Stochastic Gene Expression in Retroviral Latency

描述（由申请人提供）：逆转录病毒感染对人类健康造成巨大损失。人类免疫缺陷病毒1型（HIV- 1，或“HIV”）已在全世界造成3 000万人死亡，3 600万人感染艾滋病毒/艾滋病。没有有效的艾滋病毒疫苗。现有的治疗艾滋病毒的抗逆转录病毒疗法（ART）无法治愈受感染的患者。ART必须终生服用，因为HIV可以通过潜伏感染CD 4 + T细胞以休眠状态存在。这些潜伏的宿主是长期存在的，确保了病毒的终身存在，并且被认为是从患者中根除艾滋病毒的最大障碍。目前正在积极寻求“激活和杀死”这些潜伏的储存库并治愈艾滋病毒感染者的方法。然而，即使在理想的实验室条件下，最强大的激活剂也只能部分地重新激活潜伏的HIV。我们已经确定，这种异质性在很大程度上是由转录中的随机波动导致的，这种随机波动驱动了HIV中的命运“转换”。如果我们希望有效地重新激活潜伏的HIV，那么描述驱动这些转录波动的分子机制并解决如何调节活动和潜伏感染之间的“开关”是至关重要的。我们的长期目标是确定有效“激活和消灭”潜伏HIV的分子途径。该项目的目标是开发一个HIV潜伏期的定量模型，在供体来源的原代CD 4 + T细胞中实验验证该模型，并干扰产生潜伏HIV部分再激活的变异性来源。基于我们广泛的初步研究，我们的中心假设是，艾滋病毒转录的随机波动（即。“噪音”）限制了HIV的再激活，而操纵噪音将增强HIV的再激活。在细菌和噬菌体中，调整基因表达的变异性可以显著改变类似的细胞命运决定。这个项目的基本原理是，确定调整HIV变异性的方法将使我们能够调整HIV潜伏的再激活和有效地清除潜伏的水库。我们将通过依赖于单细胞成像和单细胞数据的数学建模的特定目标来实现我们的目标。具体来说，我们利用一套新的微孔装置和成像方法来开发HIV在供体来源的原代CD 4 + T细胞中潜伏期的数学模型。我们将确定随机波动的分子源，以确定哪些参数对扰动最敏感。该模型将使我们能够合理地测试重新激活原代CD 4 + T细胞中潜伏的HIV的新方法。除了医学相关性外，拟议的研究具有广泛的意义，因为驱动命运决定开关可变性的机制一般尚不清楚，特别是在哺乳动物系统中。该项目将提供一个急需的定量表征的噪声驱动的发育开关在哺乳动物系统。最终，所获得的知识将指导新的方法来调整命运开关，不仅在艾滋病毒中，而且在不同的哺乳动物系统中。

DESCRIPTION (provided by applicant): Retroviral infections take an enormous toll on human health. The human immunodeficiency virus type 1 (HIV- 1, or "HIV") has killed 30 million people worldwide and 36 million people are living with HIV/AIDS. There is no effective vaccine for HIV. The available antiretroviral therapies (ARTs) for treating HIV cannot cure infected patients. ART must be taken life-long because HIV can exist in a dormant state by latently infecting CD4+ T cells. These latent reservoirs are long lived, ensuring lifelong persistence of the virus, and are recognized as the greatest obstacle to eradicating HIV from patients. Approaches to 'activate and kill' these latent reservoirs, and cure HIV-infected individuals, are being actively pursued. However, even under ideal laboratory conditions, the most powerful activators only partially reactivate latent HIV. We have established that this heterogeneity results in large part from stochastic fluctuations in transcription that drive a fate 'switch' in HIV. If we hope to efficienty reactivate latent HIV, it is critical to characterize the molecular mechanisms driving these transcriptional fluctuations and address how the 'switch' between active and latent infection is regulated. Our long-term goal is to identify the molecular pathways to efficiently 'activate and kil' latent HIV. The objectives of this project are to develop a quantitative model of HIV latency, experimentally validate this model in donor-derived primary CD4+ T cells, and perturb the sources of variability that generate partial reactivation of latent HIV. Based upon our extensive preliminary studies, our central hypothesis is that stochastic fluctuations in HIV transcription (ie. 'noise') limit HIV reactivation and that manipulating noise will enhance HIV reactivation. In bacteria and phage, tuning gene-expression variability can significantly alter similar cell-fate decisions. The rationale for this project is that identifying approaches to tune HIV variability wil enable us to tune HIV latent reactivation and efficiently purge of the latent reservoir. We will achieve our objective through specific aims that rely on single-cell imaging and mathematical modeling of single-cell data. Specifically, we capitalize on a new suite of microwell devices and imaging approaches to develop a mathematical model of HIV latency in donor-derived primary CD4+ T cells. We will identify the molecular sources of stochastic fluctuations to determine which parameters are most sensitive to perturbation. This model will enable us to rationally test new approaches for reactivating latent HIV in primary CD4+ T cells. In addition to the medical relevance, the proposed research has broad significance since the mechanisms driving variability in fate-decision switches are unclear in general, especially in mammalian systems. This project would provide a much-needed quantitative characterization of a noise-driven developmental switch in a mammalian system. Ultimately, the knowledge gained will guide new approaches to tune fate switches not just in HIV, but also in diverse mammalian systems.