The ultimate causes of correlated evolution among sites in a genome remain difficult to tease apart. To address this problem directly, we performed a high‐throughput search for correlated evolution among sites associated with resistance to a fluoroquinolone antibiotic using whole‐genome data from clinical strains of Pseudomonas aeruginosa, before validating our computational predictions experimentally. We show that for at least two sites, this correlation is underlain by epistasis. Our analysis also revealed eight additional pairs of synonymous substitutions displaying correlated evolution underlain by physical linkage, rather than selection associated with antibiotic resistance. Our results provide direct evidence that both epistasis and physical linkage among sites can drive the correlated evolution identified by high‐throughput computational tools. In other words, the observation of correlated evolution is not by itself sufficient evidence to guarantee that the sites in question are epistatic; such a claim requires additional evidence, ideally coming from direct estimates of epistasis, based on experimental evidence.

中文翻译：

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确定抗生素选择下位点之间计算检测到的相关进化的驱动因素


基因组位点之间相关进化的最终原因仍然难以梳理。为了直接解决这个问题，在通过实验验证我们的计算预测之前，我们使用铜绿假单胞菌临床菌株的全基因组数据，对与氟喹诺酮类抗生素耐药性相关的位点之间的相关进化进行了高通量搜索。我们表明，对于至少两个位点，这种相关性是由上位性所支撑的。我们的分析还揭示了另外八对同义替换，它们显示出物理联系背后的相关进化，而不是与抗生素耐药性相关的选择。我们的结果提供了直接证据，表明位点之间的上位性和物理联系都可以驱动高通量计算工具识别的相关进化。换句话说，相关进化的观察本身并不足以证明所讨论的位点是上位的；这种说法需要额外的证据，最好来自基于实验证据的上位性直接估计。