title: A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.
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.
authors.affiliation: 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.
authors.forename: Hojung
authors.initials: H
authors.lastname: Nam
authors.affiliation: 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.
authors.forename: Miguel
authors.initials: M
authors.lastname: Campodonico
authors.affiliation: Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Aarash
authors.initials: A
authors.lastname: Bordbar
authors.affiliation: Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Hyduke
authors.affiliation: Severance Biomedical Science Institute, Yonsei University College of Medicine, Seodaemun-gu, Seoul, South Korea.
authors.forename: Sangwoo
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Zielinski
authors.affiliation: 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.
authors.forename: Bernhard O
authors.initials: BO
authors.lastname: Palsson
mesh_terms: D045744:Cell Line, Tumor; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D055442:Metabolome / Q000235:genetics*; D008954:Models, Biological; D009154:Mutation / Q000235:genetics; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D049490:Systems Biology / Q000379:methods*