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Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis

OBJECTIVE: To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons. MATERIALS AND METHODS: We generated molecular models representing pathophysiological mechanisms of DN and tacrolimus mechanism of action (Mo...

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Autores principales: Aschauer, Constantin, Perco, Paul, Heinzel, Andreas, Sunzenauer, Judith, Oberbauer, Rainer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217951/
https://www.ncbi.nlm.nih.gov/pubmed/28060893
http://dx.doi.org/10.1371/journal.pone.0169518
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author Aschauer, Constantin
Perco, Paul
Heinzel, Andreas
Sunzenauer, Judith
Oberbauer, Rainer
author_facet Aschauer, Constantin
Perco, Paul
Heinzel, Andreas
Sunzenauer, Judith
Oberbauer, Rainer
author_sort Aschauer, Constantin
collection PubMed
description OBJECTIVE: To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons. MATERIALS AND 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). CONCLUSION: 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.
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spelling pubmed-52179512017-01-19 Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis Aschauer, Constantin Perco, Paul Heinzel, Andreas Sunzenauer, Judith Oberbauer, Rainer PLoS One Research Article OBJECTIVE: To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons. MATERIALS AND 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). CONCLUSION: 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. Public Library of Science 2017-01-06 /pmc/articles/PMC5217951/ /pubmed/28060893 http://dx.doi.org/10.1371/journal.pone.0169518 Text en © 2017 Aschauer et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Aschauer, Constantin
Perco, Paul
Heinzel, Andreas
Sunzenauer, Judith
Oberbauer, Rainer
Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title_full Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title_fullStr Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title_full_unstemmed Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title_short Positioning of Tacrolimus for the Treatment of Diabetic Nephropathy Based on Computational Network Analysis
title_sort positioning of tacrolimus for the treatment of diabetic nephropathy based on computational network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217951/
https://www.ncbi.nlm.nih.gov/pubmed/28060893
http://dx.doi.org/10.1371/journal.pone.0169518
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