Nakauma Alberto, van Doorn G Sander
Groningen Institute for Evolutionary Life Sciences, University of Groningen, 9700 CC Groningen, The Netherlands.
Groningen Institute for Evolutionary Life Sciences, University of Groningen, 9700 CC Groningen, The Netherlands.
J Theor Biol. 2017 May 7;420:200-212. doi: 10.1016/j.jtbi.2017.03.016. Epub 2017 Mar 18.
The signal-transduction network responsible for chemotaxis in Escherichia coli has been characterised in extraordinary detail. Yet, relatively little is known about eco-evolutionary aspects of chemotaxis, such as how the network has been shaped by selection and to what extent natural populations may fine-tune their chemotactic behaviour to the ecological conditions. To address these questions, we here develop an evolutionary-systems-biology model of the chemotaxis network of E. coli, which we apply to estimate the resource accumulation rate (here used as a proxy for fitness) of wildtype and a large number of potential mutant genotypes. Mutant genotypes differ from the wildtype in the concentrations of one or more constituent proteins of the chemotaxis signalling network or in one or more of its kinetic parameters. To guarantee model consistency across the genotype space, we explicitly incorporated biochemical constraints that underly observed phenotypic trade-offs. The model was validated by reconstructing the phenotypic properties of several known mutant genotypes. We also characterised differences in the fitness distribution between genotypes, and reconstructed adaptive walks in genotype space for populations exposed to different environmental conditions. We found that the local fitness landscape is rugged, due to non-additive interactions between mutations. When selection has a consistent direction, just a few adaptive mutations are required to reach a local peak, and different local peaks can be reached by adaptive walks starting from the same initial genotype. However, when the direction of selection is fluctuating, evolutionary paths are much longer and genotype space is explored further. Longer adaptive walks were also observed when evolution was started from a low-fitness genotype such as a CheZ knockout mutant. In line with empirical observations, the initial ΔcheZ mutant did not respond to a step-down stimulus, but a dynamic response similar to the wildtype was recovered following the fixation of compensatory mutations.
负责大肠杆菌趋化作用的信号转导网络已得到极为详细的表征。然而，对于趋化作用的生态进化方面，例如该网络是如何通过选择形成的，以及自然种群在多大程度上可以根据生态条件微调其趋化行为，我们了解得相对较少。为了解决这些问题，我们在此开发了一种大肠杆菌趋化网络的进化系统生物学模型，用于估计野生型和大量潜在突变基因型的资源积累率（这里用作适应性的代理指标）。突变基因型在趋化信号网络的一种或多种组成蛋白浓度或其一种或多种动力学参数方面与野生型不同。为确保跨基因型空间的模型一致性，我们明确纳入了构成观察到的表型权衡基础的生化约束条件。通过重建几种已知突变基因型的表型特性对该模型进行了验证。我们还表征了基因型之间适应性分布的差异，并重建了暴露于不同环境条件下种群在基因型空间中的适应性行走。我们发现，由于突变之间的非加性相互作用，局部适应性景观崎岖不平。当选择具有一致方向时，只需少数适应性突变就能达到局部峰值，并且从相同初始基因型开始的适应性行走可以到达不同的局部峰值。然而，当选择方向波动时，进化路径会长得多，并且会进一步探索基因型空间。当进化从低适应性基因型（如CheZ基因敲除突变体）开始时，也观察到了更长的适应性行走。与实证观察结果一致，最初的ΔcheZ突变体对阶跃下降刺激没有反应，但在补偿性突变固定后恢复了类似于野生型的动态反应。
