Effects of 4-Hydroxy-2,3,3′,4′,5-Pentachlorobiphenyl (4-OH-CB107) on Liver Transcriptome in Rats: Implication in the Disruption of Circadian Rhythm and Fatty Acid Metabolism

Effects of 4-Hydroxy-2,3,3′,4′,5-Pentachlorobiphenyl (4-OH-CB107) on Liver Transcriptome in... Abstract Polychlorinated biphenyls (PCBs) and their hydroxylated metabolites (OH-PCBs) have been detected in tissues of both wild animals and humans. Several previous studies have suggested adverse effects of OH-PCBs on the endocrine and nervous systems in mammals. However, there have been no studies on transcriptome analysis of the effects of OH-PCBs, and thus, the whole picture and mechanisms underlying the adverse effects induced by OH-PCBs are still poorly understood. We therefore investigated the mRNA expression profile in the liver of adult male Wistar rats treated with 4-hydroxy-2,3,3′,4′,5-pentachlorobiphenyl (4-OH-CB107) to explore the genes responsive to OH-PCBs and to understand the potential effects of the chemical. Next-generation RNA sequencing analysis revealed changes in the expression of genes involved in the circadian rhythm and fatty acid metabolism, such as nuclear receptor subfamily 1, group D, member 1, aryl hydrocarbon receptor nuclear translocator-like protein 1, cryptochrome circadian clock 1, and enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase, in 4-OH-CB107-treated rats. In addition, biochemical analysis of the plasma revealed a dose-dependent increase in the leucine aminopeptidase, indicating the onset of liver damage. These results suggest that OH-PCB exposure may induce liver injury as well as disrupt the circadian rhythm and peroxisome proliferator-activated receptor-related fatty acid metabolism. OH-PCB, transcriptome, Wistar rat, circadian rhythm, fatty acid metabolism Polychlorinated biphenyls (PCBs) are one of the most widespread and hazardous anthropogenic pollutants. The use of products containing PCBs was banned under the Stockholm Convention on Persistent Organic Pollutants, effective since May 2004. Although the production and use of PCBs have been prohibited since the early 1970s in some countries, the amounts used and persistence of these chemicals are so high that PCBs are still present in humans and wildlife (Brown et al., 2018; Tanabe, 2002; Tsai et al., 2017). Some PCB congeners are known to be metabolized to hydroxylated PCBs (OH-PCBs) in the liver (Grimm et al., 2015). OH-PCBs are detected in the human blood, including cord blood, and adipose tissue (Quinete et al., 2014). OH-PCBs are also detected in wildlife such as marine and terrestrial mammals, birds, and fish species (Gebbink et al., 2008; Kunisue and Tanabe, 2009; Montaño et al., 2013; Nomiyama et al., 2010; Ochiai et al., 2013). Among OH-PCBs, 4-hydroxy-2,3,3′,4′,5-pentachlorobiphenyl (4-OH-CB107) is 1 of the major congeners detected in humans and animal species (Hoydal et al., 2017; McKinney et al., 2006). Thus, it is abundantly found in rodents and their offspring exposed to PCBs (Meerts et al., 2002). 4-OH-CB107 is predominantly formed from PCB-105 and PCB-118 via a hydroxylation and 1,2-chlorine shift (NIH shift) by cytochrome P450 (CYP) enzymes (CYP1A and 2B) and has less potential for activating the aryl hydrocarbon receptor (AHR) compared with CB118 (Letcher et al., 2000; Mise et al., 2016). Malmberg et al. (2004) have determined the rat plasma half-life of 4-OH-CB107 to be 3.8 days after a single intravenous injection (1 μmol/kg). However, in the natural environment, animals are chronically exposed to OH-PCBs through metabolism of PCBs. Öberg et al. (2002) have investigated the residual concentration of 4-OH-CB107 in rats after a single oral dose of PCB-105 and PCB-118, and after 4 months, 4-OH-CB107 was found at concentrations comparable to those of PCBs. It has been suggested that 4-OH-CB107 disrupts endocrine signaling through its effect on estrogen and thyroid hormone receptors (Gauger et al., 2007; Meerts et al., 2004; Takeuchi et al., 2011). 4-OH-CB107 has a chemical structure resembling that of thyroxin (T4), with 2 chlorines at meta-positions of the phenyl ring on the same side as the para-substituted hydroxyl group. Thus, 4-OH-CB107 has been suggested to compete with T4 in binding human transthyretin (TTR) (Cheek et al., 1999; Lans et al., 1993), and increased exposure to OH-PCBs has been associated with reduced plasma T4 levels (Gabrielsen et al., 2015; Hisada et al., 2014). Besides, 4-OH-CB107 has been reported to exert toxic effects, including the activation of the thyroid hormone receptor (Gauger et al., 2007), estrogenic activity through the inhibition of estrogen sulfotransferase (Takeuchi et al., 2011), embryo lethality (Halldin et al., 2005), and developmental neurotoxicities, including impairment in motor development (Berghuis et al., 2014), mental development (Park et al., 2009), deficits in learning and memory, and altered neurotransmitter function (Meerts et al., 2004). Meerts et al. (2002) have experimentally determined the distribution of [14C]-4-OH-CB107 in rat tissues, and predominantly found this congener in the plasma and liver. The liver is a key organ for detoxification and energy metabolism; it is involved in the uptake, synthesis, storage, secretion, and catabolism of fatty acids, as well as in gluconeogenesis. It has been suggested that PCB/dioxin-induced changes in the expression or activities of enzymes involved in hepatic metabolism may lead to metabolic disruption and liver disease (Al-Eryani et al., 2015; Gadupudi et al., 2016). For instance, a parent compound of 4-OH-CB107, PCB118, which has the potential to bind to the peroxisome proliferator-activated receptor (PPAR) (Sheikh et al., 2016), induced metabolic disorders in the mouse liver (Mesnier et al., 2015). Although several potential effects of OH-PCBs have been reported in rodents (Halldin et al., 2005), the mode of action of these compounds in the liver is still not fully characterized. The present study investigated the effects of 4-OH-CB107 exposure on gene expression in the liver and plasma biochemical parameters of adult male Wistar rats. MATERIALS AND METHODS Chemicals A 4-OH-CB107 solution (CAS, 152969-11-4; catalog No., 4H107; MW, 342.421 g/mol; 50 ppm in nonane) was purchased from Wellington Laboratories, Inc (Guelph, ON, Canada). n-Hexane was added to the 4-OH-CB107 solution and evaporated to near dryness under a gentle stream of nitrogen gas. Fifteen microliters of dimethyl sulfoxide (DMSO) were added to the solution, and after the evaporation of the remaining n-hexane, the solvent was completely replaced with DMSO. Polyethylene glycol (PEG) 400 (Wako Pure Chemicals Industries, Ltd, Osaka, Japan) was added to the 4-OH-CB107 solution in DMSO, and the resultant solution was diluted with physiological saline to obtain a 50 nmol/ml stock solution of 4-OH-CB107 (1% DMSO and 1% PEG 400). Animals All animal experiments were approved by the Animal Regulatory Committee of Ehime University (permission number: 26-1), and carried out according to the Animal Experimentation Regulations of Ehime University. All efforts were made to alleviate the suffering of animals. Fifteen male Slc: Wistar rats (5-week-old) were provided by Japan Slc, Inc (Shizuoka, Japan). Four to 5 animals were housed in a plastic cage and kept at 22°C under a 12-h light/dark cycle. The rats were fed basic rodent pelletized diet for short/midterm to long-term breeding experiments (MF; Oriental Yeast Co. Ltd). The main ingredients of the diet were corn, wheat bran, defatted soybean, defatted rice bran, alfalfa, fish meal, soy oil, and brewer’s yeast; and the diet was certified by the supplier to contain low levels of POPs, heavy metals, and aflatoxin. Food and water were provided ad libitum. Because even a small environmental change may affect transcriptome analysis, rats were allowed an acclimation period of 3 weeks. To reduce stress, rats were provided with enrichment materials, including bedding (ALPHA-dri + Plus; Shepherd Specialty Papers, Watertown, Tennessee), housing (Shepherd Shack; Shepherd Specialty Papers), and nesting materials (Enviro-dri; Shepherd Specialty Papers). After 1 week, the weight of the animals increased to 130–150 g, and thus, the number of animals per cage was reduced to 2–3 animals. OH-PCB treatment and sample collection When the rats were 8 weeks old, OH-PCB was administered intravenously from the tail. Fifteen animals with the body weights of 225–264 g were used for the experiments. Three rats were used as a vehicle control (physiological saline with 1% DMSO and 1% PEG 400), and 4 rats each received the following OH-PCB treatments: a low dose (0.50 nmol/kg body weight; 0.11 nmol), middle dose (5.0 nmol/kg body weight; 1.1 nmol), and high dose (50 nmol/kg body weight; 11 nmol). One rat in the high-dose group died due to anesthesia overdose during the administration of 4-OH-CB107, and thus, 3 rats remained in this group. All injections were conducted during the daytime. The injection schedule (starting with control rats) was started at 9:20 am, and the last injection was performed at 11:50 am. Rats were fed diet and water ad libitum throughout the experiment. Twenty-four hours after the injection, the rats were weighed and euthanized by cardiac puncture under inhalational isoflurane anesthesia (Wako Pure Chemicals Industries, Ltd). Inhalational isoflurane anesthesia was administered to the rats to reduce handling stress during injection and for euthanasia. For the anesthesia, rats were placed individually in a 1-l container containing a piece of Kimwipe moistened with a small amount of isoflurane to make less than 5% isoflurane. The Kimwipe was placed in a small jar to avoid direct contact with the rat. Tissues and organs, including the liver, were immediately removed and frozen in liquid nitrogen after weighing. The samples were stored at −80°C in 15-ml cryogenic vials until analysis. Extraction and quantification of OH-PCBs Hydroxylated PCBs were extracted from the liver samples and determined as methoxy (MeO)-PCBs (methyl derivatives) using a gas chromatograph (HP-6890; Agilent Technologies, Inc, Santa Clara, California) coupled with a high-resolution mass spectrometer (MS-800D; JEOL, Tokyo, Japan). The procedure for the extraction and clean-up was modified following the method described elsewhere (Eguchi et al., 2014). A liver sample (0.5 g) was transferred into a 2-ml tube (XXTuff reinforced microvial; BioSpec Products, Inc, Bartlesville, Oklahoma). Two tubes were prepared per sample. To each tube, 2 stainless steel beads (5 mm; Qiagen, Hilden, Germany) and 1 ml of 6 M HCl were added. The liver tissue was homogenized using TissueLyser II (Qiagen) at a frequency of 25.0 Hz for 30 min. The 2 completely homogenized liver samples were combined in 15-ml tubes, and the 2-ml tubes were washed 3 times with methyl tert-butyl ether (MTBE)/n-hexane (1:1, vol/vol). The samples were mixed with a small amount of isopropyl alcohol using a vortex and ultrasonic extraction for 15 min. The samples were then centrifuged at 3000 × g for 5 min, and the upper layer was transferred into a test tube. This step was repeated 2 times. The extract was spiked with 5 13C12-labeled OH-PCB congeners as internal standards, including 4′-OH-CB29, 4′-OH-CB61, and 4′-OH-CB120 from Wellington Laboratories, Inc, and 4′-OH-CB79 and 4′-OH-CB107 from Cambridge Isotope Laboratories, Inc (Tewksbury, Massachusetts). Then, a NaCl solution (10%) was added and mixed, and the mixture was centrifuged at 3000 × g for 5 min. The upper layer was concentrated to 1 ml under a gentle stream of nitrogen gas and then mixed with n-hexane and acetonitrile saturated with n-hexane. The solution was mixed by vortexing and centrifuged at 1000 rpm for 5 min. To the bottom layer, n-hexane and acetonitrile (saturated with n-hexane) were added, and the extraction step was repeated 3 times. Hexane-washed water, adjusted to pH < 2 with H2SO4, was added to the extract, followed by the addition of MTBE/n-hexane (1:1, vol/vol), mixing by vortexing, and centrifugation at 3000 × g for 1 min. The upper layer was collected to a new tube, and extraction with MTBE/n-hexane was repeated 3 times. The OH-PCB fraction was cleaned up on a 5% hydrated silica gel column (Wakogel S-1; Wako Pure Chemicals Industries, Ltd) and derivatized with trimethylsilyldiazomethane. The derivatized solution was further cleaned up on an activated silica gel column (Wakogel DX; Wako Pure Chemicals Industries, Ltd). 13C12-labeled CB157 was added as a syringe spike, and the solution was concentrated to 50 μL. Average recovery rates of the 5 internal standards were as follows: 4′-OH-CB29 (59% ± 13%), 4′-OH-CB61 (65% ± 14%), 4′-OH-CB79 (54% ± 3.8%), 4-OH-CB107 (52% ± 3.1%), and 4-OH-CB120 (52% ± 3.3%). 4-OH-CB107 was quantified using an isotope dilution method to the corresponding 13C12-internal standards, as described by Kunisue and Tanabe (2009). MeO-PCB congeners were quantified when the retention time was consistent with that of the standard, the signal-to-noise ratio (S/N) was ≥3, and the isotope ratio error was ±25% of the standard compound. The method detection limit (MDL) was determined using the peak area of S/N = 3 in the chromatogram of each sample. Biochemical analysis of plasma Blood samples were collected into heparinized vacuum blood collection tubes by cardiac puncture during tissue collection. The plasma was separated by centrifugation of the blood at 1000 × g, 4°C for 20 min, and stored at −80°C. The samples were used for the analyses of 20 biochemical markers related to liver and biliary diseases, including albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH), by Oriental Yeast Co., Ltd (Supplementary Table 1). RNA isolation Total RNA was isolated from liver tissue using a Direct-zol RNA miniprep kit (Zymo Research, Irvine, California) following the manufacturer’s protocol. The RNA quantity was determined using an ND-1000 spectrophotometer (NanoDrop Technologies, LLC, Wilmington, Delaware), and the quality was evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies). mRNA sequencing, preprocessing and mapping Complementary DNA (cDNA) libraries of rat liver samples were constructed by Hokkaido System Science Co., Ltd (Sapporo, Japan) using a HiSeq 2500 system (Illumina, San Diego, California) RNA sequencing (RNA-seq), and a TruSeq RNA library kit, according to the manufacturer’s instructions. The raw read data were deposited to the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA) database under an accession number of DRA006343. The adaptor sequences and low-quality reads were trimmed and aligned to the rat reference genome, Rnor_5.0 (Ensemble release 79), using TopHat (v.2.0.14) (Kim et al., 2013). Detailed settings and functions are described in Supplementary method. Quantification of mRNA levels by real-time RT-PCR To validate the performance of RNA-seq, quantitative real-time RT-PCR (qRT-PCR) was carried out to quantify mRNA levels using SYBR premix Ex Taq II (Tli RNase H Plus), Bulk (Takara Bio, Inc, Kusatsu, Shiga, Japan), and a StepOne real-time PCR system (Life Technologies, Carlsbad, California). The specific primers used for mRNA quantification were as follows: nuclear receptor subfamily 1, group D, member 1 (Nr1d1) forward (5′-CACCAGCAACATTACCAA-3′), Nr1d1 reverse (5′-CGCACAGCGTCTCTA-3′), AHR nuclear translocator-like protein 1 (Arntl) forward (5′-TGCCACTGACTACCAAGAAA-3′), and Arntl reverse (5′-TGAACAGCCATCCTGAGC-3′). qRT-PCR was run as described previously (Iida et al., 2013). Briefly, the PCR conditions for Nr1d1 were 2 min at 95°C and 40 cycles of 30 s at 95°C and 30 s at 47°C; those for Arntl were 2 min at 95°C and 40 cycles of 30 s at 95°C and 30 s at 52°C. Calibration curves for mRNA were generated by plotting cycle threshold (Ct) values against logarithmic concentrations of serially diluted references. Each reaction was run in quadruplicate, and β-actin was used for intersample normalization. Detection of differentially expressed genes HTSeq v0.6 (https://pypi.python.org/pypi/HTSeq; last accessed May 21, 2018) (Anders et al., 2015) was used to obtain read counts for each gene from mapped reads. We removed genes that had an average log2 counts per million (CPM) among 12 groups < 1, and normalized the read counts followed by removal of the batch effect (the details are in supplemental methods). The maximum fold changes (FC) of gene expression were calculated on a log2 scale between the normalized count data of the highest exposed group and control (DMSO) group. The detailed methodology for the detection of differentially expressed genes (DEGs) has been reported in our previous study (Iida et al., 2016). Briefly, the Jonckheere-Terpstra test (Jonckheere, 1954; Terpstra, 1952) was conducted to detect genes whose expression levels were altered in an OH-PCB dose-dependent manner. Significance of DEGs was determined based on false discovery ratio (FDR)-adjusted q-values (q-value < 0.1 by Jonckheere-Terpstra test) and the maximum log2 FC (|log2FC| > 0.26 for the highest dose group). Kyoto Encyclopedia of Genes and Genomes pathway transcription factor, and phenotype enrichment analyses To identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and upstream transcription factors (TFs) for DEGs, enrichment analysis was carried out using Database for Annotation, Visualization and Integrated Discovery (DAVID) (v 6.8) (https://david.ncifcrf.gov; last accessed May 21, 2018) based on rat KEGG pathway database (Huang et al., 2009) and Expression2Kinases (version 1.6.1207) (http://www.maayanlab.net/X2K/index.html; last accessed May 21, 2018) (Chen et al., 2012), respectively. We considered a term has p-value < .1 and fold enrichment score > 7 is significant for the KEGG pathway enrichment analysis and combined scores ≥10, which represent the absolute z-score > 0.7 and a p-value < .004 (q-value < 0.02), for the TF enrichment analysis. To investigate the link between DEGs and phenotypes, the enrichment analysis tool in GeneSetDB with the rat phenotype database was used. A phenotype set with an FDR-corrected p-value of less than 10% (q-value < 0.1) was considered significant. Network analysis To visualize the relationships between DEGs, network analysis was carried out. The protein-protein interaction (PPI) data for DEGs were extracted from the functional protein association networks (STRING) database ver. 10.5 (http://string-db.org/; last accessed May 21, 2018) (Szklarczyk et al., 2017). Protein-protein interactions that had interaction scores of more than 0.4 were used for subsequent analysis. The gene clusters that had similar expression patterns were determined according to the network community structure based on the correlations among gene expression levels. We visualized clusters with 3 or more DEGs. To investigate the biological function of the clusters, KEGG pathway enrichment analysis was performed for each cluster by the Fisher’s exact test. Statistical analysis To confirm differences in expression patterns among exposed groups, we performed principal component analysis (PCA) based on log2 CPMs. One-way analysis of variance (ANOVA) with Dunnett’s Multiple Comparison Test (GraphPad Prism 5) was used to determine differences in liver concentrations of 4-OH-CB107 and plasma biochemical parameters among different treatment groups. RESULTS OH-PCB Concentrations in Rat Liver The concentrations of 4-OH-CB107 in the liver are shown in Figure 1. The mean concentrations were below MDL, 3.0, 170, and 1400 pg/g wet weight for liver samples from the control, low-, middle-, and high-dose groups, respectively. The concentrations in the high-dose group were significantly higher than those in the control (p ≤ .001). The given doses increased by 10-fold increments, and the concentrations measured in the liver samples corresponded to the low, middle, and high doses. Figure 1. View largeDownload slide OH-PCB concentrations in rat livers from different treatment groups. CTL, control; MDL, method detection limit. Only one data point is shown for low-dose and non-data points for CTL because these concentrations were below MDL of 1.7 pg/g wet weight. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. ***p < .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 1. View largeDownload slide OH-PCB concentrations in rat livers from different treatment groups. CTL, control; MDL, method detection limit. Only one data point is shown for low-dose and non-data points for CTL because these concentrations were below MDL of 1.7 pg/g wet weight. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. ***p < .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Effects of OH-PCB on Hepatic Function To investigate the effects of OH-PCB exposure on hepatic function, biochemical analysis of the plasma was carried out. The data showed that the levels of AST, glucose (GLU), total bilirubin (T-BIL), indirect bilirubin (I-BIL), and leucine aminopeptidase (LAP) as indices of hepatic injury had a tendency to increase in a 4-OH-CB107 dose-dependent manner (Figs. 2A–D;Supplementary Table 1). In particular, the levels of the LAP enzyme were significantly higher (p ≤ .01, p ≤ .001, respectively) in the middle- and high-dose 4-OH-CB107 groups than in the control group. The LAP excreted from the liver into the bile catalyzes the hydrolysis of peptides. Blood LAP level has been traditionally used as a marker to diagnose hepatobiliary disease (Abouzied et al., 2015). Albumin and the ALB/globulin ratio (A/G) can be low in liver or kidney diseases and are thus used for diagnosis. We observed decreasing trends in ALB and A/G in the OH-PCB-treated rats (Figs. 2E and F;Supplementary Table 1). Figure 2. View largeDownload slide Biochemical parameters of rat plasma in different treatment groups. A, Aspartate aminotransferase (AST); B, glucose (GLU); C, total bilirubin (T-BIL); D, leucine aminopeptidase (LAP); E, albumin (ALB); F, albumin/globulin ratio. CTL, control. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤ .01 and **p ≤ .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 2. View largeDownload slide Biochemical parameters of rat plasma in different treatment groups. A, Aspartate aminotransferase (AST); B, glucose (GLU); C, total bilirubin (T-BIL); D, leucine aminopeptidase (LAP); E, albumin (ALB); F, albumin/globulin ratio. CTL, control. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤ .01 and **p ≤ .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Identification and Validation of DEGs After preprocessing and normalization of raw reads, the Jonckheere-Terpstra test was carried out to identify OH-PCB dose-dependent DEGs at the threshold of FDR correlated p-value < .1 and |log2FC| > 0.26. A total of 108 genes, including 86 downregulated and 22 upregulated genes, were identified (Table 1; Supplementary Figure 1 and Table 2). Of these genes, the expression level of Nr1d1, which is related to the circadian rhythm, was extremely upregulated by OH-PCB exposure in a dose-dependent manner (log2FC = 11 in the high-dose group; Figure 3A). On the other hand, the expression levels of other circadian rhythm-related genes such as Arntl (Figure 3B) and cryptochrome circadian clock 1 (Cry1) were downregulated (log2FC values of −0.96 and −5.06, respectively, in the high-dose group). The expression level of the gene encoding cytochrome P450 2b1 and 2b2 (Cyp2b1 and 2), 1 of the key phase I metabolizing enzymes, was decreased by OH-PCB exposure (log2FC = −3.24 and −1.07, q-value = 0.07 and 0.09). To validate the results of RNA-seq, qRT-PCR was performed for the Nr1d1 and Arntl genes (Figure 3). Although both qRT-PCR and RNA-seq showed an increasing trend for the Nr1d1 expression, the results were different for Arntl. The mapping results for Arntl showed a clear, dose-dependent, decreasing trend only for the reads in the 3′-untranslated region; however, we designed the qRT-PCR primers for the second exon. The discrepancy in the data between RNA-seq and qRT-PCR thus could be attributed to the selection of the target region for the qRT-PCR primers. We found that the expression of PPAR signaling pathway-related genes such as Cyp27a1, the peroxisomal bifunctional enzyme enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase (Ehhadh), and angiopoietin-related protein 4 (Angptl4) was also affected by 4-OH-CB107 exposure. Furthermore, the expression levels of the ALT gene (Gpt), which is related to liver damage, also changed upon 4-OH-CB107 exposure, consistent with the data of biochemical analysis. The PCA was employed to evaluate the relationships of global gene expression profiles among the experimental groups (Figure 4). The high contribution rate of principal component (PC) 1 (99.76%) indicated that the control and OH-PCB-exposed groups had similar expression profiles. However, the control and high-dose groups were separated along the Y-axis (PC2; 0.13%), suggesting a global expression change between these groups. On the other hand, the low- and middle-dose groups were colocated, indicating that the low and middle doses of OH-PCB induced identical gene expression changes. Table 1. Genes Whose Expression Levels Dose-Dependently Changed Upon Rat Exposure to 4-OH-CB107 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. FC, fold change; CC, chemical carcinogenesis; DM, drug metabolism; FA, fatty acid degradation; PE, peroxisome; RM, retinol metabolism. Table 1. Genes Whose Expression Levels Dose-Dependently Changed Upon Rat Exposure to 4-OH-CB107 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. FC, fold change; CC, chemical carcinogenesis; DM, drug metabolism; FA, fatty acid degradation; PE, peroxisome; RM, retinol metabolism. Figure 3. View largeDownload slide Expression levels of Nr1d1 and Arntl mRNA in the liver of rats treated with 4-OH-CB107 and in the control (CTL). A and B, Log2 CPM (count per million) was determined by next-generation RNA sequencing. C and D, mRNA expression was determined by 2-step real-time RT-PCR, and the levels were normalized to those of β-actin. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤.01, **p ≤ .001, and ***p < .001 (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 3. View largeDownload slide Expression levels of Nr1d1 and Arntl mRNA in the liver of rats treated with 4-OH-CB107 and in the control (CTL). A and B, Log2 CPM (count per million) was determined by next-generation RNA sequencing. C and D, mRNA expression was determined by 2-step real-time RT-PCR, and the levels were normalized to those of β-actin. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤.01, **p ≤ .001, and ***p < .001 (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 4. View largeDownload slide Principal component (PC1 × PC2) plots generated from mRNA expression profiles of DEGs. CTL, control; Hi, high dose; Mid, middle dose. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Figure 4. View largeDownload slide Principal component (PC1 × PC2) plots generated from mRNA expression profiles of DEGs. CTL, control; Hi, high dose; Mid, middle dose. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Pathway Enrichment Analysis Pathway enrichment analysis revealed that circadian rhythm and fatty acid degradation pathways were significantly enriched (Table 2) with the circadian rhythm pathway being the most enriched pathway (q-value = 0.05). Ehhadh, which was downregulated by OH-PCB treatment, is related to fatty acid degradation, peroxisome, and PPAR signaling pathways (Table 2). Chemical carcinogenesis and retinol metabolism pathways, which include alcohol dehydrogenase 1 (Adh1), Cyp2bs (Cyp2b1 and Cyp2b2), glutathione S-transferase (Gstm1), and dehydrogenase/reductase (SDR family) member 4 (Dhrs4), showed a tendency to be enriched. Table 2. Pathways Affected by OH-PCB Exposure, Based on Pathway Enrichment Analysis Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. Table 2. Pathways Affected by OH-PCB Exposure, Based on Pathway Enrichment Analysis Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. Network Analysis To visualize the relationships between genes and pathways, network analysis was performed (Figure 5). The network had 6 clusters, and 3 of them showed significantly enriched functional pathways (Figure 5, p < .05). Module and enrichment analyses of the PPI network showed 5 hub genes (ARNTL, CLPX, LOP2, EHHADH, and ALB) connecting functional pathways. An ARNTL and an EHHADH were likely to play roles of the hub genes of the circadian rhythm and fatty acid metabolism pathways, respectively. The 2 pathways indirectly interacted via the caseinolytic mitochondrial matrix peptidase chaperone subunit (CLPX) and lon peptidase 2 (LONP2). Genes related to chemokine signaling, Huntington’s disease, and focal adhesion pathways formed a cluster including ALB as a hub. Albumin directly interacted with JunD proto-oncogene (JUND) in the enriched mitogen-activated protein kinase signaling pathway and with EHHADH, which is related to fatty acid metabolism. Figure 5. View largeDownload slide Predicted interactions among DEGs (maximum connected component). Node sizes differ depending on the node degree. Pathway names correspond to those that were significantly enriched in the module based on the KEGG pathway enrichment analysis (q-value < 0.2). Numbers in parentheses show the q-values. KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Figure 5. View largeDownload slide Predicted interactions among DEGs (maximum connected component). Node sizes differ depending on the node degree. Pathway names correspond to those that were significantly enriched in the module based on the KEGG pathway enrichment analysis (q-value < 0.2). Numbers in parentheses show the q-values. KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. TF Enrichment Analysis In addition to the pathway enrichment analysis, we attempted to determine TFs, which have an impact on dose-dependent expression changes of DEGs. To screen critical TFs, we carried out TF enrichment analysis (Table 3). Fifteen TFs, such as estrogen receptors 1 and 2 (ESR1 and ESR2), the clock circadian regulator (CLOCK), v-myc avian myelocytomatosis viral oncogene neuroblastoma-derived (MYCN), hepatocyte nuclear factor 4 alpha (HNF4A), nuclear receptor subfamily 1, group I, member 2 (NR1I2, also known as the pregnane X receptor [PXR]), Forkhead box protein A2 (FOXA2), SRY-box 2 (SOX2), CCCTC-binding factor (CTCF), Spi-1 proto-oncogene (SPI1), tet methylcytosine dioxygenase 1 (TET1), doublesex and mab-3 related TF 1 (DMRT1), PPAR gamma (PPARG), myocyte enhancer factor 2A (MEF2A), and androgen receptor (AR), were detected as candidate transcriptional regulators of DEGs. Among them, there were no TFs showing dose-dependent expression changes upon OH-PCB treatment. The TF enrichment analysis showed that the most upregulated gene, Nr1d1, could be regulated by 5 TFs, including MYCN, HNF4A, FOXA2, PPARG, and AR. Similar to Nr1d1, the most downregulated gene, Cry1, could be regulated by multiple TFs such as MYCN, ESR1, and AR. Together, these results suggest that PPARG and sex hormone receptors (AR and ESR), as well as circadian rhythm-related genes, play important roles in regulating the gene expression of DEGs in an OH-PCB dose-dependent manner. Table 3. Transcription Factors Predicted Upstream of DEGs by TF Enrichment Analysis Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. DEGs, differentially expressed genes. Table 3. Transcription Factors Predicted Upstream of DEGs by TF Enrichment Analysis Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. DEGs, differentially expressed genes. Phenotype Enrichment Analysis Finally, we predicted the outcomes of OH-PCB exposure, based on the DEG analysis, using a phenotype enrichment analysis in GeneSetDB. Expression levels of the genes related to abdominal adipose tissue accumulation, including ARNTL, CRY1, and tensin 2 (TENC1), were altered; however, no effects were observed on the weight of visceral fat, body weight, and liver weight following exposure to 4-OH-CB107 (Supplementary Table 3). Genes related to a shortened circadian period, including ARNTL, CRY1, and NR1D1, hypoactivity, including ARNTL, CRY1, and EHHADH, and oxidative stress, including peroxiredoxin 4 (PRDX4), SHC (Src homology 2 domain containing) transforming protein 1 (SHC1), and superoxide dismutase 2, mitochondrial (SOD2), were significantly enriched (q-value < 0.1) (Table 4). Based on bioinformatics analysis of DEGs, we predicted the hepatic transcriptomic alterations and the contribution of the compound to adverse outcomes (Table 4). Table 4. Potential Outcomes of 4-OH-CB107 Exposure, Predicted by Phenotype Enrichment Analysis Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 DEGs, differentially expressed genes. Table 4. Potential Outcomes of 4-OH-CB107 Exposure, Predicted by Phenotype Enrichment Analysis Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 DEGs, differentially expressed genes. DISCUSSION In the present study, we demonstrated that the transcriptome was significantly altered in the liver of adult male rats by exposure to 4-OH-CB107, which is one of the major metabolites of PCBs detected in humans and wildlife. The rat liver concentrations of 4-OH-CB107 in the current study were within the ranges detected in human serum, which have been reported to be 3.1–41 pg/g wet weight in India (Eguchi et al., 2012), 6.6–326 pg/g wet weight in Belgium (Dufour et al., 2017), and 41–1700 pg/g wet weight in pregnant women in the Faroe Islands (Fängström et al., 2002). Based on the bioinformatics analysis of DEGs, we predicted the mode of action of environmentally relevant concentrations of 4-OH-CB107 in the liver and a possible contribution of this compound to adverse outcomes. The potential effects of 4-OH-CB107 on functional pathways and inducible outcomes were analyzed by using a rat database. The upstream TFs for DEGs were predicted by using human and mouse databases because there is limited information on TF binding sites in the rats. Hepatic transcriptome analysis showed that 4-OH-CB107 induced expression changes for 108 genes, such as Arntl, Cry1, Nr1d1, Rorc (RAR-related orphan receptor C), and Ehhadh, related to the circadian rhythm, fatty acid degradation, and the PPAR signaling pathway. The effects of the anesthetic on the liver transcriptome can be ruled out because all rats were treated with isoflurane under the same conditions regardless of the treatment with 4-OH-CB107. The KEGG pathway and phenotype enrichment analyses also indicated that 4-OH-CB107 exposure might lead to reduction of the abdominal adipose tissue mass and to shortening of the circadian cycle by perturbing fatty acid degradation and circadian rhythm. Our transcriptome and phenotype enrichment analyses indicated that the expression levels of circadian rhythm-related genes (Arntl, Cry1, Nr1d1, Nr1d2, and Rorc) were notably changed in the rat liver following 4-OH-CB107 exposure. The daily maximum FC for Cry1 and Nr1d1 in the rat liver have been reported to be up to 4- and 100-fold (2 and 6.6 in log2, respectively) (Ovacik et al., 2010; Yamajuku et al., 2012). These values were approximately 10 and 20 (ie, 3.6 and 4.4 in log2, respectively) times lower than the FC observed in this study (48.5- and 2048-fold; 5.6 and 11 in log2, respectively). Therefore, the expression of Cry1 and Nr1d1 was considered to be induced by 4-OH-CB107 exposures rather than the physiologic changes. It has been reported that the knockouts of Nr1d1, Cry1, and Arntl resulted in a shorter circadian period (Lowrey and Takahashi, 2011). 4-OH-CB107 exposure might disrupt the circadian rhythm in the rat liver through the alteration of clock gene expression levels. Our TF enrichment analysis suggested that 4-OH-CB107 affects circadian rhythm through several TFs, including ESRs, CLOCK, and PPARs (Table 3). In addition, 4-OH-CB107 could bind to AHR (Mise et al., 2016) that regulates the expression of circadian clock genes (Jaeger and Tischkau, 2016). Crosstalks exist between AHR and ESRs, CLOCK and PPAR (Jaeger and Tischkau, 2016). Taken together, we suggest that 4-OH-CB107 may affect the circadian rhythm directly or indirectly through ESRs, CLOCK, PPARs, and AHR (Jaeger and Tischkau, 2016). However, the specific mechanism of action should be investigated in a future study. Similar to our results showing that 4-OH-CB107 might affect circadian rhythms, the circadian period has been found to increase in 4-OH-CB106-exposed male rats (Lesmana et al., 2014). A modification of the circadian rhythm has also been reported after exposure of mice to dioxin (2,3,7,8-tetrachlorodibenzodioxin) (Xu et al., 2013). However, there have been only few reports demonstrating the effects of PCB exposure on circadian rhythms. Thus, noncoplanar PCBs were shown to increase the swimming activity of adult zebrafish at night, suggesting that PCB exposure could trigger the rhythm disruption (Péan et al., 2013). These results may suggest that disruption of the circadian rhythm in vertebrates is unique to PCB metabolites and dioxins. Previous studies have reported that OH-PCBs bind to thyroid hormone-binding proteins (thyroxine-binding globulin, TTR, and ALB) and affect thyroid hormone levels (Hisada et al., 2014; Meerts et al., 2002; Nomiyama et al., 2014). No changes in the expression levels of genes associated with the thyroid hormone signaling pathway, except regulator of calcineurin 2 (Rcan2), were observed in this study. However, there is a link between thyroid hormone levels and the circadian rhythm, and it has been reported that the circadian rhythm regulates thyroid hormone levels in the central nervous system of rats (Campos-Barros et al., 2002). One of the differences between previous studies and this study was the exposure period. Whereas previous studies have focused on the chronic toxicity of OH-PCBs, we focused on the acute toxicity of 4-OH-CB107 to understand the effects of this single compound. Therefore, it cannot be excluded that the OH-PCB effects on thyroid hormone levels are subsequent to the circadian rhythm disruption. Several transcriptome studies have indicated the relationship between PCB exposure and lipid metabolism (Gadupudi et al., 2016; Yadetie et al., 2014). Gadupudi et al. (2016) have demonstrated that exposure to PCB126 for 12 days increased lipid accumulation in the rat liver, leading to steatosis, and the data indicated that PCB126 altered liver lipid metabolism through PPARα. In this study, we showed that OH-PCB exposure disrupted fatty acid degradation and the PPAR signaling pathway through changes in the mRNA expression of Acaa2, Adh1, Angptl4, Cyp27a1, Ehhadh, and Gcdh in the liver (Tables 1 and 2). Our TF enrichment study also demonstrated that ARNTL, EHHADH, and TENC1 are downstream of PPARG (Table 3). Furthermore, our phenotype enrichment analysis suggested that the altered gene expression of Arntl, Cry1, and Tenc1 might decrease the abdominal adipose tissue amount (Table 4). Fatty liver is characterized by the accumulation of triglyceride esters, derived from glycerol and free fatty acids (FFAs). Interestingly, Rabinowich and Shibolet (2015) have proposed that the increased content of hepatic FFAs may be due to their increased uptake from peripheral tissues, predominantly adipose tissue, increased de novo lipogenesis in hepatocytes, or decreased metabolism via β-oxidation in hepatocytes. Taken together, the transcriptional alteration of PPAR signaling pathway-related genes (Acaa2, Gcdh, Ehhadh, and Angptl4) and circadian rhythm-related genes (Arntl and Cry1) by OH-PCB treatment may lead to a reduced abdominal adipose tissue amount through dysregulation of fatty acid metabolism in the liver. However, no significant associations have been found between OH-PCBs/PCBs and the body size of Japanese newborn babies (Hisada et al., 2014). Consistent with the previous study, we could not detect any differences in the visceral fat weight and body weight among the groups. It has long been known that metabolism and circadian clocks are tightly intertwined, and our network analysis for DEGs also revealed indirect interaction of the circadian rhythm and PPARG signaling pathways (Figure 5). Kawai and Rosen (2010) have pointed out that PPARG is responsible for the regulation of the circadian network as well as other major circadian clock genes. The TF enrichment analysis performed in this study indicated that PPARG and HNF4A, which are involved in lipid, glucose, and fatty acid metabolism, were enriched upstream of DEGs (Table 3). These results suggest that PPARG is an important factor directly connecting circadian rhythm and metabolic pathways. Based on the biochemical analysis of the plasma, the levels of a liver functional marker, LAP, increased in the middle- and high-dose groups of 4-OH-CB107-exposed rats. We demonstrated that the activities of hepatic functional markers (ie, AST, GLU, T-BIL, LAP, and ALB) and the expression levels of oxidative stress-related genes [ie, Prdx4, Shc1, Sod2, and xeroderma pigmentosum, complementation group C (Xpc)] were changed in 4-OH-CB107-treated rats (Figure 2; Tables 2 and 4). Similarly, another study showed that PCB exposure increased levels of enzymes (AST, ALT, and alkaline phosphatase) together with the injury in the liver of rats, whereas coexposure to antioxidants protected against the PCB-induced effects (De Oliveira et al., 2014). These results indicate that PCBs and their metabolites may cause liver injury by inducing oxidative stress. This research contributes to the elucidation of potential toxicities of OH-PCBs and their mechanism of action. However, several limitations need to be considered when interpreting our findings. First, our transcriptome results indicated strong effects on the circadian rhythm in the rat liver, but we cannot conclude that 4-OH-CB107 disrupted the circadian rhythm at an individual level because we did not carry out a time-course study. Second, we were not able to detect the weight change of any organ and tissue because of a short experimental period (24 h), although the expression levels of metabolism- and adipose tissue accumulation-related genes changed in a dose-dependent manner. Finally, we demonstrated the changes in the markers (AST, GLU, T-BIL, LAP, and ALB) of hepatic damage induced by OH-PCB exposure; however, we did not evaluate the pathologic effects on the liver because of sample size limitation. However, our findings advance the understanding of OH-PCB toxicity and provide new hypotheses for future studies. SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. ACKNOWLEDGMENTS This study was supported by the Department of Bioscience, Division of Medical Bioscience, Integrated Center for Sciences (INCS), Ehime University for sharing the facility to conduct animal studies, and Japanese Association for Experimental Animal Technologies. We sincerely thank Dr Ken-ichi Okugawa for his generously training on the handling and administration of animals. We would like to thank Mr Yusuke Tsujisawa and Mr Yasuo Yamamoto for their assistance for animal necropsy. FUNDING Joint Usage/Research Center – Leading Academia in Marine and Environment Pollution Research (LaMer) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; Grant-in-Aid KAKENHI for Scientific Research (S) (No. 26220103) from the Japan Society for the Promotion of Science (JSPS), which was given to Dr Hisato Iwata, and Grant-in-Aid for JSPS Fellows (No. 26-6348) for Dr Mari Ochiai, and Scientific Research (B) (No. 16H02989) for Dr Kei Nomiyama, and Grant-in-Aid for Scientific Research on Innovative Areas (No. 24118008) to Dr Satoshi Fujii. REFERENCES Abouzied M. M. , Eltahir H. M. , Fawzy M. A. , Abdel-Hamid N. M. , Gerges A. S. , El-Ibiari H. M. , Nazmy M. H. ( 2015 ). Estimation of leucine aminopeptidase and 5-nucleotidase increases alpha-fetoprotein sensitivity in human hepatocellular carcinoma cases . Asian Pac. J. Cancer Prev . 16 , 959 – 963 . Google Scholar Crossref Search ADS PubMed Al-Eryani L. , Wahlang B. , Falkner K. C. , Guardiola J. 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Real-time monitoring in three-dimensional hepatocytes reveals that insulin acts as a synchronizer for liver clock . Sci. Rep. 2 , 439 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Toxicological Sciences Oxford University Press

Effects of 4-Hydroxy-2,3,3′,4′,5-Pentachlorobiphenyl (4-OH-CB107) on Liver Transcriptome in Rats: Implication in the Disruption of Circadian Rhythm and Fatty Acid Metabolism

Toxicological Sciences , Volume 165 (1) – Sep 1, 2018

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Abstract

Abstract Polychlorinated biphenyls (PCBs) and their hydroxylated metabolites (OH-PCBs) have been detected in tissues of both wild animals and humans. Several previous studies have suggested adverse effects of OH-PCBs on the endocrine and nervous systems in mammals. However, there have been no studies on transcriptome analysis of the effects of OH-PCBs, and thus, the whole picture and mechanisms underlying the adverse effects induced by OH-PCBs are still poorly understood. We therefore investigated the mRNA expression profile in the liver of adult male Wistar rats treated with 4-hydroxy-2,3,3′,4′,5-pentachlorobiphenyl (4-OH-CB107) to explore the genes responsive to OH-PCBs and to understand the potential effects of the chemical. Next-generation RNA sequencing analysis revealed changes in the expression of genes involved in the circadian rhythm and fatty acid metabolism, such as nuclear receptor subfamily 1, group D, member 1, aryl hydrocarbon receptor nuclear translocator-like protein 1, cryptochrome circadian clock 1, and enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase, in 4-OH-CB107-treated rats. In addition, biochemical analysis of the plasma revealed a dose-dependent increase in the leucine aminopeptidase, indicating the onset of liver damage. These results suggest that OH-PCB exposure may induce liver injury as well as disrupt the circadian rhythm and peroxisome proliferator-activated receptor-related fatty acid metabolism. OH-PCB, transcriptome, Wistar rat, circadian rhythm, fatty acid metabolism Polychlorinated biphenyls (PCBs) are one of the most widespread and hazardous anthropogenic pollutants. The use of products containing PCBs was banned under the Stockholm Convention on Persistent Organic Pollutants, effective since May 2004. Although the production and use of PCBs have been prohibited since the early 1970s in some countries, the amounts used and persistence of these chemicals are so high that PCBs are still present in humans and wildlife (Brown et al., 2018; Tanabe, 2002; Tsai et al., 2017). Some PCB congeners are known to be metabolized to hydroxylated PCBs (OH-PCBs) in the liver (Grimm et al., 2015). OH-PCBs are detected in the human blood, including cord blood, and adipose tissue (Quinete et al., 2014). OH-PCBs are also detected in wildlife such as marine and terrestrial mammals, birds, and fish species (Gebbink et al., 2008; Kunisue and Tanabe, 2009; Montaño et al., 2013; Nomiyama et al., 2010; Ochiai et al., 2013). Among OH-PCBs, 4-hydroxy-2,3,3′,4′,5-pentachlorobiphenyl (4-OH-CB107) is 1 of the major congeners detected in humans and animal species (Hoydal et al., 2017; McKinney et al., 2006). Thus, it is abundantly found in rodents and their offspring exposed to PCBs (Meerts et al., 2002). 4-OH-CB107 is predominantly formed from PCB-105 and PCB-118 via a hydroxylation and 1,2-chlorine shift (NIH shift) by cytochrome P450 (CYP) enzymes (CYP1A and 2B) and has less potential for activating the aryl hydrocarbon receptor (AHR) compared with CB118 (Letcher et al., 2000; Mise et al., 2016). Malmberg et al. (2004) have determined the rat plasma half-life of 4-OH-CB107 to be 3.8 days after a single intravenous injection (1 μmol/kg). However, in the natural environment, animals are chronically exposed to OH-PCBs through metabolism of PCBs. Öberg et al. (2002) have investigated the residual concentration of 4-OH-CB107 in rats after a single oral dose of PCB-105 and PCB-118, and after 4 months, 4-OH-CB107 was found at concentrations comparable to those of PCBs. It has been suggested that 4-OH-CB107 disrupts endocrine signaling through its effect on estrogen and thyroid hormone receptors (Gauger et al., 2007; Meerts et al., 2004; Takeuchi et al., 2011). 4-OH-CB107 has a chemical structure resembling that of thyroxin (T4), with 2 chlorines at meta-positions of the phenyl ring on the same side as the para-substituted hydroxyl group. Thus, 4-OH-CB107 has been suggested to compete with T4 in binding human transthyretin (TTR) (Cheek et al., 1999; Lans et al., 1993), and increased exposure to OH-PCBs has been associated with reduced plasma T4 levels (Gabrielsen et al., 2015; Hisada et al., 2014). Besides, 4-OH-CB107 has been reported to exert toxic effects, including the activation of the thyroid hormone receptor (Gauger et al., 2007), estrogenic activity through the inhibition of estrogen sulfotransferase (Takeuchi et al., 2011), embryo lethality (Halldin et al., 2005), and developmental neurotoxicities, including impairment in motor development (Berghuis et al., 2014), mental development (Park et al., 2009), deficits in learning and memory, and altered neurotransmitter function (Meerts et al., 2004). Meerts et al. (2002) have experimentally determined the distribution of [14C]-4-OH-CB107 in rat tissues, and predominantly found this congener in the plasma and liver. The liver is a key organ for detoxification and energy metabolism; it is involved in the uptake, synthesis, storage, secretion, and catabolism of fatty acids, as well as in gluconeogenesis. It has been suggested that PCB/dioxin-induced changes in the expression or activities of enzymes involved in hepatic metabolism may lead to metabolic disruption and liver disease (Al-Eryani et al., 2015; Gadupudi et al., 2016). For instance, a parent compound of 4-OH-CB107, PCB118, which has the potential to bind to the peroxisome proliferator-activated receptor (PPAR) (Sheikh et al., 2016), induced metabolic disorders in the mouse liver (Mesnier et al., 2015). Although several potential effects of OH-PCBs have been reported in rodents (Halldin et al., 2005), the mode of action of these compounds in the liver is still not fully characterized. The present study investigated the effects of 4-OH-CB107 exposure on gene expression in the liver and plasma biochemical parameters of adult male Wistar rats. MATERIALS AND METHODS Chemicals A 4-OH-CB107 solution (CAS, 152969-11-4; catalog No., 4H107; MW, 342.421 g/mol; 50 ppm in nonane) was purchased from Wellington Laboratories, Inc (Guelph, ON, Canada). n-Hexane was added to the 4-OH-CB107 solution and evaporated to near dryness under a gentle stream of nitrogen gas. Fifteen microliters of dimethyl sulfoxide (DMSO) were added to the solution, and after the evaporation of the remaining n-hexane, the solvent was completely replaced with DMSO. Polyethylene glycol (PEG) 400 (Wako Pure Chemicals Industries, Ltd, Osaka, Japan) was added to the 4-OH-CB107 solution in DMSO, and the resultant solution was diluted with physiological saline to obtain a 50 nmol/ml stock solution of 4-OH-CB107 (1% DMSO and 1% PEG 400). Animals All animal experiments were approved by the Animal Regulatory Committee of Ehime University (permission number: 26-1), and carried out according to the Animal Experimentation Regulations of Ehime University. All efforts were made to alleviate the suffering of animals. Fifteen male Slc: Wistar rats (5-week-old) were provided by Japan Slc, Inc (Shizuoka, Japan). Four to 5 animals were housed in a plastic cage and kept at 22°C under a 12-h light/dark cycle. The rats were fed basic rodent pelletized diet for short/midterm to long-term breeding experiments (MF; Oriental Yeast Co. Ltd). The main ingredients of the diet were corn, wheat bran, defatted soybean, defatted rice bran, alfalfa, fish meal, soy oil, and brewer’s yeast; and the diet was certified by the supplier to contain low levels of POPs, heavy metals, and aflatoxin. Food and water were provided ad libitum. Because even a small environmental change may affect transcriptome analysis, rats were allowed an acclimation period of 3 weeks. To reduce stress, rats were provided with enrichment materials, including bedding (ALPHA-dri + Plus; Shepherd Specialty Papers, Watertown, Tennessee), housing (Shepherd Shack; Shepherd Specialty Papers), and nesting materials (Enviro-dri; Shepherd Specialty Papers). After 1 week, the weight of the animals increased to 130–150 g, and thus, the number of animals per cage was reduced to 2–3 animals. OH-PCB treatment and sample collection When the rats were 8 weeks old, OH-PCB was administered intravenously from the tail. Fifteen animals with the body weights of 225–264 g were used for the experiments. Three rats were used as a vehicle control (physiological saline with 1% DMSO and 1% PEG 400), and 4 rats each received the following OH-PCB treatments: a low dose (0.50 nmol/kg body weight; 0.11 nmol), middle dose (5.0 nmol/kg body weight; 1.1 nmol), and high dose (50 nmol/kg body weight; 11 nmol). One rat in the high-dose group died due to anesthesia overdose during the administration of 4-OH-CB107, and thus, 3 rats remained in this group. All injections were conducted during the daytime. The injection schedule (starting with control rats) was started at 9:20 am, and the last injection was performed at 11:50 am. Rats were fed diet and water ad libitum throughout the experiment. Twenty-four hours after the injection, the rats were weighed and euthanized by cardiac puncture under inhalational isoflurane anesthesia (Wako Pure Chemicals Industries, Ltd). Inhalational isoflurane anesthesia was administered to the rats to reduce handling stress during injection and for euthanasia. For the anesthesia, rats were placed individually in a 1-l container containing a piece of Kimwipe moistened with a small amount of isoflurane to make less than 5% isoflurane. The Kimwipe was placed in a small jar to avoid direct contact with the rat. Tissues and organs, including the liver, were immediately removed and frozen in liquid nitrogen after weighing. The samples were stored at −80°C in 15-ml cryogenic vials until analysis. Extraction and quantification of OH-PCBs Hydroxylated PCBs were extracted from the liver samples and determined as methoxy (MeO)-PCBs (methyl derivatives) using a gas chromatograph (HP-6890; Agilent Technologies, Inc, Santa Clara, California) coupled with a high-resolution mass spectrometer (MS-800D; JEOL, Tokyo, Japan). The procedure for the extraction and clean-up was modified following the method described elsewhere (Eguchi et al., 2014). A liver sample (0.5 g) was transferred into a 2-ml tube (XXTuff reinforced microvial; BioSpec Products, Inc, Bartlesville, Oklahoma). Two tubes were prepared per sample. To each tube, 2 stainless steel beads (5 mm; Qiagen, Hilden, Germany) and 1 ml of 6 M HCl were added. The liver tissue was homogenized using TissueLyser II (Qiagen) at a frequency of 25.0 Hz for 30 min. The 2 completely homogenized liver samples were combined in 15-ml tubes, and the 2-ml tubes were washed 3 times with methyl tert-butyl ether (MTBE)/n-hexane (1:1, vol/vol). The samples were mixed with a small amount of isopropyl alcohol using a vortex and ultrasonic extraction for 15 min. The samples were then centrifuged at 3000 × g for 5 min, and the upper layer was transferred into a test tube. This step was repeated 2 times. The extract was spiked with 5 13C12-labeled OH-PCB congeners as internal standards, including 4′-OH-CB29, 4′-OH-CB61, and 4′-OH-CB120 from Wellington Laboratories, Inc, and 4′-OH-CB79 and 4′-OH-CB107 from Cambridge Isotope Laboratories, Inc (Tewksbury, Massachusetts). Then, a NaCl solution (10%) was added and mixed, and the mixture was centrifuged at 3000 × g for 5 min. The upper layer was concentrated to 1 ml under a gentle stream of nitrogen gas and then mixed with n-hexane and acetonitrile saturated with n-hexane. The solution was mixed by vortexing and centrifuged at 1000 rpm for 5 min. To the bottom layer, n-hexane and acetonitrile (saturated with n-hexane) were added, and the extraction step was repeated 3 times. Hexane-washed water, adjusted to pH < 2 with H2SO4, was added to the extract, followed by the addition of MTBE/n-hexane (1:1, vol/vol), mixing by vortexing, and centrifugation at 3000 × g for 1 min. The upper layer was collected to a new tube, and extraction with MTBE/n-hexane was repeated 3 times. The OH-PCB fraction was cleaned up on a 5% hydrated silica gel column (Wakogel S-1; Wako Pure Chemicals Industries, Ltd) and derivatized with trimethylsilyldiazomethane. The derivatized solution was further cleaned up on an activated silica gel column (Wakogel DX; Wako Pure Chemicals Industries, Ltd). 13C12-labeled CB157 was added as a syringe spike, and the solution was concentrated to 50 μL. Average recovery rates of the 5 internal standards were as follows: 4′-OH-CB29 (59% ± 13%), 4′-OH-CB61 (65% ± 14%), 4′-OH-CB79 (54% ± 3.8%), 4-OH-CB107 (52% ± 3.1%), and 4-OH-CB120 (52% ± 3.3%). 4-OH-CB107 was quantified using an isotope dilution method to the corresponding 13C12-internal standards, as described by Kunisue and Tanabe (2009). MeO-PCB congeners were quantified when the retention time was consistent with that of the standard, the signal-to-noise ratio (S/N) was ≥3, and the isotope ratio error was ±25% of the standard compound. The method detection limit (MDL) was determined using the peak area of S/N = 3 in the chromatogram of each sample. Biochemical analysis of plasma Blood samples were collected into heparinized vacuum blood collection tubes by cardiac puncture during tissue collection. The plasma was separated by centrifugation of the blood at 1000 × g, 4°C for 20 min, and stored at −80°C. The samples were used for the analyses of 20 biochemical markers related to liver and biliary diseases, including albumin (ALB), aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH), by Oriental Yeast Co., Ltd (Supplementary Table 1). RNA isolation Total RNA was isolated from liver tissue using a Direct-zol RNA miniprep kit (Zymo Research, Irvine, California) following the manufacturer’s protocol. The RNA quantity was determined using an ND-1000 spectrophotometer (NanoDrop Technologies, LLC, Wilmington, Delaware), and the quality was evaluated using an Agilent 2100 bioanalyzer (Agilent Technologies). mRNA sequencing, preprocessing and mapping Complementary DNA (cDNA) libraries of rat liver samples were constructed by Hokkaido System Science Co., Ltd (Sapporo, Japan) using a HiSeq 2500 system (Illumina, San Diego, California) RNA sequencing (RNA-seq), and a TruSeq RNA library kit, according to the manufacturer’s instructions. The raw read data were deposited to the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA) database under an accession number of DRA006343. The adaptor sequences and low-quality reads were trimmed and aligned to the rat reference genome, Rnor_5.0 (Ensemble release 79), using TopHat (v.2.0.14) (Kim et al., 2013). Detailed settings and functions are described in Supplementary method. Quantification of mRNA levels by real-time RT-PCR To validate the performance of RNA-seq, quantitative real-time RT-PCR (qRT-PCR) was carried out to quantify mRNA levels using SYBR premix Ex Taq II (Tli RNase H Plus), Bulk (Takara Bio, Inc, Kusatsu, Shiga, Japan), and a StepOne real-time PCR system (Life Technologies, Carlsbad, California). The specific primers used for mRNA quantification were as follows: nuclear receptor subfamily 1, group D, member 1 (Nr1d1) forward (5′-CACCAGCAACATTACCAA-3′), Nr1d1 reverse (5′-CGCACAGCGTCTCTA-3′), AHR nuclear translocator-like protein 1 (Arntl) forward (5′-TGCCACTGACTACCAAGAAA-3′), and Arntl reverse (5′-TGAACAGCCATCCTGAGC-3′). qRT-PCR was run as described previously (Iida et al., 2013). Briefly, the PCR conditions for Nr1d1 were 2 min at 95°C and 40 cycles of 30 s at 95°C and 30 s at 47°C; those for Arntl were 2 min at 95°C and 40 cycles of 30 s at 95°C and 30 s at 52°C. Calibration curves for mRNA were generated by plotting cycle threshold (Ct) values against logarithmic concentrations of serially diluted references. Each reaction was run in quadruplicate, and β-actin was used for intersample normalization. Detection of differentially expressed genes HTSeq v0.6 (https://pypi.python.org/pypi/HTSeq; last accessed May 21, 2018) (Anders et al., 2015) was used to obtain read counts for each gene from mapped reads. We removed genes that had an average log2 counts per million (CPM) among 12 groups < 1, and normalized the read counts followed by removal of the batch effect (the details are in supplemental methods). The maximum fold changes (FC) of gene expression were calculated on a log2 scale between the normalized count data of the highest exposed group and control (DMSO) group. The detailed methodology for the detection of differentially expressed genes (DEGs) has been reported in our previous study (Iida et al., 2016). Briefly, the Jonckheere-Terpstra test (Jonckheere, 1954; Terpstra, 1952) was conducted to detect genes whose expression levels were altered in an OH-PCB dose-dependent manner. Significance of DEGs was determined based on false discovery ratio (FDR)-adjusted q-values (q-value < 0.1 by Jonckheere-Terpstra test) and the maximum log2 FC (|log2FC| > 0.26 for the highest dose group). Kyoto Encyclopedia of Genes and Genomes pathway transcription factor, and phenotype enrichment analyses To identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and upstream transcription factors (TFs) for DEGs, enrichment analysis was carried out using Database for Annotation, Visualization and Integrated Discovery (DAVID) (v 6.8) (https://david.ncifcrf.gov; last accessed May 21, 2018) based on rat KEGG pathway database (Huang et al., 2009) and Expression2Kinases (version 1.6.1207) (http://www.maayanlab.net/X2K/index.html; last accessed May 21, 2018) (Chen et al., 2012), respectively. We considered a term has p-value < .1 and fold enrichment score > 7 is significant for the KEGG pathway enrichment analysis and combined scores ≥10, which represent the absolute z-score > 0.7 and a p-value < .004 (q-value < 0.02), for the TF enrichment analysis. To investigate the link between DEGs and phenotypes, the enrichment analysis tool in GeneSetDB with the rat phenotype database was used. A phenotype set with an FDR-corrected p-value of less than 10% (q-value < 0.1) was considered significant. Network analysis To visualize the relationships between DEGs, network analysis was carried out. The protein-protein interaction (PPI) data for DEGs were extracted from the functional protein association networks (STRING) database ver. 10.5 (http://string-db.org/; last accessed May 21, 2018) (Szklarczyk et al., 2017). Protein-protein interactions that had interaction scores of more than 0.4 were used for subsequent analysis. The gene clusters that had similar expression patterns were determined according to the network community structure based on the correlations among gene expression levels. We visualized clusters with 3 or more DEGs. To investigate the biological function of the clusters, KEGG pathway enrichment analysis was performed for each cluster by the Fisher’s exact test. Statistical analysis To confirm differences in expression patterns among exposed groups, we performed principal component analysis (PCA) based on log2 CPMs. One-way analysis of variance (ANOVA) with Dunnett’s Multiple Comparison Test (GraphPad Prism 5) was used to determine differences in liver concentrations of 4-OH-CB107 and plasma biochemical parameters among different treatment groups. RESULTS OH-PCB Concentrations in Rat Liver The concentrations of 4-OH-CB107 in the liver are shown in Figure 1. The mean concentrations were below MDL, 3.0, 170, and 1400 pg/g wet weight for liver samples from the control, low-, middle-, and high-dose groups, respectively. The concentrations in the high-dose group were significantly higher than those in the control (p ≤ .001). The given doses increased by 10-fold increments, and the concentrations measured in the liver samples corresponded to the low, middle, and high doses. Figure 1. View largeDownload slide OH-PCB concentrations in rat livers from different treatment groups. CTL, control; MDL, method detection limit. Only one data point is shown for low-dose and non-data points for CTL because these concentrations were below MDL of 1.7 pg/g wet weight. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. ***p < .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 1. View largeDownload slide OH-PCB concentrations in rat livers from different treatment groups. CTL, control; MDL, method detection limit. Only one data point is shown for low-dose and non-data points for CTL because these concentrations were below MDL of 1.7 pg/g wet weight. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. ***p < .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Effects of OH-PCB on Hepatic Function To investigate the effects of OH-PCB exposure on hepatic function, biochemical analysis of the plasma was carried out. The data showed that the levels of AST, glucose (GLU), total bilirubin (T-BIL), indirect bilirubin (I-BIL), and leucine aminopeptidase (LAP) as indices of hepatic injury had a tendency to increase in a 4-OH-CB107 dose-dependent manner (Figs. 2A–D;Supplementary Table 1). In particular, the levels of the LAP enzyme were significantly higher (p ≤ .01, p ≤ .001, respectively) in the middle- and high-dose 4-OH-CB107 groups than in the control group. The LAP excreted from the liver into the bile catalyzes the hydrolysis of peptides. Blood LAP level has been traditionally used as a marker to diagnose hepatobiliary disease (Abouzied et al., 2015). Albumin and the ALB/globulin ratio (A/G) can be low in liver or kidney diseases and are thus used for diagnosis. We observed decreasing trends in ALB and A/G in the OH-PCB-treated rats (Figs. 2E and F;Supplementary Table 1). Figure 2. View largeDownload slide Biochemical parameters of rat plasma in different treatment groups. A, Aspartate aminotransferase (AST); B, glucose (GLU); C, total bilirubin (T-BIL); D, leucine aminopeptidase (LAP); E, albumin (ALB); F, albumin/globulin ratio. CTL, control. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤ .01 and **p ≤ .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 2. View largeDownload slide Biochemical parameters of rat plasma in different treatment groups. A, Aspartate aminotransferase (AST); B, glucose (GLU); C, total bilirubin (T-BIL); D, leucine aminopeptidase (LAP); E, albumin (ALB); F, albumin/globulin ratio. CTL, control. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤ .01 and **p ≤ .001 compared with the control (one-way ANOVA, followed by Dunnett’s multiple comparison test). Identification and Validation of DEGs After preprocessing and normalization of raw reads, the Jonckheere-Terpstra test was carried out to identify OH-PCB dose-dependent DEGs at the threshold of FDR correlated p-value < .1 and |log2FC| > 0.26. A total of 108 genes, including 86 downregulated and 22 upregulated genes, were identified (Table 1; Supplementary Figure 1 and Table 2). Of these genes, the expression level of Nr1d1, which is related to the circadian rhythm, was extremely upregulated by OH-PCB exposure in a dose-dependent manner (log2FC = 11 in the high-dose group; Figure 3A). On the other hand, the expression levels of other circadian rhythm-related genes such as Arntl (Figure 3B) and cryptochrome circadian clock 1 (Cry1) were downregulated (log2FC values of −0.96 and −5.06, respectively, in the high-dose group). The expression level of the gene encoding cytochrome P450 2b1 and 2b2 (Cyp2b1 and 2), 1 of the key phase I metabolizing enzymes, was decreased by OH-PCB exposure (log2FC = −3.24 and −1.07, q-value = 0.07 and 0.09). To validate the results of RNA-seq, qRT-PCR was performed for the Nr1d1 and Arntl genes (Figure 3). Although both qRT-PCR and RNA-seq showed an increasing trend for the Nr1d1 expression, the results were different for Arntl. The mapping results for Arntl showed a clear, dose-dependent, decreasing trend only for the reads in the 3′-untranslated region; however, we designed the qRT-PCR primers for the second exon. The discrepancy in the data between RNA-seq and qRT-PCR thus could be attributed to the selection of the target region for the qRT-PCR primers. We found that the expression of PPAR signaling pathway-related genes such as Cyp27a1, the peroxisomal bifunctional enzyme enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase (Ehhadh), and angiopoietin-related protein 4 (Angptl4) was also affected by 4-OH-CB107 exposure. Furthermore, the expression levels of the ALT gene (Gpt), which is related to liver damage, also changed upon 4-OH-CB107 exposure, consistent with the data of biochemical analysis. The PCA was employed to evaluate the relationships of global gene expression profiles among the experimental groups (Figure 4). The high contribution rate of principal component (PC) 1 (99.76%) indicated that the control and OH-PCB-exposed groups had similar expression profiles. However, the control and high-dose groups were separated along the Y-axis (PC2; 0.13%), suggesting a global expression change between these groups. On the other hand, the low- and middle-dose groups were colocated, indicating that the low and middle doses of OH-PCB induced identical gene expression changes. Table 1. Genes Whose Expression Levels Dose-Dependently Changed Upon Rat Exposure to 4-OH-CB107 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. FC, fold change; CC, chemical carcinogenesis; DM, drug metabolism; FA, fatty acid degradation; PE, peroxisome; RM, retinol metabolism. Table 1. Genes Whose Expression Levels Dose-Dependently Changed Upon Rat Exposure to 4-OH-CB107 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Pathway ID Symbol Gene Name log2 FC (HIGH/CTL) Increased by OH-PCB Decreased by OH-PCB p-Value q-Value p-Value q-Value Circadian ENSRNOG00000009329 Nr1d1 Nuclear receptor subfamily 1, group D, member 1 11.00 4.00E−04 0.07 Circadian ENSRNOG00000046912 Nr1d2 Nuclear receptor subfamily 1, group D, member 2 2.67 4.00E−04 0.07 ENSRNOG00000010588 Tenc1 Tensin 2 0.77 .0012 0.09 DM ENSRNOG00000032394 Tymp Thymidine phosphorylase 0.46 8.00E−04 0.09 FA ENSRNOG00000003307 Gcdh Glutaryl-CoA dehydrogenase −0.31 .0012 0.09 PE, RM ENSRNOG00000018239 Dhrs4 Dehydrogenase/reductase (SDR family) member 4 −0.36 .0012 0.09 CC ENSRNOG00000029726 Gstm1 Glutathione S-transferase mu 1 −0.45 8.00E−04 0.09 CYP, PPAR ENSRNOG00000017188 Cyp27a1 Cytochrome P450, family 27, subfamily a, polypeptide 1 −0.53 .0012 0.09 DM ENSRNOG00000031367 Hprt1 Hypoxanthine phosphoribosyltransferase 1 −0.63 .0012 0.09 PE ENSRNOG00000019048 Sod2 Superoxide dismutase 2 −0.72 4.00E−04 0.07 PE ENSRNOG00000050424 Decr2 NME/NM23 nucleoside diphosphate kinase 4 −0.76 4.00E−04 0.07 FA, PE, PPAR ENSRNOG00000001770 Ehhadh Enoyl-CoA, hydratase/3-hydroxyacyl CoA dehydrogenase −0.85 4.00E−04 0.07 DM ENSRNOG00000015519 Ces1d Carboxylesterase 1D −0.88 4.00E−04 0.07 Circadian ENSRNOG00000014448 Arntl Aryl hydrocarbon receptor nuclear translocator-like −0.96 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000020775 Cyp2b2 Cytochrome P450, family 2, subfamily b, polypeptide 2 −1.07 8.00E−04 0.09 FA ENSRNOG00000013766 Acaa2 Acetyl-CoA acyltransferase 2 −1.01 4.00E−04 0.07 PPAR ENSRNOG00000007545 Angptl4 Angiopoietin-like 4 −1.50 .0012 0.09 FA, CC, RM ENSRNOG00000012464 Adh1 Alcohol dehydrogenase 1 −1.17 8.00E−04 0.09 Circadian ENSRNOG00000020836 Rorc RAR-related orphan receptor C −2.52 4.00E−04 0.07 CYP, CC, RM ENSRNOG00000033680 Cyp2b1 Cytochrome P450, family 2, subfamily b, polypeptide 1 −3.24 4.00E−04 0.07 Circadian ENSRNOG00000006622 Cry1 Cryptochrome circadian clock 1 −5.06 4.00E−04 0.07 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. FC, fold change; CC, chemical carcinogenesis; DM, drug metabolism; FA, fatty acid degradation; PE, peroxisome; RM, retinol metabolism. Figure 3. View largeDownload slide Expression levels of Nr1d1 and Arntl mRNA in the liver of rats treated with 4-OH-CB107 and in the control (CTL). A and B, Log2 CPM (count per million) was determined by next-generation RNA sequencing. C and D, mRNA expression was determined by 2-step real-time RT-PCR, and the levels were normalized to those of β-actin. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤.01, **p ≤ .001, and ***p < .001 (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 3. View largeDownload slide Expression levels of Nr1d1 and Arntl mRNA in the liver of rats treated with 4-OH-CB107 and in the control (CTL). A and B, Log2 CPM (count per million) was determined by next-generation RNA sequencing. C and D, mRNA expression was determined by 2-step real-time RT-PCR, and the levels were normalized to those of β-actin. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 4, middle; n = 4, high; n = 3. *p ≤.01, **p ≤ .001, and ***p < .001 (one-way ANOVA, followed by Dunnett’s multiple comparison test). Figure 4. View largeDownload slide Principal component (PC1 × PC2) plots generated from mRNA expression profiles of DEGs. CTL, control; Hi, high dose; Mid, middle dose. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Figure 4. View largeDownload slide Principal component (PC1 × PC2) plots generated from mRNA expression profiles of DEGs. CTL, control; Hi, high dose; Mid, middle dose. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Pathway Enrichment Analysis Pathway enrichment analysis revealed that circadian rhythm and fatty acid degradation pathways were significantly enriched (Table 2) with the circadian rhythm pathway being the most enriched pathway (q-value = 0.05). Ehhadh, which was downregulated by OH-PCB treatment, is related to fatty acid degradation, peroxisome, and PPAR signaling pathways (Table 2). Chemical carcinogenesis and retinol metabolism pathways, which include alcohol dehydrogenase 1 (Adh1), Cyp2bs (Cyp2b1 and Cyp2b2), glutathione S-transferase (Gstm1), and dehydrogenase/reductase (SDR family) member 4 (Dhrs4), showed a tendency to be enriched. Table 2. Pathways Affected by OH-PCB Exposure, Based on Pathway Enrichment Analysis Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. Table 2. Pathways Affected by OH-PCB Exposure, Based on Pathway Enrichment Analysis Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Rank KEGG ID Pathway Name DEGs in Pathway No. of Genes in Pathway No. of DEGs in Pathway Fold Enrichment p-Value q-Value 1 rno04710 Circadian rhythm Arntl, Cry1, Nr1d1, Rorc 30 4 24.12 2.00E-05 0.05 2 rno00071 Fatty acid degradation Acaa2, Adh1, Ehhadh, Gcdh 47 4 15.40 1.20E-04 0.06 3 rno04146 Peroxisome Decr2, Dhrs4, Ehhadh, Sod2 85 4 8.51 1.20E-03 0.16 4 rno05204 Chemical carcinogenesis Adh1, Cyp2b1, CYP2b2, Gstm1 91 4 7.95 1.50E-03 0.17 5 rno00830 Retinol metabolism Adh1, Cyp2b1, CYP2b2, Dhrs4 83 4 8.72 1.10E-03 0.18 6 rno00983 Drug metabolism - other enzymes Ces1d, Hprt1, Tymp 56 3 9.69 3.60E-03 0.37 7 rno03320 PPAR signaling pathway Angptl4, Cyp27a1, Ehhadh 77 3 7.05 8.70E-03 0.52 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes. Network Analysis To visualize the relationships between genes and pathways, network analysis was performed (Figure 5). The network had 6 clusters, and 3 of them showed significantly enriched functional pathways (Figure 5, p < .05). Module and enrichment analyses of the PPI network showed 5 hub genes (ARNTL, CLPX, LOP2, EHHADH, and ALB) connecting functional pathways. An ARNTL and an EHHADH were likely to play roles of the hub genes of the circadian rhythm and fatty acid metabolism pathways, respectively. The 2 pathways indirectly interacted via the caseinolytic mitochondrial matrix peptidase chaperone subunit (CLPX) and lon peptidase 2 (LONP2). Genes related to chemokine signaling, Huntington’s disease, and focal adhesion pathways formed a cluster including ALB as a hub. Albumin directly interacted with JunD proto-oncogene (JUND) in the enriched mitogen-activated protein kinase signaling pathway and with EHHADH, which is related to fatty acid metabolism. Figure 5. View largeDownload slide Predicted interactions among DEGs (maximum connected component). Node sizes differ depending on the node degree. Pathway names correspond to those that were significantly enriched in the module based on the KEGG pathway enrichment analysis (q-value < 0.2). Numbers in parentheses show the q-values. KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. Figure 5. View largeDownload slide Predicted interactions among DEGs (maximum connected component). Node sizes differ depending on the node degree. Pathway names correspond to those that were significantly enriched in the module based on the KEGG pathway enrichment analysis (q-value < 0.2). Numbers in parentheses show the q-values. KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase. Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. TF Enrichment Analysis In addition to the pathway enrichment analysis, we attempted to determine TFs, which have an impact on dose-dependent expression changes of DEGs. To screen critical TFs, we carried out TF enrichment analysis (Table 3). Fifteen TFs, such as estrogen receptors 1 and 2 (ESR1 and ESR2), the clock circadian regulator (CLOCK), v-myc avian myelocytomatosis viral oncogene neuroblastoma-derived (MYCN), hepatocyte nuclear factor 4 alpha (HNF4A), nuclear receptor subfamily 1, group I, member 2 (NR1I2, also known as the pregnane X receptor [PXR]), Forkhead box protein A2 (FOXA2), SRY-box 2 (SOX2), CCCTC-binding factor (CTCF), Spi-1 proto-oncogene (SPI1), tet methylcytosine dioxygenase 1 (TET1), doublesex and mab-3 related TF 1 (DMRT1), PPAR gamma (PPARG), myocyte enhancer factor 2A (MEF2A), and androgen receptor (AR), were detected as candidate transcriptional regulators of DEGs. Among them, there were no TFs showing dose-dependent expression changes upon OH-PCB treatment. The TF enrichment analysis showed that the most upregulated gene, Nr1d1, could be regulated by 5 TFs, including MYCN, HNF4A, FOXA2, PPARG, and AR. Similar to Nr1d1, the most downregulated gene, Cry1, could be regulated by multiple TFs such as MYCN, ESR1, and AR. Together, these results suggest that PPARG and sex hormone receptors (AR and ESR), as well as circadian rhythm-related genes, play important roles in regulating the gene expression of DEGs in an OH-PCB dose-dependent manner. Table 3. Transcription Factors Predicted Upstream of DEGs by TF Enrichment Analysis Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. DEGs, differentially expressed genes. Table 3. Transcription Factors Predicted Upstream of DEGs by TF Enrichment Analysis Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Rank TF DEGs in Downstream of TF No. of Genes in Downstream No. of DEGs in Downstream p-Value q-Value z-Score Combined Score 1 ESR1 Acaa2, Adh1, Bphl, Cry1, Cyp27a1, Hsd17b2, Lbp, Nnmt, Rorc, Sds, Spp2 444 11 6.34E-09 6.85E-07 −2.50 47.12 2 CLOCK Angptl4, Arntl, Clpx, Rorc, Slc16a1, Spon2, Tenc1, Wdr6 407 8 4.39E-06 1.58E-04 −2.06 25.41 3 MYCN Acaa2, Acad11, Cdk7, Cry1, Ctnna3, Nr1d1, Nup62, Prdx4, Rbbp8, Sc5d, Shc1, Slc16a1, Tbc1d2, Tert, Tnfaip8l1, Vps36, Wdr6, Xpc, Zfp64 2261 19 5.73E-07 3.09E-05 −1.53 22.01 4 HNF4A Acaa2, Acad11, Alb, Angptl4, Asl, Bphl, Cd302, Clpx, Ctnna3, Cyb5a, Cyp27a1, Decr2, Dhrs4, Dtnb, Ehhadh, Fermt2, Galk2, Hsd17b2, Il1rap, Lama3, Lbp, Lrig2 Ndufb6, Nnmt, Nr1d1, Pcgf5, Pcsk6, Pgrmc2, Pnrc1, Rnf8, Rorc, Sec61a2, Shc1, Sod2, Tbc1d2, Tenc1, Tmcc3, Tmem140, Tmem220, Tnfaip8l1, Tspan14, Xpc 6083 42 2.07E-12 4.48E-10 −0.73 19.71 5 NR1I2 Asl, Dapk2, Gstm1, Hsd17b2, Lama3, Nnmt, Pcsk6, Rbbp8, Tnfaip8l1 939 9 .0003 3.56E-03 −2.09 17.13 6 FOXA2 Abhd8, Angptl4, Bphl, Cd302, Chchd10, Clpx, Ctnna3, Cyp27a1, Dhrs4, Dtnb, Fermt2, Hsd17b2, Il1rap, Lama3, Nr1d1, Nr1d2, Pcgf5, Pcsk6, RNF26, Rnf8, Tenc1, Tmem140 2968 22 5.18E-07 3.09E-05 −1.11 16.02 7 ESR2 Abhd8, Angptl4, Atg16l2 Cyb5a, Itga5, Pcsk6 424 6 .0004 4.28E-03 −1.89 14.73 8 SOX2 Acad11, Apoc2, Asl, Cyb5a, Hsd17b2, Itga5, Lama3, Nmnat3, Nnmt, Nup62, Pcsk6, Pgrmc2, Prdx4, Spon2, Tbc1d2, Tjp3, Tspan14 2564 17 .0001 1.12E-03 −1.40 13.83 9 CTCF Adh1, Angptl4, Dapk2, Decr2, Dhrs4, Gstm1, Lrig2 Nnmt, Rnf8, Tert, Vps36, Zfp64 1568 12 .0002 2.96E-03 −1.52 12.88 10 SPI1 Alb, Cyp27a1, Gstm1, Hprt1, Jund, Rnf8, Rorc, Tenc1, Tmcc3, Tnfaip8l1 Zbed5 1249 11 .0001 1.94E-03 −1.41 12.75 11 TET1 Acaa2, Angptl4, Chchd10, Dtnb, Jund, Nnmt, Pm20d1, Sds, Shc1, Sigmar1, Tenc1, Tjp3, Usp21 Zfp61 1839 14 .0001 1.21E-03 −1.26 12.20 12 DMRT1 Lonp2, Shc1 Tmem144 132 3 .0035 0.02 −2.02 11.41 13 PPARG Angptl4, Apoc2, Arntl, Clpx, Ehhadh, Fam195a, Galk2, Lrig2, Nnmt, Nr1d1, Pcgf5, Pgrmc2, Plin2, Pnrc1, Rbbp8, Shc1, Slc16a1, Tenc1, Tmcc3, Tmem140, Xpc, Zfp295 3565 22 1.02E-05 2.54E-04 −0.92 10.57 14 MEF2A Cdk7, Ctnna3, Hspb6 Pcgf5, Pgrmc2, Rbbp8, Rcan2, Slc16a1 1048 8 .0025 0.01 −1.77 10.57 15 AR Acad11, Clpx, Cry1, Ctnna3 Cyb5a, Dtnb, Lama3, Lbp, Lonrf3, Nnmt, Nr1d1, Oplah, Pcsk6, Prdx4, Rbbp8, Rorc, Slc16a1, Tert, Tjp3, Zbed5 3519 20 .0001 1.60E-03 −1.07 10.03 Number of animals used in each treatment group is as follows: CTL; n = 3, low; n = 3, middle; n = 3, high; n = 3. DEGs, differentially expressed genes. Phenotype Enrichment Analysis Finally, we predicted the outcomes of OH-PCB exposure, based on the DEG analysis, using a phenotype enrichment analysis in GeneSetDB. Expression levels of the genes related to abdominal adipose tissue accumulation, including ARNTL, CRY1, and tensin 2 (TENC1), were altered; however, no effects were observed on the weight of visceral fat, body weight, and liver weight following exposure to 4-OH-CB107 (Supplementary Table 3). Genes related to a shortened circadian period, including ARNTL, CRY1, and NR1D1, hypoactivity, including ARNTL, CRY1, and EHHADH, and oxidative stress, including peroxiredoxin 4 (PRDX4), SHC (Src homology 2 domain containing) transforming protein 1 (SHC1), and superoxide dismutase 2, mitochondrial (SOD2), were significantly enriched (q-value < 0.1) (Table 4). Based on bioinformatics analysis of DEGs, we predicted the hepatic transcriptomic alterations and the contribution of the compound to adverse outcomes (Table 4). Table 4. Potential Outcomes of 4-OH-CB107 Exposure, Predicted by Phenotype Enrichment Analysis Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 DEGs, differentially expressed genes. Table 4. Potential Outcomes of 4-OH-CB107 Exposure, Predicted by Phenotype Enrichment Analysis Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 Rank Phenotype Name DEGs Related the Phenotype No. of Genes in Group No. of Genes p-value q-value 1 Decreased abdominal adipose tissue amount Arntl, Cry1, Tenc1 13 3 5.30E-05 0.03 2 Shortened circadian period Arntl, Cry1, Nr1d1 23 3 3.10E-04 0.07 3 Hypoactivity Arntl, Cry1, Ehhadh, Gstm1, Hprt1, Rcan2, Sigmar1, Sod2 306 8 3.40E-04 0.07 4 Oxidative stress Prdx4, Shc1, Sod2, Xpc 65 4 .001 0.08 DEGs, differentially expressed genes. DISCUSSION In the present study, we demonstrated that the transcriptome was significantly altered in the liver of adult male rats by exposure to 4-OH-CB107, which is one of the major metabolites of PCBs detected in humans and wildlife. The rat liver concentrations of 4-OH-CB107 in the current study were within the ranges detected in human serum, which have been reported to be 3.1–41 pg/g wet weight in India (Eguchi et al., 2012), 6.6–326 pg/g wet weight in Belgium (Dufour et al., 2017), and 41–1700 pg/g wet weight in pregnant women in the Faroe Islands (Fängström et al., 2002). Based on the bioinformatics analysis of DEGs, we predicted the mode of action of environmentally relevant concentrations of 4-OH-CB107 in the liver and a possible contribution of this compound to adverse outcomes. The potential effects of 4-OH-CB107 on functional pathways and inducible outcomes were analyzed by using a rat database. The upstream TFs for DEGs were predicted by using human and mouse databases because there is limited information on TF binding sites in the rats. Hepatic transcriptome analysis showed that 4-OH-CB107 induced expression changes for 108 genes, such as Arntl, Cry1, Nr1d1, Rorc (RAR-related orphan receptor C), and Ehhadh, related to the circadian rhythm, fatty acid degradation, and the PPAR signaling pathway. The effects of the anesthetic on the liver transcriptome can be ruled out because all rats were treated with isoflurane under the same conditions regardless of the treatment with 4-OH-CB107. The KEGG pathway and phenotype enrichment analyses also indicated that 4-OH-CB107 exposure might lead to reduction of the abdominal adipose tissue mass and to shortening of the circadian cycle by perturbing fatty acid degradation and circadian rhythm. Our transcriptome and phenotype enrichment analyses indicated that the expression levels of circadian rhythm-related genes (Arntl, Cry1, Nr1d1, Nr1d2, and Rorc) were notably changed in the rat liver following 4-OH-CB107 exposure. The daily maximum FC for Cry1 and Nr1d1 in the rat liver have been reported to be up to 4- and 100-fold (2 and 6.6 in log2, respectively) (Ovacik et al., 2010; Yamajuku et al., 2012). These values were approximately 10 and 20 (ie, 3.6 and 4.4 in log2, respectively) times lower than the FC observed in this study (48.5- and 2048-fold; 5.6 and 11 in log2, respectively). Therefore, the expression of Cry1 and Nr1d1 was considered to be induced by 4-OH-CB107 exposures rather than the physiologic changes. It has been reported that the knockouts of Nr1d1, Cry1, and Arntl resulted in a shorter circadian period (Lowrey and Takahashi, 2011). 4-OH-CB107 exposure might disrupt the circadian rhythm in the rat liver through the alteration of clock gene expression levels. Our TF enrichment analysis suggested that 4-OH-CB107 affects circadian rhythm through several TFs, including ESRs, CLOCK, and PPARs (Table 3). In addition, 4-OH-CB107 could bind to AHR (Mise et al., 2016) that regulates the expression of circadian clock genes (Jaeger and Tischkau, 2016). Crosstalks exist between AHR and ESRs, CLOCK and PPAR (Jaeger and Tischkau, 2016). Taken together, we suggest that 4-OH-CB107 may affect the circadian rhythm directly or indirectly through ESRs, CLOCK, PPARs, and AHR (Jaeger and Tischkau, 2016). However, the specific mechanism of action should be investigated in a future study. Similar to our results showing that 4-OH-CB107 might affect circadian rhythms, the circadian period has been found to increase in 4-OH-CB106-exposed male rats (Lesmana et al., 2014). A modification of the circadian rhythm has also been reported after exposure of mice to dioxin (2,3,7,8-tetrachlorodibenzodioxin) (Xu et al., 2013). However, there have been only few reports demonstrating the effects of PCB exposure on circadian rhythms. Thus, noncoplanar PCBs were shown to increase the swimming activity of adult zebrafish at night, suggesting that PCB exposure could trigger the rhythm disruption (Péan et al., 2013). These results may suggest that disruption of the circadian rhythm in vertebrates is unique to PCB metabolites and dioxins. Previous studies have reported that OH-PCBs bind to thyroid hormone-binding proteins (thyroxine-binding globulin, TTR, and ALB) and affect thyroid hormone levels (Hisada et al., 2014; Meerts et al., 2002; Nomiyama et al., 2014). No changes in the expression levels of genes associated with the thyroid hormone signaling pathway, except regulator of calcineurin 2 (Rcan2), were observed in this study. However, there is a link between thyroid hormone levels and the circadian rhythm, and it has been reported that the circadian rhythm regulates thyroid hormone levels in the central nervous system of rats (Campos-Barros et al., 2002). One of the differences between previous studies and this study was the exposure period. Whereas previous studies have focused on the chronic toxicity of OH-PCBs, we focused on the acute toxicity of 4-OH-CB107 to understand the effects of this single compound. Therefore, it cannot be excluded that the OH-PCB effects on thyroid hormone levels are subsequent to the circadian rhythm disruption. Several transcriptome studies have indicated the relationship between PCB exposure and lipid metabolism (Gadupudi et al., 2016; Yadetie et al., 2014). Gadupudi et al. (2016) have demonstrated that exposure to PCB126 for 12 days increased lipid accumulation in the rat liver, leading to steatosis, and the data indicated that PCB126 altered liver lipid metabolism through PPARα. In this study, we showed that OH-PCB exposure disrupted fatty acid degradation and the PPAR signaling pathway through changes in the mRNA expression of Acaa2, Adh1, Angptl4, Cyp27a1, Ehhadh, and Gcdh in the liver (Tables 1 and 2). Our TF enrichment study also demonstrated that ARNTL, EHHADH, and TENC1 are downstream of PPARG (Table 3). Furthermore, our phenotype enrichment analysis suggested that the altered gene expression of Arntl, Cry1, and Tenc1 might decrease the abdominal adipose tissue amount (Table 4). Fatty liver is characterized by the accumulation of triglyceride esters, derived from glycerol and free fatty acids (FFAs). Interestingly, Rabinowich and Shibolet (2015) have proposed that the increased content of hepatic FFAs may be due to their increased uptake from peripheral tissues, predominantly adipose tissue, increased de novo lipogenesis in hepatocytes, or decreased metabolism via β-oxidation in hepatocytes. Taken together, the transcriptional alteration of PPAR signaling pathway-related genes (Acaa2, Gcdh, Ehhadh, and Angptl4) and circadian rhythm-related genes (Arntl and Cry1) by OH-PCB treatment may lead to a reduced abdominal adipose tissue amount through dysregulation of fatty acid metabolism in the liver. However, no significant associations have been found between OH-PCBs/PCBs and the body size of Japanese newborn babies (Hisada et al., 2014). Consistent with the previous study, we could not detect any differences in the visceral fat weight and body weight among the groups. It has long been known that metabolism and circadian clocks are tightly intertwined, and our network analysis for DEGs also revealed indirect interaction of the circadian rhythm and PPARG signaling pathways (Figure 5). Kawai and Rosen (2010) have pointed out that PPARG is responsible for the regulation of the circadian network as well as other major circadian clock genes. The TF enrichment analysis performed in this study indicated that PPARG and HNF4A, which are involved in lipid, glucose, and fatty acid metabolism, were enriched upstream of DEGs (Table 3). These results suggest that PPARG is an important factor directly connecting circadian rhythm and metabolic pathways. Based on the biochemical analysis of the plasma, the levels of a liver functional marker, LAP, increased in the middle- and high-dose groups of 4-OH-CB107-exposed rats. We demonstrated that the activities of hepatic functional markers (ie, AST, GLU, T-BIL, LAP, and ALB) and the expression levels of oxidative stress-related genes [ie, Prdx4, Shc1, Sod2, and xeroderma pigmentosum, complementation group C (Xpc)] were changed in 4-OH-CB107-treated rats (Figure 2; Tables 2 and 4). Similarly, another study showed that PCB exposure increased levels of enzymes (AST, ALT, and alkaline phosphatase) together with the injury in the liver of rats, whereas coexposure to antioxidants protected against the PCB-induced effects (De Oliveira et al., 2014). These results indicate that PCBs and their metabolites may cause liver injury by inducing oxidative stress. This research contributes to the elucidation of potential toxicities of OH-PCBs and their mechanism of action. However, several limitations need to be considered when interpreting our findings. First, our transcriptome results indicated strong effects on the circadian rhythm in the rat liver, but we cannot conclude that 4-OH-CB107 disrupted the circadian rhythm at an individual level because we did not carry out a time-course study. Second, we were not able to detect the weight change of any organ and tissue because of a short experimental period (24 h), although the expression levels of metabolism- and adipose tissue accumulation-related genes changed in a dose-dependent manner. Finally, we demonstrated the changes in the markers (AST, GLU, T-BIL, LAP, and ALB) of hepatic damage induced by OH-PCB exposure; however, we did not evaluate the pathologic effects on the liver because of sample size limitation. However, our findings advance the understanding of OH-PCB toxicity and provide new hypotheses for future studies. SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. ACKNOWLEDGMENTS This study was supported by the Department of Bioscience, Division of Medical Bioscience, Integrated Center for Sciences (INCS), Ehime University for sharing the facility to conduct animal studies, and Japanese Association for Experimental Animal Technologies. We sincerely thank Dr Ken-ichi Okugawa for his generously training on the handling and administration of animals. We would like to thank Mr Yusuke Tsujisawa and Mr Yasuo Yamamoto for their assistance for animal necropsy. FUNDING Joint Usage/Research Center – Leading Academia in Marine and Environment Pollution Research (LaMer) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan; Grant-in-Aid KAKENHI for Scientific Research (S) (No. 26220103) from the Japan Society for the Promotion of Science (JSPS), which was given to Dr Hisato Iwata, and Grant-in-Aid for JSPS Fellows (No. 26-6348) for Dr Mari Ochiai, and Scientific Research (B) (No. 16H02989) for Dr Kei Nomiyama, and Grant-in-Aid for Scientific Research on Innovative Areas (No. 24118008) to Dr Satoshi Fujii. REFERENCES Abouzied M. M. , Eltahir H. M. , Fawzy M. A. , Abdel-Hamid N. M. , Gerges A. S. , El-Ibiari H. M. , Nazmy M. H. ( 2015 ). Estimation of leucine aminopeptidase and 5-nucleotidase increases alpha-fetoprotein sensitivity in human hepatocellular carcinoma cases . Asian Pac. J. Cancer Prev . 16 , 959 – 963 . Google Scholar Crossref Search ADS PubMed Al-Eryani L. , Wahlang B. , Falkner K. C. , Guardiola J. 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Toxicological SciencesOxford University Press

Published: Sep 1, 2018

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