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From the * Department of Urology and Pediatric
Urology and the Institute of Medical
Microbiology University of Giessen, Germany; the
Institute of Pharmaceutical Sciences
University of Freiburg, Germany; and the
Institute of Veterinary Physiology, Department
of Biomathematics, the || Department of Forensic
Medicine, and the ¶ Institute of Veterinary
Anatomy, Histology, and Embryology, University of Giessen, Germany.
| Correspondence to: Prof Dr Klaus Steger, Klinik und Poliklinik für Urologie und Kinderurologie, Rudolf-Buchheim-Strasse 7, 35385 Giessen, Germany (e-mail: Klaus.Steger{at}chiru.med.uni-giessen.de). |
| Received for publication August 2, 2007; accepted for publication November 21, 2007. |
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Abstract |
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Key words: Apoptosis, histone-deacetylase, mouse, spermatogenesis
One of the most potent reversible HDAC inhibitors (HDACi), both in vitro and in vivo, is trichostatin-A (TSA) (Yoshida et al, 1990). In immortalized somatic cells, TSA is able to induce cell cycle arrest followed by either differentiation or apoptosis (Marks et al, 2000) because of selective transcriptional stimulation of genes that negatively control the cell cycle (Johnstone, 2002). Similar effects have been described for other HDACi, suggesting that this class of substances may function as anticancer drugs (Marks et al, 2000; Yoshida et al, 2001; Vigushin and Coombes, 2004). In addition to changes in gene expression, the antineoplastic effect of HDACi may be because of induction of mitochondria-dependent cell death or deregulated histone acetylation at centromeres during mitosis leading to abnormal chromosomal segregation and apoptosis (Johnstone and Licht, 2003). Increasing evidence implicates HDACi in nonhistone protein acetylation, with variable cellular effects, including apoptosis (Luo et al, 2000; Szak et al, 2001; Terui et al, 2003; Glozak et al, 2005). It is well known that normal cells are much less sensitive to HDACi than transformed or tumor cells, but the mechanisms underlying this phenomenon are poorly understood. However, there is great interest in this class of substances, as these substances will be employed as anticancer drugs (Dokmanovic and Marks, 2005; Ungerstedt et al, 2005).
Although some HDACi of the hydroxamic acid family, such as suberoylanilide hydroxamic acid and depsipeptide, are undergoing efficacy tests in clinical trials as potential anticancer drugs (Sandor et al, 2002; Kelly et al, 2005), information on both pharmacokinetics and metabolism of TSA is still lacking, and pharmacogenomic profiles of individuals undergoing treatment will be required to decide on effective doses. These constitute important bottlenecks when evaluating the use of HDACi in clinical studies and will have to be addressed. In a previous study, we demonstrated that in vivo application of the HDACi TSA results in murine male infertility, possibly because of apoptosis of pachytene spermatocytes (Fenic et al, 2004). Consequently, the rationale of this study was to further characterize the in vivo mode of action of TSA with respect to proapoptotic pathways. We, therefore, performed gene expression analysis and assessed both HDAC activity and core histone acetylation status in mouse testes after in vivo TSA application. It is suggested that male infertility due to TSA treatment is caused by an impairment of meiosis, possibly through an indirect mechanism involving somatic Sertoli and/or Leydig cells.
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Materials and Methods |
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TopAbstractMaterials and MethodsResultsDiscussionReferences |
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Tissue and Histology
Fresh tissue from each animal was fixed in liquid nitrogen. From 3 animals
per group, 1 testis was fixed in Bouin fixative and embedded in paraffin using
standard techniques. Five-micrometer paraffin sections were stained with
hematoxylineosin and evaluated according to Russell et al
(1990).
Evaluation of Apoptosis
The terminal deoxynucleotidyl transferase–mediated nick-end labeling
(TUNEL) method was used for in situ labeling of apoptotic cells according to
the manufacturer's instructions (ApopTag 7100 Kit; Chemicon, Schwalbach,
Germany). Three animals per group and 1000 postmitotic germ cells per section
were analyzed.
Immunohistochemistry
Immunohistochemistry on paraffin material was carried out as previously
described (Fenic et al, 2004).
The following rabbit-polyclonal primary antibodies were used: antihistone H4
(1:50 in Tris-buffered saline [TBS]; Biomol, Hamburg, Germany),
anti–penta-acetylated histone H4 (1:3000 in TBS; Biomol), antiacetylated
lysine (1:50 in TBS; Biomol), and anticleaved caspase-3 (1:10 in TBS; Abcam,
Hamburg, Germany). In addition, 5-µm sections from frozen tissue samples
were fixed in acetone for 10 minutes, air-dried, blocked with 3%
H2O2 in methanol for 10, minutes and further treated
identically to the paraffin sections.
Western Blot Analyses
Cryomaterial from 3 animals per group (n = 15) was used for histone
extraction applying the acid-extraction method, as has already been described
(Sonnack et al, 2002). Four
samples containing equal amounts (5 µL) of histone extract were resolved by
SDS-PAGE on 4 identical gradient acrylamide gels (4%–12%; Invitrogen,
Karlsruhe, Germany) under reducing conditions and, subsequently,
electroblotted onto 4 nitrocellulose transfer membranes (Invitrogen). To
control the blotting performance, gels were stained with Coomassie Brilliant
Blue. Immunodetection was performed separately at room temperature overnight
with antihistone H3 (1:500; Biomol), antiacetylated histone H3 (1:40 000;
Biomol), antihistone H4 (1:500; Biomol), and anti–penta-acetylated
histone H4 (1:40 000; Biomol). The reaction was visualized with the APAAP
mouse detection system (DAKO, Hamburg, Germany) using mouse–anti-rabbit
and rabbit–anti-mouse secondary antibodies (DAKO) and NBT-BCIP (KPL,
Gaithersburg, Md) as phosphatase substrate. Negative controls were performed
omitting the primary antibodies. All experiments were performed in triplicate.
The color intensity of the bands was computationally assessed with the
TotalLab program v2005 (limited license,
www.nonlinear.com).
The band intensity that corresponds to antiacetylated H3/H4 was calculated as
fold change from that of anti-H3/H4 in each case. The degree of core histone
acetylation is reflected by the difference between antiacetylated H3 and H3 or
between anti–penta-acetylated H4 and H4.
HDAC Activity Assay
HDAC activity assay was performed as described previously with minor
modifications (Heltweg et al,
2005). Stock solutions of the fluorescent substrate ZMAL (12.6
mM), the metabolite ZML (12.6 mM), and the hydroxamic acid HDAC inhibitor SW55
(24 mM) (Wittich et al, 2002)
were prepared in DMSO. Twenty microliters of the stock solution of SW55 was
complemented with incubation buffer (see below) to 1 mL. For ZMAL and ZML, 10
µL of the stock solution was diluted to 1 mL with incubation buffer pH 7.9
(NaH2PO4 1.4 mM, Na2HPO4 18.6 mM,
EDTA 0.25 mM, NaCl 10 mM, mercaptoethanol 10 mM, and glycerol 10% [v/v])
separately. Cryomaterial from 3 animals per group served as enzyme source and
were prepared separately as follows: to 400 µL of incubation buffer in a
reaction vial were added 50 mg of cryomaterial and 3 glass beads (diameter: 2
mm). The mixture was ground in a Mikro-Dismembrator (UB Braun, Biotech Int,
Melsungen, Germany) at a shaking frequency of 2000 per minute for 3 minutes
with subsequent centrifugation at 4°C to receive a clarified supernatant.
Two hundred microliters was removed from the supernatant, diluted with
incubation buffer 1:10, and used as enzyme source.
The activity screening was performed in black 96-well plates (Greiner, Kremsmünster, Austria) as follows: 60 µL of enzyme source and 50 µL of incubation buffer were transferred into each well, and reaction was initiated by the addition of 10 µL of a ZMAL stock solution. The 100% conversion value (simulating 100% conversion of substrate ZMAL) was prepared by adding 10 µL of the metabolite ZML solution to 110 µL of incubation buffer. The blank contained 110 µLof incubation buffer and 10 µL of ZMAL, but no enzyme. The incubation was carried out at 37°C for 120 minutes. Meanwhile, stop solution was prepared by mixing 96 µL of trypsin buffer (Tris-HCl 50 mM, pH 8.0, NaCl 100 mM) with 4 µL of SW55 buffer solution and 20 µL of trypsin solution (6 mg/mL in trypsin buffer, 10 000 Na-benzoyl-L-arginine ethyl ester units/mg protein) (Sigma, Munich, Germany) per well. After 120 minutes, 120 µL of stop solution was added to each well and another 20 minutes of incubation was carried out at 37°C. Fluorescence measurement was performed in a microplate reader (Polarstar Galaxy, BMG Labtechnologies, Offenburg, Germany) with an excitation filter of 390 nm and an emission filter of 460 nm. All samples were blank-corrected and the HDAC activity in the samples was calculated relative to the 100% conversion value.
Total RNA Extraction and cRNA Target Preparation
Total RNA was extracted from frozen tissue of three animals per group using
the RNeasy Mini Kit following instructions of the manufacturers (Qiagen,
Hilden, Germany). The material from each group was pooled. The RNA was checked
on an Agilent 2100 Bioanalyzer (Agilent Technologies GmbH, Waldbronn, Germany)
for quality on the basis of electrophoretic profiles and UV spectrophotometer
for quantification and purity of the preparation.
CodeLink Target Labeling and Array Hybridization
The CodeLink UniSet Mouse 10 K Bioarray (GE Healthcare, Freiburg, Germany),
which contains a collection of approximately 10 458 probes within a single
reaction chamber on each individual slide was used in this study. All
oligonucleotide probes are 30 bases in length. The core of the CodeLink
platform is a glass slide coated with a polyacrylamide gel matrix to create a
3-dimensional aqueous hybridization environment.
For array analyses, total RNA was quantified with NanoDrop (NanoDrop Technologies, Rockland, Del) and quality of RNA was assessed using the Agilent 2100 Bioanalyzer. When the total RNA yield was >2 µg, the 260/280 ratio was in the range 1.9–2.1, and the Agilent profile showed clear and sharp ribosomal peaks, RNA was subjected to double-stranded cDNA and subsequent cRNA synthesis using the CodeLink Expression Assay Kit (GE Healthcare), according to the manufacturer's instructions. Briefly, 2 µg total RNA was subjected to cDNA synthesis using a poly-A binding primer containing the T7-polymerase promoter. Double-stranded DNA fragments were purified using the QIAquick PCR Purification Kit (Qiagen). For target labeling, the cDNA was in vitro transcribed by partially substituting UTP with bio-16-UTP in the reaction mixture. Labeled cRNA was purified using the RNeasy Mini Kit (Qiagen). The eluted cRNA was quantified with the NanoDrop (NanoDrop Technologies) and quality was assessed with the Agilent 2100 Bioanalyzer. Fragmentation of cRNA, hybridization, washing, staining, and scanning were performed as described by Lipshutz et al (1998). Briefly, 20 µg purified cRNA was fragmented in 1x fragmentation buffer (40 mM Tris-acetate pH 7.9, 100 mM KOAc, 31.5 mM MgOAc) at 94°C for 20 minutes. For hybridization on CodeLink bioarrays, 10 µg of fragmented cRNA in 260 µl of hybridization solution was added to each bioarray via the Flex Chamber port and incubated for 18 hours in a Minitron shaker incubator (Infors AG, Bottmingen, Germany) at 37°C/5xg. cRNA of each group was hybridized in duplicate (technical replicates). In total, 10 bioarrays (2 arrays per group) were processed in parallel using the CodeLink Parallel Processing Kit (Amersham Biosciences, Freiburg, Germany). Bioarrays were stained with Cy5-streptavadin (Amersham Biosciences) and scanned using the GenePix 4000 B scanner and GenePix Pro 4.0 Software (Axon Instruments, Arlington, Calif). Images were subjected to data analysis.
CodeLink Bioarray Data Analysis
Spot signals of the CodeLink bioarrays were quantified using CodeLink
Expression Software 1.21 based on ImaGene 5.5 (BioDiscovery, Marina Del Rey,
Calif). The image segmentation and quantification process is outlined in the
ImaGene 5.5 user's manual. CodeLink Expression Software 1.21 generated
background corrected raw as well as intraslide median-centered normalized
data. The intraslide normalized data were used for further analysis. The
software automatically calculated thresholds (0.062–0.211) for
intraslide normalized intensities for each array and flagged genes as
"true" when the gene intensity was higher than the threshold or
"false" when the intensity was lower than the threshold. Data of
10 arrays (2 replicates per group) were subjected to data analysis. Genes with
missing values 
50% in all arrays were excluded from the analysis. The
remaining missing values were imputed using sequential K–nearest
neighbor imputation (Kim et al,
2004). Microarray data containing 9897 genes were normalized using
quantiles normalization in R (Bolstad et
al, 2003). Correlation matrices were generated in AVADIS-Pride
(Gwadry et al, 2005) to
determine outlier arrays within the dataset. Finally, the data were subjected
to RankProducts (RP), a novel statistical method based on the calculation of
rank (Breitling et al, 2004).
It was recently shown that RP is a powerful and reliable approach for
identifying biologically relevant expression changes even in highly noisy data
and that it can lead to a sharp reduction in the number of replicate
experiments needed to obtain reproducible results. RP was used to identify
genes with significant changes in their expression level in the comparisons 1)
control vs 2.5 hours, 2) control vs 5 hours, 3) control vs 7.5 hours, and 4)
control vs 10 hours. For all comparisons, genes with a false discovery rate
<0.3 and a fold change >2 and <–2 were defined as significant.
Genes flagged as FALSE in both control and experiment were discarded from the
significant gene list. Remaining significant genes were annotated using the
web-based annotation tool SOURCE Stanford
(Diehn et al, 2003).
Functional over representation of biological processes among the significant
genes was determined using EASE (Hosack et
al, 2003). Activation of pathways was assessed using GenMAPP
(Dahlquist et al, 2002).
Hierarchical cluster analyses were performed in dChip
(Li and Wong, 2001). To track
TSA-dependent temporal changes in mouse testis, an aggregate measure
designated as the disease load index (DLI) was used as recently described
(Porter et al, 2003). The DLI
is a unitless measure, representing the sum of log2 differences of
TSA-treated mice from control (log-fold change) for selected functional gene
categories.
Quantitative Real-Time Polymerase Chain Reaction
To validate the microarray data, quantitative real-time polymerase chain
reaction (qRTPCR) was performed for 15 target murine genes (that were highly
regulated during microarray analysis and relevant for the question, namely
apoptosis) using qPCR Master Mix for SYBR Green, according to the
manufacturer's instructions (Eurogentec, Köln, Germany)
(Table 1). cDNA for each group
of mice was obtained by reverse transcription from total pooled RNA (3 animals
per group, RNA concentration 50 µg/mL) using the OmniScript Kit (Qiagen).
Four microliters of cDNA was used in each amplification reaction. The PCR
conditions were: 2 minutes at 50°C, 6 minutes at 95°C, 45x (20
seconds at 95°C, 30 seconds at 55°C, 30 seconds at 72°C), 10
seconds at 60°C, and 20 minutes dissociation at 60–95°C. All
reactions were run in duplicate. The dissociation curve analysis checked the
specificity of the products. Threshold cycle values (CT) of the tested genes
were determined and normalized expression of each target gene was given as the
Ct between the log2 transformed CT of the target gene and the log2
transformed CT of the internal control (ACTB). Both log2 transformed
microarray intensities and RT gene expression levels (Ct) of each
target gene for each condition (2.5, 5, 7.5, and 10 hours) were expressed as
log2 differences from control (=log2 Ct method
[Perkin Elmer Corporation,
1997]).
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Digoxigenin-Labeled cRNA Probes and In Situ Hybridization
The digoxigenin-labeled cRNA probes corresponding to CASP3 (NM_009810),
TRP53 (NM_011640), and PMAIP1 (NM_021451) mRNA were synthesized as reported
(Steger et al, 1998). In situ
hybridization was performed as described
(Steger et al, 1998).
Statistical Analysis
Data analyses were performed using the statistical software package BMDP
(Dixon, 1993). Since the TUNEL
analysis and the caspase-3 immunohistochemistry (IHC) produced values whose
statistical distribution was skewed to the right, a logarithmic transformation
was applied. Accordingly, data were described by the geometric means
(xg) and dispersion factors for logarithmically transformed values,
and by arithmetic means (x) and standard deviation (SD) for normally
distributed values. Regression analyses were performed for all variables. The
Western blot results for histone H4 acetylation and the data obtained from the
HDAC activity assay were not distributed on a regression line. Therefore, the
groups were compared by 1-way analysis of variance (ANOVA) followed by
pairwise comparison to the control using the Dunnett test. A statistical
significance level of .05 was used.
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Results |
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Evaluation of Apoptosis
Testes from animals of treated groups displayed no decrease in weight or
germ cell number when compared with animals of the control group. However,
histopathological alterations specific to programmed cell death (oxyphile
transformation of the cytoplasm, cytoplasmic shrinkage, and pycnosis of
nuclei) were occasionally observed in spermatogonia and spermatocytes of
groups 1 (control) and 2 (2.5 hours), but were abundant in pachytene
spermatocytes of groups 3, 4, and 5 (5, 7.5, and 10 hours). This observation
was confirmed by both TUNEL (Figure 1c and
d) and immunohistochemistry with the polyclonal antibody
anticleaved caspase-3 (Figure 1i and
k). In groups 1 (control) and 2 (2.5 hours), the number of
TUNEL-positive cells within the seminiferous tubules was around or below 1 per
1000 cells (Table 2). Apoptosis
increased in groups 3 (5 hours), 4 (7.5 hours), and 5 (10 hours)
(Table 2). The values for
caspase-3 assessed by IHC revealed a similar trend
(Table 2). Regression analyses
were significant with r = 0.87 and P < .001 for TUNEL
results and r = 0.93 and P < .001 for caspase-3 data.
Microarray Analysis
The number of germ cells remained unchanged between control and treated
testes, providing the basis for obtaining meaningful changes following
microarray analysis. The apoptotic effect on spermatocytes could be observed
as early as 5 hours after TSA application and, thereafter, significantly
increased to 10 hours (P < .001). Beyond 10 hours, a visible loss
of spermatocytes occurred within the seminiferous epithelium (data not shown).
The variability between animals in each group was minor, as demonstrated by
the histological, immunohistochemical, and in situ hybridization staining (not
shown) as well as by TUNEL, caspase-3, and HDAC activity assays
(Table 2). For TUNEL and
caspase-3 values, the higher relative dispersions between animals in group 1
(control) and 2 (2.5 hours) are meaningless because the absolute values of
measurements are very low. Consequently, RNA obtained from three different
animals per group was pooled for the microarray analysis.
Following quantile normalization, the correlation matrix among technical replicates revealed a high correlation, with r = 0.985. Statistical analyses and filtering revealed 128 differentially regulated genes (88 up, 40 down) after 2.5 hours, 259 genes (167 up, 92 down) after 5 hours, 122 genes (21 up, 101 down) after 7.5 hours and 238 genes (101 up, 137 down) after 10 hours. In total, 507 genes were differentially regulated at any time point using the criteria of significance described in "Materials and Methods." Within these genes, those clusters of genes changing from 0 (control) to 10 hours were identified by hierarchical clustering (Figure 3). Basically, three clusters were predominant: 1) genes consistently under-expressed at 2.5, 5, 7.5, and 10 hours when compared with control; 2) genes overexpressed only at 5 hours; and 3) genes consistently overexpressed at all time points. The major overrepresented functional categories of these genes were "signal transduction" followed by "receptor activity," "development," "nucleus," "defense response," and "transcription," as depicted in Figure 4. The number of overexpressed genes coding for defense response were highest after 5 hours (Figure 5a), whereas those coding for developmental processes were highest at 10 hours (Figure 5b). The number of underexpressed genes was highest after 10 hours. These genes were involved in transcriptional and apoptotic processes (Figure 5c and d). Few apoptotic genes were induced at 2.5 and 5 hours, but the majority were repressed at 10 hours (Figure 5c). The global impact of TSA on mouse testis was quantified using an aggregate DLI. Overexpressed and underexpressed genes contribute to the disease process and summing up their absolute fold change values provides a suitable transcriptional index (the DLI) to express the TSA effect. To dissect temporal gene expression signatures in mouse testis, DLI plots were constructed for selected functional categories of genes (Figure 6a and b). DLI changes during the time course suggest that TSA treatment led to an initial activation of genes involved in signal transduction and development after 2.5 hours (Figure 6a). This was accompanied by the induction of 2 pathways: 1) a delayed but strong activation of genes involved in immune response and inhibition of proteases with a maximum at 5 hours followed by a quick decline (Figure 6a), and 2) a consistent repression of genes encoding for proteins that are active in the nucleus with a maximum repression at 10 hours (Figure 6b). Biological network analysis using literature and pathway mining tools such as iHOP (Hoffmann and Valencia, 2004) and KEGG pathways, as well as comparison with published transcriptome data from mouse and rat testis (O'Shaughnessy et al, 2003; Schlecht et al, 2004; Yao et al, 2004; Pang et al, 2006) and meiosis-related gene lists (Wolgemuth et al, 2002; Cohen et al, 2006) revealed that only very few germ cell and meiosis-specific genes were regulated after TSA treatment. Many of the early-regulated genes (2.5 and 5 hours) encoded for proteins expressed in the extratubular compartment of the testis (Leydig cells, peritubular cells, macrophages, and extracellular matrix) or in Sertoli cells: neuroendocrine ligands and receptors such as ghrelin (Tena-Sempere, 2005), adenylate cyclase–activating polypeptide receptor (adcyap1r1) (McArdle, 1994), dopamine receptor (drd4), relaxin (Kawamura et al, 2005), insulinlike 5; adhesion molecules such as procollagen (Col6a3, Col16a1) (Sawada and Yazama, 1994), claudin 4; immunoactive proteins like leukemia inhibitory factor (Hedger and Meinhardt, 2003), mannose binding lectin, and interferon regulatory factor 5. At the 5-hour time point, genes involved in the defense response and protease inhibition were strongly up-regulated (Figures 5a and 6b). This group encompassed haptoglobin, hemopexin, serum albumin, apolipoproteins, serine/cysteine protease inhibitors (serpins), alpha-HS-glycoprotein, transferrin, kininogen, fetuin-beta, serum amyloid components A4 and P, and vitronectin. Another group of genes strongly stimulated at 5 hours included cytochromes P450 (cyp). Only 2 apoptosis-related genes were early–up-regulated: phorbol-12-myristate-13-acetate-induced protein 1 (pmaip1/noxa) and tumor necrosis factor receptor 12a (Tnfrsf12a). The deactivation of cellular processes (transcription, DNA binding), which reached a maximum at 10 hours (Figures 5d and 6b), could reflect additional mechanisms connected to the phenotypic cell death. The unexpected under-expression of apoptotic genes after 7.5 hours (Figures 5c and 6b), as well as the late overexpression of genes involved in developmental processes and signal transduction (Figures 5b and 6a), could be related to regeneration processes occurring within the stem cell layer of the testes as a response to the rapid clearance of spermatocytes (de Rooij, 2001).
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The complete data discussed in this publication have been deposited in the NCBI's Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) (Barrett et al, 2005) and are accessible through GEO Series accession number GSE5448. Supplementary Tables 3–9 summarizing this data are available online at www.andrologyjournal.org.
Validation of Microarray Results by qRTPCR
The validity of the microarray results was determined by quantitative
real-time PCR performed for 15 target murine genes with a broad range of
expression values. The overall correspondence between gene expression levels
by microarrays and qRTPCR was good, as indicated by r = 0.89 in the
2.5-hour group (Figure 7),
r = 0.87 in the 5-hour group, r = 0.77 in the 7.5-hour
group, and r = 0.85 in the 10-hour group (data not shown). A
gene-to-gene variation exists and may be attributable to sequence-specific
factors (ie, the labeled cRNA may hybridize to a microarray element for a
given gene that is a few hundred base pairs from the corresponding qRTPCR
primers). Nevertheless, our results support the accuracy by which the Codelink
microarray represents gene expression.
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Discussion |
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In vivo results of the present study are in contrast with in vitro findings of Hazzouri et al (2000) reporting a spectacular increase of histone acetylation in round spermatids after treatment with the HDAC inhibitor TSA. However, in vivo experiments by Wang et al (2006) involving TSA treatment of porcine oocytes demonstrated that histone deacetylation is required for accurate chromosome segregation during meiosis, corroborating our in vivo findings in male mice.
In order to gain further information on the regulatory effects mediated by TSA, we performed cRNA microarray followed by qRTPCR. A total of 507 genes changed after TSA administration, but none of the classical HDACi-responsive genes implicated in cell cycle regulation or apoptosis (de Ruijter et al, 2003) were found. Also, no regulatory effects on genes controlling meiosis (Wolgemuth et al, 2002; Cohen et al, 2006) could be detected. Comparing our microarray results with published data of transcriptomes from mouse and rat testis (O'Shaughnessy et al, 2003; Schlecht et al, 2004; Yao et al, 2004; Pong et al, 2006) revealed that only very few meiosis-specific genes are regulated after TSA treatment. In turn, transcripts of genes known to be expressed in the somatic cells of the testis (Sertoli, Leydig, peritubular cells, macrophages) or in the matrix were regulated at early time points. At 5 hours after TSA application, as many as 20 classical acute-phase genes (Wait et al, 2005) and plasma-specific proteins, typically expressed in liver, were strongly regulated. Most of them have been previously described to be expressed in Sertoli cells (Jegou, 1993; Braghiroli et al, 1998). In all above-mentioned groups, the genes were up-regulated in treated animals compared with controls. These results suggest that TSA exerted stimulatory effects on the somatic cells of the testis, leading indirectly to the spermatocytes' death. This is quite unexpected when considering that germ cell damage has usually been connected to an inhibition of the somatic cell function, as described in experimental toxic injuries to Leydig or Sertoli cells (Boekelheide, 2005). Moreover, it is curious why only 1 category of germ cells (ie, the pachytene spermatocytes) was affected. It is possible that TSA perturbed one or more paracrine balances between somatic and germ cells, and/or the homeostasis of the blood-testis barrier. The dose employed in our experiment could be sufficient to selectively disrupt spermatocyte-specific survival pathways, but too low to affect all germ cells. Similarly, the chemotherapeutic agent cisplatin is able to induce apoptosis of germ cells in the testis with the spermatocytes as the most sensitive category. Its effect is also dependent on the dose, but implies, at least in part, a toxic mechanism exerted on Sertoli and Leydig cells (Boekelheide, 2005; Sawhney et al, 2005). Interestingly, it has been suggested that Sertoli cells damaged (eg, by toxic injury) induce apoptosis of germ cells through a FAS ligand/FAS receptor or p53 pathway (Boekelheide, 2005). Moreover, FAS receptor was immunohistochemically localized exclusively in germ cells (Lee et al, 1997). In the present study, the microarray analysis did not reveal any regulation of the FAS cascade. However, the proapoptotic gene pmaip1/noxa, a downstream effector of p53 (Oda et al, 2000), was up-regulated at early time points. Quantitative RTPCR confirmed the increase of pmaip1/noxa and in addition revealed a similar variation for p53 and caspase-3. In addition, in situ hybridization demonstrated positive signals for pmaip1/noxa, p53, and caspase-3 transcripts in pachytene spermatocytes up to stage XI of the seminiferous epithelial cycle. It is known that the death pathways controlled by p53 result in caspase-3 activation (Erster and Moll, 2005). These results indicate that the programmed cell death of spermatocytes follows a p53-noxa-caspase-3–mediated pathway. Details concerning the role of Sertoli cells and probably other somatic cells of the testis in this process require further investigation. However, it seems unlikely that TSA exerted toxic effects on these cells, as indicated by the microarray data (up-regulation of somatically-expressed genes in the treated testes) and histology (no morphological changes of the somatic cells in treated testes). Regarding p53, previous investigations employing in situ hybridization and immunohistochemistry found expression only in spermatocytes (Almon et al, 1993; Schwartz et al, 1993) and failed to detect the protein in spermatogonia (Beumer et al, 1998) under normal conditions. These observations would explain why, in the present study, only the spermatocytes underwent apoptosis after TSA application.
Previous studies demonstrated that p53 and other nonhistone proteins can be activated de novo through direct acetylation (Luo et al, 2000; Terui et al, 2003; Glozak et al, 2005). Moreover, it has been reported that apoptosis induced by p53 activation through direct acetylation of lysine residues 320 and 373 included the downstream effector pmaip1/noxa in cancer cells (Terui et al, 2003). This data raise another hypothesis concerning the effect of TSA on murine spermatogenesis, namely direct acetylation and activation of p53 in spermatocytes followed by apoptosis.
Cell culture studies have demonstrated that TSA and other HDAC inhibitors are able to stimulate the transcription of some members of the cytochrome P450 family, which possibly bear a role in their metabolism (Elaut et al, 2002; Nakajima et al, 2003; Hooven et al, 2005). In the present study, 8 members of this family were up-regulated at 5 hours after TSA application, indicating that TSA may be actively metabolized in the testis.
The present study provides new data on the testis-specific effects of TSA in the mouse, suggesting that male infertility due to TSA treatment is caused by an impairment of meiosis, possibly through an indirect mechanism involving somatic Sertoli and/or Leydig cells. Although further investigations are necessary to strengthen this hypothesis, our data provide valuable reference data for in vivo responses to TSA that can be compared with early responses to other testicular toxicants. Because there are a number of testicular toxicants that arrest spermatogenesis at meiosis, resulting in apoptosis of spermatocytes and depletion of round spermatids, data of the present study are a valuable tool for comparison purposes, as the molecular mechanisms of these toxicants are dissected.
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Acknowledgments |
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Footnotes |
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# These authors contributed equally to the manuscript. 
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