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. 2016 Jul 27;153(2):327–340. doi: 10.1093/toxsci/kfw137

From the Cover: Sperm Molecular Biomarkers Are Sensitive Indicators of Testicular Injury following Subchronic Model Toxicant Exposure

Edward Dere *,†,1,2, Shelby K Wilson †,1, Linnea M Anderson †, Kim Boekelheide †
PMCID: PMC5036622  PMID: 27466211

Abstract

Traditional testis histopathology endpoints remain the gold standard for evaluating testicular insult and injury in a non-clinical setting, but are invasive and unfeasible for monitoring these effects clinically in humans. Assessing testicular injury in humans relies on semen and serum hormone analyses, both of which are insensitive and poor indicators of effect. Therefore, we hypothesized that sperm messenger RNA (mRNA) transcripts and DNA methylation marks can be used as translatable and sensitive indicators or testicular injury. Dose–response studies using adult male Fischer 344 rats subchronically exposed to model Sertoli cell toxicants (0.14, 0.21, and 0.33% 2,5-hexanedione, and 30, 50, and 70 mg/kg/day carbendazim), and a model germ cell toxicant (1.4, 3.4, and 5.1 mg/kg/day cyclophosphamide) for 3 months were evaluated for testicular injury by traditional histopathological endpoints, changes in sperm mRNA transcript levels using custom PCR arrays, and alterations in sperm DNA methylation via reduced representation bisulfite sequencing. Testis histopathological evaluation and PCR array analysis of the sperm transcriptome identified dose-dependent changes elicited by toxicant exposure (P < 0.05). Global sperm DNA methylation analysis of subchronic 0.33% 2,5-hexandione and 5.1 mg/kg/day cyclophosphamide exposure using a Monte Carlo approach did not identify differentially methylated regions (methylation difference > 10% and q < 0.05) with robust signatures. Overall, these results suggest that sperm mRNA transcripts are sensitive indicators of low dose toxicant-induced testicular injury in the rat, while sperm DNA methylation changes are not. Additionally, the Monte Carlo analysis is a powerful approach that can be used to assess the robustness of signals resulting from –omic studies.

Keywords: sperm, DNA methylation, epigenetics, biomarkers, testis, RNA

The male reproductive system is a sensitive target for a number of toxicants; however, studying the effects of exposure in humans has proven difficult. Rodent studies of testicular toxicants are replicable, consistent, and informative, but extrapolation to human exposures is complicated because of the endpoints commonly measured in rodents. These endpoints include post-necropsy quantification of testicular histopathological indicators (Moffit et al., 2007), which simply are not possible to replicate in humans because testicular tissue is most often unavailable. The most common assessment of human reproductive function, the semen analysis, while replicable in rodents, is highly variable and insensitive to small but meaningful changes in humans (Bonde et al., 1996). Therefore, it is currently very difficult to know whether a toxicant-induced reduction in fertility or reproductive function seen in rodents is relevant to humans because researchers have very few sensitive and reliable endpoints to compare across species, identifying a significant need for a new approach (Dere et al., 2013).

The spermatozoon itself is an excellent source of such potential biomarkers, as the contents and state of the sperm reflect the testicular and epididymal environments in which they developed and matured. About 40% of testicular messenger RNAs (mRNAs) are detected in sperm, indicating that the sperm transcriptome can be used to monitor gene expression during spermatogenesis (Ostermeier et al., 2002). Various studies have investigated the association between altered testicular function and sperm mRNA transcript content, finding: (1) significantly different motility-related sperm mRNA transcript abundance between normal and motility-impaired men (Wang et al., 2004), (2) altered sperm protamine mRNA levels in men with infertility (Aoki et al., 2006; Steger et al., 2008), and (3) higher sperm Bcl2 mRNA in infertile men (Steger et al., 2008). Sperm RNAs may be passively retained or may play an active role in chromatin structure, imprinting, gene silencing, and embryogenesis (Miller and Ostermeier, 2006). These results have led to the proposal that sperm RNA offers considerable potential as a marker for fertility status in humans (Miller and Ostermeier, 2006).

Sperm DNA methylation marks offer a similar potential for insight into disrupted spermatogenesis. The germline DNA is de-methylated early during embryonic development and then re-methylated in a sex-specific manner later in development and during spermatogenesis (Trasler, 2006). Specific DNA methyltransferases (DNMTs) are expressed at different stages of development, with both maintenance (DNMT1), and de novo (DNMT3a and DNMT3b) DNMTs expressed during spermatogenesis (La Salle et al., 2004). Because DNA methylation remodeling occurs during spermatogenesis, various studies have investigated the association between altered testicular function and sperm DNA methylation marks, finding aberrant DNA methylation of both imprinted (Boissonnas et al., 2010; Hammoud et al., 2010; Houshdaran et al., 2007; Kobayashi et al., 2007; Marques et al., 2004, 2008; Pathak et al., 2009; Poplinski et al., 2010; Sato et al., 2011) and non-imprinted genes (Bieber et al., 2006; Chan et al., 2012; Delbès et al., 2007; Houshdaran et al., 2007; Navarro-Costa et al., 2010; Wu et al., 2010). Defects in DNA methylation profiles for men have been associated with poor sperm motility (Pacheco et al., 2011) and with poor in vitro fertilization-related embryogenesis and abnormal sperm chromatin compaction (Aston et al., 2012). Studies of altered sperm DNA methylation following toxicant exposure in adult animals have included extensive studies of the effects of a chemotherapeutic regimen in a rat model (Chan et al., 2012), and the effects of tamoxifen exposure on imprinted gene DNA methylation in rat sperm (Pathak et al., 2009).

An important benefit of pursuing sperm contents as biomarkers is that they are easily accessible in both humans and laboratory animals. Sperm, therefore, provide a bridge between the two and are a promising source of this important information. Past work in our laboratory has explored the mechanisms of action of two well studied testicular toxicants, 2,5-hexanedione (HD) and carbendazim (CBZ) (Campion et al., 2012; Markelewicz et al., 2004), and established that testicular dysfunction and sperm mRNA changes result from subchronic low-level exposures to these Sertoli cell toxicants (Moffit et al., 2007; Pacheco et al., 2012b). In this current study, a detailed time- and dose-dependent response evaluation of sperm biomarkers of testicular injury is pursued for HD and CBZ, as well as for cyclophosphamide (CPP), a well described germ cell testicular toxicant in the rat (Codrington et al., 2004; Trasler et al., 1986, 1987). The sperm molecular biomarker changes induced by exposure to these toxicants are evaluated with reference to traditional histopathological evaluation of the testes augmented by the determination of homogenization resistant spermatid head counts (HRSH); (Pacheco et al., 2012a), quantitative measures of retained spermatid heads (RSH) (Bryant et al., 2008), and terminal dUTP nick end-labeling (TUNEL) detection (Billig et al., 1995; Print and Loveland, 2000; Yamasaki et al., 2010). Using a model of daily, subchronic exposure to HD, CBZ, and CPP, we identified multiple mRNAs that changed in a consistent and dose-dependent manner and correlated these sperm biomarkers with sensitive histopathological and quantitative testicular and epididymal endpoints.

MATERIALS AND METHODS

Ethics Statement

This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The Brown University Institutional Animal Care and Use Committee (Permit Number: 0906060) approved all experimental animal protocols in compliance with National Institute of Health guidelines. Animals were euthanized using carbon dioxide asphyxiation, and all efforts were made to minimize suffering.

Animals

Adult male Fischer 344 rats (Charles River Laboratories, Wilmington, MA) weighing 175–225 grams (approximately 56–62 days of age) were allowed to acclimate for one week prior to beginning the experiment. Throughout the study the animals were maintained in a temperature and humidity controlled AAALAC accredited vivarium with a 12 h alternating light–dark cycle. All rats were housed in community cages with free access to water and Purina Rodent Chow 5010 (Farmer’s Exchange, Framingham, Massachusetts).

Chemicals

2,5-Hexanedione (CAS# 110-13-4, >99% purity), carbendazim (CAS# 10605-21-7, >97% purity), cyclophosphamide (CAS# 6055-19-2), and all other chemicals were purchased from Sigma Aldrich (St. Louis, Missouri) unless otherwise noted.

Dose Selection

Lowest-observable-adverse-effect-level doses of HD, CBZ and CPP were chosen to produce minimal but detectable testicular injury. Based on previous studies, a 3-month exposure to 0.33% HD in the drinking water or 50 mg/kg/day CBZ delivered in a corn oil vehicle by gavage was expected to produce minimal evidence of injury in the rat testis (Boekelheide, 1988; Boekelheide and Eveleth, 1988; Bryant et al., 2008). For CPP, 5.1 mg/kg/day dose was chosen based on a previous chronic study that demonstrated decreases in testicular sperm production (Trasler et al., 1986). For each toxicant, lower doses were then evaluated to explore the relationship between molecular biomarker presence and traditional indicators of testicular injury.

Experimental Design

An initial experiment with CPP, a germ cell toxicant, was conducted to assess the toxicity due to subchronic treatment of 5.1 mg/kg/d CPP for 3 months (n = 12). Based on the results from this initial study, a subsequent dose-response study was performed to examine the toxicity of CPP at lowest-observed-effect-level (LOAEL) levels of exposure. The doses used in this second study were 0, 1.4, 3.4 and 5.1 mg/kg/day (n = 12 for each dose). The route of administration for both CPP studies was via subcutaneous injection. Dose–response studies were also performed with two well-characterized Sertoli cell toxicants; 2,5-hexanedione (HD) and carbendazim (CBZ). The doses used for HD were 0, 0.14, 0.21, and 0.33% in drinking water (n = 10 for each dose), and CBZ were 0, 30, 50, and 70 mg/kg/day (n = 12 for each dose). In each experiment, rats were exposed to the toxicant for 3 months to ensure that sperm being evaluated would have experienced a disrupted environment throughout the 57 day spermatogenesis cycle.

Rats were euthanized by carbon dioxide asphyxiation and the body weights and reproductive organ weights were recorded at necropsy. Left testes were fixed in 10% neutral-buffered formalin for histological examination, and a portion of each animal’s right testis was detunicated and snap frozen for the automated determination of homogenization resistant spermatid head (HRSH) counts (Pacheco et al., 2012a). The epididymides were weighed, and RNA and DNA were isolated from sperm from the cauda.

Histological Examination

Cross-sections from the middle of each testis were embedded in 2-hydroxyethyl methacrylate (Technovit 7100; Heraeus Kulzer GmBH, Germany) and 3 µm sections were stained with periodic acid Schiff’s reagent and hematoxylin (PASH). Testis cross-sections were viewed on an Olympus BH-2 (Waltham, Massachusetts) standard light microscope, and evaluated by a board-certified pathologist who was blinded to the treatments. The extent of germ cell depletion and disorganization of the seminiferous epithelium was graded on a scale of none, minimal (occasional seminiferous tubules affected), moderate (less than half of seminiferous tubules affected), and severe (more than half of seminiferous tubules affected), and was characterized by loss of germ cells, inappropriate germ cell morphology for the stage, and abnormal positioning of germ cells within the epithelium. The presence of retained spermatid heads (RSH) was qualitatively evaluated by examining stages IX–XI for the presence of basally located late-stage condensed spermatid heads. Occasional RSH in stages IX and XI were considered normal, while clusters of RSH and persistence of RSH into stage XI was considered abnormal. Sertoli cell vacuoles were qualitatively identified as the presence of large (>30 µm), smooth-edged, clear spaces predominantly located in the basal seminiferous epithelium. The presence of occasional Sertoli cell vacuoles (1–2 per testis cross section) was considered normal, while more numerous Sertoli cell vacuoles was considered abnormal.

Digital images of the microscope slides were created using an Aperio ScanScope (Leica Microsystems Inc., Buffalo Grove, Illinois), and RSH and germ cell apoptosis was analyzed using the ImageScope software. For the enumeration of RSH, 2 sections (3 μm) of testes from 6 randomly selected rats per treatment group were stained with periodic acid-Schiff’s reagent followed by hematoxylin counter stain (PASH). Each cross section was evaluated for seminiferous tubules in spermatogenesis stages IX–XI, each of which was required to have a major:minor axis of less than 1.5:1 (Bryant et al., 2008). The number of RSH in the basal compartment was recorded for each stage-specific seminiferous tubule, and the counts were averaged together on an individual rat basis. The counts were log-transformed to assure normally distributed errors prior to statistical analysis.

For the evaluation of apoptosis, 2 paraffin sections (5 μm) of fixed testes cross sections from the CPP dose-response study were stained using the ApopTag Peroxidase In Situ Apoptosis Detection Kit (Chemicon, Temecula, California) following the manufacturer’s protocol and were counterstained with methyl green. Apoptotic cells were counted in 50 random seminiferous tubules having a major:minor axis of less than 1.5:1. The percentage of seminiferous tubules containing TUNEL-positive cells was assessed. The minor axis diameter was also recorded.

Sperm Isolation and RNA Extraction

The cauda epididymides were punctured repeatedly with 30 and 26 gauge needles, placed into micro-centrifuge tubes containing phosphate buffered saline (Life Technologies, Grand Island, New York), and incubated in a water bath at 37°C for 10 min to allow sperm release. Following centrifugation for 3 min at 300 × g to pellet the epididymal pieces, the supernatant was removed and centrifuged for 5 min at 2000 × g to pellet the sperm. Pellets were incubated in a somatic cell lysis buffer (0.15 M ammonium chloride, 10 mM potassium bicarbonate, and 0.1 mM EDTA; Thermo Fischer Scientific Inc., Pittsburgh, Pennsylvania) for 30 s prior to centrifugation at 16 100 × g for 1 min to remove any somatic cell contaminants, leaving the sperm intact. The pellet was washed with PBS and centrifuged again at 16 100 × g for 1 min. RNA was extracted from the fresh sperm using the mirVana miRNA Isolation Kit (Applied Biosystems, Austin, Texas). Sperm purity was confirmed by the absence of somatic cell contaminants using bright phase microscopy.

Sperm mRNA Transcript Analysis

Messenger RNAs (mRNAs) were further purified and concentrated into a smaller volume. In the Preliminary Experiment, mRNAs were cleaned and concentrated using ammonium acetate and ethanol precipitation. RNAs were re-suspended in 10 μL RNAse-free water. In the time course and test experiments, mRNAs were DNase treated, and processed using Qiagen’s RNase-free DNase and RNeasy MinElute Cleanup kits (Qiagen Sciences, Germantown, Maryland) following the manufacturer’s protocol.

Sperm mRNAs from the dose-response studies with HD, CBZ and CPP (4–9 rats/group) were evaluated with SABiosciences Rat RT2 Profiler Custom qRT-PCR arrays (SABiosciences, a Qiagen Company, Frederick, Maryland). We had previously developed a custom 32 gene PCR array biomarker panel following subchronic exposures to Sertoli cell toxicants HD (0.33%) and CBZ (50 mg/kg/day) (Pacheco et al., 2012b). This panel was refined down to a 13 gene panel (Abi2, Bag1, Clu, Fank1, Gapdh, Ift81, Lrrc6, Mfap3l, Ptgds, Sclt1, Sod3, and Tbc1d5) based on statistically significant alterations in mRNA levels, and used to profile the sperm mRNAs from the HD and CBZ dose-response studies. A testicular germ cell toxicant PCR array panel was also developed in parallel with the Sertoli cell panel, using a 1-month subacute exposure to dibromochloropropane (25 mg/kg/day) and 3-month subchronic exposure to CPP (5.1 mg/kg/day). Whole-genome microarray profiling of sperm mRNAs using Affymetrix Rat Gene 1.0 ST GeneChips (Affymetrix, Santa Clara, California) was used to inform our candidate gene selection by ranking transcripts by their level of statistical significance and fold-change magnitude of mRNA alterations relative to vehicle controls. A 9 gene custom PCR array was developed which included the highest ranking transcripts from both germ and Sertoli cell toxicants (Abi2, Actb, Alox15, Clu, Gapdh, Gimap4, Ptgds, Sod3, and Tmeff1).

The qRT-PCR reactions followed the manufacturer’s instructions using an ABI 7900 HT thermocycler (Applied Biosciences, Life Technologies Corporation, Carlsbad, California). Raw Ct values were normalized to the average of the housekeeping genes (Actb and/or Gapdh) Ct values and expression of the treatments groups was analyzed using the ΔΔCt method following ABI’s guidelines. The ΔCt values of mRNA transcripts from toxicant exposed sperm in individual animals were compared to the average ΔCt values from the vehicle control group. The fold change ratio range was generated using the formula 2−ΔΔCt, with the standard error of the treatment groups were generated after calculating the average ΔCt values for each transcript.

RRBS Library Construction

Reduced representation bisulfite sequencing (RRBS) libraries were generated using previously published protocols and the gel-free technique (Boyle et al., 2012; Gu et al., 2011). Sperm genomic DNA was isolated using a modified guanidine thiocyanate DNA extraction method (Griffin, 2013) and the quality and quantity were assessed using a NanoDrop 1000 (Thermo Scientific, Wilmington, Delaware). Samples with sufficient amounts of DNA were then used to create RRBS libraries as described previously (Griffin, 2013). For the HD study, 6 vehicle control and 6 HD-treated samples were used to prepare the libraries. The preliminary CPP study used 6 vehicle controls and 4 CPP-treated samples, while the 5.1 mg/kg/day group from the dose-response study used 6 vehicle control and 7 CPP-treated samples. Briefly, DNA (500 ng) was digested with MspI (New England Biolabs, Ipswich, Massachusetts) and purified using QIAquick Nucleotide Removal Kit (Qiagen). End-repair A-tailing was performed using Klenow fragment (3′-5′ exonuclease; New England Biolabs) and TruSeq methylated indexed adaptors (Illumina, San Diego, California) were ligated with T4 DNA ligase (New England Biolabs). Size selection was performed with Agencourt AMPure XP beads (Beckman Coulter, Indianapolis, Indiana). Two rounds of bisulfite conversion were conducted using the EpiTect kit (Qiagen), following the manufacturer's instructions, to ensure complete conversion of the methylated cytosines. Following bisulfite treatment, the DNA was purified as directed and amplified using Pfu Turbo Cx Hotstart DNA polymerase (Agilent Technologies, Santa Clara, California). Library quantification was performed using the Quant-iT high sensitivity DNA assay kit (Invitrogen) and the Bioanalyzer DNA 1000 kit (Agilent Technologies). Single-end 50 bp sequence runs were performed for each constructed multiplexed libraries on an Illumina HiSeq 2500 (Illumina). The data from the sequence runs were processed using trim_galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) (accessed July 31, 2016), and then aligned to the rat reference genome RGSC Rnor_6.0 using Bismark (Krueger and Andrews, 2011), a bisulfite mapping tool.

RRBS Methylation Analysis

Differentially methylated CpGs were identified using the methylKit package (Akalin et al., 2012) for R (R Core Team, 2011). Logistic regression analysis implemented in the methylKit package (Akalin et al., 2012) was used to compare the methylation means of the CPP and vehicle treated groups to calculate methylation levels and P-values, and the P-values were corrected to genomic-wide false discovery rate (FDR)-based q-values using the SLIM method (Wang et al., 2011). The analysis was performed both on an individual CpG and 100 bp stepwise tiling window level. The methylation level of a tile represents the average of all the individual CpGs within the tile, and averaged across replicates. Differentially methylated CpGs or 100 bp tiles elicited by HD or CPP treatment were defined as having >10% methylation difference relative to vehicle controls and a q-value < 0.05. Methylation analysis was performed on both the initial 3-month subchronic 5.1 mg/kg/d CPP, the 5.1 mg/kg/d CPP level from the subchronic dose-response, and the combined samples from both of these studies.

A Monte Carlo method implemented in R was used to repeatedly identify differentially methylated CpGs and tiles from randomly selected CPP and vehicle treated samples from both the initial subchronic CPP, and the 5.1 mg/kg/d CPP level from the subchronic dose-response. For this analysis, 5 randomly selected CPP and vehicle treated samples were selected for 1000 iterations, and differentially methylated CpGs were identified for each iteration.

Statistical Analysis

Body and organ weights, and quantitative RSH, HRSH, TUNEL and sperm mRNA transcript data were analyzed using one-way ANOVA followed by the Dunnett’s multiple correction test to identify significant changes relative to vehicle control. The statistical analyses were performed using Prism 6 by GraphPad (GraphPad Software, La Jolla, California).

RESULTS

Body, Testis, and Epididymis Weights

Rats were subchronically exposed to HD (0.14, 0.21, or 0.33%), CBZ (30, 50, or 70 mg/kg/day), or CPP (1.4, 3.4, or 5.1 mg/kg/day) for 3 months at levels approaching no-observed-effect-level (NOAEL) levels of exposure. Terminal body, testis, and epididymis weights were recorded immediately following CO2 asphyxiation (Table 1). The initial 3-month 5.1 mg/kg/day CPP study resulted in a statistically significant decrease in body, testis, and epididymis weights, and a trending decrease was observed in the subsequent CPP dose–response study, with the weights in the highest dose group (5.1 mg/kg/day) significantly decreased. Similar trends in decreased body and organ weights were observed with HD, while decreased weights with CBZ exposure were observed only with the testis and epididymis.

TABLE 1.

Body, Testis and Epididymis Weights of Treatment Groups Following 3 Months of Exposure

Compounds Dose Body weight (g) Testis weighta (g) Epididymis weighta (mg)
CBZ (mg/kg/day) n = 12 Control 315.8 ± 19.8 1.512 ± 0.092 456.1 ± 34.3
30 312.3 ± 16.5 1.570 ± 0.102 459.8 ± 16.9
50 314.3 ± 16.0 1.502 ± 0.143 421.0 ± 59.9
70 307.2 ± 14.0 1.213 ± 0.143**** 379.8 ± 40.0***
HD (% v/v) n = 10 Control 331.8 ± 28.7 1.568 ± 0.103 486.8 ± 31.1
0.14% 324.5 ± 16.6 1.556 ± 0.083 466.5 ± 14.6
0.21% 312.8 ± 16.4 1.566 ± 0.112 444.1 ± 13.9**
0.33% 300.9 ± 14.3** 1.490 ± 0.120 401.6 ± 32.8****
CPP (mg/kg/day) n = 12 Control 312.8 ± 23.4 1.526 ± 0.122 479.7 ± 43.9
5.1 272.1 ± 11.8**** 1.377 ± 0.091** 411.1 ± 29.0***
Control 332.5 ± 26.2 1.559 ± 0.108 490.0 ± 74.5
1.4 335.1 ± 18.2 1.522 ± 0.142 475.7 ± 42.5
3.4 313.7 ± 13.6 1.483 ± 0.191 447.8 ± 42.2
5.1 289.5 ± 14.1**** 1.425 ± 0.171 431.8 ± 40.0*
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Weights are reported as the mean ± SD.

aOrgan weights are the average of the left and right testis or epididymis.

*P < 0.05.

**P < 0.01.

***P < 0.001.

****P < 0.0001.

Histological and Morphometric Endpoints

The testes from the exposed rats were evaluated qualitatively by light microscopy. For each of the toxicants tested, there was a dose-related effect, particularly evident with HD and CBZ, with increased seminiferous epithelial disruption, RSH, and Sertoli cell vacuolization at the mid and high doses (Figure 1 and Table 2). The histopathological alterations included a loss of clusters of spermatocytes, round spermatids, and maturing elongate spermatids, disorganization of the seminiferous epithelium, particularly in the stages following spermatid release (IX–XI), with occasional seminiferous epithelial thinning that progressed at higher doses to seminiferous tubule atrophy. Because of the evidence of RSH with increasing doses of HD and CBZ, this endpoint was quantified. Quantification of RSH supported the findings of the qualitative analysis (Figure 2), showing an overall trend toward increased basal RSH with increasing doses of HD and CBZ, while CPP failed to show this effect.

FIG. 1.

FIG. 1

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Testis histopathology from rats subchronically exposed to HD. Vehicle treated rats (A, E, G) exhibit normal testis histopathology while increasing doses of HD resulted in dose-dependent increases in germ cell depletion/Seminiferous epithelial disorganization (B: 0.14%, minimal; C: 0.21%, moderate; D: 0.33%, severe). Increased stage-specific retained spermatid heads (F; arrow heads) and Sertoli cell vacuoles (H) were observed in 0.33% HD exposed rats.

TABLE 2.

Histopathological Scoring Summary of Toxicant-Induced Testis Injury after Subchronic Exposure

HD (%)
 CBZ (mg/kg/day)
 CPP (mg/kg/day)
0 0.14 0.21 0.33 0 30 50 70 0 1.4 3.4 5.1
Germ cell depletion/ Seminiferous epithelial disorganization None 10/10 8/10 8/10 7/10 6/8 9/9 5/10 6/9 3/10 3/10 1/10
Minimal 2/10 1/10 2/10 1/8 3/10 1/7 2/9 5/10 5/10 6/10
Moderate 1/10 1/10 4/7 1/9 1/10 1/10 2/10
Severe 1/10 1/8 1/10 2/7 1/10 1/10 1/10
Retained spermatid heads Absent 10/10 2/10 1/10 7/8 6/9 6/9 5/9 6/9 6/9 6/9
Present 8/10 9/10 9/9 3/9 3/9 5/5 4/9 3/9 3/9 3/9
Sertoli cell vacuoles Absent 10/10 9/10 7/10 7/9 7/8 9/9 9/9 1/5 9/9 8/9 8/9 7/9
Present 1/10 3/10 2/9 4/5 1/9 1/9 2/9
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Note: For those slides identified as having severe germ cell depletion, evaluation of retained spermatid heads and Sertoli cell vacuoles could not be performed, reducing the number of animals evaluated for these endpoints.

FIG. 2.

FIG. 2

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Quantification of retained spermatid heads (RSH) and homogenization resistant spermatid heads (HRSH) following subchronic exposure to HD, CBZ or CPP. Values are expressed as the mean ± SEM. Testes having severe germ cell depletion were excluded from the RSH and HSH analysis. Data were analyzed using one-way ANOVA followed by the Dunnett’s multiple correction test. Significant differences relative to controls are indicated by asterisks; *P < 0.05, ***P < 0.001, ****P < 0.0001.

The effect of toxicant exposure on the quantity and quality of sperm production from the testes was evaluated by quantifying the number of homogenization resistant spermatid heads (HRSH). Both HD and CBZ at the highest levels resulted in a significant decrease of HRSH while 5.1 mg/kg/d of CPP had no effect (Figure 2). Germ cell apoptosis in the dose-response study was measured by quantifying TUNEL-positive nuclei as an indicator of programmed cell death (Figure 3). An increase in apoptotic germ cells was observed in the 5.1 mg/kg/d CPP dose group, but was not statistically significant relative to the vehicle control group (P = 0.56). Previous studies in our lab have assessed the extent of germ cell apoptosis following treatment with either CBZ or HD exposure for 3 months (Moffit et al., 2007). In these studies, only CBZ doses ranging from 40 to 200 mg/kg/day elicited a dose-dependent increase in the percent of tubules with >3 TUNEL-positive nuclei, while no differences were observed with doses of HD ranging from 0.125% to 0.625%.

FIG. 3.

FIG. 3

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Evaluation of testis germ cell apoptosis in rats subchronically exposed to CPP. Testis sections from vehicle (A) and CPP (5.1 mg/kg/d; B) treated rats were TUNEL stained, and the percentage of tubules with greater than 3 TUNEL-positive were quantified (C). Testes having severe germ cell depletion were excluded from the TUNEL analysis. Data were analyzed using one-way ANOVA followed by the Dunnett’s multiple correction test. An increase in germ cell apoptosis was observed in the 5.1 mg/kg/day dose groups, but was not statistically significant relative to vehicle control (P = 0.56).

Sperm mRNA Analysis

Custom PCR array panels were previously developed from exposures to Sertoli and germ cell toxicants. These panels were used to profile the sperm mRNA transcript levels following exposure to HD, CBZ, and CPP in this study (Figure 4). Subchronic exposure to HD significantly elevated the levels of 4 of the 12 candidate biomarker transcripts (Abi2, Clu, Ptgds, and Sod3) with fold-change values ranging from 2- to 23.7-fold. CBZ treatment significantly altered the levels of 6 transcripts, with 2 being elevated (Clu and Sod3), while the remaining 4 transcripts were significantly decreased (Ift81, Lrrc6, Sil1, and Tbc1d5). The fold-changes of the CBZ responsive transcripts ranged from 7.8- to -15.9-fold. The effects of CPP on sperm mRNA transcripts were assessed on a different custom PCR array panel with some transcript overlap and detected significant decreases in 3 of the 7 transcripts represented on the panel; Abi2, Gimap4, and Tmeff1 ranging from -2- to -2.8-fold (Figure 4).

FIG. 4.

FIG. 4

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Changes in sperm mRNA transcript levels following 3-month subchronic exposure to HD, CBZ, or CPP. For each toxicant, 3 doses were evaluated (HD: 0.14, 0.21, and 0.33%; CBZ: 30, 50, and 70 mg/kg/day; CPP: 1.4, 3.4, and 5.1 mg/kg/day). Transcript levels were assessed using custom quantitative PCR arrays. Statistical significance of ΔCt values between treatment and vehicle controls were calculated using one-way ANOVA with Dunnett’s correction for multiple testing (*P < 0.05, ***P < 0.001, ****P < 0.0001). Data are expressed as the mean fold change ± SEM, on a log2 scale.

Individual Animal Analysis of Sperm mRNA Levels

A linear regression analysis of the number of RSH, the most sensitive histopathological endpoint, versus the sperm mRNA transcript levels elicited by HD and CBZ was performed. For the CPP study, qualitative histopathological evaluation identified a high degree of variability in the assessed endpoints even among the vehicle treated animals, so this correlation analysis was not performed with these animals. This linear regression analysis demonstrated minimal to modest correlations between these endpoints (R2 values ranging from 0.0909 to 0.3545) where the slope of the fitted line was significantly different from zero (P < 0.05) for all transcripts except for Sod3 in the HD treatment (Supplementary Table 1). Results from the linear regression analysis of Abi2 and Clu transcript levels following HD treatment, and Clu and Lrrc6 following CBZ treatment are shown in Figure 5, to illustrate the correlations between the histopathologic evidence of RSH and sperm mRNA transcript levels.

FIG. 5.

FIG. 5

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Linear regression analysis of toxicant induced changes in sperm mRNA levels with changes in retained spermatid heads (RSH) in the seminiferous tubules. The left panels depict the analysis from HD exposure while the right panels are from CBZ exposure. The circles, squares and triangles represent the different doses of HD and CBZ; 0.14, 0.21, and 0.33% HD, and 30, 50, and 70 mg/kg/day CBZ, respectively. The shaded grey region is the 95% confidence interval of the regression line (solid). R2 values are shown and provide a goodness-of-fit measure. Animals with severe germ cell depletion were excluded from this analysis.

Sperm DNA Methylation Analysis

Changes in global sperm CpG DNA methylation following toxicant exposure were assessed using reduced representation bisulfite sequencing (RRBS) for the highest levels of HD and CPP. HD treatment failed to elicit any statistically significant (methylation difference > 10% compared to vehicle controls and q-value < 0.05) alterations of individual CpG methylation. However, analysis of 100 bp stepwise tiling windows that provides an average methylation value of multiple CpGs across a window identified 36 regions that were differentially methylated (Table 3). Of these, 10 were associated with mature RefSeq identifiers and gene annotation from the rat genome database (rn6) in the UCSC Genome Browser.

TABLE 3.

Differentially Methylated Regions Elicited by Subchronic Exposure to 0.33% HD

Differentially methylated region Methylation difference (%) q-value RefSeq Entrez GeneID Gene symbol
chr1: 100 578 301–100 578 400 11.7 0.0442 NM_031670 60575 Napsa
chr1: 228 382 501–228 382 600 11.5 0.0000
chr1: 277 705 301–277 705 400 −11.0 0.0013
chr2: 188 793 601–188 793 700 36.9 0.0000 NM_001008352 310645 Pmvk
chr2: 259 281 501–259 281 600 18.1 0.0247
chr3: 13 717 101–13 717 200 14.1 0.0267
chr3: 148 406 901–148 407 000 11.6 0.0471 NM_001012091 311547 Foxs1
chr3: 161 382 701–161 382 800 −12.3 0.0046
chr3: 171 295 501–171 295 600 −14.0 0.0181
chr4: 25 857 401–25 857 500 15.3 0.0063 NM_001108617 362316 Cdk14
chr4: 100 661 201–100 661 300 12.1 0.0173
chr4: 180 988 501–180 988 600 12.0 0.0498
chr5: 153 531 101–153 531 200 21.2 0.0000
chr6: 134 625 101–134 625 200 12.0 0.0296
chr6: 135 910 701–135 910 800 10.4 0.0179
chr7: 141 607 901–141 608 000 21.7 0.0001
chr7: 142 365 501–142 365 600 22.3 0.0000
chr7: 144 776 801–144 776 900 10.1 0.0404 NM_177934 315344 Smug1
chr8: 22 276 301–22 276 400 15.9 0.0474
chr8: 62 124 301–62 124 400 10.8 0.0148
chr8: 130 399 601–130 399 700 −13.4 0.0208
chr10: 61 706 401–61 706 500 −19.7 0.0219 NM_001107020 303304 Sgsm2
chr10: 73 311 101–73 311 200 11.3 0.0151
chr11: 64 645 801–64 645 900 10.3 0.0127 NM_001105879 288093 Arhgap31
chr12: 9 598 901–9 599 000 −17.5 0.0000
chr12: 26 779 701–26 779 800 10.2 0.0045
chr12: 32 784 101–32 784 200 11.5 0.0312
chr14: 16 619 201–16 619 300 16.5 0.0337
chr14: 21 005 501–21 005 600 14.9 0.0440
chr15: 106 820 301–106 820 400 14.0 0.0070 NM_057121 117261 Slc15a1
chr17: 43 831 201–43 831 300 11.5 0.0045
chr18: 76 677 801–76 677 900 14.2 0.0148
chr20: 6 173 301–6 173 400 13.3 0.0008
chr20: 18 488 801–18 488 900 13.7 0.0000 NM_134417 171458 Ipmk
chr20: 18 801 401–18 801 500 −10.5 0.0363 NM_001108531 361832 Bicc1
chr20: 47 008 701–47 008 800 17.0 0.0127
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The effects of CPP treatment were assessed in samples from both the preliminary and dose-response studies. Methylation analysis of the preliminary CPP study identified 2 CpGs that were significantly demethylated following a subchronic treatment with CPP (Supplementary Table 2). Analysis of the highest dose group from the CPP dose-response study identified 17 CpGs with significantly altered methylation status where all but one were demethylated (Supplementary Table 2). Samples from both the preliminary and highest dose group from the dose–response studies were analyzed together and identified 187 CpGs with significant alterations in their methylation status relative to the vehicle controls (Supplementary Table 2). Comparison of the analysis results from the preliminary and dose–response studies, and from combining the two independent studies did not identify any altered CpGs common to all three analyses, nor were there any shared altered CpGs in each of the pairwise comparisons between studies. The combined group of CPP samples was further analyzed using a Monte Carlo approach that performed the methylation analysis over 1000 iterations, where in each iteration 5 vehicle control and 5 CPP samples were randomly selected. The density distribution of the number of significantly altered CpGs over the 1000 iterations is shown in Supplementary Figure 1. The distribution profile illustrates that fewer than 50 CpGs were generally identified in any given iteration as significantly different (> 10% methylation difference and q-value < 0.05), suggesting that CPP was minimally altering sperm DNA methylation. Over the course of the iterative analysis, 2610 unique CpGs were detected with aberrant changes in methylation over vehicle control, and 817 of those were significant only once (Supplementary Figure 1). A single CpG located at position chr12: 23 469 668, which is 3602 bp upstream of the SH2B adaptor protein 2 (Sh2b2) transcriptional start site, was significant in 508 of the 1000 iterations, and was also identified as a differentially methylated CpG in the dose–response study.

A 100 bp stepwise tiling window analysis was also performed on the CPP RRBS datasets and identified 3, 12, and 16 differentially methylated regions from the preliminary, 5.1 mg/kg/day dose group of the dose–response and the combined studies respectively (Table 4). Similar to the analysis of the individual CpGs, none of the differentially methylated regions were common to each of the individual tiling window analyses. Applying the same Monte Carlo approach as described above, 336 unique regions were identified as differentially methylated in at least one iteration (Figure 6 and Supplementary Table 3), and only a single region was significant in over 50% of the 1000 iterations (chr5: 153 531 101–153 531 200; significant 571 times). Narrowing the list of regions to those that were associated with well annotated genes (within the genic region and 1 kb upstream of the TSS) and were significant in at least 50 iterations resulted in a list of 22 differentially methylated regions, and these were predominantly demethylated (Table 5). The strength or robustness of the methylation signal was also assessed by evaluating the directionality of the methylation level over the 1000 iterations (Robustness field in Table 5). Robustness was defined as the region’s maximum frequency of either being methylated or demethylated. For example, the region associated with Ezr, was consistently demethylated in all 112 iterations and, therefore, had a robustness score of 100%. Generally, all the regions summarized in Table 5 possessed a strong robustness score with the exception of the region annotated to Tmprss6. The methylation level for this region was increased in 57.8% of the 102 iterations, and decreased in the remaining 42.2% of the iterations. The poor robustness score in conjunction with the relatively large standard deviation of the measured methylation difference suggests that this region is an unreliable methylation biomarker.

TABLE 4.

Differentially Methylated Regions Elicited by Subchronic Exposure to 5.1 mg/kg/day CPP

Differentially methylated region Methylation difference (%) q-value RefSeq Entrez GeneID Gene Symbol
Preliminary study
chr5: 153 531 101–153 531 200 −11.60 0.0088 NM_130425 156726 Runx3
chr14: 77 665 201–77 665 300 12.71 0.0000
chr20: 13 964 601–13 964 700 −10.18 0.0436
Dose–response study
chr1: 32 456 501–32 456 600 10.39 0.0007
chr5: 172 389 701–172 389 800 11.69 0.0002
chr7: 119 679 101–119 679 200 11.10 0.0000 NM_001130556 315388 Tmprss6
chr7: 123 456 101–123 456 200 10.22 0.0000
chr8: 62 124 401–62 124 500 10.47 0.0079
chr12: 51 166 001–51 166 100 10.41 0.0002
chr14: 77 665 201–77 665 300 19.49 0.0000
chr19: 43 966 301–43 966 400 11.44 0.0269
chr19: 52 646 101–52 646 200 11.58 0.0267
chr19: 53 134 901–53 135 000 11.79 0.0000
chr19: 53 135 001–53 135 100 13.90 0.0000
chr20: 7 303 601–7 303 700 10.18 0.0064 NM_001109530 689210 Spdef
Combined studies
chr1: 91 433 601–91 433 700 −13.17 0.0001 NM_053726 114518 Slc7a10
chr1: 91 854 301–91 854 400 −11.59 0.0104
chr3: 8 615 501–8 615 600 −14.87 0.0000
chr5: 153 530 901–153 531 000 −11.87 0.0000 NM_130425 156726 Runx3
chr5: 153 531 101–153 531 200 −11.52 0.0000 NM_130425 156726 Runx3
chr7: 70 264 501–70 264 600 −12.29 0.0016
chr7: 1 40 445 701–140 445 800 −10.29 0.0000
chr9: 10 936 101–10 936 200 −13.37 0.0000
chr11: 64 884 201–64 884 300 −11.03 0.0008 NM_138882 85311 Pla1a
chr12: 37 416 101–37 416 200 −11.38 0.0000 NM_001080782 689779 Tctn2
chr12: 39 266 201–39 266 300 −10.79 0.0303 NM_031338 83506 Camkk2
chr16: 71 316 801–71 316 900 −11.13 0.0226
chr19: 53 630 301–53 630 400 −10.25 0.0120
chr20: 20 38 901–2 039 000 −13.28 0.0109 NM_001048045 499400 RT1-M5
chr20: 6 026 401–6 026 500 −20.29 0.0000 NM_019231 29513 Mapk13
chr20: 7 896 401–7 896 500 −12.41 0.0020 NM_001191718 309643 Fance
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FIG. 6.

FIG. 6

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Monte Carlo analysis of the sperm DNA methylation following CPP exposure. CPP-treated and vehicle control samples were randomly selected (n = 5 each) and analyzed to identify aberrant DNA methylated regions for 1000 iterations (> 10% methylation difference relative to vehicle control and q-value < 0.05).

TABLE 5.

Annotated Differentially Methylated Regions Elicited by Subchronic Exposure to 5.1 mg/kg/day CPP Identified From the Monte Carlo Analysis

Differentially methylated region Methylation difference (%) Std. Dev # of Times significant Robustnessa (%) RefSeq Entrez GeneID Gene symbol
chr1: 47 326 901–47 327 000 −13.96 1.49 112 100.0 NM_019357 54319 Ezr
chr1: 264 508 901–264 509 000 12.52 1.77 101 100.0 NM_001106361 293992 Pax2
chr1: 78 437 201–78 437 300 −13.35 2.92 88 98.9 NM_001107479 308387 Npas1
chr1: 217 487 901–217 488 000 −12.11 1.31 63 100.0 NM_201350 171093 Shank2
chr3: 162 796 301–162 796 400 11.21 5.49 103 95.2 NM_001034927 311642 Sulf2
chr3: 8 615 501–8 615 600 −14.68 1.68 84 100.0 NM_001005542 296618 Wdr34
chr5: 169 618 201–169 618 300 −13.27 1.35 156 100.0 NM_017304 29738 Kcnab2
chr5: 147 303 001–147 303 100 −10.94 0.82 116 100.0 NM_017122 29177 Hpca
chr5: 166 561 201–166 561 300 −11.47 1.22 77 100.0 NM_001007092 313717 Clstn1
chr5: 151 617 801–151 617 900 10.63 0.35 66 100.0 NM_012652 24782 Slc9a1
chr5: 153 530 901–153 531 000 −12.96 1.90 52 100.0 NM_130425 156726 Runx3
chr5: 153 866 801–153 866 900 −11.71 1.30 50 100.0 NM_001025772 500566 Stpg1
chr7: 119 679 101–119 679 200 1.70 11.22 102 57.8 NM_001130556 315388 Tmprss6
chr10: 14 604 801–14 604 900 −11.61 0.98 370 100.0 NM_001312663 106146145 Baiap3
chr10: 10 662 501–10 662 600 13.30 0.42 58 100.0 NM_001106976 302934 Ppl
chr12: 47 209 301–47 209 400 −12.37 1.38 240 100.0 NM_001033676 171051 Cabp1
chr12: 39 266 201–39 266 300 −17.56 1.69 142 100.0 NM_031338 83506 Camkk2
chr12: 48 302 801–48 302 900 −11.11 1.30 53 100.0 NM_134404 171442 Svop
chr16: 10 948 401–10 948 500 −12.27 1.33 146 100.0 NM_138860 192223 Opn4
chr16: 10 948 501–10 948 600 −11.50 1.11 130 100.0 NM_138860 192223 Opn4
chr20: 6 026 401–6 026 500 −18.22 3.10 134 100.0 NM_019231 29513 Mapk13
chr20: 11 356 301–11 356 400 12.43 0.96 63 100.0 NM_001003964 309680 Dnmt3l
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aValue represents the number of iterations that the change in methylation was in the same direction (i.e., increased or decreased methylation).

DISCUSSION

Toxicants and pharmaceutical compounds can adversely affect the male reproductive tract by targeting different cell types within the testis (i.e. germ, Leydig, and Sertoli cells). Decreases in testis weight can be a sensitive indicator of moderate damage in non-clinical studies, but this is not feasible clinically. Similarly, testicular histopathology, the gold standard for measuring testicular injury in animals, is not possible in a clinical setting and raises the need for a non-invasive measure. Therefore, significant effort has been devoted to finding a non-invasive biomarker for testicular injury that can bridge non-clinical toxicity studies to a clinical setting (Dere et al., 2013).

Our lab has been continuously developing and refining PCR array panels to use as a screening tool for identifying low-level testicular injury from toxicant exposures. The general process of this ongoing effort involves first globally screening toxicant-induced alterations in sperm mRNA content using Affymetrix microarrays and identifying the most robust indicators of injury. Altered transcripts were ranked and prioritized based their level of statistical significance and gene annotation to create custom PCR arrays which were further refined following repeat exposures and dose–response studies. This development strategy has been applied to both Sertoli and germ cell toxicants and described for HD and CBZ in Pacheco et al. (2012b), where a 29-transcript custom PCR array panel was developed. In this study, we have continued this effort by refining our Sertoli cell toxicant PCR array panel with the HD dose–response conducted in this study. The refined panel was then used to profile the sperm mRNA transcript levels of subchronically CBZ-exposed rats to identify any treatment-elicited alterations. This strategy has also been applied in developing a complementary germ cell toxicant panel using exposures to dibromochloropropane and CPP. The resulting germ cell PCR array panel was used to profile the dose-dependent changes in sperm mRNA levels following subchronic exposure to CPP.

Subchronic exposure to HD, CBZ and CPP significantly altered the levels of mRNA transcripts represented on the Sertoli and germ cell toxicant PCR array panels. HD and CBZ, despite both being Sertoli cell toxicants, each altered a distinct subset of transcripts, potentially reflecting the differences in their mechanisms of action (Correa and Miller, 2001; Howard and Aist, 1980; Moffit et al., 2007; Quinlan et al., 1980). However, both exposures significantly elevated the levels of Clu and Sod3, and the increased levels of Clu occurred in a dose-dependent manner. The results with Clu are in accordance with previous exposure studies in our lab with both HD and CBZ (Pacheco et al., 2012b). These alterations in transcript levels mirrored the changes in the histopathological endpoints (RSH and HRSH; Figure 2 and Table 2). However, correlation analysis of the extent of testis injury with toxicant-induced changes in sperm mRNA transcript levels identified only modest associations with RSH in the seminiferous tubules. Both Abi2 and Clu mRNA levels significantly increased in a dose-dependent manner and were associated with increased RSH (R2 values of 0.2515 and 0.2890, respectively). Changes in these transcripts may have functional consequences where ABI2 is involved in the dynamic remodeling of the actin cytoskeleton at adherens junctions and may alter the progression of germ cells from the basement membrane to the lumen of the seminiferous tubules during spermatogenesis (Cheng and Mruk, 2002; Grove et al., 2004). CBZ exposure also elevated sperm Clu levels, which were weakly correlated with increased RSH (R2 = 0.1978). CLU is secreted by Sertoli cells, and maintains the homeostatic balance between cell viability and proliferation, which may be involved in removing abnormal germ cells in the testes (Ammar and Closset, 2008; Dumont et al., 2002; Miyake et al., 2004; Trougakos et al., 2005). Additionally, CBZ treatment decreased the levels of Lrrc6 in sperm and was correlated with an increase in RSH (R2 = 0.3298). The protein product of Lrrc6 is expressed in sperm (Kott et al., 2012; McClintock et al., 2008) and Lrrc6 loss-of-function mutations in humans are associated sperm flagella defects and male infertility (Kott et al., 2012). These results suggest that sperm mRNA transcripts selected on the PCR array panels are at least as sensitive and less invasive than traditional male reproductive histopathology endpoints.

Epigenetics, specifically DNA methylation, has been explored as a potentially sensitive indicator of toxicity in different tissues (Boellmann et al., 2010; Chapman et al., 2014; Chappell et al., 2014; Conti et al., 2014; Dong et al., 2008; Koczor et al., 2015; Ozden et al., 2015). Transmission of epigenetic marks in the germ line has garnered attention recently due to increasing evidence of transgenerational inheritance and increased risk factors of developing diseases associated with these marks (Guerrero-Bosagna et al., 2012; Manikkam et al., 2013; Skinner et al., 2013; Tracey et al., 2013). To address this concern in this study, aberrations in HD- and CPP-induced sperm DNA methylation were globally evaluated in CpG enriched regions. Epigenetic analysis of 3-month subchronic exposure to 0.33% HD and 5.1 mg/kg/day CPP was comprehensively performed, and these studies identified sets of toxicant-induced differentially methylated regions (Tables 3 and 4). For CPP, the analysis of the preliminary and dose-response studies, as well as the analysis of both studies combined found that none of the CPP-induced differentially methylated regions were common to all three analyses, suggesting a lack of robustness in the DNA methylation marks detected. The issue of robustness was explored further by performing a Monte Carlo method that randomly selected samples and performed the DNA methylation analysis over 1000 iterations. This repeated sampling analysis revealed a distribution in the number of differentially methylated CpGs that was highly skewed to the left (Figure 6), demonstrating that there were few significant and repeatedly differentially methylated regions. The analysis also confirmed the lack of robustness of the DNA methylation marks; no differentially methylated region was consistently seen in all 1000 iterations and only one intergenic region was observed in 50% of the iterations (Figure 6). Although there was a general lack of robustness in the sperm DNA methylation marks, some of the methylated regions are associated with spermatogenesis and/or sperm quality (Table 5). The region associated with the Ezr annotated gene was demethylated, and its coded protein is involved in maintaining sperm membrane fluidity that is necessary for capacitation (Wang et al., 2010). It is also critical in spermatid transport and in maintaining spermatid polarity during spermatogenesis (Gungor-Ordueri et al., 2014). Increased methylation in the region associated with the Pax2 genes was observed, and PAX2 is known to promote H3K4 trimethylation and gene activation (Patel et al., 2007; Schwab et al., 2011) and may regulate the sperm chromatin structure (Vavouri and Lehner, 2011). The collective results from the analysis of the CPP-elicited DNA methylation alterations suggested that CPP had little influence on sperm DNA methylation status, and was not a robust sperm molecular biomarker for low level exposure to this toxicant. Furthermore, the results of the Monte Carlo analysis suggest that identified differentially methylated regions induced by HD exposure may also be unreliable as biomarkers of injury.

Translatable molecular-based indicators of testicular injury are the goal for replacing traditional testicular histopathological endpoints. The results from this study further support the ongoing efforts in our lab that have demonstrated that sperm mRNAs have the potential to be more sensitive than histopathology measurements. It is also clear that the sperm mRNA transcripts are better at detecting effects of the Sertoli cell toxicants than the germ cell toxicants. Epigenetic marks in the sperm, specifically DNA methylation, were also explored as potential biomarkers for testicular injury. However, our analyses suggest that DNA methylation marks lack a robustness that is an essential requirement of an effective biomarker. Collectively, these data indicate that sperm mRNA transcript profiles, but not DNA methylation marks, can be useful as biomarkers of testicular injury following subchronic, low level Sertoli and germ toxicant exposure. Additionally, the Monte Carlo analysis provides a powerful approach to systematically and rigorously tests for significance in –omic studies that can enhance risk assessments.

SUPPLEMENTARY DATA

Supplementary data are available online at http://toxsci.oxfordjournals.org/.

Supplementary Data
supp_153_2_327__index.html (968B, html)

ACKNOWLEDGMENTS

The authors would like to thank Melinda Golde and Paula Weston for preparing the testes for histopathological analyses, and Christoph Schorl of the Brown University Genomics Core Facility for preparing the Affymetrix GeneChip and running the Illumina HiSeq 2500 sequence runs.

FUNDING

National Institute of Environmental Health Sciences Training Grant (T32 ES007272) and Superfund Research Program (P42 ES013660).

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