ArticleJournal of the Royal Society, Interface2019
Diffusion-driven enhancement of the antibiotic resistance selection window.
Article in Journal of the Royal Society, Interface, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- In Vitro Resistance-Predicting Studies and In Vitro Resistance-Related Parameters-A Hit-to-Lead Perspective.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Diverse mutant selection windows shape spatial heterogeneity in evolving populations.PLoS computational biology · 2024Article
- Diverse mutant selection windows shape spatial heterogeneity in evolving populations.bioRxiv : the preprint server for biology · 2023Article
- The risk of drug resistance during long-acting antimicrobial therapy.Proceedings. Biological sciences · 2022Article
- Evolutionary History and Strength of Selection Determine the Rate of Antibiotic Resistance Adaptation.Molecular biology and evolution · 2022Article
- Fighting microbial pathogens by integrating host ecosystem interactions and evolution.BioEssays : news and reviews in molecular, cellular and developmental biology · 2021Article
- Better together-Gut microbesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current crisis of antimicrobial resistance in clinically relevant pathogens has highlighted our limited understanding of the ecological and evolutionary forces that drive drug resistance adaptation. For instance, although human tissues are highly heterogeneous, most of our mechanistic understanding about antibiotic resistance evolution is based on constant and well-mixed environmental conditions. A consequence of considering spatial heterogeneity is that, even if antibiotics are prescribed at high dosages, the penetration of drug molecules through tissues inevitably produces antibiotic gradients, exposing bacterial populations to a range of selective pressures and generating a dynamic fitness landscape that changes in space and time. In this paper, we will use a combination of mathematical modelling and computer simulations to study the population dynamics of susceptible and resistant strains competing for resources in a network of micro-environments with varying degrees of connectivity. Our main result is that highly connected environments increase diffusion of drug molecules, enabling resistant phenotypes to colonize a larger number of spatial locations. We validated this theoretical result by culturing fluorescently labelled
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Registered trials
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