A microscale mathematical model for metabolic symbiosis: Investigating the effects of metabolic inhibition on ATP turnover in tumors
Graphical abstract
Introduction
The extensive metabolic requirements for cancer cell proliferation coupled with the harsh microenvironment in solid tumors culminate in a highly adaptive and complex network for cellular energy production. The genetically altered metabolic behavior of cancer cells has led to a number of emerging metabolic paradigms, in addition to those that are universally exhibited in both cancerous and normal cells. We will investigate this complex metabolic behavior by formulating a minimal mathematical model that includes the essential metabolites of glucose, lactate and oxygen in the tissue surrounding a microvessel. The cylindrical geometry used here has been used in a similar context to consider interactions between metabolites and tumor cells with treatment effects in a simplified setting (e.g. Bertuzzi et al., 2000, Bertuzzi et al., 2007a). The model presented here will enable the quantification of various behaviors, such as the symbiotic relationship that exists between lactate-producing glycolytic cells and lactate-consuming respiratory cells, and the analysis of metabolic dependence on various physiological conditions such as hypoxia and induced metabolic inhibition. Metabolic inhibition including glycolytic inhibitors among many others targets could be very important for cancer treatment since an ATP deficit can induce apoptosis (Izyumov et al., 2004). The key consideration for addressing this problem with mathematics is the formulation of nutrient consumption rates that encompass the various primary facets of cancer cell metabolism and their corresponding ATP yields.
In normal well-oxygenated tissues the primary source of ATP is the process of cellular respiration. The complete conversion of glucose to carbon dioxide and water has an ideal yield of about 29 ATP, although realistically the yield is substantially lower (Brand, 2005). The preliminary stage of cellular respiration is glycolysis, the conversion of glucose to pyruvate; this process directly produces 2 ATP. In hypoxic conditions this pyruvate is preferentially converted into lactate via the enzyme lactate dehydrogenase (LDH) to regenerate the essential cofactor NAD+. In oxygenated conditions this pyruvate is transported across the inner mitochondrial matrix where it is decarboxylated and enters the citric acid cycle; the citric acid cycle directly generates 2 more ATP per glucose. The primary energy payoff is a result of cofactor oxidization that enables the electron transport chain to establish a proton gradient across the inner mitochondrial matrix. ATP synthase utilizes this electrochemical gradient to drive the phosphorylation of approximately 25 additional ATP per glucose molecule.
The aforementioned universal traits that cancer cells and normal cells share include cellular responses to various levels of oxygen, lactate or glucose. Examples include a Crabtree-like effect and a Pasteur-like effect (Casciari et al., 1992a). The Crabtree-like effect is when oxygen consumption decreases as glucose concentration increases. This can be explained by an increasing reliance on glycolysis for ATP when hyperglycaemic conditions are encountered. The Pasteur-like effect is decreased glucose consumption as oxygen increases. This is due primarily to the inhibition of various metabolic steps by the presence of elevated ATP and other intermediaries. However, cancer cells are unique in that they preferentially utilize glycolysis, even in the presence of oxygen, coined aerobic glycolysis. This phenomenon is generally referred to as the Warburg effect whereby cells rely primarily on glycolysis even in the presence of sufficient oxygen to perform respiration (Warburg, 1956). There is a perceived inefficiency of this metabolic strategy, namely the dramatically reduced ATP yield, just 2 per glucose instead of 29, however, it has the benefits of faster ATP production and it is likely that much of this glucose is being consumed for proliferative (Vander Heiden et al., 2009) (e.g. by the pentose phosphate pathway) purposes. In addition to the typical glycolytic phenotype exhibited in many cancers, there is also a developing story of a co-operative relationship existing between aerobic and anaerobic cancer cells. The lactate necessarily produced by glycolytic cells is being pushed back into the respiratory cycle by being converted into pyruvate (summarized in Feron, 2009, Nakajima and Van Houten, 2012); this spatial relationship is shown in Fig. 1. Lactate consumption has been observed in vitro in various models (Bouzier et al., 1998, Katz et al., 1974) as well as in vivo as early as the early 1980s (Sauer et al., 1982). However, a renewed interest in the topic was piqued when Sonveaux et al. (2008) showed that reducing lactate uptake by cancer cells led to hypoxic cell death, a particularly difficult subpopulation to target using traditional methods.
Metabolic phenomena have been studied in great detail by mathematical models, but models of tumor metabolism rarely include the interaction of the transport mechanisms of microvessels with the localized metabolic behavior of cells (with one recent exception McGillen et al., 2013). In the section to follow, we will develop a mathematical model that describes the concentrations of molecules that are important to cellular metabolism in the tissue around a single three-dimensional vessel that exhibits diffusion-dominated interstitial transport. We will then use this model to demonstrate how the properties of the tumor cell population, such as glucose, lactate and oxygen consumption rates, affect tumor hypoxia and ATP production around a single vessel. The effects of metabolic inhibitors will be investigated by parameter changes that could be elicited by the application of glycolysis inhibitors, lactate dehydrogenase (LDH) inhibitors or respiratory inhibitors. We are interested in those metabolic inhibitors that could cripple the cells׳ ability to produce ATP. Furthermore, these micrometer scale predictions give an indication of local shifts in cell metabolism, which could aid in developing combination treatments that can simultaneously hinder multiple metabolic pathways in tumors. Our simulations show that treatments targeting glycolysis via glycolytic enzyme inhibition or LDH inhibition, which have been thoroughly investigated (Pelicano et al., 2006, Gatenby and Gillies, 2007, Granchi and Minutolo, 2012), could be the most successful metabolic suppression strategy.
Section snippets
Mathematical model
A model to describe the concentrations of the major players in the metabolic pathways of respiration and glycolysis, will be outlined here. Its origins lie in a metabolic model developed by Casciari et al. (1992b) that was subsequently applied on the microscale by Molavian et al. (2009). The functional forms for the production rates are similar to those proposed by Mendoza-Juez et al. (2012) and subsequently extended to a spatial model by McGillen et al. (2013).
In hypoxic and anoxic conditions,
Base case
We will first examine how our model captures the behavior of metabolic symbiosis in tumors. Using the parameters given in Table 1, we numerically solve the nondimensional system given by (14), (15). The corresponding glucose concentration is found by using the explicit formula (19). The solutions for the nondimensional oxygen, lactate and glucose concentrations are given in Fig. 3. The oxygen concentration decreases to anoxic values approximately away from the vessel as expected. The
Limitations of predictions and future work
Possible areas for expansion include radially dependent parameters (diffusion coefficients and cell densities that modify metabolic parameters), axially dependent parameters (vessel concentrations and vessel permeability) and more generalized functional forms for the consumption rates (e.g. ATP-dependent or pH-dependent). Additional details of cellular metabolic pathways including other glycolytic and respiratory intermediaries within the pathways or alternative metabolic pathways such as
Conclusions
The mathematical model formulated and analyzed above can give insight into the metabolic behaviors of cancer cells on the microscale. The tumor microenvironment characterized by hypoxia and nutrient deprivation leads to the utilization of highly unregulated glycolytic pathways and the consumption by respiring cells of the lactate produced by these cells. These metabolic scenarios are encompassed by the functional forms proposed for glucose, lactate and oxygen consumption.
To consider the effect
Acknowledgments
M. Kohandel is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, Discovery Grants) as well as an NSERC/CIHR Collaborative Health Research Grant.
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