title: Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls.
abstract: Information on biological networks can greatly facilitate the function-orientated interpretation of high-throughput molecular data. Genome-wide metabolic network models of human cells, in particular, can be employed to contextualize gene expression profiles of patients with the goal of both, a better understanding of individual etiologies and an educated reclassification of (clinically defined) phenotypes. We analyzed publicly available expression profiles of intestinal tissues from treatment-naive pediatric inflammatory bowel disease (IBD) patients and age-matched control individuals, using a reaction-centric metabolic network derived from the Recon2 model. By way of defining a measure of 'coherence', we quantified how well individual patterns of expression changes matched the metabolic network. We observed a bimodal distribution of metabolic network coherence in both patients and controls, albeit at notably different mixture probabilities. Multidimensional scaling analysis revealed a bisectional pattern as well that overlapped widely with the metabolic network-based results. Expression differences driving the observed bimodality were related to cellular transport of thiamine and bile acid metabolism, thereby highlighting the crosstalk between metabolism and other vital pathways. We demonstrated how classical data mining and network analysis can jointly identify biologically meaningful patterns in gene expression data.
authors: Institute of Medical Informatics and Statistics, Christian-Albrechts University Kiel, Kiel, Germany.
authors: Carolin
authors: C
authors: Knecht
authors: Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany.
authors: Christoph
authors: C
authors: Fretter
authors: Institute of Clinical Molecular Biology, Center for Molecular Biosciences, Christian-Albrechts University Kiel, Kiel, Germany.
authors: Philip
authors: P
authors: Rosenstiel
authors: Institute of Medical Informatics and Statistics, Christian-Albrechts University Kiel, Kiel, Germany.
authors: Michael
authors: M
authors: Krawczak
authors: Department of Life Sciences and Chemistry, Jacobs University, Bremen, Germany.
authors: Marc-Thorsten
authors: MT
authors: Hütt
mesh_terms: D000293:Adolescent; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D057225:Data Mining; D005260:Female; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000235:genetics* / Q000378:metabolism*; D007413:Intestinal Mucosa / Q000378:metabolism; D007422:Intestines / Q000473:pathology; D008297:Male; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D010641:Phenotype