Dynamic linear models guide design and analysis of microbiota studies within artificial human guts View Full Text


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Article Info

DATE

2018-11-12

AUTHORS

Justin D. Silverman, Heather K. Durand, Rachael J. Bloom, Sayan Mukherjee, Lawrence A. David

ABSTRACT

BackgroundArtificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models’ fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets.ResultsTo address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of four replicate artificial human guts over the course of 1 month. The resulting analyses revealed how the ratio of biological variation to technical variation from sample processing depends on sampling frequency. In particular, we find that at hourly sampling frequencies, 76% of observed variation could be ascribed to technical sources, which could also skew the observed covariation between taxa. We also found that the artificial guts demonstrated replicable trajectories even after a recovery from a transient feed disruption. Additionally, we observed irregular sub-daily oscillatory dynamics associated with the bacterial family Enterobacteriaceae within all four replicate vessels.ConclusionsOur analyses suggest that, beyond variation due to sequence counting, technical variation from sample processing can obscure temporal variation from biological sources in artificial gut studies. Our analyses also supported hypotheses that human gut microbiota fluctuates on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo. More... »

PAGES

202

References to SciGraph publications

  • 2012-03-08. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2017-10-30. Cereal products derived from wheat, sorghum, rice and oats alter the infant gut microbiota in vitro in SCIENTIFIC REPORTS
  • 1989. Bayesian Forecasting and Dynamic Models in NONE
  • 2011-05-30. Moving pictures of the human microbiome in GENOME BIOLOGY
  • 2011-08-15. Temporal and spatial oscillations in bacteria in NATURE REVIEWS MICROBIOLOGY
  • 2017-10-02. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium in NATURE BIOTECHNOLOGY
  • 2015-09-30. Cultivation of stable, reproducible microbial communities from different fecal donors using minibioreactor arrays (MBRAs) in MICROBIOME
  • 2011-06-06. RNA-seq: technical variability and sampling in BMC GENOMICS
  • 2015-04-08. Temporal and technical variability of human gut metagenomes in GENOME BIOLOGY
  • 1986. The Statistical Analysis of Compositional Data in NONE
  • 1999-11. Biodiversity of plankton by species oscillations and chaos in NATURE
  • 2017-01-12. Intestinal microbiota as a tetrahydrobiopterin exogenous source in hph-1 mice in SCIENTIFIC REPORTS
  • 2009-02-25. Microbial awakenings in NATURE
  • 2010-04-11. QIIME allows analysis of high-throughput community sequencing data in NATURE METHODS
  • 2015-12-09. The microbiome quality control project: baseline study design and future directions in GENOME BIOLOGY
  • 2003-04. Isometric Logratio Transformations for Compositional Data Analysis in MATHEMATICAL GEOSCIENCES
  • 1993-05. Development of a 5-step multi-chamber reactor as a simulation of the human intestinal microbial ecosystem in APPLIED MICROBIOLOGY AND BIOTECHNOLOGY
  • 2008-02. Chaos in a long-term experiment with a plankton community in NATURE
  • 2010-09-14. Tackling the widespread and critical impact of batch effects in high-throughput data in NATURE REVIEWS GENETICS
  • 2014-07-25. Host lifestyle affects human microbiota on daily timescales in GENOME BIOLOGY
  • 2016. ggplot2, Elegant Graphics for Data Analysis in NONE
  • 2008-04-03. Characterizing mixed microbial population dynamics using time-series analysis in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
  • 2016-05-23. DADA2: High-resolution sample inference from Illumina amplicon data in NATURE METHODS
  • 2014-05-05. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis in MICROBIOME
  • 2017-11-22. Advances in Principal Balances for Compositional Data in MATHEMATICAL GEOSCIENCES
  • 2011-01-13. Microbial seed banks: the ecological and evolutionary implications of dormancy in NATURE REVIEWS MICROBIOLOGY
  • 2017-09-20. Strains, functions and dynamics in the expanded Human Microbiome Project in NATURE
  • 2011-02-24. Reproducibility and quantitation of amplicon sequencing-based detection in THE ISME JOURNAL: MULTIDISCIPLINARY JOURNAL OF MICROBIAL ECOLOGY
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    http://scigraph.springernature.com/pub.10.1186/s40168-018-0584-3

    DOI

    http://dx.doi.org/10.1186/s40168-018-0584-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1109820176

    PUBMED

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