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

Hojung Nam; Miguel Campodonico; Aarash Bordbar; Daniel R Hyduke; Sangwoo Kim; Daniel C Zielinski; Bernhard O Palsson

doi:10.1371/journal.pcbi.1003837

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

PLoS Comput Biol. 2014 Sep 18;10(9):e1003837. doi: 10.1371/journal.pcbi.1003837. eCollection 2014 Sep.

Authors

Hojung Nam¹, Miguel Campodonico², Aarash Bordbar³, Daniel R Hyduke³, Sangwoo Kim⁴, Daniel C Zielinski³, Bernhard O Palsson⁵

Affiliations

¹ Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America; School of Information & Communications, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, Republic of Korea.
² Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America; Centre for Biotechnology and Bioengineering, CeBiB, University of Chile, Santiago, Chile.
³ Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.
⁴ Severance Biomedical Science Institute, Yonsei University College of Medicine, Seodaemun-gu, Seoul, South Korea.
⁵ Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America; Department of Pediatrics, University of California San Diego, La Jolla, California, United States of America.

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.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

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*

Grants and funding

This work was supported by U.S. Department of Energy grant (DE-SC0004917), a grant from the NNF Center for Biosustainability at the Technical University of Denmark, and National Research Foundation (NRF) of Korea Grant funded by Ministry of Education, Science and Technology (MEST) (NRF-2011-357-C00142). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.