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

TitleA Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks.
Publication TypeJournal Article
Year of Publication2014
AuthorsNam H, Campodonico M, Bordbar A, Hyduke DR, Kim S, Zielinski DC, Palsson BO
JournalPLoS Comput Biol
Volume10
Issue9
Paginatione1003837
PubMed Date09/2014
ISSN1553-7358
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.

Alternate JournalPLoS Comput. Biol.
PubMed ID25232952
Cover Image: 

Location

Location

417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

User Login