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Modelling microbiome recovery after antibiotics using a stability landscape framework.

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Summary
This summary is machine-generated.

Antibiotic treatment severely impacts the human microbiome, potentially causing long-term shifts to different states. This study introduces a quantitative model to analyze these antibiotic-induced microbiome changes.

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Area of Science:

  • Microbiology
  • Computational Biology
  • Systems Biology

Background:

  • Antibiotics are major disruptors of the human microbiome, reducing diversity and potentially causing dysbiosis.
  • The 'stability landscape' concept qualitatively describes microbiome shifts but lacks quantitative modeling.
  • Previous models have not fully incorporated the stability landscape concept for antibiotic effects.

Purpose of the Study:

  • To develop and validate a quantitative model of antibiotic effects on the human microbiome based on the stability landscape concept.
  • To analyze the long-term impact of antibiotics on gut and oral microbiomes.
  • To investigate transitions to alternative microbiome states post-antibiotic treatment.

Main Methods:

  • Developed a simple, analytical impulse-response model with three parameters.
  • Applied a Bayesian framework to fit the model to existing microbiome data.
  • Utilized Bayesian model selection to compare model performance and identify significant transitions.

Main Results:

  • The quantitative stability landscape model successfully explained real microbiome data.
  • Evidence supports long-term transitions to alternative microbiome states following certain antibiotic courses.
  • Model parameters could be compared across different antibiotics and microbiome sites.

Conclusions:

  • Quantitative stability landscape models offer a powerful new framework for understanding antibiotic-microbiome interactions.
  • Antibiotic perturbations can induce lasting shifts in microbiome composition and stability.
  • Further research into quantitative microbiome modeling is warranted.