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Modelling microbiome recovery after antibiotics using a stability landscape framework
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 microb...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591120/ https://www.ncbi.nlm.nih.gov/pubmed/30877283 http://dx.doi.org/10.1038/s41396-019-0392-1 |
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author | Shaw, Liam P. Bassam, Hassan Barnes, Chris P. Walker, A. Sarah Klein, Nigel Balloux, Francois |
author_facet | Shaw, Liam P. Bassam, Hassan Barnes, Chris P. Walker, A. Sarah Klein, Nigel Balloux, Francois |
author_sort | Shaw, Liam P. |
collection | PubMed |
description | 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 data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling. |
format | Online Article Text |
id | pubmed-6591120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65911202019-09-15 Modelling microbiome recovery after antibiotics using a stability landscape framework Shaw, Liam P. Bassam, Hassan Barnes, Chris P. Walker, A. Sarah Klein, Nigel Balloux, Francois ISME J Article 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 data from a previous study of the year-long effects of short courses of four common antibiotics on the gut and oral microbiomes, allowing us to compare parameters between antibiotics and microbiomes, and further validate our model using data from another study looking at the impact of a combination of last-resort antibiotics on the gut microbiome. Using Bayesian model selection we find support for a long-term transition to an alternative microbiome state after courses of certain antibiotics in both the gut and oral microbiomes. Quantitative stability landscape frameworks are an exciting avenue for future microbiome modelling. Nature Publishing Group UK 2019-03-15 2019-07 /pmc/articles/PMC6591120/ /pubmed/30877283 http://dx.doi.org/10.1038/s41396-019-0392-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Shaw, Liam P. Bassam, Hassan Barnes, Chris P. Walker, A. Sarah Klein, Nigel Balloux, Francois Modelling microbiome recovery after antibiotics using a stability landscape framework |
title | Modelling microbiome recovery after antibiotics using a stability landscape framework |
title_full | Modelling microbiome recovery after antibiotics using a stability landscape framework |
title_fullStr | Modelling microbiome recovery after antibiotics using a stability landscape framework |
title_full_unstemmed | Modelling microbiome recovery after antibiotics using a stability landscape framework |
title_short | Modelling microbiome recovery after antibiotics using a stability landscape framework |
title_sort | modelling microbiome recovery after antibiotics using a stability landscape framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6591120/ https://www.ncbi.nlm.nih.gov/pubmed/30877283 http://dx.doi.org/10.1038/s41396-019-0392-1 |
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