Biotic interactions and temporal dynamics of the human gastrointestinal microbiota

Abstract

The human gastrointestinal (GI) microbiota is important to human health and imbalances or shifts in the gut microbial community have been linked to many diseases. Most studies of the GI microbiota only capture snapshots of this dynamic community at one or a few time points. Although this is valuable in terms of providing knowledge of community composition and variability between individuals, it does not provide the foundation for going beyond descriptive studies and toward truly predictive ecological models. In order to achieve this goal, we need longitudinal data of appropriate temporal and taxonomic resolution, so that established time series analysis tools for identifying and quantifying putative interactions among community members can be used. Here, we present new analyses of existing data to illustrate the potential usefulness of this approach. We discuss challenges related to sampling and data processing, as well as analytical approaches and considerations for future studies of the GI microbiota and other complex microbial systems.

Figure 1

Dynamic patterns in the healthy adult human gut. (a) Dynamic range of observed genera measured as the log ratio of the highest to the lowest abundances of genera found in at least two samples per subject (y axis) rank ordered from lowest to highest dynamic range (x axis). The rank orderings of the 147 genera that are observed at least twice in both individuals are highly correlated (rho=0.72, P≪0.001, Spearman's rank correlation). (b) Dynamic patterns within the order Bacteroidales are illustrated by the relative stability of some genera and abrupt fluctuations in the abundances of others. Stable taxa include Bacteroidales*, Bacteroides, Parabacteroides, Alistipes. Fluctuating taxa include Prevotella and Porphyromonas. Besides qualitative assessment of the graph, the ratio of the mean to the s.d. was used as a rough metric to support this classification. (c) Regime shift within the phylum Proteobacteria. The Proteobacteria are dominated by the gamma, delta and epsilon classes until around day 100 when the beta class becomes dominant. The small dark blue spikes at the bottom of the plot are Alphaproteobacteria. (d) Transiently dominant classes within the phylum Tenericutes are observed as blooms. Erysipelotrichi are partially displaced by ML615J-28 and then Mollicutes. Data in (b–d) are from the male subject with the taxonomic group names indicated in the colour keys above the plots. *Classified to order level.

Figure 2

Sampling effort, rare populations and diversity in the Caporaso data. (a) Observed number of genus-level OTUs plotted against number of reads per sample with data pooled from both subjects for raw data (black) and data subsampled to the lowest number of reads (red). The lines are linear regression fits. Without subsampling, there is a highly significant relationship between (R²=0.14, P≪0.001) between observed richness and sampling depth. Subsampling makes the association weaker (R²=0.03), but the relationship is still significant (P<0.001). (b) Distribution of the number of genera-level OTUs in the male subject that are found in at least the number of samples indicated on the x axis for raw data (black) and data subsampled to the lowest number of reads (red). For instance, using the raw data 38 OTUs were observed in at least 90% of the samples, 64 were observed in at least 50% of the samples, whereas 107 OTUs were observed in at least 10% of samples. For subsampled data, the corresponding numbers were 30, 53 and 91 OTUs, respectively. (c) Shannon entropy over time, as measured for genus-level OTUs, for the male (black) and female (red) subject. The dotted lines are means. The mean and the variance are elevated in the male relative to the female (P≪0.001, t-test and Levene's test, respectively). (d) Relationships between specific phylum abundances and Shannon entropy for genus-level OTUs. The green (Bacteroidetes) and blue (Firmicutes) lines are linear regression fits (R²=0.81 and 0.72, respectively, P≪0.001 in both cases). Turquoise (Actinobacteria) and pink (Proteobacteria) lines are smoothing splines fitted with three degrees of freedom (R²=0.74 and 0.07, respectively, P≪0.001 in both cases, GAMs).

Figure 3

Time series regression approach. (a) System of equations for evaluating biotic interactions assuming simple linear relationships: Δx_i,t=x_i,t+1–x_i,t, where x_i,t is the log relative abundance of taxon i at time=t. α_i,j are intercept terms, β_i,j are linear regression coefficients and x_j,t are log relative abundances of taxon j at time=t. The total number of equations is equal to n², where n is the total number of taxa. In the male time series, n=38 when using the stated filtering criteria. (b) Heat map describing the strength and direction (β_i,j in a) of highly significant interactions between genera of bacteria in a healthy adult human gut (male subject), estimated by using linear regression on the equation set in (a). Dependent variables are along the y axis and independent variables along the x axis. The colour key on the right-hand side indicates the sign and magnitude of interactions that were significant at the 99% confidence level. Cells representing nonsignificant relationships are black. Axis labels are colour coded according to phylum provenance of the genera as in (c). (c) Network representation of the biotic interactions identified in the previous steps demonstrating varying degrees of connectedness of the different genera. The network is based on a binary version of (b) where interactions significant at P⩽0.01 (coloured cells) were coded as 1 and all others (black cells) were coded as 0.