Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations
Cite this dataset
Hansen, Elsa et al. (2020). Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations [Dataset]. Dryad. https://doi.org/10.5061/dryad.s4mw6m943
Standard infectious disease practice calls for aggressive drug treatment that rapidly eliminates the pathogen population before resistance can emerge. When resistance is absent, this elimination strategy can lead to complete cure. However, when resistance is already present, removing drug-sensitive cells as quickly as possible removes competitive barriers that may slow the growth of resistant cells. In contrast to the elimination strategy, the containment strategy aims to maintain the maximum tolerable number of pathogens, exploiting competitive suppression to achieve chronic control. Here we combine in vitro experiments in computer-controlled bioreactors with mathematical modeling to investigate whether containment strategies can delay failure of antibiotic treatment regimens. To do so, we measured the "escape time" required for drug-resistant E. coli populations to eclipse a threshold density maintained by adaptive antibiotic dosing. Populations containing only resistant cells rapidly escape the threshold density, but we found that matched resistant populations that also contain the maximum possible number of sensitive cells could be contained for significantly longer. The increase in escape time occurs only when the threshold density--the acceptable bacterial burden--is sufficiently high, an effect that mathematical models attribute to increased competition. The findings provide decisive experimental confirmation that maintaining the maximum number of sensitive cells can be used to contain resistance when the size of the population is sufficiently large.
Experimental data corresponding to Figures in Hansen et al, Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations, PLOS Biology, 2020.
National Institute of General Medical Sciences, Award: 1R35GM124875
National Science Foundation, Award: 1553028