Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure

Environmental Pollution xxx (2016) 1e11

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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol

Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure* Hesham M. Korashy a, *, Ibraheem M. Attafi a, Konrad S. Famulski b, Saleh A. Bakheet a, Mohammed M. Hafez a, Abdulaziz M.S. Alsaad a, Abdul Rahman M. Al-Ghadeer c a b c

Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia Alberta Transplant Applied Genomics Centre, University of Alberta, Edmonton, AB T6G 2S2, Canada Central Laboratory, Research Center, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 April 2016 Received in revised form 9 October 2016 Accepted 19 October 2016 Available online xxx

Heavy metals are the most commonly encountered toxic substances that increase susceptibility to various diseases after prolonged exposure. We have previously shown that healthy volunteers living near a mining area had significant contamination with heavy metals associated with significant changes in the expression of some detoxifying genes, xenobiotic metabolizing enzymes, and DNA repair genes. However, alterations of most of the molecular target genes associated with diseases are still unknown. Thus, the aims of this study were to (a) evaluate the gene expression profile and (b) identify the toxicities and potentially relevant human disease outcomes associated with long-term human exposure to environmental heavy metals in mining area using microarray analysis. For this purpose, 40 healthy male volunteers who were residents of a heavy metal-polluted area (Mahd Al-Dhahab city, Saudi Arabia) and 20 healthy male volunteers who were residents of a non-heavy metal-polluted area were included in the study. Total RNA was isolated from whole blood using PAXgene Blood RNA tubes and then reversed transcribed and hybridized to the gene array using the Affymetrix U219 GeneChip. Microarray analysis showed about 2129 genes were identified and differentially altered, among which a shared set of 425 genes was differentially expressed in the heavy metal-exposed groups. Ingenuity pathway analysis revealed that the most altered gene-regulated diseases in heavy metal-exposed groups included hematological and developmental disorders and mostly renal and urological diseases. Quantitative realtime polymerase chain reaction closely matched the microarray data for some genes tested. Importantly, changes in gene-related diseases were attributed to alterations in the genes encoded for protein synthesis. Renal and urological diseases were the diseases that were most frequently associated with the heavy metal-exposed group. Therefore, there is a need for further studies to validate these genes, which could be used as early biomarkers to prevent renal injury. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Heavy metals Mining activity Mercury Lead Microarray Renal diseases

1. Introduction Over the past years, there is increasing evidence of an association between long-term exposure to heavy metals and human health risk. Chronic exposure to heavy metals has been shown to increase the susceptibility to human diseases such as diabetes,

*

This paper has been recommended for acceptance by Prof. von Hippel Frank A. * Corresponding author. Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia. E-mail address: [email protected] (H.M. Korashy).

cardiovascular disease, cancer, mutagenicity, and teratogenicity (Kakkar and Jaffery, 2005) as a result of specific changes in several signal transduction and transcription factors at cellular and molecular levels. Examples of these factors are oxidative stress, enzyme inhibition, ionic substitution, genotoxicity, and gene expression alteration (Breton et al., 2013; Edwards and Myers, 2007). Gene expression regulation is a highly complicated process and is considered one of the most important mechanisms of heavy metal toxicities. The effects of heavy metals on gene expression alterations have been reported in different animal models and cell lines (Bartosiewicz et al., 2001; Chan et al., 2006; Li et al., 2008;

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Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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H.M. Korashy et al. / Environmental Pollution xxx (2016) 1e11

Miller et al., 2004; Nair et al., 2014; Zhou et al., 2004). Thus, the development of new molecular biology techniques, such as microarray, allows for the better detection of early markers of heavy metal exposures and toxicities. Microarray provides a fast quantitative method of analyzing a huge number of transcripts from a single sample of RNA (Irwin et al., 2004). Analysis of the gene expression profile using microarray technology is considered a powerful tool in disease diagnosis, prognosis, and population risk assessment (Hamadeh et al., 2002; Lettieri, 2006). A recent massive heavy metal pollution was reported in the Mahd Ad-Dahab gold mine located in Saudi Arabia. Previous studies in the city of Mahd Ad-Dahab have demonstrated a significant contamination with more than 13 heavy metals in the soil and plants collected from different regions near the mining activities (Al-Farraj and Al-Wabel, 2007a, 2007b). Importantly, we have previously shown that healthy volunteers living near Mahd AdDahab showed a significantly high blood concentration levels of heavy metals, the highest being arsenic (As), mercury (Hg), and lead (Pb), compared with unexposed subjects (Al Bakheet et al., 2013). The increased plasma concentrations of these metals were associated with significant changes in the expression of detoxifying genes [NADP(H):quinone oxidoresuctase 1 (NQO1) and glutathione S-Transferase A1 (GSTA1)], xenobiotic metabolizing enzymes [cytochrome P450 1A1 (CYP1A1), CYP2E1, and CYP3A4], and DNA repair genes [8-oxoguanine DNA glycosylase 1 (OGG1) and apurinic-apyrimidinic endonuclease 1 (APE1)]. Furthermore, unpublished data from Mahd Ad-Dahab Hospital Medical Records have reported an increase in the incidence of several diseases such as asthma, renal failure, and teratogenicity. Although multiple transcription genes for heavy metals may be affected, most of these molecular target genes are still unknown and need to be identified. In light of this, we hypothesized that alteration of gene expression profile using microarray technology would be a guide for identifying specific events and biomarkers that can potentially be related to human diseases following heavy metal exposure. To test this hypothesis, this study was designed to (a) investigate the effects of long-term human exposure to environmental heavy metals on the gene expression profile using the microarray technique and (b) identify selective and specific gene biomarkers for a variety of diseases associated with heavy metals using the Ingenuity Pathway Analysis (IPA) application. To our knowledge, this is the first study to examine the effect of heavy metals on gene expression profile in the Saudi population in Mahd Ad-Dahab city. 2. Material and methods 2.1. Study population, ethical consideration, and consent The study groups consisted of 40 healthy male volunteers, residents of Mahd Ad-Dahab city, who were exposed to heavy metals (exposed group) (Al Bakheet et al., 2013). The control (nonexposed) group comprised 20 healthy male volunteers who were residents of Riyadh city, which was 700 km away from Mahd AdDahab polluted area. The study was approved by the Mahd AdDahab Hospital Review Committee. Each volunteer filled a questionnaire form, which was prepared according to the guidelines of the International Union of Pure and Applied Chemistry commission (Cornelis et al., 1996), and signed the consent form just before participating in the study. The questionnaire form provided the volunteers with information about the aims of study and the potential outcomes. The questionnaire gathered information about the volunteers' demographics (sex, age, job, and the residence time in the study area), lifestyle habits (smoking, eating habits, and source of drinking water), and health status (medical problem, medication chronically or recently used, and surgical history).

2.2. Inclusion and exclusion criteria for microarray analysis The medical health status of all participating volunteers was checked and confirmed from their hospital medical records to ensure that all volunteers were healthy and have no history of any chronic disease. All heavy metal-exposed volunteers were residents of Mahd Ad-Dahab city for more than 10 consecutive years. To minimize the variation between groups, exclusion criteria were applied, which included the presence of any acute and chronic diseases, smoking, use of anticoagulant medications, vitamins or minerals, diet habits, presence of dental amalgam, and working in other mine industries. 2.3. Determination of the blood levels of heavy metals and antioxidant trace elements in human volunteers A 4-ml fresh venous blood sample, collected by venipuncture into ethylenediaminetetraacetic acid tubes under sterile condition, was stored at 2e8
C in upright position during transportation (approximately 6 h). The conditions of the blood sample collection were standardized in terms of collection time (9e12 AM) and the size of needle used (23G). For the determination of heavy metals and antioxidant trace elements concentrations, a mixture of blood sample (1 ml), concentrated nitric acid (2 ml), and Triton X-100 (0.01% v/v) was incubated for 12 h at 70
C; thereafter, the concentrations of heavy metals and antioxidant trace elements were determined using Inductive Coupled Plasma-Mass Spectrometry method (Agilent 4500, Agilent Technologies, CA, USA). Blood Hg levels were determined by Direct Mercury Analyzer (DMA; Milestone Inc., CT, USA) according to U.S. EPA accredited methods (Method 7473), as previously described (Al Bakheet et al., 2013; Bazzi et al., 2008; Stube et al., 2011). 2.4. Total RNA extraction and purification The total RNA was extracted from peripheral blood using the PAXgene Blood RNA Kit for isolation and purification according to the manufacturer's instructions (PreAnalytiX, Hombrechtikon, Switzerland) and as described previously (Chai et al., 2005) with slight modification (Al Bakheet et al., 2013). Briefly, approximately 2.5 ml of blood sample from all subjects was incubated for 2 h at room temperature in PAXgene® Blood RNA tubes (PreAnalytiX, Hombrechtikon, Switzerland); the samples were then centrifuged for 10 min at 3000  g. The cell pellets were then incubated with a mixture of 300 ml of binding buffer and 40 ml of proteinase K for 10 min at 55
C while shaking. The resultant lysate was centrifuged for 3 min in a PAXgene® Shredder spin column at 16,300  g, and the supernatant was then mixed with 350 ml of 100% ethanol in PAXgene® RNA spin column followed by centrifugation for 1 min at 16,300  g. The purified RNA was eluted by elution buffer, denatured by incubation at 65
C for 5 min, chilled on iced, and finally stored at 20
C for microarray and gene expression studies. 2.5. Determination of RNA quality and integrity The RNA quality was analyzed by measuring the absorbance 260/280 ratio using Nano Drop 8000 (Thermo Scientific, Wilmington, DE) maintained at a 260/280 ratio of approximately 2, indicating pure RNA samples. In addition, the RNA integrity was assessed by conducting RNA gel electrophoresis and measuring RNA Integrity Number (RIN) using spectroscopic analysis. Electrophoresis of RNA samples showed intact RNA as evidenced by a relative 28S:18S ratio of approximately 2:1, indicating intact RNA with no genomic DNA contamination of the RNA samples (Supplement 1). The spectroscopic analysis for 18S and 28S for all

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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the samples showed high RIN values, which was indicative of high RNA integrity (Supplement 2). In addition, to improve the microarrays sensitivity, GeneChip® Globin-Reduction Kit (PreAnalytiX, Hombrechtikon, Switzerland) was used to reduce globin transcription by minimizing the amount of globin cDNA generated during the cDNA synthesis step of the protocol for GeneChip arrays. The globin reduction method for all samples was performed by Alberta Transplant Applied Genomics Centre, University of Alberta, Edmonton, Alberta, Canada. 2.6. Microarray analysis The global expression analysis using microarray technology was performed to identify sets of genes that are induced/inhibited by heavy metals. For this purpose, the isolated RNAs were reversed transcribed and hybridized to the gene arrays. A total of 1 mg of RNA from each sample was individually amplified, reverse transcribed, fragmented, and labeled using Affymetrix GeneTitan Human Genome U219 microarray (Affymetrix, Santa Clara, USA) according to the manufacturer's protocols. Microarray data were analyzed by IPA Application (Ingenuity Systems, Mountain View, CA, USA). Microarrays were hybridized, processed, and scanned at Alberta Transplant Applied Genomics Centre, the University of Alberta, Edmonton, Alberta, Canada. The selected genes identified by microarray analysis were validated by quantitative real-time polymerase chain reaction (qPCR) using ABI 7500 System (Applied Biosystems, CA, USA). Total RNA extraction, the reverse transcription reaction, and qPCR amplification were performed as reported in our previous study (Al Bakheet et al., 2013). The following human gene primers (Supplement 3) were used in the present study: metallothionein 1 (MT-1), heme oxygenase (HO-1), heat shock protein 70 (HSP70), Cytochrome P4501A1 (CYP1A1), and glyceraldehyde-3phosphate dehydrogenase (GAPDH), were synthesized and purchased from Integrated DNA technologies (Coralville, IA, USA). The endogenous reference gene, GAPDH, was used as an internal reference to calculate the relative expression of the target genes between control and exposed volunteers. The qPCR data were analyzed using the relative gene expression (i.e., DD Ct) method, as described and explained previously (Livak and Schmittgen, 2001), using the following equation: fold change ¼ 2D(DCt), where DCt ¼ Ct(target)  Ct(GAPDH) and D(DCt) ¼ DCt(exposed)  DCt(control). 2.7. Bioinformatics tools and analysis The results data sets were analyzed and evaluated using dChip software (http://www.hsph.harvard.edu/cli/complab/dchip/). The biological functions, networks, diseases, and canonical pathways analyses were conducted by the IPA application (IPA®; www. ingenuity.com). 2.8. Statistical analysis The significance of the differences between the means of the control and exposed groups was analyzed by unpaired Student's ttest using the SigmaPlot 11.0 software package for Windows 8 (Systat Software Inc., San Jose, CA). The significance of correlations between the parameters was determined by Spearman's rank correlation coefficient and simple or multiple regression analyses. Statistical significance was defined as a p-value of <0.05. Affymetrix Expression Console software was used to evaluated the microarray data (Cy5/Cy3 ratios) using Probe Logarithmic Intensity Estimation. Fisher's exact test was used to calculate a pvalue determining the probability of the associations (Calvano et al., 2005). The IPA application was used to organize differently expressed genes into networks of interacting genes and identify

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modules of functionally related genes that correspond to pathways. IPA analysis was defined using the BenjamineHochberg ratio test. IPA enabled networks, functional analyses, and canonical pathways from differentially expressed genes to be determined. Statistical significance was defined as a p-value of <0.05 with a false discovery rate (FDR) of 0.05. 3. Results and discussion International health concerns due to industrial and environmental pollution has been raised regarding the risk of human exposure to heavy metals. A recent study from our laboratory has reported increased plasma concentrations of several heavy metals in healthy volunteers residing in Mahd Ad-Dahab associated with altered expression of several antioxidants, xenobiotic metabolizing enzymes, and DNA repair genes (Al Bakheet et al., 2013), which is consistent with the heavy metal-contaminated soil and plants (AlFarraj and Al-Wabel, 2007a, c). The results of these studies warrant the need for determining the early effects of long-term heavy metal exposure on gene-regulated diseases to avoid health risk consequences. Thus, the main objectives of the present study were to (a) assess whether long-term residence near a heavy metalcontaminated area, such as Mahd Ad-Dahab in Saudi Arabia, could alter the gene expression profile of gene-regulated diseases and (b) identify the potential biomarkers that may be involved in response to heavy metal toxicity. To test this hypothesis, human subjects who lived in Mahd AdDahab (exposed) and control volunteers who lived in a noncontaminated area 700 km away from Mahd Ad-Dahab (nonexposed) were actively involved in this study. Both groups were individually matched for age. Fifty percent of all volunteers in both groups were in the age range of 25e30 years, whereas the rest were in the range of 31e36 years. None of the control volunteers had lived in Mahd Ad-Dahab before, whereas all heavy metal-exposed volunteers were residents of Mahd Ad-Dahab for 5e10 years (30.5%) or for more than 10 years (69.5%). Approximately 55% of heavy metal-exposed volunteers and 90% of control volunteers were working in schools. Most of the heavy metal-exposed and control volunteers (88% and 80%, respectively) eat meat, whereas only 8.3% of heavy metal-exposed and 20% of control volunteers eat fish. In addition, 66.7% of heavy metal-exposed volunteers used ground water as a source of drinking water, whereas all control volunteers used only bottled water. Importantly, all participant volunteers were non-smokers (Supplement 4). The increase in blood levels of heavy metals is considered the primary biomarker for the long-term exposure to environmental heavy metals and reflects the body’s burden (Barbosa et al., 2005). In our previous study, heavy metal-exposed volunteers exhibited a higher plasma concentration of several heavy metals, particularly Pb, Cd, and Hg, than the control volunteers. The mean blood levels of Pb, Cd, and Hg were approximately 21.61 ± 0.46, 2.5 ± 0.21, and 1.3 ± 0.06 mg/l, respectively, in exposed volunteers compared with 10.36 ± 0.46, 1.9 ± 0.02, and 0.98 ± 0.04 mg/l, respectively, in controls (Al Bakheet et al., 2013). This contamination may be attributed to the inhalation of heavy metals transported by dust through the air, drinking ground water, and/or by consumption of contaminated food. This is supported by the results of previous studies on the soil and plants around Mahd Ad-Dahab mine, which demonstrated massive pollution by heavy metals (Al-Farraj and Al-Wabel, 2007a, 2007b; Li et al., 2006). Spearman's rank correlation coefficient was conducted to identify and test the strength of the relationship (statistical dependence) between age, residence time, physical activities, source of drinking water, and elevated blood levels of heavy metals. The results showed that the elevated blood levels of Pb, Cd, and Hg

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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were not significantly correlated with volunteer's age and physical activities (Supplement 5). However, increased blood concentrations of Pb were significantly dependent and correlated with residence time (Spearman r ¼ 0.33, p ¼ 0.034) and ground water drinking. Moreover, although blood levels of Hg were highly dependent on residence time (Spearman r ¼ 0.42, p ¼ 0.008), blood levels of Cd did not show any significant correlation. Accordingly, it could be postulated that the inhalation of the particulates from dust storms and drinking ground water are the primary sources of exposure which warrant further investigation. Chronic exposure to heavy metals causes alteration in the metabolism of some trace elements and heme components. In general, deficiencies of some essential trace elements increase the toxicity of heavy metals, whereas excesses are protective (Chowdhury and Chandra, 1987). In the present study, plasma levels of zinc (Zn) and copper (Cu), but not selenium (Se), were significantly lesser in the heavy metal-exposed groups than in the control group (Supplement 6). In this regard, Szczurek, E. I. et al. found that Zn deficiency reduced renal zinc and metallothionein (MT) concentrations (Szczurek et al., 2009), the main transporting protein for heavy metals from blood into kidney (Leffler et al., 2000). This was also supported by the fact that Zn deficiencyinduced renal tissue damage is attenuated by Zn supplementation (Baltaci et al., 2004; Petering et al., 1984). In agreement with these observations, we have previously reported the downregulation of human MT-1 gene expression in individuals exposed to heavy metals in mining areas (Al Bakheet et al., 2013). Taken together, these results suggest that MT-1 could be used as an early biomarker to identify the susceptibility to heavy metal toxicity (Waalkes et al., 2004). In previous studies, a strong correlation between hematological parameters and heavy metal exposure was reported (Hegazy et al., 2010; Tripathi et al., 2001; Yilmaz et al., 2012). In agreement with these studies, blood profile parameters such as white blood cells (WBC), red blood cells (RBC), hemoglobin, hematocrit, lymphocytes, and RDW were altered (Supplement 6). Among these parameters, increased red cell distribution width (RDW) levels indicate an early stage of inflammation, impaired iron metabolism (Allen et al., 2010), and acute renal injury (Oh et al., 2012). 3.1. Differentially expressed genes Based on the inclusion and exclusion criteria and blood levels of heavy metals, exposed volunteers were further subdivided into three groups; Pb-, Cd-, or Hg-exposed groups (Supplement 7), in which each subgroup exhibited the highest concentration of a specific metal and lower concentrations of other metals. The mean blood levels of Pb, Cd, and Hg were approximately 27.76 ± 2.84, 2.54 ± 0.24, and 1.78 ± 0.1 mg/l, respectively, in exposed volunteers compared with 10.87 ± 0.51, 1.82 ± 0.02, and 0.92 ± 0.05 mg/l, respectively in controls. Accordingly, 24 volunteers were divided into four groups of 6 volunteers each in the microarray study as follows: control, Pb-, Cd- and Hg-exposed groups. To identify the differences in the overall gene expression profile between each heavy metal-exposed and control group, the mRNA expression levels were measured using Affymetrix GeneTitan Human Genome U219 microarray. Differentially expressed probe sets were selected using a BenjamineHochberg statistical procedure and a fold change filter of 1.5-fold change for each heavy metal. The three heavy metal distribution patterns yielded three different gene sets with some overlap. As demonstrated in Fig. 1, a total of 2129 genes were differentially expressed by all heavy metalexposed groups, i.e., approximately 1397 genes were diffentially expressed in the Pb-exposed group, whereas the expression of 508 and 1620 genes was altered in the Cd- and Hg-exposed groups, respectively (Fig. 1). Among these genes, approximately 971 (46%)

genes were overlapped and 425 (20%) genes were shared between the three groups (Fig. 1). IPA of 425 shared genes that were altered in response to all three heavy metal-exposed groups showed upregulation of 382 genes, whereas 43 genes were downregulated (Table 1). In addition, some selective genes were only altered in each specific heavy metalexposed group. For example, the microarray data showed that 434 genes (341 upregulated and 93 downregulated) were uniquely expressed in Pb-exposed group, whereas the Cd-exposed group specifically altered 20 genes (8 upregulated and 12 downregulated). Furthermore, the Hg-exposed group specifically altered 704 genes (147 upregulated and 558 downregulated). The microarray results were validated by quantified gene expression levels of selected genes by qPCR. Fig. 2 shows that the pattern and amplitude of the fold changes determined by qPCR closely matched the microarray data for the genes tested. 3.2. Hierarchical cluster analysis Hierarchical cluster analysis of 425 shared genes in all three heavy metal-exposed and the control groups, using dChip software (Amin et al., 2011; Eisen et al., 1998; Shannon et al., 2003), showed four main clusters as follows: the control group, a group consisting of a mixture of all three heavy metals, Hg- and Cd-exposed groups (similar), and Pb-exposed group (unique) (Fig. 3). The hierarchical cluster analysis revealed that the three heavy metal-exposed group was clustered separately from the control group (Fig. 3), whereas heavy metal exposed groups from the control group were completely separated, indicating that the effect on gene expression is clear. Alteration of 425 genes in the heavy metal-exposed group clearly suggests that these genes play a role in heavy metal-induced toxicity. Among these shared genes, HSPA5, the major heat shock protein 70 (HSP70) of the endoplasmic reticulum, was the most downregulated gene in response to all heavy metals. HSP70 and clathrin heavy chain (CLTC) have pivotal protective roles in renal proximal tubular epithelial cell endocytosis and the development of chronic kidney disease (Borkan et al., 1993; Saito et al., 2010; Turman and Rosenfeld, 1999). Downregulation of HSP70 in the current study in response to heavy metals using microarray is validated by a significant decrease in the mRNA expression level of HSP70 using qPCR technology (Al Bakheet et al., 2013). In contrast, HSP90, an indicative gene for renal proximal tubules injury (Satoh et al., 1994), was upregulated in all heavy metals groups. Taken together, downregulation of HSP70 and upregulation of HSP90 genes increase susceptibility of heavy metal toxicity and the risk of renal diseases (De Luca et al., 2011; Swierzcek et al., 2004). 3.3. Functional analysis To further correlate the alteration of these 425 genes with diseases and disorders associated with heavy metals exposure, IPA functional analysis was conducted. As shown in Table 2, IPA functional analysis identified four disease and disorder categories: renal and urological diseases (30 genes), developmental disorders (18 genes), hematological diseases (17 genes), and hereditary disorders (45 genes). To quickly narrow the relevant biology within the data set and area of research, function pathway analysis was performed on the statistically over-represented shared altered genes in response to heavy metals. Using microarray IPA, it was determined that the perturbed biological processes common to all three metals are protein synthesis (26 genes), RNA post-transcriptional modification (19 genes), and gene expression (16 genes) (Table 3). Importantly, IPA network analysis identified several significant diseases and disorders associated with heavy metal exposure

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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Fig. 1. The three heavy metals distribution patterns yielded three different gene sets and the degree to which genes are regulated similarly between these groups is represented by Venn diagram.

Table 1 The top 20 upregulated and top 20 downregulated expressed gene sets in Pb-, Cd-, and Hg-exposed groups. Probe Set ID

High Cd-group (FC)

High Hg-group (FC)

High Pb-group (FC)

Gene Symbol

Gene Title

Entrez Gene

11716469_x_at 11757329_s_at 11753841_x_at 11758245_s_at 11719480_a_at 11756875_x_at 11753784_x_at 11757059_x_at 11757027_x_at 200026_PM_at 11730096_a_at 200082_PM_s_at 11733496_x_at 11733816_a_at 200099_PM_s_at 11715958_s_at 11744334_x_at 11720598_x_at 11721302_a_at 11757422_x_at 11725933_at 11719212_a_at 11719051_a_at 11752003_a_at 11723349_x_at 11739029_a_at 11743279_x_at 11755520_s_at 11740423_a_at 11719857_a_at 11719409_a_at 11735494_a_at 11739009_a_at 11721132_at 11718306_a_at 11742930_a_at 11757399_s_at 11757469_s_at 11715591_s_at 11747415_a_at

[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

[ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ [ Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y

UQCRB RPL26 COMMD6 SUB1 CSTA COMMD6 RPL34 RPL36A RPL31 RPL34 BCL2A1 RPS7 COMMD6 RPL22L1 RPS3A RPL7 RPS17 SUB1 CLEC2B RPL23 HSPA5 NOTCH2NL CEP350 IQGAP1 GAPVD1 CLTC WNK1 LASS6 PRKDC ARID1A HIPK2 SIN3A MYH9 HCFC1 KIAA0182 BIRC6 PRKDC TPP1 DYNC1H1 CKAP5

ubiquinol-cytochrome c reductase binding protein ribosomal protein L26 COMM domain containing 6 SUB1 homolog (S. cerevisiae) cystatin A (stefin A) COMM domain containing 6 ribosomal protein L34 ribosomal protein L36a ribosomal protein L31 ribosomal protein L34 BCL2-related protein A1 ribosomal protein S7 COMM domain containing 6 ribosomal protein L22-like 1 ribosomal protein S3A ribosomal protein L7 ribosomal protein S17 SUB1 homolog C-type lectin domain family 2, member B ribosomal protein L23 heat shock 70 kDa protein 5 notch 2 N-terminal like centrosomal protein 350 kDa IQ motif containing GTPase activating protein 1 GTPase activating protein and VPS9 domains 1 clathrin, heavy chain (Hc) WNK lysine deficient protein kinase 1 LAG1 homolog, ceramide synthase 6 protein kinase, DNA-activated, polypeptide AT rich interactive domain 1A (SWI-like) homeodomain interacting protein kinase 2 SIN3 homolog A, transcription regulator myosin, heavy chain 9, non-muscle host cell factor C1 (VP16-accessory protein) KIAA0182 baculoviral IAP repeat-containing 6 protein kinase, DNA-activated, polypeptide tripeptidyl peptidase I dynein, cytoplasmic 1, heavy chain 1 cytoskeleton associated protein 5

7381 6154 170622 10923 1475 170622 6164 6173 6160 6164 597 6201 170622 200916 6189 6129 6218 10923 9976 9349 3309 388677 9857 8826 26130 1213 65125 253782 5591 8289 28996 25942 4627 3054 23199 57448 5591 1200 1778 9793

5.734672 4.985396 4.377595 4.5314 4.048306 3.797164 4.08367 4.090494 4.123035 3.826129 3.643493 3.567049 3.402839 2.960146 3.702061 3.424337 3.146863 3.411019 3.176872 2.880579 1.68641 1.70492 1.49559 1.54402 1.42792 1.46206 1.48191 1.58461 1.48764 1.44394 1.43793 1.39049 1.3687 1.42464 1.52501 1.38672 1.39078 1.38434 1.35744 1.47822

6.122624 6.470718 5.258143 4.79285 4.674293 5.042066 5.35359 4.201659 4.756602 4.730635 4.087232 4.069921 4.385651 3.916717 4.081881 3.906841 4.109697 3.420328 3.598287 3.678849 2.22983 1.8727 2.09995 2.24191 1.95467 2.00642 1.75985 1.82781 1.71971 1.77917 1.88572 2.11509 1.61198 1.4772Y 8 1.671Y 86 1.46Y 413 1.40976 1.46382 1.55442 1.68443

8.477019 7.119508 6.696845 6.216371 6.027134 5.916399 5.906723 5.709857 5.683662 5.320216 5.292697 4.986289 4.967141 4.905994 4.879114 4.638218 4.535621 4.277848 4.226782 4.162107 1.87508 1.86017 1.67813 1.6486 1.61274 1.61164 1.61019 1.59789 1.59564 1.58993 1.56362 1.52115 1.49499 1.46823 1.46276 1.45361 1.42961 1.4271 1.42326 1.42226

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Fig. 2. mRNA quantification and validation assays for microarray results.

Fig. 3. Cluster analysis 24 RNA samples using the common genes identified by paired t-test. The clustering display was generated by dChip software with two-way data clustering. Each column represents an individual gene, and each row corresponds to an individual array. Gene expression values were standardized and color-coded relative to the mean: green, values less than the mean; red, values greater than the mean. RNA samples of the individual in same group were labeled with the same sample ID (C¼Control, Pb ¼ Lead, Cd¼Cadmium, Hg ¼ Mercury) with the number of sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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Table 2 Diseases and disorders observed in IPA that are associated with common differentially expressed genes associated with all three heavy metal-exposed groups. Category

P-value

Molecules

Renal and Urological 3.22E-22Disease 2.95E-01 Developmental Disorder Hematological Disease Hereditary Disorder

1.1E-042.95E-01 1.1E-042.95E-01 1.1E-042.95E-01

Upregulated: UQCRH, SNRPE, SNRPG, EEF1B2, UQCRB, NDUFA1, CLEC2B, RPL7, NSA2, LSM7, ZC3H15, LSM3, AIF1, ATP5I, TAF9, ATP5O, DBI, RPL34, COX6C, DNTTIP2, PFDN5, RPL23, COX7C, NDUFS5, NDUFA6, RPL36A, HSP90AA1, PSAP. Downregulated: MYH9, WNK1. Upregulated: RPL11, PRKDC, SOD1, MKKS, DPM1, RPS6, RPS17/RPS17L, RPS7, RPS24, RPS10, ACAT1, GTF2H5. Downregulated: PSAP, HUWE1, DYNC1H1, ARID1A, TPP1, HK1. Upregulated: RPL11, SOD1, FCER1A, RPS6, RPS17/17L, RPL7, RPS7, RPS24, RPL6, RPS10, RPL35, RPS20, RPS15A, RPS27A. Downregulated: HK1, MYH9, HSPA5. Upregulated: RPL11, DYNLT1, MRPS22, DPM1, NDUFB5, ATP5L, UQCRB, RPS17/17L, DNAJA1, NDUFA1, RPS7, RPS24, HINT1, PCMT1, SUB1, GTF2H5, TXN, NDUFS4, SPTLC1, COX7A2, NDUFB3, SOD1, ATP5O, MKKS, RAB18, SLIRP, EIF3E, SIN3A, COX7C, ATP5C1, NDUFS5, RPS10, ACAT1, HSP90AA1, UQCRQ, NDUFB2. Downregulated: ARID1A, HSPA5, PSAP, DYNC1H1, HUWE1, TPP1, HK1, MYH9, WNK1,

Table 3 Partial list of the functional pathways as identified by IPA. Ingenuity Functional Pathways

B-H p-value

Molecules

Protein Synthesis

1.96E-052.61E-01 3.73E-052.61E-01 7.67E-052.61E-01

RPL24, BATF, EEF1B2, RPS6, ANAPC10, PAIP2, MRPL39, HSPA5, NRG1, RPS7, RPS24, RPS3A, PSMC2, SOD1, COPS5, CKAP5, RPL30, RPL23, EIF3E, RPS27L, FBXO8, RPS29, RPL39, TPP1, MTIF3, RPL41 LSM6, RPL11, HCFC1, POP5, PPIG, SNRPG, RPS6, RPL26, PIN4, RPS17/RPS17L, RPL7, RPS7, RPS24, SNRPB2, LSM1, SNRPD1, LSM3, SNRPD2, KIN RPL24, COPS5, RPL30, RPL23, PAIP2, EIF3E, RPS27L, MRPL39, RPS29, GTF2B, RPS24, RPS3A, RPL39, TXN, MTIF3, RPL41

RNA Post-Transcriptional Modification of Gene Expression

B-H p-value ¼ BenjaminieHochberg correction for multiple testing was applied by the IPA software.

(Supplement 8), among which renal and urological diseases were the highest and ranked among the fifth top-score network (score ¼ 39) (Table 4). These results are consistent with the findings of previous studies (Hodgson et al., 2007; Wang et al., 2005) and with the high incidence of renal diseases in Mahd Ad-Dahab area (unpublished data). In addition, functional analysis (Fig. 4) shows that approximately 30 genes were associated with renal and urological diseases. Among these genes, ribosomal protein L7 (RPL7), which is known to be associated with renal cell carcinoma (Turgut et al., 2007), was from the highest scores in our functional analysis. The discriminatory power of these gene-associated diseases is the usability in early preventative purposes (Fig. 4).

3.4. Gene-regulated renal and urological diseases To classify the genes associated with renal diseases, IPA pathway analysis was performed. Functional pathway analysis showed that shared altered genes were minor enriched with cell death and apoptosis of kidney cell lines and kidney fibrosis, whereas the chronic kidney diseases are the most significant and enrichment renal diseases (data not shown). The significant genes associated with chronic kidney diseases were identified and classified based on canonical (CP), functional (Fx), and biomarker (BM) pathways using IPA software. The network presented in Fig. 4 reveals the interaction of heavy metals exposure with genes involved in chronic kidney diseases, mitochondrial dysfunction, mitochondrial

Table 4 Partial list of the molecules in network as identified by IPA. ID Molecules in Network

Score Focus Top Functions Molecules

1 Akt, BLOC1S1, BLOC1S2, Ck2, CKAP5, DPY30, DYNC1H1, DYNLT1, DYNLT3, ERH, GPN3, GTF2B, IQGAP1, MED31, mediator, MYL6, N-cor, NCOA6, PCMT1, PRKDC, PSAP, RNA polymerase II, RPL7, RPL17, RPL26, RPL35, RPS21, RPS25, SARNP, SLIRP, SPAG7, SUB1, TAF9, TBCA, thyroid hormone receptor 2 20s proteasome, 26s Proteasome, BATF, BIRC6, CD3D, Ctbp, Cyclin E, GLRX3, GLRX, HINT1, HIPK2, HUWE1, IGBP1, MKI67IP, NFkB (complex), PIN4, PSMA2, PSMA3, PSMA4, PSMA, PSMB1, PSMC2, PSMD10, Rnr, RPL6, RPL21, RPS24, RPS29, RPS15A, RPS17/RPS17L, RPS3A, SOD1, SPTLC1, TFCP2, Ubiquitin 3 ANAPC10, APC (complex), ARID1A, ATP5C1, ATP5F1, ATP5I, ATP5L, ATP5O, ATP6V1D, BCL2A1, BPTF, Cbp/p300, CCDC25, CCNB1IP1, CDC26, CLIC1, Cyclin A, Cyclin B, ERK1/2, Fcer1, FCER1A, HAT1, LAMTOR3, LSM1, MAP2K1/2, NADH Dehydrogenase, NDUFB2, NDUFB3, PPIG, RPL9, RPL30, RPS27A, SMAD2, TRAT1, TXNDC17 4 Actin, ACTR10, AIF1, Alpha tubulin, ANXA1, BCR (complex), caspase, CLTC, COX6A1, COX6C, COX7A2, COX7C, cytochrome-c oxidase, DCTN6, ERK, F Actin, FAU, HERC1, Mlc, MRPL39, MSN, MYH9, MYL12B, p70 S6k, PAIP2, PFDN5, PTRH2, Rock, RPL11, RPL23, RPS6, RPS27L, SYF2, VBP1, WNK1 5 Cytochrome bc1, DNAJA1, EEF1B2, HCFC1, Hdac, HK1, Hsp70, Hsp90, HSP, HSPA5, HSPE1, Iga, Igm, IL12 (complex), Immunoglobulin, Interferon alpha, KLRB1, LSM3, LSM6, LSM7, PI3K (complex), PRPF18, RPL31, SNRNP27, snRNP, SNRPB2, SNRPD1, SNRPD2, SNRPE, SNRPG, Tgf beta, TPT1, UQCRB, UQCRH, UQCRQ

49

29

Cell Cycle, Reproductive System Development and Function, Carbohydrate Metabolism

44

27

Developmental Disorder, Hematological Disease, RNA Post-Transcriptional Modification

44

27

DNA Replication, Recombination, Repair, Energy Production, Nucleic Acid Metabolism

39

25

Renal and Urological Disease, Cellular Movement, Nervous System Development and Function

32

22

Renal and Urological Disease, RNA PostTranscriptional Modification, Hereditary Disorder

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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Fig. 4. The network of significant genes associated with renal disease using IPA software. Classification of genes was based on pathways canonical (CP), functional (Fx), and biomarker (BM).

complex I and III deficiency, ATP catabolism, acyl-coenzyme A metabolism, proximal tubular toxicity, eukaryotic translation initiation factors 2 signaling, nucleic acid metabolism, bladder carcinoma, renal cell carcinoma, and estrogen and glucocorticoid receptor signaling. For example, human genes such as ATP5O, COX6C, COX7C, NDUFS5, NDUFA1, UQCRB, and ATP5I, which are highly linked to chronic renal diseases (Granata et al., 2009), are highly expressed in the heavy metal-exposed group. Mechanistically, the alterations of NADH dehydrogenase (ubiquinone) Fe A1 (NDUFA1), NDUFA6, and NDUFS5 genes, which play a role in the transfer of electrons from NADH to ubiquinone, and cytochrome C oxidase 6C (COX6C) and COX7C genes, that catalyze the electron transfer from reduced cytochrome c to oxygen, result in high oxidative stress with elevated synthesis of reactive oxygen species (ROS) (Ferrando et al., 2011). This is supported by our previous observations that human exposure to Cd increased plasma level of 8-hydroxy-2'-deoxyguanosine (8-OHdG), a marker of oxidative stress to DNA (Al Bakheet et al., 2013). Other annotated genes altered in the heavy metal-exposed group and associated

with renal disease include: allograft inflammatory factor-1 (AIF-1), ubiquinol-cytochrome c reductase binding protein (UQCRB), small nuclear ribonucleoprotein polypeptide G (SNRPG), lysine deficient protein kinase-1 (WNK1), and myosin, heavy chain 9 gene (MYH9). Among these genes, AIF-1, a biomarker for glomerulonephritis (Tsubata et al., 2006) and diabetic nephropathy (Fukui et al., 2012), was significantly increased in the Pb-exposed group by 2.1-fold and by 1.8-fold in the Cd- and Hg-exposed groups. Furthermore, it has been reported that increased expression of the AIF-1 gene was associated with acute renal dysfunction (Grimm et al., 1999) secondary to the production of various pro-inflammatory cytokines in humans (Fukui et al., 2012). UQCRB, which is an important protein involved in renal diseases, was highly expressed in the Pb-, Cd-, and Hg-exposed groups by approximately 8.5-, 5.7-, and 6.1-fold, respectively. The UQCRB protein is known to play a role in the mitochondrial oxidative phosphorylation system, chemotaxis, apoptosis (Taki et al., 2009; Tsubata et al., 2006), and is thus used as a biomarker of renal toxicity. In addition, myosin heavy chain 9 (MYH9) gene is highly

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

H.M. Korashy et al. / Environmental Pollution xxx (2016) 1e11

9

Table 5 Analysis of the relationship between heavy metal-induced gene alteration and renal disease. Gene Symbol

Gene Title

Change

Effect

Ref.

UQCRB

ubiquinol-cytochrome c reductase binding protein C-type lectin domain family 2 member B NSA2, ribosome biogenesis homolog allograft inflammatory factor 1 Pb-binding polypeptide

Upregulated

increases the levels of mitochondrial reactive oxygen species.

(Powell et al., 2013) CLEC2B Upregulated has been identified as podoplanin, which is expressed on the surface of tumor cells and (Suzuki-Inoue facilitates tumor metastasis by inducing platelet activation. et al., 2011) NSA2 Upregulated is a nuclear protein involved in ribosomal biogenesis involved in diabetic nephropathy (Shahni et al., 2013) AIF-1 Upregulated is a cytoplasmic protein and highly expressed in anti-glomerular basement membrane nephritis (Tsubata et al., 2006) DBI Upregulated binds to Pb with high affinity and accounts for >35% of the total Pb in kidney cortex tissue. (Smith et al., 1998) HSP90AA1 heat shock protein 90 kDa Upregulated is a chaperon protein-related gene involved in Cd renal toxicity in human (Lee et al., alpha family class A1 2013) NDUFS5 NADH dehydrogenase Upregulated is involved in oxidative phosphorylation pathway and upregulated in nephrosis (Zhang et al., (ubiquinone) Fe-S protein 3 2012) COX6C cytochrome c oxidase Upregulated is involved in oxidative phosphorylation pathway and upregulated in nephrosis (Zhang et al., subunit 6C 2012) MYH9 myosin, heavy chain 9, non- Downregulated is decreased in idiopathic nephrotic syndrome (Miura et al., muscle 2013) Downregulated is a negative regulator of Na(þ)-Cl(-) cotransporter in the distal convoluted tubule and plays an (Liu et al., WNK1 WNK lysine deficient protein kinase 1 important role in the control of Na(þ) homeostasis and blood pressure. 2011)

expressed in kidney podocytes, peritubular capillaries, and glomerulus tubules and plays an important role in cell maintaining, motility, and contractility (Singh et al., 2009). Furthermore, Johnstone and colleagues have reported that Myh9 podocyte-deleted mice are predisposed to kidney injury associated with proteinuria and glomerulosclerosis (Johnstone et al., 2011). IPA analysis of the current data showed that the expression level of MYH9 was downregulated in the Pb-, Cd-, and Hg-exposed groups by approximately 1.5-, 1.4-, and 1.6-fold, respectively. With regard to renal disease, results summarized in Table 5 showed that exposure to heavy metals significantly upregulated the UQCRB (Powell et al., 2013), C-type lectin domain 2B (CLEC2B) (Suzuki-Inoue et al., 2011), NSA2 (Shahni et al., 2013), AIF-1 (Tsubata et al., 2006), DBI (Smith et al., 1998), HSP90AA1 (Lee et al., 2013), NDUFS5, and COX6C (Zhang et al., 2012) genes, which are all known to induce renal diseases. On the other hand, genes such as MYH9 and WNK1, which are known to play important roles in renal function and hemostasis, were downregulated. Therefore, this study provides important information for identifying the molecular mechanisms of disease onset and progression.

3.6. Conclusion Microarray technology provides powerful tools to analyze gene expression profiling alteration due to environmental heavy metal exposure. The IPA pathway analysis was used to find specific genes involved in all three heavy metals that could be used as susceptible biomarkers for heavy metal exposure. This study revealed that the subjects who were living the nearest to mining areas are significantly at higher risk of gene-regulated disease alteration. Hence, we identified the significant disease and disorder associated with the shared gene sets, in which renal and urological diseases were the highest diseases associated with all three heavy metal-exposed groups. Therefore, there is a need for further studies to validate these genes, which could be used as an early biomarker to prevent renal injury. This study not only contributes to understanding the effects of long-term exposure to heavy metals on gene expression of gene-regulated diseases, but also provides useful information and suggestions for the health risk assessment of chronic exposure to heavy metals to ultimately aid in the protection of public health. Conflict of interest

3.5. Upstream transcriptional regulator analysis To identify the cascade of the transcriptional regulators based on the direction of gene expression change, IPA analysis was conducted. The two significant upstream regulators are v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) and v-myc avian myelocytomatosis viral oncogene homolog (MYC) (Supplement 9). These transcription regulators have important roles in cell cycle machinery, proliferation, differentiation, and apoptosis. Several previous studies have demonstrated that MYC activation has been implicated in human carcinogenesis, such as hematopoietic malignancies and renal cancer (Barrett et al., 1997). Moreover, it has been reported that knockdown of MYC suppressed the expression of BCL2, an antiapoptotic gene (Tang et al., 2009). Therefore, MYC and BCL2A1 genes could be used as prognostic markers for renal diseases (Mannam et al., 2013; Patel et al., 2004; Tang et al., 2009). Future studies should be conducted to validate these results and examine the role of MYCN in the molecular basis of heavy metal-induced renal toxicity.

There are no financial or other interests with regard to this manuscript that might be construed as a conflict of interest. All the authors are aware of and agree to the content of the manuscript and their being listed as an author on the manuscript. Neither this manuscript nor any similar manuscript, either in whole or in part has been submitted to or published in Environmental Pollution or any other primary scientific journal. Upon acceptance, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder. Acknowledgments The authors are thankful to the Deanship of Scientific Research at King Saud University for funding this work (grant #NPAR3-21). The authors are also grateful to Mr. Fawaz M. Almutairi for his technical assistance. The authors would like to thank Dr. Ibrahim Sales, Assistant Professor of Clinical Pharmacy, for his critical reviewing and editing of the manuscript.

Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058

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Please cite this article in press as: Korashy, H.M., et al., Gene expression profiling to identify the toxicities and potentially relevant human disease outcomes associated with environmental heavy metal exposure, Environmental Pollution (2016), http://dx.doi.org/10.1016/ j.envpol.2016.10.058