Received Date: September 09, 2014;Accepted Date: September 25, 2014; Published Date: September 27, 2014
Citation: Yang J, Bai W, Xiao Z, Chen Y, Chen L, et al. (2014) Aberrant Hypomethylated KRAS and RASGRF2 as a Candidate Biomarker of Low Level Benzene Hematotoxicity. J Mol Biomark Diagn 5:195. doi:10.4172/2155-9929.1000195
Copyright: 2014 Yang J, 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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Benzene is an important industrial chemical and an environmental contaminant. The mechanisms of low level benzene-induced hematotoxicity are unresolved. Aberrant DNA methylation, which may lead to genomic instability and the altered gene expression, is frequently observed in hematological cancers. The purpose of the present study was to conduct a genome-wide investigation to examine comprehensively whether low level benzene induces DNA methylation alteration in the benzene-exposed workers. Infinium 450K methylation array was used to compare methylation levels of the low level benzene-exposed individuals and health controls and the differentially expressed DNA methylation pattern critical for benzene hematotoxicity were screened. Signal net analysis showed that two key hypomethylated KRAS and RASGRF2 associated with low level benzene exposure were identified. Further, the hypomethylated RASGRF2 gene played central roles through regulation of Rho protein, MAPK, small GTPase mediated signal transduction. While the hypomethylated KRAS gene played important roles through small GTPase, Ras protein, MAPK cascade, Gap junction, Axon guidance, Tight junction, GnRH, T cell receptor signaling pathway, Acute myeloid leukemia, B cell receptor signaling pathway, Chronic myeloid leukemia, ErbB signaling pathway. Our preliminary study indicated that aberrant hypomethylated KRAS and RASGRF2 might be a potential methylated biomarker of low level benzene hematotoxicity.
Low level benzene exposure; Biomarkers; DNA methylation; KRAS; RASGRF2
Benzene is an important industrial chemical and an environmental contaminant. Chronic occupational benzene exposure is associated with an increased risk of hematological malignancies. A study indicated that white blood cell counts were decreased in workers exposed to <1 ppm benzene compared with controls . However, the mechanisms of low level benzene-induced hematotoxicity remain to be elucidated.
DNA methylation alteration is very useful in the diagnosis, prognosis and prediction of disease . Methylation of 5’-CpG islands in gene promoter regions has consistently been found in malignant tissues and is hypothesized to be indicative of critical early changes in cancer development . Aberrant DNA methylation, which may lead to genomic instability and the altered gene expression, is frequently observed in AML . Repeated-element DNA hypomethylation, as well as gene-specific hypermethylation or hypomethylation are commonly seen in hematological cancers . DNA methylation has thus been proposed to reflect environmentally-induced epigenetic reprogramming and risk of future disease [6-8].
Benzene, an established human leukemogen, is known to induce global DNA hypomethylation changes through its active metabolites including hydroquinone (HQ) in human TK6 lymphoblastoid cells . Global DNA hypomethylation was reported based on PCRPyrosequencing measures in gasoline station attendants exposed to benzene  and these studies suggest that DNA hypomethylation may be key mechanism underlying the leukemogenicity of benzene. However, knowledge of the effects of benzene on DNA methylation is still limited. Therefore, we hypothesis that low level benzene exposure can induce population-specific methylation alterations in the promoter regions of specific genes, thus leading to changes in gene transcription and an increased risk for benzene hematotoxicity. To gain an insight into the molecular mechanisms and new biomarkers, microarray analysis was used to identify the differentially expressed DNA methylation pattern critical for benzene hematotoxicity. Specific CpG sites (or genes) with altered methylation in workers due to exposure to low level benzene were identified.
Characteristics of subjects
These benzene-exposed subjects are all paint sprayers and health controls are office workers without benzene exposure in the same factory. The detailed characteristics of study population can be seen in our previous published papers [11,12]. Air benzene exposure was determined by a passive sampler (stainless steel tube, internal diameter of 9 mm, length of 90 mm) containing Chromosorb 106, worn by the study subjects near the breathing zone during the work shift. Benzene was determined by thermal desorption followed by gas chromatography/flame ionization detector analysis [10,13]. Airborne benzene concentration between health controls without benzene exposure and low level benzene-exposed group without clinical symptoms is 0.06 ± 0.01mg/m3, 1.82 ± 1.16 mg/m3, respectively Each subject was given a physical exam by a study physician. A questionnaire was administered to obtain the detailed information on occupation, environmental exposures to solvents and pesticides, past and current tobacco, alcohol use and medical history including recent infections, ionizing radiation exposure, medication use and family history. The subjects in each group were matched by age, gender and life styles. These samples were used for microarray analysis. Briefly, peripheral blood samples were randomly obtained from four benzeneexposed workers (average age 44.7 (39~51) yrs, average white blood cell 6.4*109/L) and four health controls without benzene exposure (average age 43.3(34~55) yrs, average white blood cell 6.4*109/L) with informed consents, and the study was approved by the Committees for Ethical Review of Research involving Human Subjects of Capital Medical University.
Peripheral blood genomic DNA isolation and bisulphite conversion
Genomic DNA from the peripheral blood mononuclear cells of four workers exposed to benzene levels ranging < 1 ppm and four health controls was isolated by using the QIAamp DNA Mini Kit (Qiagen, Valencia, CA, USA). DNA was prepared with a Wizard Genomic DNA purification kit (Promega Corp, Madison, WI), quantified, and diluted into aliquots of 25 ng/μl for genome-wide DNA methylation analysis. One μg of genomic DNA was treated using the EZ DNA Methylation Kit (Zymo Research, Orange, CA, USA) according to the manufacturer’s protocol. Unmethylated cytosines were converted to uracils (which are then converted into thymines following subsequent PCR), while methylated cytosines remained unchanged. We used a total of 500 ng DNA for each bisulphite conversion reaction. Bisulfite treated DNA was aliquoted and stored at −80°C until ready for use.
Infinium DNA methylation analysis
The 450K DNA Methylation array includes 485,764 cytosine positions of the human genome. A general depiction of the 450K platform design, regarding functional genome distribution, CpG content and chromosome location, is reported and validated in two independent laboratories [14,15]. Genome-wide methylation analysis was performed using the high resolution Infinium 450K methylation array interrogating about 480 000 CpGs distributed in promoters, gene bodies, 3’UTRs and intergenic regions . DNA methylation data were processed using GenomeStudio software (ver.2012; Illumina, Inc.) applying the default settings. Infinium arrays were hybridized and scanned as manufacturer’s instructions (Illumina, San Diego, USA). Individual probe β values (range 0-1) are approximate representations of the absolute methylation percentage of specific CpG sites within the sample population. Beta (β)=1 indicates complete methylation; β=0 represents no methylation. The values are derived by comparing the ratio of intensities between the methylated and unmethylated alleles using the following formula: β value=Max (Signal B, 0)/[Max (Signal A, 0) + Max (Signal B, 0)]. Where Signal B is the array intensity value for the methylated allele and Signal A is the non-methylated allele. Samples were processed using the Bioconductor package, which is specifically designed for Illumina data. All the obtained DNA methylation data have been deposited in the Gene Expression Omnibus (GEO) database in the following link: acc=GSE50967
To ascertain whether these differentially expressed hypomethylated and hypermethylated genes among groups were selected correctly, hierarchical cluster analysis was performed based on differentially expressed genes using Cluster Treeview software from Stanford University.
Differentially expressed hypomethylated and hypermethylated genes were identified through random variance model (RVM)
Because of high test fee, only four samples at each group were detected. The age, gender, lifestyle such as smoking, drinking, and medical history in each group were matched to reduce the impact of these confounding factors. In addition, the effective statistical method for small samples was adopted. RVM, t-test was commonly used for comparison of two groups, because the RVM, t-test effectively increased the degrees of freedom in the cases of small samples [16,17]. After the significance analysis and false discovery rate (FDR) analysis, we selected the differentially expressed genes according to the statistical significance of t-test after adjustment with the Benjamini and Hochberg correction. The cut-off for t-test P-values is <0.001.
Gene ontology analysis and pathway analysis
Gene ontology analysis was applied to analyze the main function of differential expression genes according to the Gene ontology project . Fisher’s exact test and χ2 test were used to classify the GO category, and the FDR was calculated to correct the P value . The standard of difference screening was P<0.05. Similarly, pathway analysis was used to find out the significant pathway of the differential genes according to KEGG, Biocarta and Reatome. Fisher’s exact test and χ2 tests were used to select the significant pathway, and the threshold of significance was defined by P value and FDR [20-22]. The standard of difference screening was P<0.05.
Gene-gene interaction network was constructed based on the data of differentially expressed mRNAs. For instance, if there is confirmative evidence that two genes interact with each other, an interaction edge is assigned between the two genes. The considered evidence is the source of the interaction database from KEGG. Networks are stored and presented as graphs, where nodes are main genes (protein, compound, etc.) and edges represent relation types between the nodes, e.g. activation or phosphorylation. The graph nature of networks raised our interest to investigate them with powerful tools implemented in R . To investigate the global network, we computationally identify the most important nodes. To this end we turn to the connectivity (also known as degree) defined as the sum of connection strengths with the other network genes:In gene networks, the connectivity measures how correlated a gene is with all other network genes. For a gene in the network, the number of source genes of a gene is called the indegree of the gene and the number of target genes of a gene is its outdegree. The character of genes is described by betweenness centrality measures reflecting the importance of a node in a graph relative to other nodes. For a graph G:(V,E) with n vertices, the relative betweenness centralityis defined by: whereσ s t (v) is the number of shortest paths from s to t, andthe number of shortest paths from s to t that pass through a vertex v. [24-26]. We were thus able to search for differential expression genes. Two nodes were connected when their corresponding encoded gene products were either directly connected or indirectly connected by a linker gene in the interaction network. The network for each gene was measured by counting the number of upstream and downstream genes or binding genes which were expressed as in-degree and out-degree or degree, respectively. The higher degree of a gene indicates that it is regulating or being regulated by a greater number of genes, implying it has a more important role in the signaling network. P<0.05 was considered statistically significant. Gene interactions could then be drawn based on the data .
The differentially hypomethylated and hypermethylated CpG pattern associated with low level benzene exposure and functional analysis
Distinct genome-wide DNA methylation patterns were observed in benzene exposures workers compared with health controls. Hierarchical cluster analysis shows 1546 differential hypermethylated and 613 hypomethylated CpG sites in low level benzene exposure workers compared to health controls (Figure 1a). GO analysis indicates that these hypermethylation genes are mainly involved in cell adhesion, signal transduction, transcription, DNA-dependent, regulation of small GTPase mediated signal transduction, apoptotic process, cell differentiation, positive regulation of cell proliferation, G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger, oxidation-reduction process, Wnt receptor signaling pathway, cellular response to oxidative stress, innate immune response, induction of apoptosis, immune response, JUN phosphorylation, regulation of JUN kinase activity, leukemia inhibitory factor signaling pathway (Figure 1b); While these hypomethylation genes in low level benzene exposure workers are implicated in transcription, DNAdependent, small GTPase mediated signal transduction, cell adhesion, Ras protein signal transduction, synapse assembly, negative regulation of transcription, DNA-dependent, MAPK cascade, DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest, post-translational protein modification, protein autophosphorylation, signal transduction, negative regulation of cell proliferation, negative regulation of NF-kappaB import into nucleus, negative regulation of JAK-STAT cascade, cell differentiation (Figure 1c).
Figure 1: The differentially hypomethylated and hypermethylated CpG profiles associated with low level benzene exposure and functional analysis. (a) Hierarchical cluster of 2159 differentially methylated CpG sites in low level benzene exposure workers compared with health controls. Among them, there are 1546 differential hypermethylated and 613 hypomethylated CpG sites. The heatmap color corresponds to the Beta-value of the measured CpG-sites. The Beta-value is in the range of 0 (shown in green) and 1 (shown in red) with 0 representing purely unmethylated and 1 representing purely methylated. Each row represents a separate sample and each column represents a CpG site. (b) GO analysis of differential hypermethylated genes in low level benzene exposure workers compared to health controls. (c) GO analysis of differential hypomethylated genes in low level benzene exposure workers compared to health controls. X axis, negative logarithm of the P value (-LgP), the larger the value was, the smaller the P value was. Y axis, the name of the gene ontology category, significant GO terms of differential methylated genes.
Genomic distribution of the differentially methylated CpGs sites associated with low level benzene exposure with respect to functional genomic distribution (promoter, gene body, 3'UTR and intergenic) and CpG content (CpG island, shore, shelf and open sea)
Our preliminary study identified a set of differentially methylated CpG loci between low level benzene exposure workers and health controls. The location of differentially methylated CpG loci with respect to CpG content and functional genomic distribution was similar in our study, the majority of CpG loci were residing in gene promoters (Figure 2a) and in CpG islands (Figure 2b).
Figure 2: Genomic distribution of the differentially methylated CpGs sites in low level benzene exposure workers compared with health controls with respect to functional genomics distribution (promoter, gene body, 3′UTR and intergenic) and CpG content (CpG island, shore, shelf and open sea). The location of differentially methylated CpG loci with respect to CpG content and functional genomics distribution was similar in our study, with the majority of CpG loci residing in CpG islands and in gene promoters.
Pathway analysis of the differential methylation genes associated with low level benzene exposure
To further investigate key pathways linked to these distinct genes, the significant pathways categories (P<0.05) of differential genes associated with benzene exposure were performed. Our analysis showed that differentially hypomethylated genes were distributed in 12 significant pathways, including MAPK signaling pathway, Gap junction, Cell adhesion molecules (CAMs), p53 signaling pathway, Cytokinecytokine receptor interaction, Notch signaling pathway, GnRH signaling pathway, T cell receptor signaling pathway, Acute myeloid leukemia, Mismatch repair, B cell receptor signaling pathway, Chronic myeloid leukemia (Table 1). While these differentially hypermethylated genes associated with low level benzene exposure were distributed in 14 significant pathways, including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Apoptosis, MAPK signaling pathway, Acute myeloid leukemia, Hematopoietic cell lineage, Oxidative phosphorylation, Notch signaling pathway, Wnt signaling pathway, Toll-like receptor signaling pathway, p53 signaling pathway, Adherens junction, Tight junction, ErbB signaling pathway (Table 2 ).
|MAPK signaling pathway||6.40E-08||5.65E-06||MKNK2, CACNB4, EGF, HSPA1L, MOS, RASGRF2, MAPK8IP3, CEBPZ, HSPA1A, HSPA1A, KRAS, MAPKAPK5, PTPN7, TNF|
|Gap junction||1.09E-04||6.33E-04||GNA11, EGF, ADCY2, CDC2, CDK1, KRAS, PDGFD|
|Cell adhesion molecules (CAMs)||1.12E-04||6.33E-04||NRXN1, HLA-DQA2, HLA-F, HLA-DQA1, HLA-G, PDCD1, PVRL2|
|p53 signaling pathway||0.002349||0.005206||ATR, GTSE1, CDC2, CDK1, MDM2|
|Cytokine-cytokine receptor interaction||0.005546||0.010259||EGF, BMP7, FLT4, CCL14, RELT, TNF, TNFSF13B|
|Notch signaling pathway||0.006421||0.0117||NCOR2, CTBP2, JAG2|
|GnRH signaling pathway||0.011063||0.017584||GNA11, PLD1, ADCY2, KRAS|
|T cell receptor signaling pathway||0.011779||0.01834||PDCD1, CARD11, KRAS, TNF|
|Acute myeloid leukemia||0.011996||0.01856||JUP, KRAS, RUNX1T1|
|Mismatch repair||0.014568||0.021396||CDC2, MSH2|
|B cell receptor signaling pathway||0.022695||0.028704||CARD11, FCGR2B, KRAS|
|Chronic myeloid leukemia||0.022695||0.028704||CTBP2, KRAS, MDM2|
Table 1: Pathway analysis of differentially hypomethylated genes associated with low level benzene exposure.
|Cell adhesion molecules (CAMs)||3.27E-05||2.02E-04||HLA-DQB1, NFASC, NLGN4X, CD34, CDH2, MAG, MAL, VCAN|
|Cytokine-cytokine receptor interaction||1.57E-04||6.61E-04||TNFRSF8, IL17RB, KIT, BMPR2, CXCL1, CXCL6, IFNGR2, INHBC, TNFRSF11B, TNFRSF19|
|Apoptosis||1.64E-04||6.73E-04||BAD, MYD88, NGF, APAF1, CASP7, PRKAR2B|
|MAPK signaling pathway||9.92E-04||0.00264||CACNA1H, ECSIT, BDNF, NGF, CACNG4, DUSP10, DUSP3, JUN, MEF2C|
|Acute myeloid leukemia||0.00204||0.00477||TCF7L2, BAD, CCNA1, KIT|
|Hematopoietic cell lineage||0.008189||0.012519||KIT, CD34, CD7, MAL|
|Oxidative phosphorylation||0.008911||0.012955||NDUFV2, ATP6V0E2, ATP6V0A4, LOC642502, SDHC|
|Notch signaling pathway||0.008914||0.012955||LFNG, HES5, NUMB|
|Wnt signaling pathway||0.01276||0.016638||TCF7L2, FZD8, JUN, PRICKLE1, SFRP2|
|Toll-like receptor signaling pathway||0.014075||0.017825||TIRAP, MYD88, JUN, MAL, TLR2|
|p53 signaling pathway||0.02491||0.02784||TEP1, APAF1, CHEK2|
|Adherens junction||0.035213||0.034955||TCF7L2, ASIP, IQGAP1|
|Tight junction||0.035657||0.034955||TEP1, PRKCZ, ASIP, PARD6B|
|ErbB signaling pathway||0.0448||0.040811||BAD, NRG1, JUN|
Table 2: Pathway analysis of differentially hypermethylated genes associated with low level benzene exposure.
To find the interaction among pathways directly and systemically, the interaction net of the significant pathways associated with low level benzene exposure was built according to the KEGG database. As shown in Figure 3, key pathway interaction of differentially methylated genes associated with low level benzene exposure in this study were mainly involved in MAPK signaling pathway, p53 signaling pathway, Adherens junction, Cytokine-cytokine receptor interaction, Cell adhesion molecules (CAMs), ErbB signaling pathway. Interestingly, T cell receptor signaling pathway, Focal adhesion, B cell receptor signaling pathway were special pathways for differentially hypomethylated genes. However, apoptosis, Wnt signaling pathway, TGF-beta signaling pathway, calcium signaling pathway were special pathways for differentially hypermethylated genes
Figure 3: Pathnet analysis of differential methylation genes in low level benzene-exposed workers. Nodes represent pathways. The area of nodes displays the degree which is the number of other pathways that interact with this pathway. Lines indicate interactions between pathways, where pathways indicated by the arrow head are regulated by pathways of the arrow tail. Red denotes the hypermethylated pahtway. Blue denotes the hypomethylated pahtway. Yellow denotes the hypermethylated and hypomethylated pahtway.
Signal-net analysis of distinct methylated genes in low level benzene-exposed workers
To investigate the key genes involved in low level benzene exposure, signal-net analysis of differential methylated genes associated with low level benzene exposure showed that two key hypomethylated KRAS and RASGRF2 were identified according to the degree size (Figure 4 and Table 3). Further GO analysis indicated that the hypomethylated RASGRF2 gene played central roles through regulation of Rho protein signal transduction, MAPK signaling pathway, small GTPase mediated signal transduction, synaptic transmission (Supplemental material, Supporting Table S1) and through MAPK signaling pathway (Supplemental material, Supporting Table S2). While hypomethylated KRAS gene played important roles through small GTPase mediated signal transduction, Ras protein signal transduction, MAPK cascade, axon guidance, blood coagulation (Supplemental material, Supporting Table S1) and through MAPK signaling pathway, Gap junction, Regulation of actin cytoskeleton, Axon guidance, Tight junction, GnRH signaling pathway, T cell receptor signaling pathway, Acute myeloid leukemia, B cell receptor signaling pathway, Chronic myeloid leukemia, Colorectal cancer, ErbB signaling pathway (Supplemental material, Supporting Table S2).
Figure 4: Signal-net analysis of differential methylated genes in low level benzene-exposed workers. The molecular networks were constructed, where nodes are main genes and edges represent relation types between the nodes, e.g. activation or phosphorylation. Red denotes the hypermethylated genes. Blue denotes the hypomethylated genes.
|Gene symbol||Gene symbol Description||betweenness centrality||degree||Regulation stytle|
|KRAS||v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog||0.0377||6||low|
|RASGRF2||Ras protein-specific guanine nucleotide-releasing factor 2||0.0348||4||low|
|CACNA1H||calcium channel, voltage-dependent, T type, alpha 1H subunit||0.0239||2||high|
|RYR3||ryanodine receptor 3||0.020||3||low|
|KIT||v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog||0.010||4||high|
The degree is defined as the sum of connection strengths with the other network genes. For a gene in the network, the number of source genes of a gene is called the indegree of the gene and the number of target genes of a gene is its outdegree. The character of genes is described by betweenness centrality measures reflecting the importance of a node in a graph relative to other nodes
Table 3: The top 6 significantly methylated genes according to the degree size Supporting Information.
In this study, the overall goal is to provide early methylated biomarkers of low level benzene exposure and to have an indepth insight into the molecular mechanisms regulating benzene hematotoxicity. Our study indicated that aberrant hypomethylated KRAS and RASGRF2 might be a candidate methylation biomarker of low level benzene hematotoxicity. This study suggests that low level benzene may cause toxicity via non-mutagenicity mechanisms and benzene-induced toxicity may be mediated in part by epigenetic mechanisms.
Toxicogenomic studies of exposed population are an important alternative approach to the human health risk assessment of environmental exposures and have been used to identify potential biomarkers of early effects and mechanisms underlying associated diseases. Microarray techniques have been used successfully to monitor DNA methylation profile changes. The recent advances in methods to examine epigenetic modifications, such as DNA methylation, have led to interest in determining the genome-wide epigenetic profiles characterizing many different disease states. Various efficient technological platforms have been developed in recent years for high-throughput genome-wide analysis of DNA methylation. One of these is the Infinium 450K methylation array by Illumina . The Infinium 450k DNA methylation array from Illumina offers the possibility to analyze more than 480,000 individual CpG sites in a user friendly standardized format [14,15]. This setup enabled us to unravel alteration in DNA methylation at high resolution independent of genetic variation. This platform has been successfully applied to detect aberrantly methylated genes [28,29]. Deneberg et al. derived genomewide DNA methylation profiles of 29 childhood B-ALL patients and four normal B-cell samples using the Infinium 450 K DNA methylation Bead assay .
Compared with previously released Illumina DNA methylation platforms, the recently launched Infinium Human Methylation 450 BeadChip represents a significant increase in the CpG site density for quantifying methylation events. At the gene level, the 450K microarray covers 99% of RefSeq genes with multiple sites in the annotated promoter (1500 bp or 200 bp upstream of transcription start site), 5-UTR, first exon, gene body and 3-UTR. From the CpG context, it covers 96% of CpG islands with multiple sites in the annotated CpG Islands, shores (regions flanking island) and shelves (regions flanking shores) . While the role of DNA methylation in promoter and CpG island regions has been appreciated, the importance of DNA methylation in gene body or shore regions for transcription regulation and tumor initialization has recently come to attention [31,32]. The significantly increased coverage makes 450K microarray a powerful platform for exploring methylation profile in these annotated regions. As each targeted region contains at least one CpG site, treating the region as a unit in the differential methylation analysis might help identify regions with consistently coordinate methylation changes. The promoter, 5-UTR, first exon, gene body and 3-UTR are genebased regions. The CpG island and its surrounding shore and shelve regions are not necessary gene based, depending on their distance to the nearest genes. Our preliminary study indicated that the majority of differentially hypomethylated and hypermethylated CpG loci associated with low level benzene exposure were residing in gene promoters and in CpG islands. Our results provide direct experimental evidence that benzene, associated with increased cancer risk, cause changes in promoter CpG methylation in numerous genes.
DNA methylation is an essential epigenetic mark that is required for normal development. DNA methylation plays a vital role in transcriptional regulation and chromatin remodeling. The aberration of DNA methylation profile has been found to be associated with many human diseases including cancer. Use of DNA methylation microarray is a popular approach to characterize the epigenetic landscape of human cells . DNA methylation alterations in gene promoters have also been found repeatedly following exposure to various environmental chemicals, including pesticides , phenol and hydroquinone  and dioxidine , suggesting that DNA methylation is inducible by chemical exposures.
We previously observed DNA methylation alterations in specific genes and our data showed effects of benzene on DNA methylation of specific genes such as PARP-1 and PTEN in cells and animal modes treated by benzene [37,38]. Epigenetic mechanisms of gene regulation are heritable, reversible modifications that are critical for the organization of chromatin and regulation of tissue-specific gene expression. DNA methylation is a dynamic epigenetic mark primarily localized to cytosine residues in the context of a CpG dinucleotide in mammals. While the critical role for DNA methylation in early development is clearly established, however the role for DNA methylation in peripheral blood mononuclear cells is less understood. In this study, we conducted genome-wide DNA methylation analyses on DNA samples obtained from the peripheral blood mononuclear cells of low level benzene exposure workers (< 1 ppm) and health controls using the high resolution Infinium 450K methylation array. To our knowledge, this is the first pilot study of genome-wide DNA methylation analyses for low level benzene exposure workers (<1 ppm) using the high resolution Infinium 450K methylation array. Thus, the use of noninvasive measurements of peripheral blood samples could provide a highly feasible method to examine benzene hematotoxicity-associated epigenetic changes of certain genes. The methylation of the gene from peripheral blood cells was commonly associated with all kinds of diseases [36,39-41].
Hypermethylation of tumor suppressor p15, which leads to inactivation of the gene and subsequent uncontrolled proliferation of the cell, is commonly observed in AML and other hematological cancers [42-45]. A significant reduction in LINE-1and AluI methylation and hypermethylation in p15 and hypomethylation in MAGE-1 were associated with increasing airborne benzene levels . Seow WJ et al showed statistically significant but weak associations of LINE-1 and p15 hypomethylation with SPMA in 158 Bulgarian petrochemical workers . Carugno M et al found that low-dose benzene exposure was associated with increased mitochondrial DNA copy number. Benzene exposure may be associated with hypermethylation in ERCC3 . The melanoma antigen family A (MAGE) gene, which encodes tumor rejection antigens, is widely expressed in cells from hematological malignancies and is found to be hypomethylated with benzene exposure [10,48]. All these results suggested that effect of benzene hematotoxicity may be mediated in part by DNA methylation which can substantially affect gene transcription without changing DNA sequence.
In this study, we investigated the association between occupational benzene exposures with gene specific DNA methylation in low level benzene exposures workers. Our study indicated that aberrant hypomethylated KRAS and RASGRF2 might be a candidate methylation biomarker of low level benzene hematotoxicity. Further GO and pathway analysis indicated that hypomethylated RASGRF2 gene played central roles through regulation of small GTPase mediated signal transduction. While hypomethylated KRAS gene played important roles through small GTPase mediated signal transduction, Ras protein signal transduction, T cell receptor signaling pathway, Acute myeloid leukemia, B cell receptor signaling pathway, Chronic myeloid leukemia, ErbB signaling pathway. Functional analysis of methylation changes revealed that differential methylation genes were involved in carcinogenesis-related processes. Our results provide experimental evidence that benzene exposures may modify gene promoter DNA methylation levels, suggesting that epigenetic mechanisms may contribute to benzene-induced carcinogenesis.
Aberrant methylation of promoter CpG that causes silencing of tumor suppressor genes (TSGs) may play a key role in the carcinogenesis of many cancer types. RASGRF2 has only been reported to possibly play a role in the pathogenesis of pancreatic cancer cell lines . Frequent methylation and silencing of RASGRF2 in tumor cells may play an important role in the carcinogenesis of non-small cell lung cancer (NSCLC) . Our recent findings also showed that PIK3R1, PIK3CG, PIK3R2, GNAI3, KRAS, NRAS, NFKB1, HLADMA, and HLA-DMB played central roles in benzene hematotoxicity and immune response signaling pathways such as B/T cell receptor signaling pathway, acute myeloid leukemia, hematopoietic cell lineage and natural killer cell mediated cytotoxicity were most significantly associated with benzene exposure .
These data, coupled with human epidemiology evidence linking benzene with cancers and recent evidence indicating DNA methylation alterations as a hallmark of cancer, support the notion that benzene exposure may lead to cancer in part via inducing the alterations of DNA methylation.
Unlike genetic mutations, DNA methylation is an inherently reversible change, and therefore is of great interest as an active target of drug development [50-52]. Because DNA methylation is potentially modifiable through lifestyle and pharmacological interventions, if confirmed, our findings may open new paths for prevention against benzene-induced carcinogenesis
Previous studies have reported that the aberrant hypermethylation of promoter CpG islands is linked to gene silencing and loss of tumour suppressor function. DNA methylation of cytosine residues in CpG dinucleotides leads to transcriptional silencing of associated genes. Promoters with methylated CpG units, which have their transcriptional activity reduced, may function as an alternative mechanism of repressing tumour suppressor genes. Further research is needed to precisely define the mechanisms leading to benzene-induced DNA methylation changes. Our findings should be interpreted with caution, as there were a few limitations to this study. Further studies in other cell types and human samples are required, as well as determining the impact of these methylation changes on gene expression.
In conclusion, DNA methylation expression profiles along with GO, KEGG pathway annotation analysis have highlighted potential gene-based biomarkers of benzene exposure.
This work has been supported by grants from the National Natural Science Foundation of China (81172639, 81472957) and the Beijing Natural Science Foundation of China (7142020, 7113149); and The Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions (CIT&TCD201404187) and Program of Beijing Municipal Commission of Education (KM201410025008). The authors would like to acknowledge the technical assistance of the Genminix Company (Shanghai, China).
|Table 1||Table 2||Table 3|
|Figure 1||Figure 2||Figure 3||Figure 4|
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