Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

Purpose: For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects. Experimental design...

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Bibliographic Details
Published in:Proteomics. Clinical applications 2011-06, Vol.5 (5-6), p.354-366
Main Authors: Fechete, Raul, Heinzel, Andreas, Perco, Paul, Mönks, Konrad, Söllner, Johannes, Stelzer, Gil, Eder, Susanne, Lancet, Doron, Oberbauer, Rainer, Mayer, Gert, Mayer, Bernd
Format: Article
Language:English
Subjects:
Biomarker
Biomarkers
Biomarkers - metabolism
Case-Control Studies
Clinical trials
Computational Biology - methods
Data integration
Data Mining
Diabetes
Diabetic Nephropathies - diagnosis
Diabetic Nephropathies - drug therapy
Diabetic Nephropathies - metabolism
Disease
Drug Delivery Systems
Humans
Modeling
Network
Prognosis
Protein Interaction Mapping
Proteomics
Target
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Summary:Purpose: For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects. Experimental design: Molecular features on DN were retrieved from public domain Omics studies and by mining scientific literature, patent text and clinical trial specifications. Molecular feature sets were consolidated on a human protein interaction network and interpreted on the level of molecular pathways in the light of the pathophysiology of the disease and its clinical context defined as associated biomarkers and drug targets. Results: About 1000 gene symbols each could be assigned to the pathophysiological description of DN and to the clinical context. Direct feature comparison showed minor overlap, whereas on the level of molecular pathways, the complement and coagulation cascade, PPAR signaling, and the renin–angiotensin system linked the disease descriptor space with biomarkers and targets. Conclusion and clinical relevance: Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway‐specific biomarkers and therapy options.
ISSN:1862-8346
1862-8354
DOI:10.1002/prca.201000136