Abstract
Purpose
To develop and assess a novel custom next-generation sequencing (NGS) panel for male infertility genetic diagnosis.
Methods
A total of 241 subjects with diagnosis of idiopathic infertility ranging from azoospermia to normozoospermia were sequenced by a custom NGS panel including AR, FSHB, FSHR, KLHL10, NR5A1, NANOS1, SEPT12, SYCP3, TEX11 genes. Variants with minor allele frequency < 1% were confirmed by Sanger sequencing.
Results
Nineteen missense variants were detected in 23 subjects with abnormal sperm count, whilst no variants were identified in normozoospermic men. Of identified variants, we prioritized variants classified as pathogenic and of uncertain significance (VUS) (63.1%, 12/19). No missense variants were found in males with normal seminal parameters (0/67). Therefore, the prevalence of variants was significantly higher in patients with spermatogenic impairment (16/174 vs 0/67, p = 0.007).
Conclusion
This study confirms the utility to apply NGS panel for infertility diagnosis in order to find new genetic variants potentially linked to male infertility with much higher accuracy than standard tests suggested by guidelines. Indeed, based on biological significance, prevalence in the general population and clinical data of patients, it is plausible that identified variants in this study might be linked to quantitative spermatogenic impairment, although further studies are needed.
Electronic supplementary material
The online version of this article (10.1007/s10815-020-01747-0) contains supplementary material, which is available to authorized users.
Keywords: Idiopathic male infertility, NGS, Gene panel, DNA sequencing
Introduction
Male infertility is a complex multifactorial disorder with substantial genetic basis which affects a high number of subjects. The prevalence of infertility, defined as the inability to conceive after 1 year of unprotected intercourses, in Western countries is estimated in about 15%, and a male factor is responsible, alone or in combination with female factors, in about half of the cases [1].
Several risk factors and causes might affect male fertility, including lifestyles, endocrine diseases, testicular trauma, cryptorchidism, varicocele, genitourinary infections, iatrogenic causes and surgical therapies, metabolic diseases, but genetic defects account for a large proportion of cases [1].
Indeed, thousands of genes are implicated in spermatogenesis, testicular development and endocrine regulation of testicular function [2–12], and the genetic contribution to male infertility is considerable. Basic and clinical research in the last years found a number of genes that could potentially be used in clinical practice, and new technologies for genetic analysis, such as microarray and next-generation sequencing (NGS), have given a strong contribution to the research in this field [9–11]. However, genetic analyses currently recommended in standard clinical practice are still relatively few and limited to karyotype analysis, Y chromosome microdeletion analysis, and mutations in CFTR (cystic fibrosis transmembrane conductance regulator), AR (androgen receptor), hypogonadotropic hypogonadism (HH)-related genes in selected cases [11–18].
Genetic abnormalities are diagnosed in 10–15% of the most severe forms of male infertility, with a prevalence that is inversely related to sperm concentration [11]. However, a large proportion (30–60%) of infertile males does not receive a clear diagnosis. In these cases, generally reported as idiopathic infertility, there is a strong suspicion of genetic factors yet to be discovered.
Apart from karyotyping and PCR-based approach for Y chromosome microdeletions, Sanger sequencing has been the method of choice for screening gene variants for decades. However, as more and more genes are being suggested to be associated with male infertility, it has become time consuming and costly to keep up with the new findings [19].
New high-throughput methods have been instrumental for analysing multiple DNA molecules in parallel in large cohort studies. Indeed, NGS, such as WES (whole exome sequencing) and WGS (whole genome sequencing), allow also detecting single nucleotide variants (SNVs) and copy number variations (CNVs) potentially related to male infertility phenotypes [3, 20–27]. Whilst WES offers a global view of coding regions, it does not identify variants in non-coding regions, such as promoters, enhancer regions and untranslated region (UTR). This limitation can be resolved by large scale sequencing performed using WGS.
However, the sheer amount of variants detected by WGS can be a daunting task to compute as each individual harbours millions of variants in coding and non-coding regions, generating a substantial amount of variants with uncertain significance (VUS).
Besides the difficulty in interpreting variants, not all the regions of the genome are sequenced equally, with a potential of information loss in critical gene areas. Targeted sequencing, instead, affords the depth of sequencing exclusively for regions of interest. Therefore, it is clear that, for a diagnostic purpose, the application of NGS to panels of genes associated with the phenotype (male infertility) might represent a convenient approach. Indeed, over the last years, several groups proposed gene panel to investigate genetic causes of infertility [28–32].
The aim of this study was to assess and develop a novel target NGS panel as diagnostic genetic test in addition to classical assays for genetic screening of male infertility. Specifically, we designed a custom gene panel including genes that recent reviews have associated with male infertility [9, 11, 32] or that could be considered informative in preliminary studies, in addition to genes with strong or moderate clinical validity currently analysed in our laboratory [13, 33–37].
This NGS panel was assessed on a cohort of 241 infertile idiopathic males, who resulted negative to standard genetic screening (karyotype, Yq microdeletions and CFTR gene mutation analysis), when their total sperm count was less than 10 million [38].
Materials and methods
Patients
This study was approved by the hospital ethics committee and included a total of 1100 subjects, who provided an informed consent, referred between May 2016 and June 2018 to the Unit of Andrology and Reproductive Medicine of the University Hospital of Padova, Italy. Exclusion criteria were malignancies of any type, orchitis, clinical varicocele, testicular torsion and trauma, history of cryptorchidism, obstructive azoospermia, mutations in CFTR gene, use of gonadotoxic drugs, karyotype anomalies, Y chromosome long arm microdeletions, hypothalamic pituitary axis disturbances (such as hypogonadotropic hypogonadism and isolated gonadotropin deficiency) and spermatogenic qualitative defects (such as globozoospermia, macrocephaly, asthenozoospermia and teratozoospermia). Therefore, the subjects reported here were 241.
All selected subjects were of Caucasian ethnicity and Italian origin according to self-report. In all patients, careful history taking and physical examination were performed.
Semen analysis was performed according to World Health Organization guidelines (WHO, 2010). Total sperm count was used to define individuals into three subgroups: azoospermia (no sperm in the ejaculate after centrifugation), oligozoospermia (total sperm count > 0 < 39 million/ejaculate), and normozoospermia (total sperm count of ≥ 39 million/ejaculate). Serum levels of FSH, LH and testosterone were measured by commercial electrochemiluminescence immunoassay (Elecsys 2010; Roche Diagnostics). Testicular fine-needle aspiration cytology (FNAC) was performed to diagnose the spermatogenic alteration in cases of patients with total sperm count < 1 million/ejaculate [39].
Ten patients with known missense variants in AR and NR5A1 (nuclear receptor subfamily 5 group A member 1) genes were used as positive controls to assess the reliability of our panel.
DNA isolation and sequencing analysis
Genomic DNA was extracted from peripheral blood leucocytes of subjects using QIAamp DNA Blood Mini Kit, according to the manufacturer’s protocol (Qiagen Inc., Hilden, Germany). The quality of the DNA was determined using a NanoDrop-1000 (Thermo Fisher Scientific Inc., Waltham, MA, USA) and Qubit 2.0 fluorometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). A Qubit dsDNA BR (broad range, 2 to 1000 ng) Assay Kit and Qubit dsDNA HS (high sensitivity, 0.2 to 100 ng) Assay Kit were used with a Qubit fluorometer according to the manufacturer’s protocol.
To investigate genetic causes resulting in spermatogenic quantitative defects in our cases, we designed a target gene panel including genes that we routinely analyse in our laboratory [33–37], and, in addition, genes that recent reviews have strongly associated with male infertility [11].
Genes included in the custom panel were AR (MIM: 313700, NM_000044), FSHR (follicle stimulating hormone receptor) (MIM: 136435, NM_000145), FSHB (follicle stimulating hormone subunit beta) (MIM: 136530, NM_001018080), KLHL10 (kelch-like family member 10) (MIM: 608778, NM_152467), NR5A1 (MIM: 184757, NM_004959), NANOS1 (nanos C2HC-type zinc finger 1) (MIM: 608226, NM_199461), SEPT12 (Septin 12) (MIM: 611562, NM_144605), SYCP3 (synaptonemal complex protein 3) (MIM: 604759, NM_001177949) and TEX11 (testis expressed 11) (MIM: 300311, NM_001003811). In addition to all coding exons, the panel included splice sites, 5′ untranslated regions (UTRs), and 3′ UTRs for each of the listed genes (Supplementary Table 1) and promoter regions of FSHB and FSHR genes.
Table 1 summarizes the genes included in the panel.
Table 1.
Type of infertility, inheritance pattern and clinical validity assigned to the genes in the panel (adapted from Oud et al. 2019 and Cannarella et al. 2019)
| Gene | Cytogenetic location | Broad category | Type of infertility | Reported inheritance | Clinical validity |
|---|---|---|---|---|---|
| AR | Xq12 | Testicular (hormone resistance) | Reproductive system syndrome/isolated | XL | Definitive |
| FSHB | 11p14.1 | Pre-testicular | Endocrine disorder/reproductive system syndrome | AR | Moderate |
| FSHR | 2p16.3 | Pre-testicular/testicular (hormone resistance) | Endocrine disorder/reproductive system syndrome | AR | Moderate |
| KLHL10 | 17q21.2 | Testicular | Isolated | AD | Moderate/definitive |
| NANOS1 | 10q26.11 | Testicular | Isolated | AD | Unavailable |
| NR5A1 | 9q33.3 | Pre-testicular/testicular | Endocrine disorder/reproductive system syndrome | AD | Definitive/strong |
| SEPT12 | 16p13.3 | Testicular | Isolated | AD | Unavailable |
| SYCP3 | 12q23.2 | Testicular | Isolated | AD | Moderate |
| TEX11 | Xp11 | Testicular | Isolated | XL | Strong |
AR, autosomal recessive; AD, autosomal dominant; XL, X-linked
Probes were designed by the web-based application tool DesignStudio (Illumina Inc., San Diego, CA, USA) using the GRCh37/19 as the reference genome build, resulting in target regions covered by 169 amplicons with a mean length of 250 base pairs (bp). Probes created by DesignStudio were designed to cover 100% of the selected regions. Targeted NGS was performed on a total of 30,262 bases.
Sample libraries for sequencing were prepared starting from 80 ng of DNA by Target Sequencing Custom Amplification (TSCA) low input kit according to the manufacturer’s protocol (Illumina, San Diego, CA, USA). Amplification libraries were assessed and quantified by 4200 Agilent Tapestation System using Agilent D1000 ScreenTape (Agilent, Santa Clara, CA, USA). Equimolar concentration of libraries (4 nM) were combined, denatured and diluted to final concentration of 10 pM according to the manufacturer’s recommendation. The libraries were then loaded on a 500-cycle (2 × 250 paired ends) reagent cartridge (Illumina, San Diego, CA, USA), and run on a MiSeq sequencer (Illumina, San Diego, CA, USA).
For each run, average depth was minimum of ~ 100× coverage to allow for optimal variant calling. BWA-MEM and GATK were used as algorithms for aligning genome sequencing reads to the reference genome (GRCh37/hg19) and variant calling respectively. Variants with allelic frequency lower than 1% were confirmed by Sanger sequencing. Primers used are listed in Supplementary Table 2.
Variant filtering and interpretation of clinical relevance
Data were analysed by VariantStudio software v.3.0 (Illumina) and the Integrative Genomics Viewer (IGV) was used as visualization tool for the detected variants. HipSTR was used as bioinformatics tool to establish the number of CAG and GGC triplet repeats in AR gene.
To exclude variants occurring in general population with a frequency of 1% (referred to as polymorphisms), allele frequencies of called variants were compared with those in European-non Finish population of Genome Aggregation Database (gnomAD, http://gnomad-old.broadinstitute.org/) using Dataset v2.1.1 controls. Therefore, we included in our analysis exclusively rare non-synonymous variants (Minor Allele Frequency (MAF) <1%). We used dbSNP database (https://www.ncbi.nlm.nih.gov/snp/) and Ensembl browser (http://grch37.ensembl.org/index.html) to find location and frequency of the variants.
PolyPhen2 (http://genetics.bwh.harvard.edu/pph2) and SIFT (http://sift.jcvi.org) were used as in silico tools to predict the impact of missense changes. Variant classification was performed following criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines [43].
Statistical analysis
Statistical analysis of the data was conducted with SPSS 21.0 for Windows (SPSS, Chicago, IL). The results are expressed as means ± standard deviation (SD). Differences between groups were analysed using a Student’s t test. Differences in allele frequency between groups were measured using Fisher’s exact test when expected size was smaller than five.
Results
Table 2 shows patients’ characteristics for seminal parameters, age, reproductive hormones (luteinizing hormone (LH), follicle stimulating hormone (FSH) and testosterone (T)) and mean testicular volume. As expected, individuals with non-obstructive azoospermia (NOA) or oligozoospermia had higher FSH and LH plasma levels and lower testicular volumes than subjects with normal seminal parameters. Patients with NOA had higher FSH and LH plasma levels and lower testicular volumes with respect to subjects with oligozoospermia.
Table 2.
Clinical features of the 241 subjects
| Phenotype | Age (year) | LH (IU/L) | FSH (IU/L) | T (nmol/L) | Mean testicular volume (mL) |
|---|---|---|---|---|---|
| Non-obstructive azoospermia (NOA) (N 67) | 39.9 ± 8.6 | 10.7 ± 6.6*# | 23.7 ± 14.3*# | 14.4 ± 7.8 | 8.8 ± 2.6*# |
| Oligozoospermia (> 0 < 39 × 106 sperm/ejaculate) (N 107) | 35.8 ± 8.2 | 7.3 ± 3.3* | 12.8 ± 8.5* | 14.0 ± 4.7 | 12.1 ± 3.4* |
| Normal sperm count (≥ 39 × 106 sperm/ejaculate) (N 67) | 39.3 ± 11.5 | 4.8 ± 1.9 | 5.2 ± 2.6 | 15.7 ± 5.9 | 16.2 ± 3.8 |
Data are expressed as mean ± SD. LH, luteinizing hormone; FSH, follicle stimulating hormone; T, testosterone. Normal values: total sperm count, ≥ 39 × 106/ejaculate; LH, 1–8 IU/L; FSH, 1–8 IU/L; T, 10–29 nmol/L, testis volume, ≥ 12 mL
*P < 0.01 between subjects with abnormal seminal parameters and subjects with normal sperm count
#P < 0.01 between non-obstructive azoospermia and oligozoospermia
The mean read depth of all targeted regions was 351× and 93.5% of targeted regions were covered at least by 10 unique reads. Of 241 samples tested, 239 (99.1%) were successfully sequenced at first analysis, whilst two samples were repeated because of very low coverage and they were successfully sequenced at second sequencing effort.
Positive controls carrying known missense variants in AR and NR5A1 genes were correctly sequenced by the panel, resulting, hence, in an analytical sensitivity and specificity of the method > 99%.
A total of 5396 single nucleotide changes were detected in the target regions, including 2821 nucleotides located in non-coding regions. In the coding regions, we identified a total of 1341 synonymous and 1234 non-synonymous nucleotide variants. Nonsense and frameshift variants were not detected.
In this study, we analysed exclusively non-synonymous variants with MAF < 1%.
We found a total of nineteen rare missense variants (Table 3). Five variants, Pro392Ser in the AR gene and Ala101Gln, Gly263Ser, Pro129Leu, Ala351Val in NR5A1 gene have been already described [44–47], whilst there are not published studies for the remaining fourteen variants that might result as novel findings in idiopathic infertility.
Table 3.
Characteristics and allele frequency of missense variants detected in the study compared with the general population
| Gene | Missense variants | dbSNP accession number | Reference | ClinVar | aACMG | Allele frequency | ||||
|---|---|---|---|---|---|---|---|---|---|---|
| NOA | Oligo | Normal sperm count | EUR population (gnomAD v2.1 controls) | #P value | ||||||
| AR | c.1174 C>T, p.Pro392Ser | rs201934623 | 23, 35, 39 | Benign/Pathogenic | (PM2,PM5,PP2,BS2,BS3,BP4) VUS | 0/67 | 2/107# | 0/67 | 66/27574 | 0.01 |
| FSHB | c.59 G>T, p.Ser20Ile | rs6170 | – | US | (PM2,BS2,BP4) VUS | 3/134# | 0/214 | 0/134 | 142/48276 | 0.0009 |
| FSHB | c.327 C>A, p.Ser109Arg | rs148454792 | – | US | (PM2,PP3,BS2) VUS | 0/134 | 2/214# | 0/134 | 77/48278 | 0.04 |
| FSHR | c.604 C>T, p.Asp202Asn | rs369191560 | – | NR | (BS2,BP4) Likely benign | 0/134 | 1/214# | 0/134 | 3/5518 | 0.14 |
| FSHR | c.956 T>C, p.Glu319Gly | rs147685926 | – | NR | (BS2,BP4) Likely benign | 0/134 | 1/214# | 0/134 | 9/48282 | 0.03 |
| KLHL10 | c.242 A>T, p.Asn81Ile | rs36065902 | – | NR | (BS2,BP4,PP4) VUS | 0/134 | 2/214# | 0/134 | 26/48130 | < 0.0001 |
| KLHL10 | c.1029 T>C, p.Ile296Thr | rs61752339 | – | NR | (BS2,BP4,) Likely benign | 0/134 | 1/214# | 0/134 | 517/48112 | 0.74 |
| NANOS1 | c.235 T>A, p.Ser79Thr | rs79170274 | – | NR | (BS2,BP4) Likely benign | 0/244 | 2/214# | 0/134 | 5/8790 | 0.0009 |
| NR5A1 | c.302 C>T, p.Arg101Gln | rs750682280 | – | NR | (PM2,BP4) VUS | 0/134 | 1/214# | 0/134 | 0/42604 | < 0.0001 |
| NR5A1 | c.368 C>G, p.Gly123Ala | rs200163795 | 36, 37 | NIFSV | (BS2,BS3,BP4) Benign | 1/134# | 0/214 | 0/134 | 2/47694 | < 0.0001 |
| NR5A1 | c.386 G>A, p.Pro129Leu | rs200749741 | 36, 37 | Pathogenic | (PS3,PS4,BP4) Pathogenic | 1/134# | 0/214 | 0/134 | 2/47382 | < 0.0001 |
| NR5A1 | c.787 C>T, p.Gly263Ser | rs143355429 | – | NR | (BS2,BP4) Likely benign | 0/134 | 1/214# | 0/134 | 2/42340 | < 0.0001 |
| NR5A1 | c.1052 G>A, p.Ala351Val | rs759071081 | 38 | NR | (PM1,PM5,BS2,BP4) VUS | 1/134# | 0/214 | 0/134 | 5/40618 | 0.0006 |
| SEPT12 | c.139 C>T, p.Gly47Arg | rs1164594027 | – | NR | (PM2,PP3,PP2) VUS | 0/134 | 1/214# | 0/134 | 0/5516 | 0.01 |
| SEPT12 | c.145 C>G, p.Glu49Gln | rs1384271239 | – | NR | (PM2,PP2,BP4) VUS | 0/134 | 1/214# | 0/134 | 0/42768 | < 0.0001 |
| SEPT12 | c.253 C>T, p.Pro85Ser | rs1452958171 | – | NR | (PM2,BP4) VUS | 1/134# | 0/214 | 0/134 | NA | – |
| SEPT12 | c.638 T>C, p.Gln213Arg | rs6500633 | – | NR | (BS2,BP4) Benign | 1/134# | 0/214 | 0/134 | 7/48282 | 0.0013 |
| SYCP3 | c.73 A>G, p.Tyr25His | rs1237691411 | – | NR | (PM2,PP3) VUS | 1/134# | 0/214 | 0/134 | NA | – |
| SYCP3 | c.241 A>C, p.Ile81Leu | Not annotated | – | (PM2,BP4) VUS | 1/134# | 0/214 | 0/134 | – | – | |
VUS, variant of uncertain significance; NOA, non-obstructive azoospermia; Oligo, oligozoospermia; NA, not available; NR, not reported; US, uncertain significance; NIFSV, no interpretation for single variant. Text in bold refers to VUS and pathogenic variants
aACMG guideline: functional prediction by the American College of Medical Genetics and Genomics guideline
#P value between cases and European population
To assess the clinical relevance of detected variants, we categorized them as benign, likely benign, pathogenic and of uncertain significance (VUS) according to ACMG guidelines (Table 3). Additionally, to determine the significance of findings, allele frequency for each variant was calculated between patient subgroups and the European-non Finnish population of gnomAD browser classified as controls and we reported p value for differences in frequency (Table 3).
In total, we found 5 variants (26.3%, 5/19) classified as likely benign, 2 variants (10.5%, 2/19) classified as benign, 11 variants (57.9%, 11/19) classified as VUS and 1 variant (5.3%, 1/19) classified as pathogenic.
Of identified missense variants, we prioritized variants classified as pathogenic and VUS (63.1%, 12/19) (Supplementary Fig. 1 and Fig. 2), that were all heterozygous and approximately 6.6% of patients (16/241) harboured at least one of these variants (Table 4). Of them, one was a compound heterozygote for two NR5A1 gene variants and one patient carried two variants in two different genes (KLHL10 and SEPT12). The prevalence of missense variants was 11.9% (8/67) in patients with NOA and 7.4% (8/107) in patients with oligozoospermia. No missense variants were found in males with normal seminal parameters (0/67). Therefore, the prevalence of variants was significantly higher in patients with spermatogenic impairment (16/174 vs 0/67, p = 0.007).
Table 4.
Clinical characteristics of patients with non-synonymous variants detected
| Gene | Variant | Pt ID | Diagnosis | TSC (×106/ejaculate) | TSM (%) | LH (IU/L) | FSH (IU/L) | T (nmol/L) | TV (mL) (R; L) | Testicular histology |
|---|---|---|---|---|---|---|---|---|---|---|
| AR | p.Pro392Ser | 14781 | Idiopathic severe oligoasthenozoospermia, primary hypogonadism | 4.8 | 0 | 9.3 | 10.4 | 11.0 | 10.5; 9.8 | – |
| AR | p.Pro392Ser | 15009 | Idiopathic moderate oligoasthenozoospermia, testicular hypotrophy | 15 | 30 | 5.8 | 5.2 | 14.5 | 12.0;11.0 | – |
| FSHB | p.Ser20Ile | 5123 | Idiopathic SCOS, primary hypogonadism | 0 | – | 9.0 | 24.2 | 16.0 | 3.8; 3.0 | Bilateral SCOS |
| FSHB | p.Ser20Ile | 14869 | Idiopathic SCOS, testicular hypotrophy | 0 | – | 3.4 | 5.4 | 14.9 | 11.3;6.5 | Bilateral SCOS |
| FSHB | p.Ser20Ile | 14199 | Idiopathic hypospermatogenesis, primary hypogonadism | 0 | – | 6.6 | 12.2 | 10.9 | 9.9; 8.4 | Bilateral severe hypospermatogenesis |
| FSHB | p.Ser109Arg | 14920 | Idiopathic severe oligoasthenozoospermia | 2.5 | 10 | 3.6 | 2.7 | 11.7 | 17.8; 15.8 | – |
| FSHB | p.Ser109Arg | 14950 | Idiopathic moderate oligoasthenozoospermia | 12 | 28 | 6.2 | 7.8 | 25.8 | 12.4; 11.5 | – |
| KLHL10 | p.Asn81Ile | 14495* | Idiopathic severe oligoasthenozoospermia | 4.8 | 15 | 8.6 | 7.6 | 14.6 | 12.0; 8.5 | – |
| KLHL10 | p.Asn81Ile | 14600 | Idiopathic severe oligozoospermia, moderate asthenozoospermia, primary hypogonadism | 6.4 | 39 | 13.0 | 15.4 | 9.1 | 6.0; 5.0 | – |
| NR5A1 | p.Arg101Gln | 14760 | Idiopathic moderate oligoasthenozoospermia, testicular hypotrophy | 26.6 | 29 | 4.5 | 5.2 | 22.1 | 10.0; 9.0 | – |
| NR5A1 |
p.Gly123Ala p.Pro129Leu |
14947 | Idiopathic SCOS, primary hypogonadism | 0 | – | 7.2 | 17.0 | 9.5 | 6.8; 6.2 | Bilateral SCOS |
| NR5A1 | p.Ala351Val | 15002 | Idiopathic SCOS, primary hypogonadism | 0 | – | 12.4 | 25.4 | 8.9 | 11.3; 9.6 | Bilateral SCOS |
| SEPT12 | p.Gly47Arg | 14495* | Idiopathic severe oligoasthenozoospermia | 4.8 | 15 | 8.6 | 7.6 | 14.6 | 15.0; 10.6 | – |
| SEPT12 | p.Glu49Gln | 14954 | Idiopathic moderate oligozoospermia, severe asthenozoospermia | 38 | 1 | 3.6 | 4.4 | 18.4 | 20.0; 16.5 | – |
| SEPT12 | p.Pro85Ser | 7695 | Idiopathic SCOS, primary hypogonadism | 0 | – | 10.7 | 29.1 | 16.6 | 7.2; 8.5 | Bilateral SCOS |
| SYCP3 | p.Tyr25His | 6277 | Idiopathic SCOS, primary hypogonadism | 0 | – | 15.9 | 35.4 | 10.3 | 2.0; 2.2 | Bilateral SCOS |
| SYCP3 | p.Ile81Leu | 12867 | Idiopathic SCOS, primary hypogonadism | 0 | – | 11.5 | 20.9 | 10.3 | 8.7; 9.1 | Bilateral SCOS |
Normal values: total sperm count, ≥ 39 × 106/ejaculate; total sperm motility, ≥ 40%; LH, 1–8 IU/L; FSH, 1–8 IU/L; T, 10–29 nmol/L; testis volume, ≥ 12 mL. TSC, total sperm count; TSM, total sperm motility; T, total testosterone; TV, testes volumes; R, right; L, left; SCOS, Sertoli cell-only syndrome
*This patient has mutations in KLHL10 and SEPT12 genes
Discussion
In this study we developed and tested a novel target NGS panel including nine genes. Of these selected genes, seven are strongly or moderately linked to male infertility (AR, FSHR, FSHB, KLHL10, NR5A1, SYCP3 and TEX11) [9], whilst the causality link has not been still clearly established for two genes (NANOS1 and SEPT12). Nonetheless, we included the latter genes in panel, since they could be informative genes. Indeed, SEPT12 and NANOS1 are associated with spermatogenic failure (SPFG) in Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org/); furthermore, rare variants in both genes have been described in patients with abnormal sperm count [40, 41]. Additionally, male mice with Sept12+/− result infertile [42] and a high positive missense z score has been associated to NANOS1 gene in ExAC browser (http://exac.broadinstitute.org/), indicating an increased intolerance to missense variations.
All individuals enrolled for the study were already tested for common genetic causes suggested for male infertility (karyotype, Y chromosome microdeletions, CFTR gene mutations) [10–17, 38] with the aim of evaluating the feasibility of this new genetic test in the clinical setting of male infertility.
Although the exome sequencing allows to individuate new candidate gene-disease in individuals phenotypically well characterized, the application of a validated custom panel as diagnostic genetic test results extremely efficient and affordable.
In this study, we found rare variants classified as VUS or pathogenic accordingly ACMG guidelines in 9.1% (16/174) of patients with idiopathic non-obstructive azoospermia and oligozoospermia, a figure significantly higher with respect to men with normal sperm count (0/67, p = 0.007).
Considering that most of detected variants showed an allele frequency higher in the cases compared with healthy European population, we believe that the single variants classified as pathogenic or VUS in the present study deserve a brief discussion.
Pro392Ser variant in AR gene (NM_000044) is located in the transactivation domain of the AR protein. It has been already described in several unrelated cases of oligozoospermia with mild androgen insensitivity (MIM: 312300) and in several cases of genital malformation [33, 44, 48]. We found it in two oligozoospermic patients with testicular hypotrophy/primary hypogonadism with a prevalence significantly higher than the European population of gnomAD 2.1.1 controls (p = 0.01).
Consistently with previous evidence and with the established role of androgens and AR in testicular function [49], this finding further suggests that mutation screening in AR gene should be included in the diagnostic process of idiopathic infertile men, even when clear signs of androgen insensitivity are not evident [33]. Indeed, the identification of variants in AR gene represents an additional evidence supporting its importance during spermatogenesis.
Ser20Ile and Ser109Arg in FSHB (NM_001018080) were found in three patients with NOA, whose two with Sertoli cell-only syndrome (SCOS) and one with severe hypospermatogenesis, and in two patients with oligozoospermia respectively. It is known that alterations in the structure and activity of FSH hormone often result in partial or complete spermatogenic failure [50], as FSH is the main regulator of spermatogenesis. Although inactivating mutations of FSHB gene are relatively rare and associated with azoospermia or severe oligozoospermia [51], a number of common polymorphisms exist in this gene associated with FSH plasma levels, reproductive function in men and women and with a possible role in regulating the responsiveness to FSH treatment [34, 52–55].
No clear seminal, testicular, histologic or hormonal profile is evident in patients carrying Ser20Ile and Ser109Arg variants. Therefore, based exclusively on the very low frequency in European population of both nucleotide substitutions (p = 0.0009 and p = 0.04, respectively), it is clear that functional studies should be performed to investigate their potential effect on protein structure.
Asn81Ille in KLHL10 gene (NM_152467) was found in two oligozoospermic patients. KLHL10 is the human homologue of kelch gene in Drosophila melanogaster and encodes for a testis-specific protein interacting with Cullin3 to form a CUL3-based ubiquitin E3 ligase [56]. This protein has likely an important role in the spermiogenesis process, indeed, mice with null mutation of Klhl10 show germ cell loss and defective morphology of spermatids [57]. Variants in this gene have been associated with oligozoospermia [58]. Asn81Ile is located in BTB binding domain, a motif necessary for the interaction with the E3 ligase Cul3 [56] that, hence, might compromise protein ubiquitination during spermiogenesis. Therefore, mainly on the basis of the very low frequency in controls (p < 0.0001), further studies are indispensable to understand the real significance of this variant on male infertility phenotype.
Four rare variants in NR5A1 gene (NM_004959) were detected in three patients (one patient was a compound heterozygote): Arg101Gln, Gly123Ala, Pro129Leu and Ala351Val, confirming thus the importance of this gene in male reproductive function [45]. This gene encodes for a transcription factor that regulates the expression of a plethora of genes involved in spermatogenesis and steroidogenesis and it is the master regulator of adrenal and gonadal development [59].
Arg101Gln is located near nuclear localization signal (NLS) of NR5A1 and it was found in a patient with oligozoospermia and testicular hypotrophy. Two missense variants (Gly123Ala and Pro129Leu), previously reported in patients with male infertility phenotypes [45], were found in a NOA patient. The latter two variants, located in the hinge region of NR5A1, have been previously found in unrelated subjects of African ancestry affected by NOA and with severe oligozoospermia [45]. Similar to the cases reported, we found both variants associated in one patient. Functional studies confirmed that only the NR5A1 p.Pro129Leu alteration has a strong impact on NR5A1 transcription activity [46]. According to these data, our patient was affected by SCOS and primary hypogonadism. Therefore, although functional study of Lourenço et al. suggests a pathogenic role of Pro129Leu variant, animal models and a higher number of infertile individuals with this missense change are needed to confirm its damaging effect on male infertility phenotype.
Ala351Val variant is located in the ligand binding domain (LBD) and we found it in a NOA patient with SCOS and primary hypogonadism. A variation on this site has been previously observed in a woman with amenorrhea [47], and, furthermore, we recently reported a glutamate substitution in the same aminoacidic position in a patient with disorder of sex development (DSD), showing also loss of DNA binding and impaired transactivation ability for this mutation [37]. Although the variant observed in our study results in the substitution of Alanin with an amino acid showing the same biochemical proprieties, Valin, the reduced activity of NR5A1 caused from the change of A351, suggests an important role of this aminoacidic residue. However, as well as we suggested for the other detected variants in NR5A1, except for Pro129Leu, functional studies are essential to clarify the role of these missense variants on male infertility phenotypes.
SEPT12 (NM_144605) encodes for a testis-specific GTP-binding protein that is necessary for maintaining the structural integrity of sperm cells [60]. Loss of function of SEPT12 has been associated with morphologically abnormal and immobile sperm [40, 61]. In this study, we found three rare single nucleotide variations in GTP-binding domain of SEPT12: Gly47Arg, Glu49Gln and Pro85Ser. Variants close to these residues have been already reported in recent studies [62, 63].
SEPT12 variants in patients with asthenoteratozoospermia, oligoasthenozoospermia and teratozoospermia were also described [40, 64]. Interestingly, consistent with the role of SEPT12 in spermiogenesis [65], we found one variant (Glu49Gln) in a patient with severe defect in sperm motility, whilst Gly47Arg was found in a patient with oligoasthenozoospermia carrying also a variant in KLHL10 gene. In the latter case, it is difficult to assign a clinical significance to one or both variants and, above all, to understand whether one variant or the combination of both is causative of reduced sperm count and/or motility.
Although it is not known whether the variants in SEPT12 might cause SCOS, they are extremely rare in the general population and also Miyakawa and colleagues reported two variants in Japanese men with SCOS [62]. We found Pro85Ser variant in a patient with SCOS and primary hypogonadism.
Our data combined with those already published suggest to further investigate the contribution of these variants that might be associated with a wide range of phenotypes of male infertility.
Finally, we found two variants in SYCP3 gene (NM_001177949), Tyr25His and Ile81Leu, both located in the N-terminal of the protein. SYCP3 is an important protein for successful meiosis (Miyamoto et al., 2003) and, consistently with this role, several SYCP3 variants have been identified in subjects with NOA and recurrent miscarriages [66–68].
We detected SYCP3 variants in two NOA patients with SCOS and primary hypogonadism, and for both, information of their frequency in the European population is not available. Additionally, there is not SNP annotation for Ile81Leu. Therefore, these variants deserve detailed studies in order to explain their potential impact on male infertility phenotype.
Overall, this study shows the advantage to use our custom NGS panel as diagnostic genetic test, since it allows to find out rare variants potentially linked to infertility in patients diagnosed as idiopathic. In particular, in this study, we detected variants in eight of nine genes within panel. This finding could suggest a potential contribution of these genes to male infertility or further confirm their impact on phenotype.
Although the aim of this study was to develop a novel infertility panel, functional assays or animal models are indispensable to better elucidate the role of identified variants on phenotype, since most of them are almost absent in the general population. Furthermore, the variants for whose will be found a clear implication in infertility, the clinical consequences in the transmission to offspring after ART are to be clarified, since most of the genes within the panel have a dominant inheritance.
Furthermore, even though the use of NGS panel allowed us to highlight rare single nucleotide variants, we cannot exclude other genetic causes of idiopathic infertility in our patients. Indeed, from the beginning of this project, other studies on the application of NGS in human reproduction have been published. Therefore, although the novelty of this research has been to use a panel including only few genes in common with the other published panels and to test this panel not only in azoospermic patients but also in males with moderate oligozoospermia, the study has some limitations. Indeed, other genes with strong clinical evidence and higher number of the individuals should be added to the study in order to increase the chance to discover new causes of infertility.
In conclusion, the application of NGS panel allows to identify new variants that could be linked to male infertility increasing the knowledge on genetic causes of idiopathic infertility and the possibility to accurately diagnose it.
Electronic supplementary material
(DOCX 20 kb)
Authors’ roles
MSR and AF had the original idea for the study and designed it. AF selected the subjects, performed clinical analysis, supervised the results and wrote the manuscript. MSR and AM performed the genomic analysis, contributed to interpretation of data and to the writing of manuscript. IC performed semen analysis. MG performed clinical analysis. RC and KP have critically reviewed study design. CF supervised the project and revised manuscript critically. All authors provided feedback and contributed to the final version of the manuscript.
Compliance with ethical standards
This study was approved by the hospital ethics committee and included a total of 1100 subjects, who provided an informed consent, referred between May 2016 and June 2018 to the Unit of Andrology and Reproductive Medicine of the University Hospital of Padova, Italy
Conflict of interest
The authors declare that they have no conflict of interest.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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