ArticleProceedings of the National Academy of Sciences of the United States of America2019
Quantifying the impact of treatment history on plasmid-mediated resistance evolution in human gut microbiota.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Modelling the role of the microbiome in antimicrobial resistance across scales.Nature microbiology · 2026Review
- Genetic traits of IncK2 plasmids and theMicrobial genomics · 2026Article
- Computational and in vitro evaluation of probiotic treatments for nasalProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Urinary Plasmids Reduce Permissivity to Coliphage Infection.Microbiology spectrum · 2023Article
- A mathematician's guide to plasmids: an introduction to plasmid biology for modellers.Microbiology (Reading, England) · 2023Review
- A plasmid network from the gut microbiome of semi-isolated human groups reveals unique and shared metabolic and virulence traits.Scientific reports · 2022Article
- Profiling the plasmid conjugation potential of urinaryMicrobial genomics · 2022Article
- Models for Gut-Mediated Horizontal Gene Transfer by Bacterial Plasmid Conjugation.Frontiers in microbiology · 2022Review
- Characterizing Plasmids in Bacteria Species Relevant to Urinary Health.Microbiology spectrum · 2021Article
- Article
- Mathematical Models of Plasmid Population Dynamics.Frontiers in microbiology · 2021Review
- Modeling Competitive Mixtures With the Lotka-Volterra Framework for More Complex Fitness Assessment Between Strains.Frontiers in microbiology · 2020Article
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Authors and funding
4 authors.
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Abstract
To understand how antibiotic use affects the risk of a resistant infection, we present a computational model of the population dynamics of gut microbiota including antibiotic resistance-conferring plasmids. We then describe how this model is parameterized based on published microbiota data. Finally, we investigate how treatment history affects the prevalence of resistance among opportunistic enterobacterial pathogens. We simulate treatment histories and identify which properties of prior antibiotic exposure are most influential in determining the prevalence of resistance. We find that resistance prevalence can be predicted by 3 properties, namely the total days of drug exposure, the duration of the drug-free period after last treatment, and the center of mass of the treatment pattern. Overall this work provides a framework for capturing the role of the microbiome in the selection of antibiotic resistance and highlights the role of treatment history for the prevalence of resistance.
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