Two dynamic regimes in the human gut microbiome.
The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial tim...
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doaj-f626c994de514c819ffd340f8a1d8fda2020-11-25T01:34:03ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582017-02-01132e100536410.1371/journal.pcbi.1005364Two dynamic regimes in the human gut microbiome.Sean M GibbonsSean M KearneyChris S SmillieEric J AlmThe gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.http://europepmc.org/articles/PMC5340412?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sean M Gibbons Sean M Kearney Chris S Smillie Eric J Alm |
spellingShingle |
Sean M Gibbons Sean M Kearney Chris S Smillie Eric J Alm Two dynamic regimes in the human gut microbiome. PLoS Computational Biology |
author_facet |
Sean M Gibbons Sean M Kearney Chris S Smillie Eric J Alm |
author_sort |
Sean M Gibbons |
title |
Two dynamic regimes in the human gut microbiome. |
title_short |
Two dynamic regimes in the human gut microbiome. |
title_full |
Two dynamic regimes in the human gut microbiome. |
title_fullStr |
Two dynamic regimes in the human gut microbiome. |
title_full_unstemmed |
Two dynamic regimes in the human gut microbiome. |
title_sort |
two dynamic regimes in the human gut microbiome. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Computational Biology |
issn |
1553-734X 1553-7358 |
publishDate |
2017-02-01 |
description |
The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes. |
url |
http://europepmc.org/articles/PMC5340412?pdf=render |
work_keys_str_mv |
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