title: Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis.
abstract: OBJECTIVE
  To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons.
  
  
  METHODS
  We generated molecular models representing pathophysiological mechanisms of DN and tacrolimus mechanism of action (MoA) based on literature derived data and transcriptomics datasets. Shared enriched molecular pathways were identified based on both model datasets. A newly generated transcriptomics dataset studying the effect of tacrolimus on mesangial cells in vitro was added to identify mechanisms in DN pathophysiology. We searched for features in interference between the DN molecular model and the tacrolimus MoA molecular model already holding annotation evidence as diagnostic or prognostic biomarker in the context of DN.
  
  
  RESULTS
  Thirty nine molecular features were shared between the DN molecular model, holding 252 molecular features and the tacrolimus MoA molecular model, holding 209 molecular features, with six additional molecular features affected by tacrolimus in mesangial cells. Significantly affected molecular pathways by both molecular model sets included cytokine-cytokine receptor interactions, adherens junctions, TGF-beta signaling, MAPK signaling, and calcium signaling. Molecular features involved in inflammation and immune response contributing to DN progression were significantly downregulated by tacrolimus (e.g. the tumor necrosis factor alpha (TNF), interleukin 4, or interleukin 10). On the other hand, pro-fibrotic stimuli being detrimental to renal function were induced by tacrolimus like the transforming growth factor beta 1 (TGFB1), endothelin 1 (EDN1), or type IV collagen alpha 1 (COL4A1).
  
  
  CONCLUSIONS
  Patients with DN and elevated TNF levels might benefit from tacrolimus treatment regarding maintaining GFR and reducing inflammation. TGFB1 and EDN1 are proposed as monitoring markers to assess degree of renal damage. Next to this stratification approach, the use of drug combinations consisting of tacrolimus in addition to ACE inhibitors, angiotensin receptor blockers, TGFB1- or EDN1-receptor antagonists might warrant further studies.
authors.affiliation: Department of Nephrology, Medical University of Vienna, Vienna, Austria.
authors.forename: Constantin
authors.initials: C
authors.lastname: Aschauer
authors.affiliation: Emergentec Biodevelopment GmbH, Vienna, Austria.|Department of Internal Medicine IV, Medical University of Innsbruck, Innsbruck, Austria.
authors.forename: Paul
authors.initials: P
authors.lastname: Perco
authors.affiliation: Emergentec Biodevelopment GmbH, Vienna, Austria.
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinzel
authors.affiliation: Department of Nephrology, Medical University of Vienna, Vienna, Austria.
authors.forename: Judith
authors.initials: J
authors.lastname: Sunzenauer
authors.affiliation: Department of Nephrology, Medical University of Vienna, Vienna, Austria.
authors.forename: Rainer
authors.initials: R
authors.lastname: Oberbauer
mesh_terms: D015415:Biomarkers; D019295:Computational Biology / Q000379:methods*; D003928:Diabetic Nephropathies / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism*; D020869:Gene Expression Profiling; D006801:Humans; D007166:Immunosuppressive Agents / Q000494:pharmacology*; D050527:Mesangial Cells / Q000187:drug effects / Q000378:metabolism; D008954:Models, Biological; D016559:Tacrolimus / Q000494:pharmacology*; D059467:Transcriptome
chemical_list: D015415:Biomarkers; D007166:Immunosuppressive Agents; D016559:Tacrolimus