Data from: Fisher’s geometrical model and the mutational patterns of antibiotic resistance across dose gradients

Fisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in several situations, e.g. adaptation to antibiotics, but comes with limitations, so that more mechanistic approaches are often preferred to interpret experimental data. It might be possible however to extend FGM assumptions to better account for mutational data. This is theoretically challenging in the context of antibiotic resistance because resistance mutations are assumed to be rare. In this paper, we show with Escherichia coli how the fitness effects of resistance mutations screened at different doses of nalidixic acid vary across a dose-gradient. We found experimental patterns qualitatively consistent with the basic FGM (rate of resistance across doses, gamma distributed costs) but also unexpected patterns such as a decreasing mean cost of resistance with increasing screen-dose. We show how different extensions involving mutational modules and variations in trait covariance across environments, can be discriminated based on these data. Overall, simple extensions of the FGM accounted well for complex mutational effects of resistance mutation across antibiotic doses.

费希尔几何模型（Fisher's geometrical model, FGM）已被广泛用于描述突变的适合度效应。该模型仅依赖极少的基础假设，属于通用性极强的模型，能够全面刻画适应性演化过程的整体图景，因此在诸多场景中具有应用价值，例如对抗生素的适应性演化。但该模型也存在自身的局限性，因此在解释实验数据时，研究者通常更倾向于采用基于机制的研究方法。不过，通过拓展FGM的假设条件，或可更好地适配突变相关数据。然而在抗生素耐药性的研究语境下，这一拓展工作具有理论挑战性，因为耐药突变通常被认为是罕见事件。本文以大肠杆菌（Escherichia coli）为研究对象，分析了在不同剂量萘啶酸（nalidixic acid）筛选下获得的耐药突变的适合度效应随筛选剂量梯度的变化规律。研究发现，实验结果在定性上与基础FGM的预测相符，例如不同剂量下的耐药突变率、呈伽马分布的适应性代价，但同时也存在一些未被预期的模式，例如随着筛选剂量升高，耐药突变的平均适应性代价呈下降趋势。我们还展示了如何基于上述实验数据，区分涉及突变模块以及不同环境下性状协方差变化的多种拓展模型。总体而言，对FGM的简单拓展能够很好地解释抗生素剂量梯度下耐药突变所呈现的复杂突变效应。