RT Journal Article
SR Electronic
T1 A roadblock-and-kill model explains the dynamical response to the DNA-targeting antibiotic ciprofloxacin
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 791145
DO 10.1101/791145
A1 Nikola Ojkic
A1 Elin Lilja
A1 Susana Direito
A1 Angela Dawson
A1 Rosalind J. Allen
A1 Bartlomiej Waclaw
YR 2019
UL http://biorxiv.org/content/early/2019/10/10/791145.abstract
AB Fluoroquinolones - antibiotics that cause DNA damage by inhibiting DNA topoisomerases - are clinically important, but their mechanism of action is not yet fully understood. In particular, the dynamical response of bacterial cells to fluoroquinolone exposure has hardly been investigated, although the SOS response, triggered by DNA damage, is often thought to play a key role. Here we investigate growth inhibition of the bacterium Escherichia coli by the fluoroquinolone ciprofloxacin at low doses (up to 5x the minimum inhibitory concentration). We measure the long-term and short-term (dynamic) response of the growth rate and DNA production rate to ciprofloxacin, at both population- and single-cell level. We show that despite the molecular complexity of DNA metabolism, a simple `roadblock-and-kill’ model focusing on replication fork blockage and DNA damage by ciprofloxacin-poisoned DNA topoisomerase II (gyrase) quantitatively reproduces long-term growth rates. The model also predicts dynamical changes in DNA production rate in wild type E. coli and in an SOS-deficient mutant, following a step-up of ciprofloxacin. Our work reveals new insights into the dynamics of fluoroquinolone action, with important implications for predicting the rate of resistance evolution. Most importantly, our model explains why the response is delayed: it takes many doubling times to fragment the DNA sufficiently to inhibit gene expression. Our model also challenges the view that the SOS response plays a central role: the dynamical response is controlled by the timescale of DNA replication and gyrase binding/unbinding to the DNA rather than by the SOS response. More generally, our work highlights the importance of including biophysical processes in biochemical-systems models to fully understand bacterial response to antibiotics.