4.8 Article

Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations

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NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-35484-y

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  1. Emmy Noether Programme of the German Research Foundation [455449456]
  2. National Institute of General Medical Sciences of the National Institutes of Health [2R01GM115851-06A1, KA 4486/1-1]
  3. German Research Foundation
  4. Humboldt Professorship of the Alexander von Humboldt Foundation

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Mutation-mediated treatment resistance is a major challenge in antibiotic and anti-cancer therapy. Emerging resistant lineages can escape purifying selection through compensatory mutations. In dense microbial populations, the probability of evolutionary rescue and the long-term persistence of drug resistant mutant lineages are significantly increased.
Mutation-mediated treatment resistance is one of the primary challenges for modern antibiotic and anti-cancer therapy. Yet, many resistance mutations have a substantial fitness cost and are subject to purifying selection. How emerging resistant lineages may escape purifying selection via subsequent compensatory mutations is still unclear due to the difficulty of tracking such evolutionary rescue dynamics in space and time. Here, we introduce a system of fluorescence-coupled synthetic mutations to show that the probability of evolutionary rescue, and the resulting long-term persistence of drug resistant mutant lineages, is dramatically increased in dense microbial populations. By tracking the entire evolutionary trajectory of thousands of resistant lineages in expanding yeast colonies we uncover an underlying quasi-stable equilibrium between the opposing forces of radial expansion and natural selection, a phenomenon we term inflation-selection balance. Tailored computational models and agent-based simulations corroborate the fundamental nature of the observed effects and demonstrate the potential impact on drug resistance evolution in cancer. The described phenomena should be considered when predicting multi-step evolutionary dynamics in any mechanically compact cellular population, including pathogenic microbial biofilms and solid tumors. The insights gained will be especially valuable for the quantitative understanding of response to treatment, including emerging evolution-based therapy strategies.

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