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Microbes inhabit natural environments that are remarkably dynamic. Therefore, microbes harbor regulated genetic mechanisms to sense shifts in conditions and induce the appropriate responses. Recent studies suggest that the initial evolution of microbes occupying new niches favors mutations in regulatory pathways. However, it is not clear how this evolution is affected by how quickly conditions change (i.e. dynamics), or which mechanisms are commonly used to implement new regulation. Here, we perform experimental evolution on continuous cultures of Escherichia coli carrying the tetracycline resistance tet operon to identify specific mutations that adapt drug responses to different dynamic regimens of drug administration. We find that cultures evolved under gradually increasing tetracycline concentrations show no mutations in the tet operon, but instead a predominance of fine-tuning mutations increasing the affinity of an alternative efflux pump AcrB to tetracycline. When cultures are instead periodically exposed to large drug doses, all populations evolved transposon insertions in repressor TetR, resulting in loss of regulation and constitutive expression of efflux pump TetA. We use a mathematical model of the dynamics of antibiotic responses to show that sudden exposure to large drug concentrations overwhelm regulated responses, which cannot induce resistance fast enough, resulting in selection for constitutive expression of resistance. These results help explain the frequent loss of regulation of antibiotic resistance by pathogens evolved in clinical environments. Our experiment supports the notion that initial evolution in new ecological niches proceeds largely through regulatory mutations and suggests that transposon insertions are the main mechanism driving this process. 

Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug’s action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.