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TOXICOLOGY
Cell and Molecular Biology Branch (J.F.D., A.I.H., C.S.P., L.D.O., A.J.S., J.J.S.), Analytical Toxicology Division (C.B.), and Physiology and Immunology Branch (C.H.-H.), United States Army Medical Research Institute of Chemical Defense, Aberdeen Proving Ground, Maryland; and Medical Research and Evaluation Facility, Battelle Memorial Institute, Columbus, Ohio (R.C.K., Y.W.C., C.L.S.)
Received October 25, 2005; accepted December 22, 2005.
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Abstract |
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). SM exposure can produce debilitating pulmonary, ocular, and cutaneous injuries. After cutaneous exposure to SM, there is a dose-dependent latent phase of 8 to 24 h that precedes clinical expression of tissue damage. Erythema occurs initially and is followed by vesication because of separation at the epidermal-dermal junction. This results in large fluid-filled lesions that are long-lasting and slow to heal (Papirmeister et al., 1991; Petrali and Oglesby-Megee, 1997). The formation of blisters is accompanied by a potent inflammatory response, observed as increased production of inflammatory mediators and infiltration of the exposure area by activated immune cells (Rikimaru et al., 1991; Tsuruta et al., 1996; Ricketts et al., 2000; Sabourin et al., 2000, 2002).
The mouse ear vesicant model was developed to rapidly screen for potential SM treatment compounds (Casillas et al., 1997). In this animal model, ear weight was determined as a measure of edema and inflammation and was used as a rapid, cost-effective endpoint to determine compound efficacy. Three potential antivesicant treatment compounds were identified using this screening paradigm: 2,3-dimercapto-1-propanol (dimercaprol, British anti-Lewisite), 2-(4-hydroxy-3-methoxy-phenyl)-N-octyl-ethanamide (octyl homovanillamide; OHV), and 2-[1-(4-chlorobenzoyl)-5-methoxy-2-methyl-indole-3-yl]acetic acid (indomethacin) (Casillas et al., 2000). Dimercaprol has been used as a treatment for exposure to the chemical warfare agent Lewisite (another vesicant) and also for heavy metal poisoning. Octyl homovanillamide is a vanilloid receptor agonist, and indomethacin is a classic nonsteroidal anti-inflammatory compound.
The mechanism of toxicity of SM is not well characterized, and previous studies have used DNA microarray technology to gain greater insight into the molecular pathways perturbed by SM exposure (Rogers et al., 2004; Sabourin et al., 2004a). Based on these studies, we hypothesized that gene expression profiling of mouse ear skin exposed to SM alone or pretreated with one of these treatment compounds would provide important insight into the mechanism of cutaneous toxicity of SM and might identify genes and biological pathways involved in the response to SM exposure.
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Materials and Methods |
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Microarray Procedures. All microarray experiments were performed using Affymetrix Mouse 430A oligonucleotide arrays, as described at http://www.affymetrix.com/support/technical/datasheets/mouse430_datasheet.pdf (Affymetrix, Santa Clara, CA). RNA was isolated from frozen mouse ear biopsies using the RNeasy mini kit (QIAGEN, Valencia, CA) according to the manufacturer's instructions. The quality and amount of RNA was monitored throughout processing with an Agilent bioanalyzer (Agilent Technologies, Palo Alto, CA) and a NanoDrop ND-1000 UV-Vis spectrophotometer (Nanodrop Technologies, Rockland, DE). Purified RNA was used to prepare biotinylated target RNA, with minor modifications from the manufacturer's recommendations (http://www.affymetrix.com/support/technical/manual/expression_manual.affx). In brief, 10 µg of total RNA was used to generate first-strand cDNA by using a T7-linked oligo(dT) primer. After second-strand synthesis, in vitro transcription was performed with biotinylated nucleotides (Enzo kits; Affymetrix), resulting in approximately 100-fold amplification of cRNA. The target cRNA generated from each sample was processed as per manufacturer's recommendation using an Affymetrix GeneChip instrument system (http://www.affymetrix.com/support/technical/manual/expression_manual.affx). In brief, spiked controls were added to 15 µg of fragmented cRNA before overnight hybridization using 10 µg of cRNA. Arrays were then washed and stained with streptavidin-phycoerythrin before being scanned on an Agilent GeneArray scanner. After scanning, array images were assessed by eye to confirm scanner alignment and the absence of significant bubbles or scratches on the chip surface. The 3'/5' ratios for GAPDH were between 0.8 and 1.72, and the ratios for 
-actin were between 1.07 and 2.71. BioB spike controls were found to be present on 44 of 64 chips that were scanned (75.0%; four were called marginal and not included in the calculation), with BioC, BioD, and CreX also present in increasing intensity. When scaled to a target intensity of 150 (using Affymetrix Microarray Suite 5.0 array analysis software), scaling factors for all arrays were between 0.559 and 2.243.
Microarray Data Analysis. Sample sizes for each treatment group are indicated in the figure legends. Scanned output files from each array were inspected for quality control as described above. Raw signal intensities were normalized using the robust multiarray averaging (RMA) algorithm (Irizarry et al., 2003). The RMA-normalized data were imported into Partek Pro 6.0 (Partek, St. Charles, MO) and analyzed by principal component analysis (PCA) to determine the significant sources of variability in the data. A correlation coefficient (r) between signal intensities and ear weight was calculated for each gene, and a p value was determined. A set of genes with r 
0.90 were used to determine gene pathways and molecular networks highly correlated with ear weight (a measure of drug efficacy). Onto-Express was used to screen for significant pathways modulated by SM exposure (Khatri et al., 2002).
Quantitative Real-Time PCR. All quantitative real-time PCR (Q-PCR) was performed with Taq-Man PCR reagents and analyzed using the ABI 7500 Sequence Detection system (Applied Biosystems, Foster City, CA). All primers and probes used for Q-PCR analysis were designed using ABI Prism Primer Express version 2.0 (Applied Biosystems) and are listed in Supplemental Table 1. Primers and probes for each gene were optimized individually for maximum amplification efficiency. A validation experiment was performed to demonstrate that each target gene and endogenous control in a multiplex reaction maintained equal efficiencies (data not shown). Total RNA was purified as described above and DNase I treated on a purification column according to the manufacturer's protocol (QIAGEN). The reverse transcription reaction was carried out using 1 µg of total RNA (final concentration 50 ng/µl) using SuperScript II reverse transcriptase (Invitrogen, Carlsbad, CA). After completion of cDNA synthesis, all reactions were diluted to a final RNA input concentration of 5 ng/µl. For each gene analyzed, the experimental samples being tested (three biological replicates for each treatment group) were run in triplicate (three technical replicates) along with the corresponding no-template control and no-amplification control. The primer and probe pair concentrations used for each gene are as follows: GAPDH (endogenous control), 50 nM forward primer, 50 nM reverse primer, 200 nM VIC probe; Fgfr-3, 800 nM forward primer, 800 nM reverse primer, 100 nM Fam probe; Krt1-17, 300 nM forward primer, 50 nM reverse primer, 100 nM Fam probe; and L-myc, 300 nM forward primer, 300 nM reverse primer, 100 nM Fam probe. Amplification reactions were carried out using the instrument default cycle conditions. GAPDH was used as our internal reference gene to calculate the 
Ct for each sample assayed. The Ct was then calculated based on the average Ct of the naive control samples. The fold change in gene expression was determined as 2–Ct (Applied Biosystems User Bulletin #2, ABI Prism 7700 Sequence Detection system). Dixon's outlier test (extreme value test) was applied to all -fold change values for each gene investigated.
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Identification of Genes Whose Expression Profile Correlates with Ear Weight. The gene expression profiles were analyzed and a correlation coefficient between signal intensity (representing gene expression level) and ear weight was determined for each gene. Genes showing a positive correlation or a negative correlation with ear weight to an r 0.90 are reported in Tables 1 and 2, respectively. To determine the molecular functions and biological processes that are highly correlated with ear weight, the genes with r 0.90 were mapped to the Gene Ontology (The Gene Ontology Consortium, 2000) using the Web-based search engine Onto-Express (Khatri et al., 2002). Onto-Express translates lists of differentially regulated genes identified in high-throughput gene expression experiments into functional profiles based on the gene ontology, and a statistical significance value is calculated. Table 3 summarizes genes that represent the molecular functions most highly correlated with ear weight, and Table 4 summarizes the genes that represent the biological processes most highly correlated with ear weight. An examination of the biological processes most highly correlated with ear weight (based on genes with an r 0.90) reveals several major categories of biological processes: cell cycle regulation, inflammation, signal transduction, and cytoskeletal and cell adhesion processes. The gene expression profiles of genes that are classified in each of these biological processes are shown in Figs. 3 (cell cycle regulation), 4 (inflammation), 5 (signal transduction), and 6 (cytoskeletal and cell adhesion processes).
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Discussion |
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0.90). Gene ontology mapping of these gene sets revealed several major biological processes that were affected concomitantly with increased ear weight. These were cell cycle regulation, inflammation, signal transduction, and cytoskeletal and cell adhesion processes.
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, 2004a; Rogers et al., 2004), pig skin (Sabourin et al., 2002), cultured human keratinocytes (Schlager et al., 2002; Platteborze, 2003), HepG2 cells transfected with various stress gene reporter constructs (Schlager and Hart, 2000), and Jurkat cells exposed to the mustard-related compound chloroethyl ethyl sulfide (CEES) (Zhang et al., 2002). Our previous studies show that vanilloids reduce skin edema and cytokine mRNA expression after SM exposure in a mouse model (Sabourin et al., 2003, 2004b). This is the first study to characterize SM-induced changes in gene expression in relation to a characterized endpoint of SM exposure (ear weight) and also to examine the effect of treatment compounds on SM-induced gene expression using a global gene expression approach. These results provide important details that were previously lacking, and indicate that gene expression profiles may help to uncover the mechanisms of an observed phenotypic response (e.g., increased ear weight). Interestingly, few of the genes found to be highly correlated with increased ear weight showed what would normally be considered significant changes in gene expression (>2-fold change). Our results suggest that many of the genes highly correlated with a clinical endpoint may not show large -fold changes in gene expression. Further investigation with a broader time course and dose response may reveal more dynamic changes in the expression levels of these genes.
Cell cycle regulation has been previously implicated as a biological process affected by SM exposure. This has been attributed to the ability of SM to cross-link DNA by alkylation at the N-7 of guanine (Papirmeister et al., 1991). The cell cycle has been shown to stop at the G1/S transition after SM exposure (Smith et al., 1993). p53 is an important component of the G1/S cell cycle checkpoint. Rosenthal et al. (1998) showed that p53 accumulates after SM exposure in cultured human keratinocytes. Schlager and Hart (2000) showed increases in activity of the p53 promoter response element in HepG2 cells transfected with reporter constructs. We recently showed that rat pulmonary tissue exposed to SM via an intravenous route elicits a robust p53 response (Dillman et al., 2005). We also reported that p53 is phosphorylated on serine 15 within 15 min of SM exposure in cultured human keratinocytes (Minsavage and Dillman, 2005). Interestingly, it has been shown that there is a high incidence of lung cancer in former mustard gas workers, and there are p53 mutations in many of these cancers (Manning et al., 1981; Tokuoka et al., 1986; Easton et al., 1988; Nishimoto et al., 1988; Takeshima et al., 1994). The relationship between p53 activation, the cell cycle, and the genes identified in SM-exposed mouse skin and rat lung remains to be determined.
Up-regulation of cytokines and chemokines after SM exposure has been well characterized in a number of recent studies (Arroyo et al., 1999; Lardot et al., 1999; Sabourin et al., 2000, 2002, 2003, 2004a). Related to this, a number of cell signaling pathways have been identified that are activated or altered after SM exposure. Pharmacological inhibition of the p38 mitogen-activated protein kinase (MAPK14) has been reported to block up-regulation of cytokine production in response to SM exposure in cultured keratinocytes (Dillman et al., 2004). In addition the transcription factor nuclear factor-
B, which is involved in both inflammation and apoptosis, is activated after SM exposure in cultured cells (Atkins et al., 2000; Schlager and Hart, 2000). In other systems, nuclear factor-B has been shown to be activated by CEES in guinea pig lung (Chatterjee et al., 2003), and the Akt pathway is perturbed by CEES exposure in Jurkat cells (Zhang et al., 2002). It is not clear how CEES exposure relates to SM exposure, but it is clear that signaling pathways associated with inflammatory response are activated by SM exposure. These signaling pathways may be potential therapeutic targets for attenuating cytokine production and the subsequent recruitment of activated immune cells to the site of injury.
The cytoskeleton is an important target of SM exposure. Several studies have identified keratin proteins as direct targets of SM alkylation (van der Schans et al., 2002; Dillman et al., 2003). In addition, cross-linking of keratin filaments by SM, but not by other alkylating agents (e.g., CEES), has been observed (Dillman et al., 2003). This alkylation results in collapse of the keratin intermediate filament network and subsequent adverse changes in cellular morphology (Werrlein and Madren-Whalley, 2000, 2003). Furthermore, exposure to SM results in changes in the keratin proteins that are expressed in cultured human keratinocytes (Rosenthal et al., 1998). Western blotting of keratinocytes exposed to SM revealed that keratin-5 (K5) and keratin-14 (K14) protein levels decrease, and keratin-1 (K1) and keratin-10 (K10) protein levels increase. K5/K14 are associated with proliferating cells in the basal layer of the epidermis, whereas K1/K10 are associated with terminally differentiating cells in the suprabasal layers of the epidermis. SM exposure also results in perturbation of the actin cytoskeleton (Werrlein et al., 2005). Our data regarding altered gene expression are in agreement with these observations that cytoskeletal proteins are adversely affected by SM exposure. Although direct effects of SM on the microtubule network have not been reported to date, we observe changes in the mRNA levels of microtubule-based motor proteins from the kinesin family (Fig. 6), suggesting that changes in microtubule-based motility may result from SM exposure.
In conclusion, our results demonstrate that gene expression profiling can provide additional insight into the pathways important in SM-induced injury and which pathways may be potential targets for future development of antivesicant therapeutics. Our results suggest that gene expression profiling can be predictive of drug efficacy and imply that this could be an important tool in predictive toxicology and drug development. These approaches are of particular importance in the discovery and testing of pharmacological treatments for toxicant exposure, since in these cases traditional clinical trials for regulatory approval are not an option.
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Acknowledgements |
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Footnotes |
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ABBREVIATIONS: SM, sulfur mustard; OHV, octyl homovanillamide; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; RMA, robust multiarray averaging; PCA, principal component analysis; Q-PCR, quantitative real-time polymerase chain reaction; CEES, chloroethyl ethyl sulfide; ANOVA, analysis of variance.
The online version of this article (available at http://jpet.aspetjournals.org) contains supplemental material. 
1 Current affiliation: Biosciences and Protection Division, Human Effectiveness Directorate, Wright-Patterson AFB, Ohio.
Address correspondence to: Dr. James F. Dillman, III, Cell and Molecular Biology Branch, U.S. Army Medical Research Institute of Chemical Defense, 3100 Ricketts Point Rd., Aberdeen Proving Ground, MD 21010-5400. E-mail: james.dillman{at}us.army.mil
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