TRANSMUTATOR

Bacterial populations often contain a fraction of constitutive mutators, i.e. mutants with elevated mutation rate, which can speed up adaptation and promote the emergence of antibiotic resistance. In addition, stochasticity in intracellular reactions can create transient mutator states in single cells, which could also facilitate adaptation. In contrast to constitutive mutators, transient mutators have not been directly investigated, due to the lack of suitable tools. Due to their extensive use in agriculture and animal and human medicine, antibiotics are often found in our environment at subinhibitory concentrations. Such antibiotic doses have been suggested to promote the emergence of resistance to a wide range of antibiotics through increased mutation rate. However, it is not known if this increased mutation rate affects all the cells equally in the population, or if it is due to a subpopulation of transient phenotypic mutators. Transient mutators could in turn be of high significance for the evolution of antibiotic resistance. They could speed up adaptation without paying the cost associated with a permanently high mutation rate, namely the accumulation of many deleterious mutations. The evolution of complex traits, such as antibiotic resistance can involve several mutations, whose effects may not be independent. These mutations can for instance be deleterious alone but beneficial in combination. This suggests that mutation dynamics where mutations are acquired one by one may not lead to the same outcome under natural selection compared to dynamics allowing acquiring several mutations at the same time. Transient mutators may therefore facilitate the acquisition of a combination of mutations, which can be critical both in the emergence and decline of antibiotic resistance. Until very recently, there was no appropriate tools to investigate transient mutators, or equivalently mutation rate fluctuations. We recently developed such tools, allowing following the occurrence of mutations in real-time in single cells of Escherichia coli during steady-state growth. Our approach is based on microfluidic technologies, time-lapse imaging and a fluorescently tag of the Mismatch Repair (MMR) system. The goal of this project is to use this new approach to understand the possible origins and evolutionary consequences of mutation rate variations in bacteria. More specifically, our main objectives are to 1) estimate mutation rate fluctuations in the presence of sub-inhibitory doses of antibiotics; 2) investigate the influence of mutation rate fluctuations on the evolution of antibiotic resistance.