Determinants of Brain Cell Metabolic Phenotypes and Energy Substrate Utilization Unraveled with a Modeling Approach
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{"title"=>"Determinants of Brain Cell Metabolic Phenotypes and Energy Substrate Utilization Unraveled with a Modeling Approach", "type"=>"journal", "authors"=>[{"first_name"=>"Aitana", "last_name"=>"Neves", "scopus_author_id"=>"54796834000"}, {"first_name"=>"Robert", "last_name"=>"Costalat", "scopus_author_id"=>"6601988558"}, {"first_name"=>"Luc", "last_name"=>"Pellerin", "scopus_author_id"=>"7006987317"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84866951498", "isbn"=>"1553-734X", "scopus"=>"2-s2.0-84866951498", "pui"=>"365755957", "pmid"=>"23028284", "doi"=>"10.1371/journal.pcbi.1002686", "issn"=>"1553734X"}, "id"=>"91aadd82-58bb-3c2d-85fb-72bebc24404e", "abstract"=>"Although all brain cells bear in principle a comparable potential in terms of energetics, in reality they exhibit different metabolic profiles. The specific biochemical characteristics explaining such disparities and their relative importance are largely unknown. Using a modeling approach, we show that modifying the kinetic parameters of pyruvate dehydrogenase and mitochondrial NADH shuttling within a realistic interval can yield a striking switch in lactate flux direction. In this context, cells having essentially an oxidative profile exhibit pronounced extracellular lactate uptake and consumption. However, they can be turned into cells with prominent aerobic glycolysis by selectively reducing the aforementioned parameters. In the case of primarily oxidative cells, we also examined the role of glycolysis and lactate transport in providing pyruvate to mitochondria in order to sustain oxidative phosphorylation. The results show that changes in lactate transport capacity and extracellular lactate concentration within the range described experimentally can sustain enhanced oxidative metabolism upon activation. Such a demonstration provides key elements to understand why certain brain cell types constitutively adopt a particular metabolic profile and how specific features can be altered under different physiological and pathological conditions in order to face evolving energy demands.", "link"=>"http://www.mendeley.com/research/determinants-brain-cell-metabolic-phenotypes-energy-substrate-utilization-unraveled-modeling-approac", "reader_count"=>22, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>12, "Student > Ph. D. Student"=>4, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>12, "Student > Ph. D. Student"=>4, "Student > Master"=>1, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>12, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Physics and Astronomy"=>2, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>12}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Colombia"=>1, "Italy"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/576968"], "description"=>"<p>(A) As a starting point, it was assumed that lactate transport and glycolysis contribute 43% and 57%, respectively, to oxidative phosphorylation (<i>r</i> = 0.75 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Nehlig1\" target=\"_blank\">[29]</a>). Lactate transport was then stimulated by increasing <i>v<sub>max,MCT</sub></i> and <i>L<sub>e</sub></i> in the range 0–80%. The iso-curves show <i>J</i><sub>PDH</sub> normalized to its basal value (as a measure of oxidative phosphorylation). Note that in parallel, <i>v<sub>max,PDH</sub></i> and <i>k<sub>shuttle</sub></i> were multiplied by a factor <i>f</i> = 1.3, while the glycolytic flux remained fixed to its basal level. In all regions, the ratio <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Siegel1\" target=\"_blank\">[32]</a> and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hung1\" target=\"_blank\">[34]</a>. (B) As in (A), but <i>f</i> = 1.7. (C) As in (A), but <i>r</i> = 2.2 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hyder1\" target=\"_blank\">[28]</a>. (D) As in (A), but <i>r</i> = 2.2 <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hyder1\" target=\"_blank\">[28]</a> and <i>f</i> = 1.7. See <i>Description of the model</i> for equations and <i>Choice of parameters</i> for parameters.</p>", "links"=>[], "tags"=>["lactate", "oxidative", "phosphorylation"], "article_id"=>247463, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002686.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Importance_of_lactate_transport_for_oxidative_phosphorylation_in_oxidative_cells_/247463", "title"=>"Importance of lactate transport for oxidative phosphorylation in oxidative cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-13 02:04:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/576805"], "description"=>"<p>Glucose undergoes glycolysis and all resulting pyruvate is either further metabolized by PDH or converted to lactate by LDH before being transported out of the cell. On the other hand, lactate can be transported into the cell and then metabolized into pyruvate by LDH. Because these processes require NAD<sup>+</sup>/NADH, we also modeled the “recycling\" shuttle of NADH to NAD<sup>+</sup> by mitochondria. The red arrow shows the metabolism of a typical predominantly glycolytic cell, characterized by lactate export; the blue arrow shows a typical oxidative phenotype, where both glucose and lactate import contribute to oxidative phosphorylation. Abbreviations: L<sub>e</sub>, extracellular lactate; L<sub>i</sub>, intracellular lactate; P, pyruvate; NADH, reduced nicotinamide-adenine dinucleotide; Glc, glucose; <i>J</i><sub>MCT</sub>, transmembrane flux of lactate <i>via</i> MCTs; <i>J</i><sub>shuttle</sub>, flux of NADH to NAD<sup>+</sup> “recycling\" by the mitochondria; <i>J</i><sub>glyco</sub>, glycolytic flux; <i>J</i><sub>LDH</sub>, metabolic flux <i>via</i> LDH; <i>J</i><sub>PDH</sub>, metabolic flux <i>via</i> PDH.</p>", "links"=>[], "tags"=>["describing", "simplified", "energetics"], "article_id"=>247299, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002686.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_describing_the_simplified_energetics_of_brain_cells_/247299", "title"=>"Model describing the simplified energetics of brain cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-13 02:01:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/577132"], "description"=>"<p>Increases of oxidative metabolism, <i>J</i><sub>PDH</sub>, obtained with distinct ratios of lactate/glycolysis-derived pyruvate, <i>r</i>, and different increases in lactate transport flux, γ (cf. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686.e017\" target=\"_blank\">Eq. (1)</a>). Roman: below 10%; italic: between 10 and 29%; boldface: above 29%.</p>", "links"=>[], "tags"=>["lactate"], "article_id"=>247636, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002686.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Importance_of_lactate_transport_regulation_/247636", "title"=>"Importance of lactate transport regulation.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-09-13 02:07:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/303687", "https://ndownloader.figshare.com/files/303723", "https://ndownloader.figshare.com/files/303755", "https://ndownloader.figshare.com/files/303781", "https://ndownloader.figshare.com/files/303813", "https://ndownloader.figshare.com/files/303861", "https://ndownloader.figshare.com/files/303910", "https://ndownloader.figshare.com/files/304024"], "description"=>"<div><p>Although all brain cells bear in principle a comparable potential in terms of energetics, in reality they exhibit different metabolic profiles. The specific biochemical characteristics explaining such disparities and their relative importance are largely unknown. Using a modeling approach, we show that modifying the kinetic parameters of pyruvate dehydrogenase and mitochondrial NADH shuttling within a realistic interval can yield a striking switch in lactate flux direction. In this context, cells having essentially an oxidative profile exhibit pronounced extracellular lactate uptake and consumption. However, they can be turned into cells with prominent aerobic glycolysis by selectively reducing the aforementioned parameters. In the case of primarily oxidative cells, we also examined the role of glycolysis and lactate transport in providing pyruvate to mitochondria in order to sustain oxidative phosphorylation. The results show that changes in lactate transport capacity and extracellular lactate concentration within the range described experimentally can sustain enhanced oxidative metabolism upon activation. Such a demonstration provides key elements to understand why certain brain cell types constitutively adopt a particular metabolic profile and how specific features can be altered under different physiological and pathological conditions in order to face evolving energy demands.</p> </div>", "links"=>[], "tags"=>["determinants", "metabolic", "phenotypes", "substrate", "utilization", "unraveled", "modeling"], "article_id"=>119895, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002686.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s004", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s005", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s006", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s007", "https://dx.doi.org/10.1371/journal.pcbi.1002686.s008"], "stats"=>{"downloads"=>38, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Determinants_of_Brain_Cell_Metabolic_Phenotypes_and_Energy_Substrate_Utilization_Unraveled_with_a_Modeling_Approach/119895", "title"=>"Determinants of Brain Cell Metabolic Phenotypes and Energy Substrate Utilization Unraveled with a Modeling Approach", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-09-13 02:44:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/577059"], "description"=>"<p>(A) <i>Black</i>: <i>r</i> = <i>J<sub>MCT</sub></i>/<i>J<sub>glyco</sub></i> = 0.69/0.31 = 2.2 at basal state, as evaluated <i>in vivo</i> by Hyder et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hyder1\" target=\"_blank\">[28]</a>, with physiological values for intracellular lactate (L<sub>i</sub>) and pyruvate (P) concentrations (0.36 and 0.018 mM, respectively). L<sub>e</sub> = 1.1 mM. <i>Dark gray</i>: <i>r</i> = <i>J<sub>MCT</sub></i>/<i>J<sub>glyco</sub></i> = 0.76/0.24 = 3.2, as observed <i>in vitro</i> by Bouzier-Sore et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-BouzierSore1\" target=\"_blank\">[15]</a>, with the experimentally used <i>L<sub>e</sub></i> = 1.1 mM. <i>Light gray</i>: <i>L<sub>e</sub></i> was increased to 5.5 mM, as described in the experiment by Bouzier-Sore et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-BouzierSore2\" target=\"_blank\">[16]</a>, resulting in a higher, but still plausible, value of 0.039 mM for intracellular pyruvate; <i>r</i> = 4.1. <i>White</i>: Same conditions as in <i>light gray</i>, but the glycolytic rate was lowered by 60%, resulting in a lower intracellular pyruvate concentration of 0.036 mM and a <i>J<sub>MCT</sub></i>/<i>J<sub>glyco</sub></i> ratio equal to 0.92/0.08 = 11.5, which matches experimental results by Bouzier-Sore et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-BouzierSore2\" target=\"_blank\">[16]</a>. (B) Effect of lactate transport enhancement in the case of a basal <i>J<sub>MCT</sub></i>/<i>J<sub>glyco</sub></i> ratio (<i>r</i>) equal to 0.69/0.31 = 2.2, as evaluated <i>in vivo</i> by Hyder et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hyder1\" target=\"_blank\">[28]</a>, cf. black bar in (A). Basal state: <i>L<sub>e</sub></i> = 1.1 mM (black). Stimulations: +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub> (very dark gray, <i>ctrl</i>-bar); +0% <i>L<sub>e</sub></i>, +80% <i>v</i><sub>max,MCT</sub>, +70% <i>v</i><sub>max,PDH</sub>, +70% <i>k</i><sub>shuttle</sub> (dark gray); +80% <i>L<sub>e</sub></i>, +0% <i>v</i><sub>max,MCT</sub>, +70% <i>v</i><sub>max,PDH</sub>, +70% <i>k</i><sub>shuttle</sub> (light gray); +80% <i>L<sub>e</sub></i>, +80% <i>v</i><sub>max,MCT</sub>, +70% <i>v</i><sub>max,PDH</sub>, +70% <i>k</i><sub>shuttle</sub> (white).</p>", "links"=>[], "tags"=>["tested"], "article_id"=>247557, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002686.g004", "stats"=>{"downloads"=>3, "page_views"=>25, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Model_validity_tested_for_different_experimental_conditions_/247557", "title"=>"Model validity tested for different experimental conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-13 02:05:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/576856"], "description"=>"<p>(A) Oxidative phenotype: In the basal state (black), both lactate transport and glycolysis contribute to <i>J</i><sub>PDH</sub> (43% and 57% respectively <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Nehlig1\" target=\"_blank\">[29]</a>). We show the resulting steady state transport, glycolytic and <i>J</i><sub>PDH</sub> fluxes upon stimulation (very dark gray “<i>ctrl</i>-bar\": +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>; dark gray: +80% <i>v<sub>max</sub></i><sub>,MCT</sub>,, +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>; light gray: +80% <i>L<sub>e</sub></i>, +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>J</i><sub>max,glyco</sub>; white: +80% <i>v</i><sub>max,MCT</sub>, +80% <i>L<sub>e</sub></i>, +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>). (B) Glycolytic phenotype: In the basal state (black), parameters have been chosen such that lactate is taken out of the cell (, ). We show the resulting steady state transport, glycolytic and <i>J</i><sub>PDH</sub> fluxes upon stimulation (very dark gray “<i>ctrl</i>-bar\": +30% <i>v</i><sub>max,PDH</sub>, +30% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>; dark gray: +80% <i>v</i><sub>max,MCT</sub>, +15% <i>v</i><sub>max,PDH</sub>, +0% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>; light gray: +80% <i>L<sub>e</sub></i>, +15% <i>v</i><sub>max,PDH</sub>, +0% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>; white: +80% <i>L<sub>e</sub></i>, +80% <i>v</i><sub>max,MCT</sub>, +15% <i>v</i><sub>max,PDH</sub>, +0% <i>k</i><sub>shuttle</sub>, +48.5% <i>v</i><sub>max,glyco</sub>). See <i>Description of the model</i> for equations and <i>Choice of parameters</i> for parameters. (C–D) Basal lactate transport (C) and oxidative metabolism (D) when varying <i>v</i><sub>max,PDH</sub> and <i>k</i><sub>shuttle</sub>. We considered 20 different values evenly spaced in the range and , resulting in 400 simulations. For each simulation, we recorded the steady state value of <i>J</i><sub>MCT</sub> and <i>J</i><sub>PDH</sub>. We show the resulting iso-curves (note that <i>v</i><sub>max,PDH</sub> and <i>k</i><sub>shuttle</sub> were normalized to their basal <i>oxidative</i> value, so that (1,1) (marked by ‘<b>o</b>’) corresponds to the parameters used in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi-1002686-g002\" target=\"_blank\">Fig. 2A</a> and (0.2,0.3) (marked by ‘<b>g</b>’) corresponds to the parameters used in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi-1002686-g002\" target=\"_blank\">Fig. 2B</a>). In non-shaded regions, the ratio <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Siegel1\" target=\"_blank\">[32]</a>, and <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002686#pcbi.1002686-Hung1\" target=\"_blank\">[34]</a>.</p>", "links"=>[], "tags"=>["occurrence", "oxidative", "glycolytic"], "article_id"=>247350, "categories"=>["Biological Sciences", "Neuroscience"], "users"=>["Aitana Neves", "Robert Costalat", "Luc Pellerin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002686.g002", "stats"=>{"downloads"=>2, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_J_shuttle_and_v_max_PDH_determine_the_occurrence_of_oxidative_vs_glycolytic_phenotype_/247350", "title"=>"<i>J</i><sub>shuttle</sub> and <i>v</i><sub>max,PDH</sub> determine the occurrence of oxidative vs. glycolytic phenotype.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-09-13 02:02:30"}

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Relative Metric

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