Journal Article

Systems biology building a useful model from multiple markers and profiles

Paul Mayer, Bernd Mayer and Gert Mayer

in Nephrology Dialysis Transplantation

Published on behalf of European Renal Association - European Dialysis and Transplant Assoc

Volume 27, issue 11, pages 3995-4002
Published in print November 2012 | ISSN: 0931-0509
Published online November 2012 | e-ISSN: 1460-2385 | DOI:
Systems biology building a useful model from multiple markers and profiles

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The pathophysiology of diabetic nephropathy (DN) is driven by a complex, multi-facetted interplay of numerous molecular processes (protective as well as damaging) and the balance between these, rather than the activity of a single pathway, determines clinical presentation and outcome. We present a concept for deriving a biomarker panel aimed to represent the relevant processes involved. Our approach rests on a hybrid gene/protein interaction network that holds ample information on molecular features (nodes) and their relations (edges), as a result providing a basic structure to navigate in molecular content and context being identified as relevant in DN. Extensive literature search on omics studies in DN provided a molecular feature list mapping to a total of 2175 unique protein-coding genes [13 from single nucleotide polymorphisms (SNPs), 12 as targets from relevant miRNAs, 1583 from transcriptomics, 5 from proteomics and 53 from metabolomics via linking to enzymes; 509 features were identified from multiple sources]. Two hundred and eighty-seven further human protein-coding genes associated with DN were derived from searching NCBI Pubmed (utilizing MeSH and gene-to-pubmed). Text mining of patents and clinical trial descriptors in the context of DN further added about 1 000 features. These data were used to label the respective nodes in the interaction network, as a result obtaining a DN-specific subgraph. Application of a segmentation algorithm on this subgraph allowed the identification of DN-specific molecular units, each characterizing a cluster of genes/proteins with a high internal functional association. We interpret each such unit as a functionally relevant molecular process contributing to the presentation of DN, and the total set of such units as a molecular model of DN. We propose that selecting appropriate biomarkers from each unit might allow the description of a patient's specific ‘type’ of DN, ultimately leading to a better stratification of patients regarding progression risk and optimal interventional approach.

Keywords: biomarker; diabetic nephropathy; data integration; disease model; omics

Journal Article.  5070 words.  Illustrated.

Subjects: Nephrology

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