Adaptive landscapes of antibiotic resistance: population size and 'survival-of-the-flattest'.

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Spontaneous mutation altering genetic sequences is a key engine of evolution. However, if the rate of mutations exceeds a 'critical mutation rate', changes occur too frequently for natural selection to maintain the population's genetic makeup. For example, one can think of mutation as moving offspring away from their parents in a 'fitness landscape' where peaks are genetic sequences with high fitness. If there is too much mutation (above a critical mutation rate), selection may be able to keep a population on a broad fitness peak, but not on a narrow one, so-called 'survival-of-the-flattest'. Such critical mutation rates are generally believed to be much higher than those seen in typical biological organisms. However, we have recently discovered that, in computer simulations, critical mutation rates are much lower in very small populations (<100 individuals). This suggests that survival-of-the-flattest could be occurring within the normal range of biological mutation rates.This proposal therefore aims to test whether survival-of-the-flattest really does occur in biology, using antibiotic resistance evolution as a test case. This requires an integrated, multi-disciplinary approach, conducting decisive wet-lab experiments, aligning computer simulations with biology and developing a rigorous and predictive theoretical framework that relates findings to mathematical understanding. Our first objective will be to characterise the fitness landscapes of bacteria resistant to different antibiotics to determine whether appropriate distinctions exist in practice between narrow and broad fitness peaks. Here we will focus on environments containing antibiotics, but will use experimental evolution (at high and minimal population sizes) with complete genome sequencing to consider mutations across the genome. Our second Objective is to develop computer simulations and theory to align with the biology. Specifically we need to relate our findings about critical mutation rates in simulations to existing theory. We then need to move both theory and simulation towards more biologically realistic assumptions. For instance we shall test more realistic fitness landscapes, particularly those determined empirically through our first Objective. Our final Objective is rigorously to test the coherence of the theory, simulation and biological experiment with each other. In particular we shall use experimental evolution at small population sizes to test whether we can detect the effect of critical mutation rates as predicted and further characterise the experimentally evolved strains to test the consistency of biological mechanisms involved.Rigorously unpicking this novel population size effect in the evolution of antibiotic resistance could have broad impacts. Antibiotic resistant bacteria inevitably appear by mutation with a small population size and bacteria resistant to one antibiotic will be knocked down to small population sizes by another. The hypotheses we shall test here will determine whether the maintenance of particular antibiotic resistances at such small population sizes, could depend on the details of the different fitness landscapes imposed by different antibiotics in a theoretically predictable way - potentially crucial information in combatting antimicrobial resistance. In sum this work will closely link experimental approaches to evolution in biology, simulation and theory to determine if when and how survival-of-the-flattest impinges on the increasingly critical issue of antibiotic resistance evolution.