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

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• Artificial gut models are valuable for studying human-associated microbiota.
• Key challenges include understanding microbiota variation timescales and drivers.
• Analytical hurdles include estimating technical variation and lack of benchmark datasets.

Purpose of the Study:

• To develop a framework for analyzing microbiota dynamics in artificial gut models.
• To quantify the ratio of biological to technical variation at different sampling frequencies.
• To investigate the replicability of microbiota trajectories and identify dynamic patterns.

Main Methods:

• Developed a modeling framework using multinomial logistic-normal dynamic linear models (MALLARDs).
• Performed dense longitudinal sampling of four replicate artificial human guts over one month.
• Analyzed variation sources and microbiota temporal dynamics.

Main Results:

• At hourly sampling, 76% of variation was technical, potentially skewing covariation analysis.
• Replicate artificial guts showed reproducible trajectories post-disruption.
• Observed irregular sub-daily oscillations in Enterobacteriaceae within all replicate vessels.

Conclusions:

• Technical variation from sample processing can mask biological signals in artificial gut studies.
• Human gut microbiota can fluctuate on sub-daily timescales independently of a host.
• The developed approach can improve the design and analysis of in vivo longitudinal microbiota studies.