Macroecological dynamics of gut microbiota

Abstract

The gut microbiome is now widely recognized as a dynamic ecosystem that plays an important role in health and disease 1 . While current sequencing technologies make it possible to estimate relative abundances of host-associated bacteria over time 2, 3 , the biological processes governing their dynamics remain poorly understood. Therefore, as in other ecological systems 4, 5 , it is important to identify quantitative relationships describing global aspects of gut microbiota dynamics. Here we use multiple high-resolution time series data obtained from humans and mice [6][7][8] to demonstrate that despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, these patterns are highly similar to those previously observed across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations 9-12 and the performance of publicly traded companies 13 . Specifically, we find power law relationships describing short-and long-term changes in gut microbiota abundances, species residence and return times, and the connection between the mean and variance of species abundances. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. We use these macroecological relationships to reveal specific bacterial taxa whose dynamics are significantly affected by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamics of microbial communities..

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

“…Jaccard index, Bray-Curtis dissimilarity or Pearson correlation. Other studies used auto-correlations [11] or mean square displacement [14] of abundance time-series to investigate the long-term behavior of single OTUs. The dissimilarity measure we use is akin to the latter approach, in that it evaluates how dissimilar a single abundance time-series becomes from its past values.…”

“…Recent works have characterized the macroecological patterns of community composition [12,13]. Under the macroecological lens, community dynamics is characterized by large and rapid fluctuations with robust and reproducible statistical properties [14][15][16]. These regular patterns are well described by the stochastic logistic model with environmental noise, which assumes that fast environmental fluctuations perturb abundances around a taxa-specific carrying capacity [15,16].…”

Characterizing scaling laws in gut microbial dynamics from time series data: caution is warranted

“…Lines represent maximum likelihood estimation (MLE) fits to the data. To control for known technical factors such as sample preparation and sequencing noise, here we adopted exactly the same OTU inclusion criteria as used by Ji et al 6 The bar plots depict for each host's gut microbiota time series the percentage of OTUs with white, pink, brown, or black noise. White noise indicates the absence of temporal structure, all other noise types indicate the presence of certain levels of temporal structure: the dependency on previous time points is strongest for black noise, medium for brown noise, and weakest for pink noise.…”

“…Lines represent maximum likelihood estimation (MLE) fits to the data. To control for known technical factors such as sample preparation and sequencing noise, here we adopted exactly the same OTU inclusion criteria as used by Ji et al 6 . Columns: ( a ) Human A; ( b ) Human M3; ( c ) Mice fed on a low-fat, plant polysaccharide (LFPP) diet.…”

“… Abstract Many studies have revealed that both host and environmental factors can impact the gut microbial compositions, implying that the gut microbiota is considerably dynamic 1–5 . In their Article, Ji et al 6 performed comprehensive analysis of multiple high-resolution time series data of human and mouse gut microbiota. They found that both human and mouse gut microbiota dynamics can be characterized by several robust scaling laws describing short- and long-term changes in gut microbiota abundances, distributions of species residence and return times, and the correlation between the mean and the temporal variance of species abundances.…”