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Article
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Using a Customized DNA Microarray for Expression Profiling of the Estrogen-Responsive Genes to Evaluate Estrogen Activity among Natural Estrogens and Industrial Chemicals Shunichi Terasaka,1,2 Yukie Aita,1 Akio Inoue,1,3 Shinichi
Hayashi,3 Michiko Nishigaki,4 Kazuhiko Aoyagi,4 Hiroki
Sasaki,4 Yuko Wada-Kiyama,5 Yasuo Sakuma,5 Shuichi
Akaba,6 Junko Tanaka,7 Hideko Sone,7Junzo Yonemoto,7 Masao Tanji,8 and Ryoiti Kiyama1,8 1Research Institute for Biological Resources and Functions, National
Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki,
Japan; 2SciMedia Ltd., Taito-ku, Tokyo, Japan; 3Division
of Endocrinology, Saitama Cancer Center Research Institute, Komuro, Ina, Saitama,
Japan; 4Genetics Division, National Cancer Center Research Institute,
Tsukiji, Chuo-ku, Tokyo, Japan; 5Department of Physiology, Nippon
Medical School, Sendagi, Bunkyo-ku, Tokyo, Japan; 6Inovation Research
Center, Nissui Pharmaceutical Co., Ltd., Sugamo, Toshima-ku, Tokyo, Japan; 7Endocrine
Disruptors & Dioxin Research Project, National Institute for Environmental
Studies, Tsukuba, Japan; 8InfoGenes Co., Ltd., Tsukuba, Ibaraki,
Japan Abstract We developed a DNA microarray to evaluate the estrogen activity of natural estrogens and industrial chemicals. Using MCF-7 cells, we conducted a comprehensive analysis of estrogen-responsive genes among approximately 20,000 human genes. On the basis of reproducible and reliable responses of the genes to estrogen, we selected 172 genes to be used for developing a customized DNA microarray. Using this DNA microarray, we examined estrogen activity among natural estrogens (17ß-estradiol, estriol, estrone, genistein) , industrial chemicals (diethylstilbestrol, bisphenol A, nonylphenol, methoxychlor) , and dioxin. We obtained results identical to those for other bioassays that are used for detecting estrogen activity. On the basis of statistical correlations analysis, these bioassays have shown more sensitivity for dioxin and methoxychlor. Key words: endocrine disruptor, estrogenicity, expression profile, microarray. Environ Health Perspect 112:773-781 (2004) . doi:10.1289/txg.6753 available via http://dx.doi.org/ [Online 12 February 2004] Address correspondence to R. Kiyama, Research Institute for Biological Resources and Functions, National Institute of Advanced Industrial Science and Technology, AIST Central 6, 1-1-1 Higashi, Tsukuba, Ibaraki 305-8566, Japan. Telephone: 81 298 61 6189. Fax: 81 298 61 6190. E-mail: kiyama.r@aist.go.jp This study was funded by a grant from the Ministry of the Environment of Japan for preventing public pollution and by a grant for research and development of small businesses from the Ministry of Economy, Trade and Industry of Japan. The authors declare they have no competing financial interests. Received 22 September 2003 ; accepted 12 February 2004. |
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Endocrine disruptors mimic natural hormones, thereby causing various effects
or damage in humans and other animals. Estrogenic compounds are a particularly
serious problem because their effects can be transferred to children through
damage to the female reproductive organs in the mothers. The effects of estrogenic
compounds first appear in the estrogen-responsive genes that include estrogen
receptors (ERs), followed by changing expression levels of many other genes
and resulting in cellular responses that appear as various symptoms (McDonnell
and Norris 2002). As we identify more signaling pathways within the cell, we
become aware of more cases in which common pathways are used for different
signalings. Dioxin, for example, has an effect through the aryl hydrocarbon
receptor, which to some degree may share common cascades with the ER pathway
[Carlson and Perdew 2002; reviewed by Safe (2001)]. Therefore, unraveling signaling
pathways will provide clues not only to the estrogen signaling pathway alone
but also to other pathways.
Estrogenic chemicals can act upon the cell through two major pathways: a)
direct interaction with the ERs and b) interactions with other molecules
first. As estrogen binds to ERs more tightly than to other molecules, the major
effects originate from the first pathway. However, when the chemical has low
estrogen activity and the activity of other interactions is high, estrogen
activity can be masked or disguised by the second pathway. Furthermore, the
major estrogen activity is not conducted by a unique pathway. First, there
are at least two types of ERs, ER- (Green et al. 1986) and ER-ß (Kuiper
et al. 1996), which differ in their affinity for ligands and the way in which
they transduce signals (Katzenellenbogen and Katzenellenbogen 2000). Differences
in affinity between ER- and ER-ß were reported for methoxychlor and
its analog DDT (Jacobs et al. 2003). As for the ER- , tamoxifen is an antagonist
against natural estrogen but has agonist activity in the uterus, whereas ICI
182,780, a well-known pure antagonist, does not show such activity (Branham
et al. 1996). This difference can be explained by the difference in the ligand-dependent
or -independent activation functions assisted by coactivators and has been
observed in other chemicals [reviewed by McDonnell et al. (2002); McKenna and
O'Malley (2002)]. This indicates that, even for the first pathway, using any
one of these signaling pathways as an indicator of estrogen activity would
be biased and specific signals could be enhanced, resulting in differences
between the expected and real biological outcomes.
The second pathway is more complex. It may include various metabolic and
modification pathways for chemicals, and estrogen activity could be higher
or lower than the original, depending on the products (Beresford et al. 2000).
Methoxychlor, for example, is metabolized to mono- and bisphenolic forms by
oxygenase (Bulger et al. 1978) or by cytochrome P450 isoforms (Hu and Kupfer
2002). These metabolites have more estrogen activity than methoxychlor. Such
a metabolic activation of estrogenic chemicals was also reported for bisphenol
A and bisphenol B (Yoshihara et al. 2001), 2-nitrofluorene (Fujimoto et al.
2003), and styrenes (Kitamura et al. 2003). Metabolic inactivation or inactivation
by modification could also occur in many chemicals. As estrogen activity results
in growth and proliferation of the cell through the activity of transducing
signals by means of hormones, growth factors, cytokines, and others, monitoring
estrogen activity at the steps close to such cellular responses rather than
at the beginning (receptor binding, for example) is crucial for reliable evaluation
of estrogenicity.
Previously we found that a significant number of genes responded to estrogen
in a DNA microarray analysis and we characterized some of them, including solute
carrier family 7, member 5 (SLC7A5), retinoblastoma-binding protein
8 (RBBP8), and c-myc promoter-binding protein 1 (IRLB)
(Inoue et al. 2002b). We also found that many of these genes responded to estrogen
in a manner similar to that in cancer cells from the breast, ovary, stomach,
kidney, and other sites. Here, using a customized DNA microarray with newly
selected estrogen-responsive genes, we outline an experimental system with
more sensitivity for evaluation of estrogen activity in natural and industrial
chemicals on the basis of statistical analysis of gene response. Our goal is
to establish an experimental system with more sensitivity for the evaluation
of estrogen activity in these chemicals, which can be applied even to those
having low activity.
Materials and Methods
Cell Culture and Materials
MCF-7 cells were obtained from JCRB Cell Bank (National Institute of Health
Sciences, Tokyo, Japan) and cultured in RPMI 1640 medium supplemented with
10% fetal bovine serum (FBS) at 37°C under 5% carbon dioxide. Cells were
cultured in phenol red-free RPMI 1640 medium with 10% FBS treated with dextran-coated
charcoal for 3 days and treated with ethanol (vehicle) or a variety of chemicals
for 72 hr. 17ß-Estradiol (E2), estriol, estrone, genistein,
diethylstilbestrol (DES), bisphenol A, nonylphenol, and methoxychlor were obtained
from Sigma-Aldrich (St. Louis, MO, USA) and used at the concentrations
of 10 nM (E2, estriol, estrone, DES) or 10 µM (genistein, bisphenol
A, nonylphenol, and methoxychlor). Dioxin (2,3,7,8-tetrachlorodibenzo-p-dioxin;
purity 99.0%) was obtained from Cambridge Isotope Laboratories (Andover, MA,
USA) and used at a concentration of 50 mg/mL in dimethyl sulfoxide.
cDNA Microarray Analysis
GeneChip analysis was conducted using human U95A oligonucleotide probe arrays
(Affymetrix, Santa Clara, CA, USA) according to the supplier's protocols, as
follows. Total RNA (1 µg) was used to generate a cRNA probe by T7-transcription.
The fragmented cRNA (10 µg) was hybridized to the microarrays in 200 µL
of a hybridization cocktail by incubation at 45°C for 16 hr in a rotisserie
oven set at 60 rpm. The microarrays were then washed with a nonstringent wash
buffer [6 NaCl/NaH2PO4/EDTA
(SSPE)] at 25°C, followed
by a stringent wash buffer [100 mM MES (pH 6.7), 0.1 M NaCl, and 0.01% Tween
20] at 50°C. The microarrays were stained with streptavidin phycoerythrin
(Molecular Probes, Eugene, OR, USA), washed again with 6 SSPE, stained with
biotinylated antistreptavidin IgG followed by streptavidin phycoerythrin, and
washed a third time with 6 SSPE. The arrays were scanned using a GeneArray
scanner (Affymetrix) at a resolution of 3 µm, and the scanned image was
quantitatively analyzed with Microarray Suite 4.0 (Affymetrix). For normalizing
the data to compare mRNA expression levels among samples, we unified the values
to 1,000 as an average of average difference scores corresponding to the signal
intensities of all probe sets in each sample.
Microarray analysis using IncyteGenomics (Palo Alto, CA, USA) microarrays
was performed as reported previously (Inoue et al. 2002b).
A custom cDNA microarray (EstrArray) was manufactured by InfoGenes Co., Ltd.
(Tsukuba, Japan) by mechanical spotting of cDNA (~500 bp to ~1.5 kb) of the
genes selected from the above DNA microarray assays [see Inoue et al. (2002b)
for details]. The analysis using EstrArrays was performed as follows: After
the cells were cultured for 72 hr in the presence of chemicals at indicated
concentrations, mRNA was purified using the PolyATract System 1000 (Promega,
Madison, WI, USA) according to manufacturer instructions. The quality of mRNA
was confirmed by examining the optical density and also by reverse transcription-polymerase
chain reaction (RT-PCR) assay for several marker genes (ß-actin
for all, and pS2 and ER- for the chemicals with high estrogen activity). Each
mRNA was labeled with fluorescent Cyanine 3 (Cy3)-dUTP (for the treatment of
chemicals) or Cy5-dUTP (for the control) at 37°C for 1.5 hr using SuperScript
II (Invitrogen, Carlsbad, CA, USA) and random primers (a mixture of 6 mers
and 9 mers). Both Cy3- and Cy5-labeled probes were mixed and denatured under
alkaline conditions for 1 hr. After free fluorescent nucleotides were removed
using Microcon-30 columns (Millipore, Bedford, MA, USA), probes were hybridized
to EstrArrays for 16 hr in 5 NaCl/Na citrate (SSC) and 0.5% sodium dodecyl
sulfate at 65°C. After hybridization, slides were washed twice with 0.05 SSC for 5 min at room temperature. The fluorescent intensities were scanned
with a ChipReader (Virtek, Waterloo, Ontario, Canada), and scanned images were
analyzed using IPLab (Scanalytics, Fairfax, VA, USA) according to manufacturer
instructions. The ratio (Cy3/Cy5) was calculated for each spot, and after transforming
the ratio into a logarithmic value (log2), the value was normalized
using internal control genes. Clustering analysis was performed using the Cluster
program and the results were displayed with the TreeView program [for both
programs see Eisen et al. (1998)]. The genes spotted on EstrArray or GenBank
accession numbers (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=nucleotide)]
were as follows: ACO2, ADORA2A, AGTR1, AIM1, AKR1C4, APPL, AR (AREG), ARHGDIA,
ARNT2, ASNS, ASS, ATF3, BF, BRCA1, CAMK2A, CAPNS1, CBX1, CCNA1, CCR2, CDC14,
CDC6, CDH18, CDIPT, CDKN1A, CDSN, CEBPB, CLIC4, CPT1A, CtIP (RBBP8), CTNND2,
CTSD, D53 (TPB52L1), DAZAP2, DDEF, DHCR24, DHX29, EDN2, EFEMP1, EGR3, EIF3
(EIF3S9), ENO2, ENO3, FBP1, FOS, FRA2, FTH1, FUT8, GARS, GFPT1, GOT1, gp96
(TRA1), GRP78 (HSPA5), GUCA2B, H3F3B, HAX1, HDAC6, HMMR, HSP70, IEX-1 (IER3),
IFRD1, IGFBP4, IGFBP5, IL1R1, IL-2RB, ILK, IMP4, ISG20, JUN, KRT16, KRT8, LAMP3,
LCN2, LGALS3BP, MAL, MAN1A1, MAP1, MATN2, MBP-1 (IRLB), MGP, MIC1, MTHFD2,
NCKAP1, NPY1R, PACE4, PCK2, PCYT1A, PDZK1, PEG10, PHGDH, PI3KC3, PIG11, PMAIP1,
PMP22, PMPCA, PRKCD, PRKCSH, PSAT1, PTPN18, PVR, QSCN6, RACGAP1, RAP1GAP, RCN1,
RDH11, RHOC, RIP140, RSK, RUNX1, S100P, SCD, SECTM1, SELENBP1, SERPINA, SFTPB,
SH3BGR, SH3BP5, SHMT2, SLC12A2, SLC1A4, SLC1A5, SLC26A3, SLC7A11, SLC7A5, SORD,
STC2, SYNGR2, TACSTD2, TAF9, TCN1, TFII-I (GIF2I), TFIIS (TCEA1), TIEG, TM4SF1,
TRB3, TSPAN-1, U5-116KD, ULK1, VAMP5, WARS, XPOT, YARS, ZNF231, and expressed
sequence tags (ESTs) (L05367, NM_052965, AL109840, XM097954, NM_017867, NM_017867,
NM_014846, NM_173481, and NM_024092), along with the expression markers AHR,
CCND1, CYP19A1, CYP1A1, ERBB2, ESR1, ESR2, HSD17B2, NCOA1, NCOA3, PGR, STS, and TFF1,
and the calibration markers ACTB, ACTN1, CPEB2, FLJ12748, FUSIP1, G0S2,
G6PD, GCLM, GTF2H2, HNRPK, IL6ST, KANK, KIAA0349, KRT6E, LOC129401, NAV1, NMA,
NPM1, PAK4, PRKCD, RPL35, SDR1, SLC25A16, SLC29A2, SOCS2, TNFRSF7, and ZNF147.
Different parts of the same gene or cDNA were used for some genes (a total
of 12 genes), giving multiple plots in the figures. The expression markers
are the marker genes for estrogen [all except cytochrome P450 1A1(CYP1A1)]
or dioxin (CYP1A1) responses, and the calibration markers are the genes
for adjusting the signals between Cy3 and Cy5 labels (therefore they are not
estrogen responsive).
Real-Time Quantitative RT-PCR
mRNA was isolated using a PolyATract System 1000 (Promega) as described previously.
The first-strand cDNA was synthesized from 200 ng mRNA using SuperScript II
(Invitrogen). Quantitative PCR was carried out using a LightCycler-FastStart
DNA Master SYBR Green I kit (Roche Molecular Biochemicals, Mannheim, Germany).
The PCR conditions were as follows: denaturation at 95°C for 1 min, followed
by 40 cycles of denaturation at 94°C for 10 sec, annealing at 57°C
for 5 sec, and extension at 72°C for 20 sec. After PCR a melting curve
was constructed by increasing the temperature from 72 to 95°C. The product
was resolved in agarose gels to ensure that the correct product was amplified
in the reaction. PCR was repeated 3 times for each gene, and the average and
standard deviations were calculated. The PCR primers were as follows: SLC7A11,
5´-ACAGTGCCAGAGTGAAGAAACTC-3´ and 5´-CCAGCTAAATCCCTAACTTGGAT-3´; EGR3,
5´-CCATGATTCCTGACTACAACCTC-3´ and 5´-GTGGATCTGCTTGTCTTTGAATG-3´; PDZK1,
5´-CCTTTCTCAAGGAATGAGTTGTG-3´ and 5´-CCGCCTGTAAGACAAATGATAAC-3´; S100P,
5´-GTACTTTGAGAAGGCAGGACTCA-3´ and 5´-GGAATAATTGCCAACAAACACTT-3´; AR,
5´-AAACAAGACGGAAAGTGAAA-3´ and 5´-TTACCTTCGTGCACCTTTAT-3´; WARS,
5´- AGGCATCTTCTTCTCACACAGAG-3´and 5´- GATACTTCTCGTCATCCGTCATC-3´; SELENBP1,
5´-GAAGGTACATGGTCAGTGGAGAA-3´ and 5´-GAGATGTCATACTGCCTCAGGTC-3´; ENO2,
5´-GCACTTTCCACTTCTTCCTTTCT-3´ and 5´-AAGTGACACATGGTCCCTCTCTA-3´; ARHGDIA,
5´-CCTCACTAGCCTCTACTCCCTGT-3´ and 5´-ACTGAGGTGACTTGAGTGTTGG-3´; AGTR1,
5´-CTGAATAACTCACTGATGCCATCCCAG-3´ and 5´-GCCAGCAGCCAAATGATGATGCAGGTG-3´; IGFBP5,
5´-ATGGATTTGAGAGGAAAGAGAGG-3´ and 5´-AGCACCCTCCTAAGGTTACTCAC-3´;
and SLC12A2, 5´-GAGGAAATCATTGAGCCATACAG-3´ and 5´-GAGCACTAGACACAGCACCTTTT-3´.
Results
Figure 1. Expression profiling of the
human genes using DNA microarrays. The response to 10 nM E2 was examined with comprehensive
sets of human genes on (A) UniGem, version 2 (IncyteGenomics) and (B)
GeneChip U95A (Affymetrix). Each contains a total of 9,182 and 12,625
genes, respectively. The vertical and horizontal axes are indicated by
arbitrary units derived from fluorescent intensities.
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Table 1.
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Table 2.
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Figure 2. Clustering of gene expression after the treatment of
various estrogens and industrial chemicals examined using EstrArrays.
E2-2,
E2 twice. Gene expression profiles were obtained after treatment with
10 nM of E2, estrone, estriol, and DES, 10 µM nonylphenol, bisphenol
A, genistein, and methoxychlor, or 50 µg/mL dioxin. The results of EstrArray
analysis are shown as values of log2 (fluorescent intensity for chemical
plus/fluorescent intensity for chemical minus), which were colored according
to the color scale.
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Figure 3. Estrogenicity of chemicals examined using EstrArrays.
E2-2,
E2 twice. Gene expression profiles of estrogen-responsive genes were
compared between the independent E2 treatments and shown in a scatterplot
graph (A). The same comparison was performed for the genes of the (B)
low-expression or (C) high-expression types. Gene expression profiles
were compared between (D) E2 and estriol, (E) estrone,
(F) DES, (G) genistein, (H) nonylphenol, (I) bisphenol
A, (J) methoxychlor, and (K) dioxin. The axes are shown in log2 (fluorescent
intensity for chemical plus/fluorescent intensity for chemical minus) calculated
for each chemical. The correlation coefficient (R) between two profiles
was calculated for each graph on the basis of linear regression between the two
profiles. CYP1A1, a dioxin marker, is indicated in K. |
Figure 4. Expression profiles of the genes showing upregulation
or downregulation
in response to estrogen and estrogenic chemicals. (A) Responses to various
chemicals analyzed using EstrArrays. The vertical axis marked as log2 (C+/C-)
indicates log2 (fluorescent intensity for chemical plus/fluorescent
intensity for chemical minus) calculated for each chemical. (B) The response
to E2 examined by real-time quantitative RT-PCR. The assays were repeated
3 times and the average and the SD (bracketed) in the log2 values
are shown. The genes examined are SLC7A11 (solute carrier family 7, member
11), EGR3 (early growth response 3), PDZK1 (PDZ domain-containing
protein), S100P (S100 calcium-binding protein P), AR (amphiregulin), WARS (tryptophanyl-tRNA
synthetase), SELENBP1 (selenium binding protein 1), ENO2 (enolase
2), ARHGDIA (Rho GDP dissociation inhibitor alpha), AGTR1 (angiotensin
II receptor type 1), IGFBP5 (insulin-like growth factor binding protein
5), and SLC12A2 (solute carrier family 12, member 2). |
Figure 5. The genes responding to estrogen.
The genes were categorized into tumor-, growth- and ion-associated
genes and other genes including those for structural and neuronal
proteins. Upregulation and downregulation are indicated by arrows
on the left side.
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We first screened the estrogen-responsive genes in a human mammary tumor
cell line, MCF-7, using two different comprehensive DNA microarray systems,
UniGem, version 2 (IncyteGenomics) containing 9,182 genes and GeneChip U95A
(Affymetrix) containing 12,625 genes (Figure 1). Approximately 300 genes in
UniGem, and 850 genes in GeneChip U95A showed a response higher than 2-fold
and 3-fold, respectively. To examine the response to estrogen by monitoring
transcription of the genes, we selected 172 genes after the reproducibility
of their upregulation or downregulation on estrogen treatment (10 nM E2 for
3 days) was confirmed by repeated DNA microarray and/or RT-PCR analyses
(Inoue et al. 2002b; also, data not shown). To confirm that the data obtained
were reliable for the genes with various expression levels, we arbitrarily
divided the genes into high- and low-expression types. The genes categorized
as the high expression type characterized by abundant transcript are summarized
in Table 1. These genes had transcripts with expression levels higher than
those of the solute carrier family gene 2, member 1 (SLC2A1) and the
keratin 6B gene in each DNA microarray analysis (both appeared in both DNA
microarrays and showed identical expression levels), and included the genes
for amino acid transporters, and structural, ion-related, translation-, transcription-,
and cell cycle-associated proteins. The expression of most of the tRNA
synthetase genes and genes for the TATA-box binding protein-associated
factor and histone deacetylase was probably upregulated for enhancing protein
synthesis or transcription, respectively. Meanwhile, the genes associated with
specific tissues, such as those for the nervous system, showed downregulation.
A similar analysis was performed for the genes categorized as the low expression
type (Table 2). Upregulation of the genes related to various synthetases, transcription-related,
and cell cycle or growth-associated proteins as well as receptors and ion or
amino acid transporters was also prominent in this type. Among the tumor-associated
genes, oncogenic genes such as for c-fos, AML (acute myeloid leukemia)
1b, FOS-like antigen 2, and a v-jun homolog were upregulated, whereas
tumor suppressor-related genes (absent in melanoma 1) (Ray et al. 1996)
were slightly downregulated. Expression of the ER- gene was downregulated
as observed for progressive breast tumors (Lapidus et al. 1998; Yoshida et
al. 2000).
On the basis of the information obtained from the estrogen-responsive genes
shown above, we constructed a customized DNA microarray, EstrArray, that contains
203 genes, including genes showing either upregulation (108 genes) or downregulation
(64 genes) in their expression. EstrArray also contains calibration markers
for adjusting the fluorescent levels between Cy3- and Cy5-labeled cDNAs (28
genes) and expression markers such as the genes for trefoil factor, the ER-
and ER-ß, steroid sulfatase, and other estrogen-related proteins (14
genes, 11 showing estrogen responsiveness, resulting in a total of 203 genes).
We used this microarray system to analyze natural estrogens and industrial
chemicals (Figures 2, 3). First, we examined the reproducibility of the assay
by repeating the analysis using E2 twice (E2 and E2-2),
which resulted in very similar profiles (Figure 2) and gave a high correlation
coefficient (R = 0.928) (Figure 3A). When the reproducibility was examined
for the genes of the high and low expression types separately as examined in
Tables 1 and 2, the high expression type showed a higher score (R =
0.935) (Figure 3C) than the total gene score. Moreover, the low-expression
type also showed a relatively high score (R = 0.910) (Figure 3B), suggesting
a high reproducibility even for the low-expression type. Cluster analysis indicated
that very similar profiles were obtained among the chemicals already known
to have estrogen activity (10 nM E2, 10 nM DES, 10 µM nonylphenol,
10 nM estriol, 10 µM genistein, and 10 nM estrone) (Figure 2). Other chemicals
showed relatively high correlation coefficients (0.929 for estriol, 0.847 for
estrone, 0.692 for DES, 0.909 for genistein, and 0.862 for nonylphenol) Figure
3D-H). Relatively low scores for estrone and DES can be explained by the
low response of the genes when they were assayed at the concentration of 10
nM. Bisphenol A and methoxychlor, on the other hand, showed similar but clear
differences (0.651 for bisphenol A and 0.556 for methoxychlor) (Figure 3I-J).
Dioxin (2,3,7,8-tetrachlorodibenzo -p-dioxin; 10 nM), in contrast, showed
a very different profile, partly because most genes did not respond well, and
naturally showed a very low score (R = 0.213) (Figure 3K). A dioxin
marker, CYP1A1, responded well.
The response of a total of 12 genes (6 showing upregulation and 6 showing
downregulation) to E2 and other chemicals is summarized in Figure
4A. For example, amphiregulin (AR) showed a relatively high response
to E2 (5.4-fold increase). The response to the other chemicals with
relatively high estrogen activity (estriol, estrone, DES) was distinguishable
although low (1.6- to 1.9-fold increases). The chemicals with low estrogen
activity, however, showed a relatively high response when their concentrations
were increased to 10 µM. Expression of the AT1 receptor gene (AGTR1)
was downregulated by the treatment with E2 (2.8-fold decrease) and
all other chemicals (1.2- to 6.4-fold). We next examined the response of the
genes to E2 by the real-time PCR (Figure 4B). The degrees of response
were generally higher for the real-time PCR because of higher backgrounds in
DNA microarray assay. However, the response was confirmed by both methods.
Discussion
Customized DNA Microarray
DNA microarray technology is one of the most potentially powerful tools in
modern toxicogenomics because it can shorten the time for elucidating toxicological
phenotypes and widen the way for drug discovery (Inoue 2003). However, determining
the relationship between specific gene expression profiles and toxicological
phenotypes will be accelerated by the development of customized DNA microarrays,
the accumulation of profiles specific to chemicals, and an increase in the
knowledge of gene functions (Adachi et al. 2002; Inoue et al. 2002a; Watanabe
et al. 2002; Wong et al. 2003).
Here we developed a customized DNA microarray, EstrArray, for expression
profiling of estrogen-responsive genes. EstrArray contains 172 estrogen-responsive
genes selected from approximately 20,000 genes, almost half the estimated number
in the whole human genome. As approximately 95% of the genes examined did not
respond to estrogen or were not expressed in MCF-7 cells, the genes used for
EstrArray were considered to represent the genes most suitable for monitoring
estrogen responsiveness. As we reported previously, some of these genes were
characterized extensively to show reproducible estrogen responsiveness by Northern
blot analysis (Inoue et al. 2002b) and to examine their potential functions
(data not shown). EstrArray also contains marker genes for the calibration
of fluorescent levels that cover a wide range of expression levels for normalizing
signals between the presence and absence of chemicals. The genes, which show
estrogen responsiveness, can be classified into several types according to
their function (Tables 1 and 2; summarized in Figure 5). Among the genes related
to tumor-associated genes, oncogenes and tumor-promoting genes are generally
upregulated, whereas the genes related to tumor suppression and the ER- gene
are downregulated. This is consistent with the effects of estrogen, namely,
the promotion of tumorigenesis. For growth- and ion-associated genes and other
genes, the expression of various transporters, synthetases, transcription factors,
growth response genes, and structural genes was upregulated, indicating enhancement
of growth and proliferation of the cell. Meanwhile, the genes related to specific
differentiation of the cell, such as those for neuronal proteins, were downregulated.
Genes Responding to Estrogenic Chemicals
Among the estrogen-responsive genes used for EstrArray, the AR and AGTR1 were
examined in detail (Figure 4). Both showed a relatively high response to E2 (5.4-fold
increase for AR and 2.8-fold decrease for the AGTR1) and a similar tendency
of response to the other chemicals examined here. Estrogen responsiveness was
low for estriol, estrone, and DES compared with E2 when they were
examined at the concentration of 10 nM, except for the AGTR1 with estriol.
These data and the result of the statistical correlation study (Figure 3) indicate
that the genes responded to most chemicals analyzed here in similar ways and
suggest that these genes commonly respond to estrogen activity. The difference
in the degree of response for each gene, however, might be due to the difference
in biological effects originating from structural differences. This difference
is particularly important for the evaluation of estrogen activity, especially
when the activity is low, giving an advantage to this assay (discussed below).
The functional relationship of these genes to estrogen signaling is mostly
unknown. AR is an epidermal growth factor and is expressed in invasive
mammary tumors together with its receptor, forming a potential autocrine loop
for tumor progression (Ma et al. 2001). AR is also a target gene for
vitamin D3 (Akutsu et al. 2001) and progesterone (Das et al. 1995) and may
go through the ErbB pathway for oncogenic activity by inhibiting apoptosis
(Hurbin et al. 2002). Therefore, activation of the AR gene may well
explain the progression of estrogen-independent breast cancer. The AGTR1 is
a type 1-angiotensin II receptor whose expression is downregulated by estrogen
in several tissues. This explains the estrogen deficiency in hypertension and
other diseases (Krishnamurthi et al. 1999; Nickenig et al. 1998), although
the explanation at the molecular signaling level is not so clear. The pathways
common to the epidermal growth factor receptor or the insulin-like growth factor
could be potential signaling mechanisms (Touyz and Berry 2002).
Evaluating Estrogenicity with EstrArray
The chemicals used here have estrogen activity in reporter gene assays (Demirpence
et al. 1993; Gaido et al. 1999; Inoue
et al. 2002a; Pons et al. 1990) and cell proliferation/uterotrophic assays
[reviewed by Kanno et al. (2003)] and upregulate estrogen target genes in responsive
cells (Nagel et al. 2001; Vivacqua et al. 2003). Dioxin does not have estrogen
agonist activity (Astroff and Safe 1988; Spink et al. 1990). Cluster analysis
shown in Figure 2 clearly demonstrated similar expression profiles among estrogenic
chemicals, E2, estriol, estrone, genistein, nonylphenol, and DES.
Note that the data were obtained for 10 µM in the case of genistein, nonylphenol,
and bisphenol A, whereas a concentration of 10 nM was used for the others.
Bisphenol A at 10 µM showed less of a tendency to enhance the gene response,
although it may show a higher tendency when examined at a higher concentration.
Methoxychlor at 10 µM showed an even lower response but showed a meaningful
correlation with the profile for E2. Dioxin, as expected, was classified
as the most distant chemical in the clustering here.
The evaluation of the estrogenicity of chemicals used here is unique. First,
the estrogenicity of chemicals was compared as expression profiles of estrogen-responsive
genes, giving multiple scales provided by the expression of each gene used
here compared with the ligand-binding method and reporter gene assays. This
is even advantageous when the estrogenicity of chemicals is low, as multiple
scales can give statistically significant evaluations. The estrogenicity of
methoxychlor was not detected clearly by some assays (Shelby et al. 1996),
but here it showed a distinct tendency. Second, the estrogenicity shown here
is based on biological effects because not only the target genes of estrogen/estrogen
receptor complex but also the genes that are presumably located downstream
of the estrogen signaling pathway were included (Inoue et al., in press; Rho
et al. in press). Third, with more information, the data can be classified
according to the tendency of response among chemicals, specific to steroids,
phenol, and phthalate, for example, which are expected to have different effects
on the genes. To apply DNA microarray data for the evaluation of estrogen activity
among various compounds, we are now constructing a database consisting of DNA
microarray data of genes, chemicals, and cells. |
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Last Updated: May 4, 2004
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