Institut National de la Recherche Agronomique, UMR BGPI, Cirad TA A-54/K, Campus de Baillarguet, 34398 Montpellier cedex 5, France.
Proc Biol Sci. 2010 Mar 7;277(1682):809-17. doi: 10.1098/rspb.2009.1247. Epub 2009 Nov 11.
For positive-sense single-stranded RNA virus genomes, there is a trade-off between the mutually exclusive tasks of transcription, translation and encapsidation. The replication strategy that maximizes the intracellular growth rate of the virus requires iterative genome transcription from positive to negative, and back to positive sense. However, RNA viruses experience high mutation rates, and the proportion of genomes with lethal mutations increases with the number of replication cycles. Thus, intracellular mutant frequency will depend on the replication strategy. Introducing apparently realistic mutation rates into a model of viral replication demonstrates that strategies that maximize viral growth rate could result in an average of 26 mutations per genome by the time plausible numbers of positive strands have been generated, and that virus viability could be as low as 0.1 per cent. At high mutation rates or when a high proportion of mutations are deleterious, the optimal strategy shifts towards synthesizing more negative strands per positive strand, and in extremis towards a 'stamping-machine' replication mode where all the encapsidated genomes come from only two transcriptional steps. We conclude that if viral mutation rates are as high as current estimates suggest, either mutation frequency must be considerably higher than generally anticipated and the proportion of viable viruses produced extremely small, or replication strategies cannot be optimized to maximize viral growth rate. Mechanistic models linking mutation frequency to replication mechanisms coupled with data generated through new deep-sequencing technologies could play an important role in improving the estimates of viral mutation rate.
对于正链单链 RNA 病毒基因组而言,转录、翻译和包装这三个相互排斥的任务之间存在权衡。使病毒在细胞内的生长速率最大化的复制策略需要从正链到负链,再回到正链进行迭代基因组转录。然而,RNA 病毒的突变率很高,具有致死突变的基因组比例随着复制循环次数的增加而增加。因此,细胞内的突变频率将取决于复制策略。将明显现实的突变率引入病毒复制模型表明,当生成合理数量的正链时,最大限度地提高病毒生长速率的策略可能导致每个基因组平均发生 26 个突变,并且病毒的存活率可能低至 0.1%。在高突变率或多数突变有害的情况下,最佳策略会朝着每产生一个正链合成更多负链的方向转变,而在极端情况下,会朝着“盖章机”复制模式转变,即所有包装的基因组仅来自两个转录步骤。我们得出的结论是,如果病毒的突变率如当前估计的那样高,那么要么突变频率必须比通常预期的高得多,要么产生的有活力病毒的比例极低,要么复制策略不能优化以最大限度地提高病毒生长速率。将突变频率与复制机制联系起来的机制模型,结合通过新的深度测序技术生成的数据,可能在提高病毒突变率的估计方面发挥重要作用。