A macroecological description of alternative stable states reproduces intra- and inter-host variability of gut microbiome

Abstract

The most fundamental questions in microbial ecology concern the diversity and variability of communities. Their composition varies widely across space and time, as it is determined by a non-trivial combination of stochastic and deterministic processes. The interplay between non-linear community dynamics and environmental fluctuations determines the rich statistical structure of community variability, with both rapid temporal dynamics fluctuations and non-trivial correlations across habitats. Here we analyze long time-series of gut microbiome and compare intra- and inter-community dissimilarity. Under a macroecological framework we characterize their statistical properties. We show that most taxa have large but stationary fluctuations over time, while a minority is characterized by quick changes of average abundance which cluster in time, suggesting the presence of alternative stable states. We disentangle inter-individual variability in a major stochastic component and a deterministic one, the latter recapitulated by differences in the carrying capacities of taxa. Finally, we develop a model which includes environmental fluctuations and alternative stable states. This model quantitatively predicts the statistical properties of both intra- and inter-individual community variability, therefore summarizing variation in a unique macroecological framework.

Model-free prediction of microbiome compositions

“…Typically, the value of k min is about 10, which is only around 5 percent of the total available data. The fact that the algorithm uses only the nearest neighbors to predict the test sample most effectively is consistent with the hypothesis that microbial samples represent different alternative steady states of the ecosystem, as also suggested by previous longitudinal studies [25,26] and a macroecological description of the microbiome [27]. Accordingly, in order to make an accurate assessment of the test sample, the most relevant samples are those that are near its steady state, rather than the entire spectrum of alternative states.…”

Ecological Stability Emerges at the Level of Strains in the Human Gut Microbiome

“…Next, we sought to determine if the temporal dynamics of strains could be captured using a naive model. Recent work in microbial ecology has repeatedly demonstrated the power of such models to reproduce qualitative and quantitative features of natural microbial community dynamics [1,6,43,44]. We show that the stochastic logistic model (SLM), a minimal model itself requiring the fit of no free parameters, is a good fit for nearly all the strain time series in our cohort.…”

“…To calculate , we used the sampling-corrected estimate of the true variance as done in [1] and [43]: where T is the set of timepoints for which strain i is present, and N ( t ) is the total abundance of all species present in the sample at timepoint t , as determined by MIDAS.…”

EMBED: Essential Microbiome Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

“…A significant challenge in understanding gut microbiome dynamics is its enormous organizational complexity, comprising thousands of individual bacterial species whose abundances vary substantially across space, time, and host ecosystems [11][12][13][14][15] . Systems biology approaches are now beginning to reveal broad-scale insights into the temporal behavior of the gut microbiome, including its defining features of long-term stability and resilience to perturbations [16][17][18][19][20] . More recently, methods have also been developed to address the significant technical challenges of inferring true relative abundances of bacteria from large-scale sequencing data [21][22][23] .…”