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

Publication ,  Journal Article
Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA
Published in: Microbiome
November 12, 2018

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.

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Published In

Microbiome

DOI

EISSN

2049-2618

Publication Date

November 12, 2018

Volume

6

Issue

1

Start / End Page

202

Location

England

Related Subject Headings

  • Models, Anatomic
  • Linear Models
  • Humans
  • Gastrointestinal Tract
  • Gastrointestinal Microbiome
  • Enterobacteriaceae
  • Bacteroides
  • Artificial Organs
  • 1108 Medical Microbiology
  • 0605 Microbiology
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Silverman, J. D., Durand, H. K., Bloom, R. J., Mukherjee, S., & David, L. A. (2018). Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. Microbiome, 6(1), 202. https://doi.org/10.1186/s40168-018-0584-3
Silverman, Justin D., Heather K. Durand, Rachael J. Bloom, Sayan Mukherjee, and Lawrence A. David. “Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.Microbiome 6, no. 1 (November 12, 2018): 202. https://doi.org/10.1186/s40168-018-0584-3.
Silverman JD, Durand HK, Bloom RJ, Mukherjee S, David LA. Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. Microbiome. 2018 Nov 12;6(1):202.
Silverman, Justin D., et al. “Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.Microbiome, vol. 6, no. 1, Nov. 2018, p. 202. Pubmed, doi:10.1186/s40168-018-0584-3.
Silverman JD, Durand HK, Bloom RJ, Mukherjee S, David LA. Dynamic linear models guide design and analysis of microbiota studies within artificial human guts. Microbiome. 2018 Nov 12;6(1):202.
Journal cover image

Published In

Microbiome

DOI

EISSN

2049-2618

Publication Date

November 12, 2018

Volume

6

Issue

1

Start / End Page

202

Location

England

Related Subject Headings

  • Models, Anatomic
  • Linear Models
  • Humans
  • Gastrointestinal Tract
  • Gastrointestinal Microbiome
  • Enterobacteriaceae
  • Bacteroides
  • Artificial Organs
  • 1108 Medical Microbiology
  • 0605 Microbiology