IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1005364.html
   My bibliography  Save this article

Two dynamic regimes in the human gut microbiome

Author

Listed:
  • Sean M Gibbons
  • Sean M Kearney
  • Chris S Smillie
  • Eric J Alm

Abstract

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.Author summary: Dynamics reveal crucial information about how a system functions. In this study, we develop an approach for disentangling two types of dynamics within the human gut microbiome. We find that autoregressive dynamics involve recovery from large deviations in community structure. These recovery processes appear to involve the blooming of facultative anaerobes and aerotolerant taxa, likely due to transient shifts in redox potential, followed by re-establishment of obligate anaerobes. Non-autoregressive dynamics carry a strong phylogenetic signal, wherein highly related taxa fluctuate coherently. These non-autoregressive dynamics appear to be driven by external, non-autoregressive variables like diet. We find that most of the community variance is driven by day-to-day fluctuations in the environment, with occasional autoregressive dynamics as the system recovers from larger shocks. Despite frequently observed disruptions to the gut ecosystem, there exists a returning force that continually pushes the gut microbiome back towards its steady-state configuration.

Suggested Citation

  • Sean M Gibbons & Sean M Kearney & Chris S Smillie & Eric J Alm, 2017. "Two dynamic regimes in the human gut microbiome," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-20, February.
  • Handle: RePEc:plo:pcbi00:1005364
    DOI: 10.1371/journal.pcbi.1005364
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005364
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1005364&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1005364?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lawrence A. David & Corinne F. Maurice & Rachel N. Carmody & David B. Gootenberg & Julie E. Button & Benjamin E. Wolfe & Alisha V. Ling & A. Sloan Devlin & Yug Varma & Michael A. Fischbach & Sudha B. , 2014. "Diet rapidly and reproducibly alters the human gut microbiome," Nature, Nature, vol. 505(7484), pages 559-563, January.
    2. Ian Holmes & Keith Harris & Christopher Quince, 2012. "Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    3. Ewa M. Syczewska, 2010. "Empirical power of the Kwiatkowski-Phillips-Schmidt-Shin test," Working Papers 45, Department of Applied Econometrics, Warsaw School of Economics.
    4. Amir Bashan & Travis E. Gibson & Jonathan Friedman & Vincent J. Carey & Scott T. Weiss & Elizabeth L. Hohmann & Yang-Yu Liu, 2016. "Universality of human microbial dynamics," Nature, Nature, vol. 534(7606), pages 259-262, June.
    5. Peter J. Turnbaugh & Ruth E. Ley & Micah Hamady & Claire M. Fraser-Liggett & Rob Knight & Jeffrey I. Gordon, 2007. "The Human Microbiome Project," Nature, Nature, vol. 449(7164), pages 804-810, October.
    6. Ruth E. Ley & Peter J. Turnbaugh & Samuel Klein & Jeffrey I. Gordon, 2006. "Human gut microbes associated with obesity," Nature, Nature, vol. 444(7122), pages 1022-1023, December.
    7. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    8. Charles K Fisher & Pankaj Mehta, 2014. "Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-10, July.
    9. Tanya Yatsunenko & Federico E. Rey & Mark J. Manary & Indi Trehan & Maria Gloria Dominguez-Bello & Monica Contreras & Magda Magris & Glida Hidalgo & Robert N. Baldassano & Andrey P. Anokhin & Andrew C, 2012. "Human gut microbiome viewed across age and geography," Nature, Nature, vol. 486(7402), pages 222-227, June.
    10. Tao Ding & Patrick D. Schloss, 2014. "Dynamics and associations of microbial community types across the human body," Nature, Nature, vol. 509(7500), pages 357-360, May.
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    12. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    13. Shiri Freilich & Raphy Zarecki & Omer Eilam & Ella Shtifman Segal & Christopher S. Henry & Martin Kupiec & Uri Gophna & Roded Sharan & Eytan Ruppin, 2011. "Competitive and cooperative metabolic interactions in bacterial communities," Nature Communications, Nature, vol. 2(1), pages 1-7, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Jie & Shen, Xuzhu & Li, YaoTang, 2021. "Modeling the temporal dynamics of gut microbiota from a local community perspective," Ecological Modelling, Elsevier, vol. 460(C).
    2. Hannaford, Naomi E. & Heaps, Sarah E. & Nye, Tom M.W. & Curtis, Thomas P. & Allen, Ben & Golightly, Andrew & Wilkinson, Darren J., 2023. "A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    3. Joe J. Lim & Christian Diener & James Wilson & Jacob J. Valenzuela & Nitin S. Baliga & Sean M. Gibbons, 2023. "Growth phase estimation for abundant bacterial populations sampled longitudinally from human stool metagenomes," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Liat Shenhav & Ori Furman & Leah Briscoe & Mike Thompson & Justin D Silverman & Itzhak Mizrahi & Eran Halperin, 2019. "Modeling the temporal dynamics of the gut microbial community in adults and infants," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-21, June.
    5. Tyler A Joseph & Liat Shenhav & Joao B Xavier & Eran Halperin & Itsik Pe’er, 2020. "Compositional Lotka-Volterra describes microbial dynamics in the simplex," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-22, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lena Takayasu & Wataru Suda & Eiichiro Watanabe & Shinji Fukuda & Kageyasu Takanashi & Hiroshi Ohno & Misako Takayasu & Hideki Takayasu & Masahira Hattori, 2017. "A 3-dimensional mathematical model of microbial proliferation that generates the characteristic cumulative relative abundance distributions in gut microbiomes," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-20, August.
    2. Doris Vandeputte & Lindsey Commer & Raul Y. Tito & Gunter Kathagen & João Sabino & Séverine Vermeire & Karoline Faust & Jeroen Raes, 2021. "Temporal variability in quantitative human gut microbiome profiles and implications for clinical research," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
    4. Edoardo Pasolli & Duy Tin Truong & Faizan Malik & Levi Waldron & Nicola Segata, 2016. "Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-26, July.
    5. Sarah L Hagerty & Kent E Hutchison & Christopher A Lowry & Angela D Bryan, 2020. "An empirically derived method for measuring human gut microbiome alpha diversity: Demonstrated utility in predicting health-related outcomes among a human clinical sample," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-21, March.
    6. Mishra, Aditya & Müller, Christian L., 2022. "Robust regression with compositional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    7. Kumar P Mainali & Sharon Bewick & Peter Thielen & Thomas Mehoke & Florian P Breitwieser & Shishir Paudel & Arjun Adhikari & Joshua Wolfe & Eric V Slud & David Karig & William F Fagan, 2017. "Statistical analysis of co-occurrence patterns in microbial presence-absence datasets," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    8. Rajita Menon & Vivek Ramanan & Kirill S Korolev, 2018. "Interactions between species introduce spurious associations in microbiome studies," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-20, January.
    9. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    10. Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
    11. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    12. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    13. Margherita Giuzio, 2017. "Genetic algorithm versus classical methods in sparse index tracking," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 243-256, November.
    14. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    15. Wang, Qiao & Zhou, Wei & Cheng, Yonggang & Ma, Gang & Chang, Xiaolin & Miao, Yu & Chen, E, 2018. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 120-145.
    16. Dmitriy Drusvyatskiy & Adrian S. Lewis, 2018. "Error Bounds, Quadratic Growth, and Linear Convergence of Proximal Methods," Mathematics of Operations Research, INFORMS, vol. 43(3), pages 919-948, August.
    17. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    18. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    19. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    20. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1005364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.