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Research Article | Ecological and Evolutionary Science

Adaptive Evolution of Phosphorus Metabolism in Prochlorococcus

John R. Casey, Adil Mardinoglu, Jens Nielsen, David M. Karl
Marcelino Gutierrez, Editor
John R. Casey
aDaniel K. Inouye Center for Microbial Oceanography, Research and Education, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, Hawaii, USA
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  • ORCID record for John R. Casey
Adil Mardinoglu
bDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
cScience for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden
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Jens Nielsen
bDepartment of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
cScience for Life Laboratory, KTH—Royal Institute of Technology, Stockholm, Sweden
dNovo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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David M. Karl
aDaniel K. Inouye Center for Microbial Oceanography, Research and Education, School of Ocean and Earth Science and Technology, University of Hawaii, Honolulu, Hawaii, USA
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Marcelino Gutierrez
City of Knowledge
Roles: Editor
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DOI: 10.1128/mSystems.00065-16
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Figures

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  • FIG 1
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    FIG 1

    Diel simulation. Comparison of calculated net and gross primary production against short-term [14C]bicarbonate primary production measurements reported in reference 26. The light profile followed a gradual increase from darkness to a peak irradiance of 232 µmol photons m−2 s−1, which was held constant for 4 h, followed by a gradual decrease to darkness.

  • FIG 2
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    FIG 2

    Metabolite participation. Comparison of the 10 highest-degree metabolites (excluding H2O and H+) between the members of the ensemble, grouped by phylum, and iJC568 (■, markers). Pi, orthophosphate; PPi, diphosphate; L-Glut, l-glutamate.

  • FIG 3
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    FIG 3

    Simulated growth rates as a function of altered biomass compositions. Values represent the calculated growth rates associated with a composition of DNA, RNA, lipid, cell wall, and soluble pool which corresponds to each interval of the C/P ratio range. Growth rates were compared by constraining the orthophosphate transporter flux (red) or the carbon fixation flux (black) to suboptimal rates. The number of biomass compositions at each C/P ratio is indicated (n).

  • FIG 4
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    FIG 4

    Phenotype phase planes of light and phosphate uptake for key photosynthetic fluxes. In each panel, the white line of optimality (L.O.) indicates optimal growth and delineates LLG (above) and PLG (below) phenotypes. FdR, ferredoxin-NADP+ reductase; FQR, ferredoxin:quinone oxidoreductase; COX, cytochrome oxidase bd; Cytb6f, cytochrome b6f; NDH, NADPH dehydrogenase type 1.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    Summary of iJC568 properties

    FeatureaNo. (% of total)
    Genes568
        Complexed302 (53)
    Reactions794
        Blocked23 (3)
        Orphaned3 (<1)
        Gap filled60 (8)
        Reversible329 (42)
        Transport63 (8)
        Exchange79 (10)
    Metabolites680
        Unique597 (88)
    • ↵a Complexed, subunit-encoding genes; Blocked, reactions associated with dead-end metabolites; Orphaned, reactions not connected to the network; Gap filled, metabolic reactions with no annotated gene; Transport, including diffusive reactions and porins; Exchange, boundary transport used for modeling.

  • TABLE 2

    Crude biomass composition and growth sensitivity of iJC568a

    ComponentComposition (% of total DW)Ψ (% of total)
    DNA1.2<1
    RNA4.72
    Protein58.141
    Lipid11.535
    Pigments3.85
    Cell wall5.05
    Carbohydrate2.97
    Free nucleic acids<0.1<1
    Free amino acids2.1<1
    BioPool2.93
    Minerals and trace metals2.4<1
    • ↵a DW, ash-free dry weight; Ψ, growth sensitivity.

Supplemental Material

  • Figures
  • Tables
  • Data Set S1

    iJC568 model in Excel format. BioOpt format and RAVEN SBML format are available for download at http://biomet-toolbox.org/ . Download Data Set S1, XLS file, 0.5 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Data Set S2

    Accompanying data sets used and produced in the manuscript. readme, description of each data set, as well as hyperlinks to navigate; BOF, molar and mass-based composition of crude biomass fractions and their individual components; Elemental stoichiometry, comparison of BOF elemental stoichiometry with experimental data; Enthalpy of combustion, heats of combustion for each of the biomass components on a molar and carbon molar basis; CLG Sensitivity, carbon-limited growth biomass sensitivity; LLG Sensitivity, light-limited growth biomass sensitivity; PLG Sensitivity, phosphorus-limited growth biomass sensitivity; NLG Sensitivity, nitrogen-limited growth biomass sensitivity; repMets vs Shadow, table of the top 10 most positive and negative shadow prices for PLG conditions and their corresponding Z scores from the reporterMetabolites algorithm; Rate validations, comparison of growth rates, photosynthetic parameters, and internal fluxes for iJC568 and experimental data; Gene information, annotations and identifiers for each metabolic gene included in iJC568, gene length, strand sense, whether the gene belongs to the core or flexible Prochlorococcus pangenome, the expression level from Wang et al. (29), and the gene product molecular weight and isoelectric point; Gene essentiality, results from in silico gene knockouts (this worksheet includes essential and nonessential genes from the autotrophic and mixotrophic growth simulations; mixotrophic growth includes a third classification for “variable” essential genes which were lethal deletions only in certain medium compositions); Ensemble Models, table summarizing ensemble models (number of metabolites, reactions, metabolic genes, total genes, and essential genes); Intracellular Pi, table of intracellular phosphate concentrations in P-replete and P-limited media for Prochlorococcus marinus MED4, Synechococcus WH7803, Escherichia coli MG1655, and Saccharomyces cerevisiae; Succinate costs in mutants, table of NAD(P)H costs associated with de novo succinate synthesis for each of the strain variants (WT, +SDH, +2OGDC+SSADH, and +2OGDC+SSADH+SDH). Values for NAD(P)H-consuming reactions represent the difference between fluxes in the steady-state solution and fluxes in the forced-accumulation solution. Download Data Set S2, XLS file, 0.4 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S1

    Fractional singular values of the phosphate transformation system reaction for iJC568 and the ensemble. References for each ensemble model can be found in Table S2 in the supplemental material. Download Figure S1, EPS file, 0.01 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S2

    Elemental flux sums and turnover comparison of iJC568 and iTO977. Download Figure S2, EPS file, 0.01 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S3

    Reporter subnetworks identified from differential expression in P-replete and P-limited media. Data are from reference 11. Download Figure S3, JPG file, 1.8 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S4

    Illustration of changes to photosynthetic electron flow under optimal growth conditions, light-limited growth conditions, phosphorus-limited growth conditions, and phosphorus-limited growth conditions for the in silico SDH knock-in mutant. The center panel is a detailed view of the iJC568 photosystem, including the transport of protons across the thylakoid membrane (orange text), cofactors associated with each reaction (black arrows and numbers), and the stoichiometry of metabolites associated with each reaction (blue arrows and numbers). Reactions belonging to the LEF (pink), CEF (yellow), PCEF (orange), and succinate dehydrogenase knock-in (black) include photosystem II (PSII), ferredoxin-NADP+ reductase (FdR), photosystem I (PSI), ferredoxin:quinone oxidoreductase (FQR), cytochrome oxidase bd (COX), cytochrome b6f, NADPH dehydrogenase type 1 (NDH), ATP synthase, and succinate dehydrogenase (SDH). Reactions catalyze oxidations (toward the left) and reductions (toward the right) of ferredoxin (Fdox/Fdred), plastoquinone (PQ/PQH2), NADP+/NADPH, and plastocyanin (Cu2+/Cu+). Arrows in the condition-specific panels (top left, top right, bottom left, bottom right) are scaled by the flux of electrons, based on the individual fluxes, the stoichiometry of each metabolite, and the number of valence electrons exchanged, normalized to the incident number of photons absorbed. Download Figure S4, TIF file, 2.8 MB.

    Copyright © 2016 Casey et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

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Adaptive Evolution of Phosphorus Metabolism in Prochlorococcus
John R. Casey, Adil Mardinoglu, Jens Nielsen, David M. Karl
mSystems Nov 2016, 1 (6) e00065-16; DOI: 10.1128/mSystems.00065-16

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Adaptive Evolution of Phosphorus Metabolism in Prochlorococcus
John R. Casey, Adil Mardinoglu, Jens Nielsen, David M. Karl
mSystems Nov 2016, 1 (6) e00065-16; DOI: 10.1128/mSystems.00065-16
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    • ABSTRACT
    • INTRODUCTION
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KEYWORDS

Prochlorococcus
evolution of metabolic networks
flux balance analysis
metabolic modeling
phosphorus metabolism
succinate dehydrogenase

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