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

Journal article

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 favored 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 one-eighth 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.

Authors: Alexander, H; Maclean, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: National Academy of Sciences
• Journal: Proceedings of the National Academy of Sciences
• Volume: 117
• Issue: 32
• Pages: 19455-19464
• Publication date: 2020-07-23
• Acceptance date: 2020-06-24
• DOI: 10.1073/pnas.1919672117
• EISSN: 1091-6490
• ISSN: 0027-8424
• Language: English
• Keywords: FFR; antimicrobial resistance; mutant selection window; Pseudomonas aeruginosa; minimum inhibitory concentration 31 (MIC); extinction probability; demographic stochasticity; evolutionary rescue; inoculum effect; mathematical model