Kumawat Bhaskar, Lalejini Alexander, Acosta Monica M, Zaman Luis
Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109.
Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109.
Proc Natl Acad Sci U S A. 2025 Jan 7;122(1):e2413930121. doi: 10.1073/pnas.2413930121. Epub 2024 Dec 31.
Life at all scales is surprisingly effective at exploiting new opportunities, as demonstrated by the rapid emergence of antimicrobial resistance and novel pathogens. How populations acquire this level of evolvability and the various ways it aids survival are major open questions with direct implications for human health. Here, we use digital evolution to show that changing environments facilitate the simultaneous evolution of high mutation rates and a distribution of mutational effects skewed toward beneficial phenotypes. The evolved mutational neighborhoods allow rapid adaptation to previously encountered environments, whereas higher mutation rates aid adaptation to completely new environmental conditions. By precisely tracking evolving lineages and the phenotypes of their mutants, we show that evolving populations localize on phenotypic boundaries between distinct regions of genotype space. Our results demonstrate how evolution shapes multiple determinants of evolvability concurrently, fine-tuning a population's adaptive responses to unpredictable or recurrent environmental shifts.
正如抗菌药物耐药性和新型病原体的迅速出现所表明的那样，各个尺度的生命在利用新机会方面都出奇地有效。种群如何获得这种进化能力水平以及它有助于生存的各种方式是重大的开放性问题，对人类健康有直接影响。在这里，我们利用数字进化表明，不断变化的环境促进了高突变率的同时进化以及向有益表型倾斜的突变效应分布。进化后的突变邻域允许快速适应先前遇到的环境，而较高的突变率有助于适应全新的环境条件。通过精确跟踪进化谱系及其突变体的表型，我们表明进化种群定位在基因型空间不同区域之间的表型边界上。我们的结果证明了进化如何同时塑造进化能力的多个决定因素，微调种群对不可预测或反复出现的环境变化的适应性反应。
