Quantifying stress-induced mutagenesis in bacteria exposed to low-dose antibiotics

Abstract

Bacteria are frequently exposed to antibiotics, particularly at low doses, which induces stress responses in the cells. Some of these responses increase mutagenesis and thus potentially accelerate resistance evolution. Many studies report increased mutation rates under stress, often using the standard experimental approach of fluctuation assays. In this thesis, I extend the mathematical model behind the fluctuation assay to include within-population heterogeneity in stress responses. Our model is inspired by the DNA-damage response in Escherichia coli (SOS response). It accounts for a subpopulation with high expression of the stress response, which increases the mutation rate and decreases the division rate of a cell. In Chapter 2, I implement maximum likelihood estimation and stochastic simulations of fluctuation assays under existing and our new population dynamic model. Using the simulated data, I show that this new model, in principle, allows for estimating the increase in mutation rate specifically associated with the induction of the stress response. However, I also show that when heterogeneity is neglected, an accurate estimate of the increase in population-mean mutation rate is recovered. Moreover, in many cases, different models can explain the data equally well and, therefore, cannot be distinguished using fluctuation assay data alone. In Chapter 3, I apply our estimation method, which I converted into a user-friendly R tool, to published experimental data. I show that not all experiments that report an increase in mutation rate significantly support the hypothesis of stress-induced mutagenesis. Moreover, I find that DNA-damaging antibiotics particularly increase mutation rates and identify several signals of heterogeneity in stress-induced mutagenesis. In Chapter 4, I study how stress-induced mutagenesis depends on the antibiotic dose. By modelling different antibiotic modes of action, I determine under which conditions within-population heterogeneity can lead to a non-monotonic increase in mutation rate with antibiotic concentration. Such a maximum increase for intermediate concentrations has been previously observed empirically. Overall, this thesis improves the estimation of mutation rates in bacteria under stress, which could contribute to better predictions of the evolution of antibiotic resistance