4.6 Article

Modeling Polygenic Antibiotic Resistance Evolution in Biofilms

期刊

FRONTIERS IN MICROBIOLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fmicb.2022.916035

关键词

biofilm recalcitrance; population genetics; antibiotic resistance; resistance evolution; mathematical modeling; PK; PD

资金

  1. Volkswagen Foundation [96517, 96695]

向作者/读者索取更多资源

The resistance of biofilms to antimicrobials is a complex phenomenon involving genetic, physical, and physiological changes as well as increased genetic diversity. A polygenic model has been developed to account for multiple phenotypic mechanisms and can be used to predict the emergence of resistance and its population genetic consequences.
The recalcitrance of biofilms to antimicrobials is a multi-factorial phenomenon, including genetic, physical, and physiological changes. Individually, they often cannot account for biofilm recalcitrance. However, their combination can increase the minimal inhibitory concentration of antibiotics needed to kill bacterial cells by three orders of magnitude, explaining bacterial survival under otherwise lethal drug treatment. The relative contributions of these factors depend on the specific antibiotics, bacterial strain, as well as environmental and growth conditions. An emerging population genetic property-increased biofilm genetic diversity-further enhances biofilm recalcitrance. Here, we develop a polygenic model of biofilm recalcitrance accounting for multiple phenotypic mechanisms proposed to explain biofilm recalcitrance. The model can be used to generate predictions about the emergence of resistance-its timing and population genetic consequences. We use the model to simulate various treatments and experimental setups. Our simulations predict that the evolution of resistance is impaired in biofilms at low antimicrobial concentrations while it is facilitated at higher concentrations. In scenarios that allow bacteria exchange between planktonic and biofilm compartments, the evolution of resistance is further facilitated compared to scenarios without exchange. We compare these predictions to published experimental observations.

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