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      Reprogramming of Escherichia coli K-12 Metabolism during the Initial Phase of Transition from an Anaerobic to a Micro-Aerobic Environment

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          Abstract

          Background

          Many bacteria undergo transitions between environments with differing O 2 availabilities as part of their natural lifestyles and during biotechnological processes. However, the dynamics of adaptation when bacteria experience changes in O 2 availability are understudied. The model bacterium and facultative anaerobe Escherichia coli K-12 provides an ideal system for exploring this process.

          Methods and Findings

          Time-resolved transcript profiles of E. coli K-12 during the initial phase of transition from anaerobic to micro-aerobic conditions revealed a reprogramming of gene expression consistent with a switch from fermentative to respiratory metabolism. The changes in transcript abundance were matched by changes in the abundances of selected central metabolic proteins. A probabilistic state space model was used to infer the activities of two key regulators, FNR (O 2 sensing) and PdhR (pyruvate sensing). The model implied that both regulators were rapidly inactivated during the transition from an anaerobic to a micro-aerobic environment. Analysis of the external metabolome and protein levels suggested that the cultures transit through different physiological states during the process of adaptation, characterized by the rapid inactivation of pyruvate formate-lyase (PFL), a slower induction of pyruvate dehydrogenase complex (PDHC) activity and transient excretion of pyruvate, consistent with the predicted inactivation of PdhR and FNR.

          Conclusion

          Perturbation of anaerobic steady-state cultures by introduction of a limited supply of O 2 combined with time-resolved transcript, protein and metabolite profiling, and probabilistic modeling has revealed that pyruvate (sensed by PdhR) is a key metabolic signal in coordinating the reprogramming of E. coli K-12 gene expression by working alongside the O 2 sensor FNR during transition from anaerobic to micro-aerobic conditions.

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          Most cited references38

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          Integrating high-throughput and computational data elucidates bacterial networks.

          The flood of high-throughput biological data has led to the expectation that computational (or in silico) models can be used to direct biological discovery, enabling biologists to reconcile heterogeneous data types, find inconsistencies and systematically generate hypotheses. Such a process is fundamentally iterative, where each iteration involves making model predictions, obtaining experimental data, reconciling the predicted outcomes with experimental ones, and using discrepancies to update the in silico model. Here we have reconstructed, on the basis of information derived from literature and databases, the first integrated genome-scale computational model of a transcriptional regulatory and metabolic network. The model accounts for 1,010 genes in Escherichia coli, including 104 regulatory genes whose products together with other stimuli regulate the expression of 479 of the 906 genes in the reconstructed metabolic network. This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks. We find that a systems biology approach that combines genome-scale experimentation and computation can systematically generate hypotheses on the basis of disparate data sources.
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            EcoCyc: A comprehensive view of Escherichia coli biology

            EcoCyc (http://EcoCyc.org) provides a comprehensive encyclopedia of Escherichia coli biology. EcoCyc integrates information about the genome, genes and gene products; the metabolic network; and the regulatory network of E. coli. Recent EcoCyc developments include a new initiative to represent and curate all types of E. coli regulatory processes such as attenuation and regulation by small RNAs. EcoCyc has started to curate Gene Ontology (GO) terms for E. coli and has made a dataset of E. coli GO terms available through the GO Web site. The curation and visualization of electron transfer processes has been significantly improved. Other software and Web site enhancements include the addition of tracks to the EcoCyc genome browser, in particular a type of track designed for the display of ChIP-chip datasets, and the development of a comparative genome browser. A new Genome Omics Viewer enables users to paint omics datasets onto the full E. coli genome for analysis. A new advanced query page guides users in interactively constructing complex database queries against EcoCyc. A Macintosh version of EcoCyc is now available. A series of Webinars is available to instruct users in the use of EcoCyc.
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              Bacterial redox sensors.

              Redox reactions pervade living cells. They are central to both anabolic and catabolic metabolism. The ability to maintain redox balance is therefore vital to all organisms. Various regulatory sensors continually monitor the redox state of the internal and external environments and control the processes that work to maintain redox homeostasis. In response to redox imbalance, new metabolic pathways are initiated, the repair or bypassing of damaged cellular components is coordinated and systems that protect the cell from further damage are induced. Advances in biochemical analyses are revealing a range of elegant solutions that have evolved to allow bacteria to sense different redox signals.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2011
                27 September 2011
                : 6
                : 9
                : e25501
                Affiliations
                [1 ]Department of Molecular Biology and Biotechnology, The Krebs Institute, University of Sheffield, Sheffield, United Kingdom
                [2 ]Informatics Forum, University of Edinburgh, Edinburgh, United Kingdom
                East Carolina University School of Medicine, United States of America
                Author notes

                Conceived and designed the experiments: EWT MDR MPW CJC RKP JG. Performed the experiments: EWT MDR AMH. Analyzed the data: EWT MDR GS CJC RKP JG. Wrote the paper: JG.

                Article
                PONE-D-11-10476
                10.1371/journal.pone.0025501
                3181329
                21980479
                db96d48d-1a29-4502-9851-590389e55275
                Trotter et al. 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 credited.
                History
                : 9 June 2011
                : 6 September 2011
                Page count
                Pages: 7
                Categories
                Research Article
                Biology
                Biochemistry
                Metabolism
                Oxygen Metabolism
                Computational Biology
                Molecular Genetics
                Gene Expression
                Microarrays
                Genetics
                Gene Expression
                DNA transcription
                Molecular Genetics
                Gene Regulation
                Microbiology
                Bacterial Pathogens
                Escherichia Coli
                Bacteriology
                Bacterial Biochemistry
                Bacterial Physiology
                Microbial Metabolism
                Microbial Physiology
                Model Organisms
                Prokaryotic Models
                Escherichia Coli
                Molecular Cell Biology
                Gene Expression
                Medicine
                Infectious Diseases
                Bacterial Diseases
                Escherichia Coli
                Zoonoses
                Escherichia Coli

                Uncategorized
                Uncategorized

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