Glucose starvation as cancer treatment: Thermodynamic point of view

Abstract

To evaluate the effect of glucose deprivation on the robustness of cancer glycolysis, the total entropy production rate was calculated from the in silico modeling of the glycolytic network of HeLa cells grown under different glucose and oxygen conditions. It was shown that glucose deprivation had a deleterious effect for the cells grown under hypoglycemia and hypoxia and that the intracellular acidification therapy and the glucose deprivation treatment had synergic effects on the decrease of cancer glycolysis robustness. *Correspondence to: Ileana Durán, Department of Chemical-Physics, A. Alzola Group of Thermodynamics of Complex Systems M.V. Lomonosov Chemistry Chair, Faculty of Chemistry, University of Havana, Havana 10400, Cuba, E-mail: ileduranadn@gmail.com Nieto-Villar JM, Department of Chemical-Physics, A. Alzola Group of Thermodynamics of Complex Systems M.V. Lomonosov Chemistry Chair, Faculty of Chemistry, University of Havana, Havana 10400, Cuba, E-mail: nieto@fq.uh.cu Received: May 02, 2018; Accepted: May 28, 2018; Published: May 31, 2018 Introduction Cancer, the second leading cause of death worldwide [1], is a generic name given to a complex interaction network of malignant cells that have lost their specialization and control over normal growth [2]. This behavior stems from to the accumulation of multiple DNA mutations in specific genes called oncogenes and tumor suppressor genes [3]. The design and development of new cancer drugs may take several years and in most cases will only be effective for a fraction of patients with specific types of cancer. Therefore, it is important to develop complementary strategies that can be quickly translated into effective therapies [4]. Cancer cells are characterized for maintaining a high rate of glycolysis, thus converting glucose to lactate at high speed, even in the presence of oxygen. This phenomenon is known as “aerobic glycolysis” or “Warburg effect”. Numerous therapies against cancer are based on the inhibition of this metabolic network [5]. It is known that glucose deprivation has deleterious effects on cancer glycolysis which may even conduce to cell death [6]. Mutations and epigenetic modifications that increase growth and promote insensitivity to anti-growth signals in cancer cells, lead to the loss of appropriate responses to rapidly adapt to a variety of extreme environments including starvation [7]. Under challenging conditions such as starvation, normal cells reduce energy expenditure and divert it from growth to maintenance; thereby enhancing protection and survival [8]. However, the constitutive activation of oncoproteins can block entry into this protective mode in cancer cells; thus providing a method by which fasting induces protection in normal cells but not in oncogene-driven cancer cells, an effect called Differential Stress Resistance [4,9]. Hypoxia arises in tumors through the uncontrolled oncogene driven proliferation of cancer cells in the absence of an efficient vascular bed. Due to the rapid proliferation of cancer cells, the tumor quickly exhausts the nutrient and oxygen supply from the normal vasculature, and becomes hypoxic [10]. There is a “metabolic symbiosis” between hypoxic and aerobic cancer cells inside a tumor, in which lactate produced by hypoxic cells is taken up by aerobic cells and it is used as their principal substrate for oxidative phosphorylation. As a result, the limited glucose available to the tumor is most efficiently used [11]. On the other hand, hypoxia correlates with therapeutic resistance to both cytotoxic drugs and radiotherapy [12]. The adaptation of tumor cells to hypoxia contributes to the malignant phenotype and to aggressive tumor progression [13]. Under hypoxia, the complexity and robustness of cancer glycolysis is higher than under normoxia [14]. Another hallmark of cancer cells is the reversed pH gradient: intracellular pH (pHi) are increased compared to normal cells (∼7.3– 7.6 versus ∼7.2), while extracellular pH (pHe) is decreased (∼6.8–7.0 versus ∼7.4). The dysregulated pH of cancer cells enables cellular processes that are sensitive to small changes in , including cell proliferation, migration and metabolism. These global cell biological effects are produced by the pH-sensitive functions of proteins with activities or ligand-binding affinities that are regulated within the narrow cellular range of dynamics [15]. The aim of this work is to evaluate the effect of glucose deprivation on cancer robustness through the entropy production rate [16,17] of Durán I (2018) Glucose starvation as cancer treatment: Thermodynamic point of view Integr Cancer Sci Therap, 2018 doi: 10.15761/ICST.1000276 Volume 5(3): 2-5 the glycolytic network for HeLa cells. The paper is organized as follows: In section 2.1, the kinetic models and the experimental procedure are described. In section 2.2 the thermodynamic formalism for the entropy production rate is presented. Section 3 focuses on the results obtained and section 4 comprehends the discussion of the results. Finally, some concluding remarks are exposed. The results achieved will be useful for the design of new cancer therapies based on glucose deprivation and for the understanding of their effect on the different solid tumor areas. Materials and methods Kinetic model of cancer glycolysis The models used were proposed by Marin et al. [18] for the glycolytic network of HeLa tumor cell lines grown under three metabolic states during 24 hours, the sufficient time to induce phenotypic changes in cellular metabolism: Hypoglycemia (2.5 mM), Normoglycemia (5 mM) and Hyperglycemia (25 mM), all the three under normoxia. However, the growth saturation was not attained within this time but in a second phase where the cells were exposed to different glucose concentrations: 2.5 mM, 5 mM and 25 mM, until they reached the stationary state [18]. An intermittent fasting therapy was simulated with the 5 mM second phase models, for the three metabolic phenotypes, as the one carried out in the study of Lee et al. [7]. Heaviside Step Function [19] was used to perturb the extracellular glucose concentration (Glcout) by switching it from a fasting state (1 mM) to a normoglycemic state (5 mM) with 24 and 12 hours’ periods for the first and second experiment respectively. In such a way that, in the first experiment, starting with a glucose concentration of 1 mM during 24 hours, the extracellular glucose concentration was changed to 5 mM and maintained in that value for the entire next day, and then was returned to 1 mM and so on, as shown in figure 1. The models taken as controls were those with 5 mM constant extracellular glucose concentration, i.e. without perturbation. We also used the models proposed by Marin et al. [20] for the glycolytic network of HeLa cells grown under normoxia and hypoxia, both under hyperglycemia and then incubated at glucose 5 mM, to evaluate the effect of glucose deprivation on cancer glycolysis robustness under these metabolic conditions. The reactions Oxidative Phosphorylation (OxPhos) and the one of the lactate transporter were added to the models, in order to match the reactions of these models to those of Marin et al. [18]. The flux fixed for the reaction OxPhos in the HeLa – normoxia model was the same as the one in the models of Marin et al. [18]. For the HeLa – hipoxia model, the OxPhos flux fixed was 0.00001 mmol/min. The lactate transporter reaction MCT1 was added to the HeLa – normoxia model. It had the same Vmax, KLac(in), KLac(out) and Keq values than Marin et al. [18] for hyperglycemia. For the HeLa – hypoxia model, the lactate transporter reaction MCT4 added had KLac(in) = 0.0005 and KLac(out) = 8.5. The other reaction parameters had the same values as Marin et al. [18] for hyperglycemia. KLac(in) and KLac(out) are the affinities for intra and extracellular lactate, and Keq is the equilibrium constant of the reaction [18]. In order to represent the influence of the glucose deprivation therapy combined with an intracellular acidification treatment on the robustness of cancer glycolysis inside a tumor, all the models mentioned above were subjected to an extracellular glucose reduction from 5 mM to 1 mM and to pHi changes from 7.8 to 6.4. The former value represents an extreme pHi of cancer cells [15] and the latter one, the pHi after a cellular acidification therapy. The entropy production rate was calculated using the glycolysis network model of HeLa cell lines at the steady state. The parameters and concentration values used were obtained by modeling the metabolic network for the different metabolic conditions mentioned above. The modeling of the metabolic network was made in the biochemical network simulator COPASI v 4.16 (http://www.copasi.org). Thermodynamic framework As we have shown in previous works [21], the entropy production rate in cells due to chemical processes driven by the affinity A is calculated as: 1 1 i S G T T ξ ξ = Α = − ∆    (1) Where is the Temperature and ξ is the reaction rate. This value was obtained from COPASI simulation for each one of the reactions. The variation of free energy of reaction ( k G ∆ ) was calculated by the isotherm of reaction.