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波动的环境选择导致短期表型变异,从而导致长期探索。

Fluctuating environments select for short-term phenotypic variation leading to long-term exploration.

机构信息

Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA.

BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, USA.

出版信息

PLoS Comput Biol. 2019 Apr 19;15(4):e1006445. doi: 10.1371/journal.pcbi.1006445. eCollection 2019 Apr.

Abstract

Genetic spaces are often described in terms of fitness landscapes or genotype-to-phenotype maps, where each genetic sequence is associated with phenotypic properties and linked to other genotypes that are a single mutational step away. The positions close to a genotype make up its "mutational landscape" and, in aggregate, determine the short-term evolutionary potential of a population. Populations with wider ranges of phenotypes in their mutational neighborhood are known to be more evolvable. Likewise, those with fewer phenotypic changes available in their local neighborhoods are more mutationally robust. Here, we examine whether forces that change the distribution of phenotypes available by mutation profoundly alter subsequent evolutionary dynamics. We compare evolved populations of digital organisms that were subject to either static or cyclically-changing environments. For each of these, we examine diversity of the phenotypes that are produced through mutations in order to characterize the local genotype-phenotype map. We demonstrate that environmental change can push populations toward more evolvable mutational landscapes where many alternate phenotypes are available, though purely deleterious mutations remain suppressed. Further, we show that populations in environments with harsh changes switch phenotypes more readily than those in environments with more benign changes. We trace this effect to repeated population bottlenecks in the harsh environments, which result in shorter coalescence times and keep populations in regions of the mutational landscape where the phenotypic shifts in question are more likely to occur. Typically, static environments select solely for immediate optimization, at the expensive of long-term evolvability. In contrast, we show that with changing environments, short-term pressures to deal with immediate challenges can align with long-term pressures to explore a more productive portion of the mutational landscape.

摘要

遗传空间通常用适应度景观或基因型-表型图谱来描述,其中每个遗传序列都与表型特性相关联,并与其他仅相差一个突变步骤的基因型相关联。接近基因型的位置构成了它的“突变景观”,并共同决定了种群的短期进化潜力。突变邻域中具有更多表型范围的种群被认为更具进化能力。同样,那些在其局部邻域中可用的表型变化较少的种群更具突变稳健性。在这里,我们研究了改变突变产生的表型分布的力量是否会深刻改变后续的进化动态。我们比较了经历静态或周期性变化环境的数字生物进化种群。对于每种情况,我们都检查通过突变产生的表型多样性,以表征局部基因型-表型图谱。我们证明环境变化可以促使种群向更具进化能力的突变景观发展,在这些景观中,有许多可供选择的替代表型,尽管纯粹的有害突变仍受到抑制。此外,我们还表明,在环境变化剧烈的环境中,种群比在环境变化较温和的环境中更容易切换表型。我们将这种效应归因于在恶劣环境中反复出现的种群瓶颈,这导致合并时间更短,使种群处于突变景观的区域,在这些区域中,所讨论的表型变化更有可能发生。通常,静态环境仅选择立即进行优化,而牺牲了长期的进化能力。相比之下,我们表明,在不断变化的环境中,应对当前挑战的短期压力可以与探索更具生产力的突变景观部分的长期压力相吻合。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31d1/6474582/fce257260762/pcbi.1006445.g001.jpg

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