Harnessing the landscape of microbial culture media to predict new organism–media pairings

Abstract

Culturing microorganisms is a critical step in understanding and utilizing microbial life. Here we map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO), which includes >18,000 strain–media combinations, >3300 media variants and compound concentrations (the entire collection of the Leibniz Institute DSMZ repository). Using KOMODO, we show that although media are usually tuned for individual strains using biologically common salts, trace metals and vitamins/cofactors are the most differentiating components between defined media of strains within a genus. We leverage KOMODO to predict new organism–media pairings using a transitivity property (74% growth in new in vitro experiments) and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments and stronger growth on predicted well-scored versus poorly scored media). These resources are integrated into a web-based platform that predicts media given an organism’s 16S rDNA sequence, facilitating future cultivation efforts.

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Cited by papers we hold 4

• Portable and Rapid Sequencing Device with Microbial Community-Guided Culture Strategies for Precious Field and Environmental Samples · 2021
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• Addressing uncertainty in genome-scale metabolic model reconstruction and analysis · 2021
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• High proportions of bacteria and archaea across most biomes remain uncultured · 2019
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• Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis · 2017
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