Within-patient mutation frequencies reveal fitness costs of CpG dinucleotides and drastic amino acid changes in HIV.
Within-patient mutation frequencies reveal fitness costs of CpG dinucleotides and drastic amino acid changes in HIV.
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门内突变频率揭示了CpG二核苷酸和HIV中急剧氨基酸变化的适应性成本。
DOI：
10.1371/journal.pgen.1007420
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发表时间：
2018-06
期刊：
影响因子：
4.5
通讯作者：
Pennings PS
中科院分区：
文献类型：
--
作者：
Theys K;Feder AF;Gelbart M;Hartl M;Stern A;Pennings PS
HIV 的突变率很高，这有助于其快速进化。然而，我们对体内个体艾滋病毒突变的适应度成本、它们的分布以及塑造病毒适应度景观的不同因素知之甚少。我们计算了 160 名患者 pol 基因 870 个位点的过渡突变的平均频率，从而使我们能够确定这些突变的成本。正如预期的那样，我们发现与同义突变相比，非同义突变和无义突变的成本较高。此外，我们发现导致氨基酸剧烈变化的非同义突变的成本是不发生剧烈氨基酸变化的突变的两倍，而产生新 CpG 二核苷酸的突变的成本也是不产生新 CpG 二核苷酸的突变的两倍。我们还发现 G→A 和 C→T 突变比 A→G 突变成本更高。我们预计，我们新的基于体内频率的方法将提供对艾滋病毒以及多种微生物的适应度和进化性的见解。 HIV 的高突变率使其能够快速进化。然而，大多数突变可能会降低病毒的复制能力——它们对病毒来说代价高昂。到目前为止，突变的实际成本尚不清楚。我们使用患者内部突变频率来估计体内 870 种 HIV 突变的成本。正如预期的那样，我们发现非同义突变和无义突变的成本很高。此外，我们发现导致氨基酸剧烈变化的突变、产生新 CpG 位点的突变（可能是因为它们触发宿主的免疫系统）以及 G→A 和 C→T 突变的成本惊人地高。我们的结果证明了分析体内病毒群体的突变频率来研究突变成本的能力。更好地了解健身成本将有助于预测艾滋病毒的演变。
HIV has a high mutation rate, which contributes to its ability to evolve quickly. However, we know little about the fitness costs of individual HIV mutations in vivo, their distribution and the different factors shaping the viral fitness landscape. We calculated the mean frequency of transition mutations at 870 sites of the pol gene in 160 patients, allowing us to determine the cost of these mutations. As expected, we found high costs for non-synonymous and nonsense mutations as compared to synonymous mutations. In addition, we found that non-synonymous mutations that lead to drastic amino acid changes are twice as costly as those that do not and mutations that create new CpG dinucleotides are also twice as costly as those that do not. We also found that G→A and C→T mutations are more costly than A→G mutations. We anticipate that our new in vivo frequency-based approach will provide insights into the fitness landscape and evolvability of not only HIV, but a variety of microbes. HIV’s high mutation rate allows it to evolve quickly. However, most mutations probably reduce the virus’ ability to replicate —they are costly to the virus. Until now, the actual cost of mutations is not well understood. We used within-patient mutation frequencies to estimate the cost of 870 HIV mutations in vivo. As expected, we found high costs for non-synonymous and nonsense mutations. In addition, we found surprisingly high costs for mutations that lead to drastic amino acid changes, mutations that create new CpG sites (possibly because they trigger the host’s immune system), and G→A and C→T mutations. Our results demonstrate the power of analyzing mutant frequencies from in vivo viral populations to study costs of mutations. A better understanding of fitness costs will help to predict the evolution of HIV.
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