miRNA regulation of cytotoxic effects in mouse Sertoli cells exposed to nonylphenol

Mouse TM4 Sertoli cells were obtained from the Korean Cell Line Bank (KCLB; Seoul, Korea). TM4 cells were grown at 37°C in a humidified 5% CO₂ atmosphere in Dulbecco's Modified Eagle Medium (DMEM) containing 10% fetal bovine serum (FBS) and antibiotics (100 U/mL penicillin, 100 μg/mL streptomycin). DMEM, FBS, penicillin/streptomycin and trypsin-EDTA were obtained from HyClone (Logan, UT, USA). Cells were subcultured every 2-3 days. NP purchased from SUPELCO (Bellefonte, PA, USA) was suspended in DMSO (Sigma-Aldrich, St. Louis, MO, USA) and applied to TM4 cells at appropriate concentrations.

Cell viability was determined by a formazan assay using the Cell Counting Kit-8 (CCK-8) (Dojindo Laboratory, Kumanoto, Japan). TM4 cells suspended in culture medium were seeded in 24-well culture plates at a density of 1 × 10⁵ cells per well. They were then washed and exposed to vehicle (control) or varying concentrations of NP for 24 h. Following NP treatment, 100 μL of CCK-8 solution was added to each well and the cells were incubated at 37°C for a further 1 h. Absorbances at 450 nm were then measured using an ELISA reader (Bio-Rad, Japan). All experiments consisted of three independent replicates, each performed at least in triplicate. Data are presented as the mean ± SD.

Cells treated with 10 μg/mL NP for 3 or 24 h were harvested and total RNA extracted using the TRIzol reagent (Invitrogen, CA, USA) according to the manufacturer's instructions. RNA yield was quantified using a NanoDrop (NanoDrop, USA) and RNA quality using a 2100 Bioanalyzer (Agilent Technologies, CA, USA). Extracted RNA samples were stored at -80°C prior to microarray analysis. miRNA microarray experiments were conducted within 2 days of RNA extraction.

miRNA microarray analysis was performed using Agilent mouse miRNA v13 arrays. An miRNA Complete Labeling and Hyb Kit (Agilent Technologies) was used to label total RNA samples (100 ng) containing miRNAs with Cy3. Labeled samples were then applied to Agilent mouse miRNA v13 arrays and covered with an A4 hybridization mixer. The arrays were then incubated for 12 h at 42°C in a MAUI 12-bay Hybridization System. The hybridized slides were washed by the method described above (Gene expression microarray analysis) and dried through centrifugation (3,000 rpm, room temperature) [18].

Agilent mouse genome 4 × 44 K arrays were use to profile gene expression in TM4 cells exposed to NP. Total RNA samples (30 μg) from cells treated with 10 μg/mL NP were reverse transcribed using SuperScript (Invitrogen, CA, USA) in conjunction with dCTP labeled with Cyanine 3 (Cy3) or Cyanine 5 (Cy5) (NEN Life Science, CA, USA). The two resulting cDNA samples were combined in equal amounts. Each cDNA mixture was then applied to an Agilent mouse genome 4 × 44 K array and covered with an A4 hybridization mixer (BioMicro Systems Inc., UT). The arrays were then incubated for 12 h at 42°C in a MAUI 12-bay Hybridization System (BioMicro Systems Inc) to allow hybridization. They were next washed in wash buffer 1 (2 × SSC, 0.1% SDS) for 5 min, in wash buffer 2 (1 × SSC) for 5 min, and lastly, in wash buffer 3 (0.2 × SSC) for 10 min. Finally, they were dried through centrifugation (3,000 rpm, room temperature) [19].

Hybridized arrays were scanned using a scanner (Agilent Technologies) and the resulting scanned images analyzed using the Feature Extraction Software (v10.7; Agilent Technologies). Expression profiling, clustering, gene ontology and GeneNetworks analyses were performed using GeneSpring GX (v11.0; Agilent Technologies). The microarray data were subjected to statistical analysis. Data acquisition and cut-off creation were used to obtain quantified data from the scanned images. For each spot, the median intensity rather than the mean, was calculated. Functional analysis of selected genes was performed through Gene Ontology (GO) analysis and using the KEGG Pathway Database, GeneSpring, and JAK software (GenoCheck Co. Ltd, Korea). Hierarchical clustering analysis of global gene expression patterns in different samples was performed using the GeneSpring Cluster 3.0 software (Euclidean distance and an average linkage algorithm were used). Data for chosen genes were analyzed by 1-way ANOVA analysis with time as the selected parameter. Multiple testing correction was achieved by Benjamini Hochberg false discovery rate (FDR).

miRNA target genes were identified using the GeneSpring software and then analyzed using the Ingenuity Pathway Analysis (IPA) software to identify their biological function and canonical pathways. Fisher's exact test was used to calculate statistical significance. We then compared the results for miRNAs with those for gene expression.

We selected two miRNAs that showed aberrant expression changes and validated and quantified them in a TaqMan miRNA assay (Applied Biosystems, CA, USA), performed using miRNA-specific primers according to the manufacturer's instructions. TM4 cells were treated with 10 μg/ml NP for 24 h and total RNA was isolated using the TRIzol reagent. RNA quality was assessed using a 2100 Bioanalyzer. Duplicate total RNA samples were prepared from control and NP-treated cells and analyzed in triplicate by real-time PCR analysis, performed using a 7900 HT Fast Real-Time PCR System (Applied Biosystems). miRNA levels were calculated relative to average levels of target miRNAs and compared to controls after normalization to an internal control, snoRNA202. The amount of snoRNA202 in each sample was calculated from a standard curve.

We performed a cell viability assay using CCK-8 solution to evaluate the cytotoxic effects of NP on TM4 cells. The viability of TM4 cells was measured after growth in presence of NP (0, 3, 5, 10, 20 or 50 μg/mL) for 3 or 24 h. As shown in Figure 1-(A), the viability of TM4 cells exposed to NP for 24 h decreased, in a dose-dependent manner. Notably, we found that cytotoxicity was markedly increased in cells treated with NP at a concentration of 20 or 50 μg/mL at 24 h at both time points. We chose to use NP at a concentration of 10 μg/mL (which reduced cell viability to approximately 70% at 24 h) in subsequent microarray analyses. The cytotoxic effects of NP were also confirmed by phase contrast microscopic analysis of TM4 cells treated with 10 μg/mL NP for 3 and 24 h. At 3 h, there was no significant changes of cell morphology but cells exposed to NP for 24 h displayed morphological changes (e.g., irregular shape and cytoplasmic blebbing), were sparse, and detached from the culture plates on which they were growing (Figure 1-(B)). In addition, LDH assay showed that LDH released into the culture medium was significantly elevated after NP treatment (data not shown). The cytotoxic effect of NP in Sertoli cells was also consistent with the result of previous investigations [20].

To explore miRNA regulation in Sertoli cells following NP treatment, we performed miRNA expression analysis using Agilent mouse miRNA v13 arrays. miRNA expression profiling showed that exposure to NP significantly altered miRNA expression levels at 3 h and 24 h. In total, 186 miRNAs were > 1.3-fold up- or down-regulated following exposure to NP (59 at 3 h and 147 at 24 h). We chose the cut-off condition with 1.3-fold (P < 0.05) because a cut-off fold-change of 1.3 was considered as reasonable magnitude and statistical P value is more important rather than the intensity for the analysis of miRNA expression changes. In addition, the false positive miRNAs were filtered out in the present study using multiple test corrections as described the method section. Hierarchical clustering of differentially expressed miRNAs revealed well-coordinated expression between sample groups (Additional file 1, Figure S1). The miRNAs that displayed differential expression in NP-treated cells at each time point are listed in Table 1. Most miRNAs the expression of which was altered in NP-treated cells at 24 h were down-regulated. Of the 186 miRNAs the expression of which was altered, nine were up-regulated at both time points (miR-125a-3p, miR-297c, miR-421, miR-452, miR-483, miR-574-3p, miR-574-5p, miR-669a, miR-720) and 11 were down-regulated at both time points (let-7g, miR-107, miR-10a, miR-15a, miR-15b, miR-199b*, miR-26a, miR-29c, miR-324-5p, miR-331-3p, miR-342-3p). Two of the down-regulated miRNAs, miR-15a and miR-15b, regulate the cell cycle by controlling the expression of BCL2 and several cyclin family members (D1, D2, E1) [21, 22]. miR-125a-3p and miR-107 are also involved in cell cycle and it is known that their expression is regulated by PPARA [23]. The functions of the other miRNAs the expression of which was altered at both time points remain unclear. Figure 2 shows the miRNAs the expression of which differed the most between NP-treated and control cells. Changes in miRNA expression at 3 h ranged from 11-fold down-regulation (miR-224) to 3.8-fold up-regulation (miR-367), and at 24 h from 7-fold down-regulation (miR-222) to 20.6-fold up-regulation (miR-135a*; Figure 2).

We performed quantitative RT-PCR (qRT-PCR) to confirm the expression levels of selected miRNAs. We chose miR-135a* and miR-199a-5p, two miRNAs that were found in microarray analyses to be highly up- and down-regulated, respectively. qRT-PCR results showed that miR-135a* was up-regulated 1.9-fold and miR-199a-5p down-regulated approximately 1.7-fold in independent experiments (Additional file 2, Figure S2). These up- or down expression patterns concurred with, but were more modest than, those detected in microarray analyses. The differences in the fold changes between microarray and qRT-PCR analyses may have been caused by differences in the principle between hybridization- and amplification-based methods. It can be also arisen from the inherently different gene sets during normalization between microarray and qRT-PCR. Several reports showed that the number of true calls is greater than those calculated using qRT-PCR if the correlation is low between microarray and qRT-PCR [24]. The miRNAs can appear to be better-expressed in microarray analysis because microarray experiments employ normalization techniques within the miRNA population not by one reference gene.

Additionally, we created networks of differentially expressed miRNAs (Figure 3). At both 3 and 24 h post-NP treatment, Ppara was found to be a core miRNA-regulated gene. As mentioned above, Shah et al. reported that activated PPARA regulates miRNA expression in the mouse liver [23]. In TM4 cells exposed to NP, Ppara was down-regulated at both 3 and 24 h. We thus surmised that miRNAs regulated by Ppara may include miR-378, miR-125a-3p, and miRNA-148a at 3 h, and miR-20a, miR-203, and miR-101a at 24 h.

To evaluate the transcriptional regulation of target genes for miRNAs, we performed gene expression profiling using Agilent mouse whole genome 4 × 44 K arrays. The data generated were further combined with miRNA expression data in each time point. Each data set consisted of data from three samples each from NP-treated and control cells for each time point (3 and 24 h). At 3 and 24 h, 680 and 1,787 genes, respectively, were differentially expressed in NP-treated and control cells (fold-change > 2; P < 0.05, Welch's t-test). Of the genes the expression of which was altered in cells exposed to NP, 291 genes exhibited altered expression at both time points tested. We also analyzed gene expression using hierarchical clustering (evaluated by 1-way ANOVA; P < 0.05) to allow visual biological interpretation of the gene expression patterns. The results of this clustering analysis showed that the samples clustered according to time point and that gene expression patterns varied according to length of exposure to NP (Additional file 3, Figure S3). Functional analysis of the differentially expressed genes was performed using the GO database. These results are shown in Table 2. In total, 535 and 1,559 genes the expression of which was altered in cells treated with NP for 3 and 24 h, respectively, were functionally annotated with GO terms. The expression of genes with roles in signal transduction, transport, and transcription was highly altered in cells treated with NP for 3 h. The expression of genes with roles in growth, homeostasis, gametogenesis, spermatogenesis, and behavior was significantly altered in cells treated with NP for 24 h comparing to that of 3 h treated cells. Notably, the numbers of differentially expressed genes with roles in gametogenesis and spermatogenesis were highly increased at 24 h. Among genes with roles in gametogenesis and spermatogenesis, Rps6ka2 (ribosomal protein S6 kinase, polypeptide 2), Afp (alpha fetoprotein), Tbpl1 (TATAbox binding protein-like 1), and Mast2 (15 days embryo head cDNA) were up-regulated in NP-treated TM4 cells, while Tssk1 (testis-specific serine kinase 1), Ereg (epiregulin), and Adam25 (testase 2) were down-regulated in NP-treated TM4 cells.

To comprehensively identify target genes, we used the GeneSpring GX v11.0 software. It is known that miRNAs target multiple genes and that target genes can be regulated by negative or positive feedback mechanisms. Before analyzing the target genes for miRNAs the expression of which was altered in NP-treated cells, we filtered the target gene set to include only genes the expression of which showed the opposite pattern to that of the corresponding miRNAs. In total, 338 and 1,024 miRNA target genes were down-regulated at 3 and 24 h post-NP treatment, respectively (with the corresponding miRNAs being up-regulated). 146 and 3,128 target genes were up-regulated at 3 and 24 h post-NP treatment (with the corresponding miRNAs being down-regulated).

The detected target genes were categorized according to biological function using Ingenuity Pathway Analysis (IPA) software. The top-ranked biological functions of miRNA target genes in NP-treated TM4 cells are shown in Figure 4. Biofunctional analysis showed that target genes for differentially regulated miRNAs were, as expected, primarily involved in the control of cellular growth and proliferation, the cell cycle, and cell death. At 3 h post-NP-treatment, target genes implicated in growth and proliferation, tissue development, and genetic disorders were down-regulated by miRNAs, while several genes linked to RNA damage and repair were up-regulated to repair the cellular damage sustained. Because of the cytotoxic effects of NP, most target genes related to cell cycle or cell death were differentially expressed at 24 h post-NP treatment.

miR-135a* was significantly up-regulated after exposure to NP for 24 h. The change in miR-135a* expression was the highest in this study (20.6-fold up-regulation). We thus suggest that miR-135a* may play important roles following NP exposure. Through categorization of its target genes, we identified its potential molecular and cellular roles as being cell cycle, cell death, cell morphology, cell-to-cell signaling and interaction, and cellular assembly and organization (Table 3). Down-regulated miR-135a* target genes are listed in Table 4. Canonical pathway analysis of these genes showed that the expression of genes linked to the production of nitric oxide (NO) and reactive oxygen species (ROS; Ncf2 and Ppp2r2b), Wnt/β-catenin signaling (Wnt1), and ERK/MAP kinase signaling (Rapget3) could be down-regulated by miR-135a* in response to NP exposure

In the canonical pathway, its candidate targets fall into several categories. Genes implicated in the production of NO and ROS were predicted to be miR-135a* targets. There have been reports that NP suppresses cell growth and cellular respiration in yeast cells by inducing ROS generation, and the formation of hydroxyl radicals in rat brain striatum [25, 26]. Recently, NP was shown to induce ROS generation in human blood neutrophils and rat Sertoli cells through the activation of signal transduction pathways [20, 27]. NP increases ROS levels and lipid peroxidation and decreases the activity of antioxidant enzymes in the rat testis [28]. The results of the present study further suggest that mouse Sertoli cells are also affected by NP-induced ROS. We do not know the precise mechanism of NP-induced ROS generation in mouse Sertoli cells; this subject requires further investigation.

Other predicted target genes influence Wnt/β-catenin signaling. The Wnt/β-catenin signaling pathway is known to play fundamental roles in the regulation of proliferation, regeneration, and differentiation in many tissues and types of cells, including stem cells. Although there have been few reports on possible relationships between NP exposure and Wnt/β-catenin signaling, we suggest that key cell functions may be affected by altered expression of Wnt/β-catenin signaling pathway-related genes.

miR-135a* may target ERK/MAP kinase signaling-related genes. MAP kinases are known to be key regulators of cell differentiation, proliferation and apoptosis [29]. A study showed that 17β-estradiol and structurally diverse estrogenic compounds, including bisphenol A and NP, activated MAP kinases in MCF-7 cells [30]. Further, the three major MAPK subfamilies (ERK, JNK, and p38 MAPK) were activated by NP in SCM1 human gastric cancer cells. Notably, NP induced apoptosis by activating a Ca²⁺- and p38 MAPK-dependent signaling pathway. Although the effects of ERK during cell death remain unclear, there is a suggestion that it may promote cell survival [31]. Indeed, the activation of ERK was shown to increase neuronal survival in retinal ganglion cells [32]. Based on the results of these previous studies, we suggest that the activation of ERK/MAP kinase signaling may also promote the survival of Sertoli cells. We believe that these tentative conclusions warrant further investigation and hope that future studies we will increase our understanding of the mechanisms of NP-induced miRNA-mediated cytotoxicity in mouse Sertoli cells.