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

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.

Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics

“…MTV-LMM. MTV-LMM uses a linear mixed model for identifying autoregressive taxa and predictioning their relative abundance at future time points (see Shenhav et al 48 for more details). MTV-LMM is motivated by our assumption that the temporal changes in the relative abundance of taxa j are a time-homogeneous high-order Markov process.…”

“…We therefore sought to understand whether the initial microbiome starting point (mode of delivery) would have an impact on the identity of specific microbiome members. We applied our newly developed tool utilizing a linear mixed modelbased method to predict microbial community temporal dynamics 48 (see Methods: MTV-LMM). Using this method, we quantified the dependence and predictability of the relative abundance of each microbe in our dataset at a given time point, based on the microbial community composition at previous time points (1, .., t − 1).…”

Compositional Lotka-Volterra describes microbial dynamics in the simplex

“…Li et al [34] suggest addressing this by inference of the latent overall biomass. Alternatively, Shenhav et al [21] suggested a linear mixed model with variance components, while representing the previous state microbial community using its quantiles instead of relative abundances. Yet, binning taxa into quantiles may lose fine-grained information about interactions.…”

Sequential laxative-probiotic usage for treatment of irritable bowel syndrome: a novel method inspired by mathematical modelling of the microbiome

“…Mathematical models can describe and predict ecosystems. A previous study suggested that the composition of the human faecal microbiome at a given time point is a major factor defining microbiome composition 11 . We hypothesized that mathematical modelling can streamline the clinical management of the gut microbiota.…”