alexa Molecular Mechanisms Underlying the Nephrotoxicity of Cisplatin, Lead Acetate and Cyclosporine: Key Roles of Myc and Smad4 | Open Access Journals
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Molecular Mechanisms Underlying the Nephrotoxicity of Cisplatin, Lead Acetate and Cyclosporine: Key Roles of Myc and Smad4

Hassan Askari1, Ehsan Raeis Abdullahi1, Vahdat Poortahmasebi2, Danya Abazari3, Matin Asghari4, Solmaz Sadeghi5, Mohammad Foad Abazari6, Reza Mahmoudi7, Amir Savardashtaki8and Mansour Poorebrahim5*

1Department of Physiology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

2Hepatitis B Molecular Laboratory, Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

3Department of Biology, Centre for Biomedical Research, University of Victoria, Canada

4Department of Molecular Biotechnology, Cell Science Research Center, Royan Institute of Biotechnology, ACECR, Isfahan, Iran

5Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran

6Department of Genetics, Islamic Azad University, Tehran Medical Branch, Tehran, Iran

7Department of Agricultural Biotechnology, Faculty of Agriculture, Tarbiat Modarres University, Tehran, Iran

8Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran

*Corresponding Author:
Mansour Poorebrahim
Department of Medical Biotechnology
School of Advanced Technologies in Medicine
Tehran University of Medical Sciences
Tehran, Iran
Tel: 8(4852)460935
E-mail: [email protected]

Received date: Febraury 18, 2017; Accepted date: March 24, 2017; Published date: March 31, 2017

Citation: Askari H, Abdullahi ER, Poortahmasebi V, Abazari D, Asghari M, et al. (2017) Molecular Mechanisms Underlying the Nephrotoxicity of Cisplatin, Lead Acetate and Cyclosporine: Key Roles of Myc and Smad4. Biol Syst Open Access 6:176. doi:10.4172/2329-6577.1000176

Copyright: © 2017 Askari H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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Abstract

It is well documented that use of Cisplatin, Lead acetate and Cyclosporine in the chemotherapy and medical interventions is highly associated with nephrotoxicity and interrelated comorbidities. Here, we proposed the possible molecular mechanisms responsible for nephrotoxicity of these compounds. We utilized the microarray dataset GSE59913 consisting of approximately 600 different compounds profiled in up to 8 different tissues. After analysis with GEO2R, gene expression profiles of three aforementioned compounds were integrated with protein-protein interactions (PPI) networks and topological properties of the networks were measured using Cytoscape software. We found several key genes and signaling pathways that seem to be involved in nephrotoxicity of the examined compounds. Myc and Smad4 were identified as principal players of three compounds’ nephrotoxicity through various pathways. Our results revealed the critical functions of Il2, Jak-Stat, Mapk-Pi3k, TGFβ and Ca2+ signaling pathways as well as novel biomarkers that may mediate the nephrotoxicity of Cisplatin, Lead acetate and Cyclosporine. The significantly altered genes in the compound-treated samples were substantially correlated with regulation of cell proliferation, apoptosis, inflammatory responses and homeostatic processes. This study reveals the important hub genes, biological networks and key pathways as well as novel biomarkers involved in nephrotoxicity of Cisplatin, Lead acetate and Cyclosporine.

Keywords

Nephrotoxicity; Cisplatin; Lead acetate; Cyclosporine; PPI Networks

Introduction

The kidney is an important organ prerequisite by the body to perform several essential regulatory roles including the maintenance of homeostasis, regulation of the extracellular environment, such as detoxification, and excretion of toxic metabolites and drugs [1]. Therefore, the kidney can be considered as a vital target tissue for exogenous toxicants. Nephrotoxicity is a kidney-specific characteristic in which excretion does not go slowly owing to toxic chemicals or drugs [2,3]. Approximately 20% of nephrotoxicity in community and hospital acquired episodes is induced by drugs, among older adults, the incidence of drug-induced nephrotoxicity may rise as 66% as the average life span increases. Chemotherapy or anticancer medicine has been of limited use due to nephrotoxicity [4-7]. Cellular toxicity probably has a multifactorial etiology which is related to alterations in renal vascular cells, modifying renal hemodynamics and the relative ischemia induced by vasoconstriction potentiating sub lethal changes in renal tubular epithelial cells. Histopathological evidence of cell damage will be apparent only if the toxic injury exceeds the capacity of the cellular mechanisms to respond to the toxic insult [8]. It is widely acknowledged that patients treated with the Cyclosporine and Cisplatin are at a high risk of developing nephrotoxicity [9,10]. Studies have demonstrated that Cyclosporine causes vasoconstriction of the afferent and efferent glomerular arterioles and reductions in renal blood flow and glomerular filtration rate [11,12]. Cisplatin is an important antineoplastic drug used for the treatment of cancers. Its major dose limiting adverse effect is nephrotoxicity; 20% of patients receiving high-dose Cisplatin have intensive renal failure [10,13]. Furthermore, rat’s exposure to environmental pollutants such as Lead acetate induced nephrotoxicity [14]. However, the mechanism behind these Compounds remains a matter of debate.

Since nephrotoxicity largely affects human health and has a poorly understood pathogenesis, several studies have examined this condition. Moreover, the spectrum of temporal pathway deregulation has not been studied using integrative framework. Bearing this fact in mind, understanding the toxic mechanisms for nephrotoxicity renders advantageous information on the development of drugs with both potential therapeutic benefits and reduced adverse effects. To this end, analysis of PPI and gene regulatory networks (GRNs) has emerged as a promising tool and can help to decipher in-depth biological aspects of various disorders [15,16]. However, there are no high-throughput investigation between nephrotoxicity compounds and kidney that modulate host gene expression. Therefore, our network-based study has been carried out to investigate the specific pathways and regulatory genes that are critical for renal failure and nephrotoxicity in the rats treated with Cisplatin, Lead acetate and Cyclosporine.

Materials and Methods

Preparation of microarray data

A complete drug matrix dataset for kidney (Accession number: GSE59913) consisting of approximately 600 different compounds profiled in up to 8 different tissues was obtained from Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo/). The authors of this dataset collected 2862 samples, in biological triplicates, from test compound-treated and vehicle control-treated subjects for gene expression analysis in response to studied compounds. Owing to their important role in nephrotoxicity, we chose Cisplatin-, Lead acetateand Cyclosporine-treated samples for further analyses. The differentially expressed genes (DEGs) were determined by using GEO2R tool [17]. Subsequently, DEGs were further restricted to a log 2 fold change larger than 1 and smaller than -1 (p-value <0.05).

Functional annotation of DEGs

Functional annotation of selected DEGs was conducted utilizing Gene Ontology (GO) database [18]. In parallel, the network-based Biological Networks Gene Ontology (BiNGO) tool, a popular plugin of Cytoscape software [19], was used as an alternative tool for validating the GO results. This plugin is a flexible and extendable tool which is widely used to analyze GO terms overrepresented in a given biological network [20].

Signaling pathways data

Pathway enrichment was carried out using SPEED web tool to identify the signaling pathways underlying nephrotoxicity of Cisplatin, Lead acetate and Cyclosporine [21]. This server is an intuitive approach for discovering signaling pathways responsible for regulating various biological processes. Additionally, the signaling pathways corresponding to the DEGs of each aforementioned compounds were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) [22]. The daily updated KEGG databases consist of information about genomic, cellular pathways and chemical compounds.

Determination of regulatory relationships between the degs

A protein-protein interactions (PPIs) network was constructed for each studied compound utilizing BisoGenet, a plugin of Cytoscape [23]. BisoGenet is a multi-tier tool which constructs the PPI networks based on the regulatory relationships data accumulated from several PPI databases including Database of Interacting Proteins (DIP; http:// dip.doe-mbi.ucla.edu), BioGRID, Human protein reference database (HPRD), Biomolecular Interaction Network Database (BIND), Molecular interaction database (MINT) and IntAct [24-28]. In addition, some other PPIs between the DEGs were retrieved from the most recent studies.

Topological analysis of the PPIs networks

Topological properties of each PPIs network were measured using Network Analyzer, a network analysis plug-in of Cytoscape, to identify the most important functional hub genes within the networks and simplify interpretation of the results. We employed eight common measures including, Degree, Betweenness Centrality, Clustering Coefficient, Closeness Centrality, Eccentricity, Neighborhood Connectivity, Topological Coefficient and Average Shortest Path Length for appraisal topological properties of the PPIs networks. The nodes that were repeatedly identified as hub gene in aforementioned measures were adjudged as functionally important hub genes.

Functional motifs within the PPI networks

MCODE plugin of Cytoscape was applied to discover the functional regulatory motifs embedded in constructed PPI networks. This plugin can discern the highly interconnected complexes in a given network by finding regions of significant local density. These complexes are often associated with a specific cellular process.

Overlap nephrotoxicity mechanisms between the compounds

In order to identify the common and signature genes implicated in nephrotoxicity of Cisplatin, Lead acetate and Cyclosporine, a clustering of DEGs was conducted using Venny 2.1.0, an interactive web tool for comparing lists with venn diagrams [29]. Prior to clustering, the upand down-regulated DEGs of each compound were separated. Gene overlaps were determined with a three-way Venn diagram and were further analyzed.

Results

PPI networks of cisplatin, lead acetate and cyclosporine

By integrating the regulatory relationships obtained from BisoGenet plugin as well as published data, a PPI network was constructed for each DEG list resulted from processing Cisplatin, Lead acetat and Cyclosporine gene expression profiles. The nodes and edges number of Cisplatin, Lead acetate and Cyclosporine were 367 and 292, 467 and 408 and 502 and 436, respectively. After determination of each depicted PPI sub-network, the most integrated sub-networks were extracted and illustrated in Figure 1. Furthermore, node degree distribution of each PPI network was significantly right-skewed implying that three PPI networks have a biological scale-free pattern, as majority of nodes had low numbers of edges and only a few numbers of nodes were highly connected.

biological-systems-degree-distribution

Figure 1: The main sub-networks extracted from PPI networks of (A) Cisplatin, (B) Lead acetate and (C) Cyclosporine along with their respective degree distribution charts (below each subnetwork). The nodes with higher betweeness centrality are shown in green color.

By integrating the regulatory relationships obtained from BisoGenet plugin as well as published data, a PPI network was constructed for each DEG list resulted from processing Cisplatin, Lead acetat and Cyclosporine gene expression profiles. The nodes and edges number of Cisplatin, Lead acetate and Cyclosporine were 367 and 292, 467 and 408 and 502 and 436, respectively. After determination of each depicted PPI sub-network, the most integrated sub-networks were extracted and illustrated in Figure 1. Furthermore, node degree distribution of each PPI network was significantly right-skewed implying that three PPI networks have a biological scale-free pattern, as majority of nodes had low numbers of edges and only a few numbers of nodes were highly connected.

The most significant hub genes of PPI networks

Using various algorithms, we found a set of hub genes for each PPI network constructed for Cisplatin, Lead acetate and Cyclosporine DEGs (Table 1). Expectedly, several hub genes were identified with more than one algorithm, and therefore were propounded as important hub genes. The hub genes in Cisplatin PPI network including Ascc3l1, Bcr, Cdk9, Cdkn1a, Hnrpu, Lep, Myc, Pak1, Pcgf4, Ptpn6, Ran, Slc25a13, Slc8a1, Smad2, Smad4, Smad5, Stat5a, Tgm2, Tpm1, Ttn, Vapa, Ybx1 and Ywhae were distinguished as hub genes in more than one topological measure. Whereas, A2m, Alb, Ewsr1, Hnrpu, Iqcb1, Myc, Ncoa6, Pcgf4, Ptpn2, Smad4, Ybx1 and Ywhae were singled out as the highest-scored hub genes of Cyclosporine PPI network. The important hub genes of Lead acetate PPI network were included Adrm1, Alb, Apoa2, Apoc1, Cdk9, Cdkn1a, Hnrpu, Icam1, Myc, Pak1, Pdlim5, Prph1, Psma3, Ptpn2, Ptprr, Smad1, Smad4, Ybx1 and Ywhae . We also found several novel biomarkers that are likely involved in nephrotoxicity of each studied compound (bold genes in Table 1).

Hub Genes of Cisplatin PPI network
Average Shortest Path Length Closeness Centrality Degree Betweenness Centrality Topological Coefficient Clustering Coefficient
Oprm1 Tpm1 Smad2 Tpm1 Stat5a Stat5a
Vapa Lep Myc Lep Smad5 Smad5
Ryr1 Lcn2 Ywhae Myc` Tgm2 Tgm2
Agtpbp1 PVR Pak1 Smad2 Strap Cdk9
Arts1 Otc Ybx1 Ywhae Prkar1a Ascc3l1
Phgdhl1 Mmp9 Pcgf4 Bcr Nr5a2 Pdgfrb
Npy5r Ryk Ascc3l1 Pak1 Ngfr Ptpn2
Il2 Nrp2 Cdk9 Slc8a1 Tnfrsf1a Psmb4
Fdft1 Tomm20 Bcr Ptpn6 Ttn Gtf3c1
Asgr1 Tnfrsf12a Hnrpu Ybx1 Vapa Hnrpu
Vgcnl1 Tnnt2 Cdk5 Pcgf4 Cct2 Ybx1
Ihpk2 Ghrl Smad4 Slc25a13 Slc25a13 Smad4
Adra1b Srxn1 Cdkn1a Ran Cdc25b Myc
Ttn Ucn Ran Alb Slc8a1 Bcr
Pc Myc Ptpn6 Cdkn1a Blk Pak1
Hub Genes of Lead acetate PPI network
Average Shortest Path Length Closeness Centrality Degree Betweenness Centrality Topological Coefficient Clustering Coefficient
Gdf15 Mc4r Myc Reep6 Apoc1 Apoc1
Tpm1 Ppara Hnrpu Pdlim5 Apoa2 Apoa2
Mapk14 Sln Psma3 Hnrpu Adrm1 Adrm1
Clta Npy Ybx1 Myc Rpl13a Ptpn2
Srxn1 Wrnip1 Cdkn1a Psma3 Ptpn2 Sfrs3
Etv6 Gclm Smad4 Pak1 Prph1 Ascc3l1
Ptprr Vps26 Smad1 Cdkn1a Cd3z Syncrip
Cpb2 Nup107 Ywhae Ybx1 Paics Ghr
Otc Ryk Pak1 Vtn Cks2 Csda
Efs Prim1 Icam1 Smad4 Mme Mrps28
Vapa Prim2 Alb Lnx1 C3 S100a9
Tacc3 Pln Cdk9 Prph1 Ptprr Ccnh
Txlnb Pdlim5 Ewsr1 Ywhae Bcl2l1 Cct2
Hmmr Fabp1 H2afx Smad1 Ifit1 Cdk9
Mmp9 Txnl2 Plk1 Alb Abcc2 Icam1
Hub Genes of Cyclosporine PPI network
Average Shortest Path Length Closeness Centrality Degree Betweenness Centrality Topological Coefficient Clustering Coefficient
Pdgfrb Sstr3 Myc Myc Rps5 Ste2
Tceb1 Sstr2 Iqcb1 Ybx1 Ncoa6 Znf386
Il3ra Cxcl9 Ybx1 Iqcb1 Slc8a1 Zfx
C1s Stx7 Ywhae Ewsr1 Ngfr Zfp592
Dusp7 Onecut1 Smad4 Pcgf4 C1qb Zfp536
Ptpn2 Foxa1 Pcgf4 Hnrpu Hp Zfp496
Cish Efs Ewsr1 Ywhae Elavl3 Zfp422
Il2ra Hadh2 A2m Smad4 Zfml Ypel4
Npy5r Sftpc Hnrpu Rara Id3 Xpnpep1
Atf3 Grsf1 Rbpms Anxa2 Ttn Wrnip1
Nrcam Mapk10 Map3k1 A2m Il2 Vsnl1
Oprm1 Gfra1 Alb Vamp2 Nupr1 Vnn1
Oprk1 Tomm20 Vapa Ncoa6 Arpc1b Vgcnl1
Cln8 Gdnf Ascc3l1 Alb Ppp2r2c Vash2
Apod Gch Cdk9 Ghr Ptpn2 Usp18

Table 1: The 15 top hub genes for each constructed PPI network. The putative novel biomarkers for nephrotoxicity of each compound are shown in bold.

Myc and Il2 were found as key nodes of most significant functional motifs

In order to identify the functional motifs within constructed PPI networks, we analyzed the networks via MCODE algorithm. Prior to network analysis, the up- and down-regulated DEGs of each compound were dissected and a corresponding network was reconstructed for each DEG set. The results illustrated that a four-node complex consisting of Myc, Cdk9, Hnrpu and Ascc3l1 genes is present in the most significant up-regulated motif of three aforementioned compounds. Furthermore, Il2 and Ngfr were observed in the most significant down-regulated motif of Cisplatin and Cyclosporine PPI networks (Figure 2).

biological-systems-green-colors

Figure 2:The most significant regulatory motif of (A) Cisplatin, (B) Cyclosporine and (C) Lead acetate PPI networks along with their respective most significant down-regulated motifs. Up- and downregulated motifs are shown in red and green colors, respectively.

Functional annotation of DEGs

GO analysis unveiled that the dominant biological processes of Cisplatin, Lead acetate and Cyclosporine DEGs are obviously associated with the cell proliferation, apoptosis, inflammatory responses, homeostatic processes, response various stresses and modulation synaptic transmission responses. The overrepresented functional categories are listed in Table 2.

SPEED and KEGG results demonstrate Mapk-Pi3k as the crucial upstream signaling pathway upregulated in Cisplatin and Lead acetate nephrotoxicity. Moreover, results show that Jak-Stat signaling pathway is remarkably modulated after treatment of cells with Cisplatin and Lead acetate postulating the possible protective effect of this signaling pathway in nephrotoxicity. In contrast, Jak-Stat signaling pathway was found as the highest-scored signaling pathway upregulated in Cyclosporine-treated cells. The enriched signaling pathways are sorted by FDR score and graphical outputs aid in interpretation of the results (Figure 3).

Cisplatin
Up-regulated DEGs Down-regulated DEGs
GO Term Count P-value GO Term Count P-value
Regulation of muscle system process 11 0.0298 Chemical synaptic transmission 15 5.67E-05
Response to toxic substance 11 0.0475 Regulation of ion transmembrane transport 12 0.00794
Cellular homeostasis 23 0.00404 Regulation of homeostatic process 12 0.0372
Response to hormone 23 0.0246 Negative regulation of developmental process 15 0.0452
Chemical homeostasis 26 0.00544 Positive regulation of protein phosphorylation 17 0.0338
Response to organic cyclic compound 25 0.0165 Regulation of multicellular organismal process 19 0.00894
Regulation of cell proliferation 34 0.0304 Chemical homeostasis 17 0.0433
Cellular response to organic substance 40 0.0111 Generation of neurons 23 0.00431
Organonitrogen compound metabolic process 38 0.0314 Regulation of cell proliferation 25 0.00248
Response to stress 68 1.36E-06 Response to endogenous stimulus 22 0.0293
Regulation of cellular protein metabolic process 48 0.00191 Cell surface receptor signaling pathway 31 0.00111
Positive regulation of cellular metabolic process 51 0.0218 Regulation of intracellular signal transduction 24 0.049
Negative regulation of cellular process 74 0.000116 Animal organ development 37 0.000415
Cellular metabolic process 114 0.0267 Response to organic substance 34 0.00184
Biological process 189 0.000119 Regulation of molecular function 34 0.0169
Lead acetate
Up-regulated DEGs Down-regulated DEGs
GO Term Count P-value GO Term Count P-value
Cardiac muscle tissue development 12 0.0148 Cellular divalent inorganic cation homeostasis 11 0.0322
Regulation of blood pressure 14 0.00212 Blood circulation 12 0.0116
Response to hypoxia 20 0.000117 Cellular metal ion homeostasis 12 0.0393
Leukocyte migration 17 0.0031 Regulation of ion transport 14 0.0345
Response to oxidative stress 19 0.0135 Regulation of multicellular organismal process 26 0.000753
Positive regulation of MAPK cascade 25 0.000298 Response to organic substance 34 0.0139
Cellular response to cytokine stimulus 29 0.000262 Single-organism cellular process 87 0.0237
Inflammatory response 21 0.0423      
Negative regulation of apoptotic process 32 0.00268      
Response to organonitrogen compound 30 0.00858      
Regulation of proteolysis 27 0.0329      
Chemical homeostasis 33 0.00806      
Regulation of cell differentiation 47 0.00375      
Regulation of cell proliferation 47 0.00476      
Regulation of cellular component organization 67 6.03E-05      
Cyclosporine
Up-regulated DEGs Down-regulated DEGs
GO Term Count P-value GO Term Count P-value
Response to temperature stimulus 11 0.0494 Regulation of cytosolic Ca+2 ion concentration 10 0.0154
Regulation of muscle system process 13 0.017 Locomotory behavior 10 0.0205
Activation of immune response 19 0.0326 Modulation of synaptic transmission 15 6.76E-05
Cytokine-mediated signaling pathway 20 0.0245 Regulation of membrane potential 15 0.00077
Regulation of cytokine production 23 0.0137 Regulation of homeostatic process 16 0.00402
Negative regulation of cell proliferation 25 0.0109 Response to extracellular stimulus 15 0.0245
Immune response 38 0.000314 Positive regulation of Mapk cascade 15 0.0452
Negative regulation of cell death 30 0.0169 Regulation of hormone levels 15 0.0487
Chemical homeostasis 30 0.021 Cation transmembrane transport 17 0.015
Defense response 40 0.000663 Inorganic ion transmembrane transport 17 0.0294
Response to oxygen-containing compound 43 0.00122 Response to nitrogen compound 23 0.003
Regulation of phosphate metabolic process 44 0.0177 Positive regulation of cell differentiation 22 0.00555
Regulation of protein modification process 44 0.0361 Response to organic cyclic compound 23 0.00449
Organonitrogen compound metabolic process 47 0.0284 Response to oxygen-containing compound 36 1.69E-06
Animal organ development 66 0.0054 Response to endogenous stimulus 33 7.91E-05
      Generation of neurons 27 0.0491
      Regulation of multicellular organismal development 31 0.0463
      Cellular response to chemical stimulus 41 0.00221

Table 2: Significant overrepresented GO terms in DEGs of Cisplatin, Lead acetate and Cyclosporine.

biological-systems-signaling-pathways

Figure 3:The most enriched signaling pathways for the DEGs resulted from gene expression profile of Cisplatin, Cyclosporine and Lead acetate. Up- and down-regulated signaling pathways are shown in red and green colors, respectively.

As illustrated in Figure 4, mechanism of nephrotoxicity caused by Cisplatin, Lead acetate and Cyclosporine is noticeably mediated by upregulation of TGFβ signaling pathway. Several genes of this signaling pathway including Smad1, Smad2, Smad4, Myc and Id1 were found to be up-regulated after treatment with Cisplatin, Lead acetate and Cyclosporine. However, up-regulation of Rock1 was only observed in Lead acetate-treated samples.

biological-systems-Cisplatin-Lead

Figure 4:Representation of TGFÃź signaling pathway extracted from KEGG. Common up-regulated genes in the samples treated with three studied compounds (Cisplatin, Lead acetate and Cyclosporine) are shown in red color. The node with yellow color was only up-regulated in the samples treated with Lead acetate.

Moreover, down-regulation of Ca+2 signaling pathway was significantly observed after treatment of rats with Cisplatin, Lead acetate and Cyclosporine. This suggests that modulation in Ca+2- mediated gene expression is a common event upon nephrotoxicity of three studied compounds. Slc8a2, Chrm1, Cysltr1 and Mylk4 were found as overlap down-regulated genes of Ca+2 signaling pathway, whereas Chrna7 and Ppp3ca were down-regulated after treatment with Cyclosporine and Cisplatin, respectively (Figure 5).

biological-systems-Cisplatin-Cyclosporine

Figure 5:Representation of Ca+2 signaling pathway extracted from KEGG. Common down-regulated genes in the samples treated with three studied compounds (Cisplatin, Lead acetate and Cyclosporine) are shown in green color. The nodes with yellow and blue colors were only up-regulated in the samples treated with Cisplatin and Cyclosporine, respectively.

Cisplatin, cyclosporine and lead acetate share overlap nephrotoxicity mechanisms

In order to investigate the common nephrotoxicity pathways, a clustering was conducted according to each compound’s DEG list. The results indicated that 142 up-regulated DEGs (28.3%) and 41 downregulated DEGs (13.3%) are joint between three nephrotoxic compounds. The lowest number of overlap DEGs was found between Cisplatin and Lead acetate (Figure 6).

biological-systems-Cisplatin-Lead

Figure 6:Clustering of (A) up-regulated and (B) down-regulated DEGs obtained from analysis of gene expression profiles of Cisplatin, Lead acetate and Cyclosporine. The numbers represent number of overlap DEGs.

DEG-GO networks revealed the most important overlap DEGs

A DEG-GO network was separately constructed for both common up-regulated and down-regulated DEGs. Common up-regulated DEGs and their respective biological processes were considered as source and target nodes, respectively. The results indicated that several overlap DEGs up-regulated in all studied compounds including Cdkn1a, Myc, Smad4, Pak1, Hmgb1, Kras and Pdgfrb have significant roles in activation of nephrotoxicity pathways (Figure 7). On the other hand, among common down-regulated DEGs, Il2, Pth and Hcrt showed a pivotal role in regulation of nephrotoxicity pathways (Figure 8). These DEG-GO networks of the overlap DEGs uncovered that common upregulated DEGs are highly enriched in regulation of cell metabolism, cell proliferation and cell death pathways, whereas the common downregulated DEGs are notably involved in homeostatic processes.

biological-systems-biological-processes

Figure 7:DEG-GO network for common up-regulated DEGs between Cisplatin, Lead acetate and Cyclosporine. Nodes (common up-regulated DEGs) with higher out-degree measure are shown in bigger circles, while nodes (biological processes) with higher indegree measure tend to have red color.

biological-systems-higher-indegree

Figure 8:DEG-GO network for common up-regulated DEGs between Cisplatin, Lead acetate and Cyclosporine. Nodes (common up-regulated DEGs) with higher out-degree measure are shown in bigger circles, while nodes (biological processes) with higher indegree measure tend to have red color.

Discussion

Nephrotoxicity is a harmful repercussion of some medications and toxic chemicals on renal function which its pathogenesis is not yet fully understood [30]. All available data on protein/gene expression in nephrotoxicity provide an interesting opportunity to identify markers for the diagnosis and treatment of the disease. It is thought that the incidence of nephrotoxicity is closely correlated with the abnormal expression of multitudinous genes. We used bioinformatics methods and a consolidated view to explore a new strategy for understanding toxicity-derived renal dysfunction mechanisms and find phenotyperelated biomarkers.

GO annotation suggests that many of resulted DEGs in the samples treated with studied compounds are connected with multiple biological processes, including organonitrogen compound metabolic process, immune response, regulation of cell proliferation, regulation of apoptosis, response to oxidative stress, electrolyte abnormalities and regulation of blood pressure. Myc, Pak1, Pcgf4, Ptpn6, Ran, Smad4, Smad2, Smad5, Stat5a, Ywhae, Slc25a13 and Slc8a1 were identified as important hub genes in the PPI networks. Smad2 is actively participating in manifold cell processes such as cell differentiation, apoptosis, cell proliferation, cell-fate determination and morphogenesis. This gene is frequently upregulated in nephrotoxicity and activated by TGFβ receptor-type kinases [31]. Slc25a13 belongs to super-family of SLC proteins which comprises 55 gene families, having at least 362 putatively functional gene targets. Members of this superfamily functions in transportation of various molecules and ions across cellular and organelle membranes [32]. Recently, it has been established that cell toxicity can lead to dysregulation of these transporters [33]. Our study also provides further evidences that activation of Pak1 , presumably an adaptive reaction against cell death, is a signature of Cisplatin-induced nephrotoxicity. Delving further into the issue, Pak1 is a serine/threonine kinase that is targeted by small GTP binding proteins. This protein is presented as a regulator of cytoskeletal remodeling, apoptosis and cell motility. It has been recently demonstrated that nephrotoxicity increases matrix remodeling and apoptosis properties [34,35]. We also report Cdk9, Psma3, Icam1, Myc, Hnrpu, Pak1 and Alb as a consensus early signature of nephrotoxicity that induced by Lead acetate. During apoptosis Hnrpu is cleaved in a caspase-dependent way. Interestingly, Li et al. found that Hnrpu up-regulation is followed by cooper overload and vancomycin nephrotoxicity [36,37]. By analysis of PPI networks, we present Hnrpu as a promising biomarker for Lead acetate nephrotoxicity. Furthermore, the results indicated that Rock1 is important during Lead acetate nephrotoxicity. Interestingly, gentamicin-induced nephrotoxicity caused an increase in the activity of Rho-kinase enzyme. The activity of Rho-kinase enzyme in nephrotoxicity is highly associated with an increase in oxidative stress, leading ultimately to irreversible kidney dysfunction [38]. Moreover, a significant up-regulation of response to hypoxia, leukocyte migration and response to oxidative stresses was found in DEGs of studied compounds after GO analyses. Strikingly, these biological processes have been linked to the Lead acetate-induced nephrotoxicity [39,40]. Accordingly, we suggest the use of a specific inhibitor of Rock1 may represent a novel therapeutic approach in the prevention of nephrotoxic side effects during Lead acetate exposure. Psma3 gene was found as the most important hub gene obtained from topological analysis of Lead acetate nephrotoxicity network. The Psma3 has a key role in determination of protein’s fate via ubiquitin-independent proteasomal degradation [41]. In addition, DNA damage can induce phosphorylation of Psma3 during the cell cycle transition and apoptosis [42]. Surprisingly, Lead acetate can accelerate proteasome activity and this activity is likely associated with Mapk pathway and inflammatory response [43-45]. Taken together, we suggest that inhibition of Psma3 should be recognized as a new anti-inflammatory strategy in Lead acetate nephrotoxicity. Our results revealed that expression of Alb, Pcgf4, Ybx1 and Myc hub genes is significantly altered in Cyclosporine nephrotoxicity. Studies have shown that these genes have a role in cell toxicity of some cytotoxic compounds. Recently, it has been determined that reduction of Pcgf4 can lead to apoptosis, senescence in tumor cells, and increased cell susceptibility to cytotoxic agents [46]. In addition, Pcgf4 can prevent cell toxicity in vitro and in vivo by reducing oxidative stress [47]. The Ybx1 is a key regulator of cell growth, apoptosis, drug resistance, DNA repair and transcription. It has been shown that overexpression of Ybx1 via Erk/Akt pathway can monitor damage recognition and renal fibrosis of toxic agents such as Cyclosporine [48-50]. However, the role of Pcgf4 and Ybx1 in nephrotoxicity has not yet been fully evaluated. It has been shown that Il-2 is down-regulated in Cisplatin and Cyclosporine nephrotoxicity [51,52]. Consistent with these studies, we found Il-2 as a common hub gene in Cisplatin, Lead acetate and Cyclosporine nephrotoxicity. Our work further pinpointed two key genes including Myc and Smad4 that may play a pivotal role in the development of nephrotoxicity. Myc is a transcription factor and multifunctional gene that plays a role in the control various cellular functions such as cell proliferation, apoptosis, cell migration, biogenesis of macromolecules and protein degradation pathways [53]. We figured out that the Myc and Smad4 genes are the most important common hub genes procured from topological analysis of Cisplatin, Lead acetate and Cyclosporine PPI networks. Several studies have established that Myc has a crucial role in perturbation of gene expression of Mrp and SLC families which are efflux transporters and have important roles in the kidney function by regulating Ca2+ homeostasis and other organic, anionic conjugates [54-56]. In addition, the positive regulatory impact of Cisplatin and Cyclosporine on Myc expression in drug resistance and nephrotoxicity pathways has been previously reported [57,58]. Overall, our results suggest that Myc proto-oncogene not only regulates Cisplatin- and Cyclosporine-mediated nephrotoxicity, but also can be perceived as a key mediator of Lead acetate-induced nephrotoxicity.

Extracellular matrix proteins and tubulointerstitial fibrosis play pivotal role in nephrotoxicity [59]. Renal fibrosis is the final common result in loss of kidney function. TGFβ-Smad signaling has been determined to contribute in nephrotoxicity [60-62]. TGFβ signaling pathway plays multiple functions in cytokine-mediated signaling in many cell types, conditioning them for differentiation, survival, apoptosis and fibrosis [63]. Among the common hub genes of the present study, Smad4 is an important protein in TGFβ signaling pathway. Although, it is widely accepted that TGFβ signaling pathway induces cell death through Smad -mediated pathways, the distinct role of this signaling pathway has insufficiently investigated [64]. However, the synergetic cooperation Cyclosporine with TGFβ signaling pathway in the modulation of renal paracellular permeability has been confirmed by Feldman and colleagues [65]. In another study, expression of TGFβ has been demonstrated to be enhanced in cisplatin-induced Acute Kidney Injury (AKI) [66]. In agreement with these studies, we propose that that Cisplatin, Lead acetate and Cyclosporine can induce nephrotoxicity, at least in part, by affecting TGFβ signaling pathway, particularly Smad4 expression. Moreover, it is well established that distinct mechanisms such as inflammation, oxidative damages, and DNA damage are associated with development of nephrotoxicity [67]. Here, we found that the studied cytotoxic agents can exacerbate inflammation responses in rats. Activation of Hmgb1 in nephrotoxicity has been shown to increase inflammatory cytokine levels and tissue damages via immunological and nonimmunological pathways [68,69]. Our data lending credence to the hypothesis that blockade of Hmgb1 /TGFβ signaling cascade may constitute a therapeutic strategy for treatment of nephrotoxicityinduced tubulointerstitial fibrosis. The possible mechanisms of Myc and Smad4 in Cisplatin, Lead acetate and Cyclosporine nephrotoxicity are illustrated in Figure 9.

biological-systems-transcription-factors

Figure 9:Schematic illustration of possible mechanisms of Myc and Smad4 in Cisplatin, Lead acetate and Cyclosporine nephrotoxicity. Gene targets of Myc and Smad4 transcription factors are illustrated in bright green octagonal.

In summary, this study indicated that characteristics of Cisplatin, Lead acetate and Cyclosporine nephrotoxicity are associated with differential expressions of several genes. We reported Myc and Smad4 as a consensus early signature of in vivo toxicity in kidney. Inhibition of TGFβ signaling intermediates Smad4 and Myc could be an attractive new approach to treatment of Cisplatin, Lead acetate and Cyclosporine nephrotoxicity.

Acknowledgement

This study has supported by Tehran University of Medical Sciences.

Conflict of Interest

No conflict of interest to be declared by any of the authors.

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