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Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma.

Özcan E, Çakır T - Front Neurosci (2016)

Bottom Line: Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis.We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets.Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.

View Article: PubMed Central - PubMed

Affiliation: Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University Gebze, Turkey.

ABSTRACT
Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.

No MeSH data available.


Related in: MedlinePlus

GBM metabolic remodeling reported in literature. In TCA cycle, low-flux reactions were represented by a thinner gray arrow. Our computational results obtained by all GBM metabolic models support this remodeling topology. All reaction IDs, shown also in the figure, and corresponding reactions can be found in Supplementary File 1. The figure was drawn in PathVisio 3 toolbox (Kutmon et al., 2015).
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Figure 2: GBM metabolic remodeling reported in literature. In TCA cycle, low-flux reactions were represented by a thinner gray arrow. Our computational results obtained by all GBM metabolic models support this remodeling topology. All reaction IDs, shown also in the figure, and corresponding reactions can be found in Supplementary File 1. The figure was drawn in PathVisio 3 toolbox (Kutmon et al., 2015).

Mentions: The GBM subtypes, Mesenchymal (Mes), ProNeural (PN), and Proliferative (Pro) GBM, are classified based on the clustering of gene expression profiling (Phillips et al., 2006; Lee et al., 2008; Verhaak et al., 2010; Huse et al., 2013). In-silico GBM subtype metabolic models for Mes, PN and Pro types were reconstructed by GIMME algorithm (Becker and Palsson, 2008; see Materials and Methods) and used in the calculation of metabolic fluxes. Key fluxes and flux ratios are presented in Table 1. The results in Table 1 show high qualitative and quantitative agreements between the flux predictions and the literature results. Results reveal subtle differences between the GBM subtypes in terms of simulated metabolic flux phenotypes. However, all GBM subtype metabolic models exhibit the same behavior in terms of active flux routes. This flux routing, as shared by the three subtypes, are summarized in Figure 2. The simulation results depicted in the figure are in agreement with the major properties of GBM metabolic phenotypes reported in literature. The results confirm the study which reports similar metabolic characteristics for different GBM types derived from independent human tumors with different driver mutations (Marin-Valencia et al., 2012). Detailed metabolic remodeling observed in the GBM subtype metabolic models is discussed below.


Reconstructed Metabolic Network Models Predict Flux-Level Metabolic Reprogramming in Glioblastoma.

Özcan E, Çakır T - Front Neurosci (2016)

GBM metabolic remodeling reported in literature. In TCA cycle, low-flux reactions were represented by a thinner gray arrow. Our computational results obtained by all GBM metabolic models support this remodeling topology. All reaction IDs, shown also in the figure, and corresponding reactions can be found in Supplementary File 1. The figure was drawn in PathVisio 3 toolbox (Kutmon et al., 2015).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4834348&req=5

Figure 2: GBM metabolic remodeling reported in literature. In TCA cycle, low-flux reactions were represented by a thinner gray arrow. Our computational results obtained by all GBM metabolic models support this remodeling topology. All reaction IDs, shown also in the figure, and corresponding reactions can be found in Supplementary File 1. The figure was drawn in PathVisio 3 toolbox (Kutmon et al., 2015).
Mentions: The GBM subtypes, Mesenchymal (Mes), ProNeural (PN), and Proliferative (Pro) GBM, are classified based on the clustering of gene expression profiling (Phillips et al., 2006; Lee et al., 2008; Verhaak et al., 2010; Huse et al., 2013). In-silico GBM subtype metabolic models for Mes, PN and Pro types were reconstructed by GIMME algorithm (Becker and Palsson, 2008; see Materials and Methods) and used in the calculation of metabolic fluxes. Key fluxes and flux ratios are presented in Table 1. The results in Table 1 show high qualitative and quantitative agreements between the flux predictions and the literature results. Results reveal subtle differences between the GBM subtypes in terms of simulated metabolic flux phenotypes. However, all GBM subtype metabolic models exhibit the same behavior in terms of active flux routes. This flux routing, as shared by the three subtypes, are summarized in Figure 2. The simulation results depicted in the figure are in agreement with the major properties of GBM metabolic phenotypes reported in literature. The results confirm the study which reports similar metabolic characteristics for different GBM types derived from independent human tumors with different driver mutations (Marin-Valencia et al., 2012). Detailed metabolic remodeling observed in the GBM subtype metabolic models is discussed below.

Bottom Line: Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis.We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets.Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.

View Article: PubMed Central - PubMed

Affiliation: Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University Gebze, Turkey.

ABSTRACT
Developments in genome scale metabolic modeling techniques and omics technologies have enabled the reconstruction of context-specific metabolic models. In this study, glioblastoma multiforme (GBM), one of the most common and aggressive malignant brain tumors, is investigated by mapping GBM gene expression data on the growth-implemented brain specific genome-scale metabolic network, and GBM-specific models are generated. The models are used to calculate metabolic flux distributions in the tumor cells. Metabolic phenotypes predicted by the GBM-specific metabolic models reconstructed in this work reflect the general metabolic reprogramming of GBM, reported both in in-vitro and in-vivo experiments. The computed flux profiles quantitatively predict that major sources of the acetyl-CoA and oxaloacetic acid pool used in TCA cycle are pyruvate dehydrogenase from glycolysis and anaplerotic flux from glutaminolysis, respectively. Also, our results, in accordance with recent studies, predict a contribution of oxidative phosphorylation to ATP pool via a slightly active TCA cycle in addition to the major contributor aerobic glycolysis. We verified our results by using different computational methods that incorporate transcriptome data with genome-scale models and by using different transcriptome datasets. Correct predictions of flux distributions in glycolysis, glutaminolysis, TCA cycle and lipid precursor metabolism validate the reconstructed models for further use in future to simulate more specific metabolic patterns for GBM.

No MeSH data available.


Related in: MedlinePlus