Ex vivo analysis identifies effective HIV-1 latency-reversing drug combinations
Ex vivo analysis identifies effective HIV-1 latency-reversing drug combinations
复制标题
DOI：
10.1172/jci80142
复制
发表时间：
2015-05-01
影响因子：
15.9
通讯作者：
Siliciano, Robert F.
中科院分区：
文献类型：
--
作者：
Laird, Gregory M.;Bullen, C. Korin;Siliciano, Robert F.
用小分子逆转HIV-1潜伏期是一种潜在的治愈策略。这种方法可能需要有效的药物组合才能实现高水平的潜伏期逆转。使用来自感染者的静息CD4(+)T细胞(RCD4)，我们开发了一个实验和理论框架来确定有效的潜伏期反转剂(LRA)组合。利用细胞内HIV-1mRNA和病毒粒子产生的体外测试，我们比较了领先候选LRA的两种药物组合，并确定了多种有效逆转潜伏期的组合。我们发现，蛋白激酶C激动剂与溴结构域抑制剂JQ1或组蛋白去乙酰酶抑制剂联合使用时，与通过T细胞激活最大限度地重新激活相比，可以有力地诱导HIV-1转录和病毒产生。使用Bliss独立性模型来量化联合药物的作用，我们证明了这些联合作用能够协同诱导HIV-1转录。这种强大的潜伏期逆转发生在没有rCD4释放促炎细胞因子的情况下。为了扩展我们的发现的临床实用性，我们应用了一个数学模型，该模型通过体外病毒产生的测量来估计体内血浆HIV-1RNA的变化。我们的研究协调了先前研究的不同发现，建立了一种量化的实验方法来评估联合LRA的疗效，并提出了一个预测体内对LRA的反应的模型。
Reversal of HIV-1 latency by small molecules is a potential cure strategy. This approach will likely require effective drug combinations to achieve high levels of latency reversal. Using resting CD4(+)T cells (rCD4s) from infected individuals, we developed an experimental and theoretical framework to identify effective latency-reversing agent (LRA) combinations. Utilizing ex vivo assays for intracellular HIV-1 mRNA and virion production, we compared 2-drug combinations of leading candidate LRAs and identified multiple combinations that effectively reverse latency. We showed that protein kinase C agonists in combination with bromodomain inhibitor JQ1 or histone deacetylase inhibitors robustly induce HIV-1 transcription and virus production when directly compared with maximum reactivation by T cell activation. Using the Bliss independence model to quantitate combined drug effects, we demonstrated that these combinations synergize to induce HIV-1 transcription. This robust latency reversal occurred without release of proinflammatory cytokines by rCD4s. To extend the clinical utility of our findings, we applied a mathematical model that estimates in vivo changes in plasma HIV-1 RNA from ex vivo measurements of virus production. Our study reconciles diverse findings from previous studies, establishes a quantitative experimental approach to evaluate combinatorial LRA efficacy, and presents a model to predict in vivo responses to LRAs.