Abstract
Genetic polymorphisms may affect human male fertility. Even though TP53 plays a role in spermatogenesis we know little about the association of the functional polymorphism at codon 72 of TP53 with respect to susceptibility to male infertility. We conducted a case-control study to investigate this association in a Chinese population and performed a meta-analysis in different populations to clarify this association. The single nucleotide polymorphism (SNP) of TP53 codon 72 (rs1042522 G>C) was genotyped by PCR-RFLP in 83 Chinese male infertility patients and 401 healthy controls. Meta-analysis was performed using the data from four currently available studies. The data from our study were overlayed using the v.9.0 STATA software package. We observed no association between the TP53 codon 72 polymorphism and male infertility (p = 0.84, OR = 1.04, 95% CI, 0.74–1.45). Meta-analysis confirmed the case-control result that there was no significant association between the codon 72 polymorphism of TP53 and male infertility (Pro vs. Arg; p = 0.31, OR = 0.86, 95% CI, 0.65–1.15; Pro/Pro vs. Arg-carriers; p = 0.65, OR = 0.91, 95% CI, 0.61–1.36; Pro-carriers vs. Arg/Arg: p = 0.15, OR = 0.75, 95% CI, 0.51–1.11). The data presented in this communication supports the view that the codon 72 polymorphism of TP53 may not contribute to male infertility susceptibility in the Chinese population.
Introduction
Infertility is becoming a major global health concern as ∼7–10% of couples are unable to conceive during a year of normal unprotected intercourse, and 20–25% of these cases can be attributed to male factors [De Kretser and Baker Citation1999]. Male infertility mainly results from azoospermia or oligospermia [Filipponi and Feil Citation2009], primarily from Y chromosome microdeletions [Sadeghi-Nejad and Farrokhi Citation2009]. Multiple factors are known to impact spermatogenesis, such as environmental disruptors, genetics, testes pathologies, and multivariant lifestyles [Montjean et al. Citation2012]. The interest towards genetic susceptibility factors contributing to male infertility is increasing, However, only a small proportion of the genes and few polymorphisms have been identified in the infertile male [Carrell et al. Citation2006] due to the multifactorial nature of male infertility.
Apoptosis is a key step during normal spermiogenesis to remove defective germ cells and maintain normal spermatogenesis [Matsui Citation1998]. The tumor suppressor protein, p53, can mediate multiple cellular functions, such as tumor suppression, gene transcription, DNA synthesis and repair, angiogenesis, differentiation, cell cycle regulation, and cell senescence and apoptosis [Vousden and Lane Citation2007]. The P53 protein is present in human, mouse, and rat testes [Almon et al. Citation1993], located on chromosome 17p13 [Wu et al. Citation1995]. In general, during spermatogenesis TP53 regulates apoptosis to control germ cell quality [Zauberman et al. Citation1995]. In primary spermatocytes TP53 plays a critical role in the prophase of meiosis [Chen et al. Citation2012].
Recently, three reports have focused on the association of the codon 72 polymorphism of TP53 and spermatogenic failure [Huang et al. Citation2012; Lu et al. Citation2007; Mashayekhi and Hadiyan Citation2012]. The most important functional polymorphism at codon 72 results in either the arginine or proline form of the p53 protein. It has been suggested that the 72R variant is more efficient in inducing cell death than the 72P variant in some cell types [Thomas et al. Citation1999]. Previous small sample size studies gave way to an over-estimation of the association. We have, therefore, analyzed TP53 codon 72 polymorphism in an additional set of samples (n = 83) to further elucidate the correlation between this polymorphic variant and male infertility in the Chinese population. We also performed a meta-analysis based on the published case control studies which discussed the association of the codon 72 polymorphism of TP53 with male infertility, including our data.
Results
The association case-control study between TP53 codon 72 SNP and male infertility included 83 men with idiopathic infertility (average age of 29.92 years) and 401 controls (average age of 31.48 years). There was a significant difference in mean age, serum FSH, LH, testosterone, and mean sperm density between patients and the controls ().
Table 1. Clinical characteristics of the study subjects.
As shown in , the frequencies of arginine allele (Arg)/Arg, proline allele (Pro)/Arg, and Pro/Pro genotypes, in the controls were 24.20%, 53.40%, and 22.40%, respectively. The genotype frequencies in the infertile patients were similar to those in the controls: Arg/Arg, Pro/Arg, and Pro/Pro genotypes were 28.90%, 42.20%, and 28.90%, respectively. Using the Arg/Arg genotype as a reference, the odds ratio (OR) of the Pro/Arg and Pro/Pro genotypes were 0.66 (95% confidence interval (CI) = 0.37–1.17) and 1.08 (95% CI = 0.57–2.03), respectively. We found no difference in the allelic (p = 0.84, OR = 1.04, 95% CI, 0.74–1.45) distributions and genotypic distributions under a dominant and recessive model (p = 0.26, OR = 1.28, 95% CI, 0.75–2.16; p = 0.22, OR = 1.41, 95% CI, 0.83–2.39). The Hardy–Weinberg equilibrium (HWE) p value in controls was p > 0.05 ().
Table 2. Analysis of association between TP53 codon 72 polymorphism and the male infertility.
Table 3. Characteristics of case-control studies included in this meta-analysis of TP53 codon 72 polymorphism.
Meta-analysis
Meta-analysis of three additional studies [Jin et al. Citation2013; Lu et al. Citation2012; Mashayekhi and Hadiyan Citation2012] examining the functional relationship of the codon 72 SNP of TP53 with fertility were identified then compared to the current study. The combined study population included 1,134 patients and 1,545 controls from Asia and Europe. A summary of the meta-analysis of the association between the TP53 codon 72 polymorphism and male infertility is provided in . present forest plots of male infertility association with distribution of TP53 codon 72 polymorphism in an additive model, recessive model, and dominant model, respectively. The analysis suggests that the TP53 codon 72 polymorphism was not associated with male infertility susceptibility in any genetic model (additive model; p = 0.31, OR = 0.86, 95% CI, 0.65–1.15; recessive model; p = 0.65, OR = 0.91, 95% CI, 0.61–1.36; dominant model: p = 0.15, OR = 0.75, 95% CI, 0.51–1.11). The distribution of the TP53 codon 72 polymorphism in the control group was consistent with the HWE in all studies (). Egger’s regression test showed no evidence of publication bias in this meta-analysis of TP53 codon 72 polymorphism in any of the included studies (Egger’s regression test p > 0.1; ).
Figure 1. Forest plot of male infertility association with distribution of TP53 codon 72 polymorphism in recessive model. A forest plot of odds ratios (OR) of male infertility for Pro/Pro when compared to Arg carriers in order of publication year was constructed. The squares and horizontal lines correspond to the OR and 95% confidence interval (CI) of each study. The center of each square represents the OR; the horizontal line shows the corresponding 95% CI of the OR. The diamond represents the pooled OR and 95% CI (OR = 0.91; 95% CI: 0.61–1.36). The pooled OR was obtained using a random-effects model and is represented by hollow diamonds, where its center indicates the OR, and its ends correspond to the 95% CI. The area of the squares reflects the relative weight of each study. The weighting factors (weight %) used to calculate the aggregate OR, calculated from the inverse of the variance, is given for each study.
Figure 2. Forest plot of male infertility association with distribution of TP53 codon 72 polymorphism in additive model. A forest plot of odds ratio (OR) of male infertility for Pro allele when compared to Arg allele in order of publication year was constructed. The squares and horizontal lines correspond to the OR and 95% confidence interval (CI) of each study. The center of each square represents the OR; the horizontal line shows the corresponding 95% CI of the OR. The diamond represents the pooled OR and 95% CI (OR = 0.86; 95% CI: 0.64–1.15). The pooled OR was obtained using a random-effects model and is represented by hollow diamonds, where its center indicates the OR, and its ends correspond to the 95% CI. The area of the squares reflects the relative weight of each study. The weighting factors (weight %) used to calculate the aggregate odds ratio, calculated from the inverse of the variance, is given for each study.
Figure 3. Forest plot of male infertility association with distribution of TP53 codon 72 polymorphism in dominant model. A forest plot of odds ratio (OR) of male infertility for Pro carriers when compared to Arg/Arg in order of publication year was constructed. The squares and horizontal lines correspond to the OR and 95% confidence interval (CI) of each study. The center of each square represents the OR; the horizontal line shows the corresponding 95% CI of the OR. The diamond represents the pooled OR and 95% CI (OR = 0.75; 95% CI: 0.51–1.11). The pooled OR was obtained using a random-effects model and is represented by hollow diamonds, where its center indicates the OR, and its ends correspond to the 95% CI. The area of the squares reflects the relative weight of each study. The weighting factors (weight %) used to calculate the aggregate odds ratio, calculated from the inverse of the variance, is given for each study.
Table 4. Meta-analysis of the association between TP53 codon 72 polymorphism and male infertility.
Discussion
Evidence has been accumulated that TP53-mediated apoptosis may be involved in the pathogenesis of male infertility. As a tumor suppressor gene, the expression of P53 has been reported to be upregulated in rat testis exposed to microcystins, which showed that the P53 pathway is involved in microcystin-induced apoptosis [Li et al. Citation2011]. Variants of codon 72 of the TP53 gene were described to be associated with the differential expression of the gene in cultured cells [Shi et al. Citation2009]. TP53 may play an essential role in spermatogenesis by regulating apoptosis and thus the production of functional spermatozoa [Sadeghi-Nejad and Farrokhi Citation2009]. Furthermore, codon 72 SNP of TP53 can modify the activity or the level of p53 and influence cancer susceptibility [Bergamaschi et al. Citation2006; Bond et al. Citation2004; Dumont et al. Citation2003]. Thus, the TP53 gene polymorphism may have an effect on susceptibility to male infertility.
In our case-control study, we analyzed the frequencies of alleles and genotypes of TP53 at codon 72 in 83 male infertility patients and 401 healthy controls. No significant differences were observed between patients and controls. The results showed that the codon 72 SNP is not associated with male infertility susceptibility. The results were concordant with those of others that suggested that the TP53 codon 72 SNP was not associated with the male infertility patients [Huang et al. Citation2012; Lu et al. Citation2007]. In contrast, other studies have suggested that Arg appears to increase risk of developing idiopathic infertility in Iranian and southeast Chinese men [Jin et al. Citation2013; Mashayekhi and Hadiyan Citation2012]. Recently, Jin et al. [Citation2013] reported a significant association between the TP53 codon 72 polymorphism and male infertility. In subgroup analysis they found that the TP53 codon 72 polymorphism was associated with idiopathic infertility but not severe oligozoospermia in southeast Chinese Han males. However, this association was not confirmed in other Chinese populations [Huang et al. Citation2012] and in the study described above. The cases of previous reports by Huang et al. [Citation2012], Mashayekhi and Hadiyan [Citation2012], and our current report were not divided into subgroups. Heterogeneity was not observed in the meta-analysis for TP53 codon 72 SNP and male infertility. It is possible that different subgroup population compositions may have contributed to the distinct results. Therefore we carried out a meta-analysis with all available published studies and our present results to investigate the association between the TP53 codon 72 polymorphisms and male infertility. The result of our meta-analysis described the association of the SNP with male infertility susceptibility. Analysis without stratification by population showed no significant association of fertility with the TP53 codon 72 polymorphism.
Polymorphisms are generally viewed as co-factors. The negative result of TP53 codon 72 SNP presented in our replication study were confirmed by meta-analysis. Male infertility is multi-factorial in origin. Exposure to various environmental factors in combination with genetic susceptibility may contribute to inconsistent results. Genetic background and/or specific environment may interact with each other to affect the reproductive system of individuals [Krausz and Giachini Citation2007]. Perhaps it affects male fertility by combining with some other polymorphisms of genes or does not have inherited risk factors.
There are certain limitations in this meta-analysis. Firstly, it was affected by the low number of studies included in the comparison and the small sample size in the meta-analysis. Secondly, it was affected by the single source of samples which leads to limited application. Thirdly, the cases in three reports [Huang et al. Citation2012; Mashayekhi & Hadiyan Citation2012, and our study] were not subgrouped and it is not clear if the TP53 codon 72 polymorphism is associated with idiopathic infertility. Thus, additional stratified large sample size studies are required in other ethnic groups before a firm conclusion can be drawn. The investigation of the genetic basis of male infertility in other populations may promote the overall understanding of the pathogenesis of this disease.
Materials and Methods
Patients and healthy controls
A total of 83 idiopathic infertile men with nonobstructive azoospermia or severe oligozoospermia and 401 fertile men were recruited from The Second Hospital Affiliated Kunming Medical University. Infertility patients were subjected to three seminal examinations after 2–7 d of sexual abstinence. The healthy controls have a normal childbearing history. All enrolled cases fulfilled the followed inclusion criteria: (a) azoospermia or severe oligozoo-spermia (sperm count < × 106/ml) was verified by at least two-three times semen analyses conducted according to WHO criteria, (b) a normal male karyotype, (c) without Y chromosomal microdeletion, cystic fibrosis, varicocele, (d) lack of hypogonadotropic hypogonadism, (e) no seminal tract obstruction, and (f) no history of infection, exposure to chemotherapeutics of radiation. Informed consent was obtained from all participants. The study was approved by the Ethics Review Board of The Second Hospital Affiliated Kunming Medical University.
DNA samples and genotyping
Genomic DNA was obtained from whole blood leukocytes using the standard phenol/chloroform method. All samples were stored at −80°C prior to testing. Genotype was determined by PCR-RFLP method. The primer sequence 5′-AGC AGA GAC CTG TGG GAA GCG A (forward) and 5′-CAG GGC AAC TGA CCG TGC AAG T (reverse) were used to generate a 473 bp fragment. The products were digested overnight by the restriction enzyme BstUI (New England Biolabs, Beverly, MA). The fragment with Arg was cut into 309 bp and 164 bp, but the fragment with Pro was undigested (473 bp).
Statistical analysis
SPSS15.0 software was used to perform the data analysis. HWE of healthy controls was estimated by the Chi-square test or Fisher's exact test. OR and 95% CIs were also evaluated by the Chi-square test to compare the genotype and allele frequencies between the two groups. A probability level of <0.05 was used to indicate a statistically significant result in the two-tailed test.
Meta-analysis
PubMed was searched using the terms ‘TP53' or ‘P53', ‘polymorphism' and ‘male infertility', or ‘oligospermia and asthenospermia' (the last search update was on August 1, 2014). Studies were selected if the case-control research included the genotype frequencies of TP53 codon 72 polymorphism. Other original publications were searched by manual form. In order to avoid duplicated data the later one was used if there were two or more studies reported from the same unit.
HWE was assessed by Chi-square test in the control group of each study. Crude OR with 95% CIs were calculated to illustrate the strength of association between TP53 codon 72 polymorphism and male infertility. The pooled OR were assessed under dominant model (Pro/Pro+Pro/Arg vs. Arg/Arg), recessive model (Pro/Pro vs. Pro/Arg Arg/Arg), and additive genetic model (Pro vs. Arg), respectively. The assumption of heterogeneity was tested using chi-square based Q-test. A lack of heterogeneity among the studies was determined if a p value <0.05 for the Q-test, the random effects model was used to calculated the summary OR estimate of each study [DerSimonian and Laird Citation1986; Mantel and Haenszel Citation1959]. Begg's test and Egger’s linear regression test were used to examine the potential for publication bias (p < 0.05 considered representative of statistical significance) [Egger et al. Citation1997]. Stata software was used to perform all statistical analyses (version9.0; Stata Corporation, College Station, TX).
| Abbreviations | ||
| Arg | = | arginine allele |
| CI | = | confidence interval |
| HWE Hardy-Weinberg equilibrium; OR | = | odds ratio |
| PCR-RFLP | = | polymerase chain reaction-estriction fragment length polymorphism |
| Pro | = | proline allele |
| SNP | = | single nucleotide polymorphism |
| TP53 | = | tumor protein 53. |
Acknowledgments
We thank all participants for their participation in this study. Furthermore, we sincerely thank all subjects and all medical colleagues who participated in this study. Many thanks go to the Editor and all reviewers.
Declaration of interest
The authors report no conflicts of interest. This work was financially supported by the Fund of State Key Laboratory of Genetics Resources and Evolution (No. GREKF10-07).
Author contributions
Conceived and designed the study: YC, HJ, YL, WT; Performed the study: YC, HJ, LM, YM, DL; Analyzed and processed the data: JC, YC, WT; Wrote the manuscript: YC, HJ; Revised the manuscript: YC, WT. All the authors read and approved the final manuscript.
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