Abstract
The purpose of this study was to investigate the association between male infertility and single-nucleotide polymorphisms (SNPs) of DNA methyltransferases (DNMT) genes (DNMT3B: rs2424909, DNMT1: rs4804490, DNMT3A: rs1550117 and DNMT3L: rs7354779). Eight hundred and thirty three idiopathic infertile males and four hundred and ten fertile controls from the hospitals affiliated to Nanjing Medical University between 2010 and 2012 were recruited in the study. We demonstrated a significantly increased risk of idiopathic infertility with abnormal semen parameters in association with the heterozygous genotype of variant rs4804490. Moreover, the AA genotype of variant rs4804490 was associated with significantly decreased risk for male infertility with abnormal semen parameters. A decreased risk of idiopathic infertility with abnormal semen parameters was associated with the homozygous genotype of variant rs2424909. These results suggested that variants in different DNMT genes have different relationships with idiopathic male infertility, and Chinese men carrying these variants have an increased or decreased risk of abnormal semen parameters.
Introduction
Male infertility is a heterogeneous disorder that contributes to the impairment of spermatogenesis. Clinical investigations have confirmed that a significant proportion of male infertility is idiopathic, the molecular mechanisms underlying the defects have not been elucidated1, 2. Most studies published to date support the view that abnormal epigenetic changes also resulted in male infertility. Lande-Diner and other researchers have concluded that DNA methylation is an essential epigenetic modification specifically related to gene silencing3. Aberrant DNA methylation has been proposed as a possible mechanism compromising male fertility4–6. The aberrant DNA methylation found in infertile males may be a consequence of a dysfunction in the machinery involved in establishing and maintaining normal DNA methylation. Accumulating evidence has indicated that male infertility results from gene mutations and single-nucleotide polymorphisms (SNP)7, 8.
Mouse models have shown that aberrant DNA methylation patterns resulting from gene targeting of the DNMTs may cause spermatogenic defects9–12. DNA methyltransferases (DNMTs) are a family of proteins responsible for transferring methyl groups specifically to cytosine in CpG dinucleotides13, 14. Genes involved in the epigenetic pathways are likely to affect DNA methylation and are associated with male infertility. Researchers have analyzed the genetic variants within the genes to investigate the epigenetic processes in the aetiology of diseases15.
Four DNMTs (DNMT1 16, DNMT3A, DNMT3B 17–19, and DNMT3L 20, 21) have been extensively characterized in mammals. DNMT1, which is often referred to as the maintenance methyltransferase, is responsible for maintaining pre-existing methylation patterns during DNA replication22, 23. DNMT3A and DNMT3B are considered to be de novo DNA methyltranferases, which are critical in the dynamic DNA methylation process during embryogenesis and pathogenesis24. In addition to these active enzymes, the DNMT3L protein, catalytically inactive by itself, also contributes to de novo methylation by interacting with the catalytic domains of DNMT3A and DNMT3B and enhancing their enzymatic activity25–27. DNA methylation, mediated by DNMTs, is required for proper embryonic development18, 28 and for the formation of mature functional germ cells9, 10, 29. Dysregulation of the DNMTs may lead to various conditions30, including spermatogenic defects12, autoimmune disorders31 and cancer13, 32. To better understand the relationships between polymorphisms in DNMTs and idiopathic male infertility, we studied the associations between genetic variation in DNMT genes and altered semen parameters. We show that variants in the DNMT3B and DNMT1 may have different relationships with idiopathic male infertility. Furthermore, we compared the transcriptional factors between C allele and T allele of rs2424909, C allele and the A allele of rs4804490.
Materials and Methods
Subject recruitment and sample collection
The study was approved by the Institutional Ethics Committee of Nanjing Medical University. All activities involving human subjects were done under full compliance with government policies and the Helsinki Declaration. The participants wrote informed consents prior to the study. All study subjects were consecutively recruited from the Affiliated Hospitals of Nanjing Medical University between 2010 and 2012 (NJMU Infertility Study). The inclusion criteria consisted of five terms: (i) a normal 46, XY karyotype; (ii) absence of Y chromosomal microdeletions of AZF region; (iii) lack of hypogonadotropic hypogonadism; (iv) normal sexual and ejaculatory functions and no seminal tract obstruction, varicocele; (v) no history of infection or other diseases that could affect fertility. Finally, 833 men are included as the study subjects. The controls were healthy men who had fathered at least one healthy child, without assisted reproductive measures during the same period as those of the cases recruited in the same hospital. A scheduled interview was arranged for each subject to collect the basic information, including personal background, lifestyle factors, occupational and environmental exposures, sexual and reproduction status, genetic risk factors, medical history and physical activity (e.g. exercise status). Then, each subject donated a 5-ml peripheral blood sample and a semen sample for genetic testing. These patients and healthy donors were all ethnically Han Chinese east China.
Semen analysis
Semen samples were obtained in private by masturbation into a sterile wide-mouth and metal-free glass container after a recommended at least 3-day sexual abstinence. After liquefaction at 37 °C for 30 minutes, conventional semen analysis was conducted in accordance with guidelines of the WHO Laboratory Manual for the Examination of Human Semen, including semen volume, sperm number per ejaculum, sperm concentration, motility, progression and motion parameters by using Micro-cell slide and computer-aided semen analysis (CASA, WLJY 9000, Weili New Century Science and Tech Dev.). Each sample was assessed twice with strict quality control.
Variant selection and genotyping
Genomic DNA was extracted from leukocyte pellets of the venous blood by traditional proteinase K digestion, followed by phenol-chloroform extraction and ethanol precipitation. Genotypes were detected by the TaqMan allelic discrimination Assay on an ABI 7900HT real-time PCR system (Applied Biosystems, the United States). The gene variants studied were: DNMT1 (rs4804490); DNMT3A (rs1550117); DNMT3B (rs2424909); DNMT3L (rs7354779). We selected only variants where the frequency of the minor allele was greater than 5%. For quality control, all genotyping assays were performed without knowing the case or control status of the subjects, and a random 5% of cases and controls were genotyped twice by different individuals.
Predicting the association of variant DNMT3B rs2424909, DNMT1 rs4804490, DNMT3A rs1550117 and DNMT3L rs7354779 and the binding sites of transcription factors
AliBaba2 is a program for predicting binding sites of transcription factors. We used the program to compare putative transcriptional factor-binding sites of rs2424909 and rs4804490, rs1550117 and rs7354779. Firstly, we searched the base sequences of rs2424909, rs4804490, rs1550117 and rs7354779 (51 bases). Then, by respectively entering C allele and T allele of rs2424909, C allele and A allele of rs4804490, A allele and G allele of rs1550117, C allele and T allele of rs7354779, the different binding sites of transcription factors emerged.
Statistical analyses
Semen parameters were dichotomized based on WHO reference values (World Health Organization, 1999) for semen volume (<2 ml), sperm concentration (<20 × 106/ml), sperm number per ejaculate (<40 × 106/ml) and sperm motility (<50% motile sperm). The control group consisted of proven fertile men with all four semen parameters at or above the WHO reference value. The subjects with idiopathic infertility were first divided into two groups according to semen parameters: case Group I was defined as idiopathic infertile men with all four semen parameters at or above the WHO reference value and the subjects in case Group II had at least one of the abnormal semen parameters. Case Group II was divided into four subgroups: Subgroup I (semen volume <2 ml), Subgroup II (sperm concentration <20 × 106/ml), Subgroup III (sperm number per ejaculate <40 × 106/ml) and Subgroup IV (sperm motility <50% motile sperm). Analysis of variance was used to compare the mean age, body mass index (BMI) and pack-years of cigarette smoking between case and control groups. The χ2 test was used to evaluate the difference in smoking status and alcohol status between the two groups. Multivariate logistic regression analysis was performed to obtain the odds ratios (OR) for male infertility and 95% confidence intervals (95% CI) with adjustment for age, BMI, smoking status and alcohol drinking. All statistical analyses were carried out using Stata (Version 9.0, StataCorp LP, TX, USA), and p value of less than 0.05 was considered to be statistically significant.
Results
Characteristics of the study population
The final study consisted of 1243 men, 833 idiopathic infertile patients and 410 fertile controls. All subjects were Han Chinese, with a mean (±SD) age of 29.38 (±4.15) years. Eight hundred and thirty three men were idiopathic infertile men, 191 men (22.9%; Case Group I) with normal semen quality and 642 men (77.1%; Case Group II) with abnormal semen quality according to WHO standard. In addition, patients in the Case Group II were further divided into four subgroups: Subgroup I: patients with semen volume < 2 ml (n = 122); Subgroup II: subjects with sperm concentration <20 × 106/ml (n = 340); Subgroup III: patients with sperm number per ejaculum <40 × 106/ml (n = 311); and Subgroup IV: patients with sperm motility <50% motile sperm (n = 611). Demographic categories by fertility and semen quality are described in Table 1. Infertile males were younger, had a lower BMI and were more likely to be former drinkers than the fertile controls (Table 1). All variables were further adjusted for any residue confounding effect in later multivariate logistic regression analyses.
Table 1.
Characteristics of the study population (n = 1243).
| Characteristic | Control (n = 410)a | Case Group I (n = 191)b | Case Group II (n = 642)c | Subgroup I (n = 122)d | Subgroup II (n = 340)e | Subgroup III (n = 311)f | Subgroup IV (n = 611)g |
|---|---|---|---|---|---|---|---|
| Age (mean ± SD) | 29.90 ± 3.61 | 29.19 ± 3.91 | 29.11 ± 4.51 | 29.39 ± 4.86 | 29.15 ± 4.54 | 29.11 ± 4.59 | 29.13 ± 4.45 |
| BMI (mean ± SD)h | 23.77 ± 2.94 | 23.23 ± 2.92 | 23.27 ± 3.11 | 23.48 ± 3.13 | 23.14 ± 3.11 | 23.12 ± 3.12 | 23.26 ± 3.14 |
| Smoking status [n (%)] | |||||||
| Never smoker | 186 (45.4) | 84 (44.0) | 305 (47.5) | 48 (39.3) | 142 (41.8) | 127 (40.8) | 294 (48.1) |
| Ever smoker | 224 (54.6) | 107 (56.0) | 337 (52.5) | 74 (60.7) | 198 (58.2) | 184 (59.2) | 317 (51.9) |
| Current smoker | 199 (48.5) | 90 (47.1) | 288 (44.9) | 66 (54.1) | 169 (49.7) | 155 (49.8) | 268 (43.9) |
| Former smoker | 25 (6.1) | 17 (8.9) | 49 (7.6) | 8 (6.6) | 29 (8.5) | 29 (9.3) | 49 (8.0) |
| Pack-years (mean ± SD)f | 4.58 ± 4.70 | 5.28 ± 4.15 | 5.33 ± 5.80 | 5.92 ± 7.79 | 5.30 ± 6.04 | 5.43 ± 6.23 | 5.25 ± 5.81 |
| Drinking status [n (%)] | |||||||
| Never drinker | 187 (45.6) | 99 (51.8) | 290 (45.2) | 56 (45.9) | 148 (43.8) | 137 (44.1) | 274 (44.8) |
| Ever drinker | 223 (54.4) | 92 (48.2) | 387 (60.3) | 66 (54.1) | 192 (56.5) | 174 (55.9) | 337 (55.2) |
| Current drinker | 211 (51.5) | 74 (38.7) | 292 (45.5) | 53 (43.3) | 163 (47.9) | 147 (47.3) | 280 (45.8) |
| Former drinker | 12 (2.9) | 18 (9.4) | 95 (14.8) | 13 (10.7) | 29 (8.5) | 27 (8.7) | 57 (9.3) |
SD, standard deviation; BMI, body mass index.
aProven fertility men with semen volume ≥ 2 ml, sperm concentration ≥20 × 106/ml, sperm number per ejaculum ≥40 × 106/ml and sperm motility ≥50% motile sperm.
bIdiopathic infertile men with all four semen parameters at or above the WHO reference value.
cIdiopathic infertile men with at least one of the abnormal semen parameters (semen volume, sperm concentration, sperm number per ejaculum and sperm motility).
dIdiopathic infertile men with semen volume < 2 ml.
eIdiopathic infertile men with sperm concentration <20 × 106/ml.
fIdiopathic infertile men with sperm number per ejaculum <40 × 106/ml.
gIdiopathic infertile men with sperm motility <50% motile sperm.
hBMI: kg/m2.
Association between genotype distributions of four DNA methyltransferases and semen quality
The genotype distributions of the four DNMT genes among the cases and controls are shown in Tables 2 and 3. As shown in Table 2, we initially divided the cases into the Case Group I and Case Group II according to semen parameters. When the DNMT1 CC genotype was used as the reference group, the CA genotype was significantly associated with increased risk for male infertility in Case Group I (adjusted OR, 1.50, 95% CI = 1.00–2.25, P = 0.048) and Case Group II (adjusted OR, 1.40, 95% CI = 1.05–1.86, P = 0.023). Under the recessive model of inheritance, the DNMT1 AA genotype was associated with significantly decreased risk for male infertility with abnormal semen parameters (adjusted OR, 0.68, 95% CI = 0.50–0.92, P = 0.012), compared with other genotypes. In addition, compared to the DNMT3B CC genotype, the association between the CT genotype and male infertility was not significantly different (adjusted OR, 1.25, 95% CI = 0.85–1.83, P = 0.255), but the TT homozygous genotype was associated with significantly decreased risk for male infertility with abnormal semen parameters (adjusted OR, 0.25, 95% CI = 0.06–0.94, P = 0.040). Under the recessive model of inheritance, the DNMT3B variant homozygote was associated with significantly decreased risk for male infertility with abnormal semen parameters (adjusted OR, 0.24, 95% CI = 0.06–0.90, P = 0.034), compared with other genotypes, after adjustment for age, BMI, smoking and drinking status in the multivariate logistic regression analysis (Table 2). In contrast, no significant differences were found for rs1550117 and rs7354779 between the cases and controls.
Table 2.
Genotype frequencies of DNMTs genetic variants among the cases and controls and their association with male infertility.
| SNPs | Genotype | Control (n = 410)a | Case Group I (n = 191)b | Case Group II (n = 642)c | ||||
|---|---|---|---|---|---|---|---|---|
| n (%) | n (%) | P d,e | OR (95% CI)d,f | n (%) | P d,e | OR (95% CI)d,f | ||
| rs4804490 | CC | 144 (35.3) | 55 (29.3) | 1.00 (reference) | 204 (31.9) | 1.00 (reference) | ||
| CA | 166 (40.7) | 96 (51.1) | 0.048 | 1.50 (1.00–2.25) | 324 (50.6) | 0.023 | 1.40 (1.05–1.86) | |
| AA | 98 (24.0) | 37 (19.7) | 0.985 | 1.00 (0.61–1.63) | 112 (17.5) | 0.281 | 0.82 (0.58–1.17) | |
| C-allele carriers | 310 (76.0) | 151 (80.3) | 1.00 (reference) | 528 (82.5) | 1.00 (reference) | |||
| AA | 98 (24.0) | 37 (19.7) | 0.294 | 0.79 (0.52–1.22) | 112 (17.5) | 0.012 | 0.68 (0.50–0.92) | |
| rs1550117 | GG | 263 (64.8) | 115 (60.2) | 1.00 (reference) | 405 (64.0) | 1.00 (reference) | ||
| GA | 122 (30.0) | 70 (36.6) | 0.163 | 1.30 (0.90–1.89) | 201 (31.8) | 0.829 | 1.03 (0.78–1.36) | |
| AA | 21 (5.2) | 6 (3.1) | 0.452 | 0.69 (0.27–1.80) | 27 (4.3) | 0.476 | 0.80 (0.44–1.46) | |
| G-allele carriers | 385 (94.8) | 185 (96.9) | 1.00 (reference) | 606 (95.7) | 1.00 (reference) | |||
| AA | 21 (5.2) | 6 (3.1) | 0.315 | 0.62 (0.24–1.58) | 27 (4.3) | 0.457 | 0.80 (0.44–1.44) | |
| rs2424909 | CC | 344 (86.4) | 160 (83.8) | 1.00 (reference) | 537 (84.8) | 1.00 (reference) | ||
| CT | 46 (11.6) | 30 (15.7) | 0.246 | 1.35 (0.81–2.23) | 93 (14.7) | 0.255 | 1.25 (0.85–1.83) | |
| TT | 8 (2.0) | 1 (0.5) | 0.242 | 0.29 (0.04–2.32) | 3 (0.5) | 0.040 | 0.25 (0.06–0.94) | |
| C-allele carriers | 390 (98.0) | 190 (99.5) | 1.00 (reference) | 630 (99.5) | 1.00 (reference) | |||
| TT | 8 (2.0) | 1 (0.5) | 0.229 | 0.28 (0.03–2.25) | 3 (0.5) | 0.034 | 0.24 (0.06–0.90) | |
| rs7354779 | TT | 384 (93.7) | 177 (95.2) | 1.00 (reference) | 597 (94.1) | 1.00 (reference) | ||
| TC | 24 (5.9) | 9 (4.8) | 0.555 | 0.79 (0.35–1.75) | 36 (5.7) | 0.978 | 0.99 (0.58–1.70) | |
| CC | 2 (0.5) | 0 (0) | — | — | 2 (0.3) | 0.673 | 0.65 (0.09–4.73) | |
| T-allele carriers | 408 (99.5) | 186 (100) | 1.00 (reference) | 633 (99.7) | 1.00 (reference) | |||
| CC | 2 (0.5) | 0 (0) | 0.593 | 0.88 (0.55–1.41) | 2 (0.3) | 0.675 | 0.65 (0.09–4.74) | |
SNPs, single-nucleotide polymorphisms. OR, odds ratios; CI, confidence interval.
aProven fertility men with semen volume ≥ 2 ml, sperm concentration ≥20 × 106/ml, sperm number per ejaculum ≥40 × 106/ml and sperm motility ≥50% motile sperm.
bIdiopathic infertile men with all four semen parameters at or above the WHO reference value.
cIdiopathic infertile men with at least one of the abnormal semen parameters (semen volume, sperm concentration, sperm number per ejaculum and sperm motility).
dAdjusted for age, BMI, smoking status and drinking status.
eTwo-sided χ2 test for genotype distributions between cases and controls.
fORs were obtained from multivariate logistic regression analysis.
Table 3.
Genotype frequencies of DNMTs genetic variants among the cases and controls and their association with male infertility.
| SNPs | Genotype | Control (n = 410)a | Subgroup I (n = 122)b,f | Subgroup II (n = 340)c,f | Subgroup III (n = 311)d,f | Subgroup IV (n = 611)e,f | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n (%) | n (%) | P g,h | OR (95% CI)g,i | n (%) | P g,h | OR (95% CI)g,i | n (%) | P g,h | OR (95% CI)g,i | n (%) | P g,h | OR (95% CI)g,i | ||
| rs4804490 | CC | 144 (35.3) | 42 (34.7) | 1.00 (reference) | 108 (31.9) | 1.00 (reference) | 102 (32.9) | 1.00 (reference) | 193 (31.7) | 1.00 (reference) | ||||
| CA | 166 (40.7) | 55 (45.4) | 0.525 | 1.16 (0.73–1.84) | 170 (50.1) | 0.037 | 1.43 (1.02–1.99) | 151 (48.7) | 0.084 | 1.35 (0.96–1.90) | 309 (50.7) | 0.023 | 1.40 (1.05–1.86) | |
| AA | 98 (24.0) | 24 (19.8) | 0.629 | 0.87 (0.49–1.54) | 61 (18.0) | 0.606 | 0.90 (0.59–1.36) | 57 (18.4) | 0.624 | 0.90 (0.59–1.38) | 107 (17.6) | 0.287 | 0.83 (0.58–1.18) | |
| C-allele carriers | 310 (76.0) | 97 (80.2) | 1.00 (reference) | 278 (82.0) | 1.00 (reference) | 253 (81.6) | 1.00 (reference) | 502 (82.4) | 1.00 (reference) | |||||
| AA | 98 (24.0) | 24 (19.8) | 0.357 | 0.79 (0.48–1.31) | 61 (18.0) | 0.066 | 0.71 (0.50–1.02) | 57 (18.4) | 0.099 | 0.73 (0.51–1.06) | 107 (17.6) | 0.014 | 0.68 (0.50–0.92) | |
| rs1550117 | GG | 263 (64.8) | 82 (67.2) | 1.00 (reference) | 215 (64.2) | 1.00 (reference) | 193 (63.1) | 1.00 (reference) | 381 (63.3) | 1.00 (reference) | ||||
| GA | 122 (30.0) | 34 (27.9) | 0.600 | 0.89 (0.56–1.40) | 104 (31.0) | 0.964 | 1.01 (0.73–1.39) | 100 (32.7) | 0.597 | 1.09 (0.79–1.52) | 196 (32.6) | 0.645 | 1.07 (0.81–1.41) | |
| AA | 21 (5.2) | 6 (4.9) | 0.842 | 0.91 (0.35–2.35) | 16 (4.8) | 0.806 | 0.92 (0.46–1.83) | 13 (4.2) | 0.618 | 0.83 (0.40–1.73) | 25 (4.2) | 0.488 | 0.81 (0.44–1.48) | |
| G-allele carriers | 385 (94.8) | 116 (95.1) | 1.00 (reference) | 319 (93.2) | 1.00 (reference) | 293 (95.8) | 1.00 (reference) | 577 (95.8) | 1.00 (reference) | |||||
| AA | 21 (5.2) | 6 (4.9) | 0.911 | 0.95 (0.37–2.42) | 16 (4.8) | 0.770 | 0.90 (0.46–1.78) | 13 (4.2) | 0.527 | 0.79 (0.38–1.63) | 25 (4.2) | 0.453 | 0.79 (0.43–1.45) | |
| rs2424909 | CC | 344 (86.4) | 102 (85.0) | 1.00 (reference) | 290 (86.6) | 1.00 (reference) | 264 (86.3) | 1.00 (reference) | 509 (84.6) | 1.00 (reference) | ||||
| CT | 46 (11.6) | 18 (15.0) | 0.383 | 1.30 (0.72–2.36) | 43 (12.8) | 0.792 | 1.06 (0.68–1.67) | 40 (13.1) | 0.703 | 1.09 (0.69–1.73) | 90 (15.0) | 0.220 | 1.27 (0.87–1.87) | |
| TT | 8 (2.0) | 0 (0.0) | 0.980 | 1.00 (0.82–1.23) | 2 (0.6) | 0.145 | 0.31 (0.06–1.50) | 2 (0.7) | 0.185 | 0.35 (0.07–1.66) | 3 (0.5) | 0.046 | 0.26 (0.07–0.98) | |
| C-allele carriers | 390 (98.0) | 120 (100.0) | 1.00 (reference) | 333 (99.4) | 1.00 (reference) | 304 (99.3) | 1.00 (reference) | 599 (99.5) | 1.00 (reference) | |||||
| TT | 8 (2.0) | 0 (0.0) | 0.984 | 1.00 (0.82–1.23) | 2 (0.6) | 0.138 | 0.31 (0.06–1.47) | 2 (0.7) | 0.175 | 0.34 (0.07–1.62) | 3 (0.5) | 0.040 | 0.25 (0.06–0.94) | |
| rs7354779 | TT | 384 (93.7) | 110 (91.7) | 1.00 (reference) | 311 (92.6) | 1.00 (reference) | 282 (91.9) | 1.00 (reference) | 567 (93.9) | 1.00 (reference) | ||||
| TC | 24 (5.9) | 9 (7.5) | 0.529 | 1.29 (0.58–2.87) | 24 (7.1) | 0.443 | 1.26 (0.70–2.27) | 24 (7.8) | 0.273 | 1.39 (0.77–2.52) | 36 (6.0) | 0.862 | 1.05 (0.61–1.79) | |
| CC | 2 (0.5) | 1 (0.8) | 0.640 | 1.79 (0.16–20.28) | 1 (0.3) | 0.632 | 0.55 (0.05–6.52) | 1 (0.3) | 0.701 | 0.61 (0.05–7.40) | 1 (0.2) | 0.324 | 0.29 (0.02–3.44) | |
| T-allele carriers | 408 (99.5) | 119 (99.2) | 1.00 (reference) | 335 (99.7) | 1.00 (reference) | 306 (99.7) | 1.00 (reference) | 603 (99.8) | 1.00 (reference) | |||||
| CC | 2 (0.5) | 1 (0.8) | 0.675 | 1.68 (0.15–19.13) | 1 (0.3) | 0.626 | 0.54 (0.04–6.46) | 1 (0.3) | 0.690 | 0.60 (0.05–7.27) | 1 (0.2) | 0.325 | 0.29 (0.02–3.45) | |
SNPs, single-nucleotide polymorphisms. OR, odds ratios; CI, confidence interval.
aProven fertility men with semen volume ≥ 2 ml, sperm concentration ≥20 × 106/ml, sperm number per ejaculum ≥40 × 106/ml and sperm motility ≥50% motile sperm.
bIdiopathic infertile men with semen volume, 2 ml.
cIdiopathic infertile men with sperm concentration, 20 × 106/ml.
dIdiopathic infertile men with sperm number per ejaculum, 40 × 106/ml.
eIdiopathic infertile men with sperm motility, 50% motile sperm.
fThe semen parameter categories were not mutually exclusive; a subject may contribute data to more than one category.
gAdjusted for age, BMI, smoking status and drinking status.
hTwo-sided χ2 test for genotype distributions between cases and controls.
IORs were obtained from multivariate logistic regression analysis.
In order to further assess the possibility of an association between the four DNMTs genetic variants and a particular aspect of semen parameters, the patients in case Group II were further stratified into four subgroups on the basis of semen volume, sperm concentration, sperm number per ejaculum or sperm motility as described in the section of statistical analysis. As illustrated in Table 3, subjects in Subgroup II and Subgroup IV had a significantly increased frequency of the heterozygous DNMT1 CA genotype compared with fertile controls (adjusted OR, 1.43, 95% CI = 1.02–1.99, P = 0.037 for Subgroup II; adjusted OR, 1.40, 95% CI = 1.05–1.86, P = 0.023 for Subgroup IV). Under the recessive model of inheritance, subjects in Subgroup IV had a significantly decreased frequency of the DNMT1 variant homozygote compared with other genotypes (adjusted OR, 0.68, 95% CI = 0.50–0.92, P = 0.014). When the DNMT3B CC genotype was used as the reference group, the carrier of TT genotype in Subgroup IV had a significantly decreased risk of male infertility (adjusted OR, 0.26, 95% CI = 0.07–0.98, P = 0.046). The DNMT3B variant homozygote had a significantly decreased risk of male infertility in the Subgroup IV (adjusted OR, 0.25, 95% CI = 0.06–0.94, P = 0.040) compared with other genotypes (Table 3). In contrast, no significant differences were found for DNMT3A (rs1550117) and DNMT3L (rs7354779) between the cases and controls (Table 3).
Effects of the variant rs2424909 and rs4804490 on the binding of transcription factors
According to AliBaba2 programs, with the transition of “C” to “T” of the variant DNMT3B rs2424909 allele, new binding sites for transcription factors (Figure S1) were generated. Figure S2 indicated that the transition of “C” to “A” of the variant DNMT1 rs4804490 allele generated new binding sites for transcription factors. Figures S3 and S4 showed that the transition of “A” to “G” of the variant DNMT3A rs1550117 allele and “C” to “T” of the variant DNMT3L rs7354779 generated new binding sites for transcription factors.
Discussion
Several studies have revealed the close relationship of DNMTs with the male reproductive system, and potential associations with human infertility. Although it has been identified that DNMTs play critical roles in spermatogenesis and male fertility, a genetic study of four DNMTs in idiopathic male infertility has not been performed yet.
This case-control study analyzed DNMTs polymorphisms to define their association with semen parameters and male infertility. We have demonstrated a significantly increased risk of idiopathic male infertility with abnormal semen parameters in association with heterozygote of variant rs4804490, and a decreased risk of idiopathic infertility with abnormal semen parameters in association with variant homozygote of variant rs2424909 (P = 0.023 and 0.040, respectively). In addition, under the recessive model of inheritance, the AA genotype of variant rs4804490 and TT genotype of variant rs2424909 were significantly associated with decreased risk for male infertility with abnormal semen parameters. Among men with normal semen parameters, there were no significant differences in risks associated with these genotypes. Furthermore, no significant associations with risk of idiopathic infertility associated with either normal or abnormal semen parameters were found for the rs1550117 and rs7354779 variants. A recent study by Huang et al. investigated the association between DNMT3L polymorphisms and male infertility with azoospermia33. They found that the presence of rs2070565 allele A increased the risk of azoospermia in males. Additionally, they did not report any significant difference for rs7354779 between the infertile patients and fertile controls, which similar to our findings. These results suggest that different variants in the genes involved in epigenetic pathways may have different relationships with idiopathic male infertility and men with these variants have an increased or increased risk of abnormal semen parameters associated with male infertility.
Genetic disruption of both DNMT1 and DNMT3B nearly eliminated methyltransferase activity, and reduced genomic DNA methylation by greater than 95%34. These marked changes resulted in demethylation of repeated sequences, loss of insulin-like growth factor II (IGF2) imprinting, abrogation of silencing of the tumor suppressor gene p16, and growth suppression34. Rhee and colleagues demonstrated that these two enzymes cooperatively maintain DNA methylation and gene silencing in human cancer cells34. The precise mechanism of the variant rs4804490 and rs2424909 in male infertility remains unclear, as there is no direct functional data available. A single-base substitution may change the binding of transcription factors and consequently influence their function. We therefore raised hypotheses that these variations may be associated with changes of transcription factors. Changes of the binding of transcription factors were observed with both of the variations. Variants of DNMT1 and DNMT3B are risk factors for idiopathic male infertility.
In conclusion, our study demonstrates that some representative variants of the DNMT genes may modulate the risk of male infertility associated with abnormal semen parameters. Confirmation of similar findings in larger study groups would be needed. In summary, in this case-control study, we found that the carrier of DNMT1 CA genotype had a significantly increased risk of idiopathic male infertility compared with DNMT1 CC genotype carriers, although no significant difference was found in DNMT3A and DNMT3L. Our results support the hypothesis that DNMT1 polymorphism may be associated with an increased risk of idiopathic male infertility in a Han-Chinese population. These SNPs may alter a transcription factor binding site in the alternative promoter, and the variant genotype (s) may be in linkage disequilibrium with other untyped susceptibility loci. Moreover, the variants may alter the binding of a regulatory miRNA or contribute to the differential expression of alternatively spliced DNMT3B variants.
As shown in Fig. S1, the rs2424909 C > T variation creates a binding site for transcription factors GR, and deletes two binding sites for ADR1 and AP-2alph. GR is a ligand-activated transcription factor. In addition to activating enhancers harboring glucocorticoid response elements (GREs), it also inhibits the actions of other transcription factors, including AP1 and NF-κB. In ERα-positive breast cancer, GR expression has been associated with good clinical outcomes35, 36. AP-1 transcription factor, known to play a vital role in cell proliferation and neuronal activation, is also involved in cell apoptosis in response to stress, lack of survival signals or DNA damaging agents37. Nuclear factor-κB (NF-κB) transcription factor regulates a wide array of genes mediating numerous cellular processes such as proliferation, differentiation, motility and survival38. In Fig. S2, rs4804490 C > A variation creates two binding sites for transcription factors NF-1 and CTF, deleting two binding sites for c-Jun and Sp1. The CAAT box-binding transcription factor/nuclear factor-1 (NF1, also called CTF/NF1) consists of widely expressed transcription factors, comprising NF1-A, NF1-B, NF1-C and NF1-X subtypes39, 40. Transcription factor Specificity protein 1 (Sp1) plays a role in promoting oncogenes required for tumor survival, metastasis and progression. c-Jun, a component of the transcription factor AP-1, regulates gene expression and cell function.
At present, there is no evidence that these transcription factors interact with DNMTs to control their DNA binding specificity. We suggest that the genetic variants DNMT3B and DNMT1 could regulate gene expression by the creation or deletion of transcription factors and influence the sperm maturation process and male fertility. Microarray analysis and real time quantitative PCR has shown that the differential gene expression of DNMT1 between various forms of testicular cancer was consistent41. Adiga et al. documented that DNMT3B is differentially expressed42 in the preleptotene/zygotene and pachytene spermatocytes from fertile and infertile men. Future mechanistic studies about functional significance of the variation at DNMT1 and DNMT3B will help fully flesh out the relationship between polymorphism of DNMTs, abnormal DNA methylation patterns, and spermatogenic failure.
Electronic supplementary material
Acknowledgements
This work was supported by National Natural Science Foundation of China (Nos 81302457, 81673217, 81401213), National Key Research and Development Program (2016YFC1000207), and the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine).
Author Contributions
Conceived and designed the experiments: W.W., Q.T., Y.C. Conducted the experiments: Q.T., Y.C., Analyzed the data: W.W., Q.T., Y.C. Prepare figures: W.W., Q.T., Y.C. Contributed materials/analysis tools: W.W., Y.X., H.D., D.C., X.W. Wrote and revised the paper: W.W., Q.T., YC.
Competing Interests
The authors declare that they have no competing interests.
Footnotes
Qiuqin Tang and Yiqiu Chen contributed equally to this work.
Electronic supplementary material
Supplementary information accompanies this paper at doi:10.1038/s41598-017-11636-9
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Contributor Information
Wei Wu, Email: wwu@njmu.edu.cn.
Yankai Xia, Email: yankaixia@njmu.edu.cn.
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