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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 overcomin...

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Autores principales: Silverman, Justin D., Durand, Heather K., Bloom, Rachael J., Mukherjee, Sayan, David, Lawrence A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233358/
https://www.ncbi.nlm.nih.gov/pubmed/30419949
http://dx.doi.org/10.1186/s40168-018-0584-3
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author Silverman, Justin D.
Durand, Heather K.
Bloom, Rachael J.
Mukherjee, Sayan
David, Lawrence A.
author_facet Silverman, Justin D.
Durand, Heather K.
Bloom, Rachael J.
Mukherjee, Sayan
David, Lawrence A.
author_sort Silverman, Justin D.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0584-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-62333582018-11-20 Dynamic linear models guide design and analysis of microbiota studies within artificial human guts Silverman, Justin D. Durand, Heather K. Bloom, Rachael J. Mukherjee, Sayan David, Lawrence A. Microbiome Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40168-018-0584-3) contains supplementary material, which is available to authorized users. BioMed Central 2018-11-12 /pmc/articles/PMC6233358/ /pubmed/30419949 http://dx.doi.org/10.1186/s40168-018-0584-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Silverman, Justin D.
Durand, Heather K.
Bloom, Rachael J.
Mukherjee, Sayan
David, Lawrence A.
Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title_full Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title_fullStr Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title_full_unstemmed Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title_short Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
title_sort dynamic linear models guide design and analysis of microbiota studies within artificial human guts
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6233358/
https://www.ncbi.nlm.nih.gov/pubmed/30419949
http://dx.doi.org/10.1186/s40168-018-0584-3
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