In vivo mutation rates and the landscape of fitness costs of HIV-1.
In vivo mutation rates and the landscape of fitness costs of HIV-1.
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DOI：
10.1093/ve/vex003
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发表时间：
2017-01
期刊：
影响因子：
5.3
通讯作者：
Neher RA
中科院分区：
文献类型：
--
作者：
Zanini F;Puller V;Brodin J;Albert J;Neher RA
突变率和有害突变的适应性成本很难在体内测量，但对于定量理解进化至关重要。使用未经治疗的HIV-1感染期间纵向样本的全基因组深度测序数据，我们从遗传变异的动态中估计了HIV-1的突变率和适应性成本。在近似中性的位点，突变以每天每个位点1.2 × 10−5的速率累积，与细胞培养中测量的速率一致。我们估计从G到A的转换率最高，其次是C到T、T到C和A到G的转换，而颠换频率较低。在其他位点，突变倾向于减少病毒复制。我们使用突变选择平衡模型估计了HIV-1基因组中每个位点突变的适应性成本。大约一半的非同义突变具有较大的适应性成本（> 10%），而大多数同义突变的成本<1%。同义突变的成本在大多数pol中特别低，其中我们无法检测大多数同义突变的可测量成本。相比之下，我们发现在重要的RNA结构和调控区的同义突变的成本很高。患者内健身成本估计在多个患者中是一致的，表明健身景观的有害部分是普遍的，并解释了全球HIV-1 M组多样性的很大一部分。
Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of evolution. Using whole genome deep sequencing data from longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and fitness costs in HIV-1 from the dynamics of genetic variation. At approximately neutral sites, mutations accumulate with a rate of 1.2 × 10−5 per site per day, in agreement with the rate measured in cell cultures. We estimated the rate from G to A to be the largest, followed by the other transitions C to T, T to C, and A to G, while transversions are less frequent. At other sites, mutations tend to reduce virus replication. We estimated the fitness cost of mutations at every site in the HIV-1 genome using a model of mutation selection balance. About half of all non-synonymous mutations have large fitness costs (>10 percent), while most synonymous mutations have costs <1 percent. The cost of synonymous mutations is especially low in most of pol where we could not detect measurable costs for the majority of synonymous mutations. In contrast, we find high costs for synonymous mutations in important RNA structures and regulatory regions. The intra-patient fitness cost estimates are consistent across multiple patients, indicating that the deleterious part of the fitness landscape is universal and explains a large fraction of global HIV-1 group M diversity.