Predicting parallelism and quantifying divergence in experimental evolution
Predicting parallelism and quantifying divergence in experimental evolution
复制标题
预测实验进化中的并行性和量化分歧
DOI：
10.1101/2020.05.13.070953
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Shoemaker WR, Lennon JT
中科院分区：
文献类型：
--
作者：
Shoemaker WR, Lennon JT
环境在多大程度上决定了哪些基因有助于适应环境，这是微生物进化中的一个基本问题。微生物种群通常在不同环境中进行实验传代并测序，以确定适应特定环境的候选者。然而，仍然需要开发适当的统计框架来鉴定在一种环境中获得比另一种环境更多突变的基因（即，发散进化）。在这里，我们展示了如何在相同的环境中复制种群之间的进化结果，被称为平行进化，可以利用来构建一个直观的统计测试，以确定基因，有助于不同的进化。为了完成这一任务，我们检查了公开可用的进化和重排序实验数据集，发现基因间突变计数的分布可以使用独立泊松随机变量的集合来预测。基于这一结果，我们提出，在一个给定的基因从两个不同的环境中的种群之间的分歧进化的程度可以建模为两个泊松随机变量之间的差异，称为的Sternlam分布。然后，我们提出并应用统计检验，以确定特定的基因，有助于不同的进化。重要提示：目前还没有现有的框架，可以利用来确定基因，有助于在微生物进化实验中的分歧进化。为了纠正这种情况，我们研究了基因间突变计数的分布，以确定一个适当的空模型。我们的观察表明，在一个给定的基因内的分歧进化可以模拟为在两个环境之间观察到的突变总数的差异。这个数量是由一个概率分布来描述的，这个概率分布被称为阿拉姆分布，它为研究人员提供了一个合适的统计检验，以确定在进化实验中有助于分化进化的基因集。
The degree that the environment determines what genes contribute towards adaptation is a fundamental question in microbial evolution. Microbial populations are often experimentally passaged in different environments and sequenced in order to identify candidates for adaptation in a particular environment. However, there remains the need to develop an appropriate statistical framework to identify genes that acquired more mutations in one environment over the other (i.e., divergent evolution). Here we demonstrate how the evolutionary outcomes among replicate populations in the same environment, known as parallel evolution, can be leveraged to construct an intuitive statistical test for identifying the genes that contribute towards divergent evolution. To accomplish this task, we examined publicly available evolve-and-resequence experiment datasets and found that the distribution of mutation counts among genes can be predicted using an ensemble of independent Poisson random variables. Building on this result, we propose that the degree of divergent evolution at a given gene between populations from two different environments can be modeled as the difference between two Poisson random variables, known as the Skellam distribution. We then propose and apply a statistical test to identify specific genes that contribute towards divergent evolution. IMPORTANCE: There is currently no existing framework that can be leveraged to identify genes that contribute towards divergent evolution in microbial evolution experiments. To correct for this absence, we investigated the distribution of mutation counts among genes in order to identify an appropriate null model. Our observations suggest that divergent evolution within a given gene can be modeled as the difference in the total number of mutations observed between two environments. This quantity is described by a probability distribution known as the Skellam distribution, providing an appropriate statistical test for researchers seeking to identify the set of genes that contribute towards divergent evolution in evolution experiments.