A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines
| العنوان: | A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines |
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| المؤلفون: | Ronan M. T. Fleming, Maike K. Aurich, Ines Thiele |
| المصدر: | PLoS Computational Biology, Vol 13, Iss 8, p e1005698 (2017) PLoS Computational Biology |
| بيانات النشر: | Public Library of Science (PLoS), 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | Melanomas, 0301 basic medicine, Physiology, Cell, Biochemistry, Drug Metabolism, Metabolites, Medicine and Health Sciences, Biology (General), Databases, Protein, Melanoma, Ecology, Systems Biology, Phenotype, Isocitrate Dehydrogenase, Oxygen Metabolism, Chemistry, medicine.anatomical_structure, Oncology, Computational Theory and Mathematics, Modeling and Simulation, Physical Sciences, Metabolome, Research Article, Chemical Elements, Cell Physiology, Cell type, QH301-705.5, Biology, Models, Biological, 03 medical and health sciences, Cellular and Molecular Neuroscience, Metabolomics, Cell Line, Tumor, Biomarkers, Tumor, Genetics, medicine, Extracellular, Humans, Pharmacokinetics, Molecular Biology, Secretion, Ecology, Evolution, Behavior and Systematics, Pharmacology, Biology and Life Sciences, Cancers and Neoplasms, Cancer, Cell Biology, medicine.disease, Cell Metabolism, Oxygen, Metabolism, 030104 developmental biology, Cancer cell, Physiological Processes |
| الوصف: | The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models’ capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates. Interestingly, models based on metabolomic profiles of melanoma cells were distinguished from other models through their low oxygen uptake rates, which were associated with a glycolytic phenotype. A subset of the melanoma cell models required reductive carboxylation. The analysis of protein and RNA expression levels from the Human Protein Atlas showed that IDH2, which was an essential gene in the melanoma models, but not IDH1 protein, was detected in normal skin cell types and melanoma. Moreover, the von Hippel-Lindau tumor suppressor (VHL) protein, whose loss is associated with non-hypoxic HIF-stabilization, reductive carboxylation, and promotion of glycolysis, was uniformly absent in melanoma. Thus, the experimental data supported the predicted role of IDH2 and the absence of VHL protein supported the glycolytic and low oxygen phenotype predicted for melanoma. Taken together, our approach of integrating extracellular metabolomic data with metabolic modeling and the combination of different network interrogation methods allowed insights into the metabolism of cells. Author summary Altered metabolism is characteristic for many human diseases including cancer. Disease progression and treatment efficacy vary between patients. Hence, we need personalized approaches to define metabolic disease phenotypes. This definition will enable us to unravel the underlying disease mechanisms and to treat individuals more efficiently. Computational modeling increasingly supports the analysis of disease mechanisms and complex data sets. The interpretation of extracellular metabolomic data sets is particularly promising since this data type is proximal to the actual metabolic phenotype altered in human diseases. Moreover, it might enable us to directly interpret disease states from serum samples in the future. Herein, we took a first step towards this ambitious goal. We generated a large set of cancer metabolic models from extracellular metabolomic data and computationally stratified the models based on their metabolic characteristics into different phenotype groups. Melanoma emerged as an interesting example of how our approach can provide insights into the intracellular metabolism from extracellular measurements. Taken together, this work paves the way to generate condition-specific models from extracellular metabolomic data and demonstrates the many ways by which distinct phenotypes can be stratified and phenotype-specific intervention targets can be predicted. |
| تدمد: | 1553-7358 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::837a90c1e0c5081f9ae3c20e2ef2796f https://doi.org/10.1371/journal.pcbi.1005698 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....837a90c1e0c5081f9ae3c20e2ef2796f |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15537358 |
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