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Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):929-34. doi: 10.1073/pnas.1414218112. Epub 2015 Jan 6.

Model-driven discovery of underground metabolic functions in Escherichia coli.

Author information

1
Departments of Bioengineering.
2
Algorithmic Biology Laboratory, St. Petersburg Academic University, Russian Academy of Sciences, St. Petersburg, Russia;
3
Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA 94608; and.
4
NanoEngineering, and.
5
Departments of Bioengineering, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Pediatrics, University of California, San Diego, La Jolla, CA 92093;
6
Departments of Bioengineering, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark afeist@ucsd.edu.

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.

KEYWORDS:

genome-scale modeling; isozyme discovery; substrate promiscuity; systems biology; underground metabolism

PMID:
25564669
PMCID:
PMC4311852
DOI:
10.1073/pnas.1414218112
[Indexed for MEDLINE]
Free PMC Article

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