Annotation-free discovery of functional groups in microbial communities

An unsupervised, annotation-free method is developed that can identify microbial functional groups on the basis of variation in microbiome data and environmental variables. Here, the authors demonstrate its application in several different datasets including the Tara oceans microbiome and animal gut microbiomes. Recent studies have shown that microbial communities are composed of groups of functionally cohesive taxa whose abundance is more stable and better-associated with metabolic fluxes than that of any individual taxon. However, identifying these functional groups in a manner that is independent of error-prone functional gene annotations remains a major open problem. Here we tackle this structure-function problem by developing a novel unsupervised approach that coarse-grains taxa into functional groups, solely on the basis of the patterns of statistical variation in species abundances and functional read-outs. We demonstrate the power of this approach on three distinct datasets. On data of replicate microcosms with heterotrophic soil bacteria, our unsupervised algorithm recovered experimentally validated functional groups that divide metabolic labour and remain stable despite large variation in species composition. When leveraged against the ocean microbiome data, our approach discovered a functional group that combines aerobic and anaerobic ammonia oxidizers whose summed abundance tracks closely with nitrate concentrations in the water column. Finally, we show that our framework can enable the detection of species groups that are probably responsible for the production or consumption of metabolites abundant in animal gut microbiomes, serving as a hypothesis-generating tool for mechanistic studies. Overall, this work advances our understanding of structure-function relationships in complex microbiomes and provides a powerful approach to discover functional groups in an objective and systematic manner.

Automated summary

This study develops an unsupervised, annotation-free method that can identify microbial functional groups based on variation in microbiome data and environmental variables. The authors demonstrate its application in various datasets, including the Tara oceans microbiome and animal gut microbiomes. This work advances our understanding of structure-function relationships in complex microbiomes and provides an objective and systematic approach to discover functional groups.