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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

Recontruction of the GBM metabolic models. GBM gene expression data were integrated with the growth-implemented brain specific genome-scale metabolic model (iMS570g) by GIMME and MADE algorithms to create GBM metabolic models. The algorithms are shown in paranthesis for related GBM metabolic models. (Mes, Mesenchymal subtype of GBM; PN, ProNeural subtype of GBM; Pro, Proliferative subtype of GBM). GIMME and MADE sketches were obtained from Figure 1 of Blazier and Papin (2012).
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Figure 1: Recontruction of the GBM metabolic models. GBM gene expression data were integrated with the growth-implemented brain specific genome-scale metabolic model (iMS570g) by GIMME and MADE algorithms to create GBM metabolic models. The algorithms are shown in paranthesis for related GBM metabolic models. (Mes, Mesenchymal subtype of GBM; PN, ProNeural subtype of GBM; Pro, Proliferative subtype of GBM). GIMME and MADE sketches were obtained from Figure 1 of Blazier and Papin (2012).

Mentions: iMS570g, the growth-implemented brain specific genome-scale metabolic network, was integrated with the GBM gene expression data mentioned in the previous section to generate context-specific GBM metabolic models and metabolic flux distributions. Two alternative methods, GIMME (Becker and Palsson, 2008) and MADE (Jensen and Papin, 2011), were applied to generate GBM metabolic models and test the effect of different algorithms on the results (Figure 1). Friedmann-Morvinski et al. (2012) showed that GBM can originate not only from astrocytes but also from neurons. Therefore, GBM transcriptome data were mapped to both astrocytic and neuronal reactions in iMS570g in order to generate GBM metabolic models via GIMME and MADE. While the output of MADE is a context-specific flux distribution, the output of GIMME is a context-specific model which needs to be further processed to obtain a flux distribution.


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

Özcan E, Çakır T - Front Neurosci (2016)

Recontruction of the GBM metabolic models. GBM gene expression data were integrated with the growth-implemented brain specific genome-scale metabolic model (iMS570g) by GIMME and MADE algorithms to create GBM metabolic models. The algorithms are shown in paranthesis for related GBM metabolic models. (Mes, Mesenchymal subtype of GBM; PN, ProNeural subtype of GBM; Pro, Proliferative subtype of GBM). GIMME and MADE sketches were obtained from Figure 1 of Blazier and Papin (2012).
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Recontruction of the GBM metabolic models. GBM gene expression data were integrated with the growth-implemented brain specific genome-scale metabolic model (iMS570g) by GIMME and MADE algorithms to create GBM metabolic models. The algorithms are shown in paranthesis for related GBM metabolic models. (Mes, Mesenchymal subtype of GBM; PN, ProNeural subtype of GBM; Pro, Proliferative subtype of GBM). GIMME and MADE sketches were obtained from Figure 1 of Blazier and Papin (2012).
Mentions: iMS570g, the growth-implemented brain specific genome-scale metabolic network, was integrated with the GBM gene expression data mentioned in the previous section to generate context-specific GBM metabolic models and metabolic flux distributions. Two alternative methods, GIMME (Becker and Palsson, 2008) and MADE (Jensen and Papin, 2011), were applied to generate GBM metabolic models and test the effect of different algorithms on the results (Figure 1). Friedmann-Morvinski et al. (2012) showed that GBM can originate not only from astrocytes but also from neurons. Therefore, GBM transcriptome data were mapped to both astrocytic and neuronal reactions in iMS570g in order to generate GBM metabolic models via GIMME and MADE. While the output of MADE is a context-specific flux distribution, the output of GIMME is a context-specific model which needs to be further processed to obtain a flux distribution.

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