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

Abstract

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.

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

“…Actually, as this analysis is only made on the second part of glycolysis, it could be envisioned to merge it with the first part of this metabolic pathway to investigate the changes in terms of and pathway flux control, and then compare the results to the previous ones, where the parts were modeled separately 8 . It would be interesting to have a detailed kinetic model of glycolysis in E. histolytica combined with other major metabolic pathways (glycogen metabolism, pentose phosphate pathway) 7 , to highlight the need to inhibit or not the main controlling enzymes identified here, as was done for cancer cells 59 . Also, the addition of genetic-level regulations could help to better understand parasite metabolism, as is done for E. coli 60 .…”

The immunologic Warburg effect: Evidence and therapeutic opportunities in autoimmunity

“…As described earlier, G3P and DHAP can be non-enzymatically converted to the advanced glycation end-product MGO, which in turn is metabolized to D-lactate by glyoxalase enzymes. MGO, which increases with GAPDH inhibition, is a reactive intermediate that may serve dedicated signaling roles (Bollong et al, 2018;Gaffney et al, 2019;Galligan et al, 2018) but also is potentially toxic (Beisswenger, Howell, Smith, & Szwergold, 2003;Marín-Hernández et al, 2016). D-lactate derived from MGO can produce acidosis.…”

Protein acetylation effects on enzyme activity and metabolic pathway fluxes

“…Furthermore, a comparative analysis of the sirtuin activities with those of all other acetyl‐CoA and NAD + producers and consumers within the intermediary metabolism should be undertaken to solidly support such statements. It has to be recalled that the enzymes with the lowest catalytic efficiencies (lowest Vmax/Km ratios) may exert significant control on the metabolite concentrations 16–19 . This scenario may be more complicated because, in contrast to eukaryotes where enzymatic and nonenzymatic protein acetylation depends on acetyl‐CoA, in bacteria, acetyl‐phosphate seems to mediate most nonenzymatic acetylation 15 …”

“…It has to be recalled that the enzymes with the lowest catalytic efficiencies (lowest Vmax/Km ratios) may exert significant control on the metabolite concentrations. [16][17][18][19] This scenario may be more complicated because, in contrast to eukaryotes where enzymatic and nonenzymatic protein acetylation depends on acetyl-CoA, in bacteria, acetyl-phosphate seems to mediate most nonenzymatic acetylation. 15 Acetylation of proteins in Archaea and its effect on metabolism is still poorly understood.…”

“…For this task, the changes reported on the activity of GAPDH (40% increase), 36 PGK (63% decrease), 44 PGAM (40% increase), 45 PYKM2 (57% decrease), 48 and LDH (60% decrease) 52 induced by acetylation (Figure 4) were individually tested in the glycolysis kinetic model previously reported and experimentally validated. 16,17 The changes in the Vmax values used for pathway modeling are shown in Table S1. The in silico simulations predicted that changes in the activity of PGAM or GAPDH do not modify the glycolytic flux and only a slight decrease (2%-7%) in the concentration of Fru,1,6BP, DHAP, G3P, 1,3BPG, and 3PG was attained; this result was expected from modification of non-flux controlling enzymes (Figure 5).…”