Nahum Joshua R, Godfrey-Smith Peter, Harding Brittany N, Marcus Joseph H, Carlson-Stevermer Jared, Kerr Benjamin
BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824;
Philosophy Program, The Graduate Center, City University of New York, NY 10016; History and Philosophy of Science Unit, University of Sydney, Sydney, NSW 2006, Australia;
Proc Natl Acad Sci U S A. 2015 Jun 16;112(24):7530-5. doi: 10.1073/pnas.1410631112. Epub 2015 May 11.
In the context of Wright's adaptive landscape, genetic epistasis can yield a multipeaked or "rugged" topography. In an unstructured population, a lineage with selective access to multiple peaks is expected to fix rapidly on one, which may not be the highest peak. In a spatially structured population, on the other hand, beneficial mutations take longer to spread. This slowdown allows distant parts of the population to explore the landscape semiindependently. Such a population can simultaneously discover multiple peaks, and the genotype at the highest discovered peak is expected to dominate eventually. Thus, structured populations sacrifice initial speed of adaptation for breadth of search. As in the fable of the tortoise and the hare, the structured population (tortoise) starts relatively slow but eventually surpasses the unstructured population (hare) in average fitness. In contrast, on single-peak landscapes that lack epistasis, all uphill paths converge. Given such "smooth" topography, breadth of search is devalued and a structured population only lags behind an unstructured population in average fitness (ultimately converging). Thus, the tortoise-hare pattern is an indicator of ruggedness. After verifying these predictions in simulated populations where ruggedness is manipulable, we explore average fitness in metapopulations of Escherichia coli. Consistent with a rugged landscape topography, we find a tortoise-hare pattern. Further, we find that structured populations accumulate more mutations, suggesting that distant peaks are higher. This approach can be used to unveil landscape topography in other systems, and we discuss its application for antibiotic resistance, engineering problems, and elements of Wright's shifting balance process.
在赖特的适应性景观背景下,基因上位性可产生多峰或“崎岖”的地形。在一个无结构的种群中,一个能够选择性地到达多个山峰的谱系预计会迅速固定在其中一个山峰上,而这个山峰可能不是最高峰。另一方面,在一个空间结构化的种群中,有益突变的传播需要更长时间。这种减缓使得种群的不同部分能够半独立地探索景观。这样的种群可以同时发现多个山峰,并且预计最终在发现的最高峰处的基因型将占主导地位。因此,结构化种群为了搜索广度而牺牲了初始适应速度。就像龟兔赛跑的寓言一样,结构化种群(乌龟)开始时相对较慢,但最终在平均适应度上超过了无结构种群(兔子)。相比之下,在缺乏上位性的单峰景观中,所有上坡路径都会汇聚。鉴于这种“平滑”的地形,搜索广度的价值被贬低,结构化种群在平均适应度上仅落后于无结构种群(最终会趋同)。因此,龟兔模式是崎岖性的一个指标。在可操控崎岖性的模拟种群中验证了这些预测之后,我们探索了大肠杆菌集合种群中的平均适应度。与崎岖的景观地形一致,我们发现了龟兔模式。此外,我们发现结构化种群积累了更多的突变,这表明远处的山峰更高。这种方法可用于揭示其他系统中的景观地形,并且我们讨论了它在抗生素抗性、工程问题以及赖特的动态平衡过程要素方面的应用。