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Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics

View Article: PubMed Central - PubMed

ABSTRACT

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

No MeSH data available.


Experimental confirmation of differences in predictions between uFBA and FBA models.(a) TCA metabolites and pathways in the RBC metabolic model are shown, including changes in metabolite levels and metabolites found to be isotopically labeled after addition of fully labeled 13C citrate. Cofactor producing reactions are shown, while other cofactors and reaction names are omitted. Concentrations are shown as μmol/L of bag or mmol/L of bag. Time spans 0–45 days for insets. (b) uFBA (blue arrow) predicts that the depletion of intracellular malate produces extracellular malate and fumarate, while driving lactate production. Extracellular citrate is used to produce glutamate and lactate. Fluxes shown in μM. (c) FBA (red arrow) predicts extracellular citrate is used only to produce malate and fumarate and that MDH proceeds in the opposite direction than in uFBA. (d) uFBA and FBA predicted fluxes were integrated with a 13C MFA “forward” tracer simulation to simulate how labeled citrate would accumulate across the first two metabolic states. The three intracellular metabolites outside of citrate that were observed to be labeled and were absolutely quantified are shown. As fully labeled citrate is used, the only labeled versions of lactate and glutamate are shown. The unlabeled fraction of the metabolite (not shown) is the remainder. For malate, there is a small percentage of m + 3 labeling (see Fig. S11), and the remainder is unlabeled. uFBA predicted more correct labeling patterns than FBA. This is quantitatively corroborated by the residual sum of squares (RSS) for each. Abbreviations: oaa: oxaloacetate; akg: alpha-ketoglutarate; pep: phosphoenolpyruvate; MDH: malate dehydrogenase; PYK: pyruvate kinase; ICDH: isocitrate dehydrogenase. Vertical lines on metabolite time profiles denote the time intervals of the three metabolic states.
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f3: Experimental confirmation of differences in predictions between uFBA and FBA models.(a) TCA metabolites and pathways in the RBC metabolic model are shown, including changes in metabolite levels and metabolites found to be isotopically labeled after addition of fully labeled 13C citrate. Cofactor producing reactions are shown, while other cofactors and reaction names are omitted. Concentrations are shown as μmol/L of bag or mmol/L of bag. Time spans 0–45 days for insets. (b) uFBA (blue arrow) predicts that the depletion of intracellular malate produces extracellular malate and fumarate, while driving lactate production. Extracellular citrate is used to produce glutamate and lactate. Fluxes shown in μM. (c) FBA (red arrow) predicts extracellular citrate is used only to produce malate and fumarate and that MDH proceeds in the opposite direction than in uFBA. (d) uFBA and FBA predicted fluxes were integrated with a 13C MFA “forward” tracer simulation to simulate how labeled citrate would accumulate across the first two metabolic states. The three intracellular metabolites outside of citrate that were observed to be labeled and were absolutely quantified are shown. As fully labeled citrate is used, the only labeled versions of lactate and glutamate are shown. The unlabeled fraction of the metabolite (not shown) is the remainder. For malate, there is a small percentage of m + 3 labeling (see Fig. S11), and the remainder is unlabeled. uFBA predicted more correct labeling patterns than FBA. This is quantitatively corroborated by the residual sum of squares (RSS) for each. Abbreviations: oaa: oxaloacetate; akg: alpha-ketoglutarate; pep: phosphoenolpyruvate; MDH: malate dehydrogenase; PYK: pyruvate kinase; ICDH: isocitrate dehydrogenase. Vertical lines on metabolite time profiles denote the time intervals of the three metabolic states.

Mentions: There were considerable differences in flux predictions made by uFBA and FBA for RBC metabolism. To hone in on major discrepancies, we focused on metabolic reactions where uFBA and FBA predicted opposite directions of flux. We observed flux reversals in the cytosolic remnants of the TCA cycle reactions. These enzymes had been previously detected in RBC proteomic datasets16 (Fig. 3a). Over storage, RBCs uptake citrate (2.32 μM/day during State 1) and secrete malate (1.96 μM/day during State 1) and fumarate (0.300 μM/day during State 1). FBA predicts that nearly all citrate is converted to malate and fumarate (Fig. 3c). This is a reasonable estimation because the flux into and out of the cell is roughly balanced, and the Keq of malate dehydrogenase (Keq = 2.1 × 105) heavily favors metabolite flow in that direction. However, through intracellular metabolite profiling, we discovered that RBCs have a high concentration of intracellular malate (>1 mM). With this additional information, uFBA predicts that the secretion of malate and fumarate are due to the depletion of the large intracellular malate pool (26.1 μM/day, State 1). Further, uFBA predicts the shuttling of the majority of the intracellular malate and citrate into lower glycolysis through oxaloacetate, as well as production of glutamate from citrate (Fig. 3b). The network in Fig. 3a shows all TCA remnant enzymes previously detected in RBCs in proteomic studies16 or through literature curation17.


Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics
Experimental confirmation of differences in predictions between uFBA and FBA models.(a) TCA metabolites and pathways in the RBC metabolic model are shown, including changes in metabolite levels and metabolites found to be isotopically labeled after addition of fully labeled 13C citrate. Cofactor producing reactions are shown, while other cofactors and reaction names are omitted. Concentrations are shown as μmol/L of bag or mmol/L of bag. Time spans 0–45 days for insets. (b) uFBA (blue arrow) predicts that the depletion of intracellular malate produces extracellular malate and fumarate, while driving lactate production. Extracellular citrate is used to produce glutamate and lactate. Fluxes shown in μM. (c) FBA (red arrow) predicts extracellular citrate is used only to produce malate and fumarate and that MDH proceeds in the opposite direction than in uFBA. (d) uFBA and FBA predicted fluxes were integrated with a 13C MFA “forward” tracer simulation to simulate how labeled citrate would accumulate across the first two metabolic states. The three intracellular metabolites outside of citrate that were observed to be labeled and were absolutely quantified are shown. As fully labeled citrate is used, the only labeled versions of lactate and glutamate are shown. The unlabeled fraction of the metabolite (not shown) is the remainder. For malate, there is a small percentage of m + 3 labeling (see Fig. S11), and the remainder is unlabeled. uFBA predicted more correct labeling patterns than FBA. This is quantitatively corroborated by the residual sum of squares (RSS) for each. Abbreviations: oaa: oxaloacetate; akg: alpha-ketoglutarate; pep: phosphoenolpyruvate; MDH: malate dehydrogenase; PYK: pyruvate kinase; ICDH: isocitrate dehydrogenase. Vertical lines on metabolite time profiles denote the time intervals of the three metabolic states.
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f3: Experimental confirmation of differences in predictions between uFBA and FBA models.(a) TCA metabolites and pathways in the RBC metabolic model are shown, including changes in metabolite levels and metabolites found to be isotopically labeled after addition of fully labeled 13C citrate. Cofactor producing reactions are shown, while other cofactors and reaction names are omitted. Concentrations are shown as μmol/L of bag or mmol/L of bag. Time spans 0–45 days for insets. (b) uFBA (blue arrow) predicts that the depletion of intracellular malate produces extracellular malate and fumarate, while driving lactate production. Extracellular citrate is used to produce glutamate and lactate. Fluxes shown in μM. (c) FBA (red arrow) predicts extracellular citrate is used only to produce malate and fumarate and that MDH proceeds in the opposite direction than in uFBA. (d) uFBA and FBA predicted fluxes were integrated with a 13C MFA “forward” tracer simulation to simulate how labeled citrate would accumulate across the first two metabolic states. The three intracellular metabolites outside of citrate that were observed to be labeled and were absolutely quantified are shown. As fully labeled citrate is used, the only labeled versions of lactate and glutamate are shown. The unlabeled fraction of the metabolite (not shown) is the remainder. For malate, there is a small percentage of m + 3 labeling (see Fig. S11), and the remainder is unlabeled. uFBA predicted more correct labeling patterns than FBA. This is quantitatively corroborated by the residual sum of squares (RSS) for each. Abbreviations: oaa: oxaloacetate; akg: alpha-ketoglutarate; pep: phosphoenolpyruvate; MDH: malate dehydrogenase; PYK: pyruvate kinase; ICDH: isocitrate dehydrogenase. Vertical lines on metabolite time profiles denote the time intervals of the three metabolic states.
Mentions: There were considerable differences in flux predictions made by uFBA and FBA for RBC metabolism. To hone in on major discrepancies, we focused on metabolic reactions where uFBA and FBA predicted opposite directions of flux. We observed flux reversals in the cytosolic remnants of the TCA cycle reactions. These enzymes had been previously detected in RBC proteomic datasets16 (Fig. 3a). Over storage, RBCs uptake citrate (2.32 μM/day during State 1) and secrete malate (1.96 μM/day during State 1) and fumarate (0.300 μM/day during State 1). FBA predicts that nearly all citrate is converted to malate and fumarate (Fig. 3c). This is a reasonable estimation because the flux into and out of the cell is roughly balanced, and the Keq of malate dehydrogenase (Keq = 2.1 × 105) heavily favors metabolite flow in that direction. However, through intracellular metabolite profiling, we discovered that RBCs have a high concentration of intracellular malate (>1 mM). With this additional information, uFBA predicts that the secretion of malate and fumarate are due to the depletion of the large intracellular malate pool (26.1 μM/day, State 1). Further, uFBA predicts the shuttling of the majority of the intracellular malate and citrate into lower glycolysis through oxaloacetate, as well as production of glutamate from citrate (Fig. 3b). The network in Fig. 3a shows all TCA remnant enzymes previously detected in RBCs in proteomic studies16 or through literature curation17.

View Article: PubMed Central - PubMed

ABSTRACT

The increasing availability of metabolomics data necessitates novel methods for deeper data analysis and interpretation. We present a flux balance analysis method that allows for the computation of dynamic intracellular metabolic changes at the cellular scale through integration of time-course absolute quantitative metabolomics. This approach, termed “unsteady-state flux balance analysis” (uFBA), is applied to four cellular systems: three dynamic and one steady-state as a negative control. uFBA and FBA predictions are contrasted, and uFBA is found to be more accurate in predicting dynamic metabolic flux states for red blood cells, platelets, and Saccharomyces cerevisiae. Notably, only uFBA predicts that stored red blood cells metabolize TCA intermediates to regenerate important cofactors, such as ATP, NADH, and NADPH. These pathway usage predictions were subsequently validated through 13C isotopic labeling and metabolic flux analysis in stored red blood cells. Utilizing time-course metabolomics data, uFBA provides an accurate method to predict metabolic physiology at the cellular scale for dynamic systems.

No MeSH data available.