Quantifying The Effect Of HIV Latency

量化 HIV 潜伏期的影响

• 批准号: 8685122
• 负责人: Thomas K. Leitner
• 金额: $ 20.96万
• 依托单位: LOS ALAMOS NAT SECTY-LOS ALAMOS NAT LAB
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-18 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8685122
• 关键词: Affect; Back; Bioinformatics; Biological Models; Cells; Clinical; Clinical Data; Clinical assessments; Computer Simulation; DNA; Data; Deposition; Detection; Evolution; Follow-Up Studies; Frequencies; Future; Genetic; Genetic Variation; HIV; Highly Active Antiretroviral Therapy; Infection; Life; Maintenance; Measures; Methods; Modeling; Molecular; Mutation; Pathogenesis; Patients; Phylogenetic Analysis; Plasma; Population; Process; Proviruses; Publishing; Recording of previous events; Sampling; Scientist; Simulate; Sorting - Cell Movement; System; Testing; Time; Tissues; Treatment Protocols; Viral; Virus; base; clinical application; clinically relevant; deep sequencing; feeding; genetic evolution; novel; prevent; public health relevance; reconstruction; simulation; stem; tool

DESCRIPTION (provided by applicant): Although HIV can be treated by HAART, long-lived reservoirs prevent eradication. Thus, HIV can persist as latent provirus in cells that do not produce replicating virus. While laborious methods exist to investigate long- lived cells that may harbor HIV, little is known about how frequently HIV becomes latent, and how latency periods may affect HIV diversity, maintenance and adaptation potential. Our hypothesis is based on the fact that HIV accumulates mutations while replicating, but not when latent. The result will be that some virus lineages will have evolved less than others. We can reconstruct the entire evolutionary history of all sampled lineages using phylogenetic inference methods and estimate the different evolutionary rates in that history. The specific aims of this proposal are 1) To develop a phylogenetic framework to identify and quantify real latency in clinical data, and 2) To develop a latency model for prediction and simulation of the genetic repertoire. We will investigate 1) in silico simulated data to assess model limitations, 2) reanalyze meta-data from many published studies where plasma and reservoir sequences are available to test our method on real data as well as critically reexamine existing data, and 3) analyze new data collected from serially sampled patients before and during treatment both in plasma (when retrievable) and reservoirs using ultra-deep and limited dilution sequencing. One strength of our phylogenetic approach is that it does not depend on sequencing of cells that have been sorted by markers or specialized sampling of potential reservoir tissues. Thus, in addition to investigating reservoir virus sequences, we will also be able to measure the effect of latency in the plasma virus population if it was reactivated at any point in the patient's infection history. e show that a preliminary method, which avoids statistical issues stemming from common evolution, is able to detect latency in simulated data. Compartmental modeling systems, like ODE models of viral dynamics, typically cannot include mutation processes and simulate sequence evolution. Although other systems, like agent-based models can do this in principle, it has not been done in this context. Thus, we will develop two new frameworks i) detection/quantification of real latency in clinical sequence data using a novel molecular clock phylogenetic approach, and ii) simulation and prediction of latent reservoirs in different scenarios using a novel dynamic model that includes sequence evolution. Integrating the efforts from aims 1 and 2 will allow us to create a bioinformatic tool that can take HIV sequence data from a patient, detect and quantify effective latency, then feed those results to the latency model to project how that specific HIV population would evolve under different treatment scenarios.

描述(由申请人提供)：虽然HAART可以治疗艾滋病毒，但长寿的蓄水池会阻碍根除。因此，艾滋病毒可以作为潜伏的前病毒在不产生复制病毒的细胞中持续存在。虽然有一些费力的方法来研究可能携带艾滋病毒的长寿命细胞，但人们对艾滋病毒潜伏的频率以及潜伏期如何影响艾滋病毒的多样性、维持和适应潜力知之甚少。我们的假设是基于这样一个事实，即艾滋病毒在复制时积累突变，而不是在潜伏时。其结果将是 一些病毒谱系的进化程度将低于其他病毒。我们可以使用系统发育推断方法重建所有样本谱系的整个进化史，并估计在该历史中不同的进化率。这项建议的具体目标是1)开发一个系统发育框架来识别和量化临床数据中的真实潜伏期，以及2)开发一个用于预测和模拟遗传谱的潜伏期模型。我们将研究1)在电子计算机模拟数据中评估模型的局限性，2)重新分析来自许多已发表研究的元数据，其中血浆和存储序列可用于在真实数据上测试我们的方法，并严格地重新检查现有数据，3)使用超深和有限稀释测序分析从治疗前和治疗期间连续采样的患者在血浆(当可检索时)和存储中收集的新数据。我们的系统发育方法的一个优点是，它不依赖于已经通过标记分类的细胞的排序或潜在储藏组织的专门采样。因此，除了调查储存的病毒序列外，我们还将能够测量血浆病毒群体中潜伏期的影响，如果它在患者感染历史的任何时间点被重新激活。E表明，一种避免了共同进化引起的统计问题的初步方法能够检测模拟数据中的延迟。分区建模系统，如病毒动力学的ODE模型，通常不能包括突变过程和模拟序列进化。尽管其他系统，如基于代理的模型原则上可以做到这一点，但在这种情况下还没有做到这一点。因此，我们将开发两个新的框架：i)使用新的分子时钟系统发育方法来检测/量化临床序列数据中的真实潜伏期；ii)使用包含序列进化的新的动态模型来模拟和预测不同场景下的潜在储集层。整合AIMS 1和AIMS 2的努力将使我们能够创建一个生物信息学工具，该工具可以从患者那里获取HIV序列数据，检测并量化有效的潜伏期，然后将这些结果提供给潜伏期模型 以预测特定的艾滋病毒人群在不同的治疗方案下将如何演变。