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

Autor: Silverman JD; Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA.; Medical Scientist Training Program, Duke University, Durham, NC, 27708, USA.; Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA., Durand HK; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, 27708, USA., Bloom RJ; University Program in Genetics and Genomics, Duke University, Durham, NC, 27708, USA., Mukherjee S; Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA.; Departments of Statistical Science, Mathematics, Computer Science, Biostatistics & Bioinformatics, Duke University, Durham, NC, 27708, USA., David LA; Program in Computational Biology and Bioinformatics, Duke University, CIEMAS, Room 2171, 101 Science Drive, Box 3382, Durham, NC, 27708, USA. lawrence.david@duke.edu.; Center for Genomic and Computational Biology, Duke University, Durham, NC, 27708, USA. lawrence.david@duke.edu.; University Program in Genetics and Genomics, Duke University, Durham, NC, 27708, USA. lawrence.david@duke.edu.; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, 27708, USA. lawrence.david@duke.edu.
Jazyk: angličtina
Zdroj: Microbiome [Microbiome] 2018 Nov 12; Vol. 6 (1), pp. 202. Date of Electronic Publication: 2018 Nov 12.
DOI: 10.1186/s40168-018-0584-3
Abstrakt: 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.
Databáze: MEDLINE
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