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

Serhii Aif, Nico Appold, Lucas Kampman, Oskar Hallatschek () and Jona Kayser ()
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Serhii Aif: Max Planck Institute for the Science of Light & Max-Planck-Zentrum für Physik und Medizin
Nico Appold: Max Planck Institute for the Science of Light & Max-Planck-Zentrum für Physik und Medizin
Lucas Kampman: University of California
Oskar Hallatschek: University of California
Jona Kayser: Max Planck Institute for the Science of Light & Max-Planck-Zentrum für Physik und Medizin

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-35484-y

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