Signatures of ecological processes in microbial community time series View Full Text


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Article Info

DATE

2018-06-28

AUTHORS

Karoline Faust, Franziska Bauchinger, Béatrice Laroche, Sophie de Buyl, Leo Lahti, Alex D. Washburne, Didier Gonze, Stefanie Widder

ABSTRACT

BackgroundGrowth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.ResultsWe implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell’s neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.ConclusionsWe present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis. More... »

PAGES

120

References to SciGraph publications

  • 2016-08-16. Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets in SCIENTIFIC REPORTS
  • 2017-03-27. Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm in BMC GENOMICS
  • 2014-01-05. Gut microbiota metabolism of dietary fiber influences allergic airway disease and hematopoiesis in NATURE MEDICINE
  • 1972-08. Will a Large Complex System be Stable? in NATURE
  • 2011-03-24. Marine bacterial, archaeal and protistan association networks reveal ecological linkages in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2014-10-22. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile in NATURE
  • 2009. ggplot2, Elegant Graphics for Data Analysis in NONE
  • 2011-08-18. Defining seasonal marine microbial community dynamics in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2011-05-30. Moving pictures of the human microbiome in GENOME BIOLOGY
  • 1988-08. The variability of population densities in NATURE
  • 2016-03-24. Dynamic models of the complex microbial metapopulation of lake mendota in NPJ SYSTEMS BIOLOGY AND APPLICATIONS
  • 2017-06-06. Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis in NATURE COMMUNICATIONS
  • 2014-07-25. Host lifestyle affects human microbiota on daily timescales in GENOME BIOLOGY
  • 2012-05-30. Molecular ecological network analyses in BMC BIOINFORMATICS
  • 2015-08-21. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2012-02-19. Stability criteria for complex ecosystems in NATURE
  • 2016-06-03. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses in GENOME BIOLOGY
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    http://scigraph.springernature.com/pub.10.1186/s40168-018-0496-2

    DOI

    http://dx.doi.org/10.1186/s40168-018-0496-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1105203852

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29954432


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    36 schema:description BackgroundGrowth rates, interactions between community members, stochasticity, and immigration are important drivers of microbial community dynamics. In sequencing data analysis, such as network construction and community model parameterization, we make implicit assumptions about the nature of these drivers and thereby restrict model outcome. Despite apparent risk of methodological bias, the validity of the assumptions is rarely tested, as comprehensive procedures are lacking. Here, we propose a classification scheme to determine the processes that gave rise to the observed time series and to enable better model selection.ResultsWe implemented a three-step classification scheme in R that first determines whether dependence between successive time steps (temporal structure) is present in the time series and then assesses with a recently developed neutrality test whether interactions between species are required for the dynamics. If the first and second tests confirm the presence of temporal structure and interactions, then parameters for interaction models are estimated. To quantify the importance of temporal structure, we compute the noise-type profile of the community, which ranges from black in case of strong dependency to white in the absence of any dependency. We applied this scheme to simulated time series generated with the Dirichlet-multinomial (DM) distribution, Hubbell’s neutral model, the generalized Lotka-Volterra model and its discrete variant (the Ricker model), and a self-organized instability model, as well as to human stool microbiota time series. The noise-type profiles for all but DM data clearly indicated distinctive structures. The neutrality test correctly classified all but DM and neutral time series as non-neutral. The procedure reliably identified time series for which interaction inference was suitable. Both tests were required, as we demonstrated that all structured time series, including those generated with the neutral model, achieved a moderate to high goodness of fit to the Ricker model.ConclusionsWe present a fast and robust scheme to classify community structure and to assess the prevalence of interactions directly from microbial time series data. The procedure not only serves to determine ecological drivers of microbial dynamics, but also to guide selection of appropriate community models for prediction and follow-up analysis.
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