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<p>The microbiome actively influences antimicrobial resistance (AMR) dynamics by shaping both ecological and evolutionary processes. However, the extent of its role in resistance emergence, transmission and persistence remains unclear. Traditional AMR research has mainly focused on genetic mechanisms and pathogen-level dynamics. In contrast, the intersection of AMR and the microbiome, including resistance-gene reservoirs, microbial competition and community-mediated selection, remains poorly represented, especially in a modelling context. Here we present a structured framework for incorporating microbiome–AMR interactions into predictive models. We identify key microbiome-mediated processes shaping AMR across different levels of complexity, describe how these can be quantitatively integrated into models, and identify critical data gaps that limit current approaches. By bridging microbiome ecology, AMR biology and mathematical modelling, we set out research priorities and strategies to improve resistance prediction and guide microbiome-targeted interventions.</p>

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Modelling the role of the microbiome in antimicrobial resistance across scales

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Lisa Pagani,
  • Ricardo León-Sampedro,
  • Massimo Amicone,
  • Burcu Tepekule,
  • Christopher Witzany,
  • Silvio D. Brugger,
  • Marjon G. J. de Vos,
  • Sara Mitri,
  • Erik Bakkeren,
  • Michael J. Bottery,
  • Lulla Opatowski,
  • Gabriel E. Leventhal,
  • Karoline Faust,
  • Lucas Böttcher,
  • Sonja Lehtinen,
  • Roger D. Kouyos,
  • Sebastian Bonhoeffer

摘要

The microbiome actively influences antimicrobial resistance (AMR) dynamics by shaping both ecological and evolutionary processes. However, the extent of its role in resistance emergence, transmission and persistence remains unclear. Traditional AMR research has mainly focused on genetic mechanisms and pathogen-level dynamics. In contrast, the intersection of AMR and the microbiome, including resistance-gene reservoirs, microbial competition and community-mediated selection, remains poorly represented, especially in a modelling context. Here we present a structured framework for incorporating microbiome–AMR interactions into predictive models. We identify key microbiome-mediated processes shaping AMR across different levels of complexity, describe how these can be quantitatively integrated into models, and identify critical data gaps that limit current approaches. By bridging microbiome ecology, AMR biology and mathematical modelling, we set out research priorities and strategies to improve resistance prediction and guide microbiome-targeted interventions.