Pyruvate Dehydrogenase Pdh Research Articles

Hypoxia inducible factor −1 regulates WSSV-induced glycolytic genes in the white shrimp Litopenaeus vannamei

Hypoxia-inducible factor −1 (HIF-1) is a transcriptional factor that regulates the expression of several glycolytic genes. The white spot syndrome virus (WSSV) induces a shift in glycolysis that favors viral replication in white shrimp Litopenaeus vannamei. HIF-1 is related to the pathogenesis of the WSSV infection through the induction of metabolic changes in infected white shrimp. Although the WSSV infection is associated with metabolic changes, the role of HIF-1 on key glycolytic genes during the WSSV infection has not been examined. In this work, we evaluated the effect of HIF-1α silencing on expression and activity of glycolytic enzymes (Hexokinase-HK, phosphofructokinase-PFK and pyruvate kinase-PK) along with the glucose transporter 1 (Glut1), regulatory enzymes (glucose-6-phosphate dehydrogenase-G6PDH and pyruvate dehydrogenase-PDH), and metabolic intermediates of glycolysis (glucose-6-phosphate-G6P and pyruvate). The expression of Glut1 increased in each tissue evaluated after WSSV infection, while HK, PFK and PK gene expression and enzyme activities increased in a tissue-specific manner. G6PDH activity increased during WSSV infection, and its substrate G6P decreased, while PDH activity decreased and its substrate pyruvate increased. Silencing of HIF-1α blocked the WSSV-induced Glut1 and glycolytic genes upregulation and enzyme activity in a tissue-specific manner. We conclude that HIF-1 regulates the WSSV-induced glycolysis through induction of glycolytic genes contributing to glucose metabolism in tissues of infected shrimp. Also, the inhibition, and activation of regulatory genes are likely to decrease the availability of the raw materials essential for WSSV replication and increase oxidative metabolism.

Expression of enzymes related to glucose metabolism in non-small cell lung cancer and prognosis

ABSTRACTPurpose/Aim: Cancer cells are addicted to glycolytic anaerobic pathways, in presence or in absence of a functional Krebs' cycle (phenomenon Warburg). This metabolic predilection relies on both extracellular (impaired vascularization and oxygenation) and intracellular (oncogenic activation of genes) causes. Materials and Methods: We investigated the expression and prognostic relevance of enzymes involved in the glucose absorption and metabolism, monocarboxylate transporter (MCT) expression, MCT1 and MCT2, pentose pathway (Glucose-6-phospahte dehydrogenase G6PD), glycogene synthesis (glycogene synthase GYS1), glycolysis (Hexokinase HXKII, phosphofructokinase PFK1, fructose biphosphate aldolase), fate of pyruvate (pyruvate dehydrogenase PDH, phosphorylated pPDH, PDH kinase PDK1, lactate dehydrogenase LDH5 and LDH1) and key Kreb's cycle enzymes (citrate synthase CSynth and isocitrate dehydrogenase IDH). Results: A strong overexpression of the above enzymes/proteins was noted in a varying percentage of cases examined. An interesting significant correlation between the enzymes involved in glycolysis and with the LDH5 was noted. Adenocarcinomas expressed higher levels of GLUT1 and MCT2 compared to other subtypes. Stage (p = 0.0001), aldolase (p = 0.004), LDH5 (p = 0.008), GLUT2 (p = 0.008), MCT2 (p = 0.009), GSYS1 (p = 0.04), and GLUT1 (p = 0.05) were significantly related with poor disease specific overall survival. In multivariate analysis stage (p = 0.001), LDH5 (p = 0.04), pPDH (p = 0.04), and aldolase (p = 0.04) were independent prognostic variables. Conclusion: It is concluded that an orchestrated activation of glucose absorption and metabolism towards anaerobic pathways characterize the majority of NSCLC, and this phenotype is strongly linked with an aggressive clinical behavior. This glycolytic addiction of lung cancer cell is revealed as a key therapeutic target.

Lung cancer: An organized cellular and metabolic domain

Immunohistochemical evaluation of lung carcinomas for key enzymes involved in cellular metabolism (lactate dehydrogenase LDH 1 and 5, pyruvate dehydrogenase PDH, pyruvate dehydrogenase kinase PDHK-1, monocarboxylate transporters MCT 1, 2 and 4, glucose transporter GLUT1, hypoxia inducible factors HIF1α and 2α) show a complementary metabolic profile between cancer cells and tumor-associated stroma. Cancer cells share enzyme/transporter activities suggestive of an anaerobic metabolism with high affinity for glucose absorption, anaerobic glycolysis and lactate extrusion. On the other hand, the tumour-associated fibroblasts express patterns involved in aerobic pathways and lactate oxidation. These findings bring forward the hypothesis that tumor associated stroma is an accomplice in tumor growth and survival sustaining an independent cellular and metabolic tumor domain. The development of agents exploiting such cancer specific metabolic pathways may prove of importance in the treatment of lung cancer.

Two optimizing procedures for the solution of complex systems of equations: a powerful tool for modelling and simulation of metabolism

Introduction Standard calculations for the evaluation of indirect calorimetry (IC) are based on two-dimensional nonlinear systems of equations. For a more sophisticated evaluation metabolic models can be used, which are described by complex systems of equations. Since the solutions are multidimensional, a concrete result must be selected by means of constraints, using optimizing procedures. These multidimensional optimizations are critical concerning processing time and reproducibility of minimum detection. Methods In order to simulate the status of metabolism of ICU patients on the basis of IC data, a complex model of metabolism was developed. The model was described by a system of equations consisting of 13 equations and 25 variables. The final result is selected out of the nine-dimensional space of solutions by optimization, considering a total of 69 constraints. Two optimizing procedures (Simplex algorithm [1] SPX, Powell procedure [2] POW) were modified, and analysed referring to processing time (PT), variance and reproducibility (VK, VC) of results. Test data as well as long-term IC measurements in ICU patients were evaluated. Modifications including ‘meta procedures’, which multiply restart the ‘core’ of the optimization (SPX) or find an optimal starting point for the core by a deformation of optimal sets of IC data with already known solutions, were necessary. VC and VK were tested for four representative parameters of the metabolic simulation (enzymes pyruvate dehydrogenase PDH, citrate synthase CS, ketoglutarate decarboxylase KDC, malate dehydrogenase MDH) by means of seven sets of calculated IC data. Penalty values as a measure of the quality of the simulation and processing time (Pentium 120 MHz) were determined using real data of a ventilated patient. Results Both optimization procedures showed a high reproducibility and a low variance (all but one VK/VC values below 10%); there were no differences between Simplex and Powell algorithm (see Table 1). Evaluating real data, we found a lower frequency of outliers (nonevaluable data) for SPX (3.7% vs. 5.7%), while processing time was shorter for POW (1.95 ± 0.87 vs. 8.88 ± 3.06 min; both P < 0.05; n = 800). Table 1. Variance (VK) and reproducibility (VC) for the Simplex algorithm (SPX) and the Powell procedure (POW) for four different enzymes (PDH, CS, KDC, MDH) VC VK SPX POW SPX POW PDH 3.5% ± 1.7% 2.1% ± 1.7% 5.1% ± 1.7% 2.8% ± 2.3% CS 3.6% ± 1.6% 1.4% ± 1.4% 10.1% ± 2.1% 4.7% ± 5.8% KDC 0.1% ± 0.0% 0.0% ± 0.0% 1.5% ± 0.0% 0.0% ± 0.0% MDH 1.5% ± 0.7% 0.5% ± 0.4% 3.0% ± 1.3% 2.5% ± 1.5% Conclusion The evaluation of IC data using a model for the simulation of metabolism was successful even in clinical situations, where standard procedures usually fail. Multidimensional nonlinear systems of equations, as used for this simulation, are a considerable extension of available tools for the interpretation of IC data, if a powerful optimizing procedure is used. Both of the analysed procedures have shown to be suitable to reduce the nine-dimensional space of solutions of the system of equations to a concrete result, and can be applied to online metabolic simulations, if fast PC hardware is available.