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Determining the Control Circuitry of Redox Metabolism at the Genome-Scale
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 regu...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974632/ https://www.ncbi.nlm.nih.gov/pubmed/24699140 http://dx.doi.org/10.1371/journal.pgen.1004264 |
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author | Federowicz, Stephen Kim, Donghyuk Ebrahim, Ali Lerman, Joshua Nagarajan, Harish Cho, Byung-kwan Zengler, Karsten Palsson, Bernhard |
author_facet | Federowicz, Stephen Kim, Donghyuk Ebrahim, Ali Lerman, Joshua Nagarajan, Harish Cho, Byung-kwan Zengler, Karsten Palsson, Bernhard |
author_sort | Federowicz, Stephen |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3974632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39746322014-04-08 Determining the Control Circuitry of Redox Metabolism at the Genome-Scale Federowicz, Stephen Kim, Donghyuk Ebrahim, Ali Lerman, Joshua Nagarajan, Harish Cho, Byung-kwan Zengler, Karsten Palsson, Bernhard PLoS Genet Research Article 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. Public Library of Science 2014-04-03 /pmc/articles/PMC3974632/ /pubmed/24699140 http://dx.doi.org/10.1371/journal.pgen.1004264 Text en © 2014 Federowicz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Federowicz, Stephen Kim, Donghyuk Ebrahim, Ali Lerman, Joshua Nagarajan, Harish Cho, Byung-kwan Zengler, Karsten Palsson, Bernhard Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title_full | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title_fullStr | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title_full_unstemmed | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title_short | Determining the Control Circuitry of Redox Metabolism at the Genome-Scale |
title_sort | determining the control circuitry of redox metabolism at the genome-scale |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974632/ https://www.ncbi.nlm.nih.gov/pubmed/24699140 http://dx.doi.org/10.1371/journal.pgen.1004264 |
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