Exploiting the Host-HIV Interface To Identify Biomarkers Predicting Time to Viral Rebound after Treatment Interruption

项目总结/摘要 一套强有力的治疗方法将大大加快潜在的HIV治愈疗法的开发和测试。 生物标志物预测其临床有效性。可以作为替代终点的生物标志物仍然存在 身份不明这些生物标志物将：1）加速HIV治疗竞技场的进展，就像血浆病毒载量一样 测试推动抗病毒药物开发; 2）为参与分析治疗的患者提供中断 (ATI)试验通过减少试验数量，提供更高程度的临床保护; 3）提供生物 控制潜在水库的分子和生物化学途径的线索; 4）作为磁铁 吸引生物技术和制药公司更积极地参与艾滋病治疗研究。BioMark计划 项目团队（Warner格林、Gilad Doitsh、Garry Nolan、Katie Pollard、Satish Pillai、Nadia Roan和Robert Siliciano）将寻找能够准确预测治疗后反弹时间的强生物标志物 中断.这些生物标志物对于治疗领域具有很大的价值，因为它们可以让临床医生预测 患者可以停止ART而不出现病毒复发的时间。血细胞和血浆125 参与四项不同ATI试验的HIV感染志愿者在ATI之前和病毒感染时获得了 将对反弹进行分析。这些患者包括30名在急性感染期间接受治疗的患者， 预计会表现出更慢的反弹时间。为了鉴定病毒和宿主来源的生物标志物，研究小组将：1） 部署了一种令人兴奋的“一流”数字液滴PCR检测方法， 前病毒DNA（IPDA）在水库-因为它是总前病毒群体的这一关键小部分， 含有介导反弹的感染性前病毒，少量完整的前病毒可能会出现， 预测病毒反弹时间较长的强生物标志物; 2）利用下一代超深度测序， 分析CD 4 T和其他免疫细胞中的细胞RNA和miRNA，并平行于测序DNA、RNA和miRNA。 miRNA在血浆中（和受试者的有限子集中的脑脊液中）游离循环或结合为 细胞外囊泡中的货物，以确定病毒反弹时间的预测因子; 3）使用7个经验证的CyTOF板 包括超过200个参数，以表型研究CD 4 T细胞和其他免疫细胞， 静息和刺激条件，以鉴定病毒反弹时间的单细胞特征; 4）评估变化 在循环抗HIV抗体的滴度和亲合力或淋巴组织炎症（包括 作为表达的储库的大小的指标，其可以用作 病毒反弹的时间这些研究将产生大量的高维数据，这些数据将被汇编， 在BioMark的生物信息学和生物统计学核心中进行策划和分析。几种生物统计方法 将被用来鉴定这些生物标志物，并进行更大的Meta分析（见核心描述）。在 总而言之，BioMark提出了一种创新和全面的方法来填补艾滋病毒治疗的主要空白 缺乏关键生物标志物预测治疗中断后病毒反弹的时间。

Project Summary/Abstract The development and testing of potential HIV cure therapeutics would be greatly expedited by a robust set of biomarkers predicting their clinical effectiveness. Biomarkers that can serve as surrogate endpoints remain unidentified. Such biomarkers will: 1) accelerate progress in the HIV cure arena much like plasma viral load testing propelled antiviral drug development; 2) afford patients participating in analytical treatment interruption (ATI) trials a higher degree of clinical protection by both reducing the number of trials; 3) provide biological clues into the molecular and biochemical pathways that control the latent reservoir; and 4) serve as a magnet for attracting Biotech and Pharma to more vigorously engage in HIV cure research. The BioMark program project team (Warner Greene, Gilad Doitsh, Garry Nolan, Katie Pollard, Satish Pillai, Nadia Roan, and Robert Siliciano) will search for strong biomarkers that accurately predict time to rebound following treatment interruption. Such biomarkers would be of great value for the cure field as they would allow clinicians to predict the period of time a patient can remain off ART without viral recrudescence. Blood cells and plasma from 125 HIV-infected volunteers participating in four different ATI trials obtained before ATI and at the time of viral rebound will be analyzed. These patients include 30 individuals treated during acute infection who are expected to exhibit slower rebound times. To identify both virus- and host-derived biomarkers, the team will 1) deploy an exciting “first in class” digital droplet PCR assay that selectively detects and quantitates intact proviral DNAs (IPDA) in the reservoir––because it is this key small fraction of the total provirus population that contains the infectious proviruses mediating rebound, a low number of intact proviruses might emerge as a strong biomarker predicting a longer time to viral rebound; 2) utilize next-generation ultra-deep sequencing to profile cellular RNAs and miRNAs in CD4 T and other immune cells and in parallel to sequence DNA, RNA and miRNA circulating free in plasma (and in cerebrospinal fluid in a limited subset of subjects) or bound as cargoes in extracellular vesicles to identify predictors of time of viral rebound; 3) use 7 validated CyTOF panels comprising over >200 parameters to phenotypically study CD4 T cells and other immune cells under both resting and stimulated conditions to identify single-cell signatures of time to viral rebound; 4) assess changes in the titer and avidity of circulating anti-HIV antibodies or markers of lymphoid tissue inflammation (including products of pyroptosis) as indicators of the size of the expressed reservoir, which can serve as predictors of time to viral rebound. These studies will generate large bodies of high-dimensional data that will be compiled, curated, and analyzed in BioMark's Bioinformatics and Biostatistics Core. Several biostatistical approaches will be employed to identify these biomarkers and to perform the larger meta analysis (see Core description). In summary, BioMark proposes an innovative and comprehensive approach to fill a major gap in HIV cure research produced by a lack of key biomarkers predicting time to viral rebound after treatment interruption.