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

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Kayser,  Jona
Kayser Research Group, Guck Division, Max Planck Institute for the Science of Light, Max Planck Society;
Max-Planck-Zentrum für Physik und Medizin, Max Planck Institute for the Science of Light, Max Planck Society;

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Zitation

Aif, S., Appold, N., Kampman, L., Hallatschek, O., & Kayser, J. (2022). Evolutionary rescue of resistant mutants is governed by a balance between radial expansion and selection in compact populations. NATURE COMMUNICATIONS, 13(1): 7916. doi:10.1038/s41467-022-35484-y.


Zusammenfassung
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.