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Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics

BACKGROUND: Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in h...

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Autores principales: Fujita, Hiroaki, Ushio, Masayuki, Suzuki, Kenta, Abe, Masato S., Yamamichi, Masato, Iwayama, Koji, Canarini, Alberto, Hayashi, Ibuki, Fukushima, Keitaro, Fukuda, Shinji, Kiers, E. Toby, Toju, Hirokazu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052866/
https://www.ncbi.nlm.nih.gov/pubmed/36978146
http://dx.doi.org/10.1186/s40168-023-01474-5
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author Fujita, Hiroaki
Ushio, Masayuki
Suzuki, Kenta
Abe, Masato S.
Yamamichi, Masato
Iwayama, Koji
Canarini, Alberto
Hayashi, Ibuki
Fukushima, Keitaro
Fukuda, Shinji
Kiers, E. Toby
Toju, Hirokazu
author_facet Fujita, Hiroaki
Ushio, Masayuki
Suzuki, Kenta
Abe, Masato S.
Yamamichi, Masato
Iwayama, Koji
Canarini, Alberto
Hayashi, Ibuki
Fukushima, Keitaro
Fukuda, Shinji
Kiers, E. Toby
Toju, Hirokazu
author_sort Fujita, Hiroaki
collection PubMed
description BACKGROUND: Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. METHODS: We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS: We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS: The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-023-01474-5.
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spelling pubmed-100528662023-03-30 Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics Fujita, Hiroaki Ushio, Masayuki Suzuki, Kenta Abe, Masato S. Yamamichi, Masato Iwayama, Koji Canarini, Alberto Hayashi, Ibuki Fukushima, Keitaro Fukuda, Shinji Kiers, E. Toby Toju, Hirokazu Microbiome Research BACKGROUND: Microbiome dynamics are both crucial indicators and potential drivers of human health, agricultural output, and industrial bio-applications. However, predicting microbiome dynamics is notoriously difficult because communities often show abrupt structural changes, such as “dysbiosis” in human microbiomes. METHODS: We integrated theoretical frameworks and empirical analyses with the aim of anticipating drastic shifts of microbial communities. We monitored 48 experimental microbiomes for 110 days and observed that various community-level events, including collapse and gradual compositional changes, occurred according to a defined set of environmental conditions. We analyzed the time-series data based on statistical physics and non-linear mechanics to describe the characteristics of the microbiome dynamics and to examine the predictability of major shifts in microbial community structure. RESULTS: We confirmed that the abrupt community changes observed through the time-series could be described as shifts between “alternative stable states“ or dynamics around complex attractors. Furthermore, collapses of microbiome structure were successfully anticipated by means of the diagnostic threshold defined with the “energy landscape” analysis of statistical physics or that of a stability index of nonlinear mechanics. CONCLUSIONS: The results indicate that abrupt microbiome events in complex microbial communities can be forecasted by extending classic ecological concepts to the scale of species-rich microbial systems. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40168-023-01474-5. BioMed Central 2023-03-29 /pmc/articles/PMC10052866/ /pubmed/36978146 http://dx.doi.org/10.1186/s40168-023-01474-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fujita, Hiroaki
Ushio, Masayuki
Suzuki, Kenta
Abe, Masato S.
Yamamichi, Masato
Iwayama, Koji
Canarini, Alberto
Hayashi, Ibuki
Fukushima, Keitaro
Fukuda, Shinji
Kiers, E. Toby
Toju, Hirokazu
Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title_full Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title_fullStr Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title_full_unstemmed Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title_short Alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
title_sort alternative stable states, nonlinear behavior, and predictability of microbiome dynamics
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052866/
https://www.ncbi.nlm.nih.gov/pubmed/36978146
http://dx.doi.org/10.1186/s40168-023-01474-5
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