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Nephrol Dial Transplant. 2015 Aug;30 Suppl 4:iv17-25. doi: 10.1093/ndt/gfv267.

Molecular disease presentation in diabetic nephropathy.

Author information

1
emergentec biodevelopment GmbH, Vienna, Austria.
2
Weizmann Institute of Science, Rehovot, Israel.
3
Medical University of Vienna, Vienna, Austria.
4
EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, UK.

Abstract

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

KEYWORDS:

biological pathways; diabetic nephropathy; functional annotation; molecular disease modeling; omics profiling

PMID:
26209734
DOI:
10.1093/ndt/gfv267
[Indexed for MEDLINE]

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