Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect
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{"title"=>"Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the warburg effect", "type"=>"journal", "authors"=>[{"first_name"=>"Tomer", "last_name"=>"Shlomi", "scopus_author_id"=>"8592513400"}, {"first_name"=>"Tomer", "last_name"=>"Benyamini", "scopus_author_id"=>"36101116600"}, {"first_name"=>"Eyal", "last_name"=>"Gottlieb", "scopus_author_id"=>"7102235134"}, {"first_name"=>"Roded", "last_name"=>"Sharan", "scopus_author_id"=>"7005271135"}, {"first_name"=>"Eytan", "last_name"=>"Ruppin", "scopus_author_id"=>"7005381250"}], "year"=>2011, "source"=>"PLoS Computational Biology", "identifiers"=>{"pmid"=>"21423717", "doi"=>"10.1371/journal.pcbi.1002018", "sgr"=>"79953661070", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "scopus"=>"2-s2.0-79953661070", "issn"=>"1553734X", "pui"=>"361550350"}, "id"=>"170b7e27-23fa-3f77-beef-63825d90a389", "abstract"=>"The Warburg effect--a classical hallmark of cancer metabolism--is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.", "link"=>"http://www.mendeley.com/research/genomescale-metabolic-modeling-elucidates-role-proliferative-adaptation-causing-warburg-effect", "reader_count"=>292, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>19, "Researcher"=>92, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>86, "Student > Postgraduate"=>9, "Other"=>9, "Student > Master"=>31, "Student > Bachelor"=>18, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>12}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>19, "Researcher"=>92, "Student > Doctoral Student"=>9, "Student > Ph. D. Student"=>86, "Student > Postgraduate"=>9, "Other"=>9, "Student > Master"=>31, "Student > Bachelor"=>18, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>2, "Professor"=>12}, "reader_count_by_subject_area"=>{"Unspecified"=>19, "Agricultural and Biological Sciences"=>168, "Chemical Engineering"=>2, "Chemistry"=>5, "Computer Science"=>16, "Engineering"=>24, "Biochemistry, Genetics and Molecular Biology"=>28, "Materials Science"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>18, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>3, "Social Sciences"=>1, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>18}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>19}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Chemical Engineering"=>{"Chemical Engineering"=>2}, "Engineering"=>{"Engineering"=>24}, "Chemistry"=>{"Chemistry"=>5}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>168}, "Computer Science"=>{"Computer Science"=>16}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>28}}, "reader_count_by_country"=>{"United States"=>13, "Thailand"=>1, "Portugal"=>2, "Russia"=>1, "Sweden"=>2, "Netherlands"=>2, "South Korea"=>1, "Iran"=>2, "Luxembourg"=>2, "China"=>1, "Brazil"=>1, "Chile"=>1, "Japan"=>1, "United Kingdom"=>4, "Switzerland"=>1, "India"=>1, "Spain"=>1, "Canada"=>1, "Czech Republic"=>1, "Finland"=>1, "Taiwan"=>1, "Denmark"=>1, "Mexico"=>1, "Australia"=>2, "Germany"=>4}, "group_count"=>17}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/792135"], "description"=>"<p>(A) as predicted across phases I-III in the model and (B) based on experimental measurements taken from BJ fibroblast cell lines representing the path towards tumorigenic conversion (CL1-CL4; <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002018#pcbi.1002018-Ramanathan1\" target=\"_blank\">[32]</a>). The model's predictions are compatible with the experimental evidences for increased glycolytic activity (expressed by increased lactate production) during full cancerous development (phase III, CL4) preceded by an increase in the oxidative phosphorylation (OXPHOS) activity (expressed by the mitochondrial gene expression). Experimental results for CL2-CL4 are given as the fold change relative to the same measurement in the CL1 cell line. In (B), the bars represent the mean fold change for each set of metabolites/genes and the error bars represent the standard deviation.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "cell biology"], "article_id"=>462504, "categories"=>["Biological Sciences", "Cell Biology", "Genetics"], "users"=>["Tomer Shlomi", "Tomer Benyamini", "Eyal Gottlieb", "Roded Sharan", "Eytan Ruppin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002018.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathway_activity_differences_/462504", "title"=>"Pathway activity differences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:41:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/792188"], "description"=>"<p>The axes (A, B) describe the growth rate obtained from flux distributions A and B, respectively. The blue lines represent two different constraints on the glucose uptake rate, and the red line represents the maximal concentration constraint. Green dashed lines are the contours of the growth rate maximization objective function – the further the line is from the origin, the higher the growth rate. When the glucose uptake U is limiting (dark grey feasible region), the maximal growth rate is obtained via A only (Solution 1; left green diamond). When both the uptake and the enzyme concentration constraints are limiting (light grey feasible region), maximal growth rate (G) is obtained via a combination of A and B (Solution 2; right green diamond), resulting in a shift to a less efficient metabolism and lactate secretion. This can be explained by the fact that the slope of the growth-rate (middle green) line (-1) is larger than the slope of the enzyme concentration limit (red) line (), that is the yield-to-cost ratio of flux distribution B is greater than that of flux distribution A ().</p>", "links"=>[], "tags"=>["describing", "feasible"], "article_id"=>462555, "categories"=>["Biological Sciences", "Cell Biology", "Genetics"], "users"=>["Tomer Shlomi", "Tomer Benyamini", "Eyal Gottlieb", "Roded Sharan", "Eytan Ruppin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002018.g003", "stats"=>{"downloads"=>3, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_plane_describing_the_feasible_region_in_our_model_/462555", "title"=>"A plane describing the feasible region in our model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:42:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/396250", "https://ndownloader.figshare.com/files/396262", "https://ndownloader.figshare.com/files/396286", "https://ndownloader.figshare.com/files/396313"], "description"=>"<div><p>The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.</p> </div>", "links"=>[], "tags"=>["genome-scale", "metabolic", "modeling", "elucidates", "proliferative", "adaptation", "causing", "warburg"], "article_id"=>138269, "categories"=>["Biological Sciences", "Cell Biology", "Genetics"], "users"=>["Tomer Shlomi", "Tomer Benyamini", "Eyal Gottlieb", "Roded Sharan", "Eytan Ruppin"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002018.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002018.s002", "https://dx.doi.org/10.1371/journal.pcbi.1002018.s003", "https://dx.doi.org/10.1371/journal.pcbi.1002018.s004"], "stats"=>{"downloads"=>30, "page_views"=>41, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Genome_Scale_Metabolic_Modeling_Elucidates_the_Role_of_Proliferative_Adaptation_in_Causing_the_Warburg_Effect/138269", "title"=>"Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-03-10 02:17:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/792075"], "description"=>"<p>(A) Predicted maximalgrowth yield of human cells (per unit of glucose uptake; y-axis) for a range of growth rates (x-axis), based strictly on reactions' stoichiometry (dotted) and by considering also enzyme mass and enzyme solvent capacity (solid). Vertical dashed lines indicate the borders between: phase I (high yield, no lactate secretion), phase II (medium yield, increased oxidative phosphorylation) and phase III (low yield, lactate secretion). (B) Predicted lactate secretion flux (red lines) and oxygen consumption flux (blue lines) for a range of growth rates. Growth rates were manipulated by varying the glucose uptake rate limit from 0 until the uptake value needed to reach the maximal growth rate. Fluxes were normalized by the glucose uptake rate. (C) Experimentally determined lactate secretion rates (red; squares) and oxygen uptake rates (blue; circles) during tumor development of H-RasV12/E1A transformed fibroblasts. NRFU: Normalized relative fluorescence units; see <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002018#pcbi.1002018-deGroof1\" target=\"_blank\">[31]</a> for more details.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "cell biology"], "article_id"=>462438, "categories"=>["Biological Sciences", "Cell Biology", "Genetics"], "users"=>["Tomer Shlomi", "Tomer Benyamini", "Eyal Gottlieb", "Roded Sharan", "Eytan Ruppin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002018.g001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metabolic_behavior_across_increasing_growth_rates_/462438", "title"=>"Metabolic behavior across increasing growth rates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:40:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/792296"], "description"=>"<p>The increase in proliferation rate achievable by the increased uptake of each of the 20 amino-acids in addition to glucose (relative to the baseline growth rate achieved when only glucose is available), as predicted by the stoichiometric model (A) and by the model accounting for the solvent capacity constraint (B). Glutamine uptake (highlighted in yellow) enables to achieve the highest increase in growth rate according to the solvent capacity model, in agreement with experimental data showing preference for high glutamine uptake rates in cancer.</p>", "links"=>[], "tags"=>["genetics and genomics", "Computational biology", "cell biology"], "article_id"=>462663, "categories"=>["Biological Sciences", "Cell Biology", "Genetics"], "users"=>["Tomer Shlomi", "Tomer Benyamini", "Eyal Gottlieb", "Roded Sharan", "Eytan Ruppin"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1002018.g004", "stats"=>{"downloads"=>3, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Amino_acid_growth_rate_contribution_/462663", "title"=>"Amino-acid growth rate contribution.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-03-10 00:44:23"}

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Relative Metric

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