New Zealand Institute for Advanced Study, Massey University at Albany, Auckland, New Zealand.
Department of Molecular Biology, Umeå University, Umeå, Sweden.
Elife. 2019 Jan 8;8:e38822. doi: 10.7554/eLife.38822.
Predicting evolutionary change poses numerous challenges. Here we take advantage of the model bacterium in which the genotype-to-phenotype map determining evolution of the adaptive 'wrinkly spreader' (WS) type is known. We present mathematical descriptions of three necessary regulatory pathways and use these to predict both the rate at which each mutational route is used and the expected mutational targets. To test predictions, mutation rates and targets were determined for each pathway. Unanticipated mutational hotspots caused experimental observations to depart from predictions but additional data led to refined models. A mismatch was observed between the spectra of WS-causing mutations obtained with and without selection due to low fitness of previously undetected WS-causing mutations. Our findings contribute toward the development of mechanistic models for forecasting evolution, highlight current limitations, and draw attention to challenges in predicting locus-specific mutational biases and fitness effects.
预测进化变化带来了诸多挑战。在这里,我们利用模式细菌 ,其中决定适应性“起皱蔓延物”(WS)类型进化的基因型到表型图是已知的。我们提出了三个必要的调控途径的数学描述,并利用这些途径来预测每种突变途径的使用速度和预期的突变靶标。为了检验预测结果,我们确定了每条途径的突变率和靶标。由于以前未检测到的 WS 引起突变的适应性较低,意外的突变热点导致实验观察结果与预测结果不符,但额外的数据导致了更精细的模型。由于先前未检测到的 WS 引起突变的适应性较低,导致选择引起的 WS 突变的频谱与未选择引起的 WS 突变的频谱之间存在不匹配。我们的研究结果为预测进化的机制模型的发展做出了贡献,突出了当前的局限性,并引起了对预测特定基因座突变偏向和适应度效应的挑战的关注。