The role of migration in mutant dynamics in fragmented populations

Abstract

Mutant dynamics in fragmented populations have been studied extensively in evolutionary biology. Yet, open questions remain, both experimentally and theoretically. Some of the fundamental properties predicted by models still need to be addressed experimentally. We contribute to this by using a combination of experiments and theory to investigate the role of migration in mutant distribution. In the case of neutral mutants, while the mean frequency of mutants is not influenced by migration, the probability distribution is. To address this empirically, we performed in vitro experiments, where mixtures of GFP‐labelled (“mutant”) and non‐labelled (“wid‐type”) murine cells were grown in wells (demes), and migration was mimicked via cell transfer from well to well. In the presence of migration, we observed a change in the skewedness of the distribution of the mutant frequencies in the wells, consistent with previous and our own model predictions. In the presence of de novo mutant production, we used modelling to investigate the level at which disadvantageous mutants are predicted to exist, which has implications for the adaptive potential of the population in case of an environmental change. In panmictic populations, disadvantageous mutants can persist around a steady state, determined by the rate of mutant production and the selective disadvantage (selection‐mutation balance). In a fragmented system that consists of demes connected by migration, a steady‐state persistence of disadvantageous mutants is also observed, which, however, is fundamentally different from the mutation‐selection balance and characterized by higher mutant levels. The increase in mutant frequencies above the selection‐mutation balance can be maintained in small (N<Nc) demes as long as the migration rate is sufficiently small. The migration rate above which the mutants approach the selection‐mutation balance decays exponentially with N/Nc. The observed increase in the mutant numbers is not explained by the change in the effective population size. Implications for evolutionary processes in diseases are discussed, where the pre‐existence of disadvantageous drug‐resistant mutant cells or pathogens drives the response of the disease to treatments.

Mutant fate in spatially structured populations on graphs: connecting models to experiments

“…In our recent paper [36], we developed a model describing the evolution of spatially structured populations composed of well-mixed subpopulations or demes, each placed on a node of a graph. This model explicitly takes into account the serial passage protocol commonly used in modern evolution experiments [27][28][29][30][31][32][33][34][35]. In particular, such a protocol was used in the recent study [34] to investigate the impact of different spatial structures on the fate of beneficial mutants.…”

“…Modern evolution experiments [27][28][29][30][31][32][33][34][35] allow to investigate evolution in the lab. In particular, some experiments have investigated the impact of spatial structure on evolution, implementing spatial structure through transfers between well-mixed cultures [30-32, 34, 35].…”

“…Evolutionary graph theory assumes that the size of each deme is strictly constant (generally set to one, or to a fixed number in generalizations). This is not the case in experiments, which generally employ batch culture with serial passages (also called serial transfers), alternating phases of growth and bottlenecks [27][28][29][30][31][32][33][34][35]. Furthermore, these experiments do not have update rules or strict couplings between birth, death and migration events, in contrast to evolutionary graph theory.…”

Categorizing update mechanisms for graph-structured metapopulations

“…In graphs of individuals, three main events influence the evolution of a population: birth, mutation and death. In the long run, a mutation-selection balance is reached [8,[18][19][20][21]. Typically, the focus is on the low mutation regime in which the system reaches fixation or extinction of the mutant before the next mutation occurs.…”

“…An extension of evolutionary graph theory is given by graph-structured metapopulations in which the nodes indicate subpopulations, and the links indicate the migration between subpopulations [5][6][7][8]. Evolutionary dynamics in subdivided populations has been studied by researchers for a long time [9].…”