Fig 15 - uploaded by Gábor Somlyai
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![1 Extracellular [U-13 C 18 ]-stearic acid tracer substrate entry and its metabolic hubs by rosiglitazone action in HepG2 cells. (a) Rosiglitazone increases peroxisomal long-chain fatty acid degradation while shifting, (b) acetyl-CoA flux via, (c) malate shuttling towards, (d) triose, as well as [3,4-13 C 2 ]-D-pentose and hexose labeling. Rosiglitazone also forces long-chain fatty acid oxidation to occur in the mitochondria and acetyl-CoA disposal via glutamate synthesis as well as malate shuttling to label RNA ribose and lactate. Please note that acetyl-CoA used by citrate synthase is indistinguishable, if it was generated after peroxisomal chain shortening and mitochondrial fatty acid β carbon oxidation. Increased ribose, lactate, and glutamate labeling after a stepwise rosiglitazone dosing are used as efficacy markers to visualize, via regression analysis, 13 C tracer fate associations in HepG2 cells](profile/Gabor-Somlyai/publication/272336143/figure/fig1/AS:614261009158152@1523462645360/Extracellular-U-13-C-18-stearic-acid-tracer-substrate-entry-and-its-metabolic-hubs-by.png)
1 Extracellular [U-13 C 18 ]-stearic acid tracer substrate entry and its metabolic hubs by rosiglitazone action in HepG2 cells. (a) Rosiglitazone increases peroxisomal long-chain fatty acid degradation while shifting, (b) acetyl-CoA flux via, (c) malate shuttling towards, (d) triose, as well as [3,4-13 C 2 ]-D-pentose and hexose labeling. Rosiglitazone also forces long-chain fatty acid oxidation to occur in the mitochondria and acetyl-CoA disposal via glutamate synthesis as well as malate shuttling to label RNA ribose and lactate. Please note that acetyl-CoA used by citrate synthase is indistinguishable, if it was generated after peroxisomal chain shortening and mitochondrial fatty acid β carbon oxidation. Increased ribose, lactate, and glutamate labeling after a stepwise rosiglitazone dosing are used as efficacy markers to visualize, via regression analysis, 13 C tracer fate associations in HepG2 cells
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![Fig. 15.1 Extracellular [U-13 C 18 ]-stearic acid tracer substrate...](profile/Gabor-Somlyai/publication/272336143/figure/fig1/AS:614261009158152@1523462645360/Extracellular-U-13-C-18-stearic-acid-tracer-substrate-entry-and-its-metabolic-hubs-by_Q320.jpg)
Metabolomics technologies continue to develop not only to study endpoint steady-state concentrations of numerous metabolites in normal and cancer cells but also to examine metabolic flux and networks. These techniques are of importance for understanding tumor cell metabolism and for the development of new drugs and treatment strategies. The choice...
Citations
... Rosiglitazone (TZD class of antidiabetic drug) is an agonist of the PPAR-γ, which is found in insulin-dependent glucose-requiring tissues and causes insulin sensitization (Young et al. 1998). Rosiglitazone enters into the cell by a transporter substrate called stearate tracer (Boros et al. 2015). In the cell, it directly acts on PPAR-γ and combines with retinoid X receptor (RXR) (Rosen et al. 2000;Berger et al. 1996). ...
Diabetes is known to increase susceptibility to hypertension due to increase in inflammation, oxidative stress, and endothelial dysfunction, leading to vascular stiffness. The polytherapy might lead to several drug–drug interactions (DDIs), which cause certain life-threatening complications such as diabetic nephropathy and hypoglycaemia. So, in this review we focused on drug–drug interactions and impact of genetic factors on drug responses for better disease management. Drug–drug interactions (DDIs) may act either synergistically or antagonistically. For instance, a combination of metformin with angiotensin II receptor antagonist or angiotensin-converting enzyme inhibitors (ACEIs) synergistically improves glucose absorption, whereas the same hypertensive drug combination with sulphonylurea might cause severe hypoglycaemia sometimes. Thiazolidinediones (TDZs) can cause fluid retention and heart failure when taken alone, but a combination of angiotensin II receptor antagonist with TZDs prevents these side effects. Interindividual genetic variation affects the DDI response. We found two prominent genes, GLUT4 and PPAR-γ, which are common targets for most of the drug. So, all of these findings established a connection between drug–drug interaction and genetics, which might be used for effective disease management.
... Instead, it provides a series of qualitative or semiquantitative flux predictions around each analyzed metabolite. Strategies that couple direct interpretation of 13 C data to regression and correlation analyses are widely applied to unveil the effect of an external perturbation, such as a therapeutic intervention, on central carbon metabolism [26][27][28][29][30]. ...
... 13 C data has been widely used to assist in drug discovery. In this regard, tracer analysis coupled with regression and correlation analyses is frequently used to characterize drug response [26][27][28][29]. Such approach uses regression and correlation statistics with binary, numeric and visual analysis to integrate drug dosage, time points, as well as all necessary biological variables in order to diagnose disturbed stable isotope labeled matrices [29]. ...
... In this regard, tracer analysis coupled with regression and correlation analyses is frequently used to characterize drug response [26][27][28][29]. Such approach uses regression and correlation statistics with binary, numeric and visual analysis to integrate drug dosage, time points, as well as all necessary biological variables in order to diagnose disturbed stable isotope labeled matrices [29]. p 13 CMFA could further expand the role of 13 C in drug discovery by allowing the integration of 13 C and transcriptomic data in the framework of genome-scale metabolic models. ...
Deciphering the mechanisms of regulation of metabolic networks subjected to perturbations, including disease states and drug-induced stress, relies on tracing metabolic fluxes. One of the most informative data to predict metabolic fluxes are 13C based metabolomics, which provide information about how carbons are redistributed along central carbon metabolism. Such data can be integrated using 13C Metabolic Flux Analysis (13C MFA) to provide quantitative metabolic maps of flux distributions. However, 13C MFA might be unable to reduce the solution space towards a unique solution either in large metabolic networks or when small sets of measurements are integrated. Here we present parsimonious 13C MFA (p13CMFA), an approach that runs a secondary optimization in the 13C MFA solution space to identify the solution that minimizes the total reaction flux. Furthermore, flux minimization can be weighted by gene expression measurements allowing seamless integration of gene expression data with 13C data. As proof of concept, we demonstrate how p13CMFA can be used to estimate intracellular flux distributions from 13C measurements and transcriptomics data. We have implemented p13CMFA in Iso2Flux, our in-house developed isotopic steady-state 13C MFA software. The source code is freely available on GitHub (https://github.com/cfoguet/iso2flux/releases/tag/0.7.2).
... Recent developments in tracer substrate-based metabolomics demonstrated that the in vitro applied [1,2-13 C2]-D-glucose tracer is a precise method for characterizing fluxes of the pentose cycle, glycolysis, and the TCA cycle [20]. The use of stable isotopes has been extended to analyze de novo (new) fatty acid synthesis, turnover, and modifications via chain shortening or elongation in cell pellets, plasma or tissues [21]. Tracer substrate based metabolomics using 13 C glucose and deuterium labeling follow roadmap studies as the most efficient and pertinent tools [22] for linking phenotype with specific metabolic processes by the single 1,2 13 C2-D-glucose tracer's fate and its associations with fructose dosing. ...
... Rapid system-wide association study (SWAS) evaluation of SGBS cells was performed by the color assisted visual isotopolome data matrix screening tool [21,63] to diagnose phenotypic serine oxidation and glycine cleavage as markers of glucose-deriving substrate level phosphorylation (ATP synthesis) [28] and its response to fructose treatment. The SOGC pathway is routinely measured by the positive correlation between 13 CO2 release and 13 C lactate labeling, at the expense of ATP synthesis in mitochondria via glutamate (TCA cycle) and fatty acid labeling (citrate shuttling) as the central CO2 releasing mechanism from glucose. ...
... The p values signify how different the percent values are from each other, as groups, among variables. As can be noted, 13 CO2 and 13 C lactate percent changes are in closer numeric ranges (hundreds), than other variables, thus the high p values indicate that these are responsive markers of the SOGC pathway [21]. High correlation coefficients and close numeric ranges (no significant differences in the percent ranges among 13 CO2 release and changes in lactate release) are signs of close variable response after fructose treatment, consistent with the SOGC pathway. ...
Increased consumption of sugar and fructose as sweeteners has resulted in the utilization of fructose as an alternative metabolic fuel that may compete with glucose and alter its metabolism. To explore this, human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes were differentiated to adipocytes in the presence of 0, 1, 2.5, 5 or 10 mM of fructose added to a medium containing 5 mM of glucose representing the normal blood glucose concentration. Targeted tracer [1,2-13C2]-d-glucose fate association approach was employed to examine the influence of fructose on the intermediary metabolism of glucose. Increasing concentrations of fructose robustly increased the oxidation of [1,2-13C2]-d-glucose to 13CO2 (p < 0.000001). However, glucose-derived 13CO2 negatively correlated with 13C labeled glutamate, 13C palmitate, and M+1 labeled lactate. These are strong markers of limited tricarboxylic acid (TCA) cycle, fatty acid synthesis, pentose cycle fluxes, substrate turnover and NAD+/NADP+ or ATP production from glucose via complete oxidation, indicating diminished mitochondrial energy metabolism. Contrarily, a positive correlation was observed between glucose-derived 13CO2 formed and 13C oleate and doses of fructose which indicate the elongation and desaturation of palmitate to oleate for storage. Collectively, these results suggest that fructose preferentially drives glucose through serine oxidation glycine cleavage (SOGC pathway) one-carbon cycle for NAD+/NADP+ production that is utilized in fructose-induced lipogenesis and storage in adipocytes.
... Due to the choice of fatty acids (C22:0 and C24:0) we surely measure elongation as well as new synthesis by selecting nuclear membrane fatty acids that are not available from the culture media, while rosiglitazone, tracer glucose and cross-labeled glutamine are present. Although palmitate, stearate and oleate are much more abundant than lignocerate is, the shorter chains are more diluted from bovine serum fatty acids, externally added to culture media [33]. The high abundance of medium and long chain fatty acids in FBS-treated media is also the reason why we did not use extraction methods with higher coverage, e.g.: dual‐ phase, methoximation-MTBSTFA derivatization for aqueous metabolites, combined with trans-methylation for lipid‐ bound fatty acids. ...
Regression statistics in this targeted tracer fate association study (TTFAS) is shown to reveal associations among diverse phenotypic metabolic products in fumarate hydratase-deficient UOK kidney tumor cells with defective glutaminolysis, reductive carboxylation for lipogenic citrate production, as well as the Warburg effect, using the [1,2-13C2 ]-D-glucose tracer. These co-existing profiles were revealed in separate 13 C-glutamine and 13 C-glucose tracer experiments. Herein UOK cells show a close Warburg-type correlation between consumption of glucose for 13 C-lactic acid production (R2 >0.98; glucose to lactate). On the other hand, proliferation-related macromolecule 13C labeling, such as that of RNA-derived ribose and lignoceric acid correlates with the glucose-derived internally cross-labeled 13C-glutamine fraction (R2 >0.98; glutamate to lignocerate and RNA ribose). We conclude that the TTFAS approach reliably reproduces results and conclusions obtained via multiple 13C-tracer metabolic flux analyses using a single metabolic tracer to trim down versatilities, cost and time, involved in multiple tracer studies.
(in print)
FULL CV: https://drive.google.com/open?id=1rBus-TbpwAsvRYPCIuZApZ352Tmn8sdr
