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Modelling microbiome recovery after antibiotics using a stability landscape framework

Liam P. Shaw, Hassan Bassam, Chris P. Barnes, A. Sarah Walker, Nigel Klein, Francois Balloux
doi: https://doi.org/10.1101/222398
Liam P. Shaw
1UCL Genetics Institute, UCL, London
2CoMPLEX, UCL, London
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  • For correspondence: liam.philip.shaw@gmail.com
Hassan Bassam
2CoMPLEX, UCL, London
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Chris P. Barnes
1UCL Genetics Institute, UCL, London
4Cell and Developmental Biology, UCL, London
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A. Sarah Walker
5MRC Clinical Trials Unit at UCL, UCL, London
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Nigel Klein
3UCL Institute of Child Health, UCL, London
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Francois Balloux
1UCL Genetics Institute, UCL, London
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Abstract

Treatment with antibiotics is one of the most extreme perturbations to the human microbiome. Even standard courses of antibiotics dramatically reduce the microbiome’s diversity and can cause transitions to dysbiotic states. Conceptually, this is often described as a ‘stability landscape’: the microbiome sits in a landscape with multiple stable equilibria, and sufficiently strong perturbations can shift the microbiome from its normal equilibrium to another state. However, this picture is only qualitative and has not been incorporated in previous mathematical models of the effects of antibiotics. Here, we outline a simple quantitative model based on the stability landscape concept and demonstrate its success on real data. Our analytical impulse-response model has minimal assumptions with three parameters. We fit this model in a Bayesian framework to previously published data on the year-long effects of four common antibiotics (ciprofloxacin, clindamycin, minocycline, and amoxicillin) on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes. Furthermore, using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of ciprofloxacin and clindamycin in both the gut and salivary microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling.

Footnotes

  • (liam.philip.shaw{at}gmail.com)

  • (hassan.bassam.17{at}ucl.ac.uk)

  • (christopher.barnes{at}ucl.ac.uk)

  • (rmjlasw{at}ucl.ac.uk)

  • (n.klein{at}ucl.ac.uk)

  • (f.balloux{at}ucl.ac.uk)

  • Competing interests: The authors declare that they have no competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 20, 2018.
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Modelling microbiome recovery after antibiotics using a stability landscape framework
Liam P. Shaw, Hassan Bassam, Chris P. Barnes, A. Sarah Walker, Nigel Klein, Francois Balloux
bioRxiv 222398; doi: https://doi.org/10.1101/222398
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Modelling microbiome recovery after antibiotics using a stability landscape framework
Liam P. Shaw, Hassan Bassam, Chris P. Barnes, A. Sarah Walker, Nigel Klein, Francois Balloux
bioRxiv 222398; doi: https://doi.org/10.1101/222398

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