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

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

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

Background: Artificial 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. Results: To 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. Conclusions: Our 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.

Original languageEnglish
Article number202
JournalMicrobiome
Volume6
Issue number1
DOIs
StatePublished - 12 Nov 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Artificial gut
  • Bayesian statistics
  • Bioreactor
  • Compositional data
  • Metagenomics
  • Microbiome
  • Time series analysis

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