Abstract
Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating an idealized microbiome-metabolome dataset. We then compare observed taxon-metabolite correlations in this dataset to calculated ground-truth taxonomic contribution values. We find that correlation-based analysis poorly identifies key contributors even in these idealized settings, with extremely low predictive value and accuracy. Importantly, however, we demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.
Importance Identifying the key microbial taxa responsible for metabolic differences between individual microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in a simulated dataset of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome dataset identifies true contributions with very low accuracy, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.