Journal Article

PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways

Gunnar Schramm, Stefan Wiesberg, Nicolle Diessl, Anna-Lena Kranz, Vitalia Sagulenko, Marcus Oswald, Gerhard Reinelt, Frank Westermann, Roland Eils and Rainer König

in Bioinformatics

Volume 26, issue 9, pages 1225-1231
Published in print May 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq113
PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways

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Motivation: Gene expression profiling by microarrays or transcript sequencing enables observing the pathogenic function of tumors on a mesoscopic level.

Results: We investigated neuroblastoma tumors that clinically exhibit a very heterogeneous course ranging from rapid growth with fatal outcome to spontaneous regression and detected regulatory oncogenetic shifts in their metabolic networks. In contrast to common enrichment tests, we took network topology into account by applying adjusted wavelet transforms on an elaborated and new 2D grid representation of curated pathway maps from the Kyoto Enzyclopedia of Genes and Genomes. The aggressive form of the tumors showed regulatory shifts for purine and pyrimidine biosynthesis as well as folate-mediated metabolism of the one-carbon pool in respect to increased nucleotide production. We spotted an oncogentic regulatory switch in glutamate metabolism for which we provided experimental validation, being the first steps towards new possible drug therapy. The pattern recognition method we used complements normal enrichment tests to detect such functionally related regulation patterns.

Availability and Implementation: PathWave is implemented in a package for R (www.r-project.org) version 2.6.0 or higher. It is freely available from http://www.ichip.de/software/pathwave.html

Contact: r.koenig@dkfz.de; r.eils@dkfz.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5437 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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