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PLoS Genet. 2014 Apr 3;10(4):e1004264. doi: 10.1371/journal.pgen.1004264. eCollection 2014 Apr.

Determining the control circuitry of redox metabolism at the genome-scale.

Author information

1
Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, United States of America.
2
Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.
3
Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.

Abstract

Determining how facultative anaerobic organisms sense and direct cellular responses to electron acceptor availability has been a subject of intense study. However, even in the model organism Escherichia coli, established mechanisms only explain a small fraction of the hundreds of genes that are regulated during electron acceptor shifts. Here we propose a qualitative model that accounts for the full breadth of regulated genes by detailing how two global transcription factors (TFs), ArcA and Fnr of E. coli, sense key metabolic redox ratios and act on a genome-wide basis to regulate anabolic, catabolic, and energy generation pathways. We first fill gaps in our knowledge of this transcriptional regulatory network by carrying out ChIP-chip and gene expression experiments to identify 463 regulatory events. We then interfaced this reconstructed regulatory network with a highly curated genome-scale metabolic model to show that ArcA and Fnr regulate >80% of total metabolic flux and 96% of differential gene expression across fermentative and nitrate respiratory conditions. Based on the data, we propose a feedforward with feedback trim regulatory scheme, given the extensive repression of catabolic genes by ArcA and extensive activation of chemiosmotic genes by Fnr. We further corroborated this regulatory scheme by showing a 0.71 r(2) (p<1e-6) correlation between changes in metabolic flux and changes in regulatory activity across fermentative and nitrate respiratory conditions. Finally, we are able to relate the proposed model to a wealth of previously generated data by contextualizing the existing transcriptional regulatory network.

PMID:
24699140
PMCID:
PMC3974632
DOI:
10.1371/journal.pgen.1004264
[Indexed for MEDLINE]
Free PMC Article
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