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A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks

Authors
 Hojung Nam  ;  Miguel Campodonico  ;  Aarash Bordbar  ;  Daniel R. Hyduke  ;  Sangwoo Kim  ;  Daniel C. Zielinski  ;  Bernhard O. Palsson 
Citation
 PLOS GENETICS, Vol.10(9) : e1003837, 2014 
Journal Title
PLOS GENETICS
ISSN
 1553-7390 
Issue Date
2014
MeSH
Cell Line, Tumor ; Cluster Analysis ; Computer Simulation ; Gene Expression Profiling ; Humans ; Metabolic Networks and Pathways/genetics* ; Metabolome/genetics* ; Models, Biological ; Mutation/genetics ; Neoplasms/genetics* ; Neoplasms/metabolism* ; Systems Biology/methods*
Abstract
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.
Files in This Item:
T201403694.pdf Download
DOI
10.1371/journal.pcbi.1003837
Appears in Collections:
1. College of Medicine (의과대학) > BioMedical Science Institute (의생명과학부) > 1. Journal Papers
Yonsei Authors
Kim, Sangwoo(김상우) ORCID logo https://orcid.org/0000-0001-5356-0827
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/100148
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