RESUMO
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.
Assuntos
Desenvolvimento de Medicamentos/métodos , Metabolômica/métodos , Proteínas de Protozoários/metabolismo , Trypanosoma cruzi/metabolismo , Extratos Celulares/isolamento & purificação , Doença de Chagas/tratamento farmacológico , Doença de Chagas/parasitologia , Simulação por Computador , Glutationa/análogos & derivados , Glutationa/metabolismo , Humanos , Cinética , Redes e Vias Metabólicas/efeitos dos fármacos , Modelos Biológicos , Terapia de Alvo Molecular/métodos , Proteínas de Protozoários/antagonistas & inibidores , Proteínas de Protozoários/isolamento & purificação , Proteínas Recombinantes/isolamento & purificação , Proteínas Recombinantes/metabolismo , Espermidina/análogos & derivados , Espermidina/metabolismo , Tripanossomicidas/farmacologia , Tripanossomicidas/uso terapêutico , Trypanosoma cruzi/efeitos dos fármacosRESUMO
Trypanothione (T(SH)2) is the main antioxidant metabolite for peroxide reduction in Trypanosoma cruzi; therefore, its metabolism has attracted attention for therapeutic intervention against Chagas disease. To validate drug targets within the T(SH)2 metabolism, the strategies and methods of Metabolic Control Analysis and kinetic modeling of the metabolic pathway were used here, to identify the steps that mainly control the pathway fluxes and which could be appropriate sites for therapeutic intervention. For that purpose, gamma-glutamylcysteine synthetase (γECS), trypanothione synthetase (TryS), trypanothione reductase (TryR) and the tryparedoxin cytosolic isoform 1 (TXN1) were separately overexpressed to different levels in T. cruzi epimastigotes and their degrees of control on the pathway flux as well as their effect on drug resistance and infectivity determined. Both experimental in vivo as well as in silico analyses indicated that γECS and TryS control T(SH)2 synthesis by 60-74% and 15-31%, respectively. γECS overexpression prompted up to a 3.5-fold increase in T(SH)2 concentration, whereas TryS overexpression did not render an increase in T(SH)2 levels as a consequence of high T(SH)2 degradation. The peroxide reduction flux was controlled for 64-73% by TXN1, 17-20% by TXNPx and 11-16% by TryR. TXN1 and TryR overexpression increased H2O2 resistance, whereas TXN1 overexpression increased resistance to the benznidazole plus buthionine sulfoximine combination. γECS overexpression led to an increase in infectivity capacity whereas that of TXN increased trypomastigote bursting. The present data suggested that inhibition of high controlling enzymes such as γECS and TXN1 in the T(SH)2 antioxidant pathway may compromise the parasite's viability and infectivity.
Assuntos
Antioxidantes/metabolismo , Glutamato-Cisteína Ligase/genética , Glutationa/análogos & derivados , Proteínas de Protozoários/genética , Espermidina/análogos & derivados , Tiorredoxinas/genética , Trypanosoma cruzi/efeitos dos fármacos , Amida Sintases/genética , Amida Sintases/metabolismo , Butionina Sulfoximina/farmacologia , Linhagem Celular , Combinação de Medicamentos , Resistência a Medicamentos/genética , Fibroblastos/parasitologia , Regulação da Expressão Gênica , Glutamato-Cisteína Ligase/metabolismo , Glutationa/antagonistas & inibidores , Glutationa/biossíntese , Humanos , Peróxido de Hidrogênio/farmacologia , NADH NADPH Oxirredutases/genética , NADH NADPH Oxirredutases/metabolismo , Nitroimidazóis/farmacologia , Oxirredução , Estresse Oxidativo , Peroxidases/genética , Peroxidases/metabolismo , Proteínas de Protozoários/metabolismo , Transdução de Sinais , Espermidina/antagonistas & inibidores , Espermidina/biossíntese , Tiorredoxinas/metabolismo , Tripanossomicidas/farmacologia , Trypanosoma cruzi/enzimologia , Trypanosoma cruzi/genéticaRESUMO
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.
Assuntos
Desenvolvimento de Medicamentos/métodos , Enzimas/metabolismo , Proteínas de Protozoários/metabolismo , Trypanosoma cruzi/enzimologia , Glutationa/análogos & derivados , Glutationa/metabolismo , Glicólise/fisiologia , Cinética , Modelos Biológicos , Espermidina/análogos & derivados , Espermidina/metabolismoRESUMO
Metabolic control analysis (MCA) is a promising approach in biochemistry aimed at understanding processes in a quantitative fashion. Here the contribution of enzymes and transporters to the control of a given pathway flux and metabolite concentrations is determined and expressed quantitatively by means of numerical coefficients. Metabolic flux can be influenced by a wide variety of modulators acting on one or more metabolic steps along the pathway. We describe a laboratory exercise to study metabolic regulation of human erythrocytes (RBCs). Within the framework of MCA, students use these cells to determine the sensitivity of the glycolytic flux to two inhibitors (iodoacetic acid: IA, and iodoacetamide: IAA) known to act on the enzyme glyceraldehyde-3-phosphate-dehydrogenase. Glycolytic flux was estimated by determining the concentration of extracellular lactate, the end product of RBC glycolysis. A low-cost colorimetric assay was implemented, that takes advantage of the straightforward quantification of the absorbance signal from the photographic image of the multi-well plate taken with a standard digital camera. Students estimate flux response coefficients for each inhibitor by fitting an empirical function to the experimental data, followed by analytical derivation of this function. IA and IAA exhibit qualitatively different patterns, which are thoroughly analyzed in terms of the physicochemical properties influencing their action on the target enzyme. IA causes highest glycolytic flux inhibition at lower concentration than IAA. This work illustrates the feasibility of using the MCA approach to study key variables of a simple metabolic system, in the context of an upper level biochemistry course. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):502-515, 2018.
Assuntos
Bioquímica/educação , Eritrócitos/metabolismo , Glicólise , Colorimetria , Inibidores Enzimáticos/química , Inibidores Enzimáticos/farmacologia , Eritrócitos/efeitos dos fármacos , Gliceraldeído-3-Fosfato Desidrogenases/antagonistas & inibidores , Gliceraldeído-3-Fosfato Desidrogenases/metabolismo , Glicólise/efeitos dos fármacos , Humanos , Iodoacetamida/química , Iodoacetamida/farmacologia , Ácido Iodoacético/química , Ácido Iodoacético/farmacologia , EstudantesRESUMO
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].
Assuntos
Glicólise , Hipoglicemia/metabolismo , Neoplasias/metabolismo , Proliferação de Células , Glucose/fisiologia , Proteínas Facilitadoras de Transporte de Glucose/metabolismo , Células HeLa , Hexoquinase/química , Hexoquinase/metabolismo , Humanos , Isoenzimas/química , Isoenzimas/metabolismo , Cinética , L-Lactato Desidrogenase/metabolismo , Células MCF-7 , Modelos Biológicos , Transportadores de Ácidos Monocarboxílicos/metabolismo , Fosfofrutoquinase-1/metabolismo , Piruvato Quinase/metabolismo , Simportadores/metabolismoRESUMO
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.