Increased macrophage activation inhibited by tacrolimus in the kidney of diabetic rats

Tacrolimus ameliorates tubulointerstitial inflammation in diabetic nephropathy via inhibiting the NFATc1/TRPC6 pathway

Tubulointerstitial inflammation is crucial for the progression of diabetic nephropathy (DN), and tubular cells act as a driving force in the inflammatory cascade. Emerging data suggested that tacrolimus (TAC) ameliorates podocyte injury and macrophage infiltration in streptozotocin (STZ) mice. However, the effect of TAC on tubulointerstitial inflammation remains unknown. We found that albuminuria and tubulointerstitial damage improved in db/db mice treated with TAC. Macrophage infiltration and expression of IL‐6, TNF‐α, fibronectin, collagen 1 and cleaved caspase 3 were inhibited as well. In addition, the expression of nuclear factor of activated T cell 1 (NFATc1) and transient receptor potential channel 6 (TRPC6) was up‐regulated in the kidneys of DN patients and correlated with tubular injury and inflammation. The expression of NFATc1 and TRPC6 also increased in the kidneys of db/db mice and HK‐2 cells with high glucose (HG), while TAC inhibited these effects. HG‐induced inflammatory markers and apoptosis were reversed by TAC and NFATc1 siRNA in HK‐2 cells, which was abolished by TRPC6 plasmid. Furthermore, HG‐induced TRPC6 expression was inhibited by NFATc1 siRNA, while NFATc1 nuclear translocation was inhibited by TAC, but was restored by TRPC6 plasmid in HK‐2 cells under HG conditions. These findings suggest that TAC ameliorates tubulointerstitial inflammation in DN through NFATc1/TRPC6 feedback loop.

An integrative systems biology approach for precision medicine in diabetic kidney disease

Current therapeutic approaches are ineffective in many patients with established diabetic kidney disease (DKD), an epidemic affecting one in three patients with diabetes. Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes. To achieve this, a better understanding of the complex pathophysiology of DKD is needed. A system biology approach integrating large-scale omic data is well suited to unravel the molecular mechanisms driving DKD and may offer new perspectives how to personalize therapy. Recent studies indeed show that integrating genome scale data sets generated from prospectively designed clinical cohort studies with model systems using innovative bioinformatics analysis revealed critical molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers. This review seeks to provide an overview of the recent progress in the application of the integrative systems biology approaches specifically in the field of molecular biomarkers for DKD. We will mainly focus the discussion on how to use integrative system biology approach to first identify patients at high risk of progression, and second to identify patients who may or may not respond to treatment. Challenges and opportunities in applying precision medicine in DKD will also be discussed.

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 (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.