Model-driven discovery of underground metabolic functions in Escherichia coli

Gabriela I. Guzmán, José Utrilla, Sergey Nurk, Elizabeth Brunk, Jonathan M. Monk, Ali Ebrahim, Bernhard O. Palsson, Adam M. Feist

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Enzyme promiscuity toward substrates has been discussed in evolutionary terms as providing the flexibility to adapt to novel environments. In the present work, we describe an approach toward exploring such enzyme promiscuity in the space of a metabolic network. This approach leverages genome-scale models, which have been widely used for predicting growth phenotypes in various environments or following a genetic perturbation; however, these predictions occasionally fail. Failed predictions of gene essentiality offer an opportunity for targeting biological discovery, suggesting the presence of unknown underground pathways stemming from enzymatic cross-reactivity. We demonstrate a workflow that couples constraint-based modeling and bioinformatic tools with KO strain analysis and adaptive laboratory evolution for the purpose of predicting promiscuity at the genome scale. Three cases of genes that are incorrectly predicted as essential in Escherichia coli—aspC, argD, and gltA—are examined, and isozyme functions are uncovered for each to a different extent. Seven isozyme functions based on genetic and transcriptional evidence are suggested between the genes aspC and tyrB, argD and astC, gabT and puuE, and gltA and prpC. This study demonstrates how a targeted model-driven approach to discovery can systematically fill knowledge gaps, characterize underground metabolism, and elucidate regulatory mechanisms of adaptation in response to gene KO perturbations.
Original languageEnglish
JournalProceedings of the National Academy of Sciences
Volume112
Issue number3
Pages (from-to)929-934
Number of pages6
ISSN0027-8424
DOIs
Publication statusPublished - 2015

Keywords

  • Underground metabolism
  • Systems Biology
  • Substrate promiscuity
  • Isozyme discovery
  • Genome-scale modeling

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