Abstract
Objective
The transcriptional landscape of Klinefelter syndromeduring early embryogenesis remains elusive. This study aimed to evaluate the impact of X chromosome overdosage in 47,XXY males induced pluripotent stem cells (iPSCs) obtained from patients with different genomic backgrounds and ethnicities.
Design and method
We derived and characterized 15 iPSC lines from four Saudi 47,XXY KS patients and one Saudi 46,XY male. We performed a comparative transcriptional analysis using the Saudi KS-iPSCs and a cohort of European and North American KS-iPSCs.
Results
We identified a panel of X-linked and autosomal genes commonly dysregulated in Saudi and European/North American KS-iPSCs vs 46,XY controls. Our findings demonstrate that seven PAR1 and nine non-PAR escape genes are consistently dysregulated and mostly display comparable transcriptional levels in both groups. Finally, we focused on genes commonly dysregulated in both iPSC cohorts and identified several gene-ontology categories highly relevant to KS physiopathology, including aberrant cardiac muscle contractility, skeletal muscle defects, abnormal synaptic transmission, and behavioral alterations.
Conclusions
Our results indicate that a transcriptomic signature of X chromosome overdosage in KS is potentially attributable to a subset of X-linked genes sensitive to sex chromosome dosage and escaping X inactivation, regardless of the geographical area of origin, ethnicity, and genetic makeup.
Introduction
Klinefelter syndrome (KS) is the most common chromosome aneuploidy in males (1:600 newborns). KS is predominantly caused by sex chromosome non-disjunction at meiosis I or II during maternal or paternal gametogenesis, and it is genetically inherited in an equal ratio from the mother and the father (1, 2, 3). While the non-mosaic form of KS with 47,XXY karyotype is the most frequent (80–90%), less common non-disjunction events during the early mitotic division of the zygote result in mosaic forms of KS (47,XXY/46,XY) (4). KS has been associated with multiple manifestations with variable penetrance (5). KS traits are often undiagnosed until adulthood, while in other cases, the condition causes visible effects on growth and appearance already from childhood. The overall symptomatology varies by age. Weak muscles, slow motor development, and speech delays can be observed in newborns; taller stature, decreased muscle mass, sparse facial and body hair, intellectual disabilities, and micropenis are KS signs at puberty. Gynecomastia, increased body mass, and low or absent sperm count, eventually leading to infertility, are commonly diagnosed in adult KS males (6, 7, 8). In addition, KS patients are at higher risk of developing cardiac abnormalities, metabolic disorders, type-2 diabetes, osteoporosis, and cancers (9). Despite being a highly prevalent chromosomal male aneuploidy, there is still a significant gap in knowledge about treatments, care management of patients, and detailed mechanisms by which a supernumerary X chromosome leads to the KS pathophysiology. To close this gap in knowledge, our recent studies aimed at identifying the transcriptomic landscape of KS and high-grade sex chromosome aneuploidies (SCAs) carrying 48,XXXY and 49,XXXXY karyotypes during early embryogenesis (10). We previously reported that pseudoautosomal region 1 (PAR1) and a few non-PAR1 genes escaping X inactivation (Xi) strictly follow X chromosome dosage in a cohort of European and North American (ENA) KS- and high-grade SCA patient-derived induced pluripotent stem cells (iPSCs) (10, 11, 12, 13, 14, 15). Here, we present the first iPSC-based disease-modeling study performed on KS patients from Saudi Arabia. Saudi Arabia represents an essential resource for genetic studies because of the consanguinity rate of >50%, the high fertility levels, and the territory’s geography that favor the genetic isolation of this region (16). In fact, the high incidence of homozygous recessive mutations coupled with an average large size of families in the Saudi population could lead to the identification of novel gene candidates linked to hereditary cases of KS, as reported for the USP26 gene mutations (17).
In this study, we recruited four KS patients and one 46,XY male, from which we generated a cohort of twelve KS-iPSC and three 46,XY-iPSC clones, respectively. We profiled the transcriptome of these KS-iPSCs, virtually characterized by subdued genetic backgrounds. Moreover, we performed a comparative transcriptomic analysis to assess the aberrant gene expression profile due to X dosage imbalance in four Saudi and five ENA 47,XXY patients-derived iPSCs. Although based on a small number of patients, our results indicate that a subset of X-linked and autosomal genes is sensitive to sex chromosome dosage and consistently dysregulated, potentially indicating a transcriptomic signature of KS, regardless of the geographical area of origin, ethnicity, and genetic makeup.
Materials and methods
Recruitment of Saudi Klinefelter syndrome patients
Five Saudi men were enrolled for the collection of a skin punch biopsy after receiving ethical approval (Reference No. 242-19) from the Ethical Committee of Biomedical Research in King Abdulaziz University Hospital in addition to the patient’s consent. Patients were diagnosed during adulthood with primary infertility. All men displayed testicular atrophy and azoospermia. Karyotype analysis has confirmed a KS diagnosis (47,XXY) in four out of five patients. The fifth patient has shown a normal 46,XY karyotype. Three KS patients were obese, while one was overweight (Table 1). The intelligence quotient has not been assessed.
Summary of the clinical assessment of the recruited donors.
| 46,XY | KS1 | KS2 | KS3 | KS4 | |
|---|---|---|---|---|---|
| Karyotype | 46,XY | 47,XXY | 47,XXY | 47,XXY | 47,XXY |
| Age | 53 years | 40 years | 37 years | 44 years | 32 years |
| Height | 161 cm | 170 cm | 166 cm | 182 cm | 194 cm |
| Weight | 66 kg | 80 kg | 104.9 kga | 153.2 kga | 126 kg |
| Body mass index (kg/m2) | 25.5 | 27.7 | 38.1 | 46.3 | 33.5 |
| Glucose fasting (range: 3.9–6.1) | 11.6 mmol/La | 10.9 mmol/La | 5.1 mmol/L | - | 11.6 mmol/La |
| Hemoglobin A1C (range: 4.4–6.3) | 8.5%a | 8.9%a | 6.30% | 8.6%a | 8.6%a |
| Testosterone hormone (range: male 5.72–26.14) | 15 nmol/L | 13.12 nmol/L | 4.62 nmol/La | 5.0 nmol/La | 3.60 nmol/La |
aValue outside of the reference range.
Derivation of primary fibroblasts from skin biopsies
Skin punch biopsies (about 4 mm2) were collected from the patient's forearms after sterilization of the area using povidone-iodine, and local anesthetic was applied to the selected site. The biopsies were transported in DMEM–20% fetal bovine serum (FBS) (45–60 min) to the lab for processing. Fibroblasts were derived from skin punch biopsy as described in (18). Briefly, skin biopsies were dissociated in the presence of DMEM enriched with 20% FBS. The dissociated tissue was then moved into a 0.1% Gelatin-coated six-well plate in the presence of complete DMEM/20% FBS. Three weeks after, the confluent monolayer of fibroblasts was detached with trypsin and seeded into T75 flasks in the presence of DMEM/10% FBS. Fibroblasts were then passaged for expansion, freezing and further applications at a low passage number. Fibroblasts were incubated at 37°C, 5% CO2 and 5% O2. All procedures followed the ethical standards of the King Abdullah University of Science and Technology KAUST Institutional Biosafety and Bioethics Committee, approval number 22IBEC059.
Fibroblast reprogramming into iPSCs
Fibroblasts were cultured and expanded for at least three passages in high glucose DMEM supplemented with 15% FBS, 1% non-essential amino acids, and 1% penicillin–streptomycin prior to cell reprogramming. About 5 × 104–10 × 104 cells/well were seeded on iMatrix-coated 6-well plates 24 h before reprogramming. The reprogramming was performed using the Stemgent® StemRNA-NM Reprogramming Kit. According to the manufacturer’s instructions, fibroblasts were transfected daily with synthetic NM-RNAs in presence of NutriStem® hPSC XF medium (Stemgent) for 4 consecutive days. After 10 days, individual emerging pluripotent stem cell (PSC)-like colonies were manually picked and transferred into 96-well plates coated with Matrigel (Corning) in NutriStem® hPSC XF plus RevitaCell™ (Thermo Fisher Scientific). PSC colonies were then dissociated with Versene (Thermo Fisher Scientific) and, after the first passage, adapted to Essential 8 (E8) Medium (Thermo Fisher Scientific).
Human iPSC and hESC culture
The established hiPSC lines and WA16 hESCs were cultured on hESC-qualified Matrigel (Corning) coated six-well-plates in E8 medium and passaged with Versene in E8 medium supplemented with RevitaCell™ (Thermo Fisher Scientific). The hiPSC lines were incubated at 37°C in the presence of 5% CO2 and 5% O2.
Karyotype and KaryoStat analyses
A KaryoStat assay was used to accurately detect chromosomal abnormalities in fibroblasts obtained from the five donors. The assay, including arrays, reagents, and data analysis, was performed by Thermo Fisher Scientific.
The karyotype of iPSCs was carried out at the cytogenetic laboratory at Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah. Briefly, iPSC lines were treated with 0.3 μg/mL KaryoMAX™ Colcemid™ (Thermo Fisher Scientific) for 15 min, dissociated by TrypLE and incubated in hypotonic solution (75 mM potassium chloride) at 37°C for 20 min. iPSCs were then fixed in methanol/glacial acetic acid. At least 50 metaphases were analyzed for each cell line.
X-chromosome short tandem repeat
The short tandem repeat (STR) analysis was performed on genomic DNA amplified by PCR. Twelve X-chromosomal STR loci on the X chromosome were analyzed using the Investigator® Argus X-12 QS Kit (Qiagen). PCR amplicons were resolved on a 3730XL DNA analyzer (Thermo Fisher Scientific). Data were analyzed with GeneMapper ID-X software v1.6 (Applied Biosystems).
Teratoma assay
iPSCs (1 × 106) were dissociated at passage 10 with Versene, resuspended in Matrigel, and injected subcutaneously into the dorsal flanks of 8–10 weeks old NSG mice. Teratomas of about 1 cm diameter were collected, fixed, and sectioned for staining with antibodies for the three germinal lineages. Images were acquired on EVOS™ FL Auto 2 Imaging System.
Immunofluorescence and image acquisitions
iPSCs were plated on Matrigel-coated coverslips and fixed for 12 min with 3% paraformaldehyde for immunostaining 72 h after seeding. Fixed cells were permeabilized with 0.25% Triton-X100 in PBS, incubated overnight with primary antibodies for pluripotency markers (Supplementary Table 2, see section on supplementary materials given at the end of this article), washed, incubated with secondary antibodies, and mounted with ProLong Glass antifade Mountant (Thermo Fisher Scientific). Immunostainings for pluripotency markers were acquired using an EVOSTM FL Auto 2 Imaging System (Thermo Fisher Scientific) using a 1.30 NA/40× oil immersion objective (Olympus).
RNA extraction and qPCR
The total RNA extraction from iPSCs and fibroblasts was performed using the RNeasy Mini Kit (Quiagen) according to the manufacturer’s instructions. The RNA was subjected to DNase treatment using the RNase-free DNase Set (Qiagen). The cDNA was synthesized with the SuperScript VILO IV cDNA Synthesis Kit (Thermo Fisher Scientific). Gene expression was determined by real-time qPCR on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific) using TaqMan™ Fast Advanced Master Mix (Thermo Fisher Scientific) and TaqMan® Gene Expression Probes (Supplementary Table 3). Individual gene expression was normalized on Tata-binding protein (TBP) using the 2−ΔΔCt as a relative quantification method.
Bulk RNA-Seq library preparation and sequencing
RNA libraries were generated using the human mRNA TruSeq Stranded library preparation KIT from Illumina (San Diego, CA, USA) and profiled using a NovaSeq 6000 system with 150 bp paired-end sequencing method. An average of 50M reads were obtained for each sample. Samples with less than 18M input reads and lower than 75% assigned reads were removed from the analysis (Supplementary Table 4).
RNA-Seq data profiling
RNA-Seq data validation, pairing, and FastQC quality control were followed by trimming using BBDuk (19). High-quality reads were mapped against the reference genome (GRCh38/Ensembl release 101) using the STAR end-to-end alignment method (20), and gene expression quantification was performed with featureCounts (21). Lowly expressed genes were filtered out using HTSFilter (22). Data normalization was performed with the Trimmed Mean of M-values method. The differential expression analysis was performed using the R package edgeR (23) using the following design: ~ Origin + Origin:Karyotype:Clone + Origin:Karyotype. ‘Origin’ corresponds to the two different cohorts; ‘Karyotype’ corresponds to the two genotypes; and ‘Clone’ corresponds to the different clones (the same clones were independently processed more than once). The ‘:’ stands for ‘interaction’. For example, Origin:Karyotype indicates the four groups created by the interaction of ‘Origin’ and ‘Karyotype’. Differentially expressed genes (DEGs) were filtered for FDR < 0.05, and Log2FC > |0.4|. Gene Ontology (GO) analyses were performed with the EnrichR online tool (24).
Statistical analysis
The nonparametric Student’s t-test was used. Mean values ± s.d. are shown. *P < 0.05; **P < 0.01; ***P < 0.001. Where indicated, the Fisher’s exact statistical analysis was used to test the significance between more than two groups.
Data availability
The datasets presented in this study can be found in the online repository Gene Expression Omnibus with the following accession numbers: GSE152001 (ENA cohort) and GSE220268 (Saudi cohort).
Results
Fibroblast isolation from skin biopsies and somatic cell reprogramming
Skin biopsies from five donors were cultured to establish fibroblast cell lines with 47,XXY and 46,XY karyotypes (Table 1, Fig. 1A), as assessed by KaryoStat assay (Fig. 1B and C). The reprogramming of the five fibroblast cell lines was performed using an mRNA-based integration-free-methodology as previously described (11, 12, 13, 14, 15). For each fibroblast line, we generated three independent iPSC clones. We obtained 12 iPSCs from four patients with karyotype 47,XXY and three iPSC lines from a 46,XY subject (Fig. 1A). We thoroughly characterized the generated iPSCs to confirm the genotype of the parental cell line. A karyotype analysis by G-banding confirmed the presence of one extra X chromosome in all the established KS-iPSC clones and a normal chromosomal content in the iPSCs obtained from the 46,XY donor (Supplementary Fig. 1). Next, we assessed the mRNA levels of the long non-coding RNA (lncRNA) XIST in KS and 46,XY fibroblasts (Fig. 1D) and in the generated iPSCs (Fig. 1E). XIST is a key mediator of the X chromosome inactivation (XCI) process, which allows the silencing of the extra X in females (25, 26). We and others previously reported that XIST expression stoichiometrically mirrors the supernumerary X number in Turner, KS, and high-grade SCA patients, while it is silenced in 46,XY males (10, 27). Consequently, XIST is not expressed in skin fibroblasts obtained from the 46,XY donor, where only the Y-linked chromosome gene UTY (Fig. 1D) is detectable. Moreover, the STR analysis on the X chromosomes (Table 2) defined that all iPSCs match the genetic signature of the parental fibroblasts. The X-linked STR analysis also revealed that the ancestral non-disjunction event originating the extra X chromosomes in all KS patients results from aberrant chromosomal segregation during meiosis I.
Description and characterization of the Saudi cohort of iPSCs. (A) Schematic of the cohort of Saudi patients. Fibroblasts from five donors (one 46,XY male and four 47,XXY KS patients) were established from skin biopsies and reprogrammed into iPSCs using non-integrative reprogramming methods. For each fibroblast sample, three independent iPSC clones were generated. In total, twelve KS-iPSCs and three 46,XY iPSCs were used in this study. (B) Karyostat analysis of the whole genome performed on fibroblasts. The plots display autosomes and sex chromosomes in one frame. The y-axis represents the copy number; the x-axis shows the chromosome number. A value of 1 on the y-axis is expected for X and Y chromosomes in males 46,XY (CN = 1). A value of 2 on the y-axis represents a normal copy number state for autosomes or chromosomal gain for sex chromosomes (CN = 2), as in the case of KS fibroblasts. The pink, green, and yellow colors indicate the raw signal for each individual chromosome probe, while the blue signal represents the normalized probe signal which is used to identify copy numbers and aberrations. (C) Summary of KaryoStat results for the four KS fibroblast samples. A value of CN = 3 identifies an X chromosomal gain. (D–E) Taqman assay showing XIST and UTY mRNA levels relative to TBP (Tata binding protein) in fibroblast samples (D) or in the individual iPSC clones (E). Bars show ±s.d. (n = 3 independent experiments. (F) Analysis by Taqman assay of the mRNA levels of the pluripotency markers OCT4, NANOG, and SOX2 in the iPSCs. Bars show ±s.d. (n = 3 independent experiments). The hESC WA16 is used as a control cell line.
Citation: Endocrine Connections 12, 5; 10.1530/EC-22-0515
X-linked STR analysis on KS and control fibroblasts and iPSCs.
| Cell lines | AM | DXS7132 | DXS8378 | DXS10101 | DXS10134 | DXS10148 | D21S11 |
|---|---|---|---|---|---|---|---|
| 46,XY-Fs | X/Y | 14 | 11 | 28.2 | 38.3 | 24 | 32/33 |
| 46,XY#A | X/Y | 14 | 11 | 28.2 | 38.3 | 24 | 32/33 |
| 46,XY#B | X/Y | 14 | 11 | 28.2 | 38.3 | 24 | 32/33 |
| 46,XY#C | X/Y | 14 | 11 | 28.2 | 38.3 | 24 | 32/33 |
| KS1-Fs | X/Y | 14/16 | 10/12 | 30.2/33 | 34.3 | 23/24 | 33/34 |
| KS1#A | X/Y | 14/16 | 10/12 | 30.2/33 | 34.3 | 23/24 | 33/34 |
| KS1#B | X/Y | 14/16 | 10/12 | 30.2/33 | 34.3 | 23/24 | 33/34 |
| KS1#C | X/Y | 14/16 | 10/12 | 30.2/33 | 34.3 | 23/24 | 33/34 |
| KS2-Fs | X/Y | 14 | 10/11 | 27.2/32.2 | 35.3/36.3 | 27 | 29.2 |
| KS2#A | X/Y | 14 | 10/11 | 27.2/32.2 | 35.3/36.3 | 27 | 29.2 |
| KS2#B | X/Y | 14 | 10/11 | 27.2/32.2 | 35.3/36.3 | 27 | 29.2 |
| KS2#C | X/Y | 14 | 10/11 | 27.2/32.2 | 35.3/36.3 | 27 | 29.2 |
| KS3-Fs | X/Y | 15 | 11 | 29.2/32 | 39.3/40.3 | 24 | 28.2/32 |
| KS3#A | X/Y | 15 | 11 | 29.2/32 | 39.3/40.3 | 24 | 28.2/32 |
| KS3#B | X/Y | 15 | 11 | 29.2/32 | 39.3/40.3 | 24 | 28.2/32 |
| KS3#C | X/Y | 15 | 11 | 29.2/32 | 39.3/40.3 | 24 | 28.2/32 |
| KS4-Fs | X/Y | 15/16 | 10/12 | 30.2/32.2 | 35.3/38.3 | 25 | 29.2 |
| KS4#A | X/Y | 15/16 | 10/12 | 30.2/32.2 | 35.3/38.3 | 25 | 29.2 |
| KS4#B | X/Y | 15/16 | 10/12 | 30.2/32.2 | 35.3/38.3 | 25 | 29.2 |
| KS4#C | X/Y | 15/16 | 10/12 | 30.2/32.2 | 35.3/38.3 | 25 | 29.2 |
Fs, fibroblasts.
Pluripotency validation of 47,XXY and 46,XY iPSC clones
To verify the pluripotent characteristics of the iPSC cohort, we evaluated the endogenous expression of OCT4, NANOG, and SOX2. We confirmed by Taqman assay that the derived 46,XY and KS-iPSCs express similar mRNA levels of pluripotency markers compared to the human 47,XXY embryonic stem cell line WA16 (28) (Fig. 1F). This result was further confirmed by testing the protein expression of the markers OCT4, NANOG, SOX2, and SSEA4 by immunostaining on iPSCs (Supplementary Fig. 2). Moreover, we assessed the in vivo developmental potential of 47,XXY and 46,XY-iPSCs by teratoma formation assay through subcutaneous injection into immunodeficient severe combined immunodeficiency mice. All the tested iPSCs formed teratomas containing the three embryonic germ layers, as evaluated by immunostaining analysis for the ectodermal marker calcium-binding protein S100, the mesodermal marker muscle-specific intermediate filament protein Desmin, and the cytokeratin polypeptides specific of endodermal–epithelial structures cytokeratin (Supplementary Fig. 3).
The overdosage of PAR1 genes is a universal transcriptomic signature of KS
We next profiled the transcriptomic dysregulation associated with KS-iPSCs in Saudi patients by RNA-Seq analysis. We first performed a differential expression analysis comparing 47,XXY vs 46,XY transcriptomes. We identified 1834 DEGs, 1005 of which were upregulated and 829 downregulated (Fig. 2A). We found 77 X-linked DEGs (Fig. 2B); 11 genes were located within the PAR1 territory, 19 were non-PAR escape genes, 32 were previously reported as inactive or variable, and 15 as subjected to XCI or not previously included in any of these categories (Supplementary Table 1) (29, 30). Notably, none of the PAR2 genes was identified as upregulated, thus corroborating previous studies indicating PAR2 genes as mainly inactive on the inactive X (10, 31). Next, we interrogated our Saudi transcriptomic data to perform a comparative analysis of genes X-linked and differentially expressed in the Saudi KS-iPSCs cohort and in the ENA KS-iPSCs cohort previously derived by our group (10). Our analysis identified 35 X-linked genes commonly dysregulated in the two datasets of DEGs obtained comparing 47,XXY vs 46,XY Saudi Arabian or ENA-derived iPSCs (Fig. 2C and D). Strikingly, seven out of 35 DEGs are PAR1, nine are non-PAR escape genes, 15 are inactive, variable, or subject to Xi genes according to Tukiainen et al. (2017) and Balaton et al. (2015) (29, 30), and four have not been previously included in any category (Fig. 2E and F). Interestingly, the X-Linked lncRNA AL157778.1, previously identified as a novel upregulated escape gene (10), is also differentially expressed in Saudi KS vs 46,XY. Moreover, these commonly dysregulated X-linked genes show a similar fold change increase or decrease vs independent 46,XY males with a few exceptions (Fig. 2D, E and F). Finally, PAR1 genes show similar expression levels in the four Saudi and in the five ENA 47,XXY patient-derived iPSCs (10) (Fig. 2G and Supplementary Table 1). Altogether our findings prove that the upregulation of a subset of PAR1 genes expressed in iPSCs is a common signature of the KS transcriptome with no regional confinement. Moreover, we show that the level of increased expression of the lncRNAs XIST is virtually identical in the two cohorts regardless of the ethnicity or geographical origin of the KS patients.
Transcriptomic profiling of the Saudi cohort of KS-iPSCs. (A) Volcano plot showing statistically significant DEGs in the whole transcriptome in the comparison 47,XXY vs 46,XY Saudi iPSCs (n = 36 independent RNA-Seq samples from 12 KS-iPSC clones, and nine independent RNA-Seq samples from three 46,XY iPSC clones). Blue, downregulated; Red, upregulated DEGs. Black circles indicate non-significant DEGs (N.S.). PAR1 DEGs are labeled. (B) Pie chart of global (1834) or X chromosome-restricted (77) DEGs from the comparisons KS vs 46,XY Saudi iPSCs. (C) Venn diagram showing 35 shared X-linked DEGs among the comparison 47,XXY vs 46,XY in iPSCs derived from European and North American (ENA) or Saudi patients. (D) Bar plot showing the expression trends of common X-linked DEGs across the Saudi and ENA groups. Red, DEGs with common upregulation trends; Blue, DEGs with common downregulation patterns; Gray stripes, DEGs with opposite dysregulation. StXi, subject to X inactivation; No Cat., no category. (E) Log2 fold change of shared X-linked PAR1, non-PAR escape, inactive/variable/subject to Xi DEGs across the two comparisons of 47,XXY vs 46,XY. Inactive, variable or subject to XCi genes are defined by Tukiainen et al. (2017) and Balaton et al. (2015). No category, DEGs not previously annotated. (F) Log2 fold change of XIST in KS vs males in the two populations. (G) FPKM expression of shared upregulated PAR1 genes in Saudi iPSCs. The significance between the comparison of KS-iPSCs (n = 9) vs XY (n = 9) was calculated using the two-tailed Student’s t-test, *P < 0.01; **P < 0.001.
Citation: Endocrine Connections 12, 5; 10.1530/EC-22-0515
Identification of a shared global transcriptomic impact of X overdosage in Saudi and European/North American KS-iPSCs
We previously investigated the impact of X chromosome overdosage on the global transcriptome in KS and high-grade SCA iPSCs obtained from a cohort of ENA patients (10). Here, we sought to investigate whether, comparing the transcriptomes of Saudi and ENA KS-iPSCs, we could identify a KS-specific DEGs signature, independently of the ethnicity and geographical origin. Intriguingly, we identified 926 genes commonly dysregulated in both cohorts (Fisher test P value = 3.1335 × 10−46) (Fig. 3A). We performed a GO enrichment analysis on the common DEGs in the two KS-iPSC cohorts. Relevantly, among the upregulated terms, there are ontology categories associated with membrane depolarization during cardiac action potential, thyroid hormone transport, cardiac muscle hypertrophy, voltage gate channel activities, neuronal dendrite structures, and glutamate–receptor complex (Fig. 3B). The KEGG enrichment analysis highlighted terms associated to gonadotropin secretion, parathyroid hormone functions, and immune-system response to viral infections (Fig. 3C). Our data indicate that the X overdosage affects several disease-relevant processes already detectable at the pluripotent state. These include shortened QT interval and aberrant cardiac muscle contractility, skeletal muscle defects, abnormal synaptic transmission, and behavioral alterations (Fig. 3D).
The impact of X overdosage on the global transcriptomes of Saudi and ENA KS-iPSCs. (A) Venn diagram showing the DEGs shared in the contrast 47,XXY vs 46,XY in iPSCs generated from ENA and Saudi KS patients. (B) Gene Ontology analysis on common DEGs using the GO enriched for biological processes (BP), molecular functions (MF), and cellular components (CC). (C) KEGG enrichment analysis and (D) MGI mammalian phenotype disease pathway analysis on Saudi and ENA common DEGs.
Citation: Endocrine Connections 12, 5; 10.1530/EC-22-0515
Additionally, we performed an interaction analysis to exclude genes with different dysregulation trends among the common DEGs. Out of 926 common DEGs, we identified 208 genes differentially changing between the 2 cohorts. Of these, 188 genes (including 4 X-linked) displayed an opposite dysregulation pattern (Supplementary Fig. 4A, B and C). Relevantly, the GO analysis performed on the DEGs with opposite dysregulation trends did not highlight any significant term directly related to KS (Supplementary Fig. 4D and E).
Our findings suggest that the upregulation of a subset of PAR1 and a few non-PAR escape genes are the most remarkable signature of KS transcriptome and that a significant proportion of autosomal genes is consistently dysregulated comparing KS-iPSCs derived from patients with different geographical origins and genomic backgrounds.
Discussion
Despite the high prevalence, KS has been underdiagnosed and understudied. In recent years, the clinical understanding of the multiple and often subtle phenotypic traits associated with KS has greatly improved. It is now clear that early diagnosis and standardized treatment protocols are crucial in young KS boys to prevent some of the typical clinical manifestations. For example, sperm cryopreservation in puberal age could be beneficial to overcome infertility-related issues. Moreover, severe psychiatric conditions and metabolic disorders, including obesity, diabetes, and osteoporosis, frequently observed in KS patients, could be mitigated with supplementation therapies and programmed follow-up (3, 7, 32). However, a definitive cure for treating the wide range of KS symptoms is not available yet, and the molecular mechanism linking the overdosage of genes on the supernumerary X chromosome to the onset of the clinical features of KS remains obscure. Nevertheless, most ENA countries have developed national registries and implemented standardized criteria for patients’ assessment, treatment, and programmed follow-up studies. In 2020, a group of experts from the European Academy of Andrology generated a list of guidelines for assessing and scoring KS patients’ phenotypes (GRADE). The aim of GRADE is to correctly assist and manage patients from the pre-natal period until adulthood (32). However, a multidisciplinary treatment approach for KS patients from birth to adulthood is not in place yet in Saudi Arabia and is highly demanded. Worldwide, the increasing parents' age at childbirth and the more frequent use of prenatal and neonatal tests are predicted to cause a sharp increase in the diagnosis rate of SCAs, including KS. Therefore, a better understanding of the mechanisms underlying KS is crucial to developing novel treatment options for these patients. The generation of iPSCs from KS donors provides a unique tool to recapitulate the disease phenotype in vitro and unravel mechanistic insights into KS pathogenesis. In fact, the use of iPSCs will serve as the ideal cellular platform to explore the molecular consequences of X overdosage during tissue specification into disease-relevant lineages. For example, it would be particularly interesting modeling neurodevelopment and anatomical changes observed in gray and white matters in KS and high-grade SCAs by using a 3D brain-organoids approach. Previous studies have employed patients with European, North American, or Eastern origins (10, 33, 34, 35, 36). In this study, we generated 47,XXY-iPSCs from Saudi patients. These iPSCs can provide useful insights for transcriptomic studies on patients carrying a genetically confined background (37). While other continents have historically witnessed waves of admixed migration, the Arabian Peninsula has a population with a highly clustered genomic structure, suggesting a high level of genomic conservation (37). The geographic conservation of the Arabian population confers distinct phenotypic characteristics consequent to emerging mutations. In fact, hundreds of pathogenic gene variants identified in other ethnicities are not causing clinically relevant diseases in Saudis (16). For instance, mutations in the CFTR gene causing cystic fibrosis in Europeans differ from those identified in the Saudi population (38, 39). In this study, we enrolled Saudi KS patients with similar phenotypic traits to subjects from distinct geographical regions. Endocrinologists initially assessed these patients for infertility-related matters and later diagnosed them to be affected by KS through karyotyping. Lower levels of testosterone and higher levels of hemoglobin A1c (HbA1c) compared to normal ranges suggest a hypergonadotropic hypogonadism diagnosis coupled with glucose metabolic dysfunctions in three out of four patients.
To our knowledge, our study is the first to derive KS-iPSCs from Saudi men. Importantly, we generated Saudi 46,XY and KS-iPSCs using a non-integrative mRNA-based reprogramming method that establishes iPSCs free of exogenous DNA integration and with robust conservation of XCI, as described in our previous study (10). Multiple observations and findings arose from the present study when comparing Saudi 47,XXY vs 46,XY-iPSC transcriptomes with tight XCI preservation. Consistent with our previous report (10), we identified a subset of genes located on the PAR1 region that are upregulated in KS patients in a linear fashion, mirroring X chromosome dosage. Most of these PAR1 genes are also differentially expressed in KS-iPSCs with different geographical origins. We identified also nine genes escaping Xi, not located in the PAR1 region, commonly dysregulated in KS vs 46,XY from the two geographical regions. Among them, there is the long non-coding RNA XIST. Noteworthy, the transcriptomic analysis performed in this study using control 46,XY and KS-derived iPSCs from Saudi Arabia donors recapitulates some dysregulated pathways observed in an independent set of patients and controls with distinct geographical origins. Here, we suggest that only a subset of PAR1 and a few non-PAR1 genes, whose expression is X chromosome sensitive, may prime the global transcriptome dysregulations observed in KS patients with diverse genomic backgrounds. This evidence highlights the impact of PAR1 and a few non-PAR escape copy number variants on the development of KS manifestations.
Our study has the advantage of analyzing clonal-independent iPSCs, as opposed to non-clonal blood-derived peripheral blood mononuclear cells or fibroblasts with a mixed state of XCI. We predict that insights revealed by our study on Saudi patients will have important implications for geneticists and clinicians working on KS and other complex disorders.
Conclusion and future perspectives
The derivation of iPSCs from KS patients with different geographical and genomic background origins coupled with a systematic clinical history annotation will serve as a cellular platform for basic research on KS with high translational potential. The differentiation of KS-iPSCs into disease-relevant tissue types will be demanded, for the valuable insights that will convey on the earliest stages of the disease onset and progression. Additionally, the KS-iPSCs and differentiated derivatives can be used for drug discovery applications. Importantly, the prevalence of KS among Saudi males has never been estimated. In the long term, we envision that our work will foster the national networking of clinicians, patients, families, and researchers, thus improving the quality and timing of the clinical intervention for KS patients in this country.
Limitations of the study
The limitation of the current study is mostly related to the size of the cohort of KS patients and control males. For the Saudi cohort only one 46,XY has been enrolled, from which three independent iPSC clones have been derived. Moreover, the 46,XY Saudi male used as a control karyotype is affected by primary infertility and his levels of HbA1c and fasting glucose could suggest signs of pre-diabetes. For this reason, the power of the transcriptomic comparison of this 46,XY subject with KS patients could be biased in regards to ‘fertility’ and ‘glucose metabolism‘ GO terms. Therefore, further studies enrolling a larger number of 46,XY and 47,XXY males from different geographical origins would be helpful to better clarify the transcriptomic signature of KS patients.
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/EC-22-0515.
Declaration of interest
The authors declare that they have no competing financial interests to declare.
Funding
This work was supported by KAUST Smart Health Initiative grant REI/1/4471-01-01 to A A and baseline funding (BAS 1077-01-01) to A A.
Author contribution statement
H S A, J A, A K, T A H, and M F identified the patients and coordinated the execution of the skin biopsies. E F and A A isolated fibroblasts from the skin biopsies. A A performed somatic cell reprogramming. E F and V A expanded the iPSCs. E F characterized the iPSCs, by immunofluorescence, Taqman assay, and STR analysis. E F performed Teratoma assays. V A, K J C L, and I D T performed the bioinformatic analysis. A A designed the study. V A and A A drafted the manuscript. All authors wrote and edited the manuscript. V A and E F contributed equally to this study.
Acknowledgements
The authors thank the KAUST ARCL Animal Facility for their support with teratoma formation and collection.
References
- 1↑
Lanfranco F, Kamischke A, Zitzmann M, & Nieschlag E. Klinefelter’s syndrome. Lancet 2004 364 273–283. (https://doi.org/10.1016/S0140-6736(0416678-6)
- 2↑
Tuttelmann F, & Gromoll J. Novel genetic aspects of Klinefelter’s syndrome. Molecular Human Reproduction 2010 16 386–395. (https://doi.org/10.1093/molehr/gaq019)
- 3↑
Bonomi M, Rochira V, Pasquali D, Balercia G, Jannini EA, Ferlin A, Klinefelter ItaliaN Group (KING) Bonomi M, Calogero A, & Corona G. Klinefelter syndrome (KS): genetics, clinical phenotype and hypogonadism. Journal of Endocrinological Investigation 2017 40 123–134. (https://doi.org/10.1007/s40618-016-0541-6)
- 4↑
Bearelly P, & Oates R. Recent advances in managing and understanding Klinefelter syndrome. F1000Research 2019 8 112. (https://doi.org/10.12688/f1000research.16747.1)
- 5↑
Bojesen A, Juul S, & Gravholt CH. Prenatal and postnatal prevalence of Klinefelter syndrome: a national registry study. Journal of Clinical Endocrinology and Metabolism 2003 88 622–626. (https://doi.org/10.1210/jc.2002-021491)
- 6↑
Tartaglia N, Cordeiro L, Howell S, Wilson R, & Janusz J. The spectrum of the behavioral phenotype in boys and adolescents 47,XXY (Klinefelter syndrome). Pediatric Endocrinology Reviews 2010 8(Supplement 1) 151–159.
- 7↑
Davis S, Howell S, Wilson R, Tanda T, Ross J, Zeitler P, & Tartaglia N. Advances in the interdisciplinary care of children with Klinefelter syndrome. Advances in Pediatrics 2016 63 15–46. (https://doi.org/10.1016/j.yapd.2016.04.020)
- 8↑
Bojesen A, & Gravholt CH. Klinefelter syndrome in clinical practice. Nature Clinical Practice. Urology 2007 4 192–204. (https://doi.org/10.1038/ncpuro0775)
- 9↑
Groth KA, Skakkebæk A, Høst C, Gravholt CH, & Bojesen A. Clinical review: Klinefelter syndrome—A clinical update. Journal of Clinical Endocrinology and Metabolism 2013 98 20–30. (https://doi.org/10.1210/jc.2012-2382)
- 10↑
Astro V, Alowaysi M, Fiacco E, Saera-Vila A, Cardona-Londoño KJ, Cigliano RA, & Adamo A. Pseudoautosomal Region 1 overdosage affects the global transcriptome in iPSCs from patients with Klinefelter syndrome and high-grade X chromosome aneuploidies. Frontiers in Cell and Developmental Biology 2021 9 801597. (https://doi.org/10.3389/fcell.2021.801597)
- 11↑
Alowaysi M, Fiacco E, Astro V, & Adamo A. Establishment of an iPSC cohort from three unrelated 47-XXY Klinefelter syndrome patients (KAUSTi007-A, KAUSTi007-B, KAUSTi009-A, KAUSTi009-B, KAUSTi010-A, KAUSTi010-B). Stem Cell Research 2020 49 102042. (https://doi.org/10.1016/j.scr.2020.102042)
- 12↑
Alowaysi M, Fiacco E, Astro V, & Adamo A. Establishment of iPSC lines from a high-grade Klinefelter syndrome patient (49-XXXXY) and two genetically matched healthy relatives (KAUSTi003-A, KAUSTi004-A, KAUSTi004-B, KAUSTi005-A, KAUSTi005-B, KAUSTi005-C). Stem Cell Research 2020 49 102008. (https://doi.org/10.1016/j.scr.2020.102008)
- 13↑
Alowaysi M, Fiacco E, Astro V, & Adamo A. Generation of two iPSC lines (KAUSTi001-A, KAUSTi002-A) from a rare high-grade Klinefelter syndrome patient (49-XXXXY) carrying a balanced translocation t(4,11) (q35,q23). Stem Cell Research 2020 49 102098. (https://doi.org/10.1016/j.scr.2020.102098)
- 14↑
Fiacco E, Alowaysi M, Astro V, & Adamo A. Generation of an iPSC cohort of isogenic iPSC lines (46-XY and 47-XXY) from a non-mosaic Klinefelter syndrome patient (47-XXY) (KAUSTi008-A, KAUSTi008-B, KAUSTi008-C, KAUSTi008-D, KAUSTi008-E, KAUSTi008-F, KAUSTi008-G). Stem Cell Research 2020 50 102119. (https://doi.org/10.1016/j.scr.2020.102119)
- 15↑
Fiacco E, Alowaysi M, Astro V, & Adamo A. Derivation of two naturally isogenic iPSC lines (KAUSTi006-A and KAUSTi006-B) from a mosaic Klinefelter syndrome patient (47-XXY/46-XY). Stem Cell Research 2020 49 102049. (https://doi.org/10.1016/j.scr.2020.102049)
- 16↑
Kaiser J. When DNA and culture clash. Science 2016 354 1217–1221. (https://doi.org/10.1126/science.354.6317.1217)
- 17↑
Liu C, Liu H, Zhang H, Wang L, Li M, Cai F, Wang X, Wang L, Zhang R, Yang S, et al.Paternal USP26 mutations raise Klinefelter syndrome risk in the offspring of mice and humans. EMBO Journal 2021 40 e106864. (https://doi.org/10.15252/embj.2020106864)
- 18↑
Vangipuram M, Ting D, Kim S, Diaz R, & Schüle B. Skin punch biopsy explant culture for derivation of primary human fibroblasts. Journal of Visualized Experiments: JoVE 2013 77 e3779. (https://doi.org/10.3791/3779)
- 19↑
Bushnell B, Rood J, & Singer E. BBMerge – accurate paired shotgun read merging via overlap. PLoS One 2017 12 e0185056. (https://doi.org/10.1371/journal.pone.0185056)
- 20↑
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, & Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013 29 15–21. (https://doi.org/10.1093/bioinformatics/bts635)
- 21↑
Liao Y, Smyth GK, & Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014 30 923–930. (https://doi.org/10.1093/bioinformatics/btt656)
- 22↑
Rau A, Gallopin M, Celeux G, & Jaffrézic F. Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 2013 29 2146–2152. (https://doi.org/10.1093/bioinformatics/btt350)
- 23↑
Robinson MD, McCarthy DJ, & Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010 26 139–140. (https://doi.org/10.1093/bioinformatics/btp616)
- 24↑
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, et al.Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research 2016 44 W90–W97. (https://doi.org/10.1093/nar/gkw377)
- 25↑
Heard E, & Disteche CM. Dosage compensation in mammals: fine-tuning the expression of the X chromosome. Genes and Development 2006 20 1848–1867. (https://doi.org/10.1101/gad.1422906)
- 26↑
Payer B, & Lee JT. Coupling of X-chromosome reactivation with the pluripotent stem cell state. RNA Biology 2014 11 798–807. (https://doi.org/10.4161/rna.29779)
- 27↑
Nielsen MM, Trolle C, Vang S, Hornshøj H, Skakkebæk A, Hedegaard J, Nordentoft I, Pedersen JS, & Gravholt CH. Epigenetic and transcriptomic consequences of excess X‐chromosome material in 47,XXX syndrome—a comparison with Turner syndrome and 46,XX females. American Journal of Medical Genetics. Part C, Seminars in Medical Genetics 2020 184 279–293. (https://doi.org/10.1002/ajmg.c.31799)
- 28↑
Ludwig TE, Levenstein ME, Jones JM, Berggren WT, Mitchen ER, Frane JL, Crandall LJ, Daigh CA, Conard KR, Piekarczyk MS, et al.Derivation of human embryonic stem cells in defined conditions. Nature Biotechnology 2006 24 185–187. (https://doi.org/10.1038/nbt1177)
- 29↑
Tukiainen T, Villani AC, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, Gauthier L, Fleharty M, Kirby A, et al.Landscape of X chromosome inactivation across human tissues. Nature 2017 550 244–248. (https://doi.org/10.1038/nature24265)
- 30↑
Balaton BP, Cotton AM, & Brown CJ. Derivation of consensus inactivation status for X-linked genes from genome-wide studies. Biology of Sex Differences 2015 6 35. (https://doi.org/10.1186/s13293-015-0053-7)
- 31↑
Zhang X, Hong D, Ma S, Ward T, Ho M, Pattni R, Duren Z, Stankov A, Shrestha SB, Hallmayer J, et al.Integrated functional genomic analyses of Klinefelter and Turner syndromes reveal global network effects of altered X chromosome dosage. PNAS 2020 117 4864–4873. (https://doi.org/10.1073/pnas.1910003117)
- 32↑
Zitzmann M, Aksglaede L, Corona G, Isidori AM, Juul A, T’Sjoen G, Kliesch S, D’Hauwers K, Toppari J, Słowikowska‐Hilczer J, et al.European academy of andrology guidelines on Klinefelter Syndrome Endorsing Organization: European Society of Endocrinology. Andrology 2021 9 145–167. (https://doi.org/10.1111/andr.12909)
- 33↑
Ma Y, Li C, Gu J, Tang F, Li C, Li P, Ping P, Yang S, Li Z, & Jin Y. Aberrant gene expression profiles in pluripotent stem cells induced from fibroblasts of a Klinefelter syndrome patient. Journal of Biological Chemistry 2012 287 38970–38979. (https://doi.org/10.1074/jbc.M112.380204)
- 34↑
Shimizu T, Shiohara M, Tai T, Nagao K, Nakajima K, & Kobayashi H. Derivation of integration-free iPSCs from a Klinefelter syndrome patient. Reproductive Medicine and Biology 2016 15 35–43. (https://doi.org/10.1007/s12522-015-0213-9)
- 35↑
Botman O, Hibaoui Y, Giudice MG, Ambroise J, Creppe C, Feki A, & Wyns C. Modeling Klinefelter syndrome using induced pluripotent stem cells reveals impaired germ cell differentiation. Frontiers in Cell and Developmental Biology 2020 8 567454. (https://doi.org/10.3389/fcell.2020.567454)
- 36↑
Panula S, Kurek M, Kumar P, Albalushi H, Sánchez SP, Damdimopoulou P, Olofsson JI, Hovatta O, Lanner F, & Stukenborg JB. Human induced pluripotent stem cells from two azoospermic patients with Klinefelter syndrome show similar X chromosome inactivation behavior to female pluripotent stem cells. Human Reproduction 2019 34 2297–2310. (https://doi.org/10.1093/humrep/dez134)
- 37↑
Mineta K, Goto K, Gojobori T, & Alkuraya FS. Population structure of indigenous inhabitants of Arabia. PLOS Genetics 2021 17 e1009210. (https://doi.org/10.1371/journal.pgen.1009210)
- 38↑
Banjar H, Kambouris M, Meyer BF, Al-Mehaidib A, & Mogarri I. Geographic distribution of cystic fibrosis transmembrane regulator gene mutations in Saudi Arabia. Annals of Tropical Paediatrics 1999 19 69–73. (https://doi.org/10.1080/02724939992671)
- 39↑
Al‐Sadeq D, Abunada T, Dalloul R, Fahad S, Taleb S, Aljassim K, Hamed FAA, & Zayed H. Spectrum of mutations of cystic fibrosis in the 22 Arab countries: a systematic review. Respirology 2019 24 127–136. (https://doi.org/10.1111/resp.13437)
