Stochastic bacterial population dynamics restrict the establishment of antibiotic resistance from single cells

Helen K. Alexander, R. Craig MacLean

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

A better understanding of how antibiotic exposure impacts the evolution of resistance in bacterial populations is crucial for designing more sustainable treatment strategies. The conventional approach to this question is to measure the range of concentrations over which resistant strain(s) are selectively favoured over a sensitive strain. Here, we instead investigate how antibiotic concentration impacts the initial establishment of resistance from single cells, mimicking the clonal expansion of a resistant lineage following mutation or horizontal gene transfer. Using two Pseudomonas aeruginosa strains carrying resistance plasmids, we show that single resistant cells have <5% probability of detectable outgrowth at antibiotic concentrations as low as 1/8th of the resistant strain’s minimum inhibitory concentration (MIC). This low probability of establishment is due to detrimental effects of antibiotics on resistant cells, coupled with the inherently stochastic nature of cell division and death on the single-cell level, which leads to loss of many nascent resistant lineages. Our findings suggest that moderate doses of antibiotics, well below the MIC of resistant strains, may effectively restrict de novo emergence of resistance even though they cannot clear already-large resistant populations.
Original languageEnglish
Pages (from-to)19455-19464
Number of pages10
JournalProceedings of the National Academy of Sciences (PNAS)
Issue number32
Early online date23 Jul 2020
Publication statusPublished - 23 Jul 2020

Keywords / Materials (for Non-textual outputs)

  • antimicrobial resistance
  • pseudomonas aeruginosa
  • minimum inhibitory concentration (MIC)
  • mutant selection window
  • inoculum effect
  • mathematical model
  • extinction probability
  • demographic stochasticity
  • evolutionary rescue


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