Heterogeneous associations of gut microbiota with Crohn's disease activity.

The multi-factorial involvement of gut microbiota with Crohn's disease (CD) necessitates robust analysis to uncover possible associations with particular microbes. CD has been linked to specific bacteria, but reported associations vary widely across studies. This inconsistency may result from heterogeneous associations across individual patients, resulting in no apparent or only weak relationships with the means of bacterial abundances. We investigated the relationship between bacterial relative abundances and disease activity in a longitudinal cohort of CD patients (n = 57) and healthy controls (n = 15). We applied quantile regression, a statistical technique that allows investigation of possible relationships outside the mean response. We found several significant and mostly negative associations with CD, especially in lower quantiles of relative abundance on family or genus level. Associations found by quantile regression deviated from the mean response in relative abundances of Coriobacteriaceae, Pasteurellaceae, Peptostreptococcaceae, Prevotellaceae, and Ruminococcaceae. For the family Streptococcaceae we found a significant elevation in relative abundance for patients experiencing an exacerbation relative to those who remained without self-reported symptoms or measurable inflammation. Our analysis suggests that specific bacterial families are related to CD and exacerbation, but associations vary between patients due to heterogeneity in disease course, medication history, therapy response, gut microbiota composition and historical contingency. Our study underscores that microbial diversity is reduced in the gut of CD patients, but suggests that the process of diversity loss is rather irregular with respect to specific taxonomic groups. This novel insight may advance our ecological understanding of this complex disease.

Wavelet clustering analysis as a tool for characterizing community structure in the human microbiome.

Human microbiome research is helped by the characterization of microbial networks, as these may reveal key microbes that can be targeted for beneficial health effects. Prevailing methods of microbial network characterization are based on measures of association, often applied to limited sampling points in time. Here, we demonstrate the potential of wavelet clustering, a technique that clusters time series based on similarities in their spectral characteristics. We illustrate this technique with synthetic time series and apply wavelet clustering to densely sampled human gut microbiome time series. We compare our results with hierarchical clustering based on temporal correlations in abundance, within and across individuals, and show that the cluster trees obtained by using either method are significantly different in terms of elements clustered together, branching structure and total branch length. By capitalizing on the dynamic nature of the human microbiome, wavelet clustering reveals community structures that remain obscured in correlation-based methods.

Species abundance correlations carry limited information about microbial network interactions.

Unraveling the network of interactions in ecological communities is a daunting task. Common methods to infer interspecific interactions from cross-sectional data are based on co-occurrence measures. For instance, interactions in the human microbiome are often inferred from correlations between the abundances of bacterial phylogenetic groups across subjects. We tested whether such correlation-based methods are indeed reliable for inferring interaction networks. For this purpose, we simulated bacterial communities by means of the generalized Lotka-Volterra model, with variation in model parameters representing variability among hosts. Our results show that correlations can be indicative for presence of bacterial interactions, but only when measurement noise is low relative to the variation in interaction strengths between hosts. Indication of interaction was affected by type of interaction network, process noise and sampling under non-equilibrium conditions. The sign of a correlation mostly coincided with the nature of the strongest pairwise interaction, but this is not necessarily the case. For instance, under rare conditions of identical interaction strength, we found that competitive and exploitative interactions can result in positive as well as negative correlations. Thus, cross-sectional abundance data carry limited information on specific interaction types. Correlations in abundance may hint at interactions but require independent validation.