Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, we use an organism abundance correlation network to investigate factors that control community organization in mine tailings-derived laboratory microbial consortia grown under dozens of conditions. The network is overlaid with metagenomic information about functional capacities to generate testable hypotheses. We develop a metric to predict the importance of each node within its local network environments relative to correlated vitamin auxotrophs, and predict that a Variovorax species is a hub as an important source of thiamine. Quantification of thiamine during the growth of Variovorax in minimal media show high levels of thiamine production, up to 100 mg/L. A few of the correlated thiamine auxotrophs are predicted to produce pantothenate, which we show is required for growth of Variovorax, supporting that a subset of vitamin-dependent interactions are mutualistic. A Cryptococcus yeast produces the B-vitamin pantothenate, and co-culturing with Variovorax leads to a 90-130-fold fitness increase for both organisms. Our study demonstrates the predictive power of metagenome-informed, microbial consortia-based network analyses for identifying microbial interactions that underpin the structure and functioning of microbial communities. Metagenomic data and network analyses are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, Hessler et al. combine experiments with metagenome-informed, microbial consortia-based network analyses to identify interactions in microbial consortia grown under dozens of conditions.

Automated summary

Metagenomic or metabarcoding data are often used to predict microbial interactions in complex communities, but these predictions are rarely explored experimentally. Here, the authors combine experiments with metagenome-informed, microbial consortia-based network analyses to identify interactions in microbial consortia grown under dozens of conditions. They demonstrate the predictive power of this approach for understanding the structure and functioning of microbial communities.