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

Tomas Hessler, Robert J. Huddy, Rohan Sachdeva, Shufei Lei, Susan T. L. Harrison, Spencer Diamond and Jillian F. Banfield ()
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Tomas Hessler: The Innovative Genomics Institute at the University of California
Robert J. Huddy: University of Cape Town
Rohan Sachdeva: The Innovative Genomics Institute at the University of California
Shufei Lei: University of California
Susan T. L. Harrison: University of Cape Town
Spencer Diamond: The Innovative Genomics Institute at the University of California
Jillian F. Banfield: The Innovative Genomics Institute at the University of California

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-40360-4

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