Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

Author:

Persson KarlORCID,Stenberg SimonORCID,Tamás Markus J.ORCID,Warringer JonasORCID

Abstract

ABSTRACTAdaptive evolution of clonally dividing cells and microbes is the ultimate cause of cancer and infectious diseases. The possibility of constraining the adaptation of cell populations, by inhibiting proteins that enhance their evolvability has therefore attracted substantial interest. However, our current understanding of how individual genes influence the speed of adaptation is limited, partly because accurately tracking adaptation for many experimental cell populations in parallel is challenging. Here we use a high throughput artificial laboratory evolution (ALE) platform to track the adaptation of >18.000 cell populations corresponding to single gene deletion strains in the haploid yeast deletion collection. We report that the fitness of gene knockout near-perfectly (R2=0.91) predicts their adaptation dynamics under arsenic exposure, leaving virtually no role for dedicated evolvability functions in the corresponding proteins. We tracked the adaptation of another >23.000 yeast gene knockout populations to a diverse range of selection pressures and generalised the almost perfect (R2=0.72 to 0.98) capacity of initial fitness to predict the rate of adaptation. Finally, we reconstruct mutations in the genes FPS1, ASK10, and ARR3, which together account for almost all arsenic adaptation in wildtype cells, in gene deletions covering a broad fitness range. We show that the predictability of arsenic adaptation can be understood almost entirely as a global epistasis phenomenon where excluding arsenic from cells, through these mutations, is more beneficial in cells with low arsenic fitness regardless of what causes the arsenic defects. The lack of genes with a meaningful effect on the adaptation dynamics of clonally reproducing cell populations diminishes the prospects of developing adjuvant drugs aiming to slow antimicrobial and chemotherapy resistance.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3