METAtarget-Extracting key enzymes of metabolic regulation from high-throughput metabolomics data using KEGG REACTION information

Jan Budczies, Carsten Denkert, Berit M. Müller, Scarlet F. Brockmöller, Manfred Dietel, Jules L. Griffin, Matej Oresic, Oliver Fiehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

METAtarget is a new method for reverse engineering of metabolic networks and the detection of targets enzymes from high-throughput metabolomics data. Using KEGG REACTION, reactant partners are identified and the ratio of product to substrate metabolite concentrations is employed as surrogate for the reaction activity. A test statistics is introduced to assess changes in the activity of reactions between different disease states. In an application of METAtarget to breast cancer, we investigate the dependence of tumor metabolism on hormone receptor status. To this end, we analyze metabolomics data that were generated within the METAcancer project and compare the identified reactions with data on enzyme expression that are obtained from publicly available breast cancer gene expression series. As result, deregulation of key enzymes and reactions of glycolysis, glutaminolysis and other metabolic pathways are detected.

Original languageEnglish (US)
Title of host publicationGCB 2010 - German Conference on Bioinformatics 2010
Pages103-112
Number of pages10
StatePublished - 2010
EventGerman Conference on Bioinformatics 2010, GCB 2010 - Braunschweig, Germany
Duration: Sep 20 2010Sep 22 2010

Other

OtherGerman Conference on Bioinformatics 2010, GCB 2010
CountryGermany
CityBraunschweig
Period9/20/109/22/10

ASJC Scopus subject areas

  • Information Systems
  • Biomedical Engineering

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    Budczies, J., Denkert, C., Müller, B. M., Brockmöller, S. F., Dietel, M., Griffin, J. L., Oresic, M., & Fiehn, O. (2010). METAtarget-Extracting key enzymes of metabolic regulation from high-throughput metabolomics data using KEGG REACTION information. In GCB 2010 - German Conference on Bioinformatics 2010 (pp. 103-112)