Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.
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.
['Silverman|Justin D|JD|', 'Durand|Heather K|HK|', 'Bloom|Rachael J|RJ|', 'Mukherjee|Sayan|S|', 'David|Lawrence A|LA|0000-0002-3570-4767']
D001187:Artificial Organs D001439:Bacteroides D004755:Enterobacteriaceae D000069196:Gastrointestinal Microbiome D041981:Gastrointestinal Tract D006801:Humans D016014:Linear Models D008953:Models, Anatomic
Artificial gut Bayesian statistics Bioreactor Compositional data Metagenomics Microbiome Time series analysis