干扰对HIV中CD8 + T细胞逃逸率的影响
The effect of interference on the CD8+ T cell escape rates in HIV
摘要：
在早期HIV感染中，病毒群体逃避多种CD8+细胞反应。逃逸突变出现得越晚，它的生长速度就越慢。e.逃逸率较低。这种模式可能表明CD 8+细胞反应的强度正在减弱，或者后来的病毒逃逸突变体会带来更大的适应性成本。在本文中，我们调查的模式，降低逃逸率也可能是由于不同逃逸株之间的遗传干扰。为此，我们开发了一个数学多表位模型的HIV动力学，其中包括随机效应，重组和突变。我们使用累积连锁不平衡措施来量化干扰量。我们发现，在两个位点系统中，几乎同步的、同样强烈的免疫反应增强了遗传干扰的产生。这种效应，加上密集间隔的采样时间在感染开始时，导致连续逃逸率估计值下降，即使在等位基因之间没有选择差异。这些预测得到了一名艾滋病毒感染者的实验数据的支持。因此，干扰可以解释为什么后来的逃逸速度较慢。孤立地考虑逃逸突变，忽略它们的遗传连锁，掩盖了潜在的单倍型动力学，并可能影响对CD8+细胞施加的选择压力的估计。在出现多个逃逸突变的系统中，应通过测量不同逃逸突变之间的联系来评估干扰动力学的发生。
英文摘要：
In early HIV infection, the virus population escapes from multiple CD8+ cell responses. The later an escape mutation emerges, the slower it outgrows its competition, i. e. the escape rate is lower. This pattern could indicate that the strength of the CD8+ cell responses is waning, or that later viral escape mutants carry a larger fitness cost. In this paper, we investigate whether the pattern of decreasing escape rate could also be caused by genetic interference among different escape strains. To this end, we developed a mathematical multi-epitope model of HIV dynamics, which incorporates stochastic effects, recombination and mutation. We used cumulative linkage disequilibrium measures to quantify the amount of interference. We found that nearly synchronous, similarly strong immune responses in two-locus systems enhance the generation of genetic interference. This effect, combined with densely spaced sampling times at the beginning of infection, leads to decreasing successive escape rate estimates, even when there were no selection differences among alleles. These predictions are supported by experimental data from one HIV-infected patient. Thus, interference could explain why later escapes are slower. Considering escape mutations in isolation, neglecting their genetic linkage, conceals the underlying haplotype dynamics and can affect the estimation of the selective pressure exerted by CD8+ cells. In systems in which multiple escape mutations appear, the occurrence of interference dynamics should be assessed by measuring the linkage between different escape mutations.
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