Metabolic Control Analysis for Drug Target Prioritization in Trypanosomatids.

To validate therapeutic targets in metabolic pathways of trypanosomatids, the criterion of enzyme essentiality determined by gene knockout or knockdown is usually being applied. Since, it is often found that most of the enzymes/proteins analyzed are essential, additional criteria have to be implemented for drug target prioritization. Metabolic control analysis (MCA), often in conjunction with kinetic pathway modeling, offers such possibility for prioritization. MCA is a theoretical and experimental approach to analyze how metabolic pathways are controlled. It involves strategies to perform quantitative analyses to determine the degree in which an enzyme controls a pathway flux, a value called flux control coefficient ([Formula: see text]). By determining the [Formula: see text] of individual steps in a metabolic pathway, the distribution of control of the pathway is established, that is, the identification of the main flux-controlling steps. Therefore, MCA can help in ranking pathway enzymes as drug targets from a metabolic perspective. In this chapter, three approaches to determine [Formula: see text] are reviewed: (1) In vitro pathway reconstitution, (2) manipulation of enzyme activities within parasites, and (3) in silico kinetic modeling of the metabolic pathway. To perform these methods, accurate experimental data of enzyme activities, metabolite concentrations and pathway fluxes are necessary. The methodology is illustrated with the example of trypanothione metabolism of Trypanosoma cruzi and protocols to determine such experimental data for this metabolic process are also described. However, the MCA strategy can be applied to any metabolic pathway in the parasite and general directions to perform it are provided in this chapter.

Drug Target Selection for Trypanosoma cruzi Metabolism by Metabolic Control Analysis and Kinetic Modeling.

In the search for therapeutic targets in the intermediary metabolism of trypanosomatids the gene essentiality criterion as determined by using knock-out and knock-down genetic strategies is commonly applied. As most of the evaluated enzymes/transporters have turned out to be essential for parasite survival, additional criteria and approaches are clearly required for suitable drug target prioritization. The fundamentals of Metabolic Control Analysis (MCA; an approach in the study of control and regulation of metabolism) and kinetic modeling of metabolic pathways (a bottom-up systems biology approach) allow quantification of the degree of control that each enzyme exerts on the pathway flux (flux control coefficient) and metabolic intermediate concentrations (concentration control coefficient). MCA studies have demonstrated that metabolic pathways usually have two or three enzymes with the highest control of flux; their inhibition has more negative effects on the pathway function than inhibition of enzymes exerting low flux control. Therefore, the enzymes with the highest pathway control are the most convenient targets for therapeutic intervention. In this review, the fundamentals of MCA as well as experimental strategies to determine the flux control coefficients and metabolic modeling are analyzed. MCA and kinetic modeling have been applied to trypanothione metabolism in Trypanosoma cruzi and the model predictions subsequently validated in vivo. The results showed that three out of ten enzyme reactions analyzed in the T. cruzi anti-oxidant metabolism were the most controlling enzymes. Hence, MCA and metabolic modeling allow a further step in target prioritization for drug development against trypanosomatids and other parasites.

Understanding the cancer cell phenotype beyond the limitations of current omics analyses.

Efforts to understand the mechanistic principles driving cancer metabolism and proliferation have been lately governed by genomic, transcriptomic and proteomic studies. This paper analyzes the caveats of these approaches. As molecular biology's central dogma proposes a unidirectional flux of information from genes to mRNA to proteins, it has frequently been assumed that monitoring the changes in the gene sequences and in mRNA and protein contents is sufficient to explain complex cellular processes. Such a stance commonly disregards that post-translational modifications can alter the protein function/activity and also that regulatory mechanisms enter into action, to coordinate the protein activities of pathways/cellular processes, in order to keep the cellular homeostasis. Hence, the actual protein activities (as enzymes/transporters/receptors) and their regulatory mechanisms ultimately dictate the final outcomes of a pathway/cellular process. In this regard, it is here documented that the mRNA levels of many metabolic enzymes and transcriptional factors have no correlation with the respective protein contents and activities. The validity of current clinical mRNA-based tests and proposed metabolite biomarkers for cancer detection/prognosis is also discussed. Therefore, it is proposed that, to achieve a thorough understanding of the modifications undergone by proliferating cancer cells, it is mandatory to experimentally analyze the cellular processes at the functional level. This could be achieved (a) locally, by examining the actual protein activities in the cell and their kinetic properties (or at least kinetically characterize the most controlling steps of the pathway/cellular process); (b) systemically, by analyzing the main fluxes of the pathway/cellular process, and how they are modulated by metabolites, all which should contribute to comprehending the regulatory mechanisms that have been altered in cancer cells. By adopting a more holistic approach it may become possible to improve the design of therapeutic strategies that would target cancer cells more specifically.

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms.

UNLABELLED: The effect of hypoglycemia on the contents of glycolytic proteins, activities of enzymes/transporters and flux of HeLa and MCF-7 tumor cells was experimentally analyzed and modeled in silico. After 24 h hypoglycemia (2.5 mm initial glucose), significant increases in the protein levels of glucose transporters 1 and 3 (GLUT 1 and 3) (3.4 and 2.1-fold, respectively) and hexokinase I (HKI) (2.3-fold) were observed compared to the hyperglycemic standard cell culture condition (25 mm initial glucose). However, these changes did not bring about a significant increase in the total activities (Vmax ) of GLUT and HK; instead, the affinity of these proteins for glucose increased, which may explain the twofold increased glycolytic flux under hypoglycemia. Thus, an increase in more catalytically efficient isoforms for two of the main controlling steps was sufficient to induce increased flux. Further, a previous kinetic model of tumor glycolysis was updated by including the ratios of GLUT and HK isoforms, modified pyruvate kinase kinetics and an oxidative phosphorylation reaction. The updated model was robust in terms of simulating most of the metabolite levels and fluxes of the cells exposed to various glycemic conditions. Model simulations indicated that the main controlling steps were glycogen degradation > HK > hexosephosphate isomerase under hyper- and normoglycemia, and GLUT > HK > glycogen degradation under hypoglycemia. These predictions were experimentally evaluated: the glycolytic flux of hypoglycemic cells was more sensitive to cytochalasin B (a GLUT inhibitor) than that of hyperglycemic cells. The results indicated that cancer glycolysis should be inhibited at multiple controlling sites, regardless of external glucose levels, to effectively block the pathway. DATABASE: The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.mib.ac.uk/database/achcar/index.html. [Database section added 21 July 2014 after original online publication].

The 2-oxoglutarate supply exerts significant control on the lysine synthesis flux in Saccharomyces cerevisiae.

To determine the extent to which the supply of the precursor 2-oxoglutarate (2-OG) controls the synthesis of lysine in Saccharomyces cerevisiae growing exponentially in high glucose, top-down elasticity analysis was used. Three groups of reactions linked by 2-OG were defined. The 2-OG supply group comprised all metabolic steps leading to its formation, and the two 2-OG consumer groups comprised the enzymes and transporters involved in 2-OG transformation into lysine and glutamate and their further utilization for protein synthesis and storage. Various 2-OG steady-state concentrations that produced different fluxes to lysine and glutamate were attained using yeast mutants with increasing activities of Krebs cycle enzymes and decreased activities of Lys synthesis enzymes. The elasticity coefficients of the three enzyme groups were determined from the dependence of the amino acid fluxes on the 2-OG concentration. The respective degrees of control on the flux towards lysine (flux control coefficients) were determined from their elasticities, and were 1.1, 0.41 and -0.52 for the 2-OG producer group and the Lys and Glu branches, respectively. Thus, the predominant control exerted by the 2-OG supply on the rate of lysine synthesis suggests that over-expression of 2-OG producer enzymes may be a highly effective strategy to enhance Lys production.

Hexokinase and not glycogen synthase controls the flux through the glycogen synthesis pathway in frog oocytes.

Here we set out to evaluate the role of hexokinase and glycogen synthase in the control of glycogen synthesis in vivo. We used metabolic control analysis (MCA) to determine the flux control coefficient for each of the enzymes involved in the pathway. Acute microinjection experiments in frog oocytes were specifically designed to change the endogenous activities of the enzymes, either by directly injecting increasing amounts of a given enzyme (HK, PGM and UGPase) or by microinjection of a positive allosteric effector (glc-6P for GS). Values of 0.61 ± 0.07, 0.19 ± 0.03, 0.13 ± 0.03, and -0.06 ± 0.08 were obtained for the flux control coefficients of hexokinase EC 2.7.1.1 (HK), phosphoglucomutase EC 5.4.2.1 (PGM), UDPglucose pyrophosphorylase EC 2.7.7.9 (UGPase) and glycogen synthase EC 2.4.1.11 (GS), respectively. These values satisfy the summation theorem since the sum of the control coefficients for all the enzymes of the pathway is 0.87. The results show that, in frog oocytes, glycogen synthesis through the direct pathway is under the control of hexokinase. Phosphoglucomutase and UDPG-pyrophosphorylase have a modest influence, while the control exerted by glycogen synthase is null.