发表机构
The University of Tokyo; RIKEN; University of Copenhagen; Institute of Science Tokyo（东京大学; 理化学研究所; 哥本哈根大学; 东京科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过统计物理学启发的基因型-表型映射抽象模型，揭示高外显率与突变适应性的权衡，固定环境选高外显率，环境剧变选高突变可及性，环境决定进化平衡。
AI 中文摘要
变化环境中的进化既需要当前有利表型的可靠表达，这由外显率量化，也需要通过突变获得替代表型的能力。此前研究表明，高外显率可能会限制这种突变可及性，但由于这些研究聚焦于已进化的基因型和局部突变邻域，无法确定这种局部约束是否会限制环境变化下的突变适应性。这种适应性取决于基因型相对于其他环境高适应度区域的位置，解决该问题需要重构每个基因型的完整表型概率分布，以及基因型空间中由此产生的环境特异性适应度景观；但由于基因型和表型空间呈组合式增长，这种重构通常不可行。本文提出一种受统计物理学中相互作用自旋启发的随机基因型-表型映射抽象模型，可实现该映射的详尽重构。研究发现，高外显率基因型往往占据环境特异性高适应度区域的内部，且具有突变稳健性；而低外显率基因型往往位于这些区域的边界附近，对其他环境的高适应度区域具有更高的突变可及性。这种全局几何产生了外显率与突变适应性之间的权衡。进化模拟显示，固定环境中更强的表型噪声会增加可靠表达的选择优势，从而有利于高外显率和突变稳健性；频繁的环境变化则会以牺牲外显率为代价，有利于突变可及性。因此，外显率和适应性是同一全局几何的对立结果，环境条件决定了它们的进化平衡。
英文摘要
Evolution in changing environments requires both reliable expression of the currently favored phenotype, as quantified by penetrance, and the capacity to reach alternative phenotypes through mutation. Previous studies suggest that high penetrance may restrict such mutational access. However, because these studies focus on evolved genotypes and local mutational neighborhoods, they cannot determine whether this local constraint limits mutational adaptability under environmental change. Such adaptability depends on a genotype's position relative to high-fitness regions for other environments. Addressing this question requires reconstructing the full probability distribution over phenotypes for every genotype and the resulting environment-specific fitness landscapes across genotype space. Such reconstruction is generally infeasible because genotype and phenotype spaces grow combinatorially. Here, an abstract model of stochastic genotype-phenotype mapping, inspired by interacting spins in statistical physics, permits exhaustive reconstruction of the map. We find that high-penetrance genotypes tend to occupy the interior of environment-specific high-fitness regions and are mutationally robust, whereas lower-penetrance genotypes tend to lie near their boundaries and have greater mutational access to high-fitness regions for alternative environments. This global geometry generates a trade-off between penetrance and mutational adaptability. In evolutionary simulations, stronger phenotypic noise in a fixed environment increases the selective advantage of reliable expression, thereby favoring high penetrance and mutational robustness. Frequent environmental change instead favors mutational accessibility at the expense of penetrance. Thus, penetrance and adaptability are opposing consequences of the same global geometry, with environmental conditions determining their evolutionary balance.
