Project 3: Identifying plasma biomarkers predicting time to HIV rebound after treatment interruption

项目 3：识别血浆生物标志物，预测治疗中断后 HIV 反弹的时间

• 批准号: 10223997
• 负责人: Satish Kumar Pillai
• 金额: $ 39.58万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-08 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10223997
• 关键词: Aftercare; Aging; Antibody Response; Antigens; Antiviral Agents; Avidity; Bioinformatics; Biological Assay; Biological Markers; Biostatistics Core; Cells; Cerebrospinal Fluid; Chronic; Collection; Data; Development; Disease remission; Evaluation; Filtration; Flow Cytometry; Goals; HIV; HIV Antibodies; HIV Infections; HIV-1; Immune; Immunologic Factors; Immunologics; Immunoprecipitation; Individual; Inflammation; Interruption; Kinetics; Label; Luciferases; Machine Learning; Measurement; Measures; Mediating; MicroRNAs; Morbidity - disease rate; Nucleic Acids; Phase; Plasma; Protocols documentation; RNA; Recrudescences; Residual state; Resolution; Sampling; Small RNA; Specimen; Surface; System; Technology; Time; Viral; Viral Proteins; Viremia; Work; antibody detection; antiretroviral therapy; base; cell free DNA; chemokine; circulating microRNA; cohort; cost effective; cytokine; density; experience; experimental study; extracellular vesicles; interest; miRNA expression profiling; mortality; multidimensional data; multiplex assay; next generation sequencing; noninvasive diagnosis; predictive marker; prognostic significance; prognostic value; transcriptome sequencing; viral rebound; virology

PROJECT SUMMARY/ABSTRACT While the advent of antiretroviral therapy (ART) has dramatically reduced the morbidity and mortality associated with HIV infection, viral eradication is not achievable due to the persistence of latently-infected cells during treatment. ART must therefore be taken on a lifelong basis. Accumulating data suggest that HIV- infected individuals often experience persistent immune dysregulation, chronic inflammation, and accelerated aging even in the setting of ART-mediated viral suppression. These realities have created a pronounced interest in developing strategies to eradicate HIV in infected individuals. The development, evaluation and implementation of HIV curative strategies will depend critically on our capacity to determine when viral recrudescence in the near term is unlikely in an infected individual, justifying cessation of antiretroviral therapy (ART). The goal of this P01 project is to identify biomarkers that will enable us to predict the duration of the lag phase or “remission” period prior to HIV rebound following discontinuation of ART in HIV-infected individuals. In our study, a large number of virologic and immunologic parameters will be measured in a cohort of 125 well-characterized HIV-infected individuals undergoing analytical treatment interruption (ATI), to identify biomarkers that allow us to reliably predict the kinetics of viral rebound post-ART cessation. The experiments described in Project 3 of our P01 proposal will specifically examine the prognostic value of a broad spectrum of circulating factors in plasma and cerebrospinal fluid (CSF), leveraging cutting edge technologies and an unprecedented collection of longitudinal specimens from multiple ATI studies. In Aim 1, we will implement next-generation sequencing approaches to determine the relationship between circulating nucleic acids and viral rebound, focusing on microRNA profile, cell-free DNA, and residual low-level HIV viremia. In Aim 2, we will evaluate the prognostic significance of circulating extracellular vesicles, characterizing their abundance, cellular origin, and nucleic acid and viral protein cargo. Finally, in Aim 3, soluble antiviral immune factors will be evaluated as predictors of viral rebound, focusing on high-resolution analyses of anti-HIV antibody responses, cytokines and chemokines associated with HIV persistence. We will work closely with our Bioinformatics and Biostatistics Core to transform our high-dimensional data into robust predictors of HIV rebound following ART interruption, relying on sophisticated ensemble machine learning approaches. Ultimately, the identification of a reliable blood plasma-based predictor of viral rebound will represent an optimal scenario for the field, enabling rapid development, scaling and deployment of cost- effective, non-invasive diagnostic approaches to facilitate the search for an HIV cure.

项目总结/文摘