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在一个经验表型-环境-适应度景观中进行环境选择和上位性。

Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape.

机构信息

Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada.

Department of Genome Sciences, University of Washington, Seattle, WA, USA.

出版信息

Nat Ecol Evol. 2022 Apr;6(4):427-438. doi: 10.1038/s41559-022-01675-5. Epub 2022 Feb 24.

Abstract

Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.

摘要

适应景观是基因型/表型与其对适应度影响之间的映射,是进化生物化学中非常有价值的概念。尽管已经广泛讨论,但蛋白质表型适应景观的测量仍然很少。在这里,我们在氨苄青霉素浓度的 64 倍范围内量化了 VIM-2β-内酰胺酶的所有单一突变对适应度和表型(EC)的影响。然后,我们构建了一个考虑环境选择压力变化的表型适应景观。我们发现,一个简单的经验性景观可以准确地模拟大约 39000 个突变数据点,这表明可以根据选择环境来预测 VIM-2 的进化。我们的景观为β-内酰胺酶和一般蛋白质的进化提供了新的定量知识,特别是它们在亚抑菌抗生素浓度下的进化动态,以及非特异性上位性的机制和环境依赖性。

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