Aschauer Constantin, Perco Paul, Heinzel Andreas, Sunzenauer Judith, Oberbauer Rainer
Department of Nephrology, Medical University of Vienna, Vienna, Austria.
Emergentec Biodevelopment GmbH, Vienna, Austria.
PLoS One. 2017 Jan 6;12(1):e0169518. doi: 10.1371/journal.pone.0169518. eCollection 2017.
To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons.
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.
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).
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.
基于分子特征和基于网络的分子模型比较，评估他克莫司作为糖尿病肾病（DN）治疗选择的效果。
我们基于文献数据和转录组学数据集生成了代表DN病理生理机制和他克莫司作用机制（MoA）的分子模型。基于两个模型数据集确定共享的富集分子途径。添加了一个新生成的研究他克莫司对体外系膜细胞影响的转录组学数据集，以确定DN病理生理学中的机制。我们在DN背景下搜索DN分子模型与已持有作为诊断或预后生物标志物注释证据的他克莫司MoA分子模型之间干扰的特征。
在包含252个分子特征的DN分子模型和包含209个分子特征的他克莫司MoA分子模型之间共享了39个分子特征，另外有6个分子特征在系膜细胞中受他克莫司影响。两个分子模型集显著影响的分子途径包括细胞因子 - 细胞因子受体相互作用、黏附连接、TGF-β信号传导、MAPK信号传导和钙信号传导。他克莫司显著下调了参与导致DN进展的炎症和免疫反应的分子特征（例如肿瘤坏死因子α（TNF）、白细胞介素4或白细胞介素10）。另一方面，他克莫司诱导了对肾功能有害的促纤维化刺激，如转化生长因子β1（TGFB1）、内皮素1（EDN1）或IV型胶原α1（COL4A1）。
对于DN且TNF水平升高的患者，他克莫司治疗在维持肾小球滤过率（GFR）和减轻炎症方面可能有益。建议将TGFB1和EDN1作为监测标志物以评估肾损伤程度。除了这种分层方法外，除了ACE抑制剂（血管紧张素转换酶抑制剂）、血管紧张素受体阻滞剂、TGFB1或EDN1受体拮抗剂之外，联合使用他克莫司的药物组合可能值得进一步研究。
