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

Humans Microbiota
Alternative stable states Biodiversity Biological communities Chaos Community collapse Community stability Dysbiosis Empirical dynamic modeling Microbiome dynamics Non-linear dynamics

Journal

Microbiome
ISSN: 2049-2618
Titre abrégé: Microbiome
Pays: England
ID NLM: 101615147

Informations de publication

Date de publication:
29 03 2023
Historique:
received: 13 09 2022
accepted: 24 01 2023
medline: 30 3 2023
entrez: 28 3 2023
pubmed: 29 3 2023
Statut: epublish

Résumé

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. 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. 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. 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. Video Abstract.

Sections du résumé

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. Video Abstract.

Identifiants

DOI: 10.1186/s40168-023-01474-5 PMID: 36978146 PMC: PMC10052866
pubmed: 36978146
doi: 10.1186/s40168-023-01474-5
pii: 10.1186/s40168-023-01474-5
pmc: PMC10052866
doi:

Types de publication

Video-Audio Media Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

63

Informations de copyright

© 2023. The Author(s).

Références

Proc Natl Acad Sci U S A. 2014 May 20;111(20):E2149-56
pubmed: 24778240
Proc Biol Sci. 2022 Feb 23;289(1969):20212690
pubmed: 35193401
Science. 2018 Sep 7;361(6406):
pubmed: 30190378
Nat Commun. 2014 Aug 28;5:4765
pubmed: 25163855
Nature. 2001 Oct 11;413(6856):591-6
pubmed: 11595939
Nature. 2011 May 12;473(7346):174-80
pubmed: 21508958
Ecol Lett. 2021 Dec;24(12):2763-2774
pubmed: 34601794
Proc Biol Sci. 2016 Jan 13;283(1822):
pubmed: 26763700
Nature. 2012 Jun 13;486(7402):207-14
pubmed: 22699609
Ecology. 2016 Dec;97(12):3308-3314
pubmed: 27912022
Proc Biol Sci. 2017 May 31;284(1855):
pubmed: 28539525
Science. 2017 Apr 21;356(6335):315-319
pubmed: 28428425
Science. 2012 Oct 26;338(6106):496-500
pubmed: 22997134
Nat Methods. 2016 Jul;13(7):581-3
pubmed: 27214047
Nat Plants. 2018 May;4(5):247-257
pubmed: 29725101
Science. 2012 Jun 8;336(6086):1255-62
pubmed: 22674335
Nature. 2019 Oct;574(7779):543-548
pubmed: 31645720
Sci Adv. 2020 Feb 19;6(8):eaay8676
pubmed: 32128414
J Microbiol Methods. 2013 Jun;93(3):203-5
pubmed: 23541956
Acta Paediatr. 2007 May;96(5):644-7
pubmed: 17376185
Appl Environ Microbiol. 2007 Aug;73(16):5261-7
pubmed: 17586664
Nature. 1990 Apr 19;344(6268):734-41
pubmed: 2330029
Nature. 2014 Jan 23;505(7484):559-63
pubmed: 24336217
Nature. 2018 Oct;562(7728):589-594
pubmed: 30356183
Nat Methods. 2008 Mar;5(3):235-7
pubmed: 18264105
Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4516-22
pubmed: 20534432
Nucleic Acids Res. 2013 Jan;41(Database issue):D590-6
pubmed: 23193283
Nat Rev Genet. 2012 Mar 13;13(4):260-70
pubmed: 22411464
Nature. 2005 May 19;435(7040):336-40
pubmed: 15902256
Science. 2011 Oct 7;334(6052):105-8
pubmed: 21885731
Nat Rev Microbiol. 2019 Dec;17(12):754-763
pubmed: 31578461
Nature. 2008 Feb 14;451(7180):822-5
pubmed: 18273017
Commun Biol. 2019 May 13;2:179
pubmed: 31098412
Nature. 2018 Feb 15;554(7692):360-363
pubmed: 29414940
PLoS Biol. 2017 Mar 28;15(3):e2001793
pubmed: 28350798
Front Neuroinform. 2014 Feb 25;8:12
pubmed: 24611044
Nucleic Acids Res. 2001 Jan 1;29(1):181-4
pubmed: 11125085
Nat Ecol Evol. 2019 Jun;3(6):912-918
pubmed: 31036898
Nat Commun. 2014 Jul 08;5:4344
pubmed: 25003530
Microb Ecol Health Dis. 2015 Feb 02;26:26191
pubmed: 25651997
Science. 2018 Aug 3;361(6401):469-474
pubmed: 30072533
Nat Methods. 2013 Oct;10(10):999-1002
pubmed: 23995388
Science. 2018 Mar 9;359(6380):1151-1156
pubmed: 29590046
Microbiome. 2013 Dec 02;1(1):29
pubmed: 24451163

Auteurs

Hiroaki Fujita (H)

Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan. fujita.h@ecology.kyoto-u.ac.jp.

Masayuki Ushio (M)

Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.
Department of Ocean Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China.

Kenta Suzuki (K)

Integrated Bioresource Information Division, BioResource Research Center, RIKEN, Tsukuba, Ibaraki, 305-0074, Japan.

Masato S Abe (MS)

Faculty of Culture and Information Science, Doshisha University, Kyotanabe, Kyoto, 610-0321, Japan.

Masato Yamamichi (M)

School of Biological Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia.
Department of International Health and Medical Anthropology, Institute of Tropical Medicine, Nagasaki University, Nagasaki, 852-8523, Japan.

Koji Iwayama (K)

Faculty of Data Science, Shiga University, Hikone, 522-8522, Japan.

Alberto Canarini (A)

Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.

Ibuki Hayashi (I)

Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan.

Keitaro Fukushima (K)

Faculty of Food and Agricultural Sciences, Fukushima University, Kanayagawa 1, Fukushima, Fukushima, 960-1296, Japan.

Shinji Fukuda (S)

Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0052, Japan.
Gut Environmental Design Group, Kanagawa Institute of Industrial Science and Technology, Kawasaki, Kanagawa, 210-0821, Japan.
Transborder Medical Research Center, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan.

E Toby Kiers (ET)

Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

Hirokazu Toju (H)

Center for Ecological Research, Kyoto University, Otsu, Shiga, 520-2133, Japan. toju.hirokazu.4c@kyoto-u.ac.jp.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

Recherchemedicale.com › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Alternative stable states, nonlinear behavior,...
Recherchemedicale.com › Environnement et santé publique › Environnement › Écosystème › Biodiversité › Biote › Microbiote › Alternative stable states, nonlinear behavior,...