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Human exposure to arsenic and ionizing radiation occur environmentally at low levels. While the human health effects of arsenic and ionizing radiation have been examined separately, there is little information regarding their combined effects at doses approaching environmental levels. Arsenic toxicity may be affected by concurrent ionizing radiation especially given their known individual carcinogenic actions at higher doses. We found that keratinocytes responded to either low dose arsenic and/or low dose ionizing radiation exposure, resulting in differential proteomic expression based on 2DGE, immunoblotting and statistical analysis. Seven proteins were identified that passed a rigorous statistical screen for differential expression in 2DGE and also passed a strict statistical screen for follow-up immunoblotting. These included: α-enolase, epidermal-fatty acid binding protein, heat shock protein 27, histidine triad nucleotide-binding protein 1, lactate dehydrogenase A, protein disulfide isomerase precursor and S100A9. Four proteins had combined effects that were different than would be expected based on the response to either individual toxicant. These data demonstrate a possible reaction to the combined insult that is substantially different from that of either separate treatment. Several proteins had different responses than what has been seen from high dose exposures, adding to the growing literature suggesting that the cellular responses to low dose exposures are distinct.
Low level arsenic and low dose ionizing radiation are both environmental toxicants. While data exist which examine the human health effects of either toxicant separately, there are no data in the literature regarding possible combined effects at low doses. Yet, combined effects of toxicants to produce disease are a well-known phenomenon (e.g. radon and/or arsenic with tobacco smoke induce lung cancer) [1, 2]. In the context of human health, the potential effects of receiving combined or sequential exposure to these particular toxicants in a medical setting requires further biologic characterization. The FDA has approved the use of arsenic to treat acute promyelocytic leukemia, while the growing use of intensity modulated radiation therapy to treat a wide variety of cancer has resulted in increasing amounts of healthy tissue being exposed to low dose ionizing radiation (LDIR) [3, 4]. Low dose arsenic toxicity may be affected by concurrent ionizing radiation especially in light of their known carcinogenic actions individually at higher doses [3, 5].
Arsenic is a naturally occurring metalloid that can be solubilized in water, posing the threat of contaminated drinking water . It has been well documented that exposure to arsenic can contribute to skin, bladder, liver and lung cancers . While the mechanisms of arsenic toxicity are not fully understood, several ideas include the induction of oxidative stress, decreased functioning of DNA repair systems, chromosomal abnormality and altered growth factors [6-10]. There is evidence that exposure to arsenic generates an oxidative stress response, increasing reactive oxygen species (ROS) which can mutate and/or damage DNA [9, 11]. A change in expression of genes involved in the synthesis of DNA repair enzymes has been proposed as an explanation to arsenic's co-mutagenic effects [12, 13].
Ionizing radiation (IR) exposure is unavoidable in the environment and further exposure is obtained through medical imaging. There is substantial debate regarding the biological effects of LDIR in humans and no direct information on how such exposure may alter the response of cells to other environmental toxicants . As with arsenic, IR exerts the majority of its toxicity through the intracellular generation of ROS . It is plausible that oxidative stress induced from IR could substantially enhance the effects of otherwise minimally toxic, sub-lethal exposures of arsenic.
Much remains uncertain about the effects of these toxicants at low doses. Historically, radiation and arsenic studies have involved high dose exposures with the assumption that the toxicant profile could be extrapolated down in a linear manner for low dose exposures. This is the underlying assumption in the linear no-threshold model of radiation effects, which is currently undergoing challenge . Likewise, much remains unknown about the dose response curve for low level arsenic exposure. Again, it was assumed that the response for high doses of arsenic could also be applied to low dose exposures . However, current studies have yet to demonstrate a direct relationship between low dose arsenic exposure and cancer, suggesting a nonlinear relationship and supporting the idea that the dose response curve at low levels cannot be inferred from high dose studies [17, 18]. Thus, as there is no accepted, comprehensive model describing the mechanism by which low dose toxin exposures exert their effects, there is no predictive modeling that can address the potential interacting effects of co-exposures on cells. Therefore, direct empiric study remains the cornerstone of understanding potential interactions and how the co-exposures may alter the safety profile of each.
It has been shown that transcriptional changes within the cell do not correlate completely with translational data [19, 20]. Few studies have focused on the proteomic differences induced by these toxicants [21, 22]. As proteins are the molecules through which a cell enacts change, the differences found in the proteome may better reflect the actual cellular response. Using a proteomics approach in a human keratinocyte model to mimic human skin exposure, we examined the interaction of IR and arsenic.
A spontaneously immortalized human keratinocyte cell line was grown in T-75 cm2 tissue culture flasks with a lethally irradiated feeder layer of 3T3 cells obtained from ATCC #48-X (ATCC, VA). Cells were supported with a 3:1 mixture of Dulbecco-Vogt Eagle and Ham's F-12 medium with the addition of serum and other factors as previously described .
Once cells reached near confluency, half of the flasks were treated with medium containing 2 μM sodium arsenite. At 24 hours, flasks were irradiated with a Varian 2100C linear accelerator on a 30 × 30 cm2 solid water block covered with a tissue equivalent bolus to ensure accuracy of dose delivery. Dose rate was set at 80 cGy/min, and SSD was 101.3 cm (1 cGy) or 100 cm (10 cGy). Radiation free controls were maintained with and without sodium arsenite. One or four days post irradiation, flasks were rinsed with 0.02% EDTA in phosphate buffered saline to remove residual 3T3 cells and held at −80° C until protein extraction. All samples were prepared in triplicate.
One ml of Mammalian Protein Extraction Reagent (Pierce Biotechnology, IL) containing protease inhibitor (Sigma-Aldrich, MO) was added to each flask, cells were scraped and lysate transferred to a 2 ml tube on ice. Each sample was sonicated for 1 minute and centrifuged at 10,000 × g for 20 minutes at 4°C. Supernatant was removed and placed in a clean 2 ml tube. Protein quantitation was performed using Coomassie Plus Protein Assay (Pierce Biotechnology, IL).
Isoelectric focusing was performed using a Protean IEF Cell (Bio-Rad, CA). 30 μg of protein was combined with lysis solution (0.5% Triton X-100, 4% CHAPS, 7 M urea, 2 M thiourea, nanopure water), 1% Biolyte 3-10 buffer, 2% protease inhibitor cocktail (Calbiochem, CA), 0.065% ditheothreitol and a trace amount of bromophenol blue dye for a total volume of 200 μl. The protein samples were left at room temperature for one hour before loading. ReadyStrip IPG strips (pH 3-10, 11cm) were used for separation (Bio-Rad, CA).
Isoelectric focusing was conducted at 20°C using the following program: 50 V for 12- hours; 50-250 V linear ramp; 250-8000 V linear ramp and hold for a total of 42 kVh. After focusing, the strips were incubated in an equilibration buffer (5 ml consisting of 50 mM Tris, pH 8.8, 6 M urea, 30% glycerol, 2% SDS, trace bromophenol blue and 0.065% ditheothreitol (DTT)) for 15 minutes on a rocking platform. The strips were subsequently incubated with the same equilibration buffer substituting 10 mM iodoacetamide for DTT to alkylate cysteine sulfhydryls. Each strip was then placed on top of a 12% SDS Duracryl gel and sealed using 0.5% agarose (Genomic Solutions, MI).
The second dimension separation was performed in a Hoefer SE 600/SE 660 2D-PAGE system. Gels were run in buffer (25 mM Tris, 192 mM glycine, 0.1% SDS) at 15 mA per gel for 30 minutes followed by 25 mA per gel until the dye migrated to the bottom of the gel. Broad Range Precision Plus Protein Standard molecular weight protein plugs were used for mass calibration of the gels (10-250 kDa) (Bio-Rad, CA). Gels were fixed in 10% acetic acid, 40% methanol, and 50% water, silver stained and scanned with an Epson Perfection 4870 photo scanner.
The 36 gel images were processed with the analysis software Progenesis (PG240 v2006) and TT900 S2S (Nonlinear Dynamics, UK). Gel images were first warped with TT900 S2S. Warped images were then imported to Progenesis for further analysis including: spot detection, spot matching, background subtraction, spot filtering and ‘Samespot Outline’. The Samespot Outline in Progenesis copies spot outlines from gels where a spot exists to those gels missing the spot, then calculates the spot volume within the new outlines. Therefore all missing values are filled with calculated volumes.
The Nevada Proteomics Center analyzed selected proteins using MALDI TOF/TOF analysis. Spots were destained and digested using a previously described protocol with some modifications . Samples were washed twice with 25 mM ammonium bicarbonate (ABC) and 100% acetonitrile, reduced and alkylated using 10 mM DTT and 100 mM iodoacetamide and incubated with 75 ng sequencing grade modified porcine trypsin (Promega, WI) in 25 mM ABC for 6 hours at 37°C. Samples were spotted onto a MALDI target with ZipTip μ-C18 (Millipore Corp., MA). Samples were eluted with 70% acetonitrile, 0.2% formic acid and overlaid with 0.5 μl 5 mg/ml MALDI matrix (α-Cyano-4 hydroxycinnamic acid, 10 mM ammonium phosphate). All mass spectrometric data were collected using an ABI 4700 Proteomics Analyzer MALDI TOF/TOF mass spectrometer (Applied Biosystems, CA), using their 4000 Series Explorer software v. 3.6. The peptide masses were acquired in reflectron positive mode (1-keV accelerating voltage) from a mass range of 700 – 4000 Daltons and either 1250 or 2500 laser shots were averaged for each mass spectrum. Each sample was internally calibrated on trypsin's autolysis peaks 842.51 and 2211.10 to within 20 ppm. Any sample failing to internally calibrate was analyzed under default plate calibration conditions of 150 ppm. Raw spectrum filtering/peak detection settings were S/N threshold of 3, and cluster area S/N optimization enabled at S/N threshold 10, baseline subtraction enabled at peak width 50. The eight most intense ions from the MS analysis, which were not on the exclusion list, were subjected to tandem mass spectrometry. The MS/MS exclusion list included known trypsin masses along with unidentified background peaks: 842.51, 856.52, 870.54, 1011.65, 1045.56, 1126.56, 1338.83, 1666.01, 1794.9, 1940.94, 2211.10, 2225.12, 2283.18 and 3094.62. For MS/MS analysis the mass range was 70 to precursor ion with a precursor window resolution of 50 FWHM (full-width at half maximum) with an average 2500 laser shots for each spectrum, CID on, metastable suppressor on. Raw spectrum filtering/peak detection settings were S/N threshold of 5, and cluster area S/N optimization enabled at S/N threshold 6, baseline subtraction enabled at peak width 50. The data was then stored in an Oracle database (Oracle database schema v. 3.19.0, Data version 3.90.0).
The data was extracted from the Oracle database and a peak list was created by GPS Explorer software v 3.6 (Applied Biosystems). Analyses were performed as combination mass spectrometry and tandem mass spectrometry. MS peak filtering included mass range 700-4000 Da, minimum S/N filter 10, mass exclusion tolerance of 0.2 Da. Exclusion list of known trypsin fragments and unidentified background peaks: 2211.2, 2283.2, 1045.6, 842.5, 1794.9, 1011.65, 1338.83 and 1666.01. A peak density filter of 50 peaks per 200 Da with a maximum number of peaks set to 65. MS/MS peak filtering included mass range of 60 Da to 20 Da below each precursor mass. Minimum S/N filter 5, peak density filter of 50 peaks per 200 Da, cluster area filter used with maximum number of peaks 65. The filtered data were searched by Mascot v. 1.9.05 (Matrix Science) using CDS combined database (Celera Discovery System v. KBMS3.2.20040119), containing 1,416,555 sequences. Searches were performed without restriction to protein species, Mr, or pI and with variable oxidation of methionine residues and carbamidomethylation of cysteines (no fixed modifications). Maximum missed cleavage was set to 1 and limited to trypsin cleavage sites. Precursor mass tolerance and fragment mass tolerance were set to 20 ppm and ± 0.2 Da, respectively. Protein hits with high confidence identifications and statistically significant search scores, greater than 95% confidence interval (C.I.%) or p≤0.05, were accepted. The majority of the ions present in the mass spectra were accounted for and high confidence identifications were consistent with the protein experimental Mr, and pI.
10 μg of each sample were separated on 12%, Ready Gel Tris-HCl precast gels (BioRad, CA), transferred to PVDF and blocked overnight at 4°C. The primary antibodies included: pyruvate kinase (ab6191, Abcam, MA), α-enolase (sc-15343, Santa Cruz Biotechnology, CA), S100A9 (sc-8114), PDI (sc-30932), profilin-1 (sc-18346), annexin XI (sc-9322), E-FABP (sc- 16060), LDH-A (sc-27230), cytokeratin 1 (sc-17091), CaM (sc-1989), HSP27 (sc-1048), HINT-1 (10717-1-AP, Proteintech Group, IL) and cyclophilin A (10720-1-AP, Proteintech Group , IL). All primary antibodies were used at 0.2 μg/ml final concentration, typically a 1:1000 dilution. The secondary antibody (donkey anti-goat-HRP, sc-2020 or donkey anti-rabbit-HRP, sc-2004) was used at a 1:40,000 dilution. Membranes were developed using ECL Advanced (GE Healthcare, NJ) and images were captured on a ChemiDoc system with Quantity One software (BioRad, CA). A monoclonal antibody to β-actin was used as a loading control and all density readings were normalized (sc-47778). All western blots were performed in triplicate. The western blot data were analyzed using ANOVA in the R statistical software package after normalization by β-actin.
For each spot aligned across gels, the image analysis produces a spot volume, either directly or from the Same Spot method of imputation. We took the natural logarithms of the spot volumes and then used additive mean normalization. We then used two-way ANOVA with interaction on each spot to identify differentially expressed proteins with effects for the level of arsenic, the level of IR, and the interaction effect. Two methods of estimating the protein-specific variance in ANOVA were utilized, the usual mean square for error and an estimate adjusted by an empirical Bayes method originally developed for microarrays [25, 26] (manuscript submitted, Dan Li, et al). The resulting p-values were adjusted for multiple comparisons using the Benjamini-Hochberg False Discovery Rate (FDR) method .
Proteins were isolated from keratinocytes exposed to 0 or 2 μM sodium arsenite and 0, 1 or 10 cGy of irradiation for one or four days. These proteins were separated using two-dimensional gel electrophoresis. All conditions were run in triplicate and the resulting 36 gels were imaged and analyzed using Progenesis (PG240 v2006) and TT900 S2S (Nonlinear Dynamics, UK). The initial analysis included keratinocytes exposed to a 1 cGy dose. Little differential expression was detected with this low dose and these samples were omitted from further analysis, resulting in 24 remaining gels.
ANOVA was performed on each of the 2,002 spots detected separately at each time point. Thus, the ANOVA for each spot had 12 observations at each of two IR doses and each of two arsenic doses replicated in triplicate. Proteins that displayed significant differential expression (p≤0.05) after correction for multiple comparisons using either ANOVA method (see methods and materials) were selected as candidates for sequencing, resulting in 444 spots identified for further characterization.
Protein spots were chosen for sequencing only if they were distinct, outside of areas with background smearing and could be removed from the gel without excising other nearby proteins. Most of the statistically significant spots did not meet these criterions, leaving 24 of the 444 available for identification.
A total of 24 samples were sent to the Nevada Proteomics Center for analysis, nine spots from day one and fifteen from day four. Thirteen proteins with protein score confidence indices above 95% were identified by mass spectrometry (MS) and tandem mass spectrometry (MS/MS) (table 1). The remaining proteins could not be identified with confidence. The proteins identified from the day one time point included: calmodulin (CaM), heat shock protein 27 (HSP27), lactate dehydrogenase A (LDH-A) and protein disulfide isomerase precursor (PDI, synonym: thyroid hormone binding protein precursor) (table 1). The proteins found four days post exposure included: annexin XI, S100A9 (synonym: calgranulin B), cyclophilin A (synonym: peptidyl-prolyl cis-trans isomerase), α-enolase, epidermal fatty acid binding protein (E-FABP), histidine triad nucleotide-binding protein 1 (HINT-1), profilin-1, pyruvate kinase M isozyme, and R3372_1 (protein fragment) (table 1).
Twelve identified proteins with available commercial antibodies were selected for immunoblotting to confirm significant differences between the control sample, IR only, arsenic only and IR with arsenic (figure 1). This was done as an important cross-check on the ANOVA analysis on the 2D gels. Given the complexity of the required statistical analysis for the 2D gels, it is always possible that some spots identified as differentially expressed were in fact artifactual. CaM was not recognized by the antisera in detectable amounts and no protein band was verified at the appropriate molecular weight. This does not exclude the possibility that CaM may have been differentially expressed since western blots require a relatively high concentration for detection.
For the eleven proteins that had usable immunoblot data, the intensities were normalized to β-actin and these data were analyzed using ANOVA. The ANOVA analysis allows for direct comparison of a protein immunoblotted on independent gels by adjusting for density and background signal resulting in a more sensitive test. Seven of the eleven proteins (E-FABP, α-enolase, HINT-1, HSP27, LDH-A, PDI, and S100A9) showed significant (p≤0.05) differences for either IR, arsenic, or the interaction, which far exceeds the chance rate as roughly one or two false significance values could be expected out of the 11 proteins by chance alone (table 2). Four proteins (annexin XI, cyclophilin A, pyruvate kinase and profilin-1) showed no significant difference at the p≤0.05 level.
E-FABP, PDI and S100A9 decreased with exposure to arsenic. HSP27 was down-regulated and HINT-1 was up-regulated in response to individual treatments, neither protein showed an interaction effect that was significantly different than either single treatment. Four proteins showed a response to the combined insult that was different than would have been expected from either treatment alone. E-FABP and LDH-A showed an increased response while α-enolase and PDI had a response that was less than would have been expected based on the individual exposures.
When below a certain threshold, the individual toxicity of IR and arsenic are believed to be minimal. However, the effects of combining these two known carcinogens at low doses are unknown. As a model for low dose toxicant interaction, they are of significant interest as irradiation is ubiquitous and arsenic, although heavily regulated in the U.S., is still a major environmental burden in many countries. There is little in the scientific literature examining human health risks associated with possible toxicant interactions despite the known roles of such agents in malignancy [2, 6, 8, 28].
While the arsenic concentration used in this study is within environmental background levels (2 μM), the IR dose (10 cGy) is higher than the typical US average background exposure of 0.3 cGy per year. However, the natural background varies with location and Kerala Coast, India has a natural background of 1 cGy per year, while Ramsar, Iran receives 20 cGy per year. A single CT scan for medical purposes is 1 cGy, and the dose limits set by the DOE NCR for occupational exposure is 5 cGy (Office of Biological and Environmental Research, Office of Science, U.S. Department of Energy, Orders of Magnitude guide, March 2006). Higher exposures than these can occur occupationally, under medical treatment, or as a result of a nuclear accident.
The skin is the major barrier to many environmental toxicants, with keratinocytes being the most prevalent cell type. IR generally penetrates the skin, while arsenic exposure can lead to many diseases of the skin. Chronic arsenic exposure often leads to hyperpigmentation, hyperkeratosis and arsenic induced Bowen's disease . In time, Bowen's disease can progress into invasive skin cancer in the form of basal or squamous cell carcinoma . Keratinocytes, which have been used in previous sub-lethal arsenic and LDIR studies, are readily cultivated and respond to toxicant challenges with changes in transcription of up to 10% making them an ideal model for this study [23, 29, 30].
Of the twelve proteins selected for immunoblotting, seven showed significant differences in protein expression and four yielded inconsistent results (annexin XI, cyclophilin A, pyruvate kinase and profilin-1). It is possible that the expression changes detected by the ANOVA of the gel images were too small to be detected by western blotting given its semi-quantitative nature. All proteins with variable results had faint spots on the 2D gels indicating a very low protein concentration as silver staining can detect nanogram quantities of protein, a sensitivity beyond that of western blotting. Proteins that were immunoblotted included three proteins from the early time point: PDI, LDH-A and HSP27. None of these proteins were found at the later time point indicating a transient response. At four days post exposure, E-FABP, α-enolase, S100A9, and HINT-1 had altered expression. Doses examined in this study are much lower than those that are clearly cytotoxic and this proteomic response implies that the cells are actively responding to low-level exposures.
Both IR and arsenic dose responses are currently based on the idea of a linear no-threshold model [18, 31]. Many studies are revealing that low dose exposures are fundamentally different from high dose exposures [17, 31-34]. For example, it has been shown that α-enolase was up-regulated in-vivo with a high dose exposure of 9 Gy, whereas our low dose study showed the opposite expression . Prasad et al showed that a high dose of 6 Gy of irradiation led to increases in the protein levels of PDI, calreticulin and calnexin in apoptotic cells 48 hours post exposure , whereas low dose exposure of 2 μM arsenic alone or with 10 cGy IR diminished cellular amounts of both PDI and calreticulin [23, 36]. These results suggest that the response to low doses of irradiation and arsenic may be substantially different than those seen following higher dose exposures. A response pattern of that type would be consistent with emerging data from other studies dealing with LDIR and low dose arsenic [23, 31-33].
Several of the identified proteins were found to have a response to the combined exposure that was different from the response to either toxicant individually, including E-FABP, α-enolase, LDH-A and PDI. These data demonstrate a possible response to the combined insult that is significantly different than either treatment alone, making them candidates for further study as potential biomarkers. Many of the proteins found in this study are currently recognized as biomarkers of disease processes including α-enolase which is found to be increased in many tumors; S100A9 has been found as a serum component after irradiation; and LDH, where a change in the usual LDH isozyme spectrum is indicative of ischemia, radiation treatment or cancer [37-42]. The identification of biomarkers in conjunction with changing isozyme spectra that are unique to a combination of environmental toxicants may lead to novel detection panels for suspected environmental toxicant exposures. With these findings, we have begun the process of identifying potential biomarkers of these types of combined exposures.
We would like to acknowledge that the first and second authors have made equal contributions to this work. We would like to thank Rebekah Woolsey at the Nevada Proteomics Center for her technical expertise and Karen Kalanetra for her help with manuscript preparation. This work was supported by grants from a Campus Laboratory Collaboration Grant 2003-2004 from UCOP (ZG), the Air Force Office of Scientific Research FA9550-06-1-0132 and FA9550-07-1-0146 (ZG, SB, and DMR), National Cancer Institute P30 CA093373-04 (DMR), NHGRI R01-HG003352 (DMR), NIEHS P42-ES04699 (DMR), UC Davis Health Systems (DMR) and the U.S. Department of Energy Office of Biological and Environmental Research, DE-FG02-01ER63237 and DE-FG02-07ER64341 (DMR, ZG), Nevada Proteomics Center NIH Grant Number P20 RR-016464 from the INBRE Program of the National Center for Research Resources.
Conflict of Interest Statement: All authors declare that there are no conflicts of interest