Modeling the temporal dynamics of the gut microbial community in adults and infants - PubMed (original) (raw)

Modeling the temporal dynamics of the gut microbial community in adults and infants

Liat Shenhav et al. PLoS Comput Biol. 2019.

Abstract

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1

Fig 1. MTV-LMM outperforms commonly used methods in prediction accuracy (_R_2) and detection of autoregressive dynamics.

MTV-LMM predictions are in red, ARIMA Poisson regression in green, and sVAR in blue.

Fig 2

Fig 2. The first three principle components of the inferred association matrix recover known phylogenetic structure.

Closely related species, in the DIABIMUNE dataset, have similar association patterns within the microbial community. Shown on each axis is the percentage of variance explained by each principal component for the top five orders in the data.

Fig 3

Fig 3. Time-explainability distribution.

Time-explainability distribution in the DIABIMMUNE infant dataset (left) and David et al. adult dataset (right). The average time-explainability (denoted by a dashed line) in the DIABIMMUNE cohort is 23% and in David et al. is 14%.

Fig 4

Fig 4. Time-explanability differs by taxonomic order across all datasets.

In the top row, the y-axis is the average time-explainability (per order). In the bottom row, the y-axis is the proportion of data the order occupies (log scale). the x-axis shows orders with taxa that are autoregressive in at least one dataset.

Fig 5

Fig 5. The first two principal components of the temporal kinship matrix in infants.

The first two principal components of the temporal kinship matrix color coded by individual (left; 39 infant donors) and by time (right; before and after nine months) using the DIABIMMUNE data.

Fig 6

Fig 6. The first two principal components of the temporal kinship matrix in adults.

The first two principal components of the temporal kinship matrix color coded by individual. Caporaso et al. [16](left; 2 adult donors: M3, F4) and David et al. [17](right; 2 adult donors: DA, DB).

References

    1. Turnbaugh Peter J, Hamady Micah, Yatsunenko Tanya, Cantarel Brandi L, Duncan Alexis, Ley Ruth E, Sogin Mitchell L, Jones William J, Roe Bruce A, Affourtit Jason P, et al. A core gut microbiome in obese and lean twins. nature, 457(7228):480, 2009. 10.1038/nature07540 -DOI -PMC -PubMed
    1. Turnbaugh Peter J, Ley Ruth E, Mahowald Michael A, Magrini Vincent, Mardis Elaine R, and Gordon Jeffrey I. An obesity-associated gut microbiome with increased capacity for energy harvest. nature, 444(7122):1027–131, 2006. 10.1038/nature05414 -DOI -PubMed
    1. Ley Ruth E. Obesity and the human microbiome. Current opinion in gastroenterology, 26(1):5–11, 2010. 10.1097/MOG.0b013e328333d751 -DOI -PubMed
    1. Turnbaugh Peter J, Bäckhed Fredrik, Fulton Lucinda, and Gordon Jeffrey I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell host & microbe, 3(4):213–223, 2008. 10.1016/j.chom.2008.02.015 -DOI -PMC -PubMed
    1. Ley Ruth E, Bäckhed Fredrik, Turnbaugh Peter, Lozupone Catherine A, Knight Robin D, and Gordon Jeffrey I. Obesity alters gut microbial ecology. Proceedings of the National Academy of Sciences of the United States of America, 102(31):11070–11075, 2005. 10.1073/pnas.0504978102 -DOI -PMC -PubMed

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