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。作为一类仅包含少量潜在假设的普适模型，该模型能够为适应性演化过程提供全面且系统的视角，因此在诸多场景中颇具应用价值——例如抗生素适应性演化研究，但该模型也存在一定局限，因此在解读实验数据时，学界通常更倾向于采用更具机制性的研究方法。不过，我们或可通过拓展费希尔几何模型的假设前提，以更好地契合突变相关实验数据。但在抗生素耐药性的研究背景下，这一拓展会面临理论层面的挑战，因为耐药突变通常被认为是罕见事件。在本研究中，我们以大肠杆菌（Escherichia coli）为实验对象，分析了在不同浓度萘啶酸（nalidixic acid）下筛选得到的耐药突变的适合度效应如何随筛选浓度梯度发生变化。我们发现，实验结果在定性上与基础版费希尔几何模型的预测相符，例如不同浓度下的耐药突变发生率、耐药性代价的伽马分布特征，但同时也观测到了一些出乎意料的模式，例如随着筛选浓度升高，耐药突变的平均适合度代价呈下降趋势。我们展示了如何基于上述实验数据，对涉及突变模块以及不同环境下性状协方差变异的多种模型拓展方案进行甄别区分。综上，对费希尔几何模型进行简单拓展后，便可很好地解释不同抗生素浓度下耐药突变所产生的复杂突变效应。