In early studies on energy metabolism of tumor cells, it was proposed that the enhanced glycolysis was induced by a decreased oxidative phosphorylation. Since then it has been indiscriminately applied to all types of tumor cells that the ATP supply is mainly or only provided by glycolysis, without an appropriate experimental evaluation. In this review, the different genetic and biochemical mechanisms by which tumor cells achieve an enhanced glycolytic flux are analyzed. Furthermore, the proposed mechanisms that arguably lead to a decreased oxidative phosphorylation in tumor cells are discussed. As the O2 concentration in hypoxic regions of tumors seems not to be limiting for the functioning of oxidative phosphorylation, this pathway is re‐evaluated regarding oxidizable substrate utilization and its contribution to ATP supply versus glycolysis. In the tumor cell lines where the oxidative metabolism prevails over the glycolytic metabolism for ATP supply, the flux control distribution of both pathways is described. The effect of glycolytic and mitochondrial drugs on tumor energy metabolism and cellular proliferation is described and discussed. Similarly, the energy metabolic changes associated with inherent and acquired resistance to radiotherapy and chemotherapy of tumor cells, and those determined by positron emission tomography, are revised. It is proposed that energy metabolism may be an alternative therapeutic target for both hypoxic (glycolytic) and oxidative tumors.

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The traditional experimental approaches used for changing the flux or the concentration of a particular metabolite of a metabolic pathway have been mostly based on the inhibition or over-expression of the presumed rate-limiting step. However, the attempts to manipulate a metabolic pathway by following such approach have proved to be unsuccessful. Metabolic Control Analysis (MCA) establishes how to determine, quantitatively, the degree of control that a given enzyme exerts on flux and on the concentration of metabolites, thus substituting the intuitive, qualitative concept of rate limiting step. Moreover, MCA helps to understand (i) the underlying mechanisms by which a given enzyme exerts high or low control and (ii) why the control of the pathway is shared by several pathway enzymes and transporters. By applying MCA it is possible to identify the steps that should be modified to achieve a successful alteration of flux or metabolite concentration in pathways of biotechnological (e.g., large scale metabolite production) or clinical relevance (e.g., drug therapy). The different MCA experimental approaches developed for the determination of the flux-control distribution in several pathways are described. Full understanding of the pathway properties when is working under a variety of conditions can help to attain a successful manipulation of flux and metabolite concentration.

Most cancer cells exhibit an accelerated glycolysis rate compared to normal cells. This metabolic change is associated with the over-expression of all the pathway enzymes and transporters (as induced by HIF-1α and other oncogenes), and with the expression of hexokinase (HK) and phosphofructokinase type 1 (PFK-1) isoenzymes with different regulatory properties. Hence, a control distribution of tumor glycolysis, modified from that observed in normal cells, can be expected. To define the control distribution and to understand the underlying control mechanisms, kinetic models of glycolysis of rodent AS-30D hepatoma and human cervix HeLa cells were constructed with experimental data obtained here for each pathway step (enzyme kinetics; steady-state pathway metabolite concentrations and fluxes). The models predicted with high accuracy the fluxes and metabolite concentrations found in living cancer cells under physiological O(2) and glucose concentrations as well as under hypoxic and hypoglycemic conditions prevailing during tumor progression. The results indicated that HK≥HPI>GLUT in AS-30D whereas glycogen degradation≥GLUT>HK in HeLa were the main flux- and ATP concentration-control steps. Modeling also revealed that, in order to diminish the glycolytic flux or the ATP concentration by 50%, it was required to decrease GLUT or HK or HPI by 76% (AS-30D), and GLUT or glycogen degradation by 87-99% (HeLa), or decreasing simultaneously the mentioned steps by 47%. Thus, these proteins are proposed to be the foremost therapeutic targets because their simultaneous inhibition will have greater antagonistic effects on tumor energy metabolism than inhibition of all other glycolytic, non-controlling, enzymes.

Trypanosomatid parasites cause serious diseases among humans, livestock, and plants. They belong to the order of the Kinetoplastida and form, together with the Euglenida, the phylum Euglenozoa. Euglenoid algae possess plastids capable of photosynthesis, but plastids are unknown in trypanosomatids. Here we present molecular evidence that trypanosomatids possessed a plastid at some point in their evolutionary history. Extant trypanosomatid parasites, such as Trypanosoma and Leishmania, contain several ''plant-like'' genes encoding homologs of proteins found in either chloroplasts or the cytosol of plants and algae. The data suggest that kinetoplastids and euglenoids acquired plastids by endosymbiosis before their divergence and that the former lineage subsequently lost the organelle but retained numerous genes. Several of the proteins encoded by these genes are now, in the parasites, found inside highly specialized peroxisomes, called glycosomes, absent from all other eukaryotes, including euglenoids.

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Glycolysis in the human parasite Entamoeba histolytica is characterized by the absence of cooperative modulation and the prevalence of pyrophosphatedependent (over ATP-dependent) enzymes. To determine the flux-control distribution of glycolysis and understand its underlying control mechanisms, a kinetic model of the pathway was constructed by using the software gepasi. The model was based on the kinetic parameters determined in the purified recombinant enzymes, and the enzyme activities, and steady-state fluxes and metabolite concentrations determined in amoebal trophozoites. The model predicted, with a high degree of accuracy, the flux and metabolite concentrations found in trophozoites, but only when the pyrophosphate concentration was held constant; at variable pyrophosphate, the model was not able to completely account for the ATP production ⁄ consumption balance, indicating the importance of the pyrophosphate homeostasis for amoebal glycolysis. Control analysis by the model revealed that hexokinase exerted the highest flux control (73%), as a result of its low cellular activity and strong AMP inhibition. 3-Phosphoglycerate mutase also exhibited significant flux control (65%) whereas the other pathway enzymes showed little or no control. The control of the ATP concentration was also mainly exerted by ATP consuming processes and 3-phosphoglycerate mutase and hexokinase (in the producing block). The model also indicated that, in order to diminish the amoebal glycolytic flux by 50%, it was required to decrease hexokinase or 3-phosphoglycerate mutase by 24% and 55%, respectively, or by 18% for both enzymes. By contrast, to attain the same reduction in flux by inhibiting the pyrophosphate-dependent enzymes pyrophosphate-phosphofructokinase and pyruvate phosphate dikinase, they should be decreased > 70%. On the basis of metabolic control analysis, steps whose inhibition would have stronger negative effects on the energy metabolism of this parasite were identified, thus becoming alternative targets for drug design.Abbreviations ADH, alcohol dehydrogenase; AK, adenylate kinase; ALDO, fructose 1,6-bisphosphate aldolase; AldDH, aldehyde dehydrogenase; ATP-PFK, ATP-dependent phosphofructokinase; DHAP, dihydroxyacetone phosphate; ENO, enolase; EtOH, ethanol; F6P, fructose 6-phosphate; F(1,6)P 2 , fructose 1,6-bisphosphate; G6P, glucose 6-phosphate; G6PDH, glucose 6-phosphate dehydrogenase; G3P, glyceraldehyde 3-phosphate; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; Gly3PDH, glycerol 3-phosphate dehydrogenase; HK, hexokinase; HPI, hexose 6-phosphate isomerase; HXT, hexose transporter; LDH, lactate dehydrogenase; MCA, metabolic control analysis; PGAM, 3-phosphoglycerate mutase; PGK, phosphoglycerate kinase; PGM, phosphoglucomutase; 3PGDH, 3-phosphoglycerate dehydrogenase; PEP, phosphoenolpyruvate; 2PG, 2-phosphoglycerate; 3PG, 3-phosphoglycerate; PPi, pyrophosphate; PPi-PFK, pyrophosphate-dependent phosphofructokinase; PPP, pentose phosphate pathway; PFOR, pyruvate:ferredoxin oxidoreductase; PFOR-AldDH, lumped reacti...