Abstract
The stromal antigen 3 (STAG3) gene, encoding a meiosis-specific cohesin component, is a strong candidate for causing male infertility, but little is known about this gene so far. We identified STAG3 in patients with nonobstructive azoospermia (NOA) and normozoospermia in the Korean population. The coding regions and their intron boundaries of STAG3 were identified in 120 Korean men with spermatogenic impairments and 245 normal controls by using direct sequencing and haplotype analysis. A total of 30 sequence variations were identified in this study. Of the total, seven were exonic variants, 18 were intronic variants, one was in the 5’-UTR, and four were in the 3’-UTR. Pathogenic variations that directly caused NOA were not identified. However, two variants, c.3669+35C>G (rs1727130) and +198A>T (rs1052482), showed significant differences in the frequency between the patient and control groups (P = 0.021, odds ratio [OR]: 1.79, 95% confidence interval [CI]: 1.098–2.918) and were tightly linked in the linkage disequilibrium (LD) block. When pmir-rs1052482A was cotransfected with miR-3162-5p, there was a substantial decrease in luciferase activity, compared with pmir-rs1052482T. This result suggests that rs1052482 was located within a binding site of miR-3162-5p in the STAG3 3’-UTR, and the minor allele, the rs1052482T polymorphism, might offset inhibition by miR-3162-5p. We are the first to identify a total of 30 single-nucleotide variations (SNVs) of STAG3 gene in the Korean population. We found that two SNVs (rs1727130 and rs1052482) located in the 3’-UTR region may be associated with the NOA phenotype. Our findings contribute to understanding male infertility with spermatogenic impairment.
Keywords: linkage disequilibrium, meiotic-specific gene, single-nucleotide variations, spermatogenic impairment, stromal antigen 3 gene
INTRODUCTION
Azoospermia affects approximately 1% of the male population, accounts for over 15% of all male infertility,1,2,3 and includes genital tract obstruction (obstructive azoospermia) and spermatogenic impairment (nonobstructive azoospermia, NOA).4,5 NOA is mainly caused by severely impaired spermatogenesis and is reported to account for more than 70% of azoospermia in Korean patients.6 Chromosomal abnormalities, such as Klinefelter syndrome, balanced chromosomal rearrangements, and Yq microdeletions, are well known genetic causes of NOA.7 In many cases, the genetic etiology remains unknown. Matzuk and Lamb8 reviewed many genes involved in spermatogenesis and mutations in some of those genes were identified in patients with NOA.9,10,11
The stromal antigen 3 (STAG3) gene was mapped to chromosome 7 and consists of 34 exons encoding a protein involved in the meiotic cohesion complex.12 Human STAG3 is highly expressed in the testis and several other organs including the ovary.13,14 During meiosis, STAG3 forms a cohesion core with three other proteins, structural maintenance of chromosome 3 (SMC3), structural maintenance of chromosomes 1β (SMC1β), and Rad21 cohesin complex component like 1 (Rad21L1).15,16 In mice, defective Stag3 protein causes aberrant meiotic chromosomal features and infertility.17,18 In humans, a homozygous 1-bp deletion in STAG3 has been found in a consanguineous family with premature ovarian failure (POF),18 and a homozygous donor splice-site mutation has been found in two sisters with premature ovarian insufficiency (POI).19 Therefore, STAG3 has been suggested as a strong candidate gene target for causing male infertility.14,20,21 To date, no homozygous or compound heterozygotic mutations of STAG3 have been identified in patients with spermatogenic impairment, and studies of STAG3 mutations have not been examined in infertile male populations.
In this study, we investigated whether STAG3 variations may be a genetic cause of spermatogenic impairment in Korean men.
PATIENTS AND METHODS
Subjects
A total of 120 Korean men with spermatogenic impairment (43 oligozoospermic and 77 azoospermic) and 245 normal controls were obtained from the Cha Gangnam Medical Center at Cha University, Seoul, Korea, between January 2010 and December 2012. General and clinical characteristics of patients and controls are presented in Table 1. Patients with tubule obstruction, chromosomal abnormalities, or microdeletion of the Y chromosome AZF region were excluded. Normal controls had a normal sperm concentration and no history of infertility. Testicular size was measured by a Prader orchidometer (Pro-Health Product Ltd., Guangzhou, China). Serum testosterone, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were measured on a Cobas e601 analyzer (Roche Dignostics, Penzberg, Germany) using electrochemiluminescence immunoassay (ECLIA) method. Semen analysis was performed according to the World Health Organization criteria (WHO, 2010).22 The study was approved by the Institutional Review Board of Cha Gangnam Medical Center, Seoul, Korea, and written informed consent was obtained from all participants.
Table 1.
Participants’ clinical characteristics
| Characteristics | Patients with spermatogenic impairment (43 oligozoospermia + 77 azoospermia) | Controls | P |
|---|---|---|---|
| Patients (n) | 120 | 245 | |
| Age (year), mean±s.d. | 33.9±3.7 | 33.5±2.6 | 0.783 |
| Semen volumea (ml), mean±s.d. | 2.9±1.2 | 3.3±1.5 | 0.591 |
| Sperm concentrationa (106), mean±s.d. | 10.0±5.4 | 74.9±28.0 | <0.01 |
| Sperm motilitya (%), mean±s.d. | 26.8±12.6 | 45.7±11.1 | 0.013 |
| Sperm morphologya (% normal forms), mean±s.d. | 2.0±1.3 | 6.4±2.1 | <0.01 |
| Rt. testis volume (ml), mean±s.d. | 16.0±6.2 | 23.0±2.7 | 0.020 |
| Lt. testis volume (ml), mean±s.d. | 17.0±6.2 | 23.0±2.7 | 0.025 |
| Serum FSH (mIU ml−1), mean±s.d. | 17.6±10.7 | 4.4±1.6 | <0.01 |
| Serum testosterone (ng ml−1), mean±s.d. | 4.0±1.1 | 3.6±1.5 | 0.486 |
| Serum LH (mIU ml−1), mean±s.d. | 5.5±2.4 | 3.9±2.0 | 0.106 |
aData of the azoospermic patients were excluded. s.d.: standard deviation; Rt: right; Lt: left; FSH: follicle-stimulating hormone; LH: luteinizing hormone
DNA extraction
Genomic DNA was isolated from peripheral blood of the patient and control samples with the QuickGene DNA blood kit (KURABO industries, Neyagawa, Japan), according to the manufacturer's instructions. DNA yield was quantified with the NanoDrop™ spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The extracted DNA was stored at −80°C until further analysis.
Polymerase chain reaction
Coding regions of STAG3 (NM_001282716.1) were amplified for genetic screening by polymerase chain reaction (PCR), using primers for the 34 exons and their intron boundaries that were designed by Primer 3 (http://primer3.ut.ee). As this gene has multiple pseudogenes, we performed long-range PCR that produced eleven fragments and designed eleven primer pairs to cover the 34 exons and avoid pseudogene sequences. The locations and sequences of primer sets are presented in Supplementary Table 1.
Supplementary Table 1.
The list of polymerase chain reaction primer and sequencing primer sequences
| n | Range | PCR primer Sequence (5’ → 3’) | Range | Sequencing primer Sequence (5’→3’) | ||
|---|---|---|---|---|---|---|
| P1 | Exon1 (404bp) | F | CGCCCAATGGAGTAGGAGAT | Exon1 (404bp) | F | CGCCCAATGGAGTAGGAGAT |
| R | ACCTGTCAGAGCCTGGAAGA | R | ACCTGTCAGAGCCTGGAAGA | |||
| P2 | Exon2-Exon5 (7,413bp) | F | TACCACACCCAGTGTGCAAT | Exon2 (294bp) | F | GCCCTTTCTTCTCTTTCTTCC |
| R | GGGGGTACCACAGCTACAGA | R | TCCCACGCATATTATCATCAA | |||
| Exon3 (394bp) | F | AAAAAGACTTTGTCCCAACTTCC | ||||
| R | CGGCTCACTGCAAGCTCT | |||||
| Exon4 (538bp) | F | GGTTCAGGTGATACGGTTCAT | ||||
| R | TGTTCACGTCAAATCAAGTTTGT | |||||
| Exon5 (450bp) | F | TTGTTTACCTCCCAGGGTTG | ||||
| R | AGTGCCCGGCCTAAATAAGT | |||||
| P3 | Exon6-Exon8 (7,971bp) | F | CTTATTGCCATGGCTTCGTT | Exon6 (297bp) | F | CCCACCTAAGCTCTTTGCAG |
| R | CCTGTGGCACATTTTGGTAA | R | TTCCTCCTTCTAAAAGCTACCC | |||
| Exon7 (365bp) | F | GCCCCTATGACTTCATGGAC | ||||
| R | AGCCAAGATGCAGGTAGGAA | |||||
| Exon8 (395bp) | F | TCATTGCCCTTCTTTCCTTC | ||||
| R | ACCCCTTACAGGATGGGTCT | |||||
| P4 | Exon9-Exon13 (3,748bp) | F | TCCGAATAACCACATGCAGA | Exon9 (330bp) | F | CGGGGGTTCACACTATCCTA |
| R | GCTCAGCACAACAGGAAACA | R | ATTTTTGCTCCAGCTGCATT | |||
| Exon10 (433bp) | F | CCATGAGAGGGAGTTATCTGG | ||||
| R | CTCCCCGTACCTCAGGTTTT | |||||
| Exon11 (300bp) | F | AATGAGGGATCGGAGAGG | ||||
| R | GCTGGGATAGCCAAGACATC | |||||
| Exon12 (399bp) | F | TCTTGGCTATCCCAGCATCT | ||||
| R | CCCCCTCAACATACTGCAAC | |||||
| Exon13 (425bp) | F | TGCAGTATGTTGAGGGGGTA | ||||
| R | GCTGCGAGAAGAAAGGAGAC | |||||
| P5 | Exon14-Exon17 (1,893bp) | F | ATCTGCTGCTGCCCTACCTA | Exon14 (417bp) | F | TCTCCCTGGTGTCTCCTTTC |
| R | AAGCAGCTGAGAAGCTGGAG | R | AGGCTGGTCTCAAACTCCTG | |||
| Exon15 (352bp) | F | AATGGAGAAGGATGGGAGTG | ||||
| R | CACCTTCCAACTCCAAGCTC | |||||
| Exon16 (460bp) | F | TGCTGGAGAAGGACCAGAGT | ||||
| R | TGCTGGGATTATAGGCGTAA | |||||
| Exon17 (400bp) | F | AAATCTCGTGGGAGCTACTGA | ||||
| R | AAGCAGCTGAGAAGCTGGAG | |||||
| P6 | Exon18-Exon21 (1,082bp) | F | GGGGGTGGGAGTAGGAATTA | Exon18 (248bp) | F | GGGGGTGGGAGTAGGAATTA |
| R | CTTCCTCGCTTTGTCCACTC | R | GGAACCCAAGTTCTTAGGAAAAA | |||
| Exon19 (387bp) | F | AATGCTTTTAACCCCGTTCC | ||||
| R | CAATAGCATTTCCCCCAGAA | |||||
| Exon20~21 (526bp) | F | AGCAGGAGCTTGAAGAGCTG | ||||
| R | CTTCCTCGCTTTGTCCACTC | |||||
| P7 | Exon22-Exon25 (1,237bp) | F | GAGTGGACAAAGCGAGGAAG | Exon22~23 (553bp) | F | GATGCCTCTGAAGAATGTCCA |
| R | TTGGATATCCCCCACCTGTA | R | AAAAGCCTGTAGGGGGAAAA | |||
| Exon24~25 (626bp) | F | GGAGCAACAAGGCGAGTATC | ||||
| R | TTGGATATCCCCCACCTGTA | |||||
| P8 | Exon26-Exon29 (1,565bp) | F | TTATTTTGGGCTTTGCACCT | Exon26 (344bp) | F | GGAGTTTGGGAGGGAGACAT |
| R | TACCCACACACAGCACCCTA | R | AAGAATGAAGGAACCTATCACG | |||
| Exon27 (371bp) | F | CAAGGCCTTTGGAATTTCTG | ||||
| R | AAGGCATACCCACCCCTAAC | |||||
| Exon28~29 (602bp) | F | GGGTATGCCTTTGGAGACAA | ||||
| R | CCCTGAATGACAGTAGATGCTC | |||||
| P9 | Exon30-Exon32 (2,057bp) | F | AGCCCAGGGGTATGTCTCTT | Exon30 (427bp) | F | TAGGGCTATGCCCATTTGAG |
| R | GGAGGATAGGGGGTCATGTT | R | ACAGCAGGGAACCATGAAAC | |||
| Exon31 (387bp) | F | CTCCCACATTGTTGGGTTCT | ||||
| R | TGACAGGAAGTGCTCTGTGG | |||||
| Exon32 (394bp) | F | CTCACCCATTGCCTCTCTGT | ||||
| R | TCTAGATTCATTCAGCTTTTCCA | |||||
| P10 | Exon33 (307bp) | F | TTTGCGAAGTGACAGGAGTG | Exon33 (307bp) | F | TTTGCGAAGTGACAGGAGTG |
| R | TACACAGGACACAGCAACGG | R | TACACAGGACACAGCAACGG | |||
| P11 | Exon34 (490bp) | F | GGGCTTTGAGGGTAACCCAGGG | Exon34 (490bp) | F | GGGCTTTGAGGGTAACCCAGGG |
| R | CGATCTCAAGCCACACCTTGG | R | CGATCTCAAGCCACACCTTGG | |||
PCR: polymerase chain reaction
Sequencing analysis
All eleven PCR products were purified with ExoSAP-IT (USB Corporation, Cleveland, OH, USA). To sequence the eleven fragments, we designed 30 sequencing primers to cover 34 exons (Supplementary Table 1). All the samples were amplified by PCR and sequenced bidirectionally using the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Austin, TX, USA) and BigDye® X-Terminator™ solutions (Applied Biosystems, Bedford, MA, USA) with standard conditions. The sample supernatant was loaded on the ABI 3130XL Genetic Analyzer (Applied Biosystems, Foster City, CA, USA) and processed with a BigDye® X-Terminator run module. All assays were repeated once for confirmation and the results matched over 99.0%.
Statistical analyses and database search
For each sequence variation, the data were statistically analyzed with Statistical Package for the Social Sciences software (SPSS version 22, IBM Software Group, Chicago, IL, USA). To evaluate the association between patient and control groups, odds ratios (OR), 95% confidence intervals (95% CI), and the applied P values were calculated from both the Chi-squared test and Fisher's exact test (two-tailed). Applied P < 0.05 were considered statistically significant. Three databases, Polyphen-2, SIFT, and Mutation Tester, were used to predict potentially damaging effects due to amino acid changes.
Multiple hypothesis testing was performed with the Benjamini–Hochberg method23 to control false discovery rate (FDR) in the logistic regression analysis. Calculating the FDR is a way to address problems associated with multiple comparisons, and FDR provides a measure of the expected proportion of false-positives in the data.
Haplotype block structure was established by HaploView 4.1 software (https://www.broadinstitute.org) using the method of block definition of Gabriel et al.24 Haplotype association tests were also conducted with this software.
Molecular cloning
The entire 3’-untranslated region (3’-UTR) of STAG3 was amplified from genomic DNA, which contained the rs1052482 A or T alleles, using primers that included SacI and XbaI restriction sites. Primer sequences of the STAG3 3’-UTR were: forward: 5’-GAGCTCccgttgctgtgtcctgtgta, reverse: 5’-TCTAGAgaccaagaacctgacctcca (for a predicted 476 bp product). PCR products were cloned into the pmirGLO vector (Promega, Madison, WI, USA) via the SacI and XbaI sites and all constructs were verified by DNA sequencing.
Dual luciferase assay
HEK293T cells were seeded in 48-well plates (3 × 104 per well). After 24 h, 200 ng pmir-rs1052482A, pmir-rs1052482T, or pmir-empty vector was transiently transfected with Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA). The Renilla vector was used as an internal control for transfection efficiency. After 48 h, the transfected cells were harvested and lysed with a dual-luciferase reporter assay system (Promega), and the activities of Firefly and Renilla were measured in a Luminometer, Centro XS LB960 (Berthold Technologies, Bad Wildbad, Germany), and MikroWin2000 software (https://mikrowin-20001.software.informer.com). Transfection experiments were performed in triplicate, and activity measurements were done for three times. Relative luciferase activity was determined by normalizing firefly luciferase activity against Renilla luciferase activity. An average value of firefly/Renilla was calculated and then normalized to the average value of the empty vector to yield the vector-normalized ratio.
MicroRNAs (miRNAs)
Computational prediction of putative targets for STAG3 mRNA was performed by searching mirmap.ezlab.org, www.targetscan.org, www.microrna.org, and www.mirdb.org for the target prediction algorithms. From the potential miRNAs interacting with STAG3 mRNA 3’-UTR, we selected seven candidate miRNAs (miR-148a, miR-2909, miR-3162-5p, miR-33a-5p, miR-33b-5p, miR-4739, and miR-6508-3p) that allowed rs1052482A or T alleles to be included in the seed sequence. Seven miRNAs and their inhibitors were constructed by Bioneer (Daejeon, Korea). The prediction score of the seven miRNAs and the 3’-UTR of STAG3 and details for their in silico interactions are presented in Supplementary Figure 1 (536.3KB, tif) and Supplementary Table 2. Mimics (5–10 pmol) were transiently cotransfected with pmir-vectors with Lipofectamine 2000 according to the manufacturer's instructions.
Supplementary Table 2.
miRNA target-site prediction software score
| Software | mirmap.org | TargetScan | microrna.org | mirdb.org |
|---|---|---|---|---|
| Relevant score threshold | ||||
| >90 | <−0.4 | <−0.1 | >60 | |
| miR-148a | 87.6 | −0.20 | −0.27 | |
| miR-2909 | 26.3 | |||
| miR-3162-5p | 93.0 | −0.38 | ||
| miR-33a-5p | 7.8 | |||
| miR-33b-5p | 8.2 | |||
| miR-4739 | 90.6 | −0.40 | 55 | |
| miR-6508-3p | 46.7 | −0.19 | ||
miRmap: miRmap score; TargetScan: context ++ score; microrna.org: mirSVR score; mirdb.org: Target Score
RESULTS
Thirty single-nucleotide variations (SNVs) in STAG3 were identified in this study. The locations, types, frequencies, and P values of the variations are presented in Supplementary Table 3. The distributions of genotypes of all the SNVs followed the Hardy–Weinberg equilibrium in patients and controls. Seven were exonic, 18 were intronic, one was in the 5’-UTR, and four were in the 3’-UTR of the 30 variations. Three exonic variants were nonsynonymous and the other four variants were synonymous. Three variants were found in a patient but not in controls (c.1269C>T p. Asp423, +112G>A, +315C>T); two variants, c.3669+35C>G and +198A>T, showed significant differences in the frequency between patient and control groups (P = 0.021, OR: 1.79, 95% CI: 1.098–2.918). Haplotype analysis by HaploView 4.1 showed that nineteen variants were separated into five linkage disequilibrium (LD) blocks (Figure 1). As shown in Table 2, in particular, the frequencies of G-C-A (case: control = 0.504: 0.606, P = 0.009), G-G-T (case: control = 0.162: 0.075, P < 0.001), and C-C-A (case: control = 0.075: 0.018, P < 0.001) haplotypes in block 5 were significantly different between patients and controls.
Supplementary Table 3.
Genotypes and allele distributions of STAG3 gene variations
| n | db SNP ID | Gene Location | SNP function | Genotype | Case (n=120) (%) | Control (n=245) (%) | OR | 95% CI | P (Fisher) | *FDR-p |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | rs188290003 | c.-125C>A | 5’-UTR | CC | 117 (97.5) | 239 (97.6) | 1.000 | |||
| CA | 3 (2.5) | 6 (2.4) | 1.021 | 0.251–4.156 | 1.000 | 1.000 | ||||
| AA | 0 (0) | 0 (0) | – | – | – | – | ||||
| A allele | 3 | 6 | 1.021 | 0.253–4.119 | 1.000 | 1.000 | ||||
| 2 | rs7457787 | c.-64-247A>C | Intron | AA | 101 (84.2) | 198 (80.8) | 1.000 | |||
| AC | 19 (15.8) | 42 (17.1) | 0.887 | 0.490–1.604 | 0.767 | 0.767 | ||||
| CC | 0 (0.0) | 5 (2.0) | – | – | 0.174 | 0.432 | ||||
| C allele | 19 | 52 | 0.724 | 0.418–1.255 | 0.288 | 0.432 | ||||
| 3 | rs12666107 | c.-64-97G>C | Intron | GG | 16 (13.3) | 37 (15.1) | 1.000 | |||
| GC | 62 (51.7) | 111 (45.3) | 1.292 | 0.665–2.508 | 0.511 | 1.000 | ||||
| CC | 42 (35.0) | 97 (39.6) | 1.001 | 0.503–1.995 | 1.000 | 1.000 | ||||
| C allele | 146 | 305 | 0.942 | 0.686–1.294 | 0.746 | 1.000 | ||||
| 4 | rs11531577 | c.48G>T | p.Leu16Phe | GG | 105 (87.5) | 218 (89.0) | 1.000 | |||
| GT | 15 (12.5) | 26 (10.6) | 1.198 | 0.609–2.357 | 0.725 | 0.867 | ||||
| TT | 0 (0.0) | 1 (0.4) | – | – | – | |||||
| T allele | 15 | 28 | 1.1 | 0.576–2.101 | 0.867 | 0.867 | ||||
| 5 | rs2272343 | c.106A>C | p.Thr36Pro | AA | 105 (87.5) | 218 (89.0) | 1.000 | |||
| AC | 15 (12.5) | 26 (10.6) | 1.1978 | 0.609–2.357 | 0.725 | 0.867 | ||||
| CC | 0 (0.0) | 1 (0.4) | – | – | – | |||||
| C allele | 15 | 28 | 1.1 | 0.576–2.101 | 0.867 | 0.867 | ||||
| 6 | rs6465764 | c.219+71G>A | Intron | GG | 16 (13.3) | 37 (15.1) | 1.000 | |||
| GA | 62 (51.7) | 111 (45.3) | 1.2917 | 0.665–2.508 | 0.511 | 1.000 | ||||
| AA | 42 (35.0) | 97 (39.6) | 1.0013 | 0.503–1.995 | 1.000 | 1.000 | ||||
| A allele | 146 | 305 | 0.9421 | 0.686–1.294 | 0.746 | 1.000 | ||||
| 7 | rs761620488 | c.198A>C | p.Lys66Asn | AA | 120 (100.0) | 244 (99.6) | 1.000 | |||
| AC | 0 (0.0) | 1 (0.4) | – | – | – | |||||
| CC | 0 (0.0) | 0 (0) | – | – | – | |||||
| C allele | 0 | 1 | – | – | 1.000 | 1.000 | ||||
| 8 | rs4729579 | c.220-64C>G | Intron | CC | 16 (13.3) | 37 (15.1) | 1.000 | |||
| CG | 62 (51.7) | 111 (45.3) | 1.292 | 0.665–2.508 | 0.511 | 1.000 | ||||
| GG | 42 (35.0) | 97 (39.6) | 1.001 | 0.503–1.995 | 1.000 | 1.000 | ||||
| G allele | 146 | 305 | 0.942 | 0.686–11.294 | 0.746 | 1.000 | ||||
| 9 | rs2056726 | c.220-63G>A | Intron | GG | 105 (87.5) | 218 (89.0) | 1.000 | |||
| GA | 15 (12.5) | 26 (10.6) | 1.198 | 0.609–2.357 | 0.725 | 0.867 | ||||
| AA | 0 (0.0) | 1 (0.4) | – | – | – | |||||
| A allele | 15 | 28 | 1.100 | 0.576–2.101 | 0.867 | 0.867 | ||||
| 10 | rs6960458 | c.337-109G>T | Intron | GG | 16 (13.3) | 37 (15.1) | 1.000 | |||
| GT | 62 (51.7) | 111 (45.3) | 1.2917 | 0.6651–2.5084 | 0.511 | 1.000 | ||||
| TT | 42 (35) | 97 (39.6) | 1.001 | 0.503–1.995 | 1.000 | 1.000 | ||||
| T allele | 146 | 305 | 0.942 | 0.686–1.294 | 0.746 | 1.000 | ||||
| 11 | rs2272344 | c.715+180C>T | Intron | CC | 16 (13.3) | 37 (15.1) | 1.000 | |||
| CT | 62 (51.7) | 111 (45.3) | 1.292 | 0.665–2.508 | 0.511 | 1.000 | ||||
| TT | 42 (35.0) | 97 (39.6) | 1.001 | 0.503–1.995 | 1.000 | 1.000 | ||||
| T allele | 146 | 305 | 0.942 | 0.686–1.294 | 0.746 | 1.000 | ||||
| 12 | rs11764176 | c.716-104G>T | Intron | GG | 16 (13.3) | 37 (15.1) | 1.000 | |||
| GT | 62 (51.7) | 111 (45.3) | 1.292 | 0.665–2.508 | 0.511 | 1.000 | ||||
| TT | 42 (35.0) | 97 (39.6) | 1.001 | 0.503–1.995 | 1.000 | 1.000 | ||||
| T allele | 146 | 305 | 0.942 | 0.686–1.294 | 0.746 | 1.000 | ||||
| 13 | rs200131656 | c.1035A>G | p.Leu345 | AA | 111 (92.5) | 225 (91.8) | 1.000 | |||
| AG | 9 (7.5) | 20 (8.2) | 0.912 | 0.402–2.069 | 1.000 | 1.000 | ||||
| GG | 0 (0) | 0 (0) | – | – | – | |||||
| G allele | 9 | 20 | 0.916 | 0.410–2.042 | 1.000 | 1.000 | ||||
| 14 | rs62482167 | c.1066-186C>G | Intron | CC | 105 (87.5) | 215 (87.8) | 1.000 | |||
| CG | 15 (12.5) | 30 (12.2) | 1.024 | 0.528–1.985 | 1.000 | 1.000 | ||||
| GG | 0 (0) | 0 (0) | – | – | – | |||||
| G allele | 15 | 30 | 1.022 | 0.539–1.939 | 1.000 | 1.000 | ||||
| 15 | rs3823642 | c.1245-26T>C | Intron | TT | 17 (14.2) | 39 (15.9) | 1.000 | |||
| TC | 50 (41.7) | 96 (39.2) | 1.195 | 0.615–2.322 | 0.621 | 0.934 | ||||
| CC | 53 (44.2) | 110 (44.9) | 1.105 | 0.573–2.133 | 0.868 | 0.934 | ||||
| C allele | 156 | 316 | 1.023 | 0.740–1.413 | 0.934 | 0.934 | ||||
| 16 | rs755877186 | c.1269C>T | p. Asp423 | CC | 119 (99.2) | 245 (100.0) | 1.000 | |||
| CT | 1 (0.8) | 0 (0.0) | – | – | – | |||||
| TT | 0 (0) | 0 (0) | – | – | – | |||||
| T allele | 1 | 0 | – | – | 0.329 | 0.329 | ||||
| 17 | rs3735241 | c.1293A>C | p.Pro431 | AA | 17 (14.2) | 38 (15.5) | 1.000 | |||
| AC | 50 (41.7) | 96 (39.2) | 1.195 | 0.615–2.322 | 0.621 | 0.934 | ||||
| CC | 53 (44.2) | 111 (45.3) | 1.105 | 0.573–2.133 | 0.868 | 0.934 | ||||
| C allele | 156 | 318 | 1.023 | 0.740–1.413 | 0.934 | 0.934 | ||||
| 18 | rs2272345 | c.1573+41C>G | Intron | CC | 18 (15.0) | 41 (16.7) | 1.000 | |||
| CG | 59 (49.2) | 107 (43.7) | 1.256 | 0.663–2.379 | 0.526 | 1.000 | ||||
| GG | 43 (35.8) | 97 (39.6) | 1.010 | 0.522–1.954 | 1.000 | 1.000 | ||||
| G allele | 145 | 301 | 0.958 | 0.699–1.315 | 0.809 | 1.000 | ||||
| 19 | rs13230744 | c.1678-67A>G | Intron | AA | 12 (10.0) | 35 (14.3) | 1.000 | |||
| AG | 58 (48.3) | 96 (39.2) | 1.762 | 0.847–3.665 | 0.162 | 0.485 | ||||
| GG | 50 (41.7) | 114 (46.5) | 1.279 | 0.613–2.668 | 0.588 | 0.882 | ||||
| G allele | 158 | 324 | 0.987 | 0.713–1.367 | 1.000 | 1.000 | ||||
| 20 | rs117672080 | c.1678-58A>G | Intron | AA | 107 (89.2) | 221 (90.2) | 1.000 | |||
| AG | 13 (10.8) | 23 (9.4) | 1.167 | 0.569–2.394 | 0.710 | 0.860 | ||||
| GG | 0 (0) | 1 (0.4) | – | – | – | |||||
| G allele | 13 | 25 | 1.065 | 0.535–2.121 | 0.860 | 0.860 | ||||
| 21 | rs200967267 | c.2133-36C>A | Intron | CC | 117 (97.5) | 242 (98.8) | 1.000 | |||
| CA | 3 (2.5) | 3 (1.2) | 2.068 | 0.411–10.405 | 0.399 | 0.401 | ||||
| AA | 0 (0) | 0 (0) | – | – | – | |||||
| A allele | 3 | 3 | 2.055 | 0.412–10.258 | 0.401 | 0.401 | ||||
| 22 | rs1043915 | c.2445T>A | p. Ile815 | TT | 14 (11.7) | 39 (15.9) | 1.000 | |||
| TA | 63 (52.5) | 109 (44.5) | 1.61 | 0.812–3.194 | 0.189 | 0.556 | ||||
| AA | 43 (35.8) | 97 (39.6) | 1.235 | 0.608–2.508 | 0.600 | 0.900 | ||||
| A allele | 149 | 303 | 1.011 | 0.735–1.389 | 1.000 | 1.000 | ||||
| 23 | rs150085849 | c.2395-20C>T | Intron | CC | 118 (98.3) | 241 (98.4) | 1.000 | |||
| CT | 2 (1.7) | 4 (1.6) | 1.021 | 0.184–5.655 | 1.000 | 1.000 | ||||
| TT | 0 (0) | 0 (0) | – | – | – | |||||
| T allele | 2 | 4 | 1.021 | 0.186–5.614 | 1.000 | 1.000 | ||||
| 24 | rs79986079 | c.2803-206C>T | Intron | CC | 106 (88.3) | 216 (88.2) | 1.000 | |||
| CT | 14 (11.7) | 28 (11.4) | 1.019 | 0.515–2.016 | 1.000 | 1.000 | ||||
| TT | 0 (0.0) | 1 (0.4) | – | – | – | |||||
| T allele | 14 | 30 | 0.95 | 0.494–1.827 | 1.000 | 1.000 | ||||
| 25 | rs2246713 | c.3081-38G>C | Intron | GG | 52 (43.3) | 114 (46.5) | 1.000 | |||
| GC | 55 (45.8) | 106 (43.3) | 1.138 | 0.717–1.806 | 0.638 | 0.847 | ||||
| CC | 13 (10.8) | 25 (10.2) | 1.140 | 0.541–2.404 | 0.847 | 0.847 | ||||
| C allele | 81 | 156 | 1.091 | 0.786–1.514 | 0.614 | 0.847 | ||||
| 26 | rs1727130 | c.3669+35C>G | Intron | CC | 36 (30.0) | 101 (41.2) | 1.000 | |||
| CG | 67 (55.8) | 105 (42.9) | 1.79 | 1.098–2.918 | 0.021 | 0.063 | ||||
| GG | 17 (14.2) | 39 (15.9) | 1.223 | 0.617–2.426 | 0.596 | 0.596 | ||||
| G allele | 101 | 183 | 1.219 | 0.890–1.670 | 0.226 | 0.339 | ||||
| 27 | rs188384958 | +112G>A | 3’-UTR | GG | 119 (99.2) | 245 (100.0) | 1.000 | |||
| GA | 1 (0.8) | 0 (0.0) | – | – | 0.329 | 0.329 | ||||
| AA | 0 (0) | 0 (0) | – | – | – | |||||
| A allele | 1 | 0 | – | – | 0.329 | 0.329 | ||||
| 28 | rs1052482 | +198A>T | 3’-UTR | AA | 36 (30.0) | 101 (41.2) | 1.000 | |||
| AT | 67 (55.8) | 105 (42.9) | 1.79 | 1.098–2.918 | 0.021 | 0.063 | ||||
| TT | 17 (14.2) | 39 (15.9) | 1.223 | 0.617–2.426 | 0.596 | 0.596 | ||||
| T allele | 101 | 183 | 1.219 | 0.890–1.670 | 0.226 | 0.339 | ||||
| 29 | rs1727131 | +315C>T | 3’-UTR | CC | 119 (99.2) | 245 (100.0) | 1.000 | |||
| CT | 1 (0.8) | 0 (0.0) | – | – | 0.329 | 0.329 | ||||
| TT | 0 (0) | 0 (0) | – | – | – | |||||
| T allele | 1 | 0 | – | – | 0.329 | 0.329 | ||||
| 30 | rs12056000 | +370G>A | 3’-UTR | GG | 105 (87.5) | 216 (88.2) | 1.000 | |||
| GA | 13 (10.8) | 18 (7.3) | 1.486 | 0.701–3.147 | 0.322 | 0.483 | ||||
| AA | 2 (1.7) | 11 (4.5) | 0.374 | 0.081–1.718 | 0.238 | 0.483 | ||||
| A allele | 17 | 40 | 0.858 | 0.476–1.547 | 0.662 | 0.662 | ||||
STAG3: stromal antigen 3; SNPs: single-nucleotide polymorphism; NOA: nonobstructive azoospermia; OR: odds ratio; CI: confidence interval; FDR-P: false discovery rate-adjusted P value; –: 0, NaN (not a number) or infinity
Figure 1.
LD pattern in the locus of STAG3 gene. Nineteen variants were separated into five LD blocks. Numbers in the squares indicate D’ index (level of LD) between the corresponding SNPs. STAG3: stromal antigen 3; SNPs: single-nucleotide polymorphism; LD: linkage disequilibrium.
Table 2.
Haplotype analysis between the SNPs of STAG3 and nonobstructive azoospermia
| db SNP ID | Haplotype | Frequency | OR (95% CI) | P (Fisher) | FDR-P | |
|---|---|---|---|---|---|---|
| Case | Control | |||||
| rs12666107 rs11531577 rs2272343 rs6465764 rs4729579 rs2056726 rs6960458 rs2272344 rs11764176 |
Block 1 | |||||
| CGAAGGTTT | 0.608 | 0.622 | 0.136 | 0.712 | 0.812 | |
| GGAGCGGCG | 0.329 | 0.320 | 0.056 | 0.812 | 0.812 | |
| GTCGCAGCG | 0.062 | 0.057 | 0.083 | 0.773 | 0.812 | |
| rs62482167 rs3823642 rs3735241 | Block 2 | |||||
| CCC | 0.650 | 0.645 | 0.018 | 0.892 | 0.949 | |
| CTA | 0.287 | 0.290 | 0.004 | 0.949 | 0.949 | |
| GTA | 0.062 | 0.061 | 0.005 | 0.946 | 0.949 | |
| rs2272345 rs13230744 |
Block 3 | |||||
| GG | 0.595 | 0.604 | 0.047 | 0.829 | 0.900 | |
| CA | 0.333 | 0.328 | 0.016 | 0.900 | 0.900 | |
| CG | 0.063 | 0.058 | 0.086 | 0.769 | 0.900 | |
| GA | 0.009 | 0.011 | 0.050 | 0.823 | 0.900 | |
| rs1043915 rs79986079 |
Block 4 | |||||
| AC | 0.621 | 0.618 | 0.004 | 0.949 | 0.991 | |
| TC | 0.321 | 0.320 | 0.000 | 0.991 | 0.991 | |
| TT | 0.058 | 0.061 | 0.264 | 0.878 | 0.991 | |
| rs2246713 rs1727130 rs1052482 |
Block 5 | |||||
| GCA | 0.504 | 0.606 | 6.817 | 0.009 | 0.012 | |
| CGT | 0.259 | 0.300 | 1.356 | 0.244 | 0.244 | |
| GGT | 0.162 | 0.075 | 13.028 | 0.0003 | 0.001 | |
| CCA | 0.075 | 0.018 | 14.460 | 0.0001 | 0.0004 | |
STAG3: stromal antigen 3; SNPs: single-nucleotide polymorphism; NOA: nonobstructive azoospermia; OR: odds ratio; CI: confidence interval; FDR-P: false discovery rate-adjusted P value
Table 3 shows a genetic model of the 3 SNVs (rs2246713, rs1727130, and rs1052482) between the cases and the controls. We found that the individuals with the CG genotype of rs1727130 and AT genotype of rs1052482 had an increased risk susceptibility to NOA in the codominant model, and those with the minor allele G of rs1727130 and T of rs1052482 had an increased risk susceptibility to NOA in the dominant model (P = 0.039, OR: 1.64, 95% CI: 1.030–2.608).
Table 3.
The genotype distributions of STAG3 rs2246713, rs1727130, and rs1052482 in the cases and the controls
| SNPs | Model | Genotype | Case, n (%) | Control, n (%) | P (Fisher) | OR | 95% CI | FDR-P |
|---|---|---|---|---|---|---|---|---|
| rs2246713 | Codominant | GG | 52 (43.3) | 114 (46.5) | ||||
| GC | 55 (45.9) | 106 (43.3) | 0.638 | 1.138 | 0.717–1.806 | 0.638 | ||
| CC | 13 (10.8) | 25 (10.2) | 0.847 | 1.140 | 0.541–2.404 | 0.847 | ||
| Dominant | GG | 52 (43.3) | 114 (46.5) | |||||
| GC + CC | 68 (56.7) | 131 (53.5) | 0.578 | 0.879 | 0.566–1.364 | 0.578 | ||
| Recessive | CC | 13 (10.8) | 25 (10.2) | |||||
| GC + GG | 107 (89.2) | 220 (89.8) | 0.857 | 1.069 | 0.526–2.172 | 0.857 | ||
| rs1727130 | Codominant | CC | 36 (30.0) | 101 (41.2) | ||||
| CG | 67 (55.8) | 105 (42.9) | 0.021 | 1.790 | 1.098–2.918 | 0.032 | ||
| GG | 17 (14.2) | 39 (15.9) | 0.596 | 1.223 | 0.617–2.426 | 0.847 | ||
| Dominant | CC | 36 (30.0) | 101 (41.2) | |||||
| CG + GG | 84 (70.0) | 144 (58.8) | 0.039 | 1.637 | 1.030–2.608 | 0.059 | ||
| Recessive | GG | 17 (14.2) | 39 (15.9) | |||||
| CG + CC | 103 (85.8) | 206 (84.1) | 0.758 | 0.872 | 0.471–1.615 | 0.857 | ||
| rs1052482 | Codominant | AA | 36 (30.0) | 101 (41.2) | ||||
| AT | 67 (55.8) | 105 (42.9) | 0.021 | 1.79 | 1.098–2.918 | 0.032 | ||
| TT | 17 (14.2) | 39 (15.9) | 0.596 | 1.223 | 0.617–2.426 | 0.847 | ||
| Dominant | AA | 36 (30.0) | 101 (41.2) | |||||
| AT + TT | 84 (70.0) | 144 (58.8) | 0.039 | 1.637 | 1.030–2.608 | 0.059 | ||
| Recessive | TT | 17 (14.2) | 39 (15.9) | |||||
| AT + AA | 103 (85.8) | 206 (84.1) | 0.758 | 0.872 | 0.471–1.615 | 0.857 |
STAG3: stromal antigen 3; SNPs: single-nucleotide polymorphism; OR: odds ratio; CI: confidence interval; FDR-P: false discovery rate-adjusted P value
To determine whether variations in the 3’-UTR region affected miRNA-mediated gene expression regulation, miRNAs predicted to interact with STAG3 were examined by in silico analysis, and seven miRNAs, miR-148a, miR-2909, miR-3162-5p, miR-33a-5p, miR-33b-5p, miR-4739, and miR-6508-3p, were found potentially to interact with the 3’-UTR of STAG3 mRNA. The effect of these seven miRNAs on +198A>T variation (rs1052482) was examined by a luciferase assay. There was no significant difference in relative luciferase activities between rs1052482A and rs1052482T (Figure 2a), but a substantial decrease in luciferase activity was observed in cells cotransfected with pmir-rs1052482A and miR-3162-5p (mean ± standard deviation [s.d.]: 51.6% ± 2.5%, P = 0.002), compared with control vector pmir-GLO or pmir-rs1052482T (mean ± s.d.: 85.9% ± 3.6%, P = 0.061) (Figure 2b). A sequential decrease was observed in cells cotransfected with pmir-rs1052482A and miR-3162-5p in proportion to the amount of the mimic (rs1052482A [mean ± s.d.], 63.7% ± 1.7%: 45.0% ± 2.7%; rs1052482T [mean ± s.d.], 89.2 ± 5.0%: 78.3% ± 1.4%, respectively), and this reduction was inhibited when the mir-3162-5p inhibitor was cotransfected with pmir-rs1052482A and miR-3162-5p (Figure 2c). The data indicate that miR-3162-5p targets the rs1052482A sequence more efficiently than that of rs1052482T.
Figure 2.
Relative luciferase activities of pmir-rs1052482A and pmir-rs1052482T in HEK293T cells. a–c represent the mean RLU values ± s.d. of triplicates. (a) No significant differences in relative luciferase activity among three vectors. (b) Luciferase activity was substantially decreased in cells cotransfected with pmir-rs1052482A and miR-3162-5p compared to the cells cotransfected with pmir-control and miR-3162-5p (P < 0.01). (c) A sequential decrease was observed in cells cotransfected with pmir-rs1052482A and miR-3162-5p in proportion to the amount of the mimic and this reduction was rescued when the mir-3162-5p inhibitor was cotransfected with pmir-rs1052482A and miR-3162-5p. -: absence of the indicated one; +: presence of the indicated one; *P < 0.05 (pmir-rs1052482A + mir3162-5p [5 pmol] compared to pmir-rs1052482A); **P < 0.01 (pmir-rs1052482A + mir3162-5p [10 pmol] compared to pmir-rs1052482A). CTL: control; RLU: relative light unit; s.d.: standard deviation.
DISCUSSION
Many autosomal genes such as ring finger protein 212(Rnf212), testis expressed 15(Tex15), syntaxin 2(Stx2), and siah E3 ubiquitin protein ligase 1A(Siah1a)are reported to be crucial factors for the meiotic process and spermatogenesis in mouse studies.25,26,27,28 Human homologs of these genes also play a role in meiosis, and variations of these genes are thought to induce spermatogenic impairment.8,29,30,31 In recent studies, the homologous deletion of Stag3 has been shown to induce sterility associated with the premature arrest of meiotic prophase I in both male and female mice.18,32 Therefore, we have investigated the association between STAG3 gene variations and male infertility.
We identified 30 variations in the coding regions and intron boundariesof STAG3 in patients with NOA and in control samples. Of the 30 variations, seven were exonic and three were found only in different infertile patients. For these variations, we evaluated the potential pathogenic effects by three prediction methods, Polyphen-2, SIFT, and Mutation Taster (Table 4). Most variations (six of nine) were considered benign and three variations did not show consistent results in the three predictive programs. Minor allele frequencies (MAFs) of these variations were not significantly different between patients and controls, and these MAFs were similar to those of other Asian populations on the NCBI SNP database from the 1000 Genomes Project. Considering these data, the above-mentioned variations are unlikely to be related to the spermatogenic impairment in our Korean male population. Interestingly, two variations, c.3669+35C>G and +198A>T (rs1727130 and rs1052482) located in 3’-UTR, had a significantly different frequency between the patient and control groups. However, there is a discrepancy in this result. According to Yu et al.,33 there is no significant difference in the frequencies of allele and genotype at SNP rs1052482 between patients with NOA and controls and they suggested that this SNP is not associated with azoospermia. Two variants of rs1727130 and rs1052482 are close together (372 bp apart) and tightly linked, as shown in the LD block analysis. The allele distribution of 2 SNVs between the patient and control groups is more evident in the LD block analysis. On the basis of the data, we propose that multiple SNVs linked to a block can interact with each other to regulate gene function, rather than allowing each SNV to function independently.
Table 4.
In silico analysis for exonic variations and three variations found only in patients
| n | db SNP ID | Gene location | AA change | In silico variant analysis | ||
|---|---|---|---|---|---|---|
| PolyPhen-2a | SIFTb | Mutation Tasterc | ||||
| 1 | rs11531577 | c.48G>T | p.Leu16Phe | Benign (1.00/0.00) | Not tolerated | Polymorphism |
| 2 | rs2272343 | c.106A>C | p.Thr36Pro | Benign (1.00/0.00) | Not tolerated | Polymorphism |
| 3 | rs761620488 | c.198A>C | p.Lys66Asn | Benign (0.98/0.44) | Tolerated | Polymorphism |
| 4 | rs200131656 | c.1035A>G | p.Leu345 | – | Tolerated | Disease causing |
| 5 | rs755877186 | c.1269C>T | p.Asp423 | – | Tolerated | Polymorphism |
| 6 | rs3735241 | c.1293A>C | p.Pro431 | – | Tolerated | Polymorphism |
| 7 | rs1043915 | c.2445T>A | p.Ile815 | – | Tolerated | Polymorphism |
| 8 | rs188384958 | +112G>A | 3’-UTR | – | – | Polymorphism |
| 9 | rs1727131 | +315C>T | 3’-UTR | – | – | Polymorphism |
ahttp://genetics.bwh.harvard.edu/pph 2/; bSIFT, http://siftdna.org/; cwww.mutationtaster.org. AA: amino acid; –: no result; SIFT: sorting intolerant from tolerant
The presence of SNVs in the 3’-UTR of genes may interfere with mRNA stability and translation through effects on polyadenylation and regulatory protein–mRNA and miRNA–mRNA interactions, or may locally alter secondary structures of mRNAs, affecting the accessibility of binding sites for interacting transelements.34,35,36
We investigated whether rs1052482, the SNV in the 3’-UTRof STAG3, could affect interaction with miRNAs and thus affect posttranscriptional repression of STAG3. When pmir-rs1052482A was cotransfected with miR-3162-5p, a substantial decrease was observed in luciferase activity compared with pmir-rs1052482T. This result suggests that rs1052482 is located within a binding site for miR-3162-5p in the STAG3 3’-UTR, and the minor rs1052482T allele may offset the inhibition by miR-3162-5p.
According to Fukuda et al.,17,37 STAG3 interacts with the three different α-kleisin subunits present in mammalian meiotic cells, depending on the temporal and spatial distribution. STAG3 combined with meiotic recombination protein (REC8), one of the α-kleisin subunits, and promoted synapsis between homologous chromosomes, while the same complexes inhibited synaptonemal complex assembly between sister chromatids. Therefore, we hypothesize that STAG3 with the rs1052482T variation reduces the normal inhibitory function of mir-3162-5p, thereby increasing the amount of STAG3 protein and ultimately disturbing synapses between homologous chromosomes or sister chromatids. However, it is unclear how elevated STAG3 may affect meiotic chromosome dynamics. In previous studies, mir-3162-5p was identified as a regulator of prostate or cervical antigen in cell carcinomas.38,39,40 However, little is known about mir-3162-5p's regulation of human meiosis, because of the difficulties in uncovering the spatiotemporal and sequential expression of miRNAs in human germ cells, and identifying which miRNAs are the actual operators for the onset of meiosis or spermatogenesis.
CONCLUSION
We have identified 30 SNVs of STAG3 in the Korean population. Pathogenic variations that directly cause NOA were not identified. However, we found that two SNVs, rs1727130 and rs1052482, located in the 3’-UTR region may be associated with the NOA phenotype through the regulation of miRNA. Further studies are needed to determine whether variations in the 3’-UTR region of STAG3 actually affect gene expression through miRNAs, including mir-3162-5p in germ cells. While there is still much to learn about the exact mechanisms regulating human meiosis or spermatogenesis, our findings contribute to the understanding of spermatogenic impairment, as well as the identification of predictive susceptibility biomarkers.
AUTHOR CONTRIBUTIONS
SH Shim conceived and designed the article; SH Shim, YN, and KMK drafted the manuscript; YN, KMK, and SRS designed and performed the experiments; SRS, JEP, and YJS analyzed data and interpreted findings. SH Song collected the samples and performed andrology workup. JTS and TKY prepared the publication. All authors read, edited, and approved the final manuscript.
COMPETING INTERESTS
All authors declared no competing interests.
Schematic diagram of luciferase reporter construct and in silico interaction of the potential microRNAs with rs1052482 of STAG3 Seven candidate miRNAs (miR-148a, miR-2909, miR-3162-5p, miR-33a-5p, miR-33b-5p, miR-4739, and miR-6508-3p) that allow rs1052482A or T alleles to be included in the seed sequence. STAG3: stromal antigen 3.
ACKNOWLEDGMENTS
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant No. H14C0106020014). Funding for this study was obtained by SH Shim.
Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.
REFERENCES
- 1.Cooper TG, Hellenkemper B, Jonckheere J, Callewaert N, Grootenhuis AJ, et al. Azoospermia: virtual reality or possible to quantify? J Androl. 2006;27:483–90. doi: 10.2164/jandrol.05210. [DOI] [PubMed] [Google Scholar]
- 2.Esteves SC, Miyaoka R, Agarwal A. An update on the clinical assessment of the infertile male.[corrected] Clinics (Sao Paulo) 2011;66:691–700. doi: 10.1590/S1807-59322011000400026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Gudeloglu A, Parekattil SJ. Update in the evaluation of the azoospermic male. Clinics (Sao Paulo) 2013;68(Suppl 1):27–34. doi: 10.6061/clinics/2013(Sup01)04. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kolettis PN. The evaluation and management of the azoospermic patient. J Androl. 2002;23:293–305. [PubMed] [Google Scholar]
- 5.Esteves SC, Prudencio C, Seol B, Verza S, Knoedler C, et al. Comparison of sperm retrieval and reproductive outcome in azoospermic men with testicular failure and obstructive azoospermia treated for infertility. Asian J Androl. 2014;16:602–6. doi: 10.4103/1008-682X.126015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Lee HD, Lee HS, Park SH, Jo DG, Choe JH, et al. Causes and classification of male infertility in Korea. Clin Exp Reprod Med. 2012;39:172–5. doi: 10.5653/cerm.2012.39.4.172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Esteves SC. Clinical management of infertile men with nonobstructive azoospermia. Asian J Androl. 2015;17:459–70. doi: 10.4103/1008-682X.148719. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Matzuk MM, Lamb DJ. The biology of infertility: research advances and clinical challenges. Nat Med. 2008;14:1197–213. doi: 10.1038/nm.f.1895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sun F, Turek P, Greene C, Ko E, Rademaker A, et al. Abnormal progression through meiosis in men with nonobstructive azoospermia. Fertil Steril. 2007;87:565–71. doi: 10.1016/j.fertnstert.2006.07.1531. [DOI] [PubMed] [Google Scholar]
- 10.Sun F, Trpkov K, Rademaker A, Ko E, Martin RH. Variation in meiotic recombination frequencies among human males. Hum Genet. 2005;116:172–8. doi: 10.1007/s00439-004-1215-6. [DOI] [PubMed] [Google Scholar]
- 11.Sun F, Kozak G, Scott S, Trpkov K, Ko E, et al. Meiotic defects in a man with non-obstructive azoospermia: case report. Hum Reprod. 2004;19:1770–3. doi: 10.1093/humrep/deh335. [DOI] [PubMed] [Google Scholar]
- 12.Pezzi N, Prieto I, Kremer L, Perez Jurado LA, Valero C, et al. STAG3, a novel gene encoding a protein involved in meiotic chromosome pairing and location of STAG3-related genes flanking the Williams-Beuren syndrome deletion. FASEB J. 2000;14:581–92. doi: 10.1096/fasebj.14.3.581. [DOI] [PubMed] [Google Scholar]
- 13.Houmard B, Small C, Yang L, Naluai-Cecchini T, Cheng E, et al. Global gene expression in the human fetal testis and ovary. Biol Reprod. 2009;81:438–43. doi: 10.1095/biolreprod.108.075747. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Nogues C, Fernandez C, Rajmil O, Templado C. Baseline expression profile of meiotic-specific genes in healthy fertile males. Fertil Steril. 2009;92:578–82. doi: 10.1016/j.fertnstert.2008.06.034. [DOI] [PubMed] [Google Scholar]
- 15.Rankin S. Complex elaboration: making sense of meiotic cohesin dynamics. FEBS J. 2015;282:2426–43. doi: 10.1111/febs.13301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Garcia-Cruz R, Brieno MA, Roig I, Grossmann M, Velilla E, et al. Dynamics of cohesin proteins REC8, STAG3, SMC1β and SMC3 are consistent with a role in sister chromatid cohesion during meiosis in human oocytes. Hum Reprod. 2010;25:2316–27. doi: 10.1093/humrep/deq180. [DOI] [PubMed] [Google Scholar]
- 17.Fukuda T, Fukuda N, Agostinho A, Hernandez-Hernandez A, Kouznetsova A, et al. STAG3-mediated stabilization of REC8 cohesin complexes promotes chromosome synapsis during meiosis. EMBO J. 2014;33:1243–55. doi: 10.1002/embj.201387329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Caburet S, Arboleda VA, Llano E, Overbeek PA, Barbero JL, et al. Mutant cohesin in premature ovarian failure. N Engl J Med. 2014;370:943–9. doi: 10.1056/NEJMoa1309635. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.He WB, Banerjee S, Meng LL, Du J, Gong F, et al. Whole-exome sequencing identifies a homozygous donor splice-site mutation inSTAG3 that causes primary ovarian insufficiency. Clin Genet. 2018;93:340–4. doi: 10.1111/cge.13034. [DOI] [PubMed] [Google Scholar]
- 20.Llano E, Gomez HL, Garcia-Tunon I, Sanchez-Martin M, Caburet S, et al. STAG3 is a strong candidate gene for male infertility. Hum Mol Genet. 2014;23:3421–31. doi: 10.1093/hmg/ddu051. [DOI] [PubMed] [Google Scholar]
- 21.Bayes M, Prieto I, Noguchi J, Barbero JL, Perez Jurado LA. Evaluation of the Stage3 gene and the synaptonemal complex in a rat model (as/as) for male infertility. Mol Reprod Dev. 2001;60:414–7. doi: 10.1002/mrd.1104. [DOI] [PubMed] [Google Scholar]
- 22.Murray KS, James A, McGeady JB, Reed ML, Kuang WW, et al. The effect of the new 2010 World Health Organization criteria for semen analyses on male infertility. Fertil Steril. 2012;98:1428–31. doi: 10.1016/j.fertnstert.2012.07.1130. [DOI] [PubMed] [Google Scholar]
- 23.Hsueh HM, Chen JJ, Kodell RL. Comparison of methods for estimating the number of true null hypotheses in multiplicity testing. J Biopharm Stat. 2003;13:675–89. doi: 10.1081/BIP-120024202. [DOI] [PubMed] [Google Scholar]
- 24.Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, et al. The structure of haplotype blocks in the human genome. Science. 2002;296:2225–9. doi: 10.1126/science.1069424. [DOI] [PubMed] [Google Scholar]
- 25.Reynolds A, Qiao H, Yang Y, Chen JK, Jackson N, et al. RNF212 is a dosage-sensitive regulator of crossing-over during mammalian meiosis. Nat Genet. 2013;45:269–78. doi: 10.1038/ng.2541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yang F, Eckardt S, Leu NA, McLaughlin KJ, Wang PJ. Mouse TEX15 is essential for DNA double-strand break repair and chromosomal synapsis during male meiosis. J Cell Biol. 2008;180:673–9. doi: 10.1083/jcb.200709057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Fujiwara Y, Ogonuki N, Inoue K, Ogura A, Handel MA, et al. t-SNARE Syntaxin2 (STX2) is implicated in intracellular transport of sulfoglycolipids during meiotic prophase in mouse spermatogenesis. Biol Reprod. 2013;88:141. doi: 10.1095/biolreprod.112.107110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Dickins RA, Frew IJ, House CM, O’Bryan MK, Holloway AJ, et al. The ubiquitin ligase component Siahla is required for completion of meiosis I in male mice. Mol Cell Biol. 2002;22:2294–303. doi: 10.1128/MCB.22.7.2294-2303.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kong A, Thorleifsson G, Stefansson H, Masson G, Helgason A, et al. Sequence variants in the RNF212 gene associate with genome-wide recombination rate. Science. 2008;319:1398–401. doi: 10.1126/science.1152422. [DOI] [PubMed] [Google Scholar]
- 30.Okutman O, Muller J, Baert Y, Serdarogullari M, Gultomruk M, et al. Exome sequencing reveals a nonsense mutation in TEX15 causing spermatogenic failure in a Turkish family. Hum Mol Genet. 2015;24:5581–8. doi: 10.1093/hmg/ddv290. [DOI] [PubMed] [Google Scholar]
- 31.Choi Y, Jeon S, Choi M, Lee MH, Park M, et al. Mutations in SOHLH1 gene associate with nonobstructive azoospermia. Hum Mutat. 2010;31:788–93. doi: 10.1002/humu.21264. [DOI] [PubMed] [Google Scholar]
- 32.Hopkins J, Hwang G, Jacob J, Sapp N, Bedigian R, et al. Meiosis-specific cohesin component, Stage3 is essential for maintaining centromere chromatid cohesion, and required for DNA repair and synapsis between homologous chromosomes. PLoS Genet. 2014;10:e1004413. doi: 10.1371/journal.pgen.1004413. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Yu CH, Xie T, Zhang RP, A ZC. Association of the common SNPs in RNF212, STAG3 and RFX2 gene with male infertility with azoospermia in Chinese population. Eur J Obstet Gynecol Reprod Biol. 2018;221:109–12. doi: 10.1016/j.ejogrb.2017.12.030. [DOI] [PubMed] [Google Scholar]
- 34.Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell. 2004;116:281–97. doi: 10.1016/s0092-8674(04)00045-5. [DOI] [PubMed] [Google Scholar]
- 35.Moreno-Moya JM, Vilella F, Simon C. MicroRNA: key gene expression regulators. Fertil Steril. 2014;101:1516–23. doi: 10.1016/j.fertnstert.2013.10.042. [DOI] [PubMed] [Google Scholar]
- 36.Kotaja N. MicroRNAs and spermatogenesis. Fertil Steril. 2014;101:1552–62. doi: 10.1016/j.fertnstert.2014.04.025. [DOI] [PubMed] [Google Scholar]
- 37.Fukuda T, Pratto F, Schimenti JC, Turner JM, Camerini-Otero RD, et al. Phosphorylation of chromosome core components may serve as axis marks for the status of chromosomal events during mammalian meiosis. PLoS Genet. 2012;8:e1002485. doi: 10.1371/journal.pgen.1002485. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Stegeman S, Amankwah E, Klein K, O’Mara TA, Kim D, et al. A large-scale analysis of genetic variants within putative miRNA binding sites in prostate cancer. Cancer Discov. 2015;5:368–79. doi: 10.1158/2159-8290.CD-14-1057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Yousef GM. miRSNP-based approach identifies a miRNA that regulates prostate-specific antigen in an allele-specific manner. Cancer Discov. 2015;5:351–2. doi: 10.1158/2159-8290.CD-15-0230. [DOI] [PubMed] [Google Scholar]
- 40.Chen J, Yao D, Li Y, Chen H, He C, et al. Serum microRNA expression levels can predict lymph node metastasis in patients with early-stage cervical squamous cell carcinoma. Int J Mol Med. 2013;32:557–67. doi: 10.3892/ijmm.2013.1424. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Schematic diagram of luciferase reporter construct and in silico interaction of the potential microRNAs with rs1052482 of STAG3 Seven candidate miRNAs (miR-148a, miR-2909, miR-3162-5p, miR-33a-5p, miR-33b-5p, miR-4739, and miR-6508-3p) that allow rs1052482A or T alleles to be included in the seed sequence. STAG3: stromal antigen 3.