A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase...

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Bibliographic Details
Published in:PLoS computational biology 2014-09, Vol.10 (9), p.e1003837-e1003837
Main Authors: Nam, Hojung, Campodonico, Miguel, Bordbar, Aarash, Hyduke, Daniel R, Kim, Sangwoo, Zielinski, Daniel C, Palsson, Bernhard O
Format: Article
Language:English
Subjects:
BASIC BIOLOGICAL SCIENCES
Biology and Life Sciences
breast cancer
Cancer
cancers and neoplasms
Cell Line, Tumor
Cell metabolism
Cluster Analysis
Computer Simulation
Dehydrogenases
enzyme metabolism
Enzymes
Epigenetic inheritance
Epigenetics
gastric cancer
Gene Expression Profiling
Genetic aspects
Genetic regulation
Genomes
hepatocellular carcinoma
Humans
lung and intrathoracic tumors
MATHEMATICS AND COMPUTING
Metabolic Networks and Pathways - genetics
Metabolism
Metabolites
Metabolome - genetics
Models, Biological
Mutation
Mutation - genetics
Neoplasms - genetics
Neoplasms - metabolism
Physiological aspects
renal cancer
Studies
Systems Biology - methods
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Summary:Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003837