Modelling and in vitro testing of the HIV-1 Nef fitness landscape
Modelling and in vitro testing of the HIV-1 Nef fitness landscape
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DOI：
10.1093/ve/vez029
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发表时间：
2019-07-01
期刊：
影响因子：
5.3
通讯作者：
Ndung'u, Thumbi
中科院分区：
文献类型：
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作者：
Barton, John P.;Rajkoomar, Erasha;Ndung'u, Thumbi
迫切需要一种有效的疫苗来遏制 HIV-1 的流行。我们之前描述了一种对几种 HIV-1 蛋白的适应性景观进行建模的方法，并根据实验和临床数据验证了结果。适应度景观可用于识别对病毒活力有害的突变模式，从而为可以针对这些区域进行免疫控制的免疫原的设计提供信息。在这里，我们将这样的分析和补充实验应用于 HIV-1 Nef，这是一种在 HIV-1 发病机制中发挥关键作用的多功能蛋白。我们测量了 32 个不同 Nef 突变体的 Nef 驱动的复制能力以及 Nef 介导的 CD4 和 HLA-I 下调能力，并根据这些结果测试了模型预测。此外，我们使用 448 个患者衍生的 Nef 序列评估了模型，之前测量了这些序列的一些 Nef 活性。模型预测与各种 Nef 突变体的 Nef 驱动的复制和 CD4 下调能力显着相关，但与 HLA-I 下调能力无关。同样，在我们对患者来源的 Nef 序列的分析中，CD4 下调能力与模型预测的相关性最显着，表明在测试的 Nef 功能中，这是体内最重要的。总体而言，我们的结果强调了从患者来源的序列推断出的健康状况如何至少部分地捕获 Nef 突变的体内功能影响。然而，适应度景观的预测与 Nef 函数测量参数之间的相关性并不像过去对其他蛋白质的研究中观察到的相关性那么准确。这可能是因为推断 Nef 不同功能的突变成本会带来额外的复杂性。
An effective vaccine is urgently required to curb the HIV-1 epidemic. We have previously described an approach to model the fitness landscape of several HIV-1 proteins, and have validated the results against experimental and clinical data. The fitness landscape may be used to identify mutation patterns harmful to virus viability, and consequently inform the design of immunogens that can target such regions for immunological control. Here we apply such an analysis and complementary experiments to HIV-1 Nef, a multifunctional protein which plays a key role in HIV-1 pathogenesis. We measured Nef-driven replication capacities as well as Nef-mediated CD4 and HLA-I down-modulation capacities of thirty-two different Nef mutants, and tested model predictions against these results. Furthermore, we evaluated the models using 448 patient-derived Nef sequences for which several Nef activities were previously measured. Model predictions correlated significantly with Nef-driven replication and CD4 down-modulation capacities, but not HLA-I down-modulation capacities, of the various Nef mutants. Similarly, in our analysis of patient-derived Nef sequences, CD4 down-modulation capacity correlated the most significantly with model predictions, suggesting that of the tested Nef functions, this is the most important in vivo. Overall, our results highlight how the fitness landscape inferred from patient-derived sequences captures, at least in part, the in vivo functional effects of mutations to Nef. However, the correlation between predictions of the fitness landscape and measured parameters of Nef function is not as accurate as the correlation observed in past studies for other proteins. This may be because of the additional complexity associated with inferring the cost of mutations on the diverse functions of Nef.