Hypoglycemia Enhances Epithelial-Mesenchymal Transition and Invasiveness, and Restrains the Warburg Phenotype, in Hypoxic HeLa Cell Cultures and Microspheroids.

The accelerated growth of solid tumors leads to episodes of both hypoxia and hypoglycemia (HH) affecting their intermediary metabolism, signal transduction, and transcriptional activity. A previous study showed that normoxia (20% O2 ) plus 24 h hypoglycemia (2.5 mM glucose) increased glycolytic flux whereas oxidative phosphorylation (OxPhos) was unchanged versus normoglycemia in HeLa cells. However, the simultaneous effect of HH on energy metabolism has not been yet examined. Therefore, the effect of hypoxia (0.1-1% O2 ) plus hypoglycemia on the energy metabolism of HeLa cells was analyzed by evaluating protein content and activity, along with fluxes of both glycolysis and OxPhos. Under hypoxia, in which cell growth ceased and OxPhos enzyme activities, ΔΨm and flux were depressed, hypoglycemia did not stimulate glycolytic flux despite increasing H-RAS, p-AMPK, GLUT1, GLUT3, and HKI levels, and further decreasing mitochondrial enzyme content. The impaired mitochondrial function in HH cells correlated with mitophagy activation. The depressed OxPhos and unchanged glycolysis pattern was also observed in quiescent cells from mature multicellular tumor spheroids, suggesting that these inner cell layers are similarly subjected to HH. The principal ATP supplier was glycolysis for HH 2D monolayer and 3D quiescent spheroid cells. Accordingly, the glycolytic inhibitors iodoacetate and gossypol were more effective than mitochondrial inhibitors in decreasing HH-cancer cell viability. Under HH, stem cell-, angiogenic-, and EMT-biomarkers, as well as glycoprotein-P content and invasiveness, were also enhanced. These observations indicate that HH cancer cells develop an attenuated Warburg and pronounced EMT- and invasive-phenotype. J. Cell. Physiol. 232: 1346-1359, 2017. © 2016 Wiley Periodicals, Inc.

Assessment of the low inhibitory specificity of oxamate, aminooxyacetate and dichloroacetate on cancer energy metabolism.

BACKGROUND: Exceedingly high therapeutic/experimental doses of metabolic drugs such as oxamate, aminooxyacetate (AOA) and dichloroacetate (DCA) are required to diminish growth, glycolysis and oxidative phosphorylation (OxPhos) of different cancer cells. To identify the mechanisms of action of these drugs on cancer energy metabolism, a systematic analysis of their specificities was undertaken. METHODS: Hepatocarcinoma AS-30D cells were treated with the inhibitors and glycolysis and OxPhos enzyme activities, metabolites and fluxes were analyzed. Kinetic modeling of glycolysis was used to identify the regulatory mechanisms. RESULTS: Oxamate (i) not only inhibited LDH, but also PYK and ENO activities inducing an increase in the cytosolic NAD(P)H, Fru1,6BP and DHAP levels in AS-30D cells; (ii) it slightly inhibited HPI, ALD and Glc6PDH; and (iii) it inhibited pyruvate-driven OxPhos in isolated heart mitochondria. AOA (i) strongly inhibited both AAT and AlaT, and 2-OGDH and glutamate-driven OxPhos; and (ii) moderately affected GAPDH and TPI. DCA slightly affected pyruvate-driven OxPhos and Glc6PDH. Kinetic modeling of cancer glycolysis revealed that oxamate inhibition of LDH, PYK and ENO was insufficient to achieve glycolysis flux inhibition. To do so, HK, HPI, TPI and GAPDH have to be also inhibited by the accumulated Fru1,6BP and DHAP induced by oxamate. CONCLUSION: Oxamate, AOA, and DCA are not specific drugs since they inhibit several enzymes/transporters of the glycolytic and OxPhos pathways through direct interaction or indirect mechanisms. GENERAL SIGNIFICANCE: These data explain why oxamate or AOA, through their multisite inhibitory actions on glycolysis or OxPhos, may be able to decrease the proliferation of cancer cells.

Inhibition of Non-flux-Controlling Enzymes Deters Cancer Glycolysis by Accumulation of Regulatory Metabolites of Controlling Steps.

Glycolysis provides precursors for the synthesis of macromolecules and may contribute to the ATP supply required for the constant and accelerated cellular duplication in cancer cells. In consequence, inhibition of glycolysis has been reiteratively considered as an anti-cancer therapeutic option. In previous studies, kinetic modeling of glycolysis in cancer cells allowed the identification of the main steps that control the glycolytic flux: glucose transporter, hexokinase (HK), hexose phosphate isomerase (HPI), and glycogen degradation in human cervix HeLa cancer cells and rat AS-30D ascites hepatocarcinoma. It was also previously experimentally determined that simultaneous inhibition of the non-controlling enzymes lactate dehydrogenase (LDH), pyruvate kinase (PYK), and enolase (ENO) brings about significant decrease in the glycolytic flux of cancer cells and accumulation of intermediate metabolites, mainly fructose-1,6-bisphosphate (Fru1,6BP), and dihydroxyacetone phosphate (DHAP), which are inhibitors of HK and HPI, respectively. Here it was found by kinetic modeling that inhibition of cancer glycolysis can be attained by blocking downstream non flux-controlling steps as long as Fru1,6BP and DHAP, regulatory metabolites of flux-controlling enzymes, are accumulated. Furthermore, experimental results and further modeling showed that oxamate and iodoacetate inhibitions of PYK, ENO, and glyceraldehyde3-phosphate dehydrogenase (GAPDH), but not of LDH and phosphoglycerate kinase, induced accumulation of Fru1,6BP and DHAP in AS-30D hepatoma cells. Indeed, PYK, ENO, and GAPDH exerted the highest control on the Fru1,6BP and DHAP concentrations. The high levels of these metabolites inhibited HK and HPI and led to glycolytic flux inhibition, ATP diminution, and accumulation of toxic methylglyoxal. Hence, the anticancer effects of downstream glycolytic inhibitors are very likely mediated by this mechanism. In parallel, it was also found that uncompetitive inhibition of the flux-controlling steps is a more potent mechanism than competitive and mixed-type inhibition to efficiently perturb cancer glycolysis.

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

UNLABELLED: 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 (Vmax ) 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. DATABASE: The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.mib.ac.uk/database/achcar/index.html. [Database section added 21 July 2014 after original online publication].