Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms

Abstract

The effect of hypoglycemia on the contents of glycolytic proteins, activities of enzymes/transporters and flux of HeLa and MCF-7 tumor cells was experimentally analyzed and modeled in silico. After 24 h hypoglycemia (2.5 mM initial glucose), significant increases in the protein levels of glucose transporters 1 and 3 (GLUT 1 and 3) (3.4 and 2.1-fold, respectively) and hexokinase I (HKI) (2.3-fold) were observed compared to the hyperglycemic standard cell culture condition (25 mM initial glucose). However, these changes did not bring about a significant increase in the total activities (V max ) of GLUT and HK; instead, the affinity of these proteins for glucose increased, which may explain the twofold increased glycolytic flux under hypoglycemia. Thus, an increase in more catalytically efficient isoforms for two of the main controlling steps was sufficient to induce increased flux. Further, a previous kinetic model of tumor glycolysis was updated by including the ratios of GLUT and HK isoforms, modified pyruvate kinase kinetics and an oxidative phosphorylation reaction. The updated model was robust in terms of simulating most of the metabolite levels and fluxes of the cells exposed to various glycemic conditions. Model simulations indicated that the main controlling steps were glycogen degradation > HK > hexosephosphate isomerase under hyper-and normoglycemia, and GLUT > HK > glycogen degradation under hypoglycemia. These predictions were experimentally evaluated: the glycolytic flux of hypoglycemic cells was more sensitive to cytochalasin B (a GLUT inhibitor) than that of hyperglycemic cells. The results indicated that cancer glycolysis should be inhibited at multiple controlling sites, regardless of external glucose levels, to effectively block the pathway. DatabaseThe mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at

Computational Model Predicts the Effects of Targeting Cellular Metabolism in Pancreatic Cancer

“…We constructed a kinetic model of pancreatic cancer cell metabolism using previously published models of metabolism from various cell types (Mulukutla et al, 2010; Marín-Hernández et al, 2011; Mulukutla et al, 2012; Marín-Hernández et al, 2014; Shestov et al, 2014; Mulukutla et al, 2015). Our model is comprised of a total of 46 metabolites and 53 enzymatic reactions including glycolysis, glutaminolysis, the TCA cycle, the PPP, and malate-aspartate-ketoglutarate-glutamate shuttles between the cytosolic and mitochondrial compartments (Figure 1).…”

“…Abbreviations for the metabolites and reaction names are given in Supplementary File S3 and the values of the fixed parameters are listed in Supplementary File S4. The detailed rate equations for glycolysis and their corresponding kinetic constants are primarily based on the glycolysis model for HeLa cells (Marín-Hernández et al, 2011, 2014). This glycolysis reaction network was extended to include reactions from the TCA cycle and PPP using kinetic rate laws and parameters from Mulukutla and coworkers (Wu et al, 2007; Mulukutla et al, 2010, 2012, 2015).…”

“…Therefore, we allowed the initial concentration for each metabolite to vary within a specified range. We specified the concentration ranges based on published measurements obtained from various cell lines, including human cervical cancer (Marín-Hernández et al, 2011, 2014), diseased and surrounding normal tissue samples from stomach and colon cancer patients (Hirayama et al, 2009), breast cancer cell extracts (Le Guennec et al, 2012), PDAC cancer patient samples (Fontana et al, 2016) and mouse myeloma and CHO cell lines (Mulukutla et al, 2012, 2015). Additional uncertainty for pancreatic cancer cells was considered by increasing and decreasing the upper and lower bounds, respectively, by 20%.…”

“…In these cases, only the forward reaction velocity is fit to the experimental data, thereby reducing the number of fitted parameters. The V eq is calculated using the published works from which our model is derived (Wu et al, 2007; Mulukutla et al, 2010, 2012, 2015; Marín-Hernández et al, 2011, 2014). …”

Liposomes co-Loaded with 6-Phosphofructo-2-Kinase/Fructose-2, 6-Biphosphatase 3 (PFKFB3) shRNA Plasmid and Docetaxel for the Treatment of non-small Cell Lung Cancer

“…The glycolytic flux is 2 to 17-folds higher in tumor cells compared to normal cells (4). This is because of the high expression of all the glycolytic enzymes such as phosphofructokinase (6-Phosphofructo-2-Kinase/Fructose-2, 6-Biphosphatase-3, PFKFB3), hexokinase, pyruvate kinase, etc.…”