Determining the Control Circuitry of Redox Metabolism at the Genome-Scale

• Published in: PLoS genetics Vol. 10; no. 4; p. e1004264
• Main Authors: Federowicz, Stephen, Kim, Donghyuk, Ebrahim, Ali, Lerman, Joshua, Nagarajan, Harish, Cho, Byung-kwan, Zengler, Karsten, Palsson, Bernhard
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2014; Public Library of Science (PLoS)
• Subjects: Anaerobiosis - genetics; BASIC BIOLOGICAL SCIENCES; Biology and Life Sciences; Computer and Information Sciences; E coli; Electron Transport - genetics; Electrons; Energy Metabolism - genetics; enzyme regulation; Enzymes; Escherichia coli - genetics; Escherichia coli Proteins - genetics; Experiments; Gene expression; Gene Expression Regulation, Bacterial - genetics; gene regulation; Gene Regulatory Networks - genetics; Genetic aspects; Genetic research; Genomes; genomics; metabolic networks; Metabolism; Metabolism - genetics; Metabolites; Neural circuitry; Neurological research; Nitrates; Oxidation-Reduction; oxidation-reduction reactions; Transcription factors; Transcription Factors - genetics; Transcription, Genetic - genetics; transcriptional control; United States
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1004264

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.