Genetic Variants,EDCs Exposure And Their Interactions On Spermatogenesis

Infertility affects about one in six couples in preparation for pregnancy, with the man being responsible for approximately half of the cases. A significant proportion of male infertility is accompanied by spermatogenesis failure. It has suggested that genetic alterations (single nucleotide polymorphism) and environment risk factors (EDCs) may be associated with spermatogenesis failure.Therefore, we hypothesized that:1. Polymorphisms in spermatogenesis related genes are associated with spermatogenesis failure and male infertility;2. Specific genetic background may confer risks of abnormal male reproduction following certain exposures.There may be interactions between individual’s genetic background (SNPs) and EDCsexposure on spermatogenesis failure and male infertility. To test our hypothesis1,Through Next generation sequencing and Arrays, we studied the associations between spermatogenesis failure and polymorphisms in spermatogenesis related genes.To test our hypothesis2, we detected human exposure levels of the presentive EDCs (phenols and phytoestrogen) in urin samples. Combining the exposure data and genetic data, we conducted the analysis of interactions between EDCs exposure and SNPs on sepermatogeneisis failure and male infertility. Part I Genetic Variants in NOA GWAS Identified Risk Loci, Key Genesand Spermatogenesis FailureChapter I Genetic Variants in NOA GWAS Identified Risk LociandSpermatogenesis FailureObjectiveNon-obstructive azoospermia (NOA) is one of the most severe forms of spermatogenesis failure and a recent, genome-wide associationstudy(GWAS) has identified four risk loci associated with NOA.However, a large portion of the heritability ofNOAhas not been well explainedby GWAS.MethodsBy hypothesizing that rare, low-frequency and common genetic variants might point toward a causal relation between candidate genesandNOA, we performed a two-stage study including deep exon sequencing in96NOAcases and96healthy controls and a replication study in alarger population containing522NOA cases and484healthy controls.ResultsIn the solexa sequencing stage, a total of two rare mutations (chr20.1902132and chr20.1902301in SIRPA), four common mutations (rs1048055and rs2281807in SIRPG, rs11046992and rsl46039840inSOX5) were identified by using next generation sequencing (NGS). In the validation stage, subjects in theNOAgroup had a significantly decreasedfrequency of the heterozygousGAgenotype in SIRPA (4.23%,22out of520) than that in the control group (8.60%,41out of477)[odds ratios(OR)0.47,95%confidence intervals (CI)0.28-0.80](P=6.00×10-3). The rs1048055in SIRPG was associated with a significantly increased risk ofspermatogenic impairment, compared with theCCgenotype (OR3.93,95%CI1.59-9.70)(P=3.00×10-3).Conclusion Our study provides evidence ofindependent NOA risk alleles driven by variants in the protein-coding sequence of two of the genes(SIRPA and SIRPG) discovered by GWAS.Further investigation in larger populations and functional characterizations are needed to validate our findings. Chapter ⅡNOA GWAS BasedPathway Analysis Identifies Associations with Spermatogenesis FailureObjectivePrevious genome-wide association studies (GWAS) have identified three loci for NO A, whereas less attention was given to those markers that did not exceed the genome-wide significance threshold. We conducted a two-stage association study containing1,653NOA cases and2,329controls to investigate the susceptibility makers for NOA.MethodsImputation and pathway-based approaches can be applied to identify additional causal makers with small effects on NOA. In this study, we performed candidate pathway-based association study using imputed-genotyping data for24,238single nucleotide polymorphisms (SNPs) estimated from NOA GWAS. Remarkably,40makers were associated with NOA in both imputation analysis and NOA GWAS (Stage1) after linkage disequilibrium (LD) analysis and selected for validation (Stage2) in another population.ResultsCombined analysis using directly genotyped data for Stages1and2revealed that rs1406714in CHD2was associated with decreased risk of NOA (odds ratio (OR)= 0.78,95%confidence interval (CI)=0.68-0.89, Pmeta=1.7×10-4), whereas rs2126986in GNAO1and rs7226979in BCL2were both risk makers for NOA (rs2126986:OR=1.28,95%CI=1.15-1.41,Pmeta=2.3×10-6; rs7226979:OR=1.21,95%CI=1.11-1.33, Pmeta=4.5×10-5).ConclusionOur study would open avenues for the identification of other novel causal makers that are related to NOA. It will also provide a new paradigm of understanding of male infertility etiology and contribute to the development of targeted therapies. Part II Interactions betweenEDCsExposure and Key Genes Polymorphisms on Spermatogenesis FailureChapter I Comboined Effects of Phytoestrogens Exposure and Polymorphisms in Metabolic Enzymes on Spermatogenesis FailureObjectivePhytoestrogens are plant-derived compounds that may interact with estrogen receptors and mimic estrogenic effects. It remains unclear whether the individual variability in metabolizing phytoestrogens contributes to phytoestrogens-induced beneficial or detrimental effects. Our aim was to determine whether there is any interaction between metabolic rates (MR) of phytoestrogens and genetic polymorphisms in related xenobiotic metabolizing enzyme genes.MR was used to assess phytoestrogen exposure and mdividualmetabolic ability.MethodsThe amount of phytoestrogens in urine was measured by ultra-high performance liquid chromatography-tandem mass spectrometry in600idiopathic infertile male patients and401controls. Polymorphisms were genotyped using the Taqman method.ResultsIt was found that low MR of SEC increased the risk of male infertility (odds ratio (OR)=2.49,95%confidence interval (CI)=1.78-3.48, Ptrend=8.00×10-8). Novel interactions were also observed between the MR of SEC and rs1042389in CYP2B6, rs1048943in CYP1A1, and rs1799931in NAT2on male infertility (Pinter=1.06×10-4,1.14×10-3,3.55×10-3, respectively).ConclusionBy analyzing the relationships between urinary phytoestrogen concentrations, their metabolites and male infertility, we found that individual variability in metabolizing SEC contributed to the interpersonal differences in SEC’s effects on malereproduction. Chapter Ⅲnteractions between Phenol Exposure and Metabolism Enzyme Gene Variants on Spermatogenesis FailureObjectiveOctylphenol (OP) and Trichlorophenol (TCP) act as endocrine disruptors and have effects on male reproductive function. We studied the interactions between4-tert-Octylphenol (4-t-OP),4-n-Octylphenol (4-n-OP),2,3,4-Trichlorophenol (2,3,4-TCP),2,4,5-Trichlorophenol (2,4,5-TCP) urinary exposure levels and polymorphisms in selected xenobiotic metabolism enzyme genes among589idiopathic male infertile patients and396controls in a Han-Chinese population.MethodsUltra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to measure alkylphenols and chlorophenols in urine. Polymorphisms were genotyped using the Taqman method.ResultsAmong four phenols that were detected, we found that only exposure to4-t-OP increased the risk of male infertility (Ptrend=1.70×10-7). The strongest interaction was between4-t-OP and rs4918758in CYP2C9(Pinter=6.05×10-7). It presented a significant monotonic increase in risk estimates for male infertility with increasing4-t-OP exposure levels among men with TC/CC genotype (low level compared with non-exposed, odds ratio (OR)=2.26,95%confidence intervals (CI)=1.06-4.83; high level compared with non-exposed, OR=9.22,95%CI=2.78-30.59), but no associations observed among men with TT genotype. We also found interactions between4-t-OP and rs4986894in CYP2C19, and between rs1048943in CYP1A1, on male infertile risk (Pinter=8.09×10-7, Pinter=3.73×10-4, respectively). ConclusionWe observed notable interactions between4-t-OP exposure and metabolism enzyme gene polymorphisms on idiopathic infertility in Han-Chinese men.