TY - JOUR
AU1 - McDougal, James, N.
AU2 - Garrett, Carol, M.
AU3 - Amato, Carol, M.
AU4 - Berberich, Steven, J.
AB - Abstract The jet fuel jet propulsion fuel 8 (JP-8) has been shown to cause an inflammatory response in the skin, which is characterized histologically by erythema, edema, and hyperplasia. Studies in laboratory animal skin and cultured keratinocytes have identified a variety of changes in protein levels related to inflammation, oxidative damage, apoptosis, and cellular growth. Most of these studies have focused on prolonged exposures and subsequent effects. In an attempt to understand the earliest responses of the skin to JP-8, we have investigated changes in gene expression in the epidermis for up to 8 h after a 1-h cutaneous exposure in rats. After exposure, we separated the epidermis from the rest of the skin with a cryotome and isolated total mRNA. Gene expression was studied with microarray techniques, and changes from sham treatments were analyzed and characterized. We found consistent twofold increases in gene expression of 27 transcripts at 1, 4, and 8 h after the beginning of the 1-h exposure that were related primarily to structural proteins, cell signaling, inflammatory mediators, growth factors, and enzymes. Analysis of pathways changed showed that several signaling pathways were increased at 1 h and that the most significant changes at 8 h were in metabolic pathways, many of which were downregulated. These results confirm and expand many of the previous molecular studies with JP-8. Based on the 1-h changes in gene expression, we hypothesize that the trigger of the JP-8–induced, epidermal stress response is a physical disruption of osmotic, oxidative, and membrane stability which activates gene expression in the signaling pathways and results in the inflammatory, apoptotic, and growth responses that have been previously identified. cutaneous, jet fuel, signaling pathway, inflammatory, apoptotic, response, JP-8 Jet propulsion fuel 8 (JP-8) is a multipurpose hydrocarbon fuel that is related to kerosene and used by the U.S. and North Atlantic Treaty Organization (NATO) military organizations. Starting in the 1980's, a 20-year conversion process has led to JP-8 being the primary fuel for aircraft, ground vehicles, cooking stoves, and personnel heaters for the U.S. and NATO forces (National Research Council, 2003). Compared to the fuel predecessor, JP-4, the desirable characteristics of JP-8 are a higher flash point and lower proportions of the cancer-causing, low–molecular weight hydrocarbons such as benzene, toluene, and xylene (Zeiger and Smith, 1998). Greater potential for cutaneous exposures is a consequence of the reduced volatility and increased use. There have been anecdotal reports of increased skin irritation in jet mechanics with JP-8 compared to JP-4, and JP-8 has been shown to be more irritating than JP-4 in rat studies (Baker et al., 1999). JP-8 exposure to the skin of laboratory animals causes erythema and edema, as indicated visually and histologically, at periods of 1 h to 4 weeks in hairless rats, rats, and pigs (Kabbur et al., 2001; Kanikkannan et al., 2001, 2002; Monteiro-Riviere et al., 2001). Local and systemic toxicity of JP-8 from cutaneous exposures has been recently reviewed (McDougal and Rogers, 2004). The physical signs of JP-8 skin irritation are well characterized in laboratory animals, but the molecular mechanisms are not well understood. JP-8–induced irritation of the epidermis has been characterized histologically by erythema, edema, and hyperplasia, which increased in severity with exposure time (Baker et al., 1999; Monteiro-Riviere et al., 2001, 2004). Reversible epidermal barrier disruption is seen histologically (Monteiro-Riviere et al., 2001) and functionally with measurements of transepidermal water loss (Kanikkannan et al., 2001, 2002; Monteiro-Riviere et al., 2001). Swollen mitochondria with disrupted cristae in the epidermis can occur after 5 h of JP-8 exposure in the pig (Monteiro-Riviere et al., 2004). These early-occurring mitochondrial changes are consistent with JP-8–induced oxidative stress (Rogers et al., 2001) and reversal of changes in the delayed hypersensitivity response with antioxidants (Ramos et al., 2004). Nucleolar margination and segregation found after 4 days of treatment in pigs suggest that JP-8 induces abnormalities in DNA signal transduction (Monteiro-Riviere et al., 2004). Seven-day cutaneous treatment with JP-8 in rats showed increased neutrophilic infiltration and increases in transcripts of some of the inflammatory chemokines (CXCL1, CXCL2, CCL2, CCL3, and CCL11) and interleukin-6 (IL-6) in the skin (Gallucci et al., 2004). In this same study, a cytokine (CCL2), tumor necrosis factor (TNF-α), and IL-1β protein levels were increased. JP-8–exposed whole-skin levels of IL-1α and inducible nitric oxide synthase protein have been shown to change as early as 1 h, before any histological or visual changes occur (Kabbur et al., 2001). The immunoregulatory cytokine IL-10 has been detected in the serum of mice beginning 48 h after JP-8 cutaneous exposure (Ullrich, 1999). JP-8 suppresses cell-mediated immunity in mice after inhalation (Harris et al., 2000) and cutaneous (Ramos et al., 2002) exposures. These studies suggest that the inflammatory responses (oxidative damage, necrosis, apoptosis, lipid synthesis, and components of an immune response) of the skin to JP-8 involve a variety of pathways. It appears that some of these molecular changes precede the visible or microscopic changes that are identified after exposure, but the trigger and sequence of the pathway responses is unclear. It has been suggested that preformed and biologically active IL-1α is present in the epidermis (Corsini and Galli, 2000; Kupper, 1990; Luster et al., 1999). IL-1α is one of the inflammatory cytokines, along with TNF-α and IL-1β, that initiates the production of secondary cytokines involved in the inflammatory process (Kupper, 1990). The mechanism by which IL-1α is released is unknown. In general, gene expression studies are a useful approach to increase understanding of the mechanisms of toxicity (Battershill, 2005; Coe et al., 2006; Currie et al., 2005). JP-8 has been shown to change gene expression in brain and lung (after repeated exposures) and in cultured cells. Studies of gene expression in the rat brain after JP-8 inhalation for 6 h/day and 91 consecutive days resulted in neurotransmitter signaling and stress response changes that increased with dose (Lin et al., 2001, 2004). An inhalation study monitoring gene changes in lung tissue after 1-h JP-8 exposures on seven consecutive days revealed that genes associated with antioxidant and detoxification mechanisms increased with dose (Espinoza et al., 2005). Exposure of Jurkat cells (a human T-lymphocyte line) to JP-8 for 4 h resulted in changes in gene expression patterns associated with the cell cycle, transcriptional, apoptotic, stress, and metabolic genes (Espinoza and Smulson, 2003). Human epidermal keratinocytes treated with JP-8 for 1–7 days responded with changes in genes related to cytoskeleton, enzymes, and signaling (Espinoza et al., 2004). Taken together, these studies demonstrate that a variety of inflammatory, stress, and damage-related genes respond to prolonged or repeated JP-8 exposures. We are interested in the trigger or initiating signal that starts the variety of inflammatory responses that occur with JP-8. Therefore, the purpose of our study was to investigate the early (1–8 h) time course of gene responses in the epidermis after a brief exposure to JP-8 to help identify early responses. MATERIALS AND METHODS Rat exposures. Male Fischer rats (CDF/CrlBR, Charles River Laboratories Raleigh NC, 250–350 g) were housed one per cage and provided food and water ad libitum. On the study day, rats were anesthetized with isoflurane (1-chloro-2,2,2-trifluoroethyl difluoromethyl ether) using a vaporizer (Ohio Medical Products, Madison WI). The back of the animal was closely clipped of fur, taking care not to damage the skin (Jepson and McDougal, 1997). A Hill Top Chamber (Hill Top Research Inc., Miamiville, OH) containing 500 μl of JP-8 (Air Force Research Laboratory, WPAFB, OH) was placed on the exposure site and secured with a modified rat jacket (Lomir Biomedical, Inc., Quebec, Canada). This dose is 156 μl/cm2 in the cotton-filled plastic chamber. Rats were placed back in their cage and exposed for 1 h (Fig. 1). Sham exposures on other rats (unloaded chambers placed on the skin and removed at the same time as treated samples) were used to control for diurnal and occlusive effects. At the end of the 1-h exposure, the exposure chamber and harness were removed, residual chemical was wiped off the skin, and the rats were euthanized or returned to the home cage until time for euthanasia at 4 or 8 h after the beginning of the exposure. JP-8 exposures for real-time RT-PCR studies were conducted at separate times than the microarray studies. Procedures involving rats were approved both by the Wright State University and the Air Force Institutional Animal Care and Use Committee. FIG. 1 Open in new tabDownload slide Schematic of the study design showing the times rat skin samples were taken after the 1-h exposure (hatched area). FIG. 1 Open in new tabDownload slide Schematic of the study design showing the times rat skin samples were taken after the 1-h exposure (hatched area). Skin collection and processing. Samples were taken after euthanasia with CO2 according to approved methods (National Research Council, 1996). A permanent marker was used to outline the exposure site on the back of each animal after removing the chamber and wiping the skin. Exposed skin samples were excised to the underlying muscle fascia and subcutaneous fat was then removed with an industrial single-edge razor blade (#9, VWR, Media, PA). This was accomplished on a polypropylene cutting board that was cleaned with RNase Zap (Ambion, Austin, TX) before each sample to remove RNase contamination. The whole-skin sample was trimmed to the circular outline, placed hypodermis-side down on a Sylgard-coated petri dish (30 mm) and completely submerged in ice-cold RNAlater (Ambion) and stored at − 20°C overnight. The next morning five 8-mm biopsy punches were taken from the exposed area of rat skin taking care to keep the whole-skin samples cold at all times. Each biopsy skin punch was carefully flattened between two coverslips and mounted to the modified cryotome chuck with superglue and immediately sectioned on the cryotome. Skin layer separations. Frozen biopsy punches from exposed and unexposed skin were sectioned horizontally (parallel to the skin surface) to provide epidermal skin sections according to modifications of previously published procedures (Babu et al., 2004a; Riviere et al., 1999). A CM3050 Research Cryostat (Leica Microsystems Inc., Bannockburn, IL) set at − 32°C was used to section frozen skin into five to eight 5-μm sections. The number of sections was determined visually using a mild color change of the sample from white to pink when entering the dermis. These epidermal sections from five biopsy punches were combined, and they weighed an average of 30.6 ± 9.6 mg for the microarray studies and 24.9 ± 8.0 mg for the real-time RT-PCR studies. Because of the undulating nature of the epidermal-dermal junction, these samples are expected to contain primarily epidermis but also some dermis. Gene expression with microarrays. Total RNA was extracted using TRIreagent (Molecular Research Center, Cincinnati, OH) and cleaned up using the RNeasy Mini Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The concentration and quality of total RNA was determined using the Experion RNA Standard Sensitivity chips in the Experion Automated Electrophoresis System (Bio-Rad, Hercules, CA). Double-stranded cDNA was generated using a Superscript II cDNA synthesis kit (Invitrogen, Carlsbad, CA) with an oligo(dT)24 primer containing a T7 RNA polymerase promoter binding site (Invitrogen). Labeled cRNA was prepared from double-stranded cDNA by in vitro transcription using a T7 polymerase (MEGAscript T7 kit; Ambion) in the presence of biotin-11-CTP and biotin-16-UTP (Enzo Diagnostics, Farmingdale, NY) and purified using RNeasy columns (Qiagen). Fifteen micrograms of biotinylated cRNA was fragmented and hybridized to RGU-34A GeneChip arrays (Affymetrix, Santa Clara, CA) that contain probe sets representing approximately 8798 genes. Chip hybridization, washing, and staining were performed according to the Affymetrix-recommended protocols. After scanning, the digitalized image data were processed using GCOS software (Affymetrix) and analyzed with GeneSpring GX 7.3 (Agilent Technologies, Palo Alto, CA). The GCOS software globally scaled all chips to a fluorescent value of 150. GeneSpring data entry. After analysis of the report file for each chip (Table 1) and confirmation that the chip image was not smeared or distorted, the Affymetrix “CHP” file for each sample was imported into GeneSpring. This file contained signal values and detection calls (present, marginally present, or absent) for each gene. The detection calls were made by comparing the signals for the perfect match and the mismatch probe sets at the Affymetrix default detection p values of 0.04 and 0.06. In GeneSpring, signals that were less than 0.01 were set to 0.01 to minimize noise, and the signal value for each transcript was normalized to the median of the control samples. A time parameter (1, 4, or 8) was assigned to each chip, and ratio of signal to control (used for fold changes) and log of ratio of signal to control (used for clustering and statistics) was assigned to each of the samples. TABLE 1 Summary of the Quality Control Reports from Each Affymetrix RGU-34A GeneChip Metric Sample N Mean SD Minimum Maximum Background 1 h treated 4a 46.04 5.53 41.04 51.99 1 h sham 5 48.29 2.41 45.66 52.25 4 h treated 5 77.16 8.69 66.32 89.51 4 h sham 5 91.11 5.23 84.66 94.93 8 h treated 5 86.06 5.09 78.63 92.08 8 h sham 5 85.46 13.03 71.33 106.52 Noise (raw Q) 1 h treated 4 1.82 0.25 1.58 2.14 1 h sham 5 2.01 0.20 1.78 2.28 4 h treated 5 3.46 0.49 2.92 4.19 4 h sham 5 4.36 0.50 3.80 4.87 8 h treated 5 3.35 0.41 2.80 3.95 8 h sham 5 3.66 0.74 3.04 4.93 % Present 1 h treated 4 38.9 2.4 35.9 41.0 1 h sham 5 40.3 2.4 37.4 43.0 4 h treated 5 46.2 3.2 42.6 49.2 4 h sham 5 46.0 1.4 44.1 47.5 8 h treated 5 46.0 1.7 43.3 47.8 8 h sham 5 48.2 1.8 46.7 51.1 % Absent 1 h treated 4 58.6 2.2 56.7 61.4 1 h sham 5 57.3 2.3 54.7 60.1 4 h treated 5 51.4 3.1 48.6 54.8 4 h sham 5 51.6 1.3 50.1 53.3 8 h treated 5 52.0 1.9 49.9 54.7 8 h sham 5 49.7 1.8 46.8 51.0 % Marginal 1 h treated 4 2.6 0.2 2.3 2.8 1 h sham 5 2.5 0.1 2.3 2.6 4 h treated 5 2.4 0.1 2.2 2.6 4 h sham 5 2.4 0.1 2.2 2.6 8 h treated 5 2.0 0.2 1.8 2.2 8 h sham 5 2.1 0.1 2.0 2.3 3′/5′ Ratio GAPDH 1 h treated 4 2.68 0.43 2.07 3.07 1 h sham 5 2.84 0.58 2.13 3.48 4 h treated 5 1.57 0.45 1.21 2.17 4 h sham 5 1.35 0.28 1.12 1.81 8 h treated 5 2.65 0.53 2.06 3.52 8 h sham 5 1.89 0.23 1.53 2.10 Metric Sample N Mean SD Minimum Maximum Background 1 h treated 4a 46.04 5.53 41.04 51.99 1 h sham 5 48.29 2.41 45.66 52.25 4 h treated 5 77.16 8.69 66.32 89.51 4 h sham 5 91.11 5.23 84.66 94.93 8 h treated 5 86.06 5.09 78.63 92.08 8 h sham 5 85.46 13.03 71.33 106.52 Noise (raw Q) 1 h treated 4 1.82 0.25 1.58 2.14 1 h sham 5 2.01 0.20 1.78 2.28 4 h treated 5 3.46 0.49 2.92 4.19 4 h sham 5 4.36 0.50 3.80 4.87 8 h treated 5 3.35 0.41 2.80 3.95 8 h sham 5 3.66 0.74 3.04 4.93 % Present 1 h treated 4 38.9 2.4 35.9 41.0 1 h sham 5 40.3 2.4 37.4 43.0 4 h treated 5 46.2 3.2 42.6 49.2 4 h sham 5 46.0 1.4 44.1 47.5 8 h treated 5 46.0 1.7 43.3 47.8 8 h sham 5 48.2 1.8 46.7 51.1 % Absent 1 h treated 4 58.6 2.2 56.7 61.4 1 h sham 5 57.3 2.3 54.7 60.1 4 h treated 5 51.4 3.1 48.6 54.8 4 h sham 5 51.6 1.3 50.1 53.3 8 h treated 5 52.0 1.9 49.9 54.7 8 h sham 5 49.7 1.8 46.8 51.0 % Marginal 1 h treated 4 2.6 0.2 2.3 2.8 1 h sham 5 2.5 0.1 2.3 2.6 4 h treated 5 2.4 0.1 2.2 2.6 4 h sham 5 2.4 0.1 2.2 2.6 8 h treated 5 2.0 0.2 1.8 2.2 8 h sham 5 2.1 0.1 2.0 2.3 3′/5′ Ratio GAPDH 1 h treated 4 2.68 0.43 2.07 3.07 1 h sham 5 2.84 0.58 2.13 3.48 4 h treated 5 1.57 0.45 1.21 2.17 4 h sham 5 1.35 0.28 1.12 1.81 8 h treated 5 2.65 0.53 2.06 3.52 8 h sham 5 1.89 0.23 1.53 2.10 a One 1-h treated sample was lost. Open in new tab TABLE 1 Summary of the Quality Control Reports from Each Affymetrix RGU-34A GeneChip Metric Sample N Mean SD Minimum Maximum Background 1 h treated 4a 46.04 5.53 41.04 51.99 1 h sham 5 48.29 2.41 45.66 52.25 4 h treated 5 77.16 8.69 66.32 89.51 4 h sham 5 91.11 5.23 84.66 94.93 8 h treated 5 86.06 5.09 78.63 92.08 8 h sham 5 85.46 13.03 71.33 106.52 Noise (raw Q) 1 h treated 4 1.82 0.25 1.58 2.14 1 h sham 5 2.01 0.20 1.78 2.28 4 h treated 5 3.46 0.49 2.92 4.19 4 h sham 5 4.36 0.50 3.80 4.87 8 h treated 5 3.35 0.41 2.80 3.95 8 h sham 5 3.66 0.74 3.04 4.93 % Present 1 h treated 4 38.9 2.4 35.9 41.0 1 h sham 5 40.3 2.4 37.4 43.0 4 h treated 5 46.2 3.2 42.6 49.2 4 h sham 5 46.0 1.4 44.1 47.5 8 h treated 5 46.0 1.7 43.3 47.8 8 h sham 5 48.2 1.8 46.7 51.1 % Absent 1 h treated 4 58.6 2.2 56.7 61.4 1 h sham 5 57.3 2.3 54.7 60.1 4 h treated 5 51.4 3.1 48.6 54.8 4 h sham 5 51.6 1.3 50.1 53.3 8 h treated 5 52.0 1.9 49.9 54.7 8 h sham 5 49.7 1.8 46.8 51.0 % Marginal 1 h treated 4 2.6 0.2 2.3 2.8 1 h sham 5 2.5 0.1 2.3 2.6 4 h treated 5 2.4 0.1 2.2 2.6 4 h sham 5 2.4 0.1 2.2 2.6 8 h treated 5 2.0 0.2 1.8 2.2 8 h sham 5 2.1 0.1 2.0 2.3 3′/5′ Ratio GAPDH 1 h treated 4 2.68 0.43 2.07 3.07 1 h sham 5 2.84 0.58 2.13 3.48 4 h treated 5 1.57 0.45 1.21 2.17 4 h sham 5 1.35 0.28 1.12 1.81 8 h treated 5 2.65 0.53 2.06 3.52 8 h sham 5 1.89 0.23 1.53 2.10 Metric Sample N Mean SD Minimum Maximum Background 1 h treated 4a 46.04 5.53 41.04 51.99 1 h sham 5 48.29 2.41 45.66 52.25 4 h treated 5 77.16 8.69 66.32 89.51 4 h sham 5 91.11 5.23 84.66 94.93 8 h treated 5 86.06 5.09 78.63 92.08 8 h sham 5 85.46 13.03 71.33 106.52 Noise (raw Q) 1 h treated 4 1.82 0.25 1.58 2.14 1 h sham 5 2.01 0.20 1.78 2.28 4 h treated 5 3.46 0.49 2.92 4.19 4 h sham 5 4.36 0.50 3.80 4.87 8 h treated 5 3.35 0.41 2.80 3.95 8 h sham 5 3.66 0.74 3.04 4.93 % Present 1 h treated 4 38.9 2.4 35.9 41.0 1 h sham 5 40.3 2.4 37.4 43.0 4 h treated 5 46.2 3.2 42.6 49.2 4 h sham 5 46.0 1.4 44.1 47.5 8 h treated 5 46.0 1.7 43.3 47.8 8 h sham 5 48.2 1.8 46.7 51.1 % Absent 1 h treated 4 58.6 2.2 56.7 61.4 1 h sham 5 57.3 2.3 54.7 60.1 4 h treated 5 51.4 3.1 48.6 54.8 4 h sham 5 51.6 1.3 50.1 53.3 8 h treated 5 52.0 1.9 49.9 54.7 8 h sham 5 49.7 1.8 46.8 51.0 % Marginal 1 h treated 4 2.6 0.2 2.3 2.8 1 h sham 5 2.5 0.1 2.3 2.6 4 h treated 5 2.4 0.1 2.2 2.6 4 h sham 5 2.4 0.1 2.2 2.6 8 h treated 5 2.0 0.2 1.8 2.2 8 h sham 5 2.1 0.1 2.0 2.3 3′/5′ Ratio GAPDH 1 h treated 4 2.68 0.43 2.07 3.07 1 h sham 5 2.84 0.58 2.13 3.48 4 h treated 5 1.57 0.45 1.21 2.17 4 h sham 5 1.35 0.28 1.12 1.81 8 h treated 5 2.65 0.53 2.06 3.52 8 h sham 5 1.89 0.23 1.53 2.10 a One 1-h treated sample was lost. Open in new tab Data analysis approach. Of the 8798 probe sets on the RGU-34A microarray, only 5216 were present or marginally present in at least three of the 29 epidermal samples. Removing probe sets with absent calls in more than 26 samples produced a set of genes used to determine expression changes due to the treatments. Filtering these data for genes that changed twofold compared to sham treated resulted in genes that were used to understand the time course of the response. The Welch T-test, which assumed that variances are not equal, was used to test the probes showing a twofold change for statistical significance at p ≤ 0.05. This approach selects genes that were both twofold changed and statistically significant from the 29 samples. The heat map in Figure 2 demonstrates the results of normalization and quality control and reveals that the response to JP-8 was time dependent. A principal component analysis (GeneSpring) shows that the probe sets changed at each of the time points clustered together (Fig. 3). FIG. 2 Open in new tabDownload slide Heat map showing different expression with time of 961 significantly (p = 0.05) changed probes. Probes with similar behavior across the time points cluster together. Probes in red were increased (brightest red is a fivefold increase), and transcripts in green were decreased (brightest green is a fivefold decrease). Probes with unchanged expression were colored black. FIG. 2 Open in new tabDownload slide Heat map showing different expression with time of 961 significantly (p = 0.05) changed probes. Probes with similar behavior across the time points cluster together. Probes in red were increased (brightest red is a fivefold increase), and transcripts in green were decreased (brightest green is a fivefold decrease). Probes with unchanged expression were colored black. FIG. 3 Open in new tabDownload slide Principal component plot of the probe sets after quality control filtering, showing that the probes changed (compared to the individual sham treatments) cluster with time after the beginning of the JP-8 treatment. Right-most circles are 1-h samples, upper-most circles are 4-h samples, and left-most circles are 8-h samples. The x-axis encompasses most of the variability (47.0%). The y-axis and z-axis account for 18.8 and 9.7% of the variability, respectively. FIG. 3 Open in new tabDownload slide Principal component plot of the probe sets after quality control filtering, showing that the probes changed (compared to the individual sham treatments) cluster with time after the beginning of the JP-8 treatment. Right-most circles are 1-h samples, upper-most circles are 4-h samples, and left-most circles are 8-h samples. The x-axis encompasses most of the variability (47.0%). The y-axis and z-axis account for 18.8 and 9.7% of the variability, respectively. Gene expression with real-time RT-PCR. Total RNA was purified as described above. The RNA was treated with Turbo DNase (Ambion) to remove any contaminating genomic DNA and quantified using the Quant-iT RiboGreen fluorescence assay kit (Invitrogen). Total mRNA quality was assessed by agarose gel electrophoresis (FisherBiotech Electrophoresis Systems Mini Horizontal Unit, Fisher Scientific, Hanover Park, IL). One-hundred nanograms of total mRNA was then amplified using a Full Spectrum Complete Transcriptome RNA Amplification Kit (System Biosciences, Mountain View, CA) according to the manufacturer's protocol. Resultant cDNA from the amplification was quantified using the Fluorescent DNA Quantitation Kit (Bio-Rad), and quality was assessed by electrophoresis. Real-time RT-PCR analysis of gene transcripts involved the use of the Applied Biosystems 7900HT Sequence Detection System, TaqMan chemistry, and Applied Biosystems TaqMan Gene Expression Assays according to manufacturers' recommended procedures. Data were analyzed using the comparative Ct method (also known as the ΔΔCt method) to calculate relative changes in gene expression. Mean and SD values of the replicate samples were calculated for both the treated and control groups. Fold change (the relative quantitation, or RQ, value of the target gene) was then calculated from the ΔΔCt. This fold change was normalized to the endogenous reference gene GAPDH and was reported relative to the sham-treated samples. These calculations were repeated using each sham-treated sample as the calibrator, for a total of four different RQ values for each sample (RQ1, RQ2, RQ3, and RQ4). RQ values for each sample were averaged, and the SD was calculated, yielding the average fold change of the gene of interest. A two-tailed paired t-test of the control and treated sample groups for each gene of interest at each time point was performed to determine statistical significance at p ≤ 0.05. Functional/pathway analysis. Lists of genes with significant changes in gene expression (increases and decreases) based on the microarray experiments were exported from GeneSpring and imported into Ingenuity Pathways Analysis (IPA) 4.0 (Ingenuity Systems, Redwood City CA, http://www.ingenuity.com). Canonical pathways analysis identified the pathways from the IPA library of canonical pathways that were most significant to each of the data sets from the time course. Genes from the data sets that were associated with a canonical pathway in the Ingenuity Pathways Knowledge Base were considered for the analysis. The significance of the association between the data set and the canonical pathway was measured in two ways: (1) a ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway; and (2) Fisher's exact test was used to calculate a p value determining the probability that the association between the genes in the data set and the canonical pathway was explained by chance alone. RESULTS Visual Effects of JP-8 on the Skin These brief 1-h cutaneous exposures to neat JP-8 (156 μl/cm2) did not produce any visible signs of irritation or redness on the rat skin seen with longer exposures. A double-walled ring on the skin, where the ribbed outer boundary of the Hill Top Chamber had been, was visible in both treated and sham-treated rats at the time of removal of the chamber. No signs of physical distress were apparent with any of the rats during or after the exposures. GeneChip Probe Expression in Sham-Treated Skin Sham-treated samples were expected to reflect the transcript levels in normal untreated skin and were used to estimate normal gene expression in the skin. ANOVA showed no significant differences (p ≤ 0.05) between any of the sham-treated groups at 1, 4, and 8 h after exposure; therefore, they were combined for analysis of transcript levels in untreated skin. Gene expression in sham-treated epidermis indicated that constituent transcript levels can be measured for about 1/2 of the transcripts in the genome. Specifically, 3737 probes were detected as present or marginally present in at least 10 of the 14 sham-treated epidermal samples. According to generic Gene Ontology slim, the most significant biological process being expressed in the sham-treated epidermal tissue was metabolism with 908 of the metabolism probes (45%) in the genome constitutively present in the set of 1904 metabolism genes in the genome. The molecular function most highly represented constituently was binding with 1008 of 2457 probes (41%) present or marginally present. In this category, nucleic acid–binding, nucleotide-binding, mRNA-binding, and translation factor activity probes were significantly expressed in greatest numbers. Overall Changes in GeneChip Probe Expression due to JP-8 Alterations in gene expression were detected using Affymetrix GeneChips following 1-h JP-8 exposures. Immediately after the end of the exposure, 133 probes were increased or decreased significantly when compared with the 1-h sham-treated controls (supplementary Table 1). Two hundred and seventy five probes were changed after 4 h (supplementary Table 2). Eight hours after the beginning of the exposure, 800 probes were changed (supplementary Table 3). At 1 h the number of probes increased was greater than the number of probes decreased, but the ratio of probes increased to those decreased was reversed by 8 h (Fig. 4). Table 2 shows that the number of probes changed became greater with increasing time after the beginning of the exposure, and the categories of genes changed depended on time. Consistent with a model whereby early gene alterations trigger subsequent new gene expression changes, there was very little overlap between the gene ontology categories of the changed genes when 1- and 8 h responses are compared. The only category with significant changes at both the earliest and latest time points was metabolism. FIG. 4 Open in new tabDownload slide Probes changed (compared with individual sham treatments) up and down with time after the beginning of the 1-h JP-8 exposure. FIG. 4 Open in new tabDownload slide Probes changed (compared with individual sham treatments) up and down with time after the beginning of the 1-h JP-8 exposure. TABLE 2 Number of Probes with Significant Changes (ANOVA p ≤ 0.05) in Gene Ontology (generic GO slim) Categories Sorted by the Times They Occurred 1 h 4 h 8 h Biological process     GO:30154: cell differentiation 8     GO:4: biological process unknown 6     GO:8152: metabolism 54 90 249     GO:9790: embryonic development 5 7     GO:7582: physiological process 352     GO:8219: cell death 26     GO:9058: biosynthesis 55     GO:19748: secondary metabolism 6 Cellular component     GO:5634: nucleus 40     GO:5635: nuclear membrane 3     GO:5783: endoplasmic reticulum 13 32     GO:5622: intracellular 248     GO:5737: cytoplasm 154     GO:5739: mitochondrion 42     GO:5773: vacuole 9 Molecular function     GO:3676: nucleic acid binding 35     GO:3677: DNA binding 34 24     GO:30528: transcription regulator activity 25 18     GO:3700: transcription factor activity 21 16     GO:5515: protein binding 43     GO:3824: catalytic activity 72 230     GO:5102: receptor binding 20 36     GO:16209: antioxidant activity 4 7 1 h 4 h 8 h Biological process     GO:30154: cell differentiation 8     GO:4: biological process unknown 6     GO:8152: metabolism 54 90 249     GO:9790: embryonic development 5 7     GO:7582: physiological process 352     GO:8219: cell death 26     GO:9058: biosynthesis 55     GO:19748: secondary metabolism 6 Cellular component     GO:5634: nucleus 40     GO:5635: nuclear membrane 3     GO:5783: endoplasmic reticulum 13 32     GO:5622: intracellular 248     GO:5737: cytoplasm 154     GO:5739: mitochondrion 42     GO:5773: vacuole 9 Molecular function     GO:3676: nucleic acid binding 35     GO:3677: DNA binding 34 24     GO:30528: transcription regulator activity 25 18     GO:3700: transcription factor activity 21 16     GO:5515: protein binding 43     GO:3824: catalytic activity 72 230     GO:5102: receptor binding 20 36     GO:16209: antioxidant activity 4 7 Note. Level of indentation indicates the relationship between the categories. The number of genes in the indented categories may be included in a category with less indentation. Open in new tab TABLE 2 Number of Probes with Significant Changes (ANOVA p ≤ 0.05) in Gene Ontology (generic GO slim) Categories Sorted by the Times They Occurred 1 h 4 h 8 h Biological process     GO:30154: cell differentiation 8     GO:4: biological process unknown 6     GO:8152: metabolism 54 90 249     GO:9790: embryonic development 5 7     GO:7582: physiological process 352     GO:8219: cell death 26     GO:9058: biosynthesis 55     GO:19748: secondary metabolism 6 Cellular component     GO:5634: nucleus 40     GO:5635: nuclear membrane 3     GO:5783: endoplasmic reticulum 13 32     GO:5622: intracellular 248     GO:5737: cytoplasm 154     GO:5739: mitochondrion 42     GO:5773: vacuole 9 Molecular function     GO:3676: nucleic acid binding 35     GO:3677: DNA binding 34 24     GO:30528: transcription regulator activity 25 18     GO:3700: transcription factor activity 21 16     GO:5515: protein binding 43     GO:3824: catalytic activity 72 230     GO:5102: receptor binding 20 36     GO:16209: antioxidant activity 4 7 1 h 4 h 8 h Biological process     GO:30154: cell differentiation 8     GO:4: biological process unknown 6     GO:8152: metabolism 54 90 249     GO:9790: embryonic development 5 7     GO:7582: physiological process 352     GO:8219: cell death 26     GO:9058: biosynthesis 55     GO:19748: secondary metabolism 6 Cellular component     GO:5634: nucleus 40     GO:5635: nuclear membrane 3     GO:5783: endoplasmic reticulum 13 32     GO:5622: intracellular 248     GO:5737: cytoplasm 154     GO:5739: mitochondrion 42     GO:5773: vacuole 9 Molecular function     GO:3676: nucleic acid binding 35     GO:3677: DNA binding 34 24     GO:30528: transcription regulator activity 25 18     GO:3700: transcription factor activity 21 16     GO:5515: protein binding 43     GO:3824: catalytic activity 72 230     GO:5102: receptor binding 20 36     GO:16209: antioxidant activity 4 7 Note. Level of indentation indicates the relationship between the categories. The number of genes in the indented categories may be included in a category with less indentation. Open in new tab Persistent Changes in GeneChip Probe Expression at All Times Even though the gene expression profiles differed over time, there were several gene expression changes that were consistent at all postexposure times studied. An analysis by Venn diagram of the significant changes for the increased and decreased probes is shown in Figure 5. Twenty-seven probes representing 23 genes were increased at 1, 4, and 8 h after the beginning of the exposure (Table 3). Four genes had two different probe sets that were changed at each of the times. Figure 6 shows the cellular location and relationships between some of the upregulated genes according to IPA. The genes increased at all time points are primarily related to the growth and apoptosis pathways activated by epidermal growth factor (EGF) and TNF. The proteins coded for by these upregulated transcripts are about equally divided between extracellular space, plasma membrane, cytoplasm, and nucleus. One probe for the gene TXNIP (AI014169, upregulated by 1,25-dihydroxyvitamin D-3, thioredoxin-interacting protein) was consistently decreased at 1, 4, and 8 h after the beginning of the JP-8 exposure. TXNIP is annotated in the generic Gene Ontology slim database as a cytoplasmic enzyme inhibitor responsive to oxidative stress and regulation of cell proliferation. FIG. 5 Open in new tabDownload slide Venn diagram showing the intersections of probes that were increased and decreased in the skin (compared to sham treatments) after the cutaneous 1-h JP-8 exposure. The total number of probes increased at any time was 460, and the total number of probes decreased at any time was 509. FIG. 5 Open in new tabDownload slide Venn diagram showing the intersections of probes that were increased and decreased in the skin (compared to sham treatments) after the cutaneous 1-h JP-8 exposure. The total number of probes increased at any time was 460, and the total number of probes decreased at any time was 509. TABLE 3 Probes Significantly Increased More Than Twofold at All Time Points after a 1-h JP-8 Exposure Fold change Probe set ID 1 h 4 h 8 h GenBank ID Symbol Gene name X55183_at 6.0 11.7 3.2 X55183 AREG Amphiregulin D17695_s_at 2.6 2.3 2.4 D17695 AQP3 MIP (major intrinsic protein) family water channel; Rattus rattus AQP3 mRNA for AQP3, complete cds. X17053mRNA_s_at 3.6 2.8 4.3 X17053 CCL2 Rat immediate-early serum-responsive JE gene, chemokine (C-C motif) ligand 2 S77528cds_s_at 5.2 3.2 4.2 S77528 CEBPB NFIL-6; Rattus sp. CEBP-related transcription factor (Nfil6) mRNA, complete cds. X60769mRNA_at 3.8 2.4 2.7 X60769 CEBPB CCAAT/enhancer-binding protein (CEBP), beta rc_AI176856_at 4.8 10.4 10.5 AI176856 CYP1B1 Cytochrome P450, subfamily 1B, polypeptide 1 L05489_at 3.4 4.2 4.4 L05489 DTR Diphtheria toxin receptor (heparin-binding EGF-like growth factor) U42627_at 2.3 4.5 4.1 U42627 DUSP6 Dual specificity phosphatase 6 M19651_at 3.6 11.8 8.5 M19651 FOSL1 FOS-like antigen 1 rc_AA858520_at 2.1 3.6 4.9 AA858520 FST Follistatin rc_AA858520_g_at 2.6 4.0 9.2 AA858520 FST Follistatin rc_AA891041_at 4.8 2.3 2.1 AA891041 JUNB JUNB oncogene rc_AI169756_s_at 4.2 2.1 2.0 AI169756 MIG6 Similar to mitogen-inducible gene 6 protein homolog (Mig-6) (Gene 33 polypeptide) (predicted) rc_AI176710_at 7.4 3.9 2.5 AI176710 NR4A3 Nuclear receptor subfamily 4, group A, member 3 J04791_s_at 6.4 4.1 4.8 J04791 ODC1 Ornithine decarboxylase 1 X07944exon#1-12_s_at 3.5 3.9 2.8 X07944 ODC1 Rat ornithine decarboxylase gene (EC 4.1.1.17). X71898_at 2.8 3.1 3.2 X71898 PLAUR Plasminogen activator, urokinase receptor X96437mRNA_g_at 3.3 2.8 2.2 X96437 PRG1 Rattus norvegicus PRG1 gene, proteoglycan1, secretory granule, IER3 S67722_s_at 4.0 6.8 2.3 S67722 PTGS2 Prostaglandin–endoperoxide synthase 2 L12025_at 2.8 3.9 4.1 L12025 PVR Tumor-associated antigen 1 rc_AA892750_at 7.8 4.4 2.3 AA892750 RGS1 Regulator of G-protein signaling 1 rc_AA891911_at 4.5 3.3 13.3 AA891911 SPRR1 Similar to cornifin alpha (small proline-rich protein 1) (SPRR1) (LOC365848), mRNA L46593cds_at 4.2 3.0 13.1 L46593 SPRR1 Rattus norvegicus small proline-rich protein (spr) gene, complete cds. M57263_at 2.6 3.9 10.0 M57263 TGM1 Transglutaminase 1 rc_AI169327_g_at 2.9 4.8 6.5 AI169327 TIMP1 Tissue inhibitor of metalloproteinase 1 rc_AI639058_s_at 2.2 2.7 2.5 AI639058 TMEPAI Transmembrane, prostate androgen–induced RNA (predicted) rc_Al236158_at 10.7 2.7 2.8 AI236158 XCL1 Small inducible cytokine subfamily C, member 1 (lymphotactin) Fold change Probe set ID 1 h 4 h 8 h GenBank ID Symbol Gene name X55183_at 6.0 11.7 3.2 X55183 AREG Amphiregulin D17695_s_at 2.6 2.3 2.4 D17695 AQP3 MIP (major intrinsic protein) family water channel; Rattus rattus AQP3 mRNA for AQP3, complete cds. X17053mRNA_s_at 3.6 2.8 4.3 X17053 CCL2 Rat immediate-early serum-responsive JE gene, chemokine (C-C motif) ligand 2 S77528cds_s_at 5.2 3.2 4.2 S77528 CEBPB NFIL-6; Rattus sp. CEBP-related transcription factor (Nfil6) mRNA, complete cds. X60769mRNA_at 3.8 2.4 2.7 X60769 CEBPB CCAAT/enhancer-binding protein (CEBP), beta rc_AI176856_at 4.8 10.4 10.5 AI176856 CYP1B1 Cytochrome P450, subfamily 1B, polypeptide 1 L05489_at 3.4 4.2 4.4 L05489 DTR Diphtheria toxin receptor (heparin-binding EGF-like growth factor) U42627_at 2.3 4.5 4.1 U42627 DUSP6 Dual specificity phosphatase 6 M19651_at 3.6 11.8 8.5 M19651 FOSL1 FOS-like antigen 1 rc_AA858520_at 2.1 3.6 4.9 AA858520 FST Follistatin rc_AA858520_g_at 2.6 4.0 9.2 AA858520 FST Follistatin rc_AA891041_at 4.8 2.3 2.1 AA891041 JUNB JUNB oncogene rc_AI169756_s_at 4.2 2.1 2.0 AI169756 MIG6 Similar to mitogen-inducible gene 6 protein homolog (Mig-6) (Gene 33 polypeptide) (predicted) rc_AI176710_at 7.4 3.9 2.5 AI176710 NR4A3 Nuclear receptor subfamily 4, group A, member 3 J04791_s_at 6.4 4.1 4.8 J04791 ODC1 Ornithine decarboxylase 1 X07944exon#1-12_s_at 3.5 3.9 2.8 X07944 ODC1 Rat ornithine decarboxylase gene (EC 4.1.1.17). X71898_at 2.8 3.1 3.2 X71898 PLAUR Plasminogen activator, urokinase receptor X96437mRNA_g_at 3.3 2.8 2.2 X96437 PRG1 Rattus norvegicus PRG1 gene, proteoglycan1, secretory granule, IER3 S67722_s_at 4.0 6.8 2.3 S67722 PTGS2 Prostaglandin–endoperoxide synthase 2 L12025_at 2.8 3.9 4.1 L12025 PVR Tumor-associated antigen 1 rc_AA892750_at 7.8 4.4 2.3 AA892750 RGS1 Regulator of G-protein signaling 1 rc_AA891911_at 4.5 3.3 13.3 AA891911 SPRR1 Similar to cornifin alpha (small proline-rich protein 1) (SPRR1) (LOC365848), mRNA L46593cds_at 4.2 3.0 13.1 L46593 SPRR1 Rattus norvegicus small proline-rich protein (spr) gene, complete cds. M57263_at 2.6 3.9 10.0 M57263 TGM1 Transglutaminase 1 rc_AI169327_g_at 2.9 4.8 6.5 AI169327 TIMP1 Tissue inhibitor of metalloproteinase 1 rc_AI639058_s_at 2.2 2.7 2.5 AI639058 TMEPAI Transmembrane, prostate androgen–induced RNA (predicted) rc_Al236158_at 10.7 2.7 2.8 AI236158 XCL1 Small inducible cytokine subfamily C, member 1 (lymphotactin) Open in new tab TABLE 3 Probes Significantly Increased More Than Twofold at All Time Points after a 1-h JP-8 Exposure Fold change Probe set ID 1 h 4 h 8 h GenBank ID Symbol Gene name X55183_at 6.0 11.7 3.2 X55183 AREG Amphiregulin D17695_s_at 2.6 2.3 2.4 D17695 AQP3 MIP (major intrinsic protein) family water channel; Rattus rattus AQP3 mRNA for AQP3, complete cds. X17053mRNA_s_at 3.6 2.8 4.3 X17053 CCL2 Rat immediate-early serum-responsive JE gene, chemokine (C-C motif) ligand 2 S77528cds_s_at 5.2 3.2 4.2 S77528 CEBPB NFIL-6; Rattus sp. CEBP-related transcription factor (Nfil6) mRNA, complete cds. X60769mRNA_at 3.8 2.4 2.7 X60769 CEBPB CCAAT/enhancer-binding protein (CEBP), beta rc_AI176856_at 4.8 10.4 10.5 AI176856 CYP1B1 Cytochrome P450, subfamily 1B, polypeptide 1 L05489_at 3.4 4.2 4.4 L05489 DTR Diphtheria toxin receptor (heparin-binding EGF-like growth factor) U42627_at 2.3 4.5 4.1 U42627 DUSP6 Dual specificity phosphatase 6 M19651_at 3.6 11.8 8.5 M19651 FOSL1 FOS-like antigen 1 rc_AA858520_at 2.1 3.6 4.9 AA858520 FST Follistatin rc_AA858520_g_at 2.6 4.0 9.2 AA858520 FST Follistatin rc_AA891041_at 4.8 2.3 2.1 AA891041 JUNB JUNB oncogene rc_AI169756_s_at 4.2 2.1 2.0 AI169756 MIG6 Similar to mitogen-inducible gene 6 protein homolog (Mig-6) (Gene 33 polypeptide) (predicted) rc_AI176710_at 7.4 3.9 2.5 AI176710 NR4A3 Nuclear receptor subfamily 4, group A, member 3 J04791_s_at 6.4 4.1 4.8 J04791 ODC1 Ornithine decarboxylase 1 X07944exon#1-12_s_at 3.5 3.9 2.8 X07944 ODC1 Rat ornithine decarboxylase gene (EC 4.1.1.17). X71898_at 2.8 3.1 3.2 X71898 PLAUR Plasminogen activator, urokinase receptor X96437mRNA_g_at 3.3 2.8 2.2 X96437 PRG1 Rattus norvegicus PRG1 gene, proteoglycan1, secretory granule, IER3 S67722_s_at 4.0 6.8 2.3 S67722 PTGS2 Prostaglandin–endoperoxide synthase 2 L12025_at 2.8 3.9 4.1 L12025 PVR Tumor-associated antigen 1 rc_AA892750_at 7.8 4.4 2.3 AA892750 RGS1 Regulator of G-protein signaling 1 rc_AA891911_at 4.5 3.3 13.3 AA891911 SPRR1 Similar to cornifin alpha (small proline-rich protein 1) (SPRR1) (LOC365848), mRNA L46593cds_at 4.2 3.0 13.1 L46593 SPRR1 Rattus norvegicus small proline-rich protein (spr) gene, complete cds. M57263_at 2.6 3.9 10.0 M57263 TGM1 Transglutaminase 1 rc_AI169327_g_at 2.9 4.8 6.5 AI169327 TIMP1 Tissue inhibitor of metalloproteinase 1 rc_AI639058_s_at 2.2 2.7 2.5 AI639058 TMEPAI Transmembrane, prostate androgen–induced RNA (predicted) rc_Al236158_at 10.7 2.7 2.8 AI236158 XCL1 Small inducible cytokine subfamily C, member 1 (lymphotactin) Fold change Probe set ID 1 h 4 h 8 h GenBank ID Symbol Gene name X55183_at 6.0 11.7 3.2 X55183 AREG Amphiregulin D17695_s_at 2.6 2.3 2.4 D17695 AQP3 MIP (major intrinsic protein) family water channel; Rattus rattus AQP3 mRNA for AQP3, complete cds. X17053mRNA_s_at 3.6 2.8 4.3 X17053 CCL2 Rat immediate-early serum-responsive JE gene, chemokine (C-C motif) ligand 2 S77528cds_s_at 5.2 3.2 4.2 S77528 CEBPB NFIL-6; Rattus sp. CEBP-related transcription factor (Nfil6) mRNA, complete cds. X60769mRNA_at 3.8 2.4 2.7 X60769 CEBPB CCAAT/enhancer-binding protein (CEBP), beta rc_AI176856_at 4.8 10.4 10.5 AI176856 CYP1B1 Cytochrome P450, subfamily 1B, polypeptide 1 L05489_at 3.4 4.2 4.4 L05489 DTR Diphtheria toxin receptor (heparin-binding EGF-like growth factor) U42627_at 2.3 4.5 4.1 U42627 DUSP6 Dual specificity phosphatase 6 M19651_at 3.6 11.8 8.5 M19651 FOSL1 FOS-like antigen 1 rc_AA858520_at 2.1 3.6 4.9 AA858520 FST Follistatin rc_AA858520_g_at 2.6 4.0 9.2 AA858520 FST Follistatin rc_AA891041_at 4.8 2.3 2.1 AA891041 JUNB JUNB oncogene rc_AI169756_s_at 4.2 2.1 2.0 AI169756 MIG6 Similar to mitogen-inducible gene 6 protein homolog (Mig-6) (Gene 33 polypeptide) (predicted) rc_AI176710_at 7.4 3.9 2.5 AI176710 NR4A3 Nuclear receptor subfamily 4, group A, member 3 J04791_s_at 6.4 4.1 4.8 J04791 ODC1 Ornithine decarboxylase 1 X07944exon#1-12_s_at 3.5 3.9 2.8 X07944 ODC1 Rat ornithine decarboxylase gene (EC 4.1.1.17). X71898_at 2.8 3.1 3.2 X71898 PLAUR Plasminogen activator, urokinase receptor X96437mRNA_g_at 3.3 2.8 2.2 X96437 PRG1 Rattus norvegicus PRG1 gene, proteoglycan1, secretory granule, IER3 S67722_s_at 4.0 6.8 2.3 S67722 PTGS2 Prostaglandin–endoperoxide synthase 2 L12025_at 2.8 3.9 4.1 L12025 PVR Tumor-associated antigen 1 rc_AA892750_at 7.8 4.4 2.3 AA892750 RGS1 Regulator of G-protein signaling 1 rc_AA891911_at 4.5 3.3 13.3 AA891911 SPRR1 Similar to cornifin alpha (small proline-rich protein 1) (SPRR1) (LOC365848), mRNA L46593cds_at 4.2 3.0 13.1 L46593 SPRR1 Rattus norvegicus small proline-rich protein (spr) gene, complete cds. M57263_at 2.6 3.9 10.0 M57263 TGM1 Transglutaminase 1 rc_AI169327_g_at 2.9 4.8 6.5 AI169327 TIMP1 Tissue inhibitor of metalloproteinase 1 rc_AI639058_s_at 2.2 2.7 2.5 AI639058 TMEPAI Transmembrane, prostate androgen–induced RNA (predicted) rc_Al236158_at 10.7 2.7 2.8 AI236158 XCL1 Small inducible cytokine subfamily C, member 1 (lymphotactin) Open in new tab FIG. 6 Open in new tabDownload slide IPA of cellular locations and relationships between transcripts that were upregulated at each of the time points. Gene abbreviations are in Table 3. The transcripts are related to EGF and TNF signaling. The lines connecting the nodes are curated relationships from the literature. Solid lines are direct relationships (where the gene products make direct physical contact with each other), and the dashed lines are indirect relationships (relationships that are not known to require physical contact between the gene products). FIG. 6 Open in new tabDownload slide IPA of cellular locations and relationships between transcripts that were upregulated at each of the time points. Gene abbreviations are in Table 3. The transcripts are related to EGF and TNF signaling. The lines connecting the nodes are curated relationships from the literature. Solid lines are direct relationships (where the gene products make direct physical contact with each other), and the dashed lines are indirect relationships (relationships that are not known to require physical contact between the gene products). Early (1-h) Changes in GeneChip Probe Expression As mentioned above, most of the 1-h changes that remained changed significantly at later time points were related to transcription activity in the nucleus. Genes that were up at 1 h in the epidermis may encode proteins whose activities serve as triggers or initiators of the JP-8–induced inflammatory process. Genes associated with the biological processes (generic Gene Ontology slim) of metabolism (54), development (14), and death (5) were most changed right at the end of the exposure (supplementary Fig. 1). Molecular functions (generic Gene Ontology slim) represented by changed genes were nucleic acid binding (35), catalytic activity (28), transcription regulator activity (25), protein binding (20), and signal transducer activity (17) (supplementary Fig. 2). The cellular component genes (generic Gene Ontology slim) changed were primarily related to the nucleus (41) at the end of the exposure (supplementary Fig. 3). Analysis of the transcription factor probes that are changed by the end of the exposure help identify some of the signaling pathways that are activated by JP-8 early in the inflammatory sequence. The IPA Functional Analysis identified the 10 signaling pathways that were most significant in the Ingenuity Pathways Knowledge Base (Fig. 7). Of these 10 pathways, ERK/MAPK-, IL-6-, PDGF, and P38 MAPK-signaling pathways had the most significant changes in gene expression due to JP-8 exposure. Table 4 shows 13 transcription factor probes related to the changed signaling pathways that were changed at the first skin sample collection time. Many of these transcription factors, especially FOS, JUN, and PIK3R2, are associated with several signaling pathways in the Ingenuity Pathways Knowledge Base. FIG. 7 Open in new tabDownload slide Signaling pathways associated with genes upregulated at 1 h. The significance is expressed as negative log of the p value calculated from Fisher's exact test (IPA). FIG. 7 Open in new tabDownload slide Signaling pathways associated with genes upregulated at 1 h. The significance is expressed as negative log of the p value calculated from Fisher's exact test (IPA). TABLE 4 Signaling Genes That Were Changed Twofold and Statistically Significant (p = 0.05) at the End of the 1-h Exposure and the Signaling Pathways That They Are Associated with According to IPA ERK/MAPK IL-6 PDGF P38 MAPK IGF-1 EGF IL-2 JAK/stat Neurotrophin/Trk Chemokine CCL2 × CDKN1A × CEPB × DDIT3 × × DUSP1 × × DUSP6 × FOS × × × × × × × × HSPB1 × × × JUN × × × × × × × MYC × × × PIK3R2 × × × × × × × SOCS3 × YWHAG × × ERK/MAPK IL-6 PDGF P38 MAPK IGF-1 EGF IL-2 JAK/stat Neurotrophin/Trk Chemokine CCL2 × CDKN1A × CEPB × DDIT3 × × DUSP1 × × DUSP6 × FOS × × × × × × × × HSPB1 × × × JUN × × × × × × × MYC × × × PIK3R2 × × × × × × × SOCS3 × YWHAG × × Open in new tab TABLE 4 Signaling Genes That Were Changed Twofold and Statistically Significant (p = 0.05) at the End of the 1-h Exposure and the Signaling Pathways That They Are Associated with According to IPA ERK/MAPK IL-6 PDGF P38 MAPK IGF-1 EGF IL-2 JAK/stat Neurotrophin/Trk Chemokine CCL2 × CDKN1A × CEPB × DDIT3 × × DUSP1 × × DUSP6 × FOS × × × × × × × × HSPB1 × × × JUN × × × × × × × MYC × × × PIK3R2 × × × × × × × SOCS3 × YWHAG × × ERK/MAPK IL-6 PDGF P38 MAPK IGF-1 EGF IL-2 JAK/stat Neurotrophin/Trk Chemokine CCL2 × CDKN1A × CEPB × DDIT3 × × DUSP1 × × DUSP6 × FOS × × × × × × × × HSPB1 × × × JUN × × × × × × × MYC × × × PIK3R2 × × × × × × × SOCS3 × YWHAG × × Open in new tab Later Changes in GeneChip Probe Expression Four hours following the exposure, the chemokine and PDGF-signaling pathways were still altered according to the IPA. At 4 h, T-cell signaling and G-coupled receptor–signaling pathways also were significantly changed (Fig. 8). Metabolic pathways for (1) sterol biosynthesis; (2) synthesis and degradation of ketone bodies; and (3) valine, leucine, and isoleucine degradation were altered for the first time at 4 h after the beginning of the JP-8 exposure. FIG. 8 Open in new tabDownload slide Signaling and metabolic pathways associated with genes changed at 4 h. The significance is expressed as negative log of the p value calculated from Fisher's exact test (IPA). FIG. 8 Open in new tabDownload slide Signaling and metabolic pathways associated with genes changed at 4 h. The significance is expressed as negative log of the p value calculated from Fisher's exact test (IPA). Eight hours after the beginning of the exposure, there were no signaling pathways activated at a level greater than chance according to the IPA. By 8 h, 13 additional metabolic pathways were significantly altered in addition to the 3 metabolic pathways altered at 4 h (Table 5). The majority (64%) of the gene transcripts related to metabolism that were changed at 8 h were decreased; the exceptions were pathways for sterol biosynthesis, as well as synthesis and degradation of ketone bodies. Although none of the metabolic pathways that were significantly changed in the later time points were changed at 1 h, there were two metabolism-related genes in those pathways that were increased at all three skin-sampling points (Table 3). The probe for a cytochrome P450 isoform (CYP1B1) was increased about 5-fold at 1 h and about 10-fold at 4 and 8 h. Similarly, the >ornithine decarboxylase (ODC1) probes were increased between about three- and sixfold depending on the sampling point. TABLE 5 Metabolism Pathways in the Epidermis Changed at 8 h after the Beginning of the 1-h JP-8 Exposure as Determined by IPA (p < 0.05) Pathway Number of genes associated with pathway Ratio of genes upregulated to downregulated Fatty acid metabolism 20 0.4 Tryptophan metabolism 19 0.5 Valine, leucine, and isoleucine degradation (+) 17 0.2 Arginine and proline metabolism 12 0.3 Butanoate metabolism 12 0.3 Glutathione metabolism 10 0.3 Pyruvate metabolism 10 0.1 Lysine degradation 9 0.3 Propanoate metabolism 9 0.1 Fatty acid biosynthesis 8 0.1 Sterol biosynthesis (+) 8 7.0 Bile acid biosynthesis 7 All down Urea cycle and metabolism of amino groups 7 0.2 Glutamate metabolism 6 0.2 Nitrogen metabolism 6 0.5 Synthesis and degradation of ketone bodies (+) 4 1.0 Pathway Number of genes associated with pathway Ratio of genes upregulated to downregulated Fatty acid metabolism 20 0.4 Tryptophan metabolism 19 0.5 Valine, leucine, and isoleucine degradation (+) 17 0.2 Arginine and proline metabolism 12 0.3 Butanoate metabolism 12 0.3 Glutathione metabolism 10 0.3 Pyruvate metabolism 10 0.1 Lysine degradation 9 0.3 Propanoate metabolism 9 0.1 Fatty acid biosynthesis 8 0.1 Sterol biosynthesis (+) 8 7.0 Bile acid biosynthesis 7 All down Urea cycle and metabolism of amino groups 7 0.2 Glutamate metabolism 6 0.2 Nitrogen metabolism 6 0.5 Synthesis and degradation of ketone bodies (+) 4 1.0 Note. Pathways marked with a plus were also altered at 4 h after exposure. Open in new tab TABLE 5 Metabolism Pathways in the Epidermis Changed at 8 h after the Beginning of the 1-h JP-8 Exposure as Determined by IPA (p < 0.05) Pathway Number of genes associated with pathway Ratio of genes upregulated to downregulated Fatty acid metabolism 20 0.4 Tryptophan metabolism 19 0.5 Valine, leucine, and isoleucine degradation (+) 17 0.2 Arginine and proline metabolism 12 0.3 Butanoate metabolism 12 0.3 Glutathione metabolism 10 0.3 Pyruvate metabolism 10 0.1 Lysine degradation 9 0.3 Propanoate metabolism 9 0.1 Fatty acid biosynthesis 8 0.1 Sterol biosynthesis (+) 8 7.0 Bile acid biosynthesis 7 All down Urea cycle and metabolism of amino groups 7 0.2 Glutamate metabolism 6 0.2 Nitrogen metabolism 6 0.5 Synthesis and degradation of ketone bodies (+) 4 1.0 Pathway Number of genes associated with pathway Ratio of genes upregulated to downregulated Fatty acid metabolism 20 0.4 Tryptophan metabolism 19 0.5 Valine, leucine, and isoleucine degradation (+) 17 0.2 Arginine and proline metabolism 12 0.3 Butanoate metabolism 12 0.3 Glutathione metabolism 10 0.3 Pyruvate metabolism 10 0.1 Lysine degradation 9 0.3 Propanoate metabolism 9 0.1 Fatty acid biosynthesis 8 0.1 Sterol biosynthesis (+) 8 7.0 Bile acid biosynthesis 7 All down Urea cycle and metabolism of amino groups 7 0.2 Glutamate metabolism 6 0.2 Nitrogen metabolism 6 0.5 Synthesis and degradation of ketone bodies (+) 4 1.0 Note. Pathways marked with a plus were also altered at 4 h after exposure. Open in new tab Validation of GeneChip Expression with Real-Time RT-PCR Twenty-seven genes, expected to be changed and related to inflammation and measured in the microarray studies, were also measured by real-time RT-PCR. Comparisons were made between GeneChip probes and RT-PCR probes for the same gene (Table 6). For the purpose of complete comparison, ratios of transcripts from control and treated genes were used even if they did not meet the requirements (fold change cutoff and statistical significance) to be considered confirmed differences. In general, the fold-change values for the real-time RT-PCR were greater than the fold-change values for the GeneChip microarray studies. The variability increased with the magnitude of the change in expression levels, i.e., the 8-h fold changes did not match as well as the 1-h fold changes. Figure 9 shows a good correlation between the real-time RT-PCR and microarray fold changes for all measurements (p ≤ 0.001, Pearson correlation coefficient was R = 0.654). TABLE 6 Comparisons of GeneChip and Real-Time RT-PCR Results Fold changea Gene transcript Probe set Identificationb 1 h 4 h 8 h ADM GeneChip D15069_s_at 3.1 1.7 2.0 RT-PCR Rn00562327_m1 5.5 13.9 2.0 AREG GeneChip X55183_at 6.0 11.7 3.2 RT-PCR Rn00567471_m1 18.6 23.0 15.9 Chemokine (CCL2) GeneChip X17053mRNA_s_at 3.6 2.8 4.3 RT-PCR Rn00580555_m1 37.4 20.5 24.3 Chemokine (CCL3) GeneChip U22414_at 1.3 2.1 9.4 RT-PCR Rn00564660_m1 11.1 33.1 22.4 Chemokine (CCL4) GeneChip U06434_at 1.3 1.1 1.3 RT-PCR Rn00587826_m1 31.8 16.9 24.6 CCNL1 GeneChip AF030091UTR#1_at 1.9 1.5 1.5 RT-PCR Rn00490819_m1 5.2 2.0 0.7 CREM GeneChip S66024_at 5.0 5.9 2.3 RT-PCR Rn00569145_m1 8.5 5.0 1.8 Chemokine (CXCL1) GeneChip D11445exon#1-4 1.7 4.5 2.3 RT-PCR Rn00578225_m1 20.3 38.4 24.7 Chemokine (CXCL2) GeneChip U45965_at 5.9 14.3 11.5 RT-PCR Rn00586403_m1 54.9 26.2 16.8 EGR1 GeneChip M18416_at 3.2 1.5 1.2 RT-PCR Rn00561138_m1 3.4 0.8 0.5 GBP2 GeneChip M80367_at 0.6 1.2 3.2 RT-PCR Rn00592467_m1 1.6 4.0 2.0 IL-1α GeneChip D00403_at 1.5 0.9 3.2 GeneChip D00403_g_at 0.9 0.9 2.4 RT-PCR Rn00566700_m1 0.5 0.5 0.7 IL-1β GeneChip M98820_g_at 1.5 0.7 5.4 GeneChip M98820_at 1.6 0.9 4.1 RT-PCR Rn00580432_m1 1.0 0.9 0.6 IL-10 GeneChip X60675_at 1.1 2.0 2.8 RT-PCR Rn01483989_m1 10.2 24.6 10.4 IL-1 rec I GeneChip M95578_g_at 0.9 0.6 1.1 GeneChip U14010_at 1.2 0.6 1.4 RT-PCR Rn00565482_m1 0.7 1.7 1.6 IL-1 rec II GeneChip Z22812_at 1.3 0.8 1.2 RT-PCR Rn00588589_m1 1.4 1.1 0.5 IL-1 rec acc protein GeneChip U48592_at 0.9 0.6 0.8 RT-PCR Rn00492642_m1 0.6 0.7 0.8 IL-6 GeneChip M26744_at 16.1 2.1 3.0 RT-PCR Rn00561420_m1 89.6 15.0 2.6 IL-6 rec GeneChip M58587_at 1.1 1.1 0.8 RT-PCR Rn00566707_m1 1.1 1.3 0.5 MT3 GeneChip rc_AA924772_at 0.7 1.1 1.0 RT-PCR Rn00588658_g1 1.1 0.7 0.5 NF-κB GeneChip L26267_at 0.7 1.8 1.3 RT-PCR Rn01502266_m1 0.8 4.2 1.2 PTGDS2 GeneChip D82071_at 0.9. 1.7 3.2 RT-PCR Rn00581276_m1 1.1 1.1 1.2 PAI2A GeneChip X64563cds_at 0.9 2.6 1.3 RT-PCR Rn00572553_m1 0.9 11.9 2.0 S100A9 GeneChip L18948_at 1.4 8.6 14.6 RT-PCR Rn00585879_m1 4.0 38.0 150.0 Chemokine (CCL20) GeneChip AF053312_s_at 1.7 20.2 18.4 RT-PCR Rn00570287_m1 6.0 46.2 23.3 SOD2 GeneChip Y00497_s_at 0.9 1.0 1.0 RT-PCR Rn00566942_g1 1.3 3.8 6.4 TNF rec 1a GeneChip M63122_at 1.2 1.5 1.5 RT-PCR Rn00565310_m1 2.8 2.0 1.1 Fold changea Gene transcript Probe set Identificationb 1 h 4 h 8 h ADM GeneChip D15069_s_at 3.1 1.7 2.0 RT-PCR Rn00562327_m1 5.5 13.9 2.0 AREG GeneChip X55183_at 6.0 11.7 3.2 RT-PCR Rn00567471_m1 18.6 23.0 15.9 Chemokine (CCL2) GeneChip X17053mRNA_s_at 3.6 2.8 4.3 RT-PCR Rn00580555_m1 37.4 20.5 24.3 Chemokine (CCL3) GeneChip U22414_at 1.3 2.1 9.4 RT-PCR Rn00564660_m1 11.1 33.1 22.4 Chemokine (CCL4) GeneChip U06434_at 1.3 1.1 1.3 RT-PCR Rn00587826_m1 31.8 16.9 24.6 CCNL1 GeneChip AF030091UTR#1_at 1.9 1.5 1.5 RT-PCR Rn00490819_m1 5.2 2.0 0.7 CREM GeneChip S66024_at 5.0 5.9 2.3 RT-PCR Rn00569145_m1 8.5 5.0 1.8 Chemokine (CXCL1) GeneChip D11445exon#1-4 1.7 4.5 2.3 RT-PCR Rn00578225_m1 20.3 38.4 24.7 Chemokine (CXCL2) GeneChip U45965_at 5.9 14.3 11.5 RT-PCR Rn00586403_m1 54.9 26.2 16.8 EGR1 GeneChip M18416_at 3.2 1.5 1.2 RT-PCR Rn00561138_m1 3.4 0.8 0.5 GBP2 GeneChip M80367_at 0.6 1.2 3.2 RT-PCR Rn00592467_m1 1.6 4.0 2.0 IL-1α GeneChip D00403_at 1.5 0.9 3.2 GeneChip D00403_g_at 0.9 0.9 2.4 RT-PCR Rn00566700_m1 0.5 0.5 0.7 IL-1β GeneChip M98820_g_at 1.5 0.7 5.4 GeneChip M98820_at 1.6 0.9 4.1 RT-PCR Rn00580432_m1 1.0 0.9 0.6 IL-10 GeneChip X60675_at 1.1 2.0 2.8 RT-PCR Rn01483989_m1 10.2 24.6 10.4 IL-1 rec I GeneChip M95578_g_at 0.9 0.6 1.1 GeneChip U14010_at 1.2 0.6 1.4 RT-PCR Rn00565482_m1 0.7 1.7 1.6 IL-1 rec II GeneChip Z22812_at 1.3 0.8 1.2 RT-PCR Rn00588589_m1 1.4 1.1 0.5 IL-1 rec acc protein GeneChip U48592_at 0.9 0.6 0.8 RT-PCR Rn00492642_m1 0.6 0.7 0.8 IL-6 GeneChip M26744_at 16.1 2.1 3.0 RT-PCR Rn00561420_m1 89.6 15.0 2.6 IL-6 rec GeneChip M58587_at 1.1 1.1 0.8 RT-PCR Rn00566707_m1 1.1 1.3 0.5 MT3 GeneChip rc_AA924772_at 0.7 1.1 1.0 RT-PCR Rn00588658_g1 1.1 0.7 0.5 NF-κB GeneChip L26267_at 0.7 1.8 1.3 RT-PCR Rn01502266_m1 0.8 4.2 1.2 PTGDS2 GeneChip D82071_at 0.9. 1.7 3.2 RT-PCR Rn00581276_m1 1.1 1.1 1.2 PAI2A GeneChip X64563cds_at 0.9 2.6 1.3 RT-PCR Rn00572553_m1 0.9 11.9 2.0 S100A9 GeneChip L18948_at 1.4 8.6 14.6 RT-PCR Rn00585879_m1 4.0 38.0 150.0 Chemokine (CCL20) GeneChip AF053312_s_at 1.7 20.2 18.4 RT-PCR Rn00570287_m1 6.0 46.2 23.3 SOD2 GeneChip Y00497_s_at 0.9 1.0 1.0 RT-PCR Rn00566942_g1 1.3 3.8 6.4 TNF rec 1a GeneChip M63122_at 1.2 1.5 1.5 RT-PCR Rn00565310_m1 2.8 2.0 1.1 a Two-fold decreases in gene expression have values less than 0.5 in the table. To convert them to negative fold change, divide the fractional fold change into − 1, e.g., − 1/0.306 = − 3.27. Fold changes were not tested for statistical significance to facilitate comparisons between the methods. b Probe set identification is specific to the company providing the probe. GeneChip identification is the Affymetrix probe set number for the RG-U34A array. The RT-PCR identification is the TaqMan Gene Expression Assay number from Applied Biosystems, Foster City CA. Open in new tab TABLE 6 Comparisons of GeneChip and Real-Time RT-PCR Results Fold changea Gene transcript Probe set Identificationb 1 h 4 h 8 h ADM GeneChip D15069_s_at 3.1 1.7 2.0 RT-PCR Rn00562327_m1 5.5 13.9 2.0 AREG GeneChip X55183_at 6.0 11.7 3.2 RT-PCR Rn00567471_m1 18.6 23.0 15.9 Chemokine (CCL2) GeneChip X17053mRNA_s_at 3.6 2.8 4.3 RT-PCR Rn00580555_m1 37.4 20.5 24.3 Chemokine (CCL3) GeneChip U22414_at 1.3 2.1 9.4 RT-PCR Rn00564660_m1 11.1 33.1 22.4 Chemokine (CCL4) GeneChip U06434_at 1.3 1.1 1.3 RT-PCR Rn00587826_m1 31.8 16.9 24.6 CCNL1 GeneChip AF030091UTR#1_at 1.9 1.5 1.5 RT-PCR Rn00490819_m1 5.2 2.0 0.7 CREM GeneChip S66024_at 5.0 5.9 2.3 RT-PCR Rn00569145_m1 8.5 5.0 1.8 Chemokine (CXCL1) GeneChip D11445exon#1-4 1.7 4.5 2.3 RT-PCR Rn00578225_m1 20.3 38.4 24.7 Chemokine (CXCL2) GeneChip U45965_at 5.9 14.3 11.5 RT-PCR Rn00586403_m1 54.9 26.2 16.8 EGR1 GeneChip M18416_at 3.2 1.5 1.2 RT-PCR Rn00561138_m1 3.4 0.8 0.5 GBP2 GeneChip M80367_at 0.6 1.2 3.2 RT-PCR Rn00592467_m1 1.6 4.0 2.0 IL-1α GeneChip D00403_at 1.5 0.9 3.2 GeneChip D00403_g_at 0.9 0.9 2.4 RT-PCR Rn00566700_m1 0.5 0.5 0.7 IL-1β GeneChip M98820_g_at 1.5 0.7 5.4 GeneChip M98820_at 1.6 0.9 4.1 RT-PCR Rn00580432_m1 1.0 0.9 0.6 IL-10 GeneChip X60675_at 1.1 2.0 2.8 RT-PCR Rn01483989_m1 10.2 24.6 10.4 IL-1 rec I GeneChip M95578_g_at 0.9 0.6 1.1 GeneChip U14010_at 1.2 0.6 1.4 RT-PCR Rn00565482_m1 0.7 1.7 1.6 IL-1 rec II GeneChip Z22812_at 1.3 0.8 1.2 RT-PCR Rn00588589_m1 1.4 1.1 0.5 IL-1 rec acc protein GeneChip U48592_at 0.9 0.6 0.8 RT-PCR Rn00492642_m1 0.6 0.7 0.8 IL-6 GeneChip M26744_at 16.1 2.1 3.0 RT-PCR Rn00561420_m1 89.6 15.0 2.6 IL-6 rec GeneChip M58587_at 1.1 1.1 0.8 RT-PCR Rn00566707_m1 1.1 1.3 0.5 MT3 GeneChip rc_AA924772_at 0.7 1.1 1.0 RT-PCR Rn00588658_g1 1.1 0.7 0.5 NF-κB GeneChip L26267_at 0.7 1.8 1.3 RT-PCR Rn01502266_m1 0.8 4.2 1.2 PTGDS2 GeneChip D82071_at 0.9. 1.7 3.2 RT-PCR Rn00581276_m1 1.1 1.1 1.2 PAI2A GeneChip X64563cds_at 0.9 2.6 1.3 RT-PCR Rn00572553_m1 0.9 11.9 2.0 S100A9 GeneChip L18948_at 1.4 8.6 14.6 RT-PCR Rn00585879_m1 4.0 38.0 150.0 Chemokine (CCL20) GeneChip AF053312_s_at 1.7 20.2 18.4 RT-PCR Rn00570287_m1 6.0 46.2 23.3 SOD2 GeneChip Y00497_s_at 0.9 1.0 1.0 RT-PCR Rn00566942_g1 1.3 3.8 6.4 TNF rec 1a GeneChip M63122_at 1.2 1.5 1.5 RT-PCR Rn00565310_m1 2.8 2.0 1.1 Fold changea Gene transcript Probe set Identificationb 1 h 4 h 8 h ADM GeneChip D15069_s_at 3.1 1.7 2.0 RT-PCR Rn00562327_m1 5.5 13.9 2.0 AREG GeneChip X55183_at 6.0 11.7 3.2 RT-PCR Rn00567471_m1 18.6 23.0 15.9 Chemokine (CCL2) GeneChip X17053mRNA_s_at 3.6 2.8 4.3 RT-PCR Rn00580555_m1 37.4 20.5 24.3 Chemokine (CCL3) GeneChip U22414_at 1.3 2.1 9.4 RT-PCR Rn00564660_m1 11.1 33.1 22.4 Chemokine (CCL4) GeneChip U06434_at 1.3 1.1 1.3 RT-PCR Rn00587826_m1 31.8 16.9 24.6 CCNL1 GeneChip AF030091UTR#1_at 1.9 1.5 1.5 RT-PCR Rn00490819_m1 5.2 2.0 0.7 CREM GeneChip S66024_at 5.0 5.9 2.3 RT-PCR Rn00569145_m1 8.5 5.0 1.8 Chemokine (CXCL1) GeneChip D11445exon#1-4 1.7 4.5 2.3 RT-PCR Rn00578225_m1 20.3 38.4 24.7 Chemokine (CXCL2) GeneChip U45965_at 5.9 14.3 11.5 RT-PCR Rn00586403_m1 54.9 26.2 16.8 EGR1 GeneChip M18416_at 3.2 1.5 1.2 RT-PCR Rn00561138_m1 3.4 0.8 0.5 GBP2 GeneChip M80367_at 0.6 1.2 3.2 RT-PCR Rn00592467_m1 1.6 4.0 2.0 IL-1α GeneChip D00403_at 1.5 0.9 3.2 GeneChip D00403_g_at 0.9 0.9 2.4 RT-PCR Rn00566700_m1 0.5 0.5 0.7 IL-1β GeneChip M98820_g_at 1.5 0.7 5.4 GeneChip M98820_at 1.6 0.9 4.1 RT-PCR Rn00580432_m1 1.0 0.9 0.6 IL-10 GeneChip X60675_at 1.1 2.0 2.8 RT-PCR Rn01483989_m1 10.2 24.6 10.4 IL-1 rec I GeneChip M95578_g_at 0.9 0.6 1.1 GeneChip U14010_at 1.2 0.6 1.4 RT-PCR Rn00565482_m1 0.7 1.7 1.6 IL-1 rec II GeneChip Z22812_at 1.3 0.8 1.2 RT-PCR Rn00588589_m1 1.4 1.1 0.5 IL-1 rec acc protein GeneChip U48592_at 0.9 0.6 0.8 RT-PCR Rn00492642_m1 0.6 0.7 0.8 IL-6 GeneChip M26744_at 16.1 2.1 3.0 RT-PCR Rn00561420_m1 89.6 15.0 2.6 IL-6 rec GeneChip M58587_at 1.1 1.1 0.8 RT-PCR Rn00566707_m1 1.1 1.3 0.5 MT3 GeneChip rc_AA924772_at 0.7 1.1 1.0 RT-PCR Rn00588658_g1 1.1 0.7 0.5 NF-κB GeneChip L26267_at 0.7 1.8 1.3 RT-PCR Rn01502266_m1 0.8 4.2 1.2 PTGDS2 GeneChip D82071_at 0.9. 1.7 3.2 RT-PCR Rn00581276_m1 1.1 1.1 1.2 PAI2A GeneChip X64563cds_at 0.9 2.6 1.3 RT-PCR Rn00572553_m1 0.9 11.9 2.0 S100A9 GeneChip L18948_at 1.4 8.6 14.6 RT-PCR Rn00585879_m1 4.0 38.0 150.0 Chemokine (CCL20) GeneChip AF053312_s_at 1.7 20.2 18.4 RT-PCR Rn00570287_m1 6.0 46.2 23.3 SOD2 GeneChip Y00497_s_at 0.9 1.0 1.0 RT-PCR Rn00566942_g1 1.3 3.8 6.4 TNF rec 1a GeneChip M63122_at 1.2 1.5 1.5 RT-PCR Rn00565310_m1 2.8 2.0 1.1 a Two-fold decreases in gene expression have values less than 0.5 in the table. To convert them to negative fold change, divide the fractional fold change into − 1, e.g., − 1/0.306 = − 3.27. Fold changes were not tested for statistical significance to facilitate comparisons between the methods. b Probe set identification is specific to the company providing the probe. GeneChip identification is the Affymetrix probe set number for the RG-U34A array. The RT-PCR identification is the TaqMan Gene Expression Assay number from Applied Biosystems, Foster City CA. Open in new tab FIG. 9 Open in new tabDownload slide Correlation between the quantification of specific transcripts using microarray and real-time RT-PCR. The data represent 27 transcripts that were analyzed using both techniques at three time points, 1, 4, and 8 h after the beginning of a 1-h exposure. The Pearson correlation coefficient (R) was 0.654 with p < 0.0001. FIG. 9 Open in new tabDownload slide Correlation between the quantification of specific transcripts using microarray and real-time RT-PCR. The data represent 27 transcripts that were analyzed using both techniques at three time points, 1, 4, and 8 h after the beginning of a 1-h exposure. The Pearson correlation coefficient (R) was 0.654 with p < 0.0001. DISCUSSION It is important to understand the early events in the inflammatory process due to JP-8 if we want to be able to intervene in the process, prophylactically or therapeutically. Our objective was to evaluate the initial changes in gene expression so that the triggers or initiating events might become apparent. The mRNA has been shown to respond rapidly to chemical-induced inflammatory events (Boniface et al., 2005; Chou et al., 2006; Espinoza et al., 2004; Rogers et al., 2003; Rosenthal et al., 2001) and is necessary for new expression of protein. We used microarrays to measure the changes in mRNA levels in the epidermis immediately and for a short period after a brief JP-8 exposure. We also used IPA to identify the signaling pathways involved. Pathway analysis of genome-wide expression data has been shown to provide useful information about network perturbation with disease (Calvano et al., 2005; Janes et al., 2003; Nakatani et al., 2006) and toxicant effects (Coe et al., 2006; Currie et al., 2005). Gene expression in normal skin has not been completely characterized. Our previous gene expression study on untreated skin using the Affymetrix RT-U34 (850 probes) showed that the majority of the genes normally expressed in whole skin were related to metabolism, oxidative/cellular stress and signal transduction (Rogers et al., 2003). In the current study in the epidermis with a much larger GeneChip (8798 probes) and a sham treatment, metabolic transcripts were also significantly expressed to a greater extent. Metabolism in the epidermis is active and primarily localized in the endoplasmic reticulum (Merk et al., 1996). Nucleic acid–binding, nucleotide-binding, RNA-binding, and translation factor activity probes were also well represented in the epidermal tissue sections, as they were in whole skin. These results are not surprising because the epidermis is a metabolically active tissue involved in the process of proliferation and differentiation and is the first line of defense for foreign objects on the skin (Mack et al., 2005; Madison, 2003; O'Shaughnessy and Christiano, 2001). When categorized with gene ontology (i.e., biological process, cellular component, and molecular function), the significant changes in gene expression showed a distinct shift with time (Table 2). The sequential expression of genes in the skin reflects an integrated response to differentiation, injury, or toxic insult (Franco et al., 2005; Lee et al., 2005; Petersen et al., 1995). Both normal function of the epidermis and the process of repair depend on a well-controlled balance between keratinocyte differentiation and proliferation (Werner and Smola, 2001). Keratinocytes induced to differentiate by calcium show a sequential gene expression pattern over a period of 14 days (Seo et al., 2005). This in vivo exposure shows that genes involved in cell differentiation respond within an hour to JP-8 but return to normal at 4 or 8 h (Table 2), suggesting a very rapid induction of this biological process. A recent study of human epidermal keratinocytes exposed to JP-8 for only 1 min states that transcription and translation were both upregulated at 12 and 24 h after exposure (Chou et al., 2006). As might be expected, regulation of transcription with DNA- and nucleic acid–binding genes responds rapidly, followed in sequence by genes involved with protein-binding, catalytic, and antioxidant activity (Table 2). Oxidative stress in the skin has previously been suggested to be caused by JP-8 and other irritating chemicals (Ramos et al., 2004; Rogers et al., 2001, 2003). Probably, the most interesting shift in gene expression with time after exposure is the genes associated with metabolism, which are altered at 1-h but dramatically altered at 8 h. These metabolic changes at the latest time point will be discussed below. The mRNA transcript levels that were changed and remained changed at 4 and 8 h after initiation of a 1-h exposure (Table 3) can help elucidate events in the JP-8–induced inflammatory process. The nature of these responses at each of the sampling times suggests that the transcript changes probably reflect important events induced by the fuel in the skin. JP-8 exposure resulted in persistent changes in genes related to structural proteins, cell signaling, growth, chemokines, and enzymes. Some genes fit into more than one category. In general, these changes confirm many of the JP-8–induced responses from longer exposures and cultured cells, and they allow us begin to develop hypotheses about the mechanisms of action. The mRNA coding for some structural proteins (aquaporin 3 [AQP3], SPRR1, tissue inhibitor of metalloproteinase 1 [TIMP1], and plasminogen activator receptor [PLAUR]) was robustly increased (Table 3). AQP3 is involved in water transport and found in the basal epidermis (Hara-Chikuma and Verkman, 2005; Nejsum et al., 2002). Increased levels of AQP3 are found in eczema compared to healthy skin (Olsson et al., 2006) and may contribute to increased transepidermal water loss found with JP-8 (Kanikkannan et al., 2001, 2002; Monteiro-Riviere et al., 2001). Cornifin (SPRR1) is a structural component of the cytoskeleton (Vollberg et al., 1992) whose expression is increased by FOS-like antigen 1 (FOSL1) (Patterson et al., 2001) which was also robustly increased in our study. SPRR1 protein is overexpressed in inflammatory and neoplastic skin (De Heller-Milev et al., 2000; Koizumi et al., 1996) but has not previously been implicated in chemical exposure. TIMP1, a representative of the natural inhibitors of the matrix metalloproteinases that degrades the extracellular matrix (ECM) (Castoldi et al., 2003), was also increased in our study. Degradation and remodeling of the ECM is crucial for proliferation and differentiation (Van et al., 1995), and the robust activation of this transcript appears to confirm JP-8 as an initiator of these processes. The transcription factor JUNB, which was also increased at each time of sampling in our study, increases the expression of TIMP1 mRNA (Lan et al., 2005) among other effects on proliferation and differentiation (Angel and Szabowski, 2002; Werner and Smola, 2001). The final increased structural protein transcript was for the urokinase-type PLAUR which influences many normal and pathological processes related to cell-surface plasminogen activation and localized degradation of the ECM (Braungart et al., 2001). Espinoza (2004) found that transcripts for PLAUR, vimentin and keratin 18 (other structurally related genes) were increased 7 days after JP-8 treatment in keratinocytes. Transcription of PLAUR is increased by FOSL1 (Andersen et al., 2002) which was also increased at each time point in our study. Rapid and continued induction of these structural protein–related genes suggest that JP-8 interferes with structural integrity of the epidermis and be an early response to epidermal damage reported previously (Kanikkannan et al., 2002; Monteiro-Riviere et al., 2001, 2004). Cell signaling and interaction with DNA was a main category of transcript robustly changed by JP-8 exposure (Table 3). The changed transcripts were CEBPB, DUSP6, MIG6, FOSL1, JUNB, NR4A3, and RGS1. These regulatory proteins and transcription factors are shared in many signaling pathways and are indications of activation of the machinery of cellular growth and regulation. Rogers et al. (2003) identified several cell-signaling genes in the whole skin that were increased with in vivo exposures in a similar rat model with other irritating chemicals, including m-xylene which is a JP-8 component. We observed increased expression of the chemokines CCL2 and XCL1 (Table 3) at each of the time points. The proteins from these chemokine genes are involved in recruitment and subsequent extravasation of polymorphonuclear cells from the capillaries in the upper dermis and their migration in the tissue (Uchi et al., 2000). In a study with JP-8, Gallucci et al. (2004) showed changes in chemokine mRNA within hours after a 7-day treatment in rats. Moreover, we observed increased expression of the polio virus receptor, PVR (Table 3), which is expressed at cell junctions on primary vascular endothelial cells and is involved in transmigration of monocytes through the epithelium (Reymond et al., 2004). These results are consistent with previous work demonstrating the potential for JP-8–induced skin inflammation (Allen et al., 2000; Babu et al., 2004b; Gallucci et al., 2004; Ullrich and Lyons, 2000), and it is possible that CEBPB-mediated induction of CCL2, XCL1, and PVR may play an important role. Another category of transcripts that was robustly increased in number (Table 3) was those growth factors related to growth, apoptosis, differentiation, and proliferation. Amphiregulin (AREG) is a member of the EGF family, and it inhibits growth of certain aggressive carcinoma cell lines. The expression of AREG is regulated by inducible prostaglandin–endoperoxide synthase (PTGS2) also known as cyclooxygenase-2 (COX-2) (Chang et al., 2005), a key enzyme in prostaglandin biosynthesis that was also increased by JP-8 in our study. PTGS2 has been implicated in the JP-8–induced reduction in delayed-type hypersensitivity and contact hypersensitivity (Ramos et al., 2002; Ullrich and Lyons, 2000). Heparin-binding EGF-like growth factor also known as diphtheria toxin receptor (DTR) increases the expression of COX-2 protein (Han et al., 2002). Follistatin (FST) is an activin antagonist involved in inflammation, cellular growth, and proliferation (Sulyok et al., 2004; Wankell et al., 2003). As far as we know, this is the first evidence of FST involvement in chemical-induced inflammation. Proteoglycan1, secretory granule serves as a mediator of granule-mediated apoptosis (Arlt et al., 2001; Zhuang et al., 2004) but has not been previously shown to be in the epidermis. Transmembrane, prostate androgen–induced mRNA negatively regulates growth of androgen-responsive prostate cancer cells (Xu et al., 2003), but its function in the epidermis is unknown. Treatment of psoriasis with topical 1,25-dihydroxyvitamin D3 returned transglutaminase (TGM1) protein levels toward those in normal skin (Reichrath et al., 1997), but TGM1 has not been previously implicated in chemical-induced inflammation. These changes suggest mechanisms for the JP-8–induced effects on transcripts and protein levels related to growth and apoptosis in keratinocytes and fibroblasts (Espinoza et al., 2004; Rosenthal et al., 2001). AREG and DTR are both growth factors that rapidly increased in response to the JP-8 exposure. AREG is related to EGF and transforming growth factor-α, and overexpression of the protein in transgenic mice results in a psoriasis-like phenotype (Cook et al., 1999). DTR affects keratinocyte growth (Varani et al., 2001). Four enzymes (CYP1B1, PTGS2, ODC1, and TGM1) were also increased robustly at each time point (Table 3). CYP1B1 is a member of the P450 superfamily of enzymes and is involved in metabolism of polycyclic aromatic hydrocarbons (Nebert et al., 2004; Shimada and Fujii-Kuriyama, 2004). Although the specific enzyme or enzymes responsible for the metabolism of kerosene-like fuels (including JP-8) have not been identified, it is reasonable that CYP1B1 is involved. Inducible prostaglandin–endoperoxide synthase (PTGS2), also known as COX-2, is a key enzyme in prostaglandin biosynthesis which was increased at all time points. This is consistent with several studies with COX inhibitors that have been shown to alter the inflammatory response of tissues to JP-8 (Ramos et al., 2002, 2004; Ullrich and Lyons, 2000). Ornithine decarbolylase (ODC1), the rate-limiting enzyme of the polyamine biosynthesis pathway, is upregulated by growth-promoting stimuli such as CEBPB (Cortes-Canteli et al., 2004). ODC has been shown to increase in UVB-induced photocarcinogenis in mice (Ahmad et al., 2001) and activate keratinocytes and stimulate vascularization in the dermal layer in a manner similar to wound healing when stimulated by an inducing agent, 4-hydroxytamoxifen (Lan et al., 2005). ODC activity has been shown to increase with topical application of petroleum middle distillates (Walborg et al., 1998), and the prolonged increase in ODC transcript levels in this study with JP-8 is consistent with those effects. ODC mRNA has also been shown to be increased in the skin with sulfur mustard exposure (Rogers et al., 2004). TGM1, an enzyme in cellular membranes involved in cross-linking of the stratum corneum (Matsuki et al., 1998) and in terminal differentiation of keratinocytes (Sturniolo et al., 2003), is also increased robustly. Changes in enzymes at 8 h (Table 5) were dramatic and appear to increase the epidermal capability for xenobiotic metabolism and repair of the barrier properties. Although genes for most enzymes were downregulated at 8 h, some monooxygenase enzymes localized to the endoplasmic reticulum (CYP1B1, CYP2F1, CYP3A7, CYP4B1, and CYP51A1) were upregulated. Many phase I enzymes' isoforms of the P450 monooxygenase family are involved in the metabolism of exogenous chemicals, specifically solvents, alkanes, and aromatics (Hodgson and Rose, 2005). The upregulation of genes related to these enzymes is consistent with rapid induction of hydrocarbon metabolism in the skin. Upregulation also occurred in the sterol biosynthetic pathway, where seven (FDPS, HMGCR, IDI1, LSS, MVD, NQO1 and SQLE) of the eight changed genes had increased levels of transcripts at 8 h. Sterol biosynthesis in the stratum granulosum of the epidermis is stimulated by perturbation of the permeability barrier (Elias and Feingold, 1988). The observation that JP-8 caused the downregulation of transcripts for most enzymes, while important metabolic pathways for xenobiotic metabolism and sterol synthesis were upregulated, suggests that the epidermis responds to a chemical insult by diverting limited metabolic energy to critical pathways. IPA (Fig. 6) of these 27 robustly increased genes (Table 3) suggests strong relationships with EGF and TNF, but transcripts for these proteins were not found to change. The Affymetrix probes for EGF (U04842_at) and TNF (E02468cds_s_at, L00981mRNA#2_at, and U03470_at) were not detected as present in either sham-treated or JP-8–treated epidermis. Allen et al. (2000) detected TNF-α mRNA using PCR in cultured human keratinocytes exposed to JP-8 for up to 24 h. Our 1-h exposure may not have been long enough to upregulate transcripts for TNF. Another possible explanation for the differences is in vivo and in vitro exposures, which may include communication between other skin cells in the epidermis and dermis as well as potential differences in JP-8 concentration in the target cells. EGF mRNA has not been demonstrated to be induced by JP-8 or UVB treatments. Both EGF and TNF are involved in growth, differentiation, and apoptosis in the epidermis (Corsini et al., 1997; Jost et al., 2000; Luster et al., 1999; Mimeault et al., 2004). Taken together, the changes in gene expression of structural, cell signaling, growth, apoptotic, chemokine, and enzyme proteins consistently found at 1, 4, and 8 h confirm, augment, and expand many of the previous molecular studies of JP-8–induced skin irritation. They provide insights into the processes involved in JP-8 irritation, but they do not clearly elucidate the trigger or initiating event. JP-8–induced changes in expression of the signaling genes at 1-h demonstrate that one or several pathways are quickly activated by JP-8 (Table 4 and Fig. 7). These changes reveal a great deal of cross talk and redundancy between signaling pathways which has been previously described (Angel and Szabowski, 2002; Iversen et al., 2005). Signaling and transcription factors are common to many pathways; therefore, we cannot deduce initiation of a specific pathway based on the signaling genes alone, but ERK/MAPK-, P38 MAPK-, and IL-6–signaling pathways contain most of the signaling genes that were changed at 1 h. Figure 10 shows our working hypothesis about the specific initiating mechanisms of JP-8–induced gene changes in the epidermis. FIG. 10 Open in new tabDownload slide Schematic of the proposed mechanism of action of JP-8 on the epidermis. JP-8 passes the stratum corneum barrier and partitions into keratinocytes and the spaces between them. JP-8 causes the release of preformed and biologically active IL-1α from unknown parts of the epidermis and causes stress to keratinocytes and membranes through a physical interaction and increased rates of oxidative metabolism. These stresses activate three signaling pathways that result in gene changes that will ultimately result in inflammation, apoptosis, growth, and proliferation. FIG. 10 Open in new tabDownload slide Schematic of the proposed mechanism of action of JP-8 on the epidermis. JP-8 passes the stratum corneum barrier and partitions into keratinocytes and the spaces between them. JP-8 causes the release of preformed and biologically active IL-1α from unknown parts of the epidermis and causes stress to keratinocytes and membranes through a physical interaction and increased rates of oxidative metabolism. These stresses activate three signaling pathways that result in gene changes that will ultimately result in inflammation, apoptosis, growth, and proliferation. The very rapid response in gene expression is consistent with a physical stimulus due to interaction of the JP-8 with critical epidermal structures. JP-8 contains a mixture of aromatic and aliphatic hydrocarbons with log octanol/water partition coefficients between 2.7 (toluene) and 7.6 (tridecane) that have been shown to penetrate into the skin (McDougal et al., 2000). Once in the epidermis, the JP-8 could move into the extracellular space between keratinocytes and partition into cellular, mitochondrial, and nuclear membranes where they could initiate the stress responses that trigger signaling events seen in this study. The interaction of JP-8 with membranes could be similar to the classic physical effect of ethanol on membranes (Goldstein, 1984). One fairly well-characterized response to stress of the epidermis is the release of preformed biologically active IL-1α (Corsini and Galli, 2000; Kupper and Groves, 1995; Wood et al., 1996) which can be found in the nucleated epidermal layers in a diffuse generalized pattern (Wood et al., 1996). We would not be able to see the IL-1α release directly in this gene expression study, but rapid IL-1α release is consistent with both the rapid activation of the IL-6–signaling pathway and the 16-fold burst of IL-6 mRNA found at 1 h (supplementary Table 1). The mechanism of the IL-1α release is unknown but could be due to a physical effect such as changes in an osmotic or calcium gradient (Eckert et al., 2004; Seo et al., 2005) or disruption of membranes by JP-8 in the epidermis. The gene expression effects seen here are consistent with the ultrastructural changes seen in mitochondria and membranes by Montiero-Riviere and coworkers (2004). Oxidative or mitochondrial stress could be due to the metabolism of JP-8 in the keratinocytes and other epidermal cells. There have been several previous studies suggesting oxidative stress results from JP-8 (Ramos et al., 2004; Rogers et al., 2001, 2003) and UV radiation (Mantena and Katiyar, 2006; Matsuura et al., 1999). Another physical disruption or stress could be disruption of the ECM, which fills the extracellular space in the basal epidermis and communicates and exerts control over epithelial cells through integrins, a family of cell-surface receptors. Integrin signaling of contact between epidermal basal cells and the ECM is an important process that allows cells to survive and proliferate (Giancotti and Ruoslahti, 1999). Loss of attachment to the ECM causes apoptosis (Meredith et al., 1993). Physical disruption of membranes or oxidative stress may be the initial essential event that triggers what are generally categorized as stress responses (inflammation, growth, proliferation, and apoptosis) (Lewis et al., 1998). In summary, we have shown that JP-8 causes rapid transcriptional responses in the epidermis with brief exposures on the skin surface. We have identified changes in gene expression, which occur at 1, 4, and 8 h after the initiation of exposure and are related to structural proteins, cell signaling, growth, inflammation, and metabolism. We have shown that the effects of JP-8 increase with time after exposure and result in the ultimate decrease in mRNA levels for a large number of genes. We have shown that several signaling pathways related to inflammation, apoptosis, cell growth, and proliferation are rapidly activated by JP-8 exposure. From these results, we hypothesize that the trigger for the inflammatory process is physical stress involving JP-8 disruption of membrane integrity and the oxidative or osmotic balance which ultimately activate signaling pathways which result in the recognized effects of JP-8 on the skin. Real-time RT-PCR studies were conducted primarily by C.M.A. as partial fulfillment for the requirement for a MS degree in Pharmacology and Toxicology. We thank Dr James Rogers (Battelle's Medical Research and Evaluation Facility) for reading of the manuscript and providing valuable comments. The authors gratefully acknowledge grant support from the Air Force Office of Scientific Research (AFOSR/NL) and a seed grant from the Wright State University Center for Genomics Research and the Kettering Fund. Conflicts of interest: none declared. References Ahmad N , Gilliam AC , Katiyar SK , O'Brien TG , Mukhtar H . A definitive role of ornithine decarboxylase in photocarcinogenesis , Am. J. 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TI - Effects of Brief Cutaneous JP-8 Jet Fuel Exposures on Time Course of Gene Expression in the Epidermis
JF - Toxicological Sciences
DO - 10.1093/toxsci/kfl154
DA - 2007-02-01
UR - https://www.deepdyve.com/lp/oxford-university-press/effects-of-brief-cutaneous-jp-8-jet-fuel-exposures-on-time-course-of-fTwDi0UcGB
SP - 495
EP - 510
VL - 95
IS - 2
DP - DeepDyve
ER -