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      A curated genome-scale metabolic model of Bordetella pertussis metabolism

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          Abstract

          The Gram-negative bacterium Bordetella pertussis is the causative agent of whooping cough, a serious respiratory infection causing hundreds of thousands of deaths annually worldwide. There are effective vaccines, but their production requires growing large quantities of B. pertussis. Unfortunately, B. pertussis has relatively slow growth in culture, with low biomass yields and variable growth characteristics. B. pertussis also requires a relatively expensive growth medium. We present a new, curated flux balance analysis-based model of B. pertussis metabolism. We enhance the model with an experimentally-determined biomass objective function, and we perform extensive manual curation. We test the model’s predictions with a genome-wide screen for essential genes using a transposon-directed insertional sequencing (TraDIS) approach. We test its predictions of growth for different carbon sources in the medium. The model predicts essentiality with an accuracy of 83% and correctly predicts improvements in growth under increased glutamate:fumarate ratios. We provide the model in SBML format, along with gene essentiality predictions.

          Author summary

          Metabolic flux models have been used to understand how organisms adapt their metabolism under different growth conditions, and are finding increasing application in synthetic biology and biotechnology. One barrier to progress in this field is the construction and curation of metabolic flux models for new organisms. Here we present a curated genome-scale metabolic flux model for Bordetella pertussis, the causative agent of whooping cough. Producing vaccines against whooping cough requires growing B. pertussis in large volumes. However, its growth is relatively slow, final yields of biomass are relatively low and growth characteristics can be variable. Understanding B. pertussis metabolism has applications to improving vaccine production, as well as in understanding the basic biology of this organism.

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          A rapid method of total lipid extraction and purification.

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            A protocol for generating a high-quality genome-scale metabolic reconstruction.

            Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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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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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: InvestigationRole: ValidationRole: Visualization
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                July 2017
                17 July 2017
                : 13
                : 7
                : e1005639
                Affiliations
                [1 ] Department of Mathematics, Imperial College, London, UK
                [2 ] The Milner Centre for Evolution and Department of Biology and Biochemistry, University of Bath, Bath, UK
                Centre for Research and Technology-Hellas, GREECE
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6097-6708
                Article
                PCOMPBIOL-D-16-01545
                10.1371/journal.pcbi.1005639
                5553986
                28715411
                af0661f8-1a12-49c9-9c62-62baca92dd58
                © 2017 Fyson 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
                : 23 September 2016
                : 15 June 2017
                Page count
                Figures: 3, Tables: 2, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BBSRC BB/I00713X/2
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/K026003/1
                Award Recipient :
                This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) www.epsrc.ac.uk grant EP/K026003/1 and by the Biotechnology and Biological Sciences Research Council (BBSRC) www.bbsrc.ac.uk grant BB/I00713X/2. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Organisms
                Bacteria
                Bordetella
                Bordetella Pertussis
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbial Pathogens
                Bacterial Pathogens
                Bordetella
                Bordetella Pertussis
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Pathogens
                Microbial Pathogens
                Bacterial Pathogens
                Bordetella
                Bordetella Pertussis
                Physical Sciences
                Chemistry
                Chemical Compounds
                Ammonia
                Biology and Life Sciences
                Biochemistry
                Neurochemistry
                Neurotransmitters
                Glutamate
                Biology and Life Sciences
                Neuroscience
                Neurochemistry
                Neurotransmitters
                Glutamate
                Computer and Information Sciences
                Network Analysis
                Metabolic Networks
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Medicine and Health Sciences
                Infectious Diseases
                Infectious Disease Control
                Vaccines
                Booster Doses
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Nitrogen Metabolism
                Custom metadata
                vor-update-to-uncorrected-proof
                2017-08-11
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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