Kavli Institute for Theoretical Physics, University of California, Santa Barbara, California, United States of America.
PLoS Comput Biol. 2010 Jan 29;6(1):e1000660. doi: 10.1371/journal.pcbi.1000660.
The evolutionary dynamics of HIV during the chronic phase of infection is driven by the host immune response and by selective pressures exerted through drug treatment. To understand and model the evolution of HIV quantitatively, the parameters governing genetic diversification and the strength of selection need to be known. While mutation rates can be measured in single replication cycles, the relevant effective recombination rate depends on the probability of coinfection of a cell with more than one virus and can only be inferred from population data. However, most population genetic estimators for recombination rates assume absence of selection and are hence of limited applicability to HIV, since positive and purifying selection are important in HIV evolution. Yet, little is known about the distribution of selection differentials between individual viruses and the impact of single polymorphisms on viral fitness. Here, we estimate the rate of recombination and the distribution of selection coefficients from time series sequence data tracking the evolution of HIV within single patients. By examining temporal changes in the genetic composition of the population, we estimate the effective recombination to be rho = 1.4+/-0.6 x 10(-5) recombinations per site and generation. Furthermore, we provide evidence that the selection coefficients of at least 15% of the observed non-synonymous polymorphisms exceed 0.8% per generation. These results provide a basis for a more detailed understanding of the evolution of HIV. A particularly interesting case is evolution in response to drug treatment, where recombination can facilitate the rapid acquisition of multiple resistance mutations. With the methods developed here, more precise and more detailed studies will be possible as soon as data with higher time resolution and greater sample sizes are available.
HIV 在感染慢性期的进化动态是由宿主免疫反应和药物治疗施加的选择压力驱动的。为了定量理解和模拟 HIV 的进化，需要知道控制遗传多样化和选择强度的参数。虽然可以在单个复制周期中测量突变率，但相关的有效重组率取决于一个细胞同时感染多种病毒的概率，并且只能从群体数据中推断出来。然而，大多数用于重组率的群体遗传估计器都假设不存在选择，因此在 HIV 中应用有限，因为正选择和净化选择在 HIV 进化中很重要。然而，关于个体病毒之间选择差异的分布以及单个多态性对病毒适应性的影响知之甚少。在这里，我们从跟踪单个患者内 HIV 进化的时间序列序列数据估计重组率和选择系数的分布。通过检查群体遗传组成的时间变化，我们估计有效重组率为 rho = 1.4+/-0.6 x 10(-5) 个重组/位点和代。此外，我们提供的证据表明，至少 15%观察到的非同义多态性的选择系数超过每代 0.8%。这些结果为更详细地了解 HIV 的进化提供了基础。一个特别有趣的情况是对药物治疗的反应进化，其中重组可以促进多种耐药突变的快速获得。使用这里开发的方法，只要有更高时间分辨率和更大样本量的数据可用，就可以进行更精确和更详细的研究。
