Centro de Astrobiología (INTA-CSIC), Madrid, Spain.
BMC Evol Biol. 2010 Feb 17;10:46. doi: 10.1186/1471-2148-10-46.
The secondary structure of folded RNA sequences is a good model to map phenotype onto genotype, as represented by the RNA sequence. Computational studies of the evolution of ensembles of RNA molecules towards target secondary structures yield valuable clues to the mechanisms behind adaptation of complex populations. The relationship between the space of sequences and structures, the organization of RNA ensembles at mutation-selection equilibrium, the time of adaptation as a function of the population parameters, the presence of collective effects in quasispecies, or the optimal mutation rates to promote adaptation all are issues that can be explored within this framework.
We investigate the effect of microscopic mutations on the phenotype of RNA molecules during their in silico evolution and adaptation. We calculate the distribution of the effects of mutations on fitness, the relative fractions of beneficial and deleterious mutations and the corresponding selection coefficients for populations evolving under different mutation rates. Three different situations are explored: the mutation-selection equilibrium (optimized population) in three different fitness landscapes, the dynamics during adaptation towards a goal structure (adapting population), and the behavior under periodic population bottlenecks (perturbed population).
The ratio between the number of beneficial and deleterious mutations experienced by a population of RNA sequences increases with the value of the mutation rate mu at which evolution proceeds. In contrast, the selective value of mutations remains almost constant, independent of mu, indicating that adaptation occurs through an increase in the amount of beneficial mutations, with little variations in the average effect they have on fitness. Statistical analyses of the distribution of fitness effects reveal that small effects, either beneficial or deleterious, are well described by a Pareto distribution. These results are robust under changes in the fitness landscape, remarkably when, in addition to selecting a target secondary structure, specific subsequences or low-energy folds are required. A population perturbed by bottlenecks behaves similarly to an adapting population, struggling to return to the optimized state. Whether it can survive in the long run or whether it goes extinct depends critically on the length of the time interval between bottlenecks.
折叠 RNA 序列的二级结构是将表型映射到基因型的良好模型，由 RNA 序列表示。对 RNA 分子集合向目标二级结构进化的计算研究为复杂群体适应背后的机制提供了有价值的线索。序列和结构之间的空间关系、RNA 集合在突变-选择平衡时的组织、作为群体参数函数的适应时间、准种中集体效应的存在或促进适应的最佳突变率，所有这些问题都可以在这个框架内探讨。
我们研究了微观突变对 RNA 分子表型的影响，这些 RNA 分子在其计算机进化和适应过程中发生突变。我们计算了突变对适合度的影响的分布、有利突变和有害突变的相对分数以及在不同突变率下进化的群体的相应选择系数。我们探索了三种不同情况：在三个不同的适合度景观中的突变-选择平衡（优化种群）、适应目标结构时的动力学（适应种群）和周期性群体瓶颈下的行为（受扰种群）。
在 RNA 序列种群中经历有利突变和有害突变的数量比随着进化过程中突变率 mu 的增加而增加。相比之下，突变的选择值几乎保持不变，与 mu 无关，这表明适应是通过增加有利突变的数量来实现的，而它们对适合度的平均影响变化不大。对适合度效应分布的统计分析表明，无论是有利还是有害的小效应，都可以很好地用帕累托分布来描述。当改变适合度景观时，这些结果是稳健的，当除了选择目标二级结构外，还需要特定的子序列或低能折叠时，这些结果尤其显著。受瓶颈影响的种群与适应种群的行为相似，努力恢复到优化状态。它能否在长期内生存或灭绝，取决于瓶颈之间的时间间隔的长度。
