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Organization of enzyme concentration across the metabolic network in cancer cells.

Madhukar NS, Warmoes MO, Locasale JW - PLoS ONE (2015)

Bottom Line: We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network.At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome.We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding.

View Article: PubMed Central - PubMed

Affiliation: Tri-Institutional Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, Weill Cornell Medical College, New York, New York, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.

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Related in: MedlinePlus

Analysis of enzyme concentrations in relation to kinetic and thermodynamic properties.(a) Scatter plot of the log of the average KM and ΔG° value with each dot representing a different metabolic protein. (b) Scatter plot of the log of the average cell protein copy (CPC) and ΔG° values with each dot representing a different metabolic protein. (c) Scatter plot of the log of both the average KM and average CPC value with each dot representing a different metabolic protein. (d) Connectivity analysis of CPC, ΔG° and KM values for the various enzymes.
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pone.0117131.g006: Analysis of enzyme concentrations in relation to kinetic and thermodynamic properties.(a) Scatter plot of the log of the average KM and ΔG° value with each dot representing a different metabolic protein. (b) Scatter plot of the log of the average cell protein copy (CPC) and ΔG° values with each dot representing a different metabolic protein. (c) Scatter plot of the log of both the average KM and average CPC value with each dot representing a different metabolic protein. (d) Connectivity analysis of CPC, ΔG° and KM values for the various enzymes.

Mentions: Ultimately, we note that that the reaction rate or flux through a point in metabolism involves not only the enzyme concentration, but kinetic parameters and the thermodynamics of the chemical compounds involved in the reaction as well. These parameters include the Michaelis constant (KM) and standard Gibbs Free Energies (ΔG°) of the reactions (S2 Table). We therefore investigated the relationship between these three fundamental parameters. Surprisingly no correlation between average protein level and ΔG° (Fig. 6a), KM values and ΔG° (Fig. 6b), and average protein level and KM values (Fig. 6c) were observed. To gain additional insight into the relationship between these three variables, we also visualized these three variables together for each enzyme. Using this approach we distinguished 4 groups (Fig. 6d). The bulk of the enzymes visualized in this manner had moderate ΔG°, KM values and protein copy numbers (group 1) and were therefore responsible for the overall lack of correlation between these three variables. This finding is in contrast to a previously held assumption that larger protein concentrations are required for reactions close to equilibrium[37]. Furthermore, this analysis also provides strong evidence that each of these fundamental variables for a cellular metabolic reaction is uncoupled allowing for independent tuning of these three parameters for the evolution of the human metabolic network. Also, this lack of correlation suggests that overall, protein expression is emblematic of reaction rate since the KM and ΔG° for each reaction involving a given protein concentration appears uncorrelated. There were however a few exceptions to this general rule. First, the proteins in group 2 (Fig. 6d) corresponded largely to the previously mentioned glycolytic proteins with no apparent large KM or very low ΔG° values. This corresponds with the notion that these proteins need to be highly expressed to ensure a high glycolytic flux that may result in the buildup of glycolytic intermediates and subsequent enhanced flux into the various biosynthetic branches. The other two groups (group 3 and 4) contained enzymes with either very large KM values or very low ΔG°. The extreme KM values indicate that these enzymes need a substantial buildup of substrates in order to result in an appreciable forward flux while reactions with very low ΔG° are highly irreversible reactions. Indeed, three enzymes with large KM values (GNPDA, GPT2 and GART) directly drain glycolytic intermediates into biosynthetic pathways while three enzymes with very low ΔG° (ATIC, PPAT and QPRT) utilize phosphoribosyl diphosphate (PRRP) derived from the pentose phosphate pathway for NAD(P)H and nucleotide synthesis. Also several enzymes in group 3 (GLS, NAGS, OAT, GOT1 and ASL) are involved in arginine, aspartate, glutamine metabolism, for which the metabolites have some of the highest intracellular concentrations and/or fluxes.


Organization of enzyme concentration across the metabolic network in cancer cells.

Madhukar NS, Warmoes MO, Locasale JW - PLoS ONE (2015)

Analysis of enzyme concentrations in relation to kinetic and thermodynamic properties.(a) Scatter plot of the log of the average KM and ΔG° value with each dot representing a different metabolic protein. (b) Scatter plot of the log of the average cell protein copy (CPC) and ΔG° values with each dot representing a different metabolic protein. (c) Scatter plot of the log of both the average KM and average CPC value with each dot representing a different metabolic protein. (d) Connectivity analysis of CPC, ΔG° and KM values for the various enzymes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4306493&req=5

pone.0117131.g006: Analysis of enzyme concentrations in relation to kinetic and thermodynamic properties.(a) Scatter plot of the log of the average KM and ΔG° value with each dot representing a different metabolic protein. (b) Scatter plot of the log of the average cell protein copy (CPC) and ΔG° values with each dot representing a different metabolic protein. (c) Scatter plot of the log of both the average KM and average CPC value with each dot representing a different metabolic protein. (d) Connectivity analysis of CPC, ΔG° and KM values for the various enzymes.
Mentions: Ultimately, we note that that the reaction rate or flux through a point in metabolism involves not only the enzyme concentration, but kinetic parameters and the thermodynamics of the chemical compounds involved in the reaction as well. These parameters include the Michaelis constant (KM) and standard Gibbs Free Energies (ΔG°) of the reactions (S2 Table). We therefore investigated the relationship between these three fundamental parameters. Surprisingly no correlation between average protein level and ΔG° (Fig. 6a), KM values and ΔG° (Fig. 6b), and average protein level and KM values (Fig. 6c) were observed. To gain additional insight into the relationship between these three variables, we also visualized these three variables together for each enzyme. Using this approach we distinguished 4 groups (Fig. 6d). The bulk of the enzymes visualized in this manner had moderate ΔG°, KM values and protein copy numbers (group 1) and were therefore responsible for the overall lack of correlation between these three variables. This finding is in contrast to a previously held assumption that larger protein concentrations are required for reactions close to equilibrium[37]. Furthermore, this analysis also provides strong evidence that each of these fundamental variables for a cellular metabolic reaction is uncoupled allowing for independent tuning of these three parameters for the evolution of the human metabolic network. Also, this lack of correlation suggests that overall, protein expression is emblematic of reaction rate since the KM and ΔG° for each reaction involving a given protein concentration appears uncorrelated. There were however a few exceptions to this general rule. First, the proteins in group 2 (Fig. 6d) corresponded largely to the previously mentioned glycolytic proteins with no apparent large KM or very low ΔG° values. This corresponds with the notion that these proteins need to be highly expressed to ensure a high glycolytic flux that may result in the buildup of glycolytic intermediates and subsequent enhanced flux into the various biosynthetic branches. The other two groups (group 3 and 4) contained enzymes with either very large KM values or very low ΔG°. The extreme KM values indicate that these enzymes need a substantial buildup of substrates in order to result in an appreciable forward flux while reactions with very low ΔG° are highly irreversible reactions. Indeed, three enzymes with large KM values (GNPDA, GPT2 and GART) directly drain glycolytic intermediates into biosynthetic pathways while three enzymes with very low ΔG° (ATIC, PPAT and QPRT) utilize phosphoribosyl diphosphate (PRRP) derived from the pentose phosphate pathway for NAD(P)H and nucleotide synthesis. Also several enzymes in group 3 (GLS, NAGS, OAT, GOT1 and ASL) are involved in arginine, aspartate, glutamine metabolism, for which the metabolites have some of the highest intracellular concentrations and/or fluxes.

Bottom Line: We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network.At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome.We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding.

View Article: PubMed Central - PubMed

Affiliation: Tri-Institutional Program in Computational Biology and Medicine, Cornell University, Ithaca, New York, Weill Cornell Medical College, New York, New York, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America; Division of Nutritional Sciences, Cornell University, Ithaca, New York, United States of America.

ABSTRACT
Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.

Show MeSH
Related in: MedlinePlus