Abstract

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.

Introduction

   Tacrolimus is a powerful immunosuppressive drug belonging to the group
   of calcineurin inhibitors (CNIs), which was introduced in clinical use
   to tackle organ rejection in solid organ transplantation [[32]1].
   Tacrolimus nephrotoxicity is a dose-dependent side effect and people
   have started to investigate the role of pharmacogenetics in tacrolimus
   pharmacodynamics by looking at single nucleotide polymorphisms in genes
   of the cytochrome P450 family as well as in ABC transporters in order
   to optimize dosing on an individual patient level [[33]2].

   Tacrolimus is used for the treatment of lupus nephritis and also a
   clinical trial in the context of IgA glomerulonephritis has recently
   been conducted [[34]3][[35]NCT01224028]. There is also evidence on the
   positive effects of tacrolimus on diabetic nephropathy available, both
   on the level of animal models but also in human subjects with one human
   trial reporting beneficial effects of tacrolimus in a combination
   therapy with valsartan on renal function in a set of patients with
   diabetic nephropathies [[36]4][[37]5][[38]6]. These animal studies show
   that next to the anti-inflammatory effect of tacrolimus, there is
   evidence of direct effects on signalling cascades in renal cells. The
   effect of tacrolimus on individual molecules, especially components of
   the TGF-beta signalling cascade, in mesangial cells has been studied
   previously with negative as well as positive effects being reported
   [[39]7][[40]8][[41]9]. Mesangial cells are a good in-vitro model in
   order to study mesangial proliferation, matrix accumulation, fibrosis,
   and glomerulosclerosis, all hallmarks of diabetic nephropathy [[42]10].

   In this study we investigated the effects of tacrolimus on
   pathophysiological mechanisms of diabetic nephropathy on the level of
   in-silico constructed mechanism of action and disease pathophysiology
   molecular models respectively. We furthermore searched for molecular
   markers showing the potential for identifying the cohort of diabetic
   nephropathy patients benefitting the most from the immunosuppressive
   properties of tacrolimus.

Materials and Methods

Molecular models of tacrolimus mechanism of action (MOA) and diabetic
nephropathies pathophysiology

   Molecular features affected by tacrolimus were extracted from
   scientific literature taking into consideration genes linked via NCBI
   gene2pubmed associations to publications which were annotated with the
   MeSH term “tacrolimus”. In addition deregulated transcripts were
   extracted from two published transcriptomics studies from Kern and
   colleagues as well as Maluf and colleagues focusing on the effect of
   tacrolimus on renal tissue, in particular on renal fibroblasts and
   post-kidney transplants, respectively [[43]11][[44]12]. Transcripts
   from scientific literature as well as from the two omics studies were
   further mapped to their Ensembl Gene IDs and the set of unique
   protein-coding Ensembl Gene IDs was used as input set for generating a
   tacrolimus mechanism of action molecular model. In brief these
   molecular features were mapped on a hybrid interaction network
   including protein-protein interaction data from IntAct, BioGrid, and
   Reactome together with computationally inferred relations [[45]13]. The
   induced subgraph was extracted after mapping the features onto the
   network, including all molecular features holding an interaction to at
   least one other feature from the input set. The induced subgraph was
   further segmented into clusters of proteins using the Molecular Complex
   Detection algorithm following a procedure as described previously
   [[46]14][[47]15].

   Construction of a diabetic nephropathy molecular model followed the
   same procedure as construction of the tacrolimus MoA model using as
   input features associated via gene2pubmed to publications holding
   “diabetic nephropathies” as major MeSH term.

   A molecular pathway enrichment analysis was conducted for the two
   constructed molecular models using the Database for Annotation,
   Visualization and Integrated Discovery (DAVID) version 6.7 [[48]16].
   Results were corrected for multiple testing setting the false discovery
   rate to <5% for identification of significantly enriched molecular
   pathways. Disease specific molecular pathways like e.g. pathways in
   cancer or type II diabetes mellitus were excluded from the output thus
   focusing on molecular signalling cascades as well as metabolic
   pathways.

Cell culture experiments

   Primary human mesangial cells (Lonza, Clonetics, CC-2559) were cultured
   in MCDB-131 media (Gibco-invitrogen), supplemented with 10% FBS, 100U
   penicillin/ml 100ug/ml streptomycin and 2mmol L-Glutamine (all supplied
   from Gibco-Invitrogen) at 37°C in humid atmosphere with 5%CO[2] in air.
   Cells were seeded onto six-well cell culture plates (Greiner Bio one)
   and were used for experiments at the fifth passage. Confluent cells
   were cultured in serum free media and FK 506 (Abcam) dissolved in DMSO
   (Sigma) in a final concentration of 1 μM for 24h. The control group was
   treated in serum free media and DMSO only and a total of four
   independent experiments were conducted. Trizol® and chloroform reagent
   was used to extract RNA (Invitrogen). The RNA pellet was dissolved in
   10 μl sterile water and stored at –80°C.

Transcriptomics profiling

   Total RNA from human mesangial cells was reverse transcribed, labelled
   and hybridised to Affymetrix Human Gene (HuGene) 2.0 ST Array
   (Affymetrix) according to the manufacturer’s protocol. The oligo R
   package was used for pre-processing and normalization of expression
   data. The statistical analysis of microarrays (SAM) method available in
   the TIGR MultiExperimentViewer was used in order to identify
   differentially regulated transcripts between tacrolimus treated and
   untreated cells [[49]17][[50]18]. Transcripts were mapped to the
   respective Ensembl Gene IDs and the set of unique protein-coding genes
   was used for further analysis.The microarray data have been deposited
   in NCBI's Gene Expression Omnibus and are accessible through GEO Series
   accession number [51]GSE84908
   ([52]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84908).

Model interference analysis and biomarker annotation

   The two constructed molecular models, namely the one representing
   tacrolimus MoA and the one reflecting diabetic nephropathy
   pathophysiology on a molecular network level were compared to each
   other with respect to molecular feature overlap as previously described
   [[53]19]. Deregulated transcripts by tacrolimus in the in-vitro
   mesangial cell model were further mapped onto the diabetic nephropathy
   molecular model and considered as relevant when analyzing the effect of
   tacrolimus on diabetic nephropathy and also when further selecting
   biomarker candidates.

   Biomarker evidence for diabetic nephropathy diagnosis and prognosis was
   gathered from scientific literature for molecular features in the
   diabetic nephropathies molecular model being affected by tacrolimus.
   Therefore molecular features were extracted from publications annotated
   with the major MeSH term “diabetic nephropathies” as well as the MeSH
   terms “biological markers” OR “genetic markers” neglecting profiling
   studies by excluding publications annotated with one of the following
   MeSH terms: “gene expression studies”, “microarray analysis”,
   “proteomics”, “metabolomics”, or “genome-wide association study”.
   Publications however had to be either of publication type “clinical
   trial” or annotated with “humans” or “disease models, animal”.
   Prognostic markers were extracted from publications further holding the
   MeSH term “prognosis”, whereas diagnostic markers were extracted from
   publications annotated either with “diagnosis”, “diagnosis,
   differential”, “early diagnosis”, or “diabetic
   nephropathies/diagnosis”. Next to considering molecular features
   assigned via gene2pubmed to the respective marker papers, we mined for
   molecular features using their respective gene names and considered
   perfect matches as hits.

Results and Discussion

Molecular models of diabetic nephropathy and tacrolimus MOA

   583 molecular features could be extracted from scientific literature
   being associated with diabetic nephropathy. The constructed molecular
   model for diabetic nephropathies in total held 252 molecular features
   in 15 process units ranging in size from 3 to 58 molecular features.
   The top enriched molecular pathway of the 17 identified molecular
   pathways was calcium signaling with 28 molecular features out of the
   252 being assigned to this pathway (p-value < 0.001). A set of
   signaling cascades were among the list of enriched molecular pathways
   like e.g. Adipocytokine signaling (p-value < 0.001), MAPK signaling
   (p-value < 0.001), TGF-beta signaling (p-value < 0.001), or PPAR
   signaling (p-value = 0.002). Next to these signaling cascades,
   molecular pathways linked to inflammation and immune response were
   enriched like for example cytokine-cytokine receptor interactions
   (p-value = 0.002), Toll-like receptor signaling (p-value 0.002), or
   chemokine signaling (p-value = 0.003). In addition the
   renin-angiotensin system (p-value < 0.001) as well as the complement
   and coagulation system (p-value < 0.001) were enriched with molecular
   features from the constructed DN molecular model.

   The tacrolimus MoA molecular model held 209 molecular features assigned
   to 19 process units ranging in size from 3 to 38 molecular features.
   From the 785 unique protein coding genes forming the input for model
   construction, 582 protein-coding genes originated from the two
   transcriptomics studies with 217 genes coming from literature mining
   for tacrolimus-associated molecular features. Five of the enriched
   molecular pathways in DN were also significantly enriched based on the
   set of molecular features in the tacrolimus MoA molecular model, namely
   cytokine-cytokine receptor interactions, adherens junctions, TGF-beta
   signaling, MAPK signaling, as well as calcium signaling ([54]Fig 1).

Fig 1. Molecular models.

   [55]Fig 1
   [56]Open in a new tab

   Schematic representation of the constructed DN molecular model and the
   tacrolimus MoA molecular model with the list of significantly enriched
   molecular pathways based on the two molecular model features sets.
   Molecular pathways in bold font within the red border were
   significantly enriched in molecular features of both molecular model
   sets. Each box in the molecular model representations depicts one
   process unit, characterized by highly inter-connected proteins, with
   individual nodes representing protein coding genes.

   The use of heterogeneous data sets for constructing the tacrolimus MoA
   molecular model could be seen very critically but can also be seen from
   a positive angle. With input data coming from fibroblasts as well as
   from post-transplant kidneys it is not possible to reconstruct
   cell-specific molecular pathways, but on the other hand it allows
   assessing the effect of tacrolimus on kidney as a whole. Following this
   line we furthermore assessed the effect of tacrolimus on mesangial
   cells using gene expression profiling.

Tacrolimus effect on mesangial cells

   21534 transcripts remained after preprocessing and normalization steps
   which were used for hierarchical cluster analysis in order to see
   whether there was an effect of tacrolimus on mesangial cells in
   culture. 1—Pearson correlation and average linkage were used as
   distance measure and linkage rule. In the cluster dendrogram, a clear
   separation between the tacrolimus-treated and the untreated control
   samples was detectable although absolute differences in gene expression
   between samples were only minor. Nevertheless 222 differentially
   regulated transcripts were identified using the SAM with a false
   discovery rate of lower than 5%. 154 transcripts were upregulated after
   tacrolimus treatment with 68 being downregulated. The deregulated
   transcripts could be mapped to 204 unique protein coding genes on the
   level of Ensembl Gene IDs with 141 upregulated and 63 downregulated
   genes.

Interference and biomarker analysis

   39 of the 252 individual molecular features of the DN molecular model
   were also part of the tacrolimus MoA molecular model thus being
   affected by tacrolimus. In addition six further molecular features of
   the DN molecular model could be identified as deregulated after
   tacrolimus treatment in mesangial cells ([57]Fig 2).

Fig 2. Interference network.

   [58]Fig 2
   [59]Open in a new tab

   Depicted is the diabetic nephropathy molecular model with molecular
   features affected by tacrolimus being displayed with larger nodes and
   annotated with the official Gene Symbols. Molecular features
   upregulated by tacrolimus are highlighted in red, with downregulated
   molecular features displayed in green. Molecular features with
   contradictory evidence, i.e. up- and down-regulated are depicted in
   yellow. Annotated grey nodes depict molecular features being associated
   with effects of tacrolimus, in most cases SNPs in these genes having
   impact on tacrolimus efficacy. Prognostic and diagnostic biomarkers are
   indicated by a P or D respectively.

   Three molecular features had annotation evidence as marker for diabetic
   nephropathy, with TNF being reported as diagnostic marker for DN and
   TGFB1 and EDN1 showing evidence as prognostic marker for DN.

   A set of molecular features involved in inflammation and immune
   response like the tumor necrosis factor alpha (TNF), interleukins 4 and
   10 (IL4, IL10), or the heat shock protein family A (Hsp70) member 1B
   (HSPA1B) were all downregulated by tacrolimus.

   Inflammation is recognized as a major factor in diabetic nephropathy
   [[60]20][[61]21], subsequently leading to abnormal tissue repair and
   progressive fibrosis with alteration of tissue structure and loss of
   kidney function [[62]22]. Recent studies indicate that treatment with
   anti-inflammatory agents leads to reduction of subclinical
   inflammation, giving account for the potential positive effect of
   tacrolimus on the course of diabetic nephropathy
   [[63]23][[64]24][[65]25].

   TNF was previously reported as biomarker for diagnosing diabetic
   nephropathy and also proposed as potential drug target in order to slow
   disease progression [[66]26]. Next to TNF itself, soluble TNF receptors
   1 and 2 were evaluated in large patient cohort for their prognostic
   value in predicting the course of the disease [[67]27][[68]28][[69]29].
   Induction of TNF via the calcium/calcineurin/NFAT pathway was shown by
   Canellada and colleagues which was inhibited by tacrolimus [[70]30].
   Aomatsu et al. could demonstrate that tacrolimus suppresses TNF-induced
   CCL2 and CXCL10 expression in human colonic myofibroblasts [[71]31].
   CCL2 is next to the soluble TNF receptors another widely discussed and
   studied prognostic marker in the context of diabetic nephropathy
   [[72]32][[73]33][[74]34]. A very recent review discussed in more detail
   the involvement of TNF as well as CCL2 in the context of diabetic
   nephropathy [[75]35]. Evidence on the beneficial effect of blocking the
   CCL2/CCR2 signaling cascade is available on the level of animal models
   as demonstrated by Seok and colleagues in db/db mice [[76]36]. In
   another animal model on diabetic nephropathy, blockade of the TNF-TNFR2
   pathway was shown to have beneficial impact on disease progression
   [[77]37]. This all supports using tacrolimus in order to slow disease
   progression in patients with diabetic nephropathy.

   The downside of tacrolimus on renal tissue becomes evident when looking
   at upregulated molecular features in the DN molecular model ([78]Fig
   2). Especially molecular features linked to transforming growth factor
   beta 1 (TGFB1) signaling were upregulated by tacrolimus, namely TGFB1
   itself as well as COL4A1, NADPH oxidase 4 (NOX4), or thrombospondin 1
   (THBS1). Upregulation of TGFB signaling is one of the major components
   reported to be responsible for chronic nephrotoxicity of tacrolimus
   [[79]38]. SMAD family member 2/3 (SMAD2/3)-TGFB signaling mediates
   renal fibrosis and is next to targeting inflammation in DN a second
   pillar of current therapeutic strategies in DN [[80]39][[81]40].
   Interestingly SMAD7, an intracellular antagonist of TGFB1-mediated
   fibrosis, was upregulated in our mesangial cell experiment based on
   tacrolimus treatment [[82]41].

   Another pro-fibrotic molecule, namely endothelin 1 (EDN1), was
   upregulated by tacrolimus in mesangial cells, thus reinforcing the
   finding that tacrolimus rather activates than inhibits TGF-beta
   signaling [[83]42].

   Based on these findings further studies are warranted stratifying DN
   patients based on TNF levels in order to identify patients being prone
   for treatment with low-dose tacrolimus [[84]43][[85]44]. TGFB1 as well
   as EDN1 should however be used as monitoring markers in order to assess
   potential nephrotoxic effects of higher doses of tacrolimus.

   A further strategy might be to use drugs that block detrimental effects
   induced by tacrolimus like e.g. blocking TGF-beta signaling cascade
   with TGFB1 antibodies or using endothelin 1 receptor antagonists which
   are currently in clinical testing in the context of diabetic
   nephropathy. Such strategies were already proposed as alternative to
   conventional strategies of calcineurin inhibitor avoidance, withdrawal,
   or minimization in the context of renal transplantation [[86]38]. The
   endothelin receptor antagonist ambrisentan was shown to reduce
   nephrotoxicity in a rat model of liver transplantation [[87]45]. The
   angiotensin receptor blocker losartan in addition was shown to
   significantly decrease TGFB and endothelin levels in human plasma
   [[88]46][[89]47]. A first trial in human DN patients with tacrolimus
   was very recently published showing the beneficial effect of tacrolimus
   on diabetic nephropathy when administered in combination with valsartan
   [[90]6].

Conclusions

   Inflammatory mechanisms, and in particular the tumor necrosis factor
   signaling cascade, contributing to progression of diabetic nephropathy
   might be counterbalanced by tacrolimus treatment. Using TNF as
   predictive marker and TGFB1 and EDN1 as monitoring markers might be a
   valuable strategy finding the subset of diabetic nephropathy patients
   benefitting the most from tacrolimus treatment.

Acknowledgments