm. Combining with the ample bNAb neutralization data from literature and Aim 1 of this project, trained computational models can be fast, accurate, and high-throughput to predict bNAb susceptibility. Since the detection of a low yield of functional Env sequences and minority variants from participant’s HIV DNA reservoir is essential, we propose in Aim 3 to investigate whether a virion capture assay could be applied to enrich functional Envs. We will also apply a DNA analysis to enrich minor variants from the viral reservoir. We have biospecimens stored onsite and access to a wide distribution of HIV subtypes from the AIDS Clinical Trials Group (ACTG) and HIV Vaccine Trials Network (HVTN) biorepositories for assay development and validation. All proposed R61 Aims have clear milestones defined and the R61 assays will be developed with streamlined and standard operating procedure (SOP) for adaptation to clinical labs. The Columbia Clinical Trials Support Laboratory is fully equipped and staffed to perform the SOPs and provide input and validation on assay performance. Successful development of an accurate and sensitive bNAb susceptibility assay with quick turnaround will alter the enrollment for bNAb trials and greatly improve clinical development of bNAb products.
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