title: Two dynamic regimes in the human gut microbiome.
abstract: 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.
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|The Broad Institute, Cambridge, MA, United States of America.|The Center for Microbiome Informatics and Therapeutics, Cambridge, MA, United States of America.
authors.forename: Sean M
authors.identifier: 0000-0002-8724-7916
authors.initials: SM
authors.lastname: Gibbons
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|The Broad Institute, Cambridge, MA, United States of America.|The Center for Microbiome Informatics and Therapeutics, Cambridge, MA, United States of America.
authors.forename: Sean M
authors.identifier: 0000-0002-8033-8380
authors.initials: SM
authors.lastname: Kearney
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|The Broad Institute, Cambridge, MA, United States of America.|The Center for Microbiome Informatics and Therapeutics, Cambridge, MA, United States of America.
authors.forename: Chris S
authors.initials: CS
authors.lastname: Smillie
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America.|The Broad Institute, Cambridge, MA, United States of America.|The Center for Microbiome Informatics and Therapeutics, Cambridge, MA, United States of America.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Alm
mesh_terms: D000818:Animals; D001419:Bacteria / Q000145:classification / Q000235:genetics*; D003198:Computer Simulation; D004063:Digestion / Q000502:physiology*; D004435:Eating / Q000235:genetics*; D000069196:Gastrointestinal Microbiome / Q000235:genetics* / Q000502:physiology*; D041981:Gastrointestinal Tract / Q000382:microbiology* / Q000502:physiology; D006801:Humans; D056265:Microbial Interactions / Q000235:genetics; D008954:Models, Biological; D015233:Models, Statistical; D012044:Regression Analysis