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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5217951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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