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Exclusion rules, bottlenecks and the evolution of stochastic phenotype switching


Rainey,  Paul B.
External Scientific Member Group Experimental and Evolutionary Genetics, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Libby, E., & Rainey, P. B. (2011). Exclusion rules, bottlenecks and the evolution of stochastic phenotype switching. Proceedings of the Royal Society of London. Series B: Biological Sciences (London), 278(1724), 3574-3583. doi:10.1098/rspb.2011.0146.

Stochastic phenotype switching—often considered a bet hedging or risk-reducing strategy—can enhance the probability of survival in fluctuating environments. A recent experiment provided direct evidence for an adaptive origin by showing the de novo evolution of switching in bacterial populations propagated under a selective regime that captured essential features of the host immune response. The regime involved strong frequency-dependent selection realized via dual imposition of an exclusion rule and population bottleneck. Applied at the point of transfer between environments, the phenotype common in the current environment was assigned a fitness of zero and was thus excluded from participating in the next round (the exclusion rule). In addition, also at the point of transfer, and so as to found the next bout of selection, a single phenotypically distinct type was selected at random from among the survivors (the bottleneck). Motivated by this experiment, we develop a mathematical model to explore the broader significance of key features of the selective regime. Through a combination of analytical and numerical results, we show that exclusion rules and population bottlenecks act in tandem as potent selective agents for stochastic phenotype switching, such that even when initially rare, and when switching engenders a cost in Malthusian fitness, organisms with the capacity to switch can invade non-switching populations and replace non-switching types. Simulations demonstrate the robustness of our findings to alterations in switching rate, fidelity of exclusion, bottleneck size, duration of environmental state and growth rate. We also demonstrate the relevance of our model to a range of biological scenarios such as bacterial persistence and the evolution of sex.