Molecular models of the cardiorenal syndrome. Issue 11 (8th May 2013)

• Record Type: Journal Article
• Authors: Heinzel, Andreas; Fechete, Raul; Mühlberger, Irmgard; Perco, Paul; Mayer, Bernd; Lukas, Arno
• Is Part Of: Electrophoresis. Volume 34:Issue 11(2013:Jun.)
• Page Start: 1649
• Page End: 1656
• Publication Date: 2013-05-08
• Subjects: Biomarker -- Cross‐omics -- Integration -- Process -- Systems biology; Electrophoresis -- Periodicals; Electrophoresis -- Periodicals; 541.372
• DOI: 10.1002/elps.201200642
• Languages: English
• ISSNs: 0173-0835

Abstract

Abstract : Molecular profiling techniques have provided extensive sets of molecular features characterizing clinical phenotypes, but further extrapolation to mechanistic molecular models of disease pathophysiology faces major challenges. Here, we describe a computational procedure for delineating molecular disease models utilizing omics profiles, and exemplify the methodology on aspects of the cardiorenal syndrome describing the clinical association of declining kidney function and increased cardiovascular event rates. Individual molecular features as well as selected molecular processes were identified as linking cardiovascular and renal pathology as a combination of cross‐organ mediators and common pathophysiology. The molecular characterization of the disease presents as a set of molecular processes together with their interactions, composing a molecular disease model of the cardiorenal syndrome. Integrating omics profiles describing aspects of cardiovascular disease and respective profiles for advanced chronic kidney disease on molecular interaction networks, computation of disease term‐specific subgraphs, and complemented by subgraph segmentation allowed delineation of disease term‐specific molecular models, at their intersection providing contributors to cardiorenal pathology. Building such molecular disease models allows in a generic way to integrate multi‐omics sources for generating comprehensive sets of molecular processes, on such basis providing rationale for biomarker panel selection for further characterizing clinical phenotypes.