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      High‐quality genome‐scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

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          Summary

          Genome‐scale reconstructions of metabolism are computational species‐specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock‐out library and Bar‐seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

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          Most cited references65

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          Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0.

          Over the past decade, a growing community of researchers has emerged around the use of constraint-based reconstruction and analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a substantial update of this in silico toolbox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include (i) network gap filling, (ii) (13)C analysis, (iii) metabolic engineering, (iv) omics-guided analysis and (v) visualization. As with the first version, the COBRA Toolbox reads and writes systems biology markup language-formatted models. In version 2.0, we improved performance, usability and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the toolbox and validate results. This toolbox lowers the barrier of entry to use powerful COBRA methods.
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            A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information

            An updated genome-scale reconstruction of the metabolic network in Escherichia coli K-12 MG1655 is presented. This updated metabolic reconstruction includes: (1) an alignment with the latest genome annotation and the metabolic content of EcoCyc leading to the inclusion of the activities of 1260 ORFs, (2) characterization and quantification of the biomass components and maintenance requirements associated with growth of E. coli and (3) thermodynamic information for the included chemical reactions. The conversion of this metabolic network reconstruction into an in silico model is detailed. A new step in the metabolic reconstruction process, termed thermodynamic consistency analysis, is introduced, in which reactions were checked for consistency with thermodynamic reversibility estimates. Applications demonstrating the capabilities of the genome-scale metabolic model to predict high-throughput experimental growth and gene deletion phenotypic screens are presented. The increased scope and computational capability using this new reconstruction is expected to broaden the spectrum of both basic biology and applied systems biology studies of E. coli metabolism.
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              High-throughput generation, optimization and analysis of genome-scale metabolic models.

              Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
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                Author and article information

                Contributors
                jnogales@cnb.csic.es
                Journal
                Environ Microbiol
                Environ. Microbiol
                10.1111/(ISSN)1462-2920
                EMI
                Environmental Microbiology
                John Wiley & Sons, Inc. (Hoboken, USA )
                1462-2912
                1462-2920
                11 November 2019
                January 2020
                : 22
                : 1 ( doiID: 10.1111/emi.v22.1 )
                : 255-269
                Affiliations
                [ 1 ] Department of Systems Biology Centro Nacional de Biotecnología (CNB‐CSIC) Madrid Spain
                [ 2 ] Department of Bioengineering University of California, San Diego La Jolla CA USA
                [ 3 ] Department of Chemical and Biomolecular Engineering University of Nebraska‐Lincoln Lincoln NE USA
                [ 4 ] Center for Systems Biology, University of Iceland Reykjavík Iceland
                [ 5 ] Department of Environmental Protection Estación Experimental del Zaidín (CSIC) Granada Spain
                Author notes
                [*] [* ]For correspondence. E‐mail jnogales@ 123456cnb.csic.es ; Tel. +34 91585 4557; Fax: +34 91585 4506.
                [†]

                These two authors contributed equally.

                Author information
                https://orcid.org/0000-0002-4961-0833
                https://orcid.org/0000-0002-8731-7435
                Article
                EMI14843
                10.1111/1462-2920.14843
                7078882
                31657101
                279af337-d125-49b7-8843-cc69707ba1fb
                © 2019 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 22 May 2019
                : 27 September 2019
                : 23 October 2019
                Page count
                Figures: 5, Tables: 3, Pages: 15, Words: 10163
                Funding
                Funded by: H2020 Future and Emerging Technologies , open-funder-registry 10.13039/100010664;
                Award ID: 686585
                Funded by: H2020 LEIT Biotechnology , open-funder-registry 10.13039/100010689;
                Award ID: 635536
                Award ID: 814650
                Funded by: Ministerio de Economía y Competitividad , open-funder-registry 10.13039/501100003329;
                Award ID: BIO2014‐59528‐JIN
                Award ID: RTI‐2018‐094370‐B‐I00
                Funded by: National Science Foundation Graduate Research Fellowship Program , open-funder-registry 10.13039/100000001;
                Award ID: 1610400
                Funded by: Novo Nordisk , open-funder-registry 10.13039/501100004191;
                Award ID: NNF10CC1016517
                Funded by: U.S. Department of Energy , open-funder-registry 10.13039/100000015;
                Award ID: DE‐AC02‐05CH11231
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                January 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.8 mode:remove_FC converted:18.03.2020

                Microbiology & Virology
                Microbiology & Virology

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