Open Access

Research Article

Materials and Methods Top

Subject selection

This project used samples from an epidemiologic case–control study in New Hampshire described previously (e.g., Karagas et al. 1998, 2004). One of our major goals of this study was to investigate the bladder and nonmelanoma skin cancer risks associated with arsenic exposure in New Hampshire. Controls younger than 65 years were selected using population lists obtained from the New Hampshire Department of Transportation. Controls 65 or more years of age were chosen from data files provided by the Centers for Medicare and Medicaid Services of New Hampshire. Subjects were sent an introductory recruitment letter followed by a phone call to arrange an in-person interview. For this project, we selected subjects from a group of 606 controls who were interviewed between 1999 and 2002. The overall study participation rate was approximately 70% of the controls confirmed to be eligible for the study. Of those from whom we requested samples, the participation rate was approximately 72% for the blood draw, 90% for toenail clippings, 95% for urine collection, and more than 95% for water. Data on subjects’ exposure history were available through a personal interview covering demographic information, history of tobacco use, and other lifestyle factors. Informed consent was obtained from each participant, and all procedures and study materials were approved by the Committee for the Protection of Human Subjects at Dartmouth College.

Selection of the 21 control subjects (i.e., who did not have cancer) who were used for this present project was based on internal biomarkers (toenail or urine) and household drinking-water arsenic exposure levels from individuals on whom cryopreserved lymphocytes were available at the time the project began. The selected subjects had agreed to provide a venous blood sample that was drawn into cell preparation tubes (CPTs) containing citrate and a lymphocyte isolation gradient (Becton, Dickinson and Co., Franklin Lakes, NJ). Blood tubes were maintained at 4°C and sent to the study laboratory for processing and analysis. No later than 24 hr after the blood draw, lymphocytes collected in CPTs containing sodium citrate were isolated according to the manufacturer’s instructions using standard buoyant density centrifugation methods. After centrifugation, first plasma was removed, aliquoted, and frozen at −80°C, then the mononuclear cells were removed by pipette and cryopreserved (−120°C) using freezing media at a controlled rate of 1°C per minute. The viability of the cells after thawing was assessed to be 98% using trypan blue, as reported previously for this method (Wei et al. 1994).

A water sample from the current household drawn into commercially washed (mineral-free) high-density polyethylene bottles that met U.S. Environmental Protection Agency standards for water collection (I-Chem vials; Fisher Scientific, Pittsburgh, PA) was analyzed for arsenic concentration using an Agilent 7500c Octopole inductively coupled plasma mass spectrometer (Agilent Technologies, Palo Alto, CA) in the Dartmouth Trace Element Analysis Core Facility. Toenail clipping samples collected at the time of interview were analyzed for arsenic and other trace elements by instrumental neutron activation analysis (INAA) at the University of Missouri Research Reactor, using a standard comparison approach as described previously (Cheng et al. 1995). The detection limit for arsenic measured by INAA is approximately 0.001 μg/g. First morning void urine samples were obtained in 100-mL polypropylene bottles and kept on ice. Within 6 hr, cooled samples were taken to the laboratory and kept frozen at −80°C until the analysis of total arsenic and arsenic species was performed as described previously (Meza et al. 2005). The detection limits were 0.42–1.08 μg/L for arsenic compounds.

Individuals from whom we had collected cryopreserved lymphocyte, drinking-water, and either toenail or urine samples were candidates for the microarray project. From this subset, we first selected individuals for the high-arsenic group based on arsenic exposure criteria defined by drinking-water arsenic concentration combined with urinary or toenail arsenic levels as internal biomarkers of exposure, as detailed below. We then selected a set of low arsenic exposure subjects matched for age, sex, and smoking status. Within the subset selected for the microarray project, the drinking-water arsenic levels of the high-exposure group (n = 11) averaged 32 μg/L (range, 10.4–74.7 μg/L), whereas the levels for the low-exposure group (n = 10) averaged 0.7 μg/L (range, 0.007–5.3 μg/L). Individuals with inorganic urinary arsenic levels (overnight > 5 μg/L, spot > 1 μg/L) or > 0.11 μg/g toenail arsenic were considered to have high arsenic exposure.

Gene expression analysis

RNA was harvested from peripheral blood lymphocytes using Trizol reagent (Gibco/BRL Life Technologies, Gaithersburg, MD) followed by DNase digestion using DNAfree (Ambion Inc., Austin, TX) according to the manufacturer’s instructions and quantitated by spectrophotometric absorbance at 260 nm. RNA quality was evaluated using A260/A280 ratio (> 1.8) and the RNA 6000 Nano Chips in the Agilent 2100 Bioanalyzer (Agilent Technologies). The expression profiles were generated using the Affymetrix GeneChip Technology Human Genome U133 Plus 2.0 oligonucleotide arrays (Affymetrix, Santa Clara, CA), which simultaneously tested more than 47,000 transcripts for each subject on the integrated GeneChip Instrument System in the Dartmouth Microarray Core Facility. Our experiment was performed in compliance with the Minimum Information About a Micro-array Experiment (MIAME) checklist for standardization guidelines for microarray experiments. Array data from this experiment will be available on the National Institutes of Health GEO database or by contacting the author. Affymetrix chip CEL files were imported into the statistical programming language R and analyzed using the Bioconductor package “affy” (Gentlemen et al. 2005). Data were normalized using robust multichip analysis (RMA) implemented with Bioconductor software (Irizarry et al. 2003) followed by empirical Bayes adjustment procedures implemented on the R platform, as described previously (Johnson et al. 2007). Analysis using the Statistical Analysis of Microarrays (SAM) package (Stanford University, Stanford, CA; http://www-stat.stanford.edu/~tibs/SAM) was performed with a two-class comparison between the high- versus low-arsenic exposure groups, defined as described above by drinking-water arsenic concentration combined with urinary and toenail arsenic levels. We used 1,000 permutations and selected significant genes with a false discovery rate (FDR) of < 5% from a delta value of 0.7 to identify statistically significant differences in gene expression, accounting for multiple comparisons (Tusher et al. 2001). We constructed a heat map (Figure 1) for these 259 genes, and their successful agglomerative hierarchical clustering into high- and low-arsenic-exposed groups was assessed via bootstrap evaluation with confidence levels of > 98% [for a complete list, see Supplemental Material (http://www.ehponline.org/members/2008/10​861/suppl.pdf )] (Kamimura et al. 2003).

We performed an additional analysis restricted to a subset of 10 individuals with urinary arsenic exposure data as an internal measure of very recent arsenic exposure from the originally selected set of 21 subjects (Biggs et al. 1997). This additional analysis excluded subjects on whom we did not have urinary arsenic data, because toenail arsenic reflects chronic exposure (previous 12–18 months) (Slotnick and Nriagu 2006). Microarray data on subjects with urinary arsenic levels were normalized using RMA implemented with Bioconductor software on the R platform (Irizarry et al. 2003). Subsequent SAM analysis using a delta of 0.2 yielded 38 modified genes at or below 5% FDR for the high- versus low-arsenic-exposure groups [for a list, see Supplemental Material (http://www.ehponline.org/members/2008/10​861/suppl.pdf )].

We selected transcripts for validation by real-time polymerase chain reaction (PCR) using independent primer sets based on the microarray results. TaqMan primer-probe sets for each selected transcript were obtained from Applied Biosystems Inc. (Foster City, CA): perforin 1 (PRF1), interleukin 2 receptor, beta (IL2RB), killer cell immunoglobulin-like receptor, three domains, long cytoplasmic tail, 1 (KIR3DL1), and major histocompatibility complex (MHC), class II, DR beta 1 (HLA-DRB1). Real-time reverse transcription (RT)-PCR was performed using the Applied Biosystems Inc. PRISM sequence detection system and software. Briefly, total RNA (1.0 μg) was reverse transcribed using 100 U Moloney murine leukemia virus reverse transcriptase in a mixture with oligo-dT and dNTPs according to the instructions provided with the Omniscript kit (QIAGEN Inc., Valencia, CA). Samples were reverse transcribed in a PTC-100 thermocycler (MJ Research Inc., Watertown, MA) for 60 min at 44°C, and the reaction was terminated by heating to 95°C for 10 min. Expression of specific genes was assessed by real-time PCR using 10 ng total RNA, 400 nM primers, 200 nM probe, and TaqMan Universal PCR Master Mix (Applied Biosystems Inc.). Relative quantitation was performed using a standard curve consisting of serial dilutions of pooled sample cDNA from the same source as the test RNA with each plate. Relative expression levels of each gene were normalized to the cDNA concentrations and plotted against the creatinine-normalized urinary arsenic level.

Biological function analysis

We further characterized the functional effects of the arsenic-modified genes by implementing the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Gene Ontology (GO) search engine (http://www.geneontology.org/GO.tools.mic​roarray.shtml ; Dennis et al. 2003). This bioinformatic tool identifies functional processes that are overrepresented by the modified genes. Genes with statistically significant expression differences were also mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/pathway.html) biopathways, including the natural killer cell–mediated cytotoxicity pathway, to investigate their systemic roles.

Biologic roles of the arsenic-modified genes were then queried against the Pathway Studio ResNet 5.0 database (Ariadne Genomics, Rockville, MD). This database catalogs relationships between biologic entities based on the published literature and was used to identify the direct interactions between the arsenic-modified genes. These relations are depicted by colored lines and arrows in Figure 2.

We also implemented the Exploratory Visual Analysis (EVA) software package to facilitate graphical interpretation of our microarray data in the context of biological information, as described previously (Reif et al. 2005; Reif and Moore 2006). We prefiltered the normalized data from all 21 individuals using the ReliefF algorithm (Kononenko et al. 1997), and the top-ranked 12,484 genes were queried against KEGG biopathways. This strategy allowed us to explore broader trends in the data that may have been missed on the more restrictive SAM list.

Results Top

To evaluate the possibility of gene expression modification by arsenic, we selected subjects according to individual exposure status from those who provided a blood sample. The high-and low-arsenic-exposure groups are comparable for sex (high arsenic: 82% male, 18% female; low arsenic: 80% male, 20% female) and age (high arsenic: mean, 66 years; low arsenic: mean, 67 years). The high-arsenic-exposure group had a slightly higher proportion of smokers (high arsenic: no, 73%, yes, 27%; low arsenic: no, 90%, yes, 10%). This selected subset is similar to the population of total controls for age, sex, and smoking status (Andrew et al. 2006).

We identified 259 genes with statistically significant expression differences (high vs. low arsenic exposure status in 21 individuals) by the SAM test [Figure 1; Supplemental Material, Table 1 (http://www.ehponline.org/members/2008/10​861/suppl.pdf )]. The expression of the statistically significant genes reliably reproduced the dose–response clustering of subjects by arsenic exposure level (Figure 1, columns). With the exception of subject 9, all of the individuals exposed to high arsenic levels clustered separately (dark gray bars) from those exposed to low levels (light gray bars). Although there is some variability, as expected in a study of humans, the clustering of the rows (genes) on the heat map reveals distinct patterns of transcript abundance between lymphocyte samples that clearly separate the high- from the low-arsenic-exposed subjects (between subjects 13 and 17). We also performed a sensitivity analysis by running the algorithm with and without women or individuals who had a history of smoking. The genes with modified transcript abundance show similar patterns regardless of smoking status or sex.

Functional categories of these SAM-selected significant transcripts detected by DAVID functional annotation clustering indicated enrichment (p < 0.05) for genes involved in defense response (p < 0.001), immune response (p = 0.003), cell growth (p = 0.001), signal transduction (p = 0.01), apoptosis (p = 0.01), regulation of cell cycle (p = 0.02), JAK-STAT cascade (p = 0.004), T-cell receptor signaling pathway (p = 0.01), GTPase activity (p = 0.008), and type 1 diabetes mellitus (p = 0.002). The selected transcripts involved in diabetes included PRF1, HLA-DQA1, HLA-DQB1, SOCS3, HLA-DPA1, and GZM2. We also saw significant differences in transcripts involved in the nervous system (e.g., MATK, NCAM1, GPR56, MEF2C, ZFHX1B) and other aspects of development (e.g., AES, TAGLN, ARIH2, WNT10A, MRAS, ITGA6, ANXA2, VAMP5) [Supplemental Material, Table 1 (http://www.ehponline.org/members/2008/10​861/suppl.pdf ]. In the ubiquitin cycle, we saw differences between high- and low-arsenic groups for F-box protein transcripts FBXO32, FBX03, TRIAD3, and ariadne homolog 2 (ARIH2).

The pathways with the most statistically significant level of enrichment and largest number of modified transcripts associated with arsenic exposure were those involved in defense and immune response (listed in Table 1). These included induction of many probe sets that detected various isoforms of the killer cell immunoglobulin-like receptor that inhibits the activity of the MHC class I receptor. A number of the SAM-selected genes that showed increased or decreased transcript abundance in lymphocytes from the high- versus low-exposure groups are part of the natural killer cell cytotoxic KEGG pathway [Supplemental Material, Figure 1 (http://www.ehponline.org/members/2008/10​861/suppl.pdf )]. Notably, expression of the killer cell immunoglobulin-like inhibitory receptors (KIR) is increased. Arsenic exposure was also associated with modified levels of granzyme b (GZMB) and PRF1, enzymes that control natural killer cell–mediated apoptosis. Our data suggest decreases in defense response genes, including the heat-shock protein HSPA9B, CD69, and mucosa associated lymphoid tissue lymphoma translocation gene 1 (MALT1) associated with arsenic exposure. Inflammatory response pathway members selected in our analysis included IL2RB, carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 (CHST2), nuclear factor of activated T-cells (NFATC3), arachidonate 5-lipoxygenase (ALOX3), and pentraxin-related gene (PTX3).

We also mapped the SAM-selected differentially expressed genes onto a cell diagram to visualize the common regulators of the modified transcripts using Pathway Studio (Figure 2). Color intensity shows the level of abundance of each transcript (red, increased; green, decreased) in lymphocytes from the high-arsenic exposure compared with the low-arsenic-exposure group. The lines represent relationships among the top-ranked biologic molecules that have been established by previous work published in the literature. Importantly, this diagram helps predict differential expression of the genes that may have profound biologic consequences (i.e., by modulating protein stability or activation level) that are not observable in a gene expression microarray.

Using an alternative analysis strategy to more broadly characterize arsenic-associated expression modification, we used EVA software to graphically characterize functions of the top ranked 12,484 genes selected by ReliefF algorithm. Thus, we are including many genes with more subtle or less consistent regulation than in the SAM-selected set. This technique detected an overrepresentation of genes (Fisher exact p < 0.05) involved in the KEGG biopathway and Gene Ontology processes, including 7 in natural killer cell–mediated cytotoxicity and cellular defense response, 8 in antigen processing and presentation and immune response, 12 cellular adhesion molecules (CAMs) and cell adhesion, and 6 in the Wnt signaling pathway and regulation of transcription. Other high-ranking but nonsignificant categories included regulation of actin cytoskeleton, neuroactive lig-and–receptor interaction, Jak-STAT signaling, axon guidance, ABC transporters, peroxisome proliferator–activated receptor (PPAR) signaling pathway, and type 1 diabetes mellitus.

In addition, we performed an analysis restricted to the subset of 10 individuals with data on high versus low urinary arsenic levels. In this way, we avoid any potential misclassification of individuals that could be associated with relying on home drinking-water arsenic concentration or toenail levels, which are longer term biomarkers of exposure than urine. We compared expression levels for individuals with a urinary biomarker of very recent high arsenic exposure versus those with low urinary arsenic exposure using SAM. We identified a total of 38 genes that were significantly different in those with high versus low urinary arsenic levels [Supplemental Material, Table 2 (http://www.ehponline.org/members/2008/10​861/suppl.pdf )]. We used DAVID functional annotation clustering to assess the statistically significant overrepresentation of these urinary arsenic–modified genes by biological category: natural killer cell–mediated cytotoxicity (p < 0.001), immune response (p < 0.001), antigen processing and presentation (p < 0.001), and apoptosis (p = 0.009).

Real-time PCR validation using independent primer sets selected based on the micro-array results are shown in Figure 3. Relative expression levels of each gene in a lymphocyte sample are plotted against the creatinine-normalized urinary arsenic level for that subject. Despite substantial variability, there was a trend toward increased transcript abundance for PRF1, IL2RB, and KIR3DL1 with increasing urinary arsenic concentration. HLA-DRB1 expression significantly decreased with higher urinary arsenic levels (r ² = 0.8, p < 0.05).

Discussion Top

Chronic arsenic exposure at levels found in U.S. drinking water has been associated with cancer, cardiovascular disease, and diabetes (Engel et al. 1994; Karagas et al. 2001, 2004; Meliker et al. 2007; Steinmaus et al. 2003). Our microarray-based genomewide analyses detected patterns of decreased/increased transcript abundance in peripheral blood lymphocytes from the high- versus low-exposure groups representing North American drinking-water arsenic levels. The biologic functions of the transcripts with statistically significant differences included an overrepresentation of genes involved in defense response, immune function, and apoptosis. Although these observed changes in lymphocytes may be interpreted as surrogate markers for changes that occur in other cell types, they also directly reflect biologically important changes in the blood of arsenic-exposed individuals that may be directly involved in disease. For example, inhibition of immune function may play a role in promoting immune escape and tolerance of tumors (LeMaoult et al. 2005).

Despite differences in overall arsenic levels and country of origin among the previously reported studies of arsenic-exposed human lymphocytes, there are similarities in the differentially abundant transcripts between the high-and low-arsenic-exposed populations. Our data suggest decreases in defense response genes, including heat-shock proteins. Similarly, heat-shock protein HSPA1B/HSP70 expression was modified in lymphocytes from arsenic-exposed individuals from Bangladesh, as well as in several animal and cell culture studies, probably in response to the generation of reactive oxygen species (Andrew et al. 2003, 2007; Han et al. 2005; Liu et al. 2001). The Bangladesh and Taiwan studies and our U.S. study all observed a decrease in inflammatory response pathway members, including interleukins such as IL2R (Argos et al. 2006) and IL1 β (Wu M.M. et al. 2003). Argos et al. (2006) reported differential expression of USP13 and the ubiquitin-conjugating enzyme UBE2E1, and we also saw differences in several ubiquitin cycle transcripts.

Our human data also strongly support previous results from animal and in vitro studies indicating that chronic arsenic exposure modulates immune function (IARC 2004). Mice treated with arsenic had increased bacterial load accompanied by decreased adhesion, chemotactic migration, and phagocytic ability of splenic macrophages (Lewis et al. 1998). Exposure to 2 and 10 μg/L arsenic suppressed innate immune response in a zebrafish model, increasing pathogen load and reducing respiratory burst activity (Nayak et al. 2007). In vitro arsenic exposure (< 75 μg/L) inhibited maturation of monocytes to macrophages by producing an abnormal nonadhesive macrophage and reorganization of the actin cytoskeleton. These arsenic-exposed macrophages had higher granulocyte-macrophage colony-stimulating factor–stimulated MHC class II molecule HLA-DR protein levels, and decreased phagocytic ability (Lemarie et al. 2006; Sakurai et al. 2005, 2006).

We observed decreased abundance of CD69 in lymphocytes from the high-arsenic group, an effect that was observed previously at the protein level in CD4⁺ and CD8⁺ lymphocytes treated with arsenic in vitro (Conde et al. 2007; Tenorio and Saavedra 2005). Our highly exposed lymphocytes also had increased levels of IL2RB, which is necessary for activation of STAT5-dependent T-regulatory cell differentiation (Burchill et al. 2007). Similarly, Soto-Pena et al. (2006) reported reduced peripheral blood mononuclear cell mitogenic response and a modified CD4/CD8 lymphocyte cell ratio in children with high urinary arsenic levels. Moreover, mice exposed to 50 mg/L arsenite in their drinking water had inhibited T-lymphocyte proliferation in response to mitogens (Patterson et al. 2004). In vitro studies indicated that arsenic delayed proliferation of T-lymphocytes and modified DNA synthesis in a biphasic dose-dependent manner (Galicia et al. 2003; Meng and Meng 1994).

Natural killer cells act as a first line of defense in the blood and are also recruited to mucosal tissues with inflammation or infection, including the lung (Sentman et al. 2007). In our study, lymphocytes from the high-arsenic-exposure group had increased levels of a number of inhibitory killer cell immunoglobulin-like receptors, as well as the killer cell lectin-like receptors GZMB, PRF1, and NFATC3 [Table 1; Supplemental Material, Figure 1 (http://www.ehponline.org/members/2008/10​861/suppl.pdf )]. KIR genes are expressed by natural killer cells as well as memory α BT cell, serving both innate and adaptive immune response (Parham 2004). GZMB and PRF1 help the induction of apoptosis via the cytotoxic T-lymphocytes or natural killer cells in cell-mediated immune response (Adrain et al. 2006; Veugelers et al. 2006). GZMB specifically destabilizes the cell cytoskeleton during cytotoxic T-lymphocyte and natural killer cell–mediated cell killing (Adrain et al. 2006). NFATC3 is a transcription factor involved in T-cell development that is activated by hypoxia (Cante-Barrett et al. 2007).

In addition to the pervasive effects on immune response pathways, the lymphocytes from the high-arsenic-exposure group showed differences in transcripts involved in diabetes and nervous system development. Arsenic exposure has been associated with increased diabetes mellitus–related mortality in several populations, including the United States (Chen et al. 2007; Meliker et al. 2007). Similarly, our study shows differences in transcripts involved in diabetes by arsenic exposure status. The high-arsenic-exposure group also had modified levels of several transcripts involved in the nervous system and other aspects of development, supporting associations between arsenic exposure and fetal and early childhood effects (Hopenhayn et al. 2006; Rahman et al. 2007; Wasserman et al. 2004). One of our novel findings is that lymphocytes from arsenic-exposed individuals had higher levels of cytochrome P450 2E1 (CYP2E1), which metabolizes and bioactivates a number of chemicals by forming epoxides, including benzene, vinyl chloride, 1,1-dichloroethylene, trichloroethylene, 1,3-butadiene, acrylonitrile, and acrylamide, and metabolizes acetaminophen (Ghanayem 2007).

To elucidate common regulators of the differentially abundant transcripts in the high-versus low-arsenic groups, we mapped our results on a literature-based pathway diagram (Figure 2). Notably, TP53 regulates intercellular adhesion molecule 1 (ICAM-1), which controls immune cell migration and adhesion, and interferon-related developmental regulator 1 (IFRD1/PC4), which regulates p53 transcriptional activity and is required for myoblast differentiation, and also inhibits MyoD and MADS box transcription enhancer factor 2, polypeptide C (MEF2C) (Batta and Kundu 2007; Gorgoulis et al. 2005; Micheli et al. 2005; Nery et al. 2006). PPARA-α activates the nuclear hormone receptor nuclear receptor subfamily 1, group D, member 2 (NR1D2), blocking signaling from the orphan receptor-α (ROR-α) that regulates high-density lipoprotein cholesterol, lipid homeostasis, and inflammation (Ramakrishnan et al. 2005). E1A binding protein p300 is a common regulator of the transcription factor PPARA-α and the transcription factors MEF2C and signal transducer and activator of transcription 2 (STAT2) (Bhattacharya et al. 1996; Ma et al. 2005). The Rho-family small G-protein cell division cycle 42 homolog (Saccharomyces cerevisiae) (CDC42), controls progression through the cell cycle and also helps regulate degradation of the central carcinogenesis mediator, the epidermal growth factor receptor (EGFR) (Wu WJ et al. 2003). These human data suggest that future experimental studies of the regulation of these pathways by arsenic may provide insights into mechanisms of arsenic toxicity.

One possible limitation of this study is that the proportion of smokers varies slightly by arsenic exposure. This difference in smoking status is not driving the expression of genes selected for this analysis, because the gene expression changes are observed regardless of smoking status. Furthermore, there is abundant evidence that the most important health effects of arsenic may occur in combination with smoking (Chen et al. 2004; Rossman et al. 2002).

Our study is the first to investigate genomewide gene transcript abundance differences that occur in individuals exposed to drinking-water arsenic contamination in the United States. The modulated pathways, including defense response, immune function, cell growth, apoptosis, regulation of cell cycle, T-cell receptor signaling pathway, and diabetes, were consistent with previous reports from arsenic-exposed populations. Many of the genes are involved in carcinogenesis, diabetes, and immunosuppression, which are previously documented health effects of chronic arsenic exposure. Future studies are needed to elucidate the mechanisms through which chronic exposure to arsenic modulates expression of the identified genes, to clarify the direct roles of changes in the lymphocytes themselves in contrast with surrogate changes that also occur in other tissues, and to understand the health consequences of these changes. The arsenic-modulated pathway members identified in the pathogenesis of these diseases are potential targets for mechanistic studies and prophylactic or chemopreventive treatment and are candidates for biomonitoring of individuals with a history of arsenic exposure.

Figures and Table Top

Figure 1.

Heat map of genes with statistically significant expression differences by arsenic exposure level. The rows represent the genes selected by SAM analysis. The heat-map colors depict the gene expression level from low (black) to high (red). Each column represents an individual (numbered at the bottom) with either high (dark gray bar at the bottom) or low (light gray) arsenic exposure level. Genes and individuals were hierarchically clustered by expression level.

Figure 2.

Pathway Studio diagram of common regulators of differentially expressed genes. Genes with statistically significant expression differences were queried against the Pathway Studio ResNet 5.0 database to identify common regulators. Biological relationships are represented by red arrows for positive effects and green arrows for negative effects. Genes are represented by colored shapes: those with increased expression with high arsenic exposure are shown in red; those with decreased expression in response to high arsenic exposure are shown in green. Other genes that are directly involved in the pathway, but were not significantly modified at the gene expression level by arsenic exposure status, are shown in gray.

Figure 3.

RT-PCR analysis of gene expression in relation to urinary arsenic (As). The graphs depict the normalized RT-PCR gene expression versus creatinine-normalized urinary arsenic exposure levels. (A) PRF1 (r ² = 0.2). (B) IL2RB (r ² = 0.3). (C) HLA-DRB1 (r ² = 0.8). (D) KIR3DL1 (r ² = 0.1).

Table 1.

Immune response genes significantly associated with arsenic exposure level.

Supplemental Material Top

(432 KB) PDF.

References Top

1. Adrain C, Duriez PJ, Brumatti G, Delivani P, Martin SJ 2006. The cytotoxic lymphocyte protease, granzyme B, targets the cytoskeleton and perturbs microtubule polymerization dynamics J Biol Chem 281(12): 8118–8125. Find this article online
2. Andrew AS, Bernardo V, Warnke LA, Davey JC, Hampton T, Mason RA, et al. 2007. Exposure to arsenic at levels found in U.S. drinking water modifies expression in the mouse lung Toxicol Sci 100: 75–87. Find this article online
3. Andrew AS, Nelson HH, Kelsey KT, Moore JH, Meng AC, Casella DP, et al. 2006. Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility Carcinogenesis 27(5): 1030–1037. Find this article online
4. Andrew AS, Warren AJ, Barchowsky A, Temple KA, Klei L, Soucy NV, et al. 2003. Genomic and proteomic profiling of responses to toxic metals in human lung cells Environ Health Perspect 111: 825–835. Find this article online
5. Argos M, Kibriya MG, Parvez F, Jasmine F, Rakibuz-Zaman M, Ahsan H 2006. Gene expression profiles in peripheral lymphocytes by arsenic exposure and skin lesion status in a Bangladeshi population Cancer Epidemiol Biomarkers Prev 15(7): 1367–1375. Find this article online
6. ATSDR 2007. Toxicological Profile for Arsenic CAS#: 7440–38–2. Atlanta, GA: Agency for Toxic Substances and Disease Registry.
7. Batta K, Kundu TK 2007. Activation of p53 function by human transcriptional coactivator PC4: role of protein-protein interaction, DNA bending, and posttranslational modifications Mol Cell Biol 27(21): 7603–7614. Find this article online
8. Bhattacharya S, Eckner R, Grossman S, Oldread E, Arany Z, D’Andrea A, et al. 1996. Cooperation of Stat2 and p300/CBP in signalling induced by interferon-alpha Nature 383(6598): 344–347. Find this article online
9. Biggs ML, Kalman DA, Moore LE, Hopenhayn-Rich C, Smith MT, Smith AH 2007. Relationship of urinary arsenic to intake estimates and a biomarker of effect, bladder cell micro-nuclei Mutat Res 386(3): 185–195. Find this article online
10. Burchill MA, Yang J, Vogtenhuber C, Blazar BR, Farrar MA 2007. IL-2 receptor beta-dependent STAT5 activation is required for the development of Foxp3⁺ regulatory T cells J Immunol 178(1): 280–290. Find this article online
11. Cante-Barrett K, Winslow MM, Crabtree GR 2007. Selective role of NFATc3 in positive selection of thymocytes J Immunol 179(1): 103–110. Find this article online
12. Chen CJ, Wang SL, Chiou JM, Tseng CH, Chiou HY, Hsueh YM, et al. 2007. Arsenic and diabetes and hypertension in human populations: a review Toxicol Appl Pharmacol 222(3): 298–304. Find this article online
13. Chen CL, Hsu LI, Chiou HY, Hsueh YM, Chen SY, Wu MM, et al. 2004. Ingested arsenic, cigarette smoking, and lung cancer risk: a follow-up study in arseniasis-endemic areas in Taiwan JAMA 292(24): 2984–2990. Find this article online
14. Cheng T, Morris J, Koirtyohann S, Spate V, Baskett C 1995. Study of the correlation of trace elements in carpenter’s toenails J Radioanal Cucl Chem 195: 31–42. Find this article online
15. Conde P, Acosta-Saavedra LC, Goytia-Acevedo RC, Calderon-Aranda ES 2007. Sodium arsenite-induced inhibition of cell proliferation is related to inhibition of IL-2 mRNA expression in mouse activated T cells Arch Toxicol 81(4): 251–259. Find this article online
16. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. 2003. DAVID: Database for Annotation, Visualization, and Integrated Discovery Genome Biol 4(9): R60–R60.11. Find this article online
17. Engel RR, Hopenhayn-Rich C, Receveur O, Smith AH 1994. Vascular effects of chronic arsenic exposure: a review Epidemiol Rev 16(2): 184–209. Find this article online
18. Galicia G, Leyva R, Tenorio EP, Ostrosky-Wegman P, Saavedra R 2003. Sodium arsenite retards proliferation of PHA-activated T cells by delaying the production and secretion of IL-2 Int Immunopharmacol 3(5): 671–682. Find this article online
19. Gentlemen R, Carey V, Huber W, Irizarry R, Dudoit S, editors. 2005. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York: Springer. pp. 431–442.
20. Ghanayem BI 2007. Inhibition of urethane-induced carcinogenicity in cyp2e1^−/− in comparison to cyp2e1^+/+ mice Toxicol Sci 95(2): 331–339. Find this article online
21. Gorgoulis VG, Pratsinis H, Zacharatos P, Demoliou C, Sigala F, Asimacopoulos PJ, et al. 2005. p53-Dependent ICAM-1 overexpression in senescent human cells identified in atherosclerotic lesions Lab Invest 85(4): 502–511. Find this article online
22. Han SG, Castranova V, Vallyathan V 2005. Heat shock protein 70 as an indicator of early lung injury caused by exposure to arsenic Mol Cell Biochem 277(1–2): 153–164. Find this article online
23. Hopenhayn C, Bush HM, Bingcang A, Hertz-Picciotto I 2006. Association between arsenic exposure from drinking water and anemia during pregnancy J Occup Environ Med 48(6): 635–643. Find this article online
24. IARC (International Agency for Research on Cancer) 2004. Some drinking-water disinfectants and contaminants, including arsenic Monogr Eval Carcinog Risks Hum 84: 39–267. Find this article online
25. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. 2003. Exploration, normalization, and summaries of high density oligonucleotide array probe level data Biostatistics 4(2): 249–264. Find this article online
26. Johnson WE, Li C, Rabinovic A 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods Biostatistics 8(1): 118–127. Find this article online
27. Kamimura T, Shimodaira H, Imoto S, Kim S, Tashiro K, Kuhara S, et al. 2003. Multiscale bootstrap analysis of gene networks based on bayesian networks and nonparametric regression Genome Inform 14: 350–351. Find this article online
28. Karagas MR, Stukel TA, Morris JS, Tosteson TD, Weiss JE, Spencer SK, et al. 2001. Skin cancer risk in relation to toe-nail arsenic concentrations in a US population-based case-control study Am J Epidemiol 153(6): 559–565. Find this article online
29. Karagas MR, Stukel TA, Tosteson TD 2002. Assessment of cancer risk and environmental levels of arsenic in New Hampshire Int J Hyg Environ Health 205(1–2): 85–94. Find this article online
30. Karagas MR, Tosteson TD, Blum J, Morris JS, Baron JA, Klaue B 1998. Design of an epidemiologic study of drinking water arsenic exposure and skin and bladder cancer risk in a U.S. population Environ Health Perspect 106(suppl 4): 1047–1050. Find this article online
31. Karagas MR, Tosteson TD, Morris JS, Demidenko E, Mott LA, Heaney J, et al. 2004. Incidence of transitional cell carcinoma of the bladder and arsenic exposure in New Hampshire Cancer Causes Control 15(5): 465–472. Find this article online
32. Kononenko I, Simec E, Robnik-Sikonja M 1997. Overcoming the myopia of induction learning algorithms with RELIEFF Appl Intell 7(1): 39–55. Find this article online
33. LeMaoult J, Zafaranloo K, Le Danff C, Carosella ED 2005. HLA-G up-regulates ILT2, ILT3, ILT4, and KIR2DL4 in antigen presenting cells, NK cells, and T cells FASEB J 19(6): 662–664. Find this article online
34. Lemarie A, Morzadec C, Bourdonnay E, Fardel O, Vernhet L 2006. Human macrophages constitute targets for immunotoxic inorganic arsenic J Immunol 177(5): 3019–3027. Find this article online
35. Lewis TA, Hartmann CB, McCoy KL 1998. Gallium arsenide modulates proteolytic cathepsin activities and antigen processing by macrophages J Immunol 161(5): 2151–2157. Find this article online
36. Liu J, Kadiiska MB, Liu Y, Lu T, Qu W, Waalkes MP 2001. Stress-related gene expression in mice treated with inorganic arsenicals Toxicol Sci 61(2): 314–320. Find this article online
37. Lu T, Liu J, LeCluyse EL, Zhou YS, Cheng ML, Waalkes MP 2001. Application of cDNA microarray to the study of arsenic-induced liver diseases in the population of Guizhou, China Toxicol Sci 59(1): 185–192. Find this article online
38. Ma K, Chan JK, Zhu G, Wu Z 2005. Myocyte enhancer factor 2 acetylation by p300 enhances its DNA binding activity, transcriptional activity, and myogenic differentiation Mol Cell Biol 25(9): 3575–3582. Find this article online
39. Mead MN 2005. Arsenic: in search of an antidote to a global poison Environ Health Perspect 113: A378–A386. Find this article online
40. Meliker JR, Wahl RL, Cameron LL, Nriagu JO 2007. Arsenic in drinking water and cerebrovascular disease, diabetes mellitus, and kidney disease in Michigan: a standardized mortality ratio analysis Environ Health 6(4): 1–11. Find this article online
41. Meng Z, Meng N 1994. Effects of inorganic arsenicals on DNA synthesis in unsensitized human blood lymphocytes in vitro Biol Trace Elem Res 42(3): 201–208. Find this article online
42. Meza MM, Yu L, Rodriguez YY, Guild M, Thompson D, Gandolfi AJ, et al. 2005. Developmentally restricted genetic determinants of human arsenic metabolism: association between urinary methylated arsenic and CYT19 polymorphisms in children Environ Health Perspect 113: 775–781. Find this article online
43. Micheli L, Leonardi L, Conti F, Buanne P, Canu N, Caruso M, et al. 2005. PC4 coactivates MyoD by relieving the histone deacetylase 4-mediated inhibition of myocyte enhancer factor 2C Mol Cell Biol 25(6): 2242–2259. Find this article online
44. Nayak AS, Lage CR, Kim CH 2007. Effects of low concentrations of arsenic on the innate immune system of the zebrafish (Danio rerio) Toxicol Sci 98(1): 118–124. Find this article online
45. Nery FC, Rui E, Kuniyoshi TM, Kobarg J 2006. Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins Biochem Biophys Res Commun 341(3): 847–855. Find this article online
46. Parham P 2004. Killer cell immunoglobulin-like receptor diversity: balancing signals in the natural killer cell response Immunol Lett 92(1–2): 11–13. Find this article online
47. Patterson R, Vega L, Trouba K, Bortner C, Germolec D 2004. Arsenic-induced alterations in the contact hypersensitivity response in Balb/c mice Toxicol Appl Pharmacol 198(3): 434–443. Find this article online
48. Rahman A, Vahter M, Ekstrom EC, Rahman M, Golam Mustafa AH, Wahed MA, et al. 2007. Association of arsenic exposure during pregnancy with fetal loss and infant death: a cohort study in Bangladesh Am J Epidemiol 165(12): 1389–1396. Find this article online
49. Ramakrishnan SN, Lau P, Burke LJ, Muscat GE 2005. Reverb-beta regulates the expression of genes involved in lipid absorption in skeletal muscle cells: evidence for cross-talk between orphan nuclear receptors and myokines J Biol Chem 280(10): 8651–8659. Find this article online
50. Reif DM, Dudek SM, Shaffer CM, Wang J, Moore JH 2005. Exploratory visual analysis of pharmacogenomic results Pac Symp Biocomput 10: 296–307. Find this article online
51. Reif DM, Moore JH 2006. Visual analysis of statistical results from microarray studies of human breast cancer Oncol Rep 15(spec no): 1043–1047. Find this article online
52. Rossman TG, Uddin AN, Burns FJ, Bosland MC 2002. Arsenite cocarcinogenesis: an animal model derived from genetic toxicology studies Environ Health Perspect 110(suppl 5): 749–752. Find this article online
53. Sakurai T, Ohta T, Fujiwara K 2005. Inorganic arsenite alters macrophage generation from human peripheral blood monocytes Toxicol Appl Pharmacol 203(2): 145–153. Find this article online
54. Sakurai T, Ohta T, Tomita N, Kojima C, Hariya Y, Mizukami A, et al. 2006. Evaluation of immunotoxic and immunodisruptive effects of inorganic arsenite on human monocytes/macrophages Int Immunopharmacol 6(2): 304–315. Find this article online
55. Sentman C, Mselle T, Basu S 2007. Human mucosal NK cells In: Brossay L, editor. Everything You Always Wanted to Know about NK cells but Were Afraid to Ask. Kerala, India: Transworld Research Network. pp. 99–115.
56. Slotnick MJ, Nriagu JO 2006. Validity of human nails as a biomarker of arsenic and selenium exposure: a review Environ Res 102(1): 125–139. Find this article online
57. Soto-Pena GA, Luna AL, Acosta-Saavedra L, Conde-Moo P, Lopez-Carrillo L, Cebrian ME, et al. 2006. Assessment of lymphocyte subpopulations and cytokine secretion in children exposed to arsenic FASEB J 20(6): 779–781. Find this article online
58. Steinmaus C, Yuan Y, Bates MN, Smith AH 2003. Case-control study of bladder cancer and drinking water arsenic in the western United States Am J Epidemiol 158(12): 1193–1201. Find this article online
59. Tenorio EP, Saavedra R 2005. Differential effect of sodium arsenite during the activation of human CD4(+) and CD8(+) T lymphocytes Int Immunopharmacol 5(13–14): 1853–1869. Find this article online
60. Tusher VG, Tibshirani R, Chu G 2001. Significance analysis of microarrays applied to the ionizing radiation response Proc Natl Acad Sci USA 98(9): 5116–5121. Find this article online
61. Veugelers K, Motyka B, Goping IS, Shostak I, Sawchuk T, Bleackley RC 2006. Granule-mediated killing by granzyme B and perforin requires a mannose 6-phosphate receptor and is augmented by cell surface heparan sulfate Mol Biol Cell 17(2): 623–633. Find this article online
62. Wasserman GA, Liu X, Parvez F, Ahsan H, Factor-Litvak P, van Geen A, et al. 2004. Water arsenic exposure and children’s intellectual function in Araihazar, Bangladesh Environ Health Perspect 112: 1329–1333. Find this article online
63. Wei Q, Matanoski GM, Farmer ER, Hedayati MA, Grossman L 1994. DNA repair and susceptibility to basal cell carcinoma: a case-control study Am J Epidemiol 140(7): 598–607. Find this article online
64. Wu MM, Chiou HY, Ho IC, Chen CJ, Lee TC 2003. Gene expression of inflammatory molecules in circulating lymphocytes from arsenic-exposed human subjects Environ Health Perspect 111: 1429–1438. Find this article online
65. Wu WJ, Tu S, Cerione RA 2003. Activated Cdc42 sequesters c-Cbl and prevents EGF receptor degradation Cell 114(6): 715–725. Find this article online