Edited by: Osbaldo Resendis-Antonio, Instituto Nacional de Medicina Genomica, Mexico
Reviewed by: Julio Vera González, University Hospital Erlangen, Germany; Anshu Bhardwaj, Institute of Microbial Technology (CSIR), India
*Correspondence: Álvaro Marín-Hernández
Emma Saavedra
This article was submitted to Systems Biology, a section of the journal Frontiers in Physiology
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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.
In recent years it has been extensively documented that oxidative phosphorylation (OxPhos) is predominant for supplying ATP in cancer cells under aerobic conditions (Zu and Guppy,
By applying the fundamentals of metabolic control analysis (Fell,
However, non-flux-controlling glycolytic steps such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate kinase (PYK), and lactate dehydrogenase (LDH) have been also proposed as suitable targets for inhibition of cancer glycolysis (Ganapathy-Kanniappan et al.,
On the other hand, the glycolytic metabolites glucose-6-phosphate (Glc6P) and fructose-1,6-bisphosphate (Fru1,6BP) and the pentose phosphate pathway metabolites erythrose-4-phosphate (Ery4P) and 6-phosphogluconate (6PG) can modulate the activities of the controlling enzymes HK and HPI through competitive and mixed-type inhibitions. Furthermore, some metabolites that at low, physiological concentrations are innocuous, at high concentrations may become inhibitors of the controlling steps HK (Fru1,6BP) and HPI (dihydroxyacetone-phosphate; DHAP), inducing significant inhibition of the glycolytic flux in cancer cells (Moreno-Sánchez et al.,
Such a goal was pursued and resolved in the present paper by using our published AS-30D and HeLa cells glycolysis kinetic models (Marín-Hernández et al.,
It was concluded that (i) inhibition of GAPDH with iodoacetate, or PYK/ENO with oxamate but not LDH, PGK, or PGAM, induces Fru1,6BP and DHAP accumulation and methylglyoxal production, leading to significant suppression of glycolysis; and (ii) uncompetitive inhibition of the most controlling pathway steps is the most direct and potent mechanism to efficiently perturb cancer glycolysis.
HK, Glc6PDH, HPI, aldolase, α-glycerophosphate dehydrogenase, triosephosphate isomerase (TPI), LDH, and Fru6P were purchased from Roche (Mannheim, Germany). Glucose, iodoacetate, methylglyoxal, NADH, NAD+, NADP+, and oxamate were from Sigma Chemical (St Louis, MO, USA).
AS-30D hepatocarcinoma cells were propagated in 200–250 g weight female Wistar rats by intraperitoneal inoculation of 3 mL of ascitic liquid containing ~4–6 × 108 cells/mL. After 5–6 days, the intraperitoneal cavity liquid was extracted and tumor cells were isolated by centrifugation as previously described (López-Gómez et al.,
Hepatocarcinoma AS-30D cells (15 mg cell protein/mL) were incubated in saline Krebs-Ringer medium supplied with oxamate (10 or 20 mM) or iodoacetate (2 or 4 mM) for 60 min under orbital shaking at 150 rpm and 37°C; under such conditions cell viability was always higher than 90%. Thereafter, a cell sample was withdrawn (time 0) and 5 mM glucose was added; after further 10 min of incubation another cell sample (time 10) was withdrawn. The cell samples were immediately mixed with ice-cold perchloric acid (final concentration of 3% v/v), vortexed and centrifuged at 1800 × g for 1 min at 4°C. The supernatants were neutralized with 3 M KOH/0.1 M Tris, further incubated in ice for at least 30 min and then centrifuged. The supernatants were stored at −72°C until use for determination of Glc6P, Fru6P, Fru1,6BP, G3P, DHAP, ATP, ADP, and L-lactate contents as described by Bergmeyer (
Methylglyoxal was determined by gas chromatography in a Shimadzu GC2010 apparatus (Shimadzu; Kyoto, Japan) equipped with a capillary column HP-PLOT/U of 30 m length, 0.32 mm I.D. and 10 μm film (Agilent, USA), and flame ionization detector. A methylglyoxal standard curve was carried out in the range of 0.3–30 nmoles, and the time of retention was 4.7 min. The equipment conditions were FID temperature 200°C, column temperature 180°C, oven temperature 180°C, and linear velocity 26.4 cm/s. He (10 ml/min) and H2 (40 ml/min) mix was used as carrier gas. The cell sample (15 mg/ml) was withdrawn after time 10 and centrifuged at 1800 × g for 2 min. 0.5 mL of the supernatant was removed and the cell pellet was resuspended in the remaining supernatant. The suspension was sonicated with a Branson sonicator three times for 15 s at 60% of maximal output with 1 min rest, in an ice bath. The sonicate was centrifuged at 20 800 × g for 5 min. The supernantant was filtered and 1–2 μl were injected in the gas chromatograph. The limit of detection of methylglyoxal was lower than 0.3 nmoles. The concentration in the stock solution of methlyglyoxal was enzymatically calibrated by using human ALDH2 and saturating NAD+.
The previous kinetic models of glycolysis built for HeLa and AS-30D cells (Marín-Hernández et al.,
In the AS-30D model, kinetics of GLUT was defined as a monosubstrate reversible Michaelis-Menten equation [Haldane equation (Equation 1)] as it was previously determined (Rodríguez-Enríquez et al.,
in which
In the HeLa model, the rate-equation for GLUT was changed to a double mono-substrate reversible Michaelis-Menten equation (Equation 2), representing the co-existence of two isoforms (Marín-Hernández et al.,
in which
The HK rate equation (Equation 3) used for the present updated AS-30D model was a random Bi-Bi system (Segel,
where
In the HeLa model, the HK rate equation (Equation 4) was a double random-bisubstrate Michaelis-Menten to also represent the coexistence of two enzyme isoforms as previously reported (Marín-Hernández et al.,
in which
The HPI rate equation in the HeLa model was considered as a monoreactant reversible Michaelis-Menten equation (Equation 5) with (a) competitive (Marín-Hernández et al.,
The HPI rate equation in the AS-30D model was a monoreactant reversible equation (Equation 6) with competitive inhibition by four modulators: Ery4P, 6PG, Fru1,6BP and DHAP as experimentally determined (Moreno-Sánchez et al.,
The rate equation for PFK-I (Equation 7) in all kinetic models was the concerted transition model of Monod, Wyman and Changeux for exclusive ligand binding (Fru6P, activators, and inhibitors) together with mixed-type activation and simple Michaelis–Menten terms for ATP and reverse reaction (Marín-Hernández et al.,
Probably derived from the high complexity of the PFK1 rate equation, the algorithms used by COPASI to generate the ordinary differential equations to calculate the variation in the metabolite concentrations assign the role of the first substrate (or product) to that defined in the reaction specification window. If the order of substrates and products in the reaction does not match with that stated in the rate equation, then the computer program mix-up the identity of the ligands in the ordinary differential equations. Therefore, to correct for this type of errors both the syntax reaction and the rate equation have to be displayed in the same order of substrates and products; then one should be aware that the reaction syntax does not necessarily reflect the order of binding in the enzyme, which defines the type of reaction mechanism.
Reaction: ATP + Fru6P = ADP + Fru1,6BP
The ALDO rate equation was the reversible Uni-Bi random Michaelis-Menten equation (Equation 8) in all kinetic models as was reported in previous kinetic models (Marín-Hernández et al.,
Kinetics for TPI in the AS-30D model was here depicted by a mono-substrate simple reversible Michaelis-Menten equation (Equation 9) with mixed type inhibition by Fru1,6BP as it was previously determined (Moreno-Sánchez et al.,
α value is the factor by which
In the HeLa model, the TPI rate equation (Equation 10) was a mono-substrate simple reversible Michaelis-Menten equation as it was previously determined (Marín-Hernández et al.,
GAPDH kinetics in the AS-30D model was here described by a simplified ordered Ter-Bi Michaelis-Menten equation (Equation 11) with mixed type inhibition by Fru1,6BP as previously determined (Moreno-Sánchez et al.,
where
In the HeLa model, the GAPDH rate equation (Equation 12) was a simplified ordered Ter-Bi Michaelis-Menten equation as was previously determined and used in previous models (Marín-Hernández et al.,
In all models the rate equations for PGAM and ENO were depicted by mono-substrate simple reversible Michaelis-Menten equation (Equation 13):
in which [
The PYK rate equation (Equation 14) in all models was defined as simple random-bisubstrate Michaelis-Menten that represents the kinetics of the prevalent PYK isoform in cancer cells with no cooperativity or allosteric modulation by typical metabolites, as experimentally determined for AS-30D cells (Marín-Hernández et al.,
In all models, the rate equations (Equation 15) for PGK and LDH were defined by random Bi-Bi reversible Michaelis-Menten for non-interacting substrates (α and β = 1) according to the reported literature (Marín-Hernández et al.,
In the HeLa model the rate-equation (Equation 16) for the monocarboxylate transporter (MCT) activity was incorporated (Marín-Hernández et al.,
Crystal structures of human HK type I, TPI and GAPDH, and mouse HPI, were obtained from the protein data bank (accession numbers 4FOI, 1HTI and 3PFW, and 1U0F, respectively). The three dimensional models of the ligands used in the study were obtained from different crystal structures found in the protein data bank or downloaded from PubChem (
Inhibition of a low- or non-flux controlling step might be regarded as a mishap or misleading option to decrease the pathway flux because it requires almost complete inhibition (a “pharmacological knock out” or >80% inhibition) of the activity to decrease the pathway flux to the levels reached by inhibiting a flux-controlling step. However, there are experimental evidences indicating that oxamate and iodoacetate, presumed specific inhibitors of LDH and GAPDH, i.e., two non-controlling glycolytic enzymes, do affect the glycolytic flux of cancer, and non-cancer cells (Goldberg et al.,
The effect of oxamate on the activities of LDH, GAPDH, ENO, and PYK, which in turn affected the Fru1,6BP and DHAP concentrations (Moreno-Sánchez et al.,
GLUT | 1.9 | 1 | 0.66 | 0.36 | 0.72 | 0.4 | 2.5 | 1.5 |
HK | 2.3 | 1.3 | 2.3 | 1.3 | 2.4 | 1.4 | 1.9 | 1.1 |
HPI | 1.9 | 1.1 | 1.5 | 0.8 | 1.3 | 0.7 | 0.8 | 0.4 |
PFK1 | 0.2 | 0.1 | 0.7 | 0.4 | 0.9 | 0.5 | 0.7 | 0.4 |
ALDO | −0.4 | −0.003 | −0.3 | 0.0007 | −0.4 | −0.0007 | −0.6 | 0.001 |
TPI | −0.06 | −0.06 | −0.004 | −0.004 | −0.004 | −0.004 | −0.008 | −0.008 |
GAPDH | −0.27 | −0.16 | −1.3 | −0.7 | −1.2 | −0.7 | −1.2 | −0.7 |
PGK | −0.1 | −0.08 | −0.01 | −0.008 | −0.02 | 0.01 | −0.02 | −0.01 |
PGAM | −0.09 | −0.05 | −0.05 | −0.03 | −0.06 | −0.03 | −0.07 | −0.04 |
ENO | −0.99 | −0.57 | −0.06 | −0.03 | −0.06 | −0.03 | −0.07 | −0.05 |
PYK | −0.7 | −0.4 | −3 | −1.7 | −2.5 | −1.5 | −2.0 | −1.3 |
LDH | −0.04 | −0.02 | −0.03 | −0.02 | −0.02 | −0.01 | −0.03 | −0.02 |
MCT | −0.2 | −0.1 | −0.2 | −0.1 | −0.1 | −0.07 |
In the AS-30D kinetic model it was evaluated the effect of LDH, ENO or PYK inhibition on the levels of Fru1,6BP and DHAP. LDH showed low control on their concentrations (−0.4 and −0.02; Table
To further establish the influence in the glycolytic flux and ATP concentration of the inhibitory effect of Fru1,6BP and DHAP on flux-controlling (HK and HPI) and not flux-controlling (TPI and GADPH) enzymes, several simulations were made with the AS-30D model (Figure
Furthermore, the Fru1,6BP and DHAP levels indeed increased in cells treated with oxamate (reported by Moreno-Sánchez et al.,
Glc6P | 10 ± 5 (3) | 3.9 ± 2 (4) | 3.5 ± 2 (3) | 4 |
4 |
Fru6P | 2 ± 1 (3) | 1.5 ± 0.3 (3) | 1.6 ± 0.5 (3) | 1.2 |
1.3 |
Fru1,6BP | 2.4 ± 1.7 (3) | 7 ± 1 (3) |
8 (2) |
23 |
22 |
DHAP | 1.8 ± 0.4 (5) | 33 ± 9 (3) |
17 (2) |
11 |
11 |
ATP | 6 ± 2 (3) | 1.4 ± 0.5 (3) |
1.3 ± 0.3 (3) |
1.7 |
1.4 |
Glycolytic flux | 8 ± 3 (3) | 2 ± 2 (4) |
1 (2) |
4.3 |
4.2 |
Methylglyoxal |
< 0.3 (4) |
2.2 ± 0.6 (3) | N.M. | N.M. | 3.6 ± 1.6 (4) |
Another possible consequence of DHAP accumulation is the production of methylglyoxal, which can also inhibit the glycolytic flux in cancer and normal cells (Leoncini et al.,
Methylglyoxal affects PYK and GAPDH activities (Leoncini et al.,
In the previous sections it was shown that it is indeed possible to significantly inhibit glycolysis by affecting non-flux controlling enzymes. Traditionally, competitive inhibitors have been studied or designed for drug therapy. However, such type of inhibitors induces substrate accumulation which in turn eventually displaces the inhibitor from the enzyme binding site, attenuating its inhibitory impact. Therefore, it was interesting to test with the present kinetic model the effect of different types of inhibition mechanisms on the pathway flux. This is relevant because the experimental results above showed accumulation of metabolites that affect the activities of the main controlling enzymes. Therefore, the kinetics of a flux-controlling step such as HPI was analyzed. This enzyme catalyzes a monosubstrate reaction and is strongly regulated by Fru1,6BP, Ery4P, and 6PG (Supplementary Table
The first versions of the kinetic model of cancer glycolysis previously published predicted low levels of Glc6P and high glycolytic flux which were in disagreement with the experimental values. The model refinement process indicated that HPI activity should be inhibited to properly simulate the experimental values (Marín-Hernández et al.,
For competitive inhibition, the effect of changing the
When HPI inhibitors were all considered as uncompetitive or mixed type inhibitors in the kinetic model of hypoglycemic HeLa cells, it was necessary to decrease their affinities by 6–10 times (i.e., their
These
To explore why regulatory metabolites may have different potencies, a docking analysis of metabolic inhibitors was performed on HPI. This analysis showed that the HPI competitive inhibitors can indeed adequately bind to the substrate binding site and be stabilized by the same amino acids in the active site (Figure
A recent study by our group (Moreno-Sánchez et al.,
Although iodoacetate is an unspecific drug that may covalently alkylates thiol groups at the active sites of many enzymes and hence may show toxicity, treatment of Ehrlich ascites carcinoma-bearing mice with iodoacetate significantly increases the median cumulative survival time and percentage of survivors, as well as decreases the tumor size (Fahim et al.,
Although the drugs tested (oxamate and iodoacetate) might have similar effects on non-cancer cells, both inhibitors are well-tolerated in animals and human non-cancer cell lines, suggesting that normal cells are less sensitive to glycolysis inhibition, likely due to a lower dependence on glycolysis for their proliferation with respect to tumor cells. In addition, glycolysis inhibition of tumor associated fibroblasts (reverse Warburg effect) may be also beneficial to deter tumor growth (Pavlides et al.,
As suggested by the data of the present study, the anticancer effect observed of these unspecific drugs (Fahim et al.,
Based on the
Fru1,6BP is a product of the PFK-1 reaction and a weak inhibitor of HK, HPI, GAPDH, and TPI (Marín-Hernández et al.,
One attractive novel approach for targeting cancer cells, derived from the present study, which deserves further experimental assessment, is the use of inhibitors of GAPDH, ENO, and PYK together with glyoxylase inhibitors, which at relatively low doses do not perturb host homeostasis. This particular multi-drug treatment would induce DHAP accumulation which in turn would lead to enhanced levels of methylglyoxal, severely compromising cancer cell growth and viability. Although, methylglyoxal has several toxic effects, it has shown anticancer activity in tumor-bearing mice and slight side-effects (Ghosh et al.,
There are three mechanisms by which a reversible inhibitor may interact with an enzyme: competitive, uncompetitive, and mixed type inhibition (Segel,
Nevertheless, competitive inhibitors can be also readily displaced from the active site by high substrate concentrations, thereby restoring enzyme activity. Thus, the physiological effect of competitive inhibitors is to provide an immediate response of the targeted enzyme/transporter which will be attenuated in the medium term. Then, although competitive inhibitors are easier to find in nature or be designed and manufactured, they are not pharmacologically efficient drugs.
In contrast, the effects of the uncompetitive and mixed-type inhibitors cannot be overcome by increasing the substrate concentration; in fact, for uncompetitive inhibition it becomes more significant at increasing substrate concentrations (Cornish-Bowden,
Uncompetitive and mixed-type inhibitions modify the
Kinetic modeling studies have shown that only the simultaneous inhibition of several flux-controlling steps will have significant impact on glycolytic flux and ATP concentration in cancer cells. This can be accomplished by direct inhibition using, preferentially, uncompetitive specific drugs or indirectly through the accumulation of regulatory metabolites of the flux-controlling steps by inhibiting enzymes that exert low flux-control.
AM-H, IDM-M, and JSR-Z performed the
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research was partially supported by grants Nos. 180322 (AM-H), 178638 (ES), and 239930 (RM-S) from CONACyT-Mexico. Authors thank Dr. Ricardo Jasso Chávez (Instituto Nacional de Cardiología, SSA) for gas chromatography technical assistance.
The Supplementary Material for this article can be found online at:
dihydroxyacetone-phosphate
enolase
erythrose-4-phosphate
fructose-1,6-biphosphate
glyceraldehyde 3-phosphate dehydrogenase
glucose 6-phosphate
glucose transporter
hexokinase
hexosephosphate isomerase
lactate dehydrogenase
6-phosphogluconate
pyruvate kinase
triosephosphate isomerase.