Abstract

   Focal and Segmental Glomerulosclerosis (FSGS) is a severe
   glomerulopathy that frequently leads to end stage renal disease. Only a
   subset of patients responds to current therapies, making it important
   to identify alternative therapeutic options. The interleukin (IL)-1
   receptor antagonist anakinra is beneficial in several diseases with
   renal involvement. Here, we evaluated the potential of anakinra for
   FSGS treatment. Molecular process models obtained from scientific
   literature data were used to build FSGS pathology and anakinra
   mechanism of action models by exploiting information on protein
   interactions. These molecular models were compared by statistical
   interference analysis and expert based molecular signature matching.
   Experimental validation was performed in Adriamycin- and
   lipopolysaccharide (LPS)-induced nephropathy mouse models. Interference
   analysis (containing 225 protein coding genes and 8 molecular process
   segments) of the FSGS molecular pathophysiology model with the drug
   mechanism of action of anakinra identified a statistically significant
   overlap with 43 shared molecular features that were enriched in
   pathways relevant in FSGS, such as plasminogen activating cascade,
   inflammation and apoptosis. Expert adjudication of molecular signature
   matching, focusing on molecular process segments did not suggest a high
   therapeutic potential of anakinra in FSGS. In line with this,
   experimental validation did not result in altered proteinuria or
   significant changes in expression of the FSGS-relevant genes COL1A1 and
   NPHS1. In summary, an integrated bioinformatic and experimental
   workflow showed that FSGS relevant molecular processes can be
   significantly affected by anakinra beyond the direct drug target IL-1
   receptor type 1 (IL1R1) context but might not counteract central
   pathophysiology processes in FSGS. Anakinra is therefore not suggested
   for extended preclinical trials.

Introduction

   Focal and Segmental Glomerulosclerosis (FSGS) is a histopathological
   description for severe sclerotic lesions in the kidney glomeruli and a
   common cause of nephrotic syndrome characterized by proteinuria, edema,
   and hyperlipidemia [[36]1]. The etiology, histomorphology and clinical
   course of FSGS are highly heterogeneous including mutations in podocyte
   genes, drugs, viruses and hypertension [[37]2]. Some patients respond
   well to corticosteroids or calcineurin inhibition, but specific therapy
   is still lacking. FSGS frequently leads to end stage renal disease
   (ESRD) requiring renal replacement therapy. A proportion of
   transplanted patients will suffer from disease recurrence in their
   allograft [[38]3, [39]4]. Taken together, there is a major need for
   alternative treatments of FSGS. However, none of the recent clinical
   trials in this area has resulted in approval of a novel medication
   [[40]5]. Targeting neglected pathways might be a solution to this
   challenge [[41]6]. One of these pathways and targets is interleukin
   (IL)-1, a versatile pro-inflammatory cytokine that also activates T
   lymphocytes, which are involved in pathomechanisms associated with the
   nephrotic syndrome [[42]7, [43]8]. IL-1 has also been found upregulated
   in podocytes during glomerular injury [[44]9].

   Anakinra is an IL-1 receptor antagonist (IL1-RA) that blocks the
   biologic activity of IL-1 by competing with its binding to the IL-1
   receptor type I (IL-1R1), which is expressed in a wide range of cell
   types. Anakinra has shown to be effective in various inflammatory
   diseases and is currently licensed to treat rheumatoid arthritis and
   cryopyrin-associated periodic syndromes (CAPS)
   [[45]10][46]http://www.accessdata.fda.gov/drugsatfda_docs/label/2016/10
   3950s5175lbl.pdf) and has recently been commissioned for treatment of
   additional rare periodic fevers and autoinflammatory diseases [[47]11].
   Experimental evidence suggests a role of IL-1 in kidney disease and
   hypertension [[48]12]. In line with this, anakinra had beneficial
   effects including reduction of proteinuria in off-label use in patients
   with Familial Mediterranean fever, rheumatoid arthritis and
   Muckle-Wells (urticaria-deafness-amyloidosis) syndrome, a subtype of
   CAPS [[49]13–[50]15]. As the efficacy of anakinra in FSGS has not yet
   been evaluated [[51]5, [52]12, [53]16], we decided to analyze it in a
   drug repurposing approach by network-based drug-disease target
   interference at the molecular level [[54]17]. Our approach based on an
   algorithm using data from scientific literature consolidation
   integrates information from unbiased data mining and was recently used
   to test the potential of the immunosuppressive drug tacrolimus in
   diabetic nephropathy in silico [[55]18]. This network-based drug versus
   disease target interference at the molecular level integrates
   information obtained from unbiased data mining into a disease specific
   molecular pathophysiology framework.

   In this study, a molecular pathophysiology model characterizing the
   molecular processes associated with FSGS was overlapped with the
   molecular mechanism of action of anakinra to predict the therapeutic
   potential for treatment of FSGS by a workflow combining interference
   analysis, molecular signature matching and experimental validation
   [[56]18–[57]20].

Material and methods

General data sources

   Protein coding genes associated with FSGS or anakinra were collected by
   a literature mining approach based on public domain data. A PubMed
   search using the respective major medical subject headings (MeSH) term
   as query string was performed to identify publications of relevance.
   Protein coding genes explicitly discussed in these publications were
   extracted from gene2pubmed by filtering based on PubMed ID and taxonomy
   ID (9606 for human).

Molecular process models of clinical phenotype and drug mechanism of action

   Construction of molecular process models was performed as previously
   described [[58]21–[59]23]. In brief, two main steps were performed: (a)
   mapping of a feature signature being either the data set of protein
   coding genes associated with FSGS or with anakinra on the consolidated
   protein interaction network, followed by induced subgraph extraction.
   Nodes with a degree of zero are removed from the subgraph. (b)
   molecular process identification via utilizing a segmentation algorithm
   (MCODE with default settings [[60]24]).

Molecular signature comparison

   Matching signature of disease and drug relevant genes reported in
   scientific literature was used to predict therapeutic potential of
   anakinra in FSGS. To do so, regulation of a given gene in scientific
   literature associated with FSGS was compared to that reported for
   anakinra. Discordant regulation (i.e. upregulation reported with FSGS
   and downregulation with anakinra and vice versa) was used to assess
   whether anakinra was expected to reverse the FSGS specific expression
   and thus to counteract FSGS pathophysiology and ambiguous results were
   clarified by expert adjudication. Based on the assumption of
   identification of central aspects of FSGS pathophysiology by
   topological segmentation, this analysis focused on these molecular
   clusters, starting with those with the highest number of shared protein
   coding genes. Eventually contradicting data were resolved in an expert
   adjudication process.

Animal experiments

   Lipopolysaccharide (LPS) nephropathy was induced by intraperitoneal
   (i.p.) injection of 12 mg/kg LPS (serotype 0111:B4, Sigma Aldrich, St.
   Louis, MO, USA) in 0.9% saline into 10 weeks old female
   Gt(ROSA)^26Sortm4(ACTB-tdTomato,-EGFP)Luo/J x hNPHS2Cre mice (kind gift
   of Prof. Tobias B. Huber) [[61]25]. All animal experiments and handling
   were in accordance with the Austrian law for protection of animals and
   approved by the Institutional Committee for Animal Research and Care at
   the Medical University of Vienna (66.009/0053-II/3b/2014). Animals were
   housed in Makrolon cages, type II long in enriched environment and fed
   standard mouse chow and water ad libitum. For anaesthesia 100mg/kg
   Ketamine and 10mg/kg Xylazine were used. Adriamycin-dependent
   nephropathy (active compound: doxorubicin) was induced by tail vein
   injection of 9.8 mg/kg mouse (glomeruli isolation experiment) or 8.85
   mg/kg mouse (anakinra therapy experiment) in 10 weeks old female BALB/c
   [[62]26]. Anakinra (Kineret, Swedish Orphan Biovitrum AB, Solna,
   Sweden) was i.p. injected daily at a dose of 25 mg/kg mouse in 0.9%
   saline [[63]27] from day 7 on, Analysis was performed 35 days post
   induction (glomeruli isolation) or 18 days post induction (urinary
   collection in anakinra therapy experiment). Control mice were injected
   with 0.9% saline. At least 6 mice were analyzed per group. Mice were
   monitored daily for signs of pain, altered movement or reduced food
   uptake and sacrificed by cervical dislocation.

Cell isolation, RNA isolation and qPCR

   Glomeruli and podocytes were isolated as described before from 3
   animals/group [[64]25]. RNA was isolated with the Qiagen RNeasy kit
   according to manufacturer’s instructions. qPCR for IL1B, COL1A1, NPHS1
   and Cyclophilin B, as housekeeping gene for normalization (all primers
   from Qiagen, Hilden, Germany) were performed according to the
   manufacturer’s protocol on a CFX96 Real Time System and a C1000 Thermal
   Cycler (Bio-Rad, Hercules, CA, USA).

Urinary albumin and creatinine determination

   Urinary albumin levels of spot urine were assessed by ELISA (E90-134,
   Bethyl Laboratories, Montgomery, TX, USA). Creatinine levels were
   measured with the Creatinine Assay Kit (STA-378, Cell Biolabs, San
   Diego, CA, USA) according to the manufacturer’s protocols. Urine
   albumin-to-creatinine ratio (UACR) was calculated (urine albumin
   (mg/dl) / urine creatinine (g/dl) = UACR in mg/g ≈ albumin excretion in
   mg/day) to control for variation in urine concentration.

Data analysis and statistics

   Biological pathway enrichment analysis was performed for the set of
   protein-coding genes using the PANTHER database
   ([65]www.panther-db.org) as previously described [[66]28]. For
   statistical evaluation of molecular model interference, the set of
   protein-coding genes defined in the clinical phenotype molecular
   process model of FSGS was contrasted to the set of protein-coding genes
   defined in the anakinra mechanism of action molecular model with
   respect to molecular feature overlap as previously described [[67]18,
   [68]22]. With the set of protein coding genes as background the feature
   overlap was tested for over-representation using a Fisher's exact test
   with a significance level set to 0.05. Multiple tests were adjusted
   using Benjamini-Hochberg correction.

   For experimental data, statistical analyses and graphical
   representations of the results were performed using Prism 7.04
   (GraphPad, La Jolla, CA, USA). Values from different groups were
   compared using one-way ANOVA with Tukey's honest significant difference
   test as a post hoc; P values lower than 0.05 were considered
   significant.

Results

   Data mining from scientific literature extracted 225 protein coding
   genes associated with FSGS that were also part of the consolidated
   interaction network. 206 protein coding genes were identified as member
   of the induced subgraph, 19 had no interaction to any other feature of
   the FSGS data set and were therefore not included in molecular model
   computation. Following the network segmentation procedure aimed at
   identifying FSGS molecular process segments defined by topological
   characteristics of the FSGS-specific subgraph, the constructed
   molecular process model for FSGS held 64 protein coding genes in 8
   process units ranging in size from 3 to 16 protein coding genes
   ([69]Fig 1A and 1C).

Fig 1. Molecular model interference analysis.

   [70]Fig 1
   [71]Open in a new tab

   (A) Molecular process model of FSGS based on an underlying consolidated
   hybrid protein interaction network. Proteins are represented by nodes,
   interactions by lines. Eight molecular processes identified by
   topological segmentation are indicated in individual colors. (B)
   Interference of anakinra mode of action with the model representation
   of FSGS. Overlapping elements are indicated in red. (C) Interference of
   drug mode of action of anakinra with the eight process segments.
   Overlapping protein coding genes are indicated in yellow. Interactions
   between segments (molecular processes) are omitted.

   Interfering the FSGS molecular model with anakinra´s mechanism of
   action gene set on the level of feature overlap ([72]Fig 1B) identified
   specific protein coding genes of the FSGS molecular model: Of 225
   individual protein coding genes of the FSGS model, 43 genes were also
   part of the drug mechanism of action gene set of anakinra, i.e.
   predicted to be affected by anakinra (p<0.001). These 43 genes were
   further subjected to pathway analysis with the PANTHER database. Ten of
   these overlapping protein coding genes were also part of molecular
   process segments of FSGS ([73]Fig 1C). Five of these genes (COL1A1,
   MMP1, MMP2, MMP9, MMP13) were located in the same cluster.

   PANTHER pathway analysis ([74]Table 1) revealed Plasminogen activating
   cascade as top enriched of 7 molecular pathways (3 protein coding genes
   out of the 18 being assigned to this pathway; p = 0.0004). Among the
   list of enriched molecular pathways were signaling cascades linked to
   inflammation (Interleukin signaling pathway, Inflammation mediated by
   chemokine and cytokine signaling pathway) and apoptosis (Apoptosis
   signaling pathway, FAS signaling pathway). In addition, CCKR signaling
   map and Gonadotropin-releasing hormone receptor pathway were
   significantly enriched with more than 1 protein coding gene from the
   constructed FSGS molecular model.

Table 1.

   PANTHER Pathways Reference List Analyzed List Expected number   raw
   P-value FDR Genes
   fold Enrichment
   Plasminogen activating cascade 18 3 0,04 + 77,93 1,18E-05 3,83E-04
   MMP13, MMP1, MMP9
   FAS signaling pathway 34 2 0,07 + 27,51 2,68E-03 4,85E-02 CASP3, CASP8
   Interleukin signaling pathway 89 4 0,19 + 21,02 4,60E-05 1,25E-03
   CXCL8, IL6, STAT3, IL4
   CCKR signaling map 174 6 0,37 + 16,12 2,20E-06 1,19E-04 CXCL8, BCL2,
   STAT3, CASP3, NOS1, MMP9
   Apoptosis signaling pathway 120 4 0,26 + 15,59 1,41E-04 3,28E-03 BCL2,
   TNF, CASP3, CASP8
   Inflammation mediated by chemokine and cytokine signaling pathway 255 8
   0,55 + 14,67 7,61E-08 6,20E-06 CXCL8, CCL3, IL6, VWF, CCL2, IL1B,
   CCL3L1, STAT3
   Gonadotropin-releasing hormone receptor pathway 237 4 0,51 + 7,89
   1,72E-03 3,50E-02 TGFB1, MIF, STAT3, NOS1
   Unclassified 18438 21 39,43 - 0,53 4,80E-11 7,83E-09 NTRK2, LDLR,
   TIMP1, AGER, PRKDC, IL12B, NOS1, HLADRB1, CASP1, APOE, ESR1, FOXN1,
   IL2, MIF, LEP, IL10, TNFSF11, NLRP3, CTLA4, IL12A, ACE
   [75]Open in a new tab

   Genes in bold are embedded in molecular process segments.

   Matching signature of disease and drug relevant genes, starting in the
   cluster with the highest number of molecular features shared between
   the FSGS pathophysiology model and the anakinra mode of action gene
   data set showed in none of these 5 genes a discordant but in two of
   them a concordant expression pattern between FSGS and anakinra (MMP2,
   MMP9).

   Results of experimental validation of the impact of anakinra on
   experimental models of FSGS are shown in [76]Fig 2. The in silico
   prediction of involvement of IL-1β in the molecular pathophysiology of
   FSGS was supported as IL-1β was >8-fold upregulated in glomeruli of
   BALB/c mice with induced Adriamycin-nephropathy for 35d ([77]Fig 2A).
   In LPS-induced nephropathy, isolated podocytes exhibited a strong
   increase of IL-1β ([78]Fig 2B), confirming these findings in an
   unrelated in vivo model of FSGS. Treatment with anakinra did not
   counteract but rather increased the effect of LPS treatment on
   expression of COL1A1 encoding for alpha collagen type I, and the
   podocyte marker nephrin encoded by NPHS1, although not significantly
   ([79]Fig 2C). Finally, in the functional assessment of anakinra effects
   in the Adriamycin-nephropathy model of FSGS, anakinra did not
   significantly alter the albumin:creatinine ratio ([80]Fig 2D).
   Individual values are provided in [81]S1 Table.

Fig 2. Results of experimental validation of the impact of anakinra in two
independent experimental models of FSGS.

   [82]Fig 2
   [83]Open in a new tab

   IL-1β was strongly upregulated in glomeruli of BALB/c mice with
   Adriamycin-nephropathy (A) and isolated podocytes from mice with
   LPS-nephropathy (B). (C) Anakinra had no significant impact on the
   expression of COL1A1 and NPHS1 in isolated podocytes from mice with
   LPS-nephropathy. (D) Anakinra did not attenuate proteinuria in the
   Adriamycin-nephropathy model after 35d. FC: fold change; LPS:
   lipopolysaccharide; UACR: urine albumin-to-creatinine ratio.

Discussion

   Systems pharmacology using computational network analysis has been
   recently proposed as a novel approach to reposition known drugs for
   novel indications thereby fostering drug development in areas of yet
   unmet medical need [[84]29]. In our study, information at the level of
   protein coding genes associated with disease pathophysiology and with
   drug mechanism of action was identified by scientific literature
   consolidation. For bioinformatics analysis we used a human protein
   coding gene interaction network as framework to integrate information
   extracted at the level of individual protein coding genes into a
   molecular functional context [[85]19, [86]29]. Use of a consolidated
   network based on INTACT, Reactome, and BioGRID resulted in coverage of
   more protein coding genes than represented in each individual of these
   public domain databases [[87]22]. Next, we constructed a specific
   complex network (= FSGS disease pathophysiology model) by mapping the
   FSGS-associated protein coding genes (identified by unbiased data
   mining) on this consolidated protein interaction network, followed by
   network segmentation. Structuring this network into segments
   (“molecular processes”) according to topological specifics delineated
   eight gene clusters, which may reflect particularly relevant aspects
   from a systems biology perspective [[88]19, [89]20].

   This molecular process model representation of FSGS was used to assess
   the potential of anakinra as therapeutic option in FSGS [[90]19,
   [91]29]. Statistically significant interference of “disease relevant
   proteins” with “drug target proteins” suggested a possible functional
   effect. PANTHER pathway analysis was used to generate information about
   anakinra´s specific mechanism of action in the molecular landscape of
   the pathophysiology model of FSGS [[92]20]. This analysis predicted
   high interference in well-established molecular features that are
   expected within anakinra´s known drug mechanisms, such as Inflammation
   mediated by chemokine and cytokine signaling pathway, in particular the
   Interleukin signaling pathway, and Apoptosis signaling pathway, in
   particular the FAS signaling pathway [[93]1, [94]17]. Other pathways,
   such as CCKR signaling map and Gonadotropin-releasing hormone receptor
   pathway have not been previously associated with FSGS and need to be
   validated regarding their specific relevance in this context.
   Interestingly, our analysis also identified FSGS-relevant pathways
   affected by anakinra beyond those that have been previously described
   within the direct drug target (IL-1β) context. For example, the
   strongest overrepresented pathway Plasminogen activating cascade
   suggests high relevance with regards to current FSGS pathophysiology
   but has not yet been associated with anakinra´s mechanism of action.
   The soluble urokinase plasminogen activator receptor (suPAR) is
   intensively discussed as circulating factor causing FSGS [[95]30,
   [96]31].

   In the next step, we focused on anakinra´s drug mechanism of action
   applying comparison of molecular signature at the level of shared
   individual protein coding genes embedded in the molecular process
   segments–thus reflecting drug effects on central aspects of FSGS
   molecular pathophysiology [[97]19, [98]22]. This interference analysis
   revealed 10 overlapping genes. Five of these genes (COL1A1, MMP1, MMP2,
   MMP9, MMP13) are associated with extra-cellular matrix (ECM) and were
   all located in the same cluster suggesting relevant (either positive or
   negative) impact based on this in silico assessment. Qualitative
   in-depth analysis of scientific literature at the level of expert
   adjudication was used to allow prediction of the effect of anakinra on
   FSGS. Matrix metalloproteinases (MMPs) are regulators of ECM turnover
   and close regulation is essential for ECM homeostasis. MMP2 and MMP9
   were found significantly elevated in the urine of patients with
   steroid-resistant nephrotic syndrome as compared to steroid-sensitive
   patients [[99]32]. A study analyzing puromycin aminonucleoside (PAN)
   treated rats also reported the total amount of MMP9 as increased, while
   the total activity was significantly reduced [[100]33]. Interestingly,
   levels of MMP9 were reported as concordantly increased with levels of
   suPAR in inflammatory disorders [[101]34]. Collagen 1 (COL1A1) is a
   classical marker and promoter of fibrosis/sclerosis and strongly
   upregulated in human FSGS [[102]35]. Anakinra has been shown to inhibit
   COL1A1 [[103]36] and to activate several MMPs including MMP1, -2, -9,
   and -13 [[104]37, [105]38], but is only one, and not the most crucial,
   factor in the complex regulation of these genes. Dependent on defined
   thresholds for definition of suitable drug candidates in systems
   pharmacology approaches, anakinra would likely not be tested in
   respective preclinical trials. In this study, however, we decided to
   validate the effect of anakinra in FSGS to reduce the likelihood of a
   false negative decision.

   FSGS is very heterogeneous and no model covers all major disease nodes
   relevant in its pathophysiology [[106]16, [107]39]. To reduce this
   basic limitation, we chose two unrelated models covering different
   pathophysiologic aspects of FSGS [[108]26, [109]40]. Whereas clear
   upregulation of glomerular IL-1β in both FSGS models suggests a
   relevant role in FSGS, IL-1β inhibition by anakinra did neither
   counteract selected molecular processes nor result in functional
   attenuation of FSGS. In LPS-nephropathy, addition of anakinra appeared
   to slightly increase the expression of COL1A1, and decrease the
   podocyte specific marker NPHS1 in isolated podocytes, although both
   effects did not reach significance. In the long-term model of
   Adriamycin nephropathy, treatment with anakinra did not reduce
   proteinuria, the main clinical parameter in FSGS. Thus, in line with
   our predictions based on in silico data two independent mouse models
   covering different pathophysiologic aspects of FSGS, showed no efficacy
   of anakinra in FSGS treatment. Our validation assay has several
   limitations. First, levels of anakinra were neither assessed in blood
   nor tissue. Second, no positive control arm (such as steroid treatment)
   was included in the experimental design, and, third, anakinra might
   still be effective in other genetic mice models due to the multiplicity
   of etiologic factors in the pathogenesis of FSGS. However, the used
   dose in the mouse experiments corresponded to a more than 15-fold
   equivalent of the recommended human daily dose and has previously been
   reported to be effective [[110]27]. Although improved experimental
   designs and inclusion of additional experimental models might further
   increase the experimental rigor to exclude the potential of anakinra as
   a novel approach to treat human FSGS, we are confident that the
   combination of two established mouse models in this study should
   provide sufficient (negative) evidence to focus on other drug
   candidates for future preclinical development.

   In summary, our workflow demonstrated that a molecular network-based
   systems pharmacology approach might represent a rational strategy for
   drug repurposing in FSGS. It might thus represent a feasible approach
   for efficacious development of novel therapies for complex
   difficult-to-treat diseases such as FSGS due to early exclusion of drug
   candidates during the in silico stage of efficacy analysis [[111]29].

Supporting information

   S1 Table. Experimental data, individual values.

   The table provides values behind the means, standard deviations and
   other measures reported.

   (XLSX)
   [112]Click here for additional data file.^ (11.3KB, xlsx)

Acknowledgments