Abstract
The DNA double-strand breaks (DSBs) repair pathway plays a critical role in repairing double-strand breaks, and genetic variants in DSBs repair pathway genes are potential risk factors for various diseases. To test the hypothesis that polymorphisms in DSBs genes are associated with susceptibility to male infertility, we examined 11 single nucleotide polymorphisms in eight key DSBs genes (XRCC3, XRCC2, BRCA2, RAG1, XRCC5, LIG4, XRCC4 and ATM) in 580 infertility cases and 580 controls from a Chinese population-based case–control study (NJMU Infertility Study). Genotypes were determined using the OpenArray platform, and sperm DNA fragmentation was detected using the TUNEL assay. The adjusted odds ratio (OR) and 95% CI were estimated using logistic regression. The results indicate that LIG4 rs1805388 (Ex2+54C>T, Thr9Ile) T allele could increase the susceptibility to male infertility (adjusted OR=2.78; 95% CI, 1.77–4.36 for TT genotype; and adjusted OR=1.58; 95% CI, 1.77–4.36 for TC genotype respectively). In addition, the homozygous variant genotype GG of RAG1 rs2227973 (A>G, K820R) was associated with a significantly increased risk of male infertility (adjusted OR, 1.44; 95% CI, 1.01–2.04). Moreover, linear regression analysis revealed that carriers of LIG4 rs1805388 or RAG1 rs2227973 variants had a significantly higher level of sperm DNA fragmentation and that T allele carriers of LIG4 rs1805388 also had a lower level of sperm concentration when compared with common homozygous genotype carriers. This study demonstrates, for the first time, to our knowledge, that functional variants of RAG1 rs2227973 and LIG4 rs1805388 are associated with susceptibility to male infertility.
Introduction
Male infertility is a common reproductive disorder and a major cause of conception failure in humans that is responsible for 40–60% of infertility cases (Esteves et al. 2011, Ji et al. 2012). Though the aetiologies of ∼50% of cases are still poorly understood (De Kretser & Baker 1999), sperm DNA damage is thought to be a cause of male infertility and has been an area of intense research throughout the past decade (Sakkas et al. 1999). It has been demonstrated that DNA damage in human spermatozoa is associated with poor semen quality, low fertilisation rates, impaired preimplantation development, increased abortion and an elevated incidence of disease in the offspring (e.g. childhood cancer) (Ji et al. 1997, Borini et al. 2006, Cohen-Bacrie et al. 2009). Among the many types of DNA damage, DNA double-stranded breaks (DSBs) are the most severe (Khanna & Jackson 2001) and are a result of ionising radiation, chemotherapeutic drugs, defective chromatin packaging, apoptosis, oxidative damage, replication errors and genetically programmed processes such as recombination in meiosis, and variable (diversity) joining (V(D)J) recombination (Gellert et al. 1999, Dresser 2000, Khanna & Jackson 2001, Agarwal & Said 2003, Zha et al. 2007, Lieber 2010).
In mammalian cells, DSBs are mostly repaired by the DSBs repair system. The repair of DSBs is a complex process. The first response is sensing a DSB. This process is mediated by the MRN (MRE11A (MRE11), RAD50 and NBN (NBS1)) complex and ATM protein kinase (Lee & Paull 2005). Once DSBs are recognised, cells can repair a break lesion in one of the two ways: non-homologous end-joining (NHEJ) and homologous recombination (HR). HR is of high fidelity and error free, whereas NHEJ is potentially error prone (Khanna & Jackson 2001). In the NHEJ pathway, the XRCC5/XRCC6 protein dimer initially binds to broken ends of DNA and activates DNA-PKcs (Abe et al. 2008). Ligation is carried out by the LIG4–XRCC4 complex to complete the repair (Grawunder et al. 1997). This pathway is also required for the completion of RAG-initiated lymphocyte-specific V(D)J recombination (Lieber 2010). In HR pathway, RAD51 mediates the crucial step of searching for a homologous duplex template. RAD51 then interacts with other important repair proteins, including BRCA1, BRCA2, XRCC2 and XRCC3 at various steps of HR (van Gent et al. 2001, San Filippo et al. 2008).
Genetic variants of genes involved in the DSBs repair pathway have been extensively studied in various diseases (Han et al. 2004, Auranen et al. 2005, Figueroa et al. 2007, Liu et al. 2007, Tseng et al. 2009). However, whether genetic variants in the DSBs repair pathway genes affect susceptibility to male infertility remains largely unknown. Thus, the aim hereby was to investigate the relationship between 11 functional polymorphisms in eight key genes (XRCC3, XRCC2, BRCA2, RAG1, XRCC5, LIG4, XRCC4 and ATM) of the DSBs repair pathway and the risk of male infertility. In addition, we also evaluated their influence on DNA damage detected with TUNEL assay.
Results
Characteristics of the study population
The frequency distributions of selected characteristics of the case patients and control subjects are presented in Table 1. Briefly, cases and controls were well matched for age, drinking status, BMI and abstinence time (P>0.05). However, there were more smokers in the case group compared with the control group (P=0.046). Among smokers, cases also reported significantly greater cigarette consumption than controls, as assessed by the mean number of pack-years (P=0.019). Moreover, there was a significant difference between cases and controls with respect to semen concentration and sperm motility (P<0.001), but not semen volume (P=0.809).
Distribution of selected characteristics between cases and fertile controls.
| Variables | Cases (n=580); n (%) | Controls (n=580); n (%) | Pa |
|---|---|---|---|
| Age | |||
| ≥20 and <30 | 326 (56.3) | 299 (51.6) | 0.067 |
| ≥30 and <40 | 246 (42.4) | 278 (47.9) | |
| ≥40 and <50 | 8 (1.4) | 3 (0.5) | |
| Age (mean±s.d.) | 28.2±3.3 | 28.1±3.2 | 0.600 |
| Smoking status | |||
| Never | 272 (46.9) | 306 (52.8) | 0.046 |
| Ever | 308 (53.1) | 274 (47.2) | |
| Pack-years (mean±s.d.)b | 4.7±4.4 | 4.1±4.3 | 0.019 |
| Drinking status | |||
| Never | 500 (86.2) | 504 (86.8) | 0.731 |
| Ever | 80 (13.8) | 76 (13.1) | |
| BMI (kg/m2) | |||
| <25 | 423 | 413 | 0.246 |
| 25–29.9 | 149 | 153 | |
| ≥30 | 8 | 14 | |
| BMI (mean±s.d.) (kg/m2) | 23.3±0.1 | 23.4±0.1 | 0.825 |
| Abstinence time (days) | |||
| 3–5 | 304 (52.4) | 321 (55.3) | 0.272 |
| >5 | 276 (47.6) | 259 (44.6) | |
| Semen parameters (mean±s.d.) | |||
| Concentration (×106/ml) | 73.6±49.37 | 115.2±76.58 | <0.001 |
| Motility (%) | 37.9±13.49 | 65.3±12.52 | <0.001 |
| Volume (ml) | 2.78±1.44 | 2.80±1.37 | 0.809 |
| Sperm DNA fragmentation (%) | 20.98±15.30 | ND | |
ND, not detected.
P values were derived from the χ2 test for categorical variables (age, BMI, smoking and drinking status) and t-test for continuous variables (age, pack-years and BMI).
Among ever smokers.
Individual single nucleotide polymorphism, gene–gene interaction and susceptibility to male infertility
Primary information on the 11 functional single nucleotide polymorphisms (SNPs) found in the Chinese population in the HapMap database is shown in Table 2. All tested genotypes were in Hardy–Weinberg equilibrium in control groups (P>0.05). The main effect models for all the genotypes are presented in Table 3. Overall, two SNPs exhibited significant associations with the risk of male infertility. Carriers with RAG1 rs2227973 homozygous variant genotype (GG) exhibited a significantly increased risk of male infertility (adjusted odds ratio (OR), 1.44; 95% CI, 1.01–2.04) when compared with the common homozygous genotype (AA). Moreover, LIG4 rs1805388 variant genotypes (TT or CT) were associated with significantly increased risks of male infertility compared with the common homozygous CC genotype (adjusted OR, 1.58; 95% CI, 1.23–2.01 for CT; and adjusted OR, 2.78; 95% CI, 1.77–4.36 for TT respectively). Trend χ2 test showed that the male infertility risk was significantly increased in a dose-dependent manner (Ptrend<0.001).
Primary information for 11 genotyped SNPs in DNA DSBs repair pathway genes.
| Polymorphisms | |||||
|---|---|---|---|---|---|
| Symbol | Gene name | ID no. | Base (amino acid) change | MAFa | P value for HWE test |
| XRCC3 | X-ray repair cross-complementing group 3 | rs861539 | C>T (T241M) | 0.058 | 0.923 |
| rs1799794 | G>A (5′-UTR) | 0.478 | 0.156 | ||
| rs1799796 | A>T (IVS6-14) | 0.289 | 0.317 | ||
| XRCC2 | X-ray repair cross-complementing group 2 | rs3218385 | T/G (5′-UTR) | 0.155 | 0.706 |
| BRCA2 | Breast cancer 2 | rs1799943 | G>A (5′-UTR) | 0.279 | 0.410 |
| rs15869 | A>C (3′-UTR) | 0.256 | 0.135 | ||
| RAG1 | Recombination activating gene 1 | rs2227973 | A>G (K820R) | 0.465 | 0.362 |
| XRCC5 | X-ray repair cross-complementing group 5 | rs1051685 | A>G (3′-UTR) | 0.070 | 0.868 |
| LIG4 | Ligase 4 | rs1805388 | C>T (T9I) | 0.261 | 0.756 |
| XRCC4 | X-ray repair cross-complementing group 4 | rs1805377 | A>G (splice site) | 0.291 | 0.208 |
| ATM | Ataxia-telangiectasia mutated | rs189037 | G>A (5′-UTR) | 0.389 | 0.142 |
MAF, minor allele frequency; HWE, Hardy–Weinberg equilibrium.
Minor allele frequency in the Chinese (CHB, Han-Chinese in Beijing, China) population, as reported in dbSNP database.
Associations between SNPs in DNA DSBs repair pathway genes and risk of male infertility.
| Homozygotes (for common allele) | Heterozygotes | Homozygotes (for rarer allele) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| SNP | Cases | Controls | Cases | Controls | OR (95% CI)a | Cases | Controls | OR (95% CI)a | Ptrendb |
| XRCC3: rs861539 | 511 | 498 | 62 | 75 | 0.79 (0.55–1.12) | 4 | 3 | 1.27 (0.28–5.69) | 0.356 |
| XRCC3: rs1799794 | 155 | 165 | 292 | 271 | 1.09 (0.83–1.44) | 128 | 141 | 0.92 (0.66–1.27) | 0.899 |
| XRCC3: rs1799796 | 268 | 262 | 254 | 245 | 0.95 (0.75–1.22) | 56 | 69 | 0.75 (0.50–1.10) | 0.419 |
| XRCC2: rs3218385 | 420 | 432 | 141 | 134 | 1.07 (0.81–1.40) | 12 | 9 | 1.35 (0.56–3.24) | 0.411 |
| BRCA2: rs1799943 | 254 | 268 | 271 | 256 | 1.07 (0.84–1.36) | 59 | 52 | 1.14 (0.76–1.72) | 0.280 |
| BRCA2: rs15869 | 307 | 330 | 216 | 203 | 1.10 (0.86–1.41) | 52 | 43 | 1.25 (0.81–1.93) | 0.149 |
| RAG1: rs2227973 | 175 | 206 | 293 | 285 | 1.17 (0.90–1.52) | 106 | 84 | 1.44 (1.01–2.04) | 0.025 |
| XRCC5: rs1051685 | 478 | 487 | 93 | 84 | 1.11 (0.80–1.53) | 5 | 4 | 1.25 (0.33–4.69) | 0.420 |
| LIG4: rs1805388 | 249 | 330 | 257 | 213 | 1.58 (1.23–2.01) | 68 | 32 | 2.78 (1.77–4.36) | <0.001 |
| XRCC4: rs1805377 | 273 | 289 | 248 | 229 | 1.12 (0.88–1.43) | 64 | 58 | 1.14 (0.77–1.69) | 0.264 |
| ATM: rs189037 | 206 | 193 | 288 | 303 | 0.84 (0.65–1.08) | 82 | 79 | 0.92 (0.63–1.32) | 0.665 |
Data in boldface represent P<0.05.
Odds ratio was age, cigarette smoking, alcohol use and BMI.
False discovery rate (FDR) corrected P value.
We further evaluated the combined effects of the two high-risk genotypes on male infertility risk by summing the unfavourable genotypes of RAG1 rs2227973 and LIG4 rs1805388. As shown in Table 4, 46.3% of the cases and 32.5% of the controls had variant genotypes at both loci (RAG1 KR/RR and LIG4 TI/II), and carriers of both loci had an increased risk of male infertility (adjusted OR, 1.63; 95% CI, 1.20–2.21). However, no significant additive interaction between the variant genotypes of rs2227973 and rs1805388 was detected for the development of male infertility (Pinteraction=0.130).
Interaction of RAG1 R820K and LIG4 T9I for male infertility risk.
| RAG1 K820R | LIG4 T9I | Cases, n (%) | Controls, n (%) | OR (95% CI)a |
|---|---|---|---|---|
| KK | TT | 116 (20.21) | 148 (25.74) | 1.00 |
| KK | TI/II | 59 (10.28) | 58 (10.09) | 1.17 (0.75–1.80) |
| KR/RR | TT | 133 (23.17) | 182 (31.65) | 0.84 (0.60–1.16) |
| KR/RR | TI/II | 266 (46.34) | 187 (32.52) | 1.63 (1.20–2.21) |
| Pinteractionb | 0.130 |
Odds ratio was age, cigarette smoking, alcohol use and BMI.
P for additive interaction.
Association of sperm concentration and sperm DNA fragmentation with gene polymorphisms
Considering the important role of DSBs repair pathway genes in maintaining DNA integrity, we further evaluated the associations of sperm concentration and sperm DNA fragmentation with all the tested gene polymorphisms (Table 5). Despite the high variation in DNA fragmentation among the patients (Fig. 1), we observed that the RAG1 rs2227973 and the LIG4 rs1805388 variants were positively associated with higher levels of sperm DNA fragmentation (all β>0 and P<0.05). Moreover, we found that LIG4 rs1805388 variants were negatively associated with sperm concentration (β=−0.164, P=0.006).
Effects of SNPs in DNA DSBs repair pathway genes on sperm concentration and sperm DNA fragmentation.
| Sperm concentration | Sperm DNA fragmentation | |||||
|---|---|---|---|---|---|---|
| SNP | βa | 95% CI | Pb | βa | 95% CI | Pb |
| XRCC3: rs861539 | −0.085 | −0.252, 0.082 | 0.318 | −0.082 | −0.200, 0.036 | 0.173 |
| XRCC3: rs1799794 | −0.011 | −0.201, 0.179 | 0.909 | 0.049 | −0.084, 0.182 | 0.470 |
| XRCC3: rs1799796 | 0.065 | −0.052, 0.183 | 0.277 | −0.032 | −0.152, 0.089 | 0.605 |
| XRCC2: rs3218385 | −0.006 | −0.182, 0.169 | 0.943 | 0.013 | −0.110, 0.137 | 0.831 |
| BRCA2: rs1799943 | −0.004 | −0.174, 0.166 | 0.964 | 0.036 | −0.084, 0.156 | 0.554 |
| BRCA2: rs15869 | 0.103 | −0.027, 0.232 | 0.119 | −0.002 | −0.122, 0.118 | 0.973 |
| RAG1: rs2227973 | −0.149 | −0.332, 0.035 | 0.113 | 0.172 | 0.002, 0.342 | 0.047 |
| XRCC5: rs1051685 | −0.012 | −0.184, 0.181 | 0.984 | −0.026 | −0.154, 0.102 | 0.691 |
| LIG4: rs1805388 | −0.164 | −0.282, −0.047 | 0.006 | 0.216 | 0.046, 0.386 | 0.013 |
| XRCC4: rs1805377 | −0.112 | −0.281, 0.056 | 0.191 | −0.010 | −0.177, 0.158 | 0.909 |
| ATM: rs189037 | 0.150 | −0.019, 0.320 | 0.082 | −0.013 | −0.132, 0.106 | 0.829 |
β, regression coefficient. Data in boldface represent P<0.05.
All analyses were done using linear regression models, adjusted for age, smoking status, drinking status, abstinence time and BMI.
False discovery rate (FDR) corrected P value.
Box-and-whisker plots for sperm DNA fragmentation in study subjects, divided into three groups according to genotype. The boxes represent the 25th and 75th percentiles; whiskers are lines extending from each end of the box covering the extent of the data on 1.5×inter-quartile range. The median value is denoted by the line that bisects the boxes. Circles represent the outlier values.
Citation: REPRODUCTION 145, 5; 10.1530/REP-12-0370
Box-and-whisker plots for sperm DNA fragmentation in study subjects, divided into three groups according to genotype. The boxes represent the 25th and 75th percentiles; whiskers are lines extending from each end of the box covering the extent of the data on 1.5×inter-quartile range. The median value is denoted by the line that bisects the boxes. Circles represent the outlier values.
Citation: REPRODUCTION 145, 5; 10.1530/REP-12-0370
Box-and-whisker plots for sperm DNA fragmentation in study subjects, divided into three groups according to genotype. The boxes represent the 25th and 75th percentiles; whiskers are lines extending from each end of the box covering the extent of the data on 1.5×inter-quartile range. The median value is denoted by the line that bisects the boxes. Circles represent the outlier values.
Citation: REPRODUCTION 145, 5; 10.1530/REP-12-0370
Discussion
Accumulating evidence demonstrates that the DSBs repair pathway plays a critical role in repairing DSBs, and polymorphisms in DSBs repair pathway genes are potential risk factors for various diseases. Considering the fact that inadequacy or defects in sperm DSBs repair can lead to large-scale loss of genetic information and affect the germ line for generations (Lewis & Aitken 2005), we hypothesised that polymorphisms in DSBs repair pathway genes were involved in susceptibility to male infertility.
In this study, we assessed the effects of 11 functional SNPs in DSBs repair genes on male infertility and found that two nonsynonymous SNPs of RAG1 rs2227973 and LIG4 rs1805388 were associated with significantly increased male infertility risks, even after adjustment for multiple testing. In parallel, strong associations were detected between these two significant SNPs and sperm DNA fragmentation or sperm concentration. These findings support our previous hypothesis, and, to the best of our knowledge, this is the first systematic pathway-based study on the role of polymorphisms of DSBs repair pathway genes on male infertility risk.
The LIG4 protein plays a key role in NHEJ with regard to ligating and filling the gap in the broken DNA strands with XRCC4 to complete repair (Karran 2000). Northern blots have indicated that LIG4 is present at higher levels in testis than in other tissues (Wei et al. 1995). In mammalian cells, the LIG4 protein exists as a pre-adenylate complex (Robins & Lindahl 1996), with a N-terminal conserved ligase domain and two C-terminal BRCT domains (Girard et al. 2004). The LIG4 rs1805388 (T9I) polymorphism results in a nonsynonymous amino acid change from threonine to isoleucine in the N-terminal region of the LIG4 protein that is essential for its activity (Grawunder et al. 1998). It has been demonstrated that this substitution in the N-terminal region of LIG4 mildly but reproducibly reduces adenylation and ligation activities (two- to threefold) and increases the hydrophobic nature of this region of the protein (O'Driscoll et al. 2001, Girard et al. 2004). So, it is conceivable that the LIG4 T9I alteration may alter the interactions of LIG4 with XRCC4, or with other components of the NHEJ pathway. Consistent with the known functionality of the polymorphism, we found that the LIG4 9I variants were associated with significantly decreased DNA repair capacity and increased risk of male infertility.
Some previous studies have found evidence that LIG4 polymorphism is a risk factor for certain diseases. The LIG4 T9I polymorphism was initially identified in an LIG IV syndrome patient characterised by microcephaly, characteristic facial features, growth retardation, developmental delay and immunodeficiency (Roddam et al. 2002, Ben-Omran et al. 2005), thus suggesting a deleterious effect of the polymorphisms upon LIG4 function. Recently, Yin et al. (2012) reported that LIG4 T9I polymorphism had a significant effect on the risk of developing severe radiation pneumonitis among non-small-cell lung cancer (NSCLC) patients. Moreover, a significant association between LIG4 T9I polymorphism and NSCLC susceptibility was found in a Chinese case–control study with an overall adjusted OR of 1.64 (95% CI 1.03–2.62) (Tseng et al. 2009). These findings indicate that diseases may share some fraction of genetic factors.
Regarding RAG1, it initiates V(D)J recombination with RAG2 by introducing DSBs at recombination signal sequences (McBlane et al. 1995). It then aids in the DNA ligation processing along with the additional components of the NHEJ pathway (Kale et al. 2001). The RAG1 K820R polymorphism results in a nonsynonymous amino acid change from lysine to arginine at C-terminal domain, a domain that binds double-stranded DNA cooperatively and nonspecifically and mediates coding-end contacts (Arbuckle et al. 2001, Mo et al. 2001). Consistent with the functionality of the RAG1 K820R polymorphism, we found that the RAG1 K820R variant was associated with significantly decreased DNA recombining capacity and increased risk of male infertility.
To date, only a few studies have demonstrated that RAG1 K820R polymorphism is a risk factor for a particular disease. One study reported that homozygotes for the RAG1 820 R missense substitution had a 2.7-fold increased risk of non-Hodgkin lymphoma and presented data suggesting a gene dosage effect (Hill et al. 2006). Another study also found that carriers of variant RAG1 820R allele exhibited a significantly increased risk for bladder cancer (Wu et al. 2006). These findings raise the possibility that decreased RAG1 activity plays a role in human diseases and indicates that our findings are biologically plausible. Thus, studies exploring the effects of the RAG1 K820R polymorphism on susceptibility to additional conditions and diseases, including male infertility, should be conducted in the future.
In addtion to the two SNPs discussed earlier, we examined another nine SNPs in six DSB repair genes, including XRCC3 (rs861539, rs1799794 and rs1799796), XRCC2 rs3218385, BRCA2 (rs1799943 and rs15869), XRCC5 rs1051685, XRCC4 rs1805377 and ATM rs189037. XRCC3 belongs to the RecA/Rad51-related protein family, which is involved in HR repair pathway. XRCC3 rs861539 SNP changes the amino acid from a neutral hydrophilic residue with a hydroxyl group to a hydrophobic one with a methyl sulphur group, which may result in a substantial change in protein structure and function (Mittal et al. 2012). The XRCC3 rs1799794 SNP, which is located in the 5′-UTR region, causes the loss of an upstream open reading frame (Fachal et al. 2012) and then affects gene expression. XRCC3 rs1799796 is non-coding. XRCC2 is an essential part of the HR repair pathway and a functional candidate for involvement in tumour progression (Mohindra et al. 2002, Thacker & Zdzienicka 2004). The rs3218385 SNP in XRCC2 is located in the 5′-UTR, which might affect gene expression. The BRCA2 gene functions as a tumour-suppressor gene and plays a pivotal role in the control of the HR repair pathway (Venkitaraman 2002). BRCA2 rs1799943 SNP might modulate BRCA2 transcript levels as indicated by in silico analysis (Rajasekaran et al. 2008). The BRCA2 rs15869 SNP is located in the 3′-UTR, which might affect mRNA stability and translation. XRCC5 rs1051685, located in an exonic splice enhancer sequence, results in exon skipping, errors in alternative splicing patterns and affects mRNA stability and translation (Cibeira et al. 2011). XRCC4 rs1805377 may have functional significance as the nucleotide change from A to G potentially abolishes an acceptor splice site at exon 8 (Dore et al. 2006) and subsequently affects mRNA stability. The ATM rs189037 SNP, located in the promoter of the ATM gene, affects the expression of ATM mRNA by differentially binding to activator protein 2α (AP-2α; Chen et al. 2010), which then affect the process of sensing DNA damage. In this case–control study, we did not find any association between these SNPs and risk of male infertility, possibly due to insufficient statistical power. Thus, large-scale studies are needed to confirm our results in the future.
Several strengths of our study should be acknowledged. i) A pathway-based approach was conducted to estimate the effects of selected gene polymorphisms on male infertility. ii) All tested SNPs were in Hardy–Weinberg equilibrium in controls. iii) All cases and controls were racially homogeneous (all Han-Chinese) and well matched with regard to age and drinking status, which minimises potential confounding bias. iv) The genotyping method for the study was standardised, and quality control samples indicated high reproducibility of the genotyping results. However, there are some limitations of our study. i) We were unable to explore the specific biological mechanism of how RAG1 rs2227973 and LIG4 rs1805388 polymorphisms affect male infertility. ii) The moderate sample size might not be sufficient for us to adequately detect a small effect from very low-penetrance SNPs and evaluate gene–gene interactions between the two high-risk SNPs. iii) We used the TUNEL assay to detect sperm DNA fragmentation. However, the TUNEL assay measures both single- and double-strand DNA fragmentation by detecting a definitive end point (free 3-OH terminus). So, using this method might influence interpretation of the results. Among other commonly used tests to measure sperm DNA damage, such as sperm chromatin structure assay, acridine orange test, alkaline and neutral comet assay, only the neutral comet assay is specific for identification of double-stranded DNA breaks (Olive et al. 1991). Interestingly, it has been reported that the output of TUNEL and neutral comet assays are correlated to some degree (Caglar et al. 2007). So, we believe that the potential misclassification and bias, if any, is unlikely to be substantial.
In conclusion, of the 11 potential functional polymorphisms investigated here, we provide the first evidence that the LIG4 rs1805388 and RAG1 rs2227973 polymorphisms contribute to the risk of male infertility. These findings may be helpful in improving our understanding of the aetiology of male infertility. Future large-scale studies with ethnically diverse populations and functional evaluations are needed to validate our findings.
Materials and Methods
Subjects and sample collection
The study was approved by the Ethics Review Board of Nanjing Medical University. All studies involving human subjects were conducted under full compliance with government policies and the Helsinki Declaration. A total of 1657 infertile patients, diagnosed with unexplained male factor infertility, were drawn from the Centre of Clinical Reproductive Medicine (NJMU infertility centre) between April 2005 and March 2009. All patients had undergone complete historical and physical examinations. Those with a history of orchitis, cryptorchidism, obstruction, congenital bilateral absence of vas deferens, cytogenetic abnormalities and Y chromosome microdeletions were excluded from this study (Wu et al. 2007, Lu et al. 2009). A total of 580 subjects ranging from 24 to 42 years old were chosen for this study. The control group consisted of 580 fertile men ranging from 25 to 40 years old who had fathered at least one child without assisted reproductive technologies. All participants were ethnic Han-Chinese and completed an informed consent form. Participants completed a detailed questionnaire that included information pertaining to their age, cigarette smoking frequency, alcohol and tea consumption, vitamin regimen and abstinence time. Each subject then donated 5 ml blood that was used to extract genomic DNA. In addition, the semen samples were collected after at least 3 days of sexual abstinence. The liquefied samples were divided into two parts, one for the routine semen analysis and the other for the assessment of sperm DNA fragmentation. The semen was analysed for sperm concentration, motility and morphology, and the analysis was performed according to the WHO criteria (Lu et al. 2010).
SNP selection and genotyping
The SNPs were selected based on the following criteria: i) they had a high frequency of the rare allele (to allow the highest statistical power to detect associations) and ii) they were of potential functional significance, i.e. those located at 5′-flaking regions, UTRs, coding regions or 3′-UTRs, according to the databases of the International HapMap Project and NCBI dbSNPs. Finally, we identified 11 potential functional polymorphisms from eight crucial genes involved in DSBs repair pathway (Table 2).
Genomic DNA was isolated from leukocyte pellets of the venous blood by phenol–chloroform extraction with proteinase K digestion. Genotyping was performed using the OpenArray platform (Applied Biosystems), which employs a chip-based Taq-Man genotyping technology. Genotyping was conducted according to the manufacturer's protocol, and genotypes were identified by the OpenArray SNP Genotyping Analysis Software version 1.0.3. For quality control, the genotyping was performed without knowledge of the subjects' case/control status, and a random 5% of cases and controls were genotyped twice by different individuals; the reproducibility rate was 100%. To confirm the genotyping results, PCR-amplified DNA samples (n=2 for each genotype) were examined by DNA sequencing. These results were also consistent.
DNA fragmentation analysis
The TUNEL assay has been shown to be a feasible and sensitive way to detect DNA fragmentation (Muratori et al. 2008). We used the APO-DIRECT kit (BD Biosciences Pharmingen, San Diego, CA, USA) according to the manufacturer's protocol. Briefly, sperm were washed and resuspended in 2% paraformaldehyde for 30 min at room temperature. After being rinsed with PBS, samples were resuspended in permeabilisation solution (0.2% Triton X-100, 0.1% sodium citrate) for 10 min on ice. Fifty millilitres of TUNEL reagent were then added to the sample. For each batch, samples that were not treated with the Tdt enzyme were used as negative controls, and samples treated with DNase I were included as positive controls. After incubation for 1 h at 37 °C, samples were analysed immediately by flow cytometry (FACSCalibur; BD Biosciences Pharmingen) (Sergerie et al. 2000).
Statistical analysis
The statistical analyses were performed with the Statistical Analysis System (version 9.1.3, SAS Institute, Cary, NC, USA). Differences in selected demographic variables as well as smoking and alcohol status between the cases and the controls were evaluated by the χ2 test. The Student's t-test was used to evaluate continuous variables, including age and pack-years of cigarette smoking. Hardy–Weinberg equilibrium was tested by a goodness-of-fit χ2 test. The risk of male infertility was estimated as OR and 95% CI using unconditional multivariate logistic regression. When appropriate, the ORs were adjusted for age, cigarette smoking and alcohol use. Linear regression models were used to estimate the association of the natural logarithm (ln) transformation sperm concentration and In-transformed sperm DNA fragmentation values for each SNP independently.
The potential gene–gene interactions were evaluated by logistic regression analyses and tested by comparing the changes in deviance (−2 log likelihood) between models of main effects with or without the interaction term. The false discovery rate method was applied to the P values for trends to reduce the potential for spurious findings due to multiple testing. All P values were two-sided, with P<0.05 considered to be statistically significant.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
This work was supported by the Key Project of National Natural Science Foundation of China (Grant No. 30930079); National Natural Science Foundation of China (Grant No. 81202243 and Grant No. 81172694); and the Natural Science Foundation of Jiangsu Province (Grant No. BK2012087). A project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Acknowledgements
The authors thank Yongyue Wei for statistical analysis.
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