Cargando…
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...
Autores principales: | , , , , , , , , , , , |
---|---|
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 |
_version_ | 1785015256273125376 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10052866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT fujitahiroaki alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT ushiomasayuki alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT suzukikenta alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT abemasatos alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT yamamichimasato alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT iwayamakoji alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT canarinialberto alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT hayashiibuki alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT fukushimakeitaro alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT fukudashinji alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT kiersetoby alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics AT tojuhirokazu alternativestablestatesnonlinearbehaviorandpredictabilityofmicrobiomedynamics |