Correspondence: Noriko Nakamura (E-mail: Noriko.Nakamura@fda.hhs.gov)

Gene expression profiling in dorsolateral prostates of prepubertal and adult Sprague-Dawley rats dosed with

estradiol benzoate, estradiol, and testosteroneNoriko Nakamura, Vikrant Vijay and Daniel T. Sloper

Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration,

Jefferson, AR 72079, USA

(Received December 9, 2019; Accepted May 29, 2020)

ABSTRACT — The imbalance of testosterone to estradiol ratio has been related to the development of prostate diseases. Although rat models of prostate diseases induced by endocrine-disrupting chemicals (EDCs) and/or hormone exposure are commonly used to analyze gene expression profiles in the prostate, most studies utilize a single endpoint. In this study, microarray analysis was used for gene expression pro-filing in rat prostate tissue after exposure to EDCs and sex hormones over multiple time points (prepuber-tal through adulthood). We used dorsolateral prostate tissues from Sprague-Dawley rats (male offspring) and postnatally administered estradiol benzoate (EB) on postnatal days (PNDs) 1, 3, and 5, followed by treatment with additional hormones [estradiol (E) and testosterone (T)] on PNDs 90–200, as described by Ho et al. Microarray analysis was performed for gene expression profiling in the dorsolateral pros-tate, and the results were validated via qRT-PCR. The genes in cytokine-cytokine receptor interaction, cell adhesion molecules, and chemokines were upregulated in the EB+T+E group on PNDs 145 and 200. Moreover, early-stage downregulation of anti-inflammatory gene: bone morphogenetic protein 7 gene was observed. These findings suggest that exposure to EB, T, and E activates multiple pathways and simul-taneously downregulates anti-inflammatory genes. Interestingly, these genes are reportedly expressed in prostate cancer tissues/cell lines. Further studies are required to elucidate the mechanism, including anal-yses using human prostate tissues. Key words: Prostate, Rats, Testosterone, Estradiol, Chronic inflammation, Gene expression profiling

INTRODUCTION

Prostate diseases (i.e., prostatitis, enlarged prostate, and cancer) are prevalent in a specific age-group of males. Prostatitis, chronic inflammation of the prostate, is likely to occur in young or middle-aged people. Acute inflam-mation is often caused by bacterial infections. Chronic inflammation of the prostate can also be caused by bac-teria, although most cases result from unknown caus-es (Nickel, 2012). The risk of an enlarged prostate [e.g., benign prostatic hyperplasia (BPH)] is higher after 40 years of age, when 20% of men in their fifties and 60% of men in their sixties have the condition. Prostate cancer is the most common of all new cancer cases (23%) in the United States (http://www.cancer.gov/cancertopics/types/commoncancers). Although the causes of prostate diseas-es have yet to be determined in detail, recently, increased

estrogen levels in the serum or prostate have been thought to be a key factor in the progression of prostate carcino-genesis, as well as BPH development (Krieg et al., 1993; Bosland, 2005; Ho et al., 2011; Nicholson and Ricke, 2011). These studies suggest that prostate diseases are likely to occur when prostate estrogen levels increase due to decreasing serum testosterone levels brought on by aging or endocrine-disrupting chemicals (EDCs).

Early postnatal exposure to EDCs is specifically influ-ential on prostate development and testosterone produc-tion (vom Saal et al., 1997; Prins et al., 2006; Anway and Skinner, 2008; Hofkamp et al., 2008; Prins, 2008; Diamanti-Kandarakis et al., 2009). In addition, testoster-one levels in men peak at 25–30 years of age and then decline gradually. The testosterone levels show a 30–60% decrease at age 60 and older (Kaufman and Vermeulen, 2005). In contrast, there is no substantial age-relat-

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Original Article

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ed change in male serum estrogen levels (Jasuja et al., 2013); however, there is a significant age-related increase in estrogen levels in normal human prostates (Krieg et al., 1993). Further, the ratio of estrogen to testosterone levels significantly increased with aging (Suzuki et al., 1992).

Rat models are still widely used as animal models of prostate cancer and/or BPH (Noble, 1977; Pollard, 1982; Bosland et al., 1995; Russell and Voeks, 2003), includ-ing gene expression profiling (Thompson et al., 2002; Ho et al., 2006). Prins and colleagues developed rat mod-els of prostate disease (i.e., neoplasia and inflammation), induced by EDCs supplemented with additional testo-sterone and estradiol treatments, to examine gene expres-sion profiles during prostate development through adult-hood (Gilleran et al., 2003; Huang et al., 2004). However, most studies have elucidated the mechanisms underly-ing these disease-inducing treatments at a later stage (one endpoint). There are some studies for gene expression or methylation profiling during the development of prostate disease (Tang et al., 2012; Wong et al., 2015; Cheong et al., 2016; Lam et al., 2016; Prins et al., 2017).

Previously, we studied gene expression profiling in Sprague-Dawley rats (Hsd:SD) dosed with estradiol ben-zoate (EB), testosterone (T), and 17-β-estradiol (E) as described by Ho et al. (2006). Pups at postnatal days (PNDs) 1, 3, and 5 were injected with EB. From PND 90 through PND 200, they were dosed with T and E via silastic tube implants in the subcutaneous region. Simi-lar to Gilleran et al. (2003), we found the occurrence of chronic inflammation only in the dorsolateral prostate of rats dosed with EB, T, and E on PNDs 145 and 200 (Nakamura et al., in press), which can be associated with gene alteration.

The purpose of this study was to examine gene expres-sion profiling in rat prostate dosed with EB, T, and E dur-ing prepubertal through adult ages using rat prostates To perform this study, we performed microarray analysis using rat prostates dosed with EB, T, and E and verified these results by qRT-PCR.

MATERIALS AND METHODS

MaterialsAll reagents were purchased from Fisher Scientific

(Pittsburgh, PA, USA) and Sigma-Aldrich (St. Louis, MO, USA), unless otherwise indicated.

Animals and treatmentsThe dorsolateral prostates were obtained from Sprague-

Dawley (SD) rats (Nakamura et al., in press). Treatment of male offspring was carried out following the method

described by Ho et al. (2006). Figure 1 shows the experi-mental design. Briefly, male offspring were obtained from fifty 11–13-week-old time-mated female SD rats pur-chased from Envigo (Indianapolis, IN, USA) and deliv-ered to the National Center for Toxicological Research (NCTR) on gestation day (GD) 3 (day of birth = PND 0). Animals were housed individually and maintained under a 12:12-hr light-dark cycle with controlled room tempera-ture (23 °C ± 3 °C) and humidity (50% ± 20%). Their diet upon arrival consisted of a low-phytoestrogen 5K96 chow (Purina Mills, St. Louis, MO, USA). Water was provided ad libitum. All animal procedures were approved by the NCTR Institutional Animal Care and Use Committee and followed the guidelines set forth by the National Research Council’s Guide for the Care and Use of Laboratory Animals (National Research Council, 2011).

The male offspring were divided into two groups: untreated (n =148) and estradiol benzoate (EB)-treated (n =148). In the EB-treated group, male pups were inject-ed subcutaneously with 2.5 mg/kg body weight (BW) EB (Sigma-Aldrich, E8515) at PNDs 1, 3, and 5. Male pups in the untreated group (control) were injected with a vehicle (i.e., tocopherol-stripped corn oil; #0290141584-400; ICN Biomedicals, Inc., Aurora, OH, USA). At PND 90, all groups (untreated/control and EB-treated groups) were divided into two groups: control, testosterone (T) and estradiol (E) only, EB only, and EB+T+E groups (n = 8/group/collection point). All animals were then subcutaneously implanted with several tubes. Animals in the control and EB only groups were implanted with three empty silastic tubing inserts (two 2-cm tubes and one 1-cm tube), whereas animals in the T+E only and EB+T+E groups were implanted with two silastic tub-ing inserts (Dow Corning, ID; 1.47 mm; absorbance, 1.95 mm) packed with T powder (two 2-cm tubes; Sigma- Aldrich) and one tube packed with E (one 1-cm tube; Sigma-Aldrich) for additional testosterone and 17-β-estradiol treatment until PND 200. Eight weeks after the first surgery on PND 90 (on PND 146), 32 animals under-went a second surgery to replace the hormone-containing silastic tubing implants. These animals were sacrificed on PND 200.

Microarray experimentProstate RNA samples from PNDs 30, 100, and 145

rats (n = 3–4 litters/group) were extracted using a miRNe-asy Mini Kit (Qiagen, Valencia, CA, USA). After extrac-tion, individual RNA concentrations were determined via a NanoDrop 2000c spectrophotometer (version 3.0.1, Thermo Fisher Scientific, Inc., Wilmington, DE, USA). One-color microarray-based gene expression analysis was

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performed using Agilent QuickAmp Kits following the manufacturer’s instructions (Agilent Technologies, Santa Clara, CA, USA). The total RNA (~500 ng) was labeled with Cy3, and the cRNA (Cy3-labeled RNA) was purified with a RNeasy Kit (Qiagen). cRNA concentrations were determined with a Nanodrop ND 1000 spectrophotome-ter (Thermo Fisher Scientific). Equal amounts (600 ng) of purified cRNA were hybridized to Agilent SurePrint G3 rat 8 × 60K microarrays (Agilent Technologies) for 17 hr at 65°C in a hybridization oven. After washing, hybrid-ized microarray slides were scanned with an Agilent DNA Microarray Scanner C, and the images were fur-ther analyzed using Agilent’s Feature Extraction software (version 10.7.3).

Analysis of microarray dataThe Agilent rat whole-genome (8 × 60K) arrays con-

tain 62,976 spots for probe localization. Data processing, normalization, and statistical analyses were performed on all transcripts of prostates from all groups of PNDs 30, 100, and 145 rats using the SAS 9.4 (SAS, Cary, NC, USA). The 33 raw microarray data files and preprocessed normalized data (33 files) are accessible at NCBI’s Gene Expression Omnibus (GEO) website, and the GEO acces-

sion number is GSE145917 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145917). A generalized linear model procedure (proc glm) was used to measure statistical significance (p < 0.05) between the control and exposed groups. A relative change in the abundance of each transcript was calculated as a difference in the aver-age log2 intensity values. Genes with expression chang-es greater than 1.5 folds with p-values less than 0.05 were considered differentially expressed. These differential-ly expressed genes (DEGs) were used for further path-way analysis utilizing ArrayTrack software (Tong et al., 2003).

cDNA synthesisGene expression changes identified in the microar-

ray analysis were confirmed by qRT-PCR using cDNA synthesized with 1 µg RNA from the microarray sam-ples using a Super Script IV VILO Master Mix (Thermo Fisher Scientific).

Quantitative PCR (qPCR)The qPCR analyses were performed using the ABI

PRISM 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) with PowerUp SYBR

Fig. 1. Scheme of experimental design. Male SD:Hsd offspring were injected with 2.5 mg/kg body weight (BW) EB subcutane-ously at PNDs 1, 3, and 5 for the treated group. Male offspring in the control group were injected with a vehicle (i.e., corn oil). On PND 90, the animals in T+E only and EB+T+E groups were treated with testosterone and estradiol packed-silastic tube implants, while the animals in the control and EB only groups were implanted with empty silastic tubes. After 8 weeks (on PND 146) the animals received new implants.

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Green Master Mix (Thermo Fisher Scientific) per the manufacturer’s guidelines. The cDNA, synthesized as described above, served as a template in 10-μL reaction mixtures. The PowerUp SYBR Mix reaction conditions were as follows: initial denaturation steps at 50°C for 2 min and 95°C for 2 min, followed by 40–45 amplifi-cation cycles (95°C for 15 sec, 60°C for 1 min). The rel-ative steady-state transcript levels were calculated using threshold cycle (Ct) values and the following equation: relative quantity = 2-ΔΔCt (Livak and Schmittgen, 2001). The transcript levels were normalized using Rn18s as an internal control for each sample. The relative ratios of the transcript levels in each sample were calculated by setting the values for controls at each collection time (PND 30, 100, or 145) to one. qPCR was performed in triplicate for each sample (n = 5/group). Supplemental Table 1 shows the specific primer pairs used in this study.

Statistical analysisData are presented as the mean ± standard error of

the mean (SEM). Statistical analysis of qPCR data was performed using t-tests (PNDs 30 and 90) or one-way ANOVA (PNDs 100, 145, and 200) with Bonferroni adjustment (Hochberg, 1988; Shaffer, 1995). Log-trans-formation (Hellemans and Vandesompele, 2011) was per-formed on qPCR data with higher valiances before the statistical analysis. A p-value of < 0.05 was considered statistically significant.

RESULTS

Gene expression profilingMicroarray analyses of prostate gene expression pat-

terns from rats showed significant differential expres-sion of 2,084, 5,207, and 12,447 genes on PNDs 30, 100, and 145, respectively, compared to controls (p < 0.05, fold change (FC) > 1.5; ANOVA analysis on PNDs 100 and 145). A total of 219 genes overlapped in the prostate among PNDs 30, 100, and 145 (Supplemental Fig. 1).

The genes with treatment-specific changes in expression levels were identified in T+E only, EB only, or EB+T+E groups on PND 100 or 145 (FC > 5.0, p < 0.01) com-pared to the controls on PND 100 or 145 (Supplemental Tables 2 and 3) and in the EB-treated on PND 30 com-pared to the controls on PND 30 (Supplemental Table 4). To identify unique DEGs in the EB-treated on PND 30 or EB only groups on PNDs 100 and 145, we found the genes (FC > 5.0, P < 0.01) on PND 30 (EB only expo-sure) were not overlapped to the genes on the EB only on PNDs 100 and 145 via microarray analysis. Several genes in the EB-treated group on PND 30 were overlapped to

the genes on the T+E only on PNDs 100 and 145, respec-tively (Supplemental Fig. 2). Thus, most of the genes in the group at the different collection points were unique.

Principal component analysis (PCA)Using DEGs data, PCA was conducted by comparing

the overall expression profiles of the untreated and EB-treated prostates on PND 30, and of the control, T+E only, EB only, and EB+T+E groups’ prostates on PNDs 100 and 145 (Figs. 2A, B, and C) to determine the asso-ciation among the two or four groups. Principal Compo-nent 1 (PC1) captured 73.3%, PC2 captured 7.2%, and PC3 captured 5.7% of the variance in the data on PND 30 (Fig. 2A). Together, 86.2% of the variance in these sam-ples was captured. PC1 captured 50.7% and 60.4%, PC2 captured 18.5% and 15.3%, and PC3 captured 10.9% and 6.6% of the variance on PNDs 100 (Fig. 2B) and 145 (Fig. 2C), respectively. Together, 80.1% and 82.2% of the total variance on PNDs 100 and 145, respectively, were captured by the first three components. PC1 represents a treatment-related distinct separation that exists among the control and exposed groups, suggesting EB only, T+E only, or EB+T+E exposure has different effects.

Pathway analysisArrayTrack was used to determine the affected genet-

ic pathways in the prostate following EB only (PND 30) or EB, T, and E exposure (PNDs 100 and 145). In all, 63 KEGG pathways in the prostate were significantly altered among the groups on PNDs 30, 100, or 145 compared to the control collection date (Table 1). Of the 63 pathways, the significantly altered pathways in any groups on PNDs 30, 100, and 145 were eight pathways [i.e., cell adhe-sion molecules (CAMs) (rno04514); HTLV-I infection (rno05166); focal adhesion (rno04510); PI3K-Akt signal-ing pathway (rno04151) pathways in cancer (rno05200)].

The higher statistical significances were observed in the cytokine-cytokine receptor interaction (rno04060) pathway in the T+E only and EB+T+E groups on PND 100 and all treated groups on PND 145 (i.e., those with p-values < 0.05). Also, they were observed in the chem-okine signaling (rno0462) pathway related to the immune system in the EB only and EB+T+E groups. The KEGG pathways were specifically altered by the EB-treated group on PND 30 (Table 2). Interestingly, the estrogen signaling and Wnt signaling pathways were significantly altered on only the EB-treated group on PND 30. Further, Table 3 shows the pathways affected by exposure to only T and E by comparing the controls on PND 30 vs. the T+E only group on PNDs 100 and 145. Of the top 20, the pathways related to the cell proliferation/growth (e.g., cell

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cycle and DNA replication) were identified. The KEGG pathways that were specifically altered in the T+E only, EB only, or EB+T+E on PNDs 100 or 145 were exam-ined (Supplemental Tables 5 and 6).

Selection of genes to be validated via qPCRSeveral of the top ten genes on the list were of

unknown function in the prostate. Therefore, we select-ed genes from the cytokine-cytokine receptor interac-tion (rno04060), chemokine signaling (rno0462), and CAM (rno04514) pathways and evaluated their fuction

in the prostate during various treatments (Supplemental Table 7). These genes were also included in the list of significantly altered genes in the EB+T+E group on PND 145 compared to the control group (Supplemental Table 3), or their function had already been reported in the prostate.

qPCR validationUpregulated genes

The expression of genes related to inflammation [e.g., C-C motif chemokine ligand 2 (Ccl2), C-X-C motif

Fig. 2. Principal component analysis (PCA) of rat dorsolateral prostates. PCA was performed using ArrayTrack software (Tong et al., 2003). (A) PCA between the control and EB-treated groups on PND 30 rat prostate. (B) PCA among the control, T+E only, EB only and EB+T+E groups at PND 100 rat prostate. (C) PCA among the control, T+E only, EB only and EB+T+E groups at PND 145 rat prostate.

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Table 1. KEGG pathway analysis that significantly altered in the dorsolateral prostates of rats dosed with EB-treated on PND 30, or T+E only, EB only, or EB+T+E groups on PNDs 100 and 145 (p < 0.05).

Map title

PND 30 PND 100 PND 145EB-treated T+E only EB only EB+T+E T+E only EB only EB+T+E

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Cytokine-cytokine receptor interaction (rno04060) N.S. 0.0191 N.S. 0.0000538 0.00216 0.00000462 1.22E-20Staphylococcus aureus infection (rno05150) N.S. N.S. N.S. N.S. N.S. 0.00424 3.40E-11Chemokine signaling pathway (rno04062) N.S. N.S. N.S. N.S. N.S. 0.0000249 9.91E-11Cell adhesion molecules (CAMs) (rno04514) 0.0224 N.S. N.S. 0.0458 0.00308 0.0000115 2.13E-10Osteoclast differentiation (rno04380) N.S. N.S. N.S. N.S. 0.0372 0.0301 3.21E-09ECM-receptor interaction (rno04512) 0.0183 0.0373 N.S. N.S. 0.000514 N.S. 1.41E-08Leishmaniasis (rno05140) N.S. N.S. N.S. N.S. N.S. 0.0033 3.45E-08Amoebiasis (rno05146) 0.0174 N.S. 0.00634 0.0148 0.000203 0.0269 9.24E-08TNF signaling pathway (rno04668) N.S. N.S. N.S. N.S. N.S. 0.007 1.38E-07Antigen processing and presentation (rno04612) N.S. N.S. N.S. N.S. 0.000515 N.S. 1.42E-07Phagosome (rno04145) N.S. N.S. N.S. N.S. N.S. 0.0311 3.35E-07Pertussis (rno05133) N.S. N.S. 0.017 0.00103 N.S. N.S. 9.10E-07Chagas disease (American trypanosomiasis) (rno05142) N.S. N.S. N.S. N.S. N.S. 0.0375 0.0000012

Hematopoietic cell lineage (rno04640) N.S. N.S. N.S. N.S. N.S. 0.0113 0.00000128HTLV-I infection(rno05166) 0.0445 N.S. N.S. 0.00908 N.S. 0.00743 0.00000192Rheumatoid arthritis (rno05323) 0.0198 N.S. N.S. N.S. N.S. 0.00764 0.00000632NF-kappa B signaling pathway (rno04064) N.S. N.S. N.S. N.S. N.S. 0.00163 0.0000132Protein processing in endoplasmic reticulum (rno04141) 0.0363 N.S. N.S. N.S. N.S. 0.0312 0.0000147

Focal adhesion (rno04510) 0.00561 0.02 0.0387 N.S. 0.000103 N.S. 0.0000295Natural killer cell mediated cytotoxicity (rno04650) N.S. 0.00491 N.S. 0.032 N.S. 0.00988 0.0000307Primary immunodeficiency (rno05340) N.S. N.S. N.S. N.S. N.S. 0.000277 0.0000407Graft-versus-host disease (rno05332) N.S. N.S. N.S. N.S. N.S. 0.0212 0.0000553Type I diabetes mellitus (rno04940) N.S. N.S. N.S. N.S. N.S. 0.0244 0.0000778Endocytosis (rno04144) 0.0341 N.S. N.S. N.S. 0.0203 0.0165 0.000096Toxoplasmosis (rno05145) N.S. N.S. N.S. N.S. 0.00105 N.S. 0.000101Platelet activation (rno04611) 0.00388 N.S. N.S. N.S. 0.00556 N.S. 0.000216Allograft rejection (rno05330) N.S. N.S. N.S. N.S. N.S. 0.0112 0.000303Measles (rno05162) N.S. N.S. N.S. N.S. 0.0302 0.00875 0.00034Thyroid hormone synthesis (rno04918) N.S. N.S. N.S. N.S. N.S. 0.0126 0.00042PI3K-Akt signaling pathway (rno04151) 0.00796 0.022 N.S. N.S. 0.000252 N.S. 0.00129Protein digestion and absorption (rno04974) 0.0248 N.S. N.S. N.S. N.S. N.S. 0.0025Axon guidance (rno04360) 0.016 N.S. N.S. N.S. 0.0461 0.0399 0.00273Propanoate metabolism (rno00640) N.S. N.S. N.S. N.S. 0.0268 N.S. 0.004Arginine and proline metabolism (rno00330) 0.033 N.S. N.S. N.S. N.S. 0.0252 0.00456Viral myocarditis (rno05416) N.S. N.S. N.S. N.S. 0.0498 0.0298 0.00483Glutathione metabolism (rno00480) N.S. N.S. N.S. N.S. 0.00694 N.S. 0.00581Fc gamma R-mediated phagocytosis (rno04666) N.S. N.S. N.S. N.S. N.S. 0.0117 0.00601Autoimmune thyroid disease (rno05320) N.S. N.S. N.S. N.S. N.S. 0.0269 0.00618Glycine, serine and threonine metabolism (rno00260) N.S. N.S. N.S. N.S. 0.0267 N.S. 0.00789MicroRNAs in cancer (rno05206) N.S. N.S. N.S. N.S. N.S. 0.00477 0.00987Rap1 signaling pathway (rno04015) 0.00788 N.S. N.S. N.S. N.S. N.S. 0.0122Pathways in cancer (rno05200) 0.0000813 0.0256 N.S. N.S. 0.0219 N.S. 0.0138Cytosolic DNA-sensing pathway (rno04623) N.S. N.S. N.S. N.S. N.S. 0.0419 0.0195Transcriptional misregulation in cancers (rno05202) N.S. N.S. N.S. N.S. N.S. 0.00519 0.0208Small cell lung cancer(rno05222) N.S. N.S. N.S. N.S. 0.000733 N.S. 0.0236Glycosphingolipid biosynthesis - ganglio series (rno00604) N.S. N.S. N.S. N.S. N.S. 0.0482 0.0274

GABAergic synapse (rno04727) N.S. N.S. N.S. N.S. N.S. 0.0228 0.0279

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Map title

PND 30 PND 100 PND 145EB-treated T+E only EB only EB+T+E T+E only EB only EB+T+E

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Fisher P value

Cholinergic synapse (rno04725) N.S. N.S. N.S. N.S. N.S. 0.0175 0.0359Pentose phosphate pathway (rno00030) 0.0232 N.S. N.S. 0.0334 N.S. 0.0141 N.S.Complement and coagulation cascades (rno04610) N.S. N.S. N.S. N.S. 0.0267 0.0178 N.S.Melanogenesis (rno04916) 0.0000969 N.S. N.S. N.S. N.S. 0.0209 N.S.Ovarian Steroidogenesis (rno04913) 0.00762 N.S. N.S. N.S. N.S. 0.0376 N.S.Metabolic pathways(rno01100) 0.00117 0.0000295 0.0141 N.S. 0.00417 N.S. N.S.Glycosaminoglycan biosynthesis - keratan sulfate (rno00533) 0.00235 N.S. N.S. N.S. 0.0128 N.S. N.S.

Hedgehog signaling pathway(rno04340) 0.000145 N.S. N.S. N.S. 0.0227 N.S. N.S.Glycosphingolipid biosynthesis - lacto and neolacto series (rno00601) N.S. N.S. N.S. N.S. 0.0233 N.S. N.S.

Arachidonic acid metabolism (rno00590) 0.00397 N.S. N.S. N.S. 0.048 N.S. N.S.Sphingolipid metabolism (rno00600) N.S. 0.0124 N.S. 0.0429 N.S. N.S. N.S.Starch and sucrose metabolism (rno00500) 0.0426 0.0188 0.0233 0.00794 N.S. N.S. N.S.Aldosterone-regulated sodium reabsorption (rno04960) 0.0413 0.0197 N.S. N.S. N.S. N.S. N.S.

ErbB signaling pathway (rno04012) 0.0248 0.02 N.S. N.S. N.S. N.S. N.S.Steroid hormone biosynthesis (rno00140) 0.00397 0.0248 N.S. N.S. N.S. N.S. N.S.Serotonergic synapse (rno04726) 0.0107 0.0307 N.S. N.S. N.S. N.S. N.S.

N.S.: No significance. The significant differences are shown orange-highlighted. PND 30: the comparison between untreated vs EB-treated groups via Student t-tests. PNDs 100 and 145: the comparison between CTRL vs T+E only, EB only, or EB+T+E groups via Student t-test. Sorted by p-Value in the EB+T+E group on PND145 as primary.

Table 1. (Continued).

Table 2. The significant changes of KEGG pathways on PND 30 specifically.

Map title CategoryPND 30

Fisher P valueBasal cell carcinoma(rno05217) Cancers/Human Diseases 2.57E-05Hippo signaling pathway(rno04390) Signal transduction/Environmental Information Processing 0.000113Retinol metabolism(rno00830) Metabolism of cofactors and vitamins/Metabolism 0.000401Acute myeloid leukemia(rno05221) Cancers/Human Diseases 0.0026Estrogen signaling pathway(rno04915) Endocrine system/Organismal Systems 0.00279Oxytocin signaling pathway(rno04921) Endocrine system/Organismal Systems 0.00465Morphine addiction(rno05032) Substance dependence/Human Diseases 0.00469Calcium signaling pathway(rno04020) Signal transduction/Environmental Information Processing 0.0049Gastric acid secretion(rno04971) Digestive system/Organismal Systems 0.00496Collecting duct acid secretion(rno04966) Excretory system/Organismal Systems 0.00569Circadian entrainment(rno04713) Environmental adaptation/Organismal Systems 0.00675Wnt signaling pathway(rno04310) Signal transduction/Environmental Information Processing 0.00753Chemical carcinogenesis(rno05204) Cancers/Human Diseases 0.00786Lysine degradation(rno00310) Amino acid metabolism/Metabolism 0.00856Vascular smooth muscle contraction(rno04270) Circulatory system/Organismal Systems 0.00992N.S. No Significance. The significant differences are shown orange-highlighted. PND 30: the comparison between untreated vs EB-treated groups via Student t-tests.

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chemokine ligand 13 (Cxcl13), interleukin 7 receptor (Il7r), C-X-C motif chemokine receptor 6 (Cxcr6), C-C motif chemokine ligand 19 (Ccl19), C-C motif chemok-ine ligand 28 (Ccl28), neutrophil cytosolic factor 1 (Ncf)] was significantly higher in the EB+T+E group on PNDs 145 and 200 compared to the control group. The tran-scription levels of Ccl2 and Ccl19 genes were significant-ly higher in the EB+T+E group compared to the control group on PND 100 as well (Fig. 3A). The transcripts lev-els of HCK proto-oncogene, Src family tyrosine kinase (Hck) gene were significantly increased in the EB+T+E groups on PNDs 100, 145, and 200 compared to the con-trol group (Fig. 3B). Integrin alpha 4 (Itga4) and neural cell adhesion molecule 1 (Ncam1) genes, which belong to the CAM pathway, showed interesting changes in their expression levels (Fig. 3B). The transcript levels of Itga4 gene were significantly increased in the EB+T+E groups on PNDs 145 and 200 and in the EB only on PND 145. In contrast, the transcript levels of the Ncam1 gene were lower in all groups on PND 100 and in the EB only and EB+T+E group on PND 200 compared to the control group, with a significant difference in the EB+T+E group on PND200. On PND 145, its levels were higher in all groups than in the control group.

Downregulated genesThe transcript levels of the bone morphogenetic pro-

tein 7 (Bmp7) gene were downregulated in most groups on PNDs 30, 100, 145, and 200 except PND 90. Signif-icant decreases were observed in the EB-treated group on PND 30 and in the T+E only and EB+T+E groups on PND 100. In addition, the transcript levels of Bmp7 were lower in the T+E only and EB+T+E groups compared to the control group, in which changes in the EB+T+E group on PNDs 145 and 200 were statistically significant (Fig. 4).

DISCUSSION

This study examined gene expression profiling at mul-tiple time points in rat dorsolateral prostate dosed early postnatally with EB only (PND 30) and/or with T+E in adult age to determine what effect exposure to EB and/or T and E had on gene expression profiles in the rat pros-tate. Most of the DEGs and pathways in the dorsolater-al prostate were identified in the EB+T+E group on PND 145; in addition, some pathways (i.e., estrogen signaling and Wnt signaling) were altered on PND 30 (EB expo-sure) specifically and did not overlap the pathways after T and E exposure. These results are consistent with oth-er researchers’ findings (Huang et al., 2004, 2009; Ho et

Table 3. The pathways that significantly altered by only T and E exposure (p < 0.05).

Map title CategoryPND 100 PND 145

Fisher P value Fisher P valueCell cycle(rno04110) Cell growth and death/Cellular Processes 3.79E-07 0.00159Circadian entrainment(rno04713) Environmental adaptation/Organismal Systems 0.00000197 0.00654Cholinergic synapse(rno04725) Nervous system/Organismal Systems 0.0000102 N.S.Pathways in cancer(rno05200) Cancers/Human Diseases 0.0000183 0.0463DNA replication(rno03030) Replication and repair/Genetic Information Processing 0.0000237 5.81E-08Morphine addiction(rno05032) Substance dependence/Human Diseases 0.0000488 0.0123Dopaminergic synapse(rno04728) Nervous system/Organismal Systems 0.000095 0.00692Glutamatergic synapse(rno04724) Nervous system/Organismal Systems 0.000145 0.000758Chemokine signaling pathway(rno04062) Immune system/Organismal Systems 0.00019 N.S.Synaptic vesicle cycle(rno04721) Nervous system/Organismal Systems 0.000461 0.00189Ras signaling pathway(rno04014) Signal transduction/Environmental Information Processing 0.000477 N.S.GABAergic synapse(rno04727) Nervous system/Organismal Systems 0.000528 0.0168Serotonergic synapse(rno04726) Nervous system/Organismal Systems 0.000683 N.S.Pancreatic cancer(rno05212) Cancers/Human Diseases 0.000738 N.S.MicroRNAs in cancer(rno05206) Cancers/Human Diseases 0.000765 N.S.Adrenergic signaling in cardiomyocytes(rno04261) Circulatory system/Organismal Systems 0.00133 N.S.Insulin secretion(rno04911) Endocrine system/Organismal Systems 0.00138 N.S.Alcoholism(rno05034) Substance dependence/Human Diseases 0.00172 N.S.Gap junction(rno04540) Cellular commiunity/Cellular Processes 0.00197 0.0396African trypanosomiasis(rno05143) Infectious diseases/Human Diseases 0.00245 N.S.

N.S. No Significance. The comparison between CTRL on PND 30 vs T+E only on PNDs 100 and 145 via Student t-tests.

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Fig. 3. Relative transcript levels of genes related to inflammation (Ccl2, Cxcl13, Il7r, Cxcr6, Ccl19, Ccl28, and Ncf1), proto-onco-gene (Hck), and cell adhesion molecule (Itga4 and Ncam1) in the dorsolateral prostate of rats dosed postnatally with EB, T and E. Data are expressed as the mean fold change ± SEM (n = 5 per group). *p < 0.05 compared to the control group each endpoint (PND 30, 90, 100, 145 or 200) in the prostate. The relative ratios of the transcript levels in each sample were cal-culated by setting the values for controls at each collection time (PND 30, 100, or 145) to one.

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al., 2011).DEGs induced by EB exposure had few overlapping

genes at the EB only group on PNDs 100 and 145. This finding suggests unique DEGs on PND 30, 100, or 145 identified in this study may be caused by aging and/or dif-ferent mechanisms induced by EB or T and E exposure.

Cheong et al. (2016) examined gene expression profil-ing in the dorsal prostate dosed neonatally with EB using microarray or RNA-seq. Of the 15 differentially meth-ylated genes they identified, only progestin and adipoQ Receptor family member 4 gene were identified in the EB+T+E group on PND 145. There was a lack of con-currence of gene expression profiling between studies despite folling their experimental design (animal strain, dosing chemical, and concentration). This discrepancy may be caused by difference in collection points (PND 30 vs PND 90) and/or prostate lobes (dorsolateral vs dor-sal prostate). This study did not examine gene expression profiling using rat prostates on PND 90. Further analyses are necessary to clarify this matter.

This research selected and used known-function genes in prostates for qPCR validation. The genes (Ccl2, Ccl19, Ccl28, Cxcl13, and Cxcr6) are known to be inflammatory chemokines that work during injury (Mélik-Parsadaniantz

and Rostène, 2008). The proteins encoded by the Cxcl13 gene are expressed in prostate cancer cell lines, neopla-sia, and inflamed tissues and are thought to be related to prostate cancer development. The Cxcr6 gene is relat-ed to tumor growth and invasion in prostate cancer cells (Salazar et al., 2013). NFκB signaling regulates Cxcl13 and Ccl19 in prostate cancer and monocyte-derived den-dritic cells (Pietilä et al., 2007; Garg et al., 2017). NFκB also regulates Il7r gene expression stably in naïve T-cell-sthathave an anti-inflammatory role (Miller et al., 2014).

The proteins encoded by the Hck gene belong to the Src family of tyrosine kinases (Summy and Gallick, 2006; Tatarov and Edwards, 2007). Interactions between the Src family kinases and proteins in the MAPK and PI3K path-ways may induce cell proliferation. The Src family is thought to be related to prostate cancer development or tumor growth (Fizazi, 2007; Varkaris et al., 2014).

The protein encoded by the Bmp7 gene is a member of the TGF-β superfamily and plays a role in prostate devel-opment. Expression of BMP7 was highest in the normal prostate glandular tissue, whereas it tended to be lower during the development and progression of prostate can-cer (Masuda et al., 2004; Morrissey et al., 2010). BMP7 is also known to have an anti-inflammation function (Li et al., 2015; Tsujimura et al., 2016).

The genes related to CAMs have an important function in prostate development and cancer development (Hsieh, 2001). The NCAM protein is related to prostate cancer invasion (Li et al., 2003); the transcript levels of Igta4 gene were reported to be downregulated in prostate can-cer (Saramäki et al., 2006).

Comparing the DEGs obtained via microarray in the EB+T+E group on PND 145 to the EB only group in various time points (PND 30, 100 or 145; Supplemental Tables 2–4), showed that fewer genes were overlapped in the EB only group in the various time points (FC > 5.0, P < 0.01) (Supplemental Fig. 2). The Ccl28 gene was overlapped when compared between EB+T+E group on PND 145 vs EB only PND 100, but there were no observed statistically significancant variations in tran-scription levels of Ccl28 gene in the EB only group at PND 100.

Of the genes (Ccl2, Ccl19, Ccl28, Cxcl13, Cxcr6, Il7r, Ncf1, Hck, Ncam1, and Itga4) selected for qPCR valida-tion, all except Ccl2 and Ncam1 genes were significan-cantly elevated in the EB+T+E group on PNDs 145 and 200 although the transcript levels of these genes were not increased in the T+E group each point.

These findings suggest, up-regulation of the Ccl19, Cxcl13, Cxcr6, Il7r, Ncf1, Hck, and Itga4 genes requires both early neonatal exposure to EB and by T+E exposure

Fig. 4. Relative transcript levels of down-regulated gene (Bmp7) in dorsolateral prostate of rats dosed postna-tally with EB, T and E. Data are expressed as the mean fold change ± SEM (n = 5 per group). *p < 0.05 com-pared to the control group each endpoint (PND 30, 90, 100, 145 or 200) in the prostate. The relative ratios of the transcript levels in each sample were calculated by setting the values for controls at each collection time (PND 30, 100, or 145) to one.

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in adult age.However, regarding the Ccl2, Ccl19, Ccl28, Cxcl13,

Cxcr6, Il7r, Ncf1, and Hck genes, Prins, (1992) and Bosland et al. (1995) reported that due to inflammation mononuclear cells invaded into the prostate of rats dosed with EB or T and E. The elevated expression levels of these cytokineor chemokine-related genes may be caused by the prostate tissues, including mononuclear cells. Some genes were reported to have changes of expression in prostate cancer tissues. Further studies, including anal-yses of protein expression and their localization, are nec-essary to confirm qPCR results.

Figure 5 summarizes this study. The EB exposure on PNDs 1, 3, and 5 significantly changed the estrogen receptor and Wnt signaling pathways, specifically. In addition, the CAM pathway was altered on PND 30. After T and E exposure in adult age, cytokine-cytokine receptor interaction/chemokine signaling pathways were altered, as were CAMs. Itga4 and Hck genes and the genes relat-ed to cytokine-cytokine receptor interaction/chemokine signaling pathways (Ccl2, Ccl19, Ccl28, Cxcl13, Cxcr6,

Il7r, and Ncf1) were altered after T and E exposure. The expression of proto-oncogene (e.g., Hck gene), which belongs to the Src superfamily, was increased after T and E exposure. We have also identified the Bmp7 gene, which has a function of anti-inflammation. The transcript levels of Bmp7gene are downregulated via EB and/or T and E exposure.

Interestingly, the genes identified in this study were previously identified as expressed in prostate cancer cell lines or human prostate cancer. However, further stud-ies are needed in determining whether the change in gene expression levels are related to inflammation caused by mononuclear cell invasion.

In conclusions, this study found that most of the dif-ferentially expressed genes/pathways induced by EB and/or T and E exposure were unique at different collection points (PND 30, 100, or 145). Some pathways chang-es were time-specific occuring at PND 30, 100, or 145. The gene (Bmp7) related to anti-inflammation was down-regulated by both neonatal EB exposure and later expo-sure to EB and T and E. The upregulated genes (Itga4,

Fig. 5. Summary of gene and pathway changes in the dorsolateral prostate of rats dosed with EB and/or T and E. EB exposure on PNDs 1, 3 and 5 changed estrogen receptor/Wnt signaling pathways specifically on PND 30. Cell adhesion molecule path-ways were significantly altered by early neonatal exposure to EB on PND 30 and T and E exposure on PNDs 100 and 145. T and E exposure significantly altered cytokine-cytokine receptor interaction /chemokine signaling. The proto-oncogenes (Hck) and genes that related to chemokines and cytokine signaling (Cxcl13, Il7r, Ccl2) were increased. Bmp7 gene were down-regulated by neonatal EB exposure only and exposure to EB, T and E. Red arrows show an increased activity; green arrows show down-activity of the genes and pathways. Dotted lines indicate possible interactions. T: testosterone, E: estro-gen, EB: estradiol benzoate.

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Hck, Ccl2, Ccl19, Ccl28, Cxcl13, Cxcr6, Il7r, and Ncf1) occured after T and E exposure in the prostate of rats dosed with EB on PNDs 1, 3, and 5. Additional studies are required to determine whether the changes observed in the rat prostates correspond with human prostate sam-ples and whether they are specific to only the prostate.

ACKNOWLEDGMENTS

The authors thank Drs. Richard Beger, Tao Chen, and Pierre Alusta for their suggestions on the manuscript, and Dr. John K. Leighton (CDER/FDA) for his sugges-tion on this project proposal and manuscript. The authors also recognize the contributions of Dr. Tao Han for super-vising microarray procedure and Dr. Davis Kelly (TPA) for histological evaluation of prostates. Special thanks are given to Drs. Guangxu Zhou (DBB), Feng Qian (DBB), and Ryan Curtis (IT) for assisting with the analysis using ArrayTrack software, and Dr. Paul Rogers (DBB) for helping log-transformation of qPCR data. This study was funded by the NCTR/FDA (E0759701).The views expressed are those of the authors and do not represent the views of the Food and Drug Administration.

Conflict of interest---- The authors declare that there is no conflict of interest.

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