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Plasmid-bacteria associations in the clinical context.

Laura Toribio-Celestino1, Alvaro San Millan2

  • 1Centro Nacional de Biotecnología (CNB-CSIC), Madrid, Spain.

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|May 15, 2025
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Antimicrobial resistance (AMR) is a major global health threat. Understanding plasmid-host interactions is key to controlling AMR spread, requiring integrated experimental and computational approaches.

Keywords:
antibiotic resistancebioinformaticsclinical microbiologyevolutionary biologyhorizontal gene transferplasmid

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Area of Science:

  • Microbiology
  • Evolutionary Biology
  • Public Health

Background:

  • Antimicrobial resistance (AMR) poses a significant global health challenge.
  • Plasmids are key drivers in the evolution and spread of AMR.
  • Factors determining the epidemiological success of plasmid-bacterium associations are not fully understood.

Purpose of the Study:

  • To review factors influencing plasmid-host associations in AMR.
  • To emphasize the importance of studying clinically relevant plasmid-host interactions.
  • To highlight the integration of experimental and computational methods for studying AMR dynamics.

Main Methods:

  • Review of existing literature on plasmid-host dynamics and AMR.
  • Discussion of factors influencing the epidemiological success of plasmid-bacterium associations.
  • Emphasis on integrating experimental research with bioinformatics and machine learning.

Main Results:

  • Certain plasmid-bacterium associations exhibit epidemiological success due to various influencing factors.
  • Studying plasmid-host interactions is critical for understanding AMR evolution and spread.
  • Integrated approaches combining experimental and computational tools are increasingly important.

Conclusions:

  • Understanding the drivers of successful plasmid-host associations is crucial for combating AMR.
  • Integrated research strategies are essential for dissecting molecular mechanisms and predicting future AMR trends.
  • This approach will aid in developing effective strategies to prevent and predict high-risk AMR associations.