Dynamic linear models guide design and analysis of microbiota studies within artificial human guts
Abstract 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 o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | Microbiome |
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| Online Access: | http://link.springer.com/article/10.1186/s40168-018-0584-3 |
| _version_ | 1811213526982721536 |
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| author | Justin D. Silverman Heather K. Durand Rachael J. Bloom Sayan Mukherjee Lawrence A. David |
| author_facet | Justin D. Silverman Heather K. Durand Rachael J. Bloom Sayan Mukherjee Lawrence A. David |
| author_sort | Justin D. Silverman |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T05:48:00Z |
| format | Article |
| id | doaj.art-e50d088ddbc24126a18451ac98626df8 |
| institution | Directory Open Access Journal |
| issn | 2049-2618 |
| language | English |
| last_indexed | 2024-04-12T05:48:00Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj.art-e50d088ddbc24126a18451ac98626df82022-12-22T03:45:25ZengBMCMicrobiome2049-26182018-11-016112010.1186/s40168-018-0584-3Dynamic linear models guide design and analysis of microbiota studies within artificial human gutsJustin D. Silverman0Heather K. Durand1Rachael J. Bloom2Sayan Mukherjee3Lawrence A. David4Program in Computational Biology and Bioinformatics, Duke UniversityDepartment of Molecular Genetics and Microbiology, Duke UniversityUniversity Program in Genetics and Genomics, Duke UniversityProgram in Computational Biology and Bioinformatics, Duke UniversityProgram in Computational Biology and Bioinformatics, Duke UniversityAbstract 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.http://link.springer.com/article/10.1186/s40168-018-0584-3Artificial gutBioreactorMicrobiomeMetagenomicsCompositional dataBayesian statistics |
| spellingShingle | Justin D. Silverman Heather K. Durand Rachael J. Bloom Sayan Mukherjee Lawrence A. David Dynamic linear models guide design and analysis of microbiota studies within artificial human guts Microbiome Artificial gut Bioreactor Microbiome Metagenomics Compositional data Bayesian statistics |
| 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 | Artificial gut Bioreactor Microbiome Metagenomics Compositional data Bayesian statistics |
| url | http://link.springer.com/article/10.1186/s40168-018-0584-3 |
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