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, Eytan Ruppin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

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.

Original languageEnglish
Article number8493
JournalNature Communications
Volume6
DOIs
StatePublished - 13 Oct 2015

Funding

FundersFunder number
DIP
EU FP7
German-Israeli Project Cooperation
National Evolutionary Synthesis Center
Whitaker Foundation
Israel Science Foundation41/11
Israeli Centers for Research Excellence

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