Next-Generation Sequencing Infertility Panel in Turkey: First Results
Categoria dell'articolo: ORIGINAL ARTICLE
Pubblicato online: 06 mar 2025
Pagine: 49 - 57
DOI: https://doi.org/10.2478/bjmg-2024-0019
Parole chiave
© 2024 Ikbal Atli E et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
The way that male infertility is treated has undergone a significant transformation as a result of our growing understanding of the physiology of male reproduction, fertilization, and the development of increasingly potent assisted reproductive procedures. A physical exam and medical history gathering are currently part of the diagnostic procedure provided to infertile male patients. This is followed by a mix of laboratory tests specifically chosen for each case, including a thorough genetic laboratory analysis. At least a year of infertility should precede the administration of diagnostic testing. Accordingly, a couple is considered infertile if they are unable to conceive following a year of regular, unprotected sex. 15% of male patients who are infertile have genetic issues. Chromosome abnormalities or single gene mutations are examples of them. The Online Mendelian Inheritance in Man (OMIM) database contains information on more than 200 genetic conditions associated with male infertility (1–4).
Many disorders, most notably Mendelian or uncommon diseases where having causal variants significantly reduces reproductive fitness, have had exceptional results using NGS (5).
The candidate gene approach in model animals and whole genome investigations using single-nucleotide polymorphism microarray and next-generation sequencing (NGS) technologies, such as exome or whole-genome sequencing, are the two main methods for identifying the genes responsible for infertility. The reason of male infertility is still unknown in up to 70% of instances, despite extensive diagnostic testing, because traditional genetic tests sometimes fall short of making a diagnosis. Recent studies appear to address how NGS technology is increasing the rate of male infertility diagnosis. Accordingly, it has already been established that several diagnostic genes have a role in the pathophysiology of male infertility. It may be possible to make a diagnosis with the use of prediagnostic genes, such as those that have been linked to male infertility but do not yet have solid proof of a causal relationship (6, 7, 8).
To do this, the current study was conducted to assess a number of pre-diagnostic genes by contrasting the outcomes with those obtained using our standard NGS custom-made gene panel for the diagnosis of male infertility, which consists of 132 genes. The genes included in the gene panel are composed of genes that have been associated with infertility to date.
The research included 85 individuals with a clinical diagnosis of male infertility who had tested negative on diagnostic genetic testing. 84 individuals were thought to have primary spermatogenic failure, while one individual was thought to have central hypogonadism. Following the elimination of female factor infertility and acquired reasons of male infertility, main spermatogenic failure was suspected with a history of couple infertility longer than two years (e.g. male accessory gland infection, varicocele, testicular trauma, etc.). All patients are cases of infertility for two or more years. The patient group consists of patients who do not have any known additional disease or malignancy.
Additionally, individuals included in this study tested negative for early genetic anomalies such karyotype abnormalities, Y chromosome AZF microdeletions. MLPA technique was performed using the SALSA MLPA probemix P360 version B1 (MRC Holland, Amsterdam, The Netherlands) kit following the manufacturer’s instructions. The kit contained 55 probes, of which 12 were located in autosomal chromosomes (for internal control reaction), and 43 were located in Y-chromosome AZF regions (16 AZFa, 15 AZFb, and 12 AZFc regions).
Each patient provided written consent after being fully briefed. The study was conducted in accordance with the principles outlined in the Declaration of Helsinki, and the local Ethics Committee approved the study.
Conventional G-banded karyotype analysis from peripheral blood was performed as part of the initial screening tests. The study included peripheral lymphocyte culture by a standard method using the Leishman-banding technique, centromere-banding (C-banding) and nucleolar organizing region staining performed as needed according to the AGT Cytogenetics Laboratory Manual. The best metaphases were karyotyped, and the total chromosome count was usually determined in 25 cells. The International System for Human Cytogenetic Nomenclature (ISCN) was used for the nomenclature of human chromosomes. Patients with no anomalies as a result of karyotype analysis were included in the NGS analysis.
Eighty five samples were sequenced using QIAseq Targeted DNA Custom Panel (Qiagen, Hilden, Germany). 2mL of peripheral blood were collected and then preserved in anticoagulation tubes. Genomic DNA was isolated from peripheral whole blood using the EZ1 DNA Investigator Kit (Qiagen, Hilden, Germany). After DNA extraction, target sequences were enriched by using customized capture probes chips (Illumina, San Diego, CA). This kit included 132 genes targeting disease. Libraries covering the target genes were prepared according to the QIAseq Targeted DNA Panel protocol (Qiagen, Hilden, Germany). Following the target enrichment process, libraries were sequenced on the MiSeq System (Illumina, San Diego, CA, USA). OCI analysis (Qiagen, Hilden, Germany) was used for Quality control and Variant Call Format file generation. In silico evaluation of the pathogenicity of nucleotide changes in exons was performed using Polymorphism Phenotyping v2 (PolyPhen-2,
A total of 85 patients (85 males) between 21 years and 45 years old were included in the study group. NGS analysis had been applied in all the primary infertility cases. As a result of NGS analysis, 58 clinical variants in 28 genes were detected in 41 patients (%48,23- 41/85) (Table 1). Thirty-two of these variants are unknown clinical significance (VUS), 11 of them likely pathogenic, and 15 of these variants are classified as pathogenic in according to the Varsome, The Human Genomic Variant Search Engine, Franklin by Genoox, Clinvar and American College of Medical Genetics and Genomics (ACMG) databases.
NGS panel for the diagnosis of male infertility
1. | NPHP4 | 23. | DAZL | 45. | FKBPL | 67. | DDX25 | 89. | ZMYND15 | 111. | SYCP2 |
2. | C1orf167 | 24. | DNAH1 | 46. | PLG | 68. | C1RL | 90. | GP1BA | 112. | CBS |
3. | MTHFR | 25. | PROS1 | 47. | ZPBP | 69. | C1RL-AS1 | 91. | KLHL10 | 113. | DNMT3L |
4. | CLCA4 | 26. | BOC | 48. | C7orf61 | 70. | DPY19L2 | 92. | ITGB3 | 114. | POFUT2 |
5. | BRDT | 27. | CFAP44 | 49. | SERPINE1 | 71. | CHPT1 | 93. | TEX14 | 115. | GP1BB |
6. | F3 | 28. | CFAP44-AS1 | 50. | POLR2J3 | 72. | SYCP3 | 94. | ACE | 116. | POLR2F |
7. | SPAG17 | 29. | GP9 | 51. | CFTR | 73. | CCDC62 | 95. | PGS1 | 117. | SOX10 |
8. | F5 | 30. | SPATA16 | 52. | TEX15 | 74. | PIWIL1 | 96. | DNAH17 | 118. | PICK1 |
9. | SERPINC1 | 31. | CCDC39 | 53. | PLAT | 75. | CPB2 | 97. | DNAH17-AS1 | 119. | MEI1 |
10. | F13B | 32. | GP5 | 54. | CHD7 | 76. | F7 | 98. | TAF4B | 120. | ADGRG2 |
11. | MTR | 33. | CEP135 | 55. | TMEM70 | 77. | F10 | 99. | GGN | 121. | MAGEB4 |
12. | LHCGR | 34. | SPINK2 | 56. | CCIN | 78. | TDRD9 | 100. | PLAUR | 122. | MAGEB1 |
13. | FSHR | 35. | BMP3 | 57. | NR5A1 | 79. | CATSPER2 | 101. | LHB | 123. | NR0B1 |
14. | DNAH6 | 36. | FGB | 58. | ASS1 | 80. | TERB2 | 102. | NLRP7 | 124. | TBC1D25 |
15. | NPAS2 | 37. | FGA | 59. | UPF2 | 81. | NME4 | 103. | NLRP2 | 125. | AR |
16. | LOC101927142 | 38. | KLKB1 | 60. | CFAP43 | 82. | FAHD1 | 104. | AURKC | 126. | TEX11 |
17. | PROC | 39. | F11 | 61. | NANOS1 | 83. | MEIOB | 105. | SIRPG | 127. | USP26 |
18. | TFPI | 40. | MTRR | 62. | SYCE1 | 84. | SEPT12 | 106. | SIRPA | 128. | F9 |
19. | STRADB | 41. | PRDM9 | 63. | FSHB | 85. | PRM1 | 107. | THBD | 129. | F8 |
20. | C2CD6 | 42. | ITGA2 | 64. | F2 | 86. | TERB1 | 108. | SUN5 | 130. | FUNDC2 |
21. | LOC100129175 | 43. | F2R | 65. | MAJIN | 87. | SERPINF1 | 109. | E2F1 | 131. | SRY |
22. | CFAP65 | 44. | F13A1 | 66. | CATSPER1 | 88. | CXCL16 | 110. | PROCR | 132. | USP9Y |
The most frequently observed variants are those observed in the CFTR gene. 18 CFTR gene variants were detected in 16 different patients. Among these, 7 variants are pathogenic, 4 variants are likely pathogenic and 7 variants are VUS. The remaining 40 variants are distributed among the other 27 genes in the panel. Among these, 8 variants were evaluated as pathogenic, 7 variants as likely pathogenic, and 25 variants as VUS. Segregation analyses could not be performed in patients with VUS. Pathogenic and likely pathogenic variants were detected de novo. Among the variants considered as VUS, the most frequently observed variants clustered in the
Variants detected in the patient group as a result of NGS
Patient n. | VUS - inheritance | Likely pathogenic-inheritance | Pathogenic-inheritance |
---|---|---|---|
1. | NM_000130.5(F5):c.1128G>T p.R376S |
||
2. | NM_000313.4(PROS1): c.1021G>T (p.A341S) |
NM_000492.4(CFTR):c.1516A>G (p.Ile506Val) |
|
3. | NM_015512.5(DNAH1):c.8885A>C (p.Lys2962Thr) |
||
4. | NM_000492.4(CFTR):c.443T>C (p.Ile148Thr) |
||
5. | NM_000492.4(CFTR):c.2981T>G (p.Phe994Cys) |
NM_000128.4(F11):c.1556G>A (p.Trp519Ter) |
|
6. | NM_001242805.2 (BRDT):c.163C>T (p.Pro55Ser) |
||
7. | NM_001350162.2 (TEX15):c.2580_2583del (p.Asp860GlufsTer15) |
NM_000173.7(GP1BA): c.1235_1298delAGCCCAC… (p.E412fs*39) |
|
8. | NM_000789.4(ACE):c.2299G>A (p.Glu767Lys) |
||
9. | NM_000312.4(PROC):c.982C>T (p.Arg328Cys) |
NM_002203.4(ITGA2):c.981_985del (p.Lys327AsnfsTer6) |
|
10. | NM_000492.4 (CFTR):c.2491G>T (p.Glu831Ter) |
||
11. | NM_000492.3(CFTR):c.1521_1523del (p.Phe508del) |
||
12. | NM_000492.4(CFTR):c.1210-11T>G |
||
13. | NM_000492.4(CFTR):c.2991G>C (p.Leu997Phe) |
||
14. | NM_054012.4(ASS1):c.535T>C (p.Trp179Arg) |
||
15. | NM_144605.4(SEPT12): c.208T>C (p.Phe70Leu) |
NM_000492.4(CFTR): c.1397C>T (p.S466L) |
|
16. | NM_000492.4(CFTR):c.2973A>G (p.Ile991Met) |
||
17. | NM_015512.5(DNAH1):c.10164G>T (p.K3388N) |
||
18. | NM_015102.5(NPHP4):c.224G>A (p.Trp75Ter) |
||
19. | NM_000492.4(CFTR): c.1043T>A (p.M348K) |
NM_000492.4 (CFTR) : c.3038C>T (p.P1013L) |
|
20. | NM_000071.3(CBS):c.833T>C (p.Ile278Thr) |
||
21. | NM_000128.4(F11):c.325G>A (p.Ala109Thr) |
||
22. | NM_173812.5(DPY19L2): c.247C>T (p.Q83*) |
NM_000492.4(CFTR):c.1521_1523delCTT (p.F508del) |
|
23. | NM_000492.3(CFTR):c.3872A>G (p.Q1291R) |
||
24. | NM_000071.3(CBS):c.833T>C (p.I278T) |
||
25. | NM_000492.4(CFTR):c.3256A>G (p.Thr1086Ala) |
||
26. | NM_000894.2 (LHB):c.169T>C NP_000885.1:p.Tyr57His |
||
27. | NM_000301.5(PLG):c.2384G>A (p.Arg795His) |
||
28. | NNM_000789.4(ACE): c.3490G>A (p.G1164R) |
||
29. | NM_000492.4(CFTR): c.350G>A (p.R117H) |
||
30. | NM_015512.5 (DNAH1): c.8976C>G p.F2992L |
||
31. | NM_000131.4 (F7): c.805+3_805+6delGGGT (-) |
||
32. | NM_001994.3(F13B): c.209A>C (p.Q70P) |
NM_000492.4 (CFTR) : c.1521_1523delCTT (p.F508del) |
|
33. | NM_017780.4 (CHD7) :c.5995G>A (p.A1999T) |
||
34. | NM_012128.3 (CLCA4):c.575C>A NP_036260.2:p.Ser192Cys |
||
35. | NM_015512.5(DNAH1): c.9495G>A (p.Thr3165) |
||
36. | NM_001330438.2 (DDX25): c.110C>T (p.Ala37Val) |
||
37. | NM_000071.3(CBS):c.833T>C (p.Ile278Thr) |
||
38. | NM_001312675.1 (F10):c.202C>T NP_001299604.1:p.Arg68Cys |
||
39. | NM_000301.5(PLG):c.2134G>A (p.Gly712Arg) |
||
40. | NM_000492.4(CFTR):c.890G>A (p.Arg297Gln) |
||
41. | NM_173628.3(DNAH17):c.7752+2T>A |
||
Highly diverse phenotypic representation and a complicated multifactorial etiology, including environmental and genetic factors, characterize the condition of male infertility. In most cases, it is challenging to identify a genetic cause of infertility due to the large number of candidate genes (9, 10). In any case, a multi-disease gene panel can help identify the cause of male infertility. In order to categorize genetic variants, a multifactorial likelihood model can be used to assess the likelihood that a variant is pathogenic based on a previous likelihood of pathogenicity based on in silico research and the genetic and epidemiological data that are currently available (11–13). Genetic variants can be categorized into five categories according to the American College of Medical Genetics and Genomics’ references: pathogenic, likely pathogenic, variant of unknown consequence, likely benign, or benign (14). A genetic alteration known as a VUS has ambiguous effects on gene function. The interpretation of VUS is a challenging task for the clinical management of infertile male patients and genetic counseling. Since VUS are not clearly related with a phenotype currently, but could be categorized as pathogenic in the future, it is crucial to detect and assess them. An example of this situation is the NM_000071.3(
Variants in
Similarly, in our patient number 5, 2 pathogenic variants belonging to the F11 gene and a variant evaluated as VUS in
In another patient, case number 7, variants were detected in 2 separate genes. VUS evaluation was performed for
We detected variants in
In patient number 19, we detected compound heterozygous variants of the
We detected
We detected
Interestingly, variants in 3 different genes were detected in our last 2 patients. Variants considered to be VUS were detected in the
Although it is difficult to reconcile those with recessive inheritance in the detected variants with the patient clinics, those with dominant inheritance were compatible with the patient clinics.
The most frequently detected
Another gene we detected among pathogenic gene variants is the
Another gene we detected as a pathogenic variant in our patient group was
Genes that code for hormones and hormone receptors which are involved in the functioning of the human reproductive system are included in the fertility panel design. Numerous investigations have demonstrated the association between specific polymorphisms in genes encoding receptors, including those that bind to FSH and LH, and the results of an ovarian hyperstimulation cycle under control and in vitro fertilization treatment. The intended genetic panel’s results will yield the data required to ascertain the frequency of these variants in our community and assess the panel’s usefulness in clinical settings.
The involvement of the clinicians who seek this genetic investigation needs to be emphasized. If the gene panel is able to pinpoint the underlying reason of infertility, clinicians will need to have a comprehensive picture of the patient’s phenotype. “Idiopathic infertility” affects a large number of individuals, and while a genetic component may be identified in certain cases, the absence of a distinct phenotype may make it more difficult to interpret the data, particularly variants with unclear significance. Clinicians should also be aware that three factors play a major role in how these investigations are interpreted: the patient’s phenotypic characteristics, their medical history, and any pertinent family history. For the diagnostic laboratory to properly interpret variants found through testing, it is imperative that they have information about all observable traits as well as the family’s medical history (27–30).
The first unique gene sequencing panel intended for the diagnosis of hereditary infertility in males is presented here, for the first time in Turkey. The use of this panel will advance knowledge of the genetic causes of infertility, enhance genetic and reproductive counseling, and eventually lead to more accurate assisted reproductive techniques.
Consequently, pre-diagnostic genes included in a custom-made NGS panel test can enhance genetic diagnostic testing and have an impact on the clinical management of male infertility. There are currently no comprehensive systematic studies or meta-analyses on the epidemiology of male infertility, and it is unknown how common male infertility is. The need of diagnosing hereditary infertility is further supported by the epidemiological data that show infertile patients have greater morbidity and a shorter life expectancy. Finally, we demonstrated the effectiveness of NGS-based methods that additionally use pre-diagnostic genes. This gene panel may aid in determining the disorder’s underlying etiology and directing clinical treatment.