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

Matthew A. Oberhardt (), Raphy Zarecki, Sabine Gronow, Elke Lang, Hans-Peter Klenk, Uri Gophna () and Eytan Ruppin ()
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Matthew A. Oberhardt: Blavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University
Raphy Zarecki: Blavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University
Sabine Gronow: Leibniz Institute DSMZ—German Collection of Microorganisms and Cell Cultures
Elke Lang: Leibniz Institute DSMZ—German Collection of Microorganisms and Cell Cultures
Hans-Peter Klenk: School of Biology, Newcastle University
Uri Gophna: Faculty of Life Sciences, Tel Aviv University
Eytan Ruppin: Blavatnik School of Computer Sciences and Sackler School of Medicine, Tel Aviv University

Nature Communications, 2015, vol. 6, issue 1, 1-14

Abstract: 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.

Date: 2015
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DOI: 10.1038/ncomms9493

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