Stochastic Gene Expression in Retroviral Latency

项目摘要/摘要 逆转录病毒感染对人类健康造成了巨大的损害。人类免疫缺陷病毒1型(HIV- 1，或“艾滋病毒”)已导致全球3000万人死亡，3600万人感染艾滋病毒/艾滋病。没有 有效的艾滋病毒疫苗。现有的用于治疗艾滋病毒的抗逆转录病毒疗法(ARTS)不能治愈感染者 病人。抗逆转录病毒疗法必须终身服用，因为艾滋病毒可以通过潜伏感染CD4+T细胞而处于休眠状态 细胞。这些潜伏的蓄水池寿命很长，确保了病毒的终生存留，并被认为是 从患者身上根除艾滋病毒的最大障碍。激活和杀死这些潜在储藏者的方法， 并治愈艾滋病毒感染者，目前正在积极推进。然而，即使在理想的实验室里 在某些条件下，最强大的激活剂只能部分重新激活潜伏的艾滋病毒。我们已经确定这一点 异质性在很大程度上是由转录的随机波动造成的，这种随机波动驱动了艾滋病毒的命运“开关”。 如果我们希望有效地重新激活潜伏的艾滋病毒，关键是要确定驱动因素的分子机制 这些转录波动并解决了活跃感染和潜伏感染之间的“切换”是如何被调控的。 我们的长期目标是确定有效地“激活和杀死”潜伏的艾滋病毒的分子途径。这个 本项目的目标是开发一个HIV潜伏期的量化模型，并通过实验验证该模型 在供者来源的原代CD4+T细胞中，并干扰产生部分重新激活的变异源 潜伏的艾滋病病毒。基于我们广泛的初步研究，我们的中心假设是随机的 HIV转录的波动(即‘噪音’)限制了HIV的重新激活，而操纵噪音会增强HIV 重新激活。在细菌和噬菌体中，调节基因表达的可变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，确定调整艾滋病毒变异性的方法将使我们能够 调整HIV潜伏再激活，有效清除潜伏蓄水池。我们将通过以下途径实现我们的目标 具体目标依赖于单细胞成像和单细胞数据的数学建模。具体来说，我们 利用一套新的微孔设备和成像方法来开发一个数学模型 供者来源的原代CD4+T细胞中的HIV潜伏期。我们将确定随机的分子来源 波动来确定哪些参数对扰动最敏感。这种模式将使我们能够 理性地测试在原代CD4+T细胞中重新激活潜伏HIV的新方法。 除了医学上的相关性，这项拟议的研究还具有广泛的意义，因为 一般来说，命运决定开关的驱动变异性尚不清楚，特别是在哺乳动物系统中。这 该项目将为噪音驱动的发育开关提供亟需的量化表征 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，而不仅仅是 在艾滋病毒中，也在不同的哺乳动物系统中。

PROJECT SUMMARY/ABSTRACT 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 efficiently 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 kill' 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 (i.e. '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 will 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.