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Formaldehyde-induced gene expression in F344 rat nasal
respiratory epithelium
Susan D. Hester a,c,*, Gina B. Benavides b, Lawrence Yoonb,Kevin T. Morgan b, Fei Zou d, William Barry d, Douglas C. Wolfa
a
US Environmental Protection Agency, Research Triangle Park, NC, USAb GlaxoSmithKline, Inc., Research Triangle Park, NC, USA
c Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USAd Department of Biostatistics, School of Public Health, University of North Carolina, Chapel Hill, NC, USA
Received 17 October 2002; received in revised form 24 December 2002; accepted 2 January 2003
Abstract
Formaldehyde (FA), an occupational and environmental toxicant used extensively in the manufacturing of many
household and personal use products, is known to induce squamous cell carcinomas in the nasal turbinates of rats and
mice and squamous metaplasia in monkey noses. Tissue responses to FA include a dose dependent epithelialdegeneration, respiratory cell hypertrophy, and squamous metaplasia. The primary target for FA-induced toxicity in
both rodents and monkeys is the respiratory nasal epithelium. FA increases nasal epithelial cell proliferation and
DNA/protein crosslinks (DPX) that are associated with subsequent nasal cancer development. To address the acute
effects of FA exposure that might contribute to known pathological changes, cDNA gene expression analysis was used.
Two groups of male F344 rats received either 40 ul of distilled water or FA (400 mM) instilled into each nostril. Twenty-
four hours following treatment, nasal epithelium was recovered from which total RNA was used to generate cDNA
probes. Significance analysis of microarrays (SAM) hybridization data using ClontechTM Rat Atlas 1.2 arrays revealed
that 24 of the 1185 genes queried were significantly up-regulated and 22 genes were significantly downregulated. Results
for ten of the differentially expressed genes were confirmed by quantitative real time RT PCR. The identified genes with
FA-induced change in expression belong to the functional gene categories xenobiotic metabolism, cell cycle, apoptosis,
and DNA repair. These data suggest that multiple pathways are dysregulated by FA exposure, including those involved
in DNA synthesis/repair and regulation of cell proliferation. Differential gene expression profiles may pro vide clues
that could be used to define mechanisms involved in FA-induced nasal cancer.
# 2003 Elsevier Science Ireland Ltd. All rights reserved.
Keywords: Gene expression; F344 rat; cDNA array; Nasal epithelium; Rodent nasal cancer; Formaldehyde
Abbreviations: FA, formaldehyde; DPX, DNA/protein crosslink; Min, minute; mg, microgram; mM, millimolar; 8C, centrifuge; ml,
milliliter.
Supplementary data associated with this article can be found at doi:10.1016/S0300-483X(03)00008-8
* Corresponding author. Tel.: /1-919-541-1320; fax: /1-919-541-0694.
E-mail address: [email protected] (S.D. Hester).
Toxicology 187 (2003) 13/24
www.elsevier.com/locate/toxicol
0300-483X/03/$ - see front matter # 2003 Elsevier Science Ireland Ltd. All rights reserved.
doi:10.1016/S0300-483X(03)00008-8
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1. Introduction
Formaldehyde (FA) is a common environmental
contaminant found in tobacco smoke, paint,garments, medicinal and industrial products, and
is a component of diesel and gasoline exhaust
(Flyvholm and Andersen, 1993; Quievryn and
Zhitkovich, 2000). Although there is limited evi-
dence that FA is a human carcinogen, it is well
known that FA induces tumors in rodents. FA
exposure leads to the formation of DNA/protein
crosslinks (DPX), a major form of DNA damage
and the presence of DPX have been used as a
measure of delivered dose (Heck and Casanova,
1999; Heck et al., 1990).FA is also cytotoxic in the rodent nose, inducing
sustained increases in cell turnover (proliferation)
at concentrations that induce tumors (Monticello
et al., 1991b; Morgan et al., 1986). Lesions
primarily develop in the epithelium lining the nasal
septum and the ventral meatus of the turbinates.
The types of lesions following an acute 24 h nasal
instillation of 400 mM FA were squamous meta-
plasia, ulceration, hyperplasia, and goblet cell
hypertrophy, which were observed at 3 weeks
post treatment (St. Clair et al., 1990). These lesionsoccurred only in respiratory and transitional
epithelium lining the anterior nasal passages of
the rat nose. This suggests that respiratory and
transitional epitheliums are more susceptible to
FA damage than the other epithelial types present
in the nose.
In addition to DPX, FA can readily react with
DNA and RNA by forming cross-links that
connect bases exclusively through exocyclic amino
groups (Chaw et al., 1980; Kennedy et al., 1996;
Takahashi and Hashimoto, 2001). FA is known to
induce regenerative proliferation (Monticello etal., 1991a,b) and may alter apoptosis (Szende et
al., 1995, 1998; Tyihak et al., 2001). An alteration
in the balance between cell growth and cell death
may contribute to the development of squamous
cell carcinoma in the rat nose. Biologically based
risk-assessment models (CIIT, 1999) have been
proposed which integrate levels of cell prolifera-
tion, cell death, and mutation rates associated with
FA treatment (Andersen et al., 1992; Conolly and
Andersen, 1993). These models reflect a mathe-
matical approach based on the two-stage model of
cancer development proposed by Moolgavkar et
al. (1988). The model used to assess human risk to
environmental pollutants, states that any agentthat alters cell division or rates of cell differentia-
tion will cause an increase in the number of
susceptible cells and thus, an increase in a cancer-
ous outcome. To date, only cell proliferation and
DPX data have driven FA cancer risk assessments.
The endpoints needed to complete the model,
namely cell death rates associated with FA ex-
posure, have yet to be reported. The assessment of
cell death in the epithelia lining the nasal turbi-
nates poses a unique challenge because the lining is
only one to two cells thick. If a respiratory cellunderwent apoptosis it would immediately slought
off and, therefore, go undetected. Conventional
methods to quantitate cell death would not be
accurate. Since measurements of death rates of the
nasal epithelium following FA exposure are not
feasible, we chose to evaluate gene expression
profiles to gauge whether acute changes in activity
of genes that control apoptosis were induced by
FA.
The evaluation of respiratory tract toxicity
following exposure to chemicals inv
olv
es deliv
er-ing the agent to animals by inhalation. This is a
natural route of entry in the exposed animal and is
a preferred method to introduce toxicants to the
respiratory tract. However, this method cannot
always be used because of cost and the need of
specialized equipment. Alternatively, direct instil-
lation of the test substance to the respiratory tract
has been employed in many studies (Evans and
Hastings, 1992; Jeffrey et al., 2002; Wagner et al.,
2001). The advantages of direct instillation are that
the chemical is delivered to the target site (respira-
tory cells lining the nasal cavity) and it is arelatively simple procedure to carry out without
requiring special equipment. An acute dose of a
chemical in aqueous solution can be infused with
relative ease and little stress to the animal.
Although there are distinct differences in distribu-
tion, clearance, and retention of chemicals when
administered by nasal instillation versus inhala-
tion, nasal instillation can be a useful and rela-
tively inexpensive method for investigating specific
questions involving respiratory toxicants.
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The concentration of FA used in our study
induces regenerative cell proliferation, but is not
high enough to produce necrosis. St. Clair et al.,
(1990), using nasal instillation of 400 mM FAshowed increased cell proliferation of respiratory
epithelium with minimal cytotoxicity. Since the
present study represents the first look at global
gene expression changes following FA exposure, a
simple design of two groups of animals (control
and treated) was utilized. For review of experi-
mental design decisions and interpretation of gene
expression data from toxicogenomic experiments
see (Hamadeh et al., 2002). In the present study, a
single concentration and time point were used to
examine transcriptional changes associated withacute FA exposure.
2. Materials and methods
2.1. Animal dosing
Rats were maintained in plastic cages (two per
cage) with filter purified tap water and feed ad
libitum. On day of treatment, 40 ml aliquots of
water or FA (400 mM) were instilled into eachnostril using a pipette.
2.2. Epithelial cell extraction from anterior rat
noses
Eight 60-day-old F344 male rats (Charles River
Laboratory, Raleigh, NC) were euthanized by
receiving CO2 asphyxiation and nasal respiratory
cells were recovered and processed as previously
described (Hester et al., 2002). Briefly, TrizolTM
reagent was instilled into each nostril through
polyethylene tubing and incubated for 10 min.Using a syringe attached to the tubing, the cellular
TrizolTM solution was removed, placed in a micro-
fuge tube, and immediately frozen in liquid nitro-
gen for subsequent total RNA isolation and
quantitation.
2.3. Total RNA extraction
Total RNA was extracted from respiratory cells
as previously reported (Hester et al., 2002). Briefly,
phase separation was carried out after thawing the
cellular/TrizolTM samples in a warm water bath
(65/70 8C) for 10 min. Chloroform was added to
each sample and tubes were shaken followed by10 000/gcentrifugation at 8 8C for 20 min. The
phenol/chloroform phase separates to the bottom
and the upper aqueous phase contains the RNA,
which was precipitated using ice cold isopropyl
alcohol followed by centrifugation at 10 000/g
for 20 min. The isopropyl supernatant was re-
moved leaving the RNA pellet. The RNA was
resuspended with 75% alcohol, mixed by inversion,
then centrifuged at 10 000/g for 30 min. RNA
was dissolved in 100 ml of DEPC water and heated
at 70 8C for 3 min with intermittent vortexing.The RNA solution was then transferred to silico-
nized Eppendorf tubes and the concentration
(absorbance at 260 nm; 1A260 unit of single
stranded RNA/40 mg/ml) was adjusted to 2 mg/
ml with diethylene pyrocarbonate (DEPC) water.
2.4. Electrophoresis of the RNA
5 mg of the resultant RNA sample was loaded
into a denaturing 1% agarose gel containing FAaccording to the protocol described inLehrach et
al. (1977) for electrophoresis. Observation of
rRNA subunit bands at 18S and 28S indicate the
presence of intact RNA.
3. Synthesis and labeling of cDNA
About 5 mg total RNA was used to generate a
probe for hybridizations as described in Hester etal. (2002). Briefly, seven (four control and three
treated) cDNA probes were generated by reverse
transcribing the RNA with primers in the presence
of 33P. Unincorporated label was removed on a
MicrospinTM Sepharose G-50 gel filtration column
(Amersham Pharmacia Biotech, Piscataway, NJ).
For hybridization of the labeled product, 250 000
Cerenkovcpm were used. ClontechTM software was
used to analyze gene expression levels from the
hybridization membranes.
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4. Real-time quantitative PCR
Quantification of mRNA was done using Mo-
lecular Probes Ribogreen RNA Quantificationkit. To prevent amplification of genomic DNA
sequences, all RNA samples were treated with
DNase I and diluted to 10 ng/ul. Taqman probes
were labeled with FAM (carboxyfluorescein) as
the reporter dye on the 5? position and TAMRA
(carboxytetramethylrhodamine) as the quencher
dye on the 3? position. cDNA was made using
moloney murine leukemia virus (MMuLV) reverse
transcriptase. All reactions were carried out in a
single tube reaction setup on an ABI PRISM 7700
sequence detection system (Applied Biosystems,Inc). The following temperature profile was used:
30 min at 48 8C for reverse transcription and 10
min at 95 8C for reverse transcription inactivation
and AmpliTaq Gold activation, 40 cycles of 15 s at
94 8C and 1 min at 60 8C. To check for possible
contamination in the reaction mix, no template
control (NTC) wells without RNA were used. The
cycle threshold Ct (i.e. ten times the standard
deviation of the mean baseline emission calculated
during PCR cycles 3 to 15) was used to calculate
relativ
e amounts of target RNA. The delta Ctmethod was used to calculate relative fold expres-
sion levels, as described by Applied Biosystems.
Primers and probes for ten rat genes (aldr, b2mg,
calp, cof, czsod, p38mapk, gst, nmor, sc1, waf1)
were purchased from Keystone Biosource (Camar-
illo, CA). Final concentration of all primers were
900 nM and probes were at 200 nM.
After the reaction was complete, a graph of
fluorescent intensity versus cycle number was
created using the above software. For each gene
assayed, a Ct is placed at the intersection of the
linear portion of the curve, which reflects theexponential amplification. Samples, which do not
reach the threshold line, are considered not above
background. The average Ct for each gene is
calculated by subtracting the Ct of the sample
RNA from the control RNA for the same time
measurement. This value is known as the delta Ct
and reflects the relative expression of the treated
sample compared with control and becomes the
exponent in the calculation for amplification
240ct, equivalent to fold change in expression.
When the delta Ct is negative, the final calculation
is negative, and is interpreted as down regulation
of that gene.
5. Statistical analysis of microarray data
The mean and variance of the Adjusted signal
intensity (ASI) were calculated and plotted (Fig. 1)
for each gene across the control and FA-treated
samples. There is a clear trend that the variation in
expression of a gene increased along with the
intensity of the gene. Therefore, intensities were
log (base 2) transformed to stabilize the variation
across intensity levels. Next, the data was globallynormalized within each membrane by first sub-
tracting the median intensity and then scaling by
the median absolute difference (MAD); normal-
izing by the median and MAD for each membrane
is more robust against extreme values than by the
classical measures, mean and standard deviation.
Once the data were normalized, statistically sig-
nificant, differentially expressed genes were ob-
tained using the statistical software, SAM
(significance analysis of microarrays) (Tusher et
al., 2001). Because of the large number of genesincorporated into microarray experiments, adjust-
ing for multiple testing is necessary when assessing
statistical significance. Few experiments have en-
ough power to detect significance with a stringent
Bonferroni correction when the number of com-
parisons is large. In SAM, the effect of multiple
comparisons is controlled through the false dis-
covery rate (FDR; Benjamini and Hochberg,
1995), which is defined as the expected proportion
of false positives among the rejected hypotheses;
for example, an FDR of 5% in testing differential
gene expression would indicate that about 5% ofthe identified genes were spurious. Through a
permutation scheme, SAM estimates the FDR
for a set of genes whose test statistics deviate
from their expected value by more than a given
threshold level; by adjusting the threshold level
one can achieve a desired FDR. Due to the
limitation in sample size of our data, threshold
levels were chosen that resulted in FDRs of 11.3
and 5.3% as the nearest approximations of 10 and
5%, respectively.
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6. Results
6.1. SAM analysis of gene expression
The ASI were obtained from the image analysis
for 1185 genes from four control and three FA-
treated samples. The data were first transformed
and normalized as described in Section 5, SAM
was then used to identify a set of genes that were
differentially expressed across treated and control
samples. By setting a threshold level of 0.187,
SAM predicted a FDR of 11.3% for 46 signifi-
cantly altered genes; 24 genes increased in expres-
sion due to FA treatment and 22 genes decreasedin expression (Fig. 2). Among the genes listed in
Tables 1 and 2,the fold change in raw intensities
ranged from 6.1 to 2.4 (Table 1) among up-
regulated genes and 10/2.5 in the down-regulated
genes (Table 2). By setting a more stringent
threshold level of 0.232 we obtain a set of 14
significant genes with FDR 5.3%. This set of genes
corresponded to the top 13 up-regulated genes in
Table 1 and the most down-regulated gene in
Table 2.
Genes listed in Table 1 were statistically eval-
uated and sorted from the highest test statistic
value to the lowest. Ten of the most highly
expressed genes for FA-treated animals included
NMDA receptor, inducible nitric oxide synthase,
macrophage inflammatory protein 1 alpha and 2,
Wilms tumor protein, tumor necrosis factor
ligand, methyl-CpG binding protein 2, GABA
receptor, Fos-responsive gene 1, and presomato-
trophin. Of the 24 significantly up-regulated genes
six were receptors, six were involved in extracel-
lular cell signaling, and four were oncogene/tumor
suppressor genes suggesting pathways that could
be affected by FA treatment. In contrast, in Table2, of the 22 genes significantly down-regulated,
five were involved in ion channel regulation and
four were involved in protein turnover which
suggests that an early response to FA treatment
may be impaired ion channel function and inter-
ruption of protein processing. Many phase I and
phase II genes regulating xenobiotic metabolism
and oxidative stress, the family of cytochrome
P450 and glutathione, respectively, were altered in
treated versus control.
Fig. 1. Distribution of the raw intensities for each gene. The variation in intensities across treatment (gray triangles) and control
samples (black circles) is plotted against the mean intensity for all 1185 genes. It is e vident, that the variation observed within each gene
was dependent on the average intensity. Despite the smaller sample size, the variability observed in the treatment group (n/3) was
smaller on average, than in the control group (n/4).
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7. Comparison of atlas rat toxicology II array and
real-time PCR data
A subset of ten genes with a range of expression
levels from high to low were selected for quanti-
tative real-time PCR analysis to validate the
direction of gene expression observed on the
microarrays. These ten genes included aldehyde
dehydrogenase (aldr), calpactin (calp), cofillin
(cof), copper/zinc superoxide dismutase (czsod),p38 map kinase (p38mapk), gluthione (gst),
NAD(p)H quinone oxidoreductase (nmor), so-
dium chloride 1 (sc1), and p21 waf1/CIP 1
(cyclin-dependent protein kinase inhibitor)
(waf1). Fig. 3 displays gene expression changes
present in both the Atlas Toxicology II arrays and
TaqManTM assays. Ten genes were confirmed but
arrays under-estimated, on average, the true
expression differences as revealed by quantitative
RT-PCR. Eight of the ten TaqManTM confirmed
genes on the array were estimated as increased
signal and one gene, copper/zinc superoxide
dismutase (CZSOD), showed a reduced signal.
7.1. Apoptosis gene expression
Fig. 4 shows gene expression levels of nine
apoptosis genes assayed in the FA and controlgroups. None of these genes were defined as
statistically different. However, there
was a general trend of less apoptotic gene expres-
sion levels in the FA group compared with control.
The nine genes were representative of three of the
major apoptosis-regulating pathways including
receptor-mediated (Fas-L), caspase (Caspase 3),
and the mitochondrial-associated bcl2 family of
genes (bcl2-x, bcl2-associated oncogene, BCLX,
BAD).
Fig. 2. Identification of differentially expressed genes. Using SAM, the observed test statistic is plotted against the statistic expected by
chance for 1185 genes. Genes that were expressed higher in the FA-treated samples appear on the positi ve side of the x -axis. At the
threshold,D/0.187 (drawn as dashed lines) SAM predicts 46 genes (24 positive and 22 negative) as being differentially expressed. The
FDR was estimated as 11.3%.
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Table 1
24 significantly up-regulated genes
Gene
ID
Treatmenta Controla Fold
change
Test-sta-
tistic
Family Description
D11c 0.658 0.580 6.086 1.794 Receptors N-methyl-D-aspartate receptor subtype 2B (NMDAR2B; NR2B
epsilon 2B (GRIN2B)
A08f 0.116 0.759 3.856 1.415 Immune Inducible nitric oxide synthase (INOS); type II NOS (NOS2)
E02k 0.251 0.906 5.763 1.404 Extrac/sign Macrophage inflammatory protein 1 alpha (Small inducible cyt
D14k 0.154 0.673 3.536 1.281 Extrac/sign Macrophage inflammatory protein 2 (MIP2)
A10f 0.331 1.200 3.785 1.157 Onco/tumo Wilms tumor protein homolog 1 (WT1)
E02m 0.184 1.009 3.804 1.144 Extrac/sign Tumor necrosis factor (ligand) superfamily, member 6 (apoptosi
antigen ligand)
A06l 0.390 0.368 2.950 1.117 Tx fact/dBP Methyl-CpG-binding protein 2 (MECP2)
D10d 1.115 1.937 4.938 1.083 Receptors Gamma-aminobutyric-acid receptor alpha 2 subunit (GABA(A
D01l 0.153 0.790 2.641 0.991 Receptors Fos-responsive gene 1
E03j 0.697 1.393 3.207 0.988 Extrac/sign Presomatotropin
D07m 0.534 1.201 2.904 0.987 Receptors Somatostatin receptor 3 (SSTR3; SS3R)
A09e 0.463 1.144 2.900 0.986 Immune 34A transformation-associated protein; TAP-related matrix me
stromelysin 2 (SL2); transin 2
D14f 0.626 1.270 2.866 0.985 Extrac/sign Nerve growth factor 8A (VGF8A)
D12i 0.674 0.060 2.371 0.914 Receptors Metabotropic glutamate receptor 6 (GRM6; MGLUR6)
E02n 1.025 1.631 3.513 0.897 Extrac/sign Brain natriuretic peptide (BNP); 5-kDa cardiac natriuretic pept
A11a 0.852 0.195 2.456 0.887 Onco/tumo F os-like antigen 1
E03d 0.616 1.345 3.680 0.886 Extrac/sign Follicle-stimulating hormone beta subunit
F12h 0.076 0.654 2.404 0.880 Recept by
act
Adenosine A2A receptor (ADORA2A)
A11h 0.882 0.307 2.236 0.878 Onco/tumo Neogenin; DCC netrin receptor-related protein; immunoglobul
former tumor suppressor protein candidate
A11g 0.439 1.110 2.590 0.869 Onco/tumo Deleted in colcorectal cancer (rat homolog)
D05m 0.762 1.376 2.633 0.868 Receptors 5-hydroxytryptamine (serotonin) receptor 5A
B01a 0.331 0.361 2.364 0.853 Stress res Cytochrome P450 1b1C05b 0.005 0.591 2.454 0.852 Metab pat Acetyl-CoA carboxylase (ACAC; ACC); biotin carboxylase
A05b 0.651 1.420 2.599 0.839 Cell sur ag NK lymphocyte receptor; NKR-P1B
Sample means are calculated from the normalized data. Fold Change is calculated from the raw data.a Mean signal intensities.
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Table 2
Tenty-two significantly down-regulated genes
Gene
ID
Treatmenta Controla Fold
change
Test-sta-
tistic
Family Description
E09m /2.352 /1.356 9.701 /1.439 Mod/
transd
Phosphorylase B kinase gamma subunit
F07j /2.284 /1.318 7.999 /1.159 Prot turnov Proprotein convertase subtilisin/kexin type 2
B04i /1.910 /1.004 4.272 /1.124 Ion chann Sodium channel protein 6 (SCP6)
F06l /0.568 0.208 3.312 /1.094 Prot turnov Secretory granule neuroendocrine, protein 1 (7B2 protein)
E14n /2.357 /1.499 4.625 /1.060 Mod/
transd
Ras-related protein RAB26
C07g /0.238 0.369 2.489 /1.026 Metab pat Cytochrome P450 IVA8 (CYP4A8); P450-KP1; P450-PP1
B14i /1.969 /1.206 3.533 /1.012 Traff/target Fatty acid-binding protein 9 (FABP9); testis lipid-binding protei
protein (PERF15)
A07k /1.286 /0.503 3.385 /0.951 Cell cycle Cyclin-dependent kinase 4 inhibitor 2B (CDKN2B); p14-INK4B
C05d /0.768 /0.009 2.846 /0.940 Metab pat Brain long-chain fatty acid-CoA ligase (LACS); acyl-CoA synth
B12i /2.448 /1.692 12.641 /0.936 Extra matr Myelin-associated glycoprotein
B13h /1.514 /0.787 2.926 /0.934 Traff/target Gastric intrinsic factor
B10m /1.566 /0.986 2.488 /0.928 Ion chann Aquaporin 3 (AQP3); 31.4-kDa water channel protein
F07i /1.658 /0.824 2.900 /0.927 Prot turnov Dipeptidyl peptidase IV (DPPIV; DPP4); bile canaliculus domain
B01i /0.087 0.632 3.000 /0.918 Stress res Plasma glutathione peroxidase (GSHPX-P; GPX3); selenoprotei
B13k /0.994 /0.399 2.531 /0.917 Traff/target Syntaxin binding protein 1
E14l 0.212 0.819 2.656 /0.903 Mod/
transd
Ras-related protein RAB16
F09j /1.498 /0.809 3.393 /0.884 Prot turnov Interleukin 1beta converting enzyme
C11k /2.197 /1.352 2.802 /0.872 Translation Ribosomal protein S19
B02d /1.803 /1.065 2.853 /0.871 Ion chann Solute carrier family 2 A2 (gkucose transporter, type 2)
B10n /1.537 /0.829 3.079 /0.869 Ion chann Synaptic vesicle amine transporter (SVAT); monoamine transport
(VAT2)
B02j /1.621 /0.972 2.617 /0.866 Ion chann Neuronal acetylcholine receptor protein alpha 6 subunit (NACH
receptor nicotinic alpha polipeptide 6 (CHRNA6)C08c /1.596 /0.973 2.855 /0.854 Metab pat Cytochrome P-450 2C23, arachidonic acid epoxygenase
Sample means are calculated from the normalized data. Fold change is calculated from the raw data. For down-regulated genes, fold c
over treated.a Mean signal intensities.
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Fig. 3. Comparison of TaqManTM to ClontechTM array results. A subset of ten genes was verified using quantitative real time PCR.
Nine of the ten gene expression values were in good agreement. One gene value, calpactin, was under-estimated on the array compared
with the TaqManTM result.
Fig. 4. Apoptosis Genes. Mean expression levels of nine genes involved in regulating apoptosis. Seven of the nine genes on the FA-
treated group showed reduced expression values compared with control group; AO1g-Annexin V, B13c-Annexin I, C12h-Bcl2-
associated death promoter (BAD), C12i-Bcl2-associated X protein (BAX), C12j-Bcl2 associated oncogene, BCL2-L, C12k-BCL2,
C12m activator of apoptosis (HRK), FO9i- Caspase 3, and EO2m-Fas-ligand.
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8. Discussion
FA induces squamous metaplasia leading to
squamous cell carcinoma after 12 months orlonger of exposure. In the present study, we
evaluated the acute response of epithelial cells 24
h after FA treatment. Our results indicated that
the expression levels of genes in several functional
categories were altered, including those participat-
ing in xenobiotic metabolism, cell cycle regulation,
DNA synthesis and repair, oncogene, and apop-
tosis. Xenobiotic metabolism genes showed a
general trend towards increased expression. Un-
expectedly, two cytochrome P450 family members
were down-regulated, cytochrome P450 IVA8, andcytochrome P450 2C23, genes which are usually
involved in metabolizing FA (Dahl and Hadley,
1991).
Genes were considered differentially expressed if
they exceeded the calculated threshold of 0.187,
with up-regulated genes falling on the positive side
and down regulated genes appearing in the nega-
tive quadrant (Fig. 2). However, gene expression
changes, which were not statistically significant
cannot be considered to be irrelevant but rather
not included in this analysis. For example, genesexpressed at low levels in the FA-treated group
that exhibit a small change in expression could still
be responsible for a large biologic effect, especially
if their gene products control critical pathways
such as cell growth or cell death.
Seven of the nine genes regulating apoptosis
showed less expression in the FA group compared
with the control group, however, none of these
genes were identified as being statistically differ-
ent. This result may be interpreted as a negative
finding, however, the effect of FA exposure on
respiratory epithelium is unknown. Therefore, anyinformation of changes in gene expression levels
after FA treatment is both novel and informative.
The post-treatment changes in gene expression
levels may well be subtle ones rather than large
changes. In addition, the respiratory cell may be
responding to acute FA exposure by altering
metabolism preferentially to deal with the acute
toxicity of this chemical. Just as changes in
reparative cell proliferation secondary to FA
treatment require time to develop, changes in
apoptosis gene expression may require longer
exposures over time to saturate the detoxifying
capacity of the nasal cell. So perhaps the optimum
time-frame to observe apoptosis gene expressionchanges would certainly be longer than 24 h as it
takes 4 days to 6 weeks of continuous exposure for
increased cell proliferation to occur (Monticello et
al., 1991a).
FA is a potent respiratory irritant and is capable
of stimulating a number of receptors related to the
trigeminal nerve and localized in the nasal epithe-
lium (Babiuk et al., 1985; Cassee et al., 1996). In
the present study, seven of the 24 significantly up-
regulated genes (Table 1) were classified as recep-
tors. Stimulation of these receptors represent oneof many defense mechanisms of the rodent re-
spiratory tract (Babiuk et al., 1985). Other protec-
tive mechanisms include a decrease in minute
ventilation, local cellular metabolism the muco-
ciliary apparatus, and DNA repair (Swenberg et
al., 1983). Chemicals that can induce these protec-
tive mechanisms are considered to be sensory
irritants because they can stimulate the trigeminal
nerve fibers, cause a burning sensation, and
diminish respiratory rates (Alarie, 1973). Interest-
ingly, the gene expression data in this report showthat four of the seven most highly expressed genes
were neuropeptides including NMDAR2B, sero-
tonin 5A, GABA(A), and metabotrophic gluta-
mate receptor which could reflect a response to
sensory irritation caused by FA.
In summary, there were more up-regulated
genes in the FA treated group than down-regu-
lated genes. Some genes, such as cell receptors, are
reported for the first time to have changed
expression in FA-exposed respiratory cells lining
the nasal turbinates, which is consistent with the
phenotypic response of these cells. Several geno-mic techniques, including subtractive hybridiza-
tion, array based comparative genomic
hybridization (aCGH), mRNA differential display
and serial analysis of gene expression (SAGE) are
being utilized to identify gene expression profiles
which could account for differentially expressed
genes after chemical or xenobiotic treatment.
From the results reported here, it is clear that
multiple genetic changes are induced after FA
exposure. Genetic events that account for progres-
S.D. Hester et al. / Toxicology 187 (2003) 13 /2422
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sion from squamous metaplasia to squamous cell
carcinoma are yet to be identified. Genes playing
more significant roles in common or overlapping
pathways are candidates to be assessed andanalyzed further over time. Results of the above
experiments hold the promise of revealing critical
gene interactions, which may aid in the discovery
of potential molecular events operative in the
pathogenic transition of epithelium exposed to
FA. Once gene pathways are identified, the next
key step will be to conduct bioassays that could
validate gene expression changes and will reinforce
insights provided by the gene profiling experi-
ments.
Acknowledgements
This manuscript has been reviewed and ap-
proved for publication by the Environmental
Protection Agency and does not necessarily reflect
the views of the agency. Mention of trade names or
commercial products does not constitute endorse-
ments or recommendations for use. Special thanks
to Dr. Jeffrey Ross, Dr. Julian Preston, Dr. David
Threadgill, Dr. Donald Delkar, and Dr. MarilaCordeiro-Stone for review and advice of this
manuscript.
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