A novel quantitative targeted analysis of X

Scientific Reports volume 13, Article number: 12856 (2023) Cite this article

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X-chromosome inactivation (XCI) analyses often assist in diagnostics of X-linked traits, however accurate assessment remains challenging with current methods. We developed a novel strategy using amplification-free Cas9 enrichment and Oxford nanopore technologies sequencing called XCI-ONT, to investigate and rigorously quantify XCI in human androgen receptor gene (AR) and human X-linked retinitis pigmentosa 2 gene (RP2). XCI-ONT measures methylation over 116 CpGs in AR and 58 CpGs in RP2, and separate parental X-chromosomes without PCR bias. We show the usefulness of the XCI-ONT strategy over the PCR-based golden standard XCI technique that only investigates one or two CpGs per gene. The results highlight the limitations of using the golden standard technique when the XCI pattern is partially skewed and the advantages of XCI-ONT to rigorously quantify XCI. This study provides a universal XCI-method on DNA, which is highly valuable in clinical and research framework of X-linked traits.

X-chromosome inactivation (XCI) is a compensation mechanism for the difference in number of X-chromosomes between males (46XY), females (46XX) and individuals with X-aneuploidies1. The human molecular XCI mechanism is not completely understood2,3 but studies have shown that XCI is controlled by cis-acting and trans-acting factors in the X-inactivation center (Xic) including gene expression of the X-inactive specific transcript (XIST) and X-active specific transcript (XACT) genes3. This eventually leads to monoallelic expression, and accumulation of H3K27me33 and methylation of CpG sites near promoters of genes on the inactive X-chromosome (Xi) (Fig. 1A)4,5,6. Methylation has shown to be important for the maintenance of Xi’s inactive state7,8,9,10. XCI in humans is initiated in the early embryonic implantation stage3, and the choice of which X-chromosome to be inactivated is in general described as a random process, where both X-chromosomes are equally represented11,12. However, preferential inactivation of one X-chromosome, so-called skewed XCI, has been observed to modify X-linked disease manifestation in carrier females, indicating that the genotype is important in the choice of active X-chromosome (Xa), possibly by cell selection due to a mutant cell disadvantage13,14,15,16. Firstly, preferential silencing of the pathogenic allele has been associated with a selective female survival or a less severe effect of X-linked traits. Extreme skewing has been shown to be a good indicator for the presence of pathogenic X-linked variants in carrier females15,17,18 and XCI analyses in family relatives therefore often assist in the interpretation of X-linked variants. Secondly, expression of the pathogenic allele may lead to manifestation of phenotypes in carrier females of X-linked traits19,20,21,22,23,24,25,26,27,28 and can explain phenotypic differences observed in affected carrier females and individuals with X-aneuploidies21,24,29,30,31,32,33,34,35,36. Of note, XCI analyses are recommended to be performed in relevant tissues, at different ages and in non-smokers37,38,39,40,41,42,43,44. In general, the definition of skewing has been > 80:20 but due to limitations in the golden standard technique used for XCI analysis, quantification of the skewedness has not been recommended for other than 100:0 silencing, highlighting the need of a quantitative method.

(A) Schematic of the X-chromosome inactivation mechanism and its effect in carrier females of X-linked traits. Blue: active X-chromosome (Xa), red: inactive X-chromosome (Xi), yellow star: pathogenic X-linked variant. Illustrations created by using adobe illustrator 2022 (available at https://adobe.com/products/illustrator). (B) The golden standard XCI methods uses methylation sensitive restriction enzymes (HpaII) that cuts the Xa but leaves the Xi intact. HpaII targets two and one CpG site in the Androgen receptor (AR) and Retinis pigmentosa 2 (RP2) respectively, and is followed by PCR and fragment length analysis (FLA) spanning polymorphic repetitive regions (CAGn or GAAAn) that separates the parental alleles. Illustrations created by using Adobe Illustrator 2022 (available at https://adobe.com/products/illustrator). The XCI-ONT approach cuts the DNA independent of methylation status using CRISPR-Cas9 enrichment with three gRNAs (pink) flanking a ~ 3 kb region of interest (ROI) spanning 116 CpG sites in AR and 58 CpG sites in RP2. (C) XCI-ONT includes: (1) Dephosphorylation of 5’ ends to reduce ligation of sequencing adapters to off-target strands. (2) CRISPR-Cas9 system bind and cuts the ROI, and the DNA is dA-tailed for adapter ligation to Cas9 cut sides, which are both 3’ dA-tailed and 5’ phosphorylated. (3) The library is sequenced using Oxford Nanopore Technologies and Bonito base calling. (4) Calling repeats by aligning the reads to reference genomes containing all possible repeats in the regions, and the reads are divided into haplotypes by plotting the number of reads with different repeats. Lastly, methylation calling and quantification are performed using Nanopolish, and the data is visualized using Integrative Genomics Viewer (IGV). The average methylation of the ROI is calculated and the XCI ratio between the two X-chromosomes is determined.

The golden standard XCI analysis uses Methylation Sensitive Restriction Enzymes (MSREs), PCR and Fragment Length Analysis (FLA) targeting two regions on the X-chromosome; the first exon of the human androgen receptor (AR) gene45 and the promoter region of the human X-linked retinitis pigmentosa 2 (RP2) gene46. Methylation of CpG sites in these regions are associated with XCI in humans46,47,48 and by using different MSREs with various target sequences, one or a few CpG sites in these genes are often investigated using the golden standard analysis. Methylation of CpGs in AR and RP2 are of specific interest in XCI analyses due to their proximity to highly conserved polymorphic repetitive elements (CAGn in AR and GAAAn in RP2, heterozygosity rate 0.97 combined) that can be used for separating the parental alleles using FLA (Fig. 1B). However, the golden standard analysis is not robust since it relies on MSREs for digestion, is PCR-based and semi-quantitative. The results are often difficult to interpret due to PCR stutter peaks, secondary structures and/or polymorphisms in the fragment affecting the fragment size and thereby biasing the separation of alleles. In addition, PCR and FLA are not suitable for quantifying imbalanced expression of the two X-chromosomes often leaving the smaller fragment in favor49.

Quantification is necessary when the level of skewedness is < 100:0, and in the grey zone of the skewing definition (> 80:20), often used in the interpretation of X-linked variants. In addition, quantification can be very useful when XCI is modifying disease e.g. in the investigation of carrier females presenting symptoms due to expression of the pathogenic allele (incomplete silencing). Approaches for quantifying the levels of XCI on the maternal and paternal allele have been proposed before but this requires either bisulfite conversion and PCR50 or paired RNA and DNA sequencing data of the XIST gene using informative transcribed heterozygous single nucleotide variants (SNVs) to separate the parental alleles41,51. A SNV is not as informative as repeated elements and can therefore not be used to distinguish between individuals. This highlights the need for a universal quantitative XCI analysis on DNA to be used for research applications and clinical diagnostics related to X-linked traits.

Oxford nanopore technologies (ONT) sequencing can be used to detect CpG methylation revealed by changes in the raw electrical signal during the sequencing process. Long reads are powerful to investigate methylation over repetitive regions and GC-rich sequences such as CpG islands. In addition, high coverage without amplification is a very useful quantitative measurement of methylation. However, high read depth of the whole genome is expensive and therefore an enrichment approach could be advantageous. Recently, an amplification-free targeted library preparation prior to long-read sequencing was released using the CRISPR-Cas9 system to effectively sequence complicated targeted regions with high-coverage and no PCR bias52,53,54,55,56. Analysis without PCR steps are important to avoid allelic dropout and allow quantitative measurement of methylation. Here, we demonstrate a novel strategy to quantify XCI using CRISPR-Cas9 enrichment of the AR and RP2 regions along with ONT sequencing for repeat and methylation detection. The novel strategy is referred to as XCI-ONT in this paper and is compared to the golden standard XCI method (Fig. 1B,C).

In this study, XCI was first investigated using the golden standard PCR-based strategy of the AR and RP2 genes. The investigated family was previously described with X-linked intellectual disability (OMIM #300966) caused by a TAF1 variant (c.3568C > T;p.(Arg1190Cys), NM_004606.4) where carrier females had ~ 100:0 skewed XCI when investigating the AR gene57. The XCI result was confirmed in this study by investigating the RP2 locus of two asymptomatic carrier females (IV:8, III:10) and one asymptomatic non-carrier female (III:7) (Supplementary Fig. 1). The two asymptomatic carrier females presented a ~ 100:0 skewed XCI status consistent with the maternal X-chromosome being silent (carrying the pathogenic variant). The asymptomatic non-carrier female had expression of both alleles i.e. a random XCI status, however the method presents variable result between experiments and contrasting results in the different genes (63:37 in AR vs. 47:53 in RP2). In addition, three females (female I, II and III) were investigated using the golden standard method. All individuals showed different ratios of XCI for both AR (female I 62:38, female II 47:53 and female III 81:19) and RP2 (female I 81:19, female II 60:40 and female III 87:13) (Table 1, Supplementary Fig. 2).

To address the limitations of the golden standard technique and quantify the ratio of active maternal or paternal X-chromosomes, we developed XCI-ONT and applied it on the same samples (Fig. 1B,C). XCI-ONT uses the Cas9-enrichment protocol53 with three gRNAs on both sides of a ~ 3 kb region spanning the same repeats and CpG sites as the golden standard method of the AR and RP2 genes (Supplementary Table 1). The region enrichments were followed by DNA sequencing and simultaneous methylation detection using ONT sequencing. This enables direct detection of repeats without PCR-bias and methylation detection of 116 CpGs in AR (chrX: 67543761–67546170, hg38) and 58 CpGs in RP2 (chrX: 46836539–46837273, hg38), in comparison to the golden standard technique investigating only one or two CpGs per gene45,46. The XCI-ONT investigation generated between 43 and 155 reads on target of which 32–105 were used to calculate the methylation frequencies to separate the haplotypes (Supplementary Table 2). The repeats were detected by alignment to reference genome containing a range of naturally occurring repeats lengths in each gene respectively. The number of repeats in the novel XCI-ONT assay in AR and RP2 was consistent with the base pair differences detected using the golden standard assay (Supplementary Figs. 1, 2 and 3). Methylation calling and calculation of the methylation frequency were performed using Nanopolish58. The methylation status was visualized, and the XCI status was calculated using the average methylation frequency in said regions followed by calculating the ratio of the average methylation between the alleles in each gene. The two asymptomatic carrier females (IV:8 and III:10) presented a skewed XCI status using XCI-ONT with a methylation ration of 95:5 (IV:8) and 97:3 (III:10) in AR, and 91:9 (IV:8) and 91:9 (III:10) in RP2. The asymptomatic non-carrier female (III:7) presented a random XCI status with a methylation ratio of 31:69 in AR, and 34:66 in RP2 (Table 1, Fig. 2 and Supplementary Table 3). Females I and II displayed a random XCI status, and female III presented a random XCI status for AR but a skewed XCI status for RP2. Female I was analyzed two times (female I.I and female I.II) with a new DNA sample to investigate the robustness of the method. The ratios were as following; 28:72 (female I.I), 27:73 (female I.II), 54:46 (female II), and 81:19 (female III) in AR and 26:74, (female I.I) 27:73 (female I.II), 67:33 (female II), and 92:8 (female III) in RP2. The results from female I was highly consistent between the two separate runs (28:72 vs. 27:73), demonstration the robustness and specificity for XCI-ONT (Table 1, Supplementary Fig. 4 and Supplementary Table 3). The variability of methylation calls between the two haplotypes across all reads and analyzed CpG sites were investigated and visualized. A distinct pattern for the majority of CpG sites between methylated or unmethylated haplotypes can be seen for IV:8 and III:10 for both AR and RP2 (Supplementary Fig. 5A–D). For females I and II the different haplotypes cannot be distinguished using methylation status (Supplementary Fig. 5E–L). Female III exhibits no distinction between the haplotypes for AR but shows a clear division between the haplotypes for RP2 (Supplementary Fig. 5M,N).

Visualization of XCI-ONT result using Integrative Genomics Viewer (IGV) presenting methylated CpG sites (red; Xi) and unmetylated CpG sites (blue; Xa) across the reads in the Androgen receptor (AR) and Retinis pigmentosa 2 (RP2) genes. Asymptomatic carrier females (IV:8, III:10) present a skewed XCI pattern and asymptomatic non-carrier females (III:7) of the same family have a random XCI status. Top bar in each haplotype visualization indicates the read coverage (height) and the percentage of methylated and unmethylated calls at each position.

We demonstrate a combination of CRISPR-Cas9 enrichment and ONT sequencing (XCI-ONT) as a novel successful strategy to quantify XCI in females, which can be used to investigate X-linked traits. Here, XCI was investigated in a family with X-linked intellectual disability57, using the golden standard PCR-based strategy of the AR and RP2 genes, and then compared to the XCI-ONT result of the same genes. The golden standard XCI investigation of carrier females (IV:8 and III:10) revealed a ~ 100:0 skewed XCI status in the AR and RP2, resulting in silencing the pathogenic allele as a protective mechanism to disease. The golden standard XCI investigation of an asymptomatic non-carrier female of the same family (III:7) as well as three additional individuals (female I-III) expressed both alleles (random XCI), however the golden standard method presented variable result between experiments and contrasting results in the different genes (63:37 in AR, 47:53 in RP2 for III:7 and 62:38 in AR, 81:19 in RP2 for female I). The results highlight the limitations of using the golden standard method when the XCI pattern is only partially skewed45,46.

To address this, and quantify the ratio of active maternal or paternal X-chromosomes, we developed XCI-ONT, and applied it on the same samples as in the golden standard analysis. The XCI-ONT method detects 116 CpGs in the AR gene and 58 CpGs in RP2 gene, compared to only one or two CpGs per gene using the golden standard method45,46. The two asymptomatic carrier females presented a skewed XCI status with 95:5 (IV:8) or 97:3 (III:10) in the AR gene, and 91:9 in the RP2 gene (IV:8 and III:10), which is consistent with the golden standard results, affirming the established XCI-ONT protocol presented in this paper. The asymptomatic non-carrier female (III:7) and female I presented a random XCI status using XCI-ONT with a methylation ratio of 31:69 and 28:72 in AR and 34:66 and 26:74 in RP2 respectively, implying a distinctly different and robust detection using XCI-ONT compared to the golden standard investigation that presented contrasting results between genes and experiments (63:37 and 62:38 in AR, 47:53 and 81:19 in RP2). These results display a robust XCI detection of different ratios in both genes and confirm that the quantitative XCI-ONT investigation is preferred when XCI pattern is partially skewed. Despite the small sample size of this study, the results show a more accurate XCI investigation with great quantitative potential using the novel XCI-ONT strategy compared with the semi-quantitative golden standard method. We also show that reanalyzing an individual using a new DNA sample yields the same result demonstrating the robustness of the method.

To distinguish the alleles, both the golden standard and XCI-ONT strategy uses the advantage of highly conserved polymorphic repetitive elements in AR and RP2. Repeats are useful for separating chromosomes since parental alleles often differ in number of repeat units. The golden standard investigation uses FLA for an indirect detection of the repeats and separates the alleles by fragment size. Consequently, the haplotype division can be biased by indels and fluorophores or secondary structures affecting the detected fragment length. ONT has the power of direct detection of repeats, underlining the power of using XCI-ONT for haplotype division. This conclusion was supported because the difference in repeat lengths between the two haplotypes was consistent when comparing the two methods, except in female I where the second haplotype show 22 repeats by FLA and 39 repeats using XCI-ONT. This is most likely due to the fact that FLA is a PCR based method which cannot handle the large number of repeats.

To investigate XCI skewedness, both the golden standard and XCI-ONT strategy uses the methylation level of the different haplotypes in AR and RP2. However, the golden standard method has limitations since it relies on MSREs for digestion and only investigate methylation of one or a few CpG sites in the region. In comparison, the XCI-ONT strategy investigates methylation over 116 CpG sites in AR and 58 CpG sites in RP2 using ONT sequencing. To consider the methylation calling error frequency, only reads with steady methylation calls have been included in this analysis (see “Methods” section). Thus, some CpG sites may end up with no methylation status (Supplementary Fig. 5), and therefore the XCI-ONT status is reported as a ratio of the average methylation between the chromosomes. Future studies that include more samples can reveal which sites that are the most reliable to use in analysis and therefore fine-tune this method and improve accuracy even further. In theory, confirmed by the results in this study, XCI-ONT results in a more accurate XCI measurement in comparison to golden standard methods, especially with partially skewed females, indicating the power of using ONT and more than one CpG site in XCI analyses. This is confirmed by the variability of methylation seen across the CpG sites in the two haplotypes of all investigated individuals (Supplementary Fig. 5).

In this study, both the AR and RP2 gene have been used to confirm the XCI status. The methylation ratio of the alleles may differ slightly in the genes due to poor haplotype division caused by ≤ 2 repeats in difference in RP2, more CpGs in the investigated AR region or methylation calling errors. XCI-ONT holds the potential of increasing the number of gRNAs targeting additional X-linked genes, improving the accuracy further. In addition, to date, a limitation in the methylation calling only allows methylation detection within 10 base pair windows and therefore XCI-ONT cannot be compared to the golden standard method at a CpG site level. However, although this is not a possibility with Nanopolish yet, this research is constantly evolving and is expected to be possible soon.

In parallel to our study, ONT sequencing was demonstrated as a promising tool to investigate XCI across the X-chromosome6, but ONT sequencing as a targeted approach to separate the haplotypes and quantify XCI has to our knowledge not been reported before. Moreover, the performance of ONT sequencing compared to golden standard XCI methods have not been demonstrated before.

To summarize, we demonstrate for the first time, XCI-ONT as a novel successful strategy to quantify XCI in carrier females of X-linked traits. This is useful in clinical work and research when the patient is in the grey zone of the skewing definition (> 80:20), often assisting in the interpretation of X-linked variants. In addition, quantification can be highly useful in the investigation of carrier females manifesting symptoms due to expression of the pathogenic allele. Quantification of XCI can illuminate the mechanism of disease, and provide useful information for improving prenatal risk assessment36, diagnostics and treatment development of X-linked traits. Lastly, we would like to illuminate the possibility that different disorders and pathogenic variants requires different levels of expression to develop symptoms and stress the use of a quantitative XCI investigation to answer these questions. A related application for XCI-ONT is monitoring pharmacological Xi reactivation, which has been suggested as treatment for X-linked disorders due to skewed XCI59,60,61,62.

Ethical approval was received from the local ethics committee of the Swedish Ethical Review Authority for human research in Uppsala, Sweden (Dnr 2012/321). The samples were recruited at the Department of Clinical genetics, Uppsala University hospital and informed consent was received from all participants. The study was conducted according to the guidelines of the Declaration of Helsinki. HMW genomic DNA were extracted from 200 µl blood of six females using Nanobind CBB Big DNA kit (Circulomics) according to standard procedures or using the salting-out method (standard protocol available upon request) followed by clean-up with Nanobind CBB Big DNA kit (Circulomics). The family investigated in this study was previously described with X-linked intellectual disability (OMIM #300966) caused by a c.3568C > T;p.(Arg1190Cys) variant in the TAF1 gene (NM_004606.4)57. DNA from two carrier females (III:10, IV:8) and one non-carrier female (III:7) as well as three unrelated females (female I-III) was included in the current analysis.

Two hundred ng of genomic DNA were cut using methylation sensitive HpaII FastDigest in a total volume of 20 µl following manufacturer’s instructions (Thermo Fisher Scientific). PCR spanning the AR gene was performed using Long-range PCR kit (Qiagen) with input of 50 ng DNA or 2 µl digested DNA with primers and cycling conditions described before45. The RP2 gene was amplified in a 20 µl reaction containing 1X PCR reaction buffer, 250 nM dNTPs, 1uM of each primer, 1U Taq polymerase, 50 ng DNA or 2 µl digested DNA with primers and thermal cycler conditions described before46. Genotyping was performed using FLA on the 3130xl ABI Genetic Analyzer with ROX500 Size Standard (Thermo Fisher Scientific). The Amplified Fragment Length Polymorphism (AFLP) was determined using Geneious Prime version 2022.01 with microsatellite plugin (https://www.geneious.com). The peak heights were measured using ImageJ software63 followed by calculation of the height ratio. DNA from a female sample with known XCI pattern (100:0) were used as a control throughout the analysis pipeline.

Three CRISPR RNAs (crRNAs) on both sides of each region of interest in the AR and RP2 genes were designed using CHOPCHOP64 and selected according to the previously described instructions (Cas-mediated PCR-free enrichment protocol version: ENR_9084_v109_revD_04Dec2018). All custom designed Alt-R crRNAs (Integrated DNA Technologies, Supplementary Table 2) were pooled as an equimolar mix of crRNAs (100 µM) and assembled with trans-activating crRNAs (tracrRNAs) (Integrated DNA Technologies, cat 1073190) using Duplex buffer (Integrated DNA Technologies, cat 11010301) according to manufacturer’s instructions. Cas9 enrichment was performed according to manufacturer’s instructions using on 4–5 μg DNA and Long fragment buffer (Cas-mediated PCR-free enrichment protocol SQK-CS9109). Samples were run on a MinION R9.4.1 flow cell and operated using the MinKNOW software (version 20.10.3).

Base calling was performed using Bonito basecaller (version 0.3.8) and reads were aligned to the human reference genome (GRCh38/hg38) using minimap2 (version 2.18-r1015). Only reads targeting the regions of interest in the AR (chrX:67543761–67546170, hg38) and RP2 gene (chrX:46836539–46837273, hg38) were considered for further analysis. To enable repeat-calling, an in-house database was created with reference genomes in containing 5–40 CAG repeats (AR gene) and 5–30 GAAA repeats (RP2 gene). The range of repeat numbers used in the alignment has previously been described in the unaffected general population (ranging from 9 to 38 in AR and 10–20 in RP2)46,65. Repeat calling was performed by alignment to the in-house database using minimap2 (version 2.18-r1015) and number of repeats in the reads were identified using Integrative Genomics Viewer (version 2.10.0, base quality > 20). The data were visualized by plotting the number of reads (y-axis) containing any repeat in the range of 5–40 repeats in AR or 5–30 repeats in RP2 (x-axis) (Supplementary Fig. 3). The reads were divided into haplotypes based on the repeat count and comparison with the golden standard results. Methylation calling and calculation of the methylation frequency was performed using Nanopolish software package (version 0.12.0). Only sites with a log-likelihood ratio > 2.5 (methylated) or < − 2.5 (unmethylated) were included in the methylation analysis (Supplementary Fig. 5). Bam-files were converted using the “converting bam for igv” package66 and the methylation status was visualized using Integrative Genomics Viewer bisulfite mode CG (version 2.10.0). The XCI status was established by using the average methylation frequency in the chrX:67543761–67546170 region (AR gene, 116 CpG sites, hg38) and chrX:46836539–46837273 region (RP2 gene, 58 CpG sites, hg38) followed by calculating the ratio of the average methylation between the alleles in each gene.

The datasets generated and analyzed during the current study are available in the European Nucleotide Archive (ENA) repository, [Study Accession: PRJEB53974, ERP138789]”.

Lyon, M. F. Gene action in the X-chromosome of the mouse (Mus musculus L.). Nature 190, 372–373. https://doi.org/10.1038/190372a0 (1961).

Article ADS CAS PubMed Google Scholar

Loda, A., Collombet, S. & Heard, E. Gene regulation in time and space during X-chromosome inactivation. Nat. Rev. Mol. Cell Biol. https://doi.org/10.1038/s41580-021-00438-7 (2022).

Article PubMed Google Scholar

Patrat, C., Ouimette, J. F. & Rougeulle, C. X chromosome inactivation in human development. Development https://doi.org/10.1242/dev.183095 (2020).

Article PubMed Google Scholar

Sharp, A. J. et al. DNA methylation profiles of human active and inactive X chromosomes. Genome Res. 21, 1592–1600. https://doi.org/10.1101/gr.112680.110 (2011).

Article CAS PubMed PubMed Central Google Scholar

Yasukochi, Y. et al. X chromosome-wide analyses of genomic DNA methylation states and gene expression in male and female neutrophils. Proc. Natl. Acad. Sci. U. S. A. 107, 3704–3709. https://doi.org/10.1073/pnas.0914812107 (2010).

Article ADS PubMed PubMed Central Google Scholar

Gershman, A. et al. Epigenetic patterns in a complete human genome. Science 376, eabj5089. https://doi.org/10.1126/science.abj5089 (2022).

Article CAS PubMed PubMed Central Google Scholar

Venolia, L. et al. Transformation with DNA from 5-azacytidine-reactivated X chromosomes. Proc. Natl. Acad. Sci. U. S. A. 79, 2352–2354. https://doi.org/10.1073/pnas.79.7.2352 (1982).

Article ADS CAS PubMed PubMed Central Google Scholar

Mohandas, T., Sparkes, R. S. & Shapiro, L. J. Reactivation of an inactive human X chromosome: Evidence for X inactivation by DNA methylation. Science 211, 393–396. https://doi.org/10.1126/science.6164095 (1981).

Article ADS CAS PubMed Google Scholar

Lock, L. F., Takagi, N. & Martin, G. R. Methylation of the Hprt gene on the inactive X occurs after chromosome inactivation. Cell 48, 39–46. https://doi.org/10.1016/0092-8674(87)90353-9 (1987).

Article CAS PubMed Google Scholar

Csankovszki, G., Nagy, A. & Jaenisch, R. Synergism of Xist RNA, DNA methylation, and histone hypoacetylation in maintaining X chromosome inactivation. J. Cell Biol. 153, 773–784. https://doi.org/10.1083/jcb.153.4.773 (2001).

Article CAS PubMed PubMed Central Google Scholar

Lyon, M. F. X-chromosome inactivation and developmental patterns in mammals. Biol. Rev. Camb. Philos. Soc. 47, 1–35. https://doi.org/10.1111/j.1469-185x.1972.tb00969.x (1972).

Article CAS PubMed Google Scholar

Monkhorst, K., Jonkers, I., Rentmeester, E., Grosveld, F. & Gribnau, J. X inactivation counting and choice is a stochastic process: Evidence for involvement of an X-linked activator. Cell 132, 410–421. https://doi.org/10.1016/j.cell.2007.12.036 (2008).

Article CAS PubMed Google Scholar

Migeon, B. R. Non-random X chromosome inactivation in mammalian cells. Cytogenet. Cell Genet. 80, 142–148. https://doi.org/10.1159/000014971 (1998).

Article CAS PubMed Google Scholar

Muers, M. R. et al. Defining the cause of skewed X-chromosome inactivation in X-linked mental retardation by use of a mouse model. Am. J. Hum. Genet. 80, 1138–1149. https://doi.org/10.1086/518369 (2007).

Article CAS PubMed PubMed Central Google Scholar

Plenge, R. M., Stevenson, R. A., Lubs, H. A., Schwartz, C. E. & Willard, H. F. Skewed X-chromosome inactivation is a common feature of X-linked mental retardation disorders. Am. J. Hum. Genet. 71, 168–173. https://doi.org/10.1086/341123 (2002).

Article CAS PubMed PubMed Central Google Scholar

Furlan, G. & Galupa, R. Mechanisms of choice in X-chromosome inactivation. Cells https://doi.org/10.3390/cells11030535 (2022).

Article PubMed PubMed Central Google Scholar

Fieremans, N. et al. Identification of intellectual disability genes in female patients with a skewed X-inactivation pattern. Hum. Mutat. 37, 804–811. https://doi.org/10.1002/humu.23012 (2016).

Article CAS PubMed PubMed Central Google Scholar

Vianna, E. Q. et al. Understanding the landscape of X-linked variants causing intellectual disability in females through extreme X chromosome inactivation skewing. Mol. Neurobiol. 57, 3671–3684. https://doi.org/10.1007/s12035-020-01981-8 (2020).

Article CAS PubMed Google Scholar

Gribnau, J. & Stefan Barakat, T. X-chromosome inactivation and its implications for human disease. bioRxiv https://doi.org/10.1101/076950 (2017).

Article Google Scholar

Fieremans, N. et al. De novo MECP2 duplications in two females with intellectual disability and unfavorable complete skewed X-inactivation. Hum. Genet. 133, 1359–1367. https://doi.org/10.1007/s00439-014-1469-6 (2014).

Article PubMed Google Scholar

Brand, B. A., Blesson, A. E. & Smith-Hicks, C. L. The impact of X-chromosome inactivation on phenotypic expression of X-linked neurodevelopmental disorders. Brain Sci. https://doi.org/10.3390/brainsci11070904 (2021).

Article PubMed PubMed Central Google Scholar

Renault, N. K. et al. Heritable skewed X-chromosome inactivation leads to haemophilia A expression in heterozygous females. Eur. J. Hum. Genet. 15, 628–637. https://doi.org/10.1038/sj.ejhg.5201799 (2007).

Article CAS PubMed Google Scholar

Leehey, M. A. et al. FMR1 CGG repeat length predicts motor dysfunction in premutation carriers. Neurology 70, 1397–1402. https://doi.org/10.1212/01.wnl.0000281692.98200.f5 (2008).

Article ADS CAS PubMed Google Scholar

Hall, D. A. et al. X-inactivation in the clinical phenotype of fragile X premutation carrier sisters. Neurol. Genet. 2, e45. https://doi.org/10.1212/nxg.0000000000000045 (2016).

Article PubMed PubMed Central Google Scholar

Berry-Kravis, E., Potanos, K., Weinberg, D., Zhou, L. & Goetz, C. G. Fragile X-associated tremor/ataxia syndrome in sisters related to X-inactivation. Ann. Neurol. 57, 144–147. https://doi.org/10.1002/ana.20360 (2005).

Article CAS PubMed Google Scholar

Yano, S. et al. Familial Simpson-Golabi-Behmel syndrome: studies of X-chromosome inactivation and clinical phenotypes in two female individuals with GPC3 mutations. Clin. Genet. 80, 466–471. https://doi.org/10.1111/j.1399-0004.2010.01554.x (2011).

Article CAS PubMed Google Scholar

Shimojima, K. et al. Loss-of-function mutations and global rearrangements in GPC3 in patients with Simpson–Golabi–Behmel syndrome. Hum. Genome Var. 3, 16033. https://doi.org/10.1038/hgv.2016.33 (2016).

Article PubMed PubMed Central Google Scholar

Schirwani, S. et al. Duplications of GPC3 and GPC4 genes in symptomatic female carriers of Simpson-Golabi-Behmel syndrome type 1. Eur. J. Med. Genet. 62, 243–247. https://doi.org/10.1016/j.ejmg.2018.07.022 (2019).

Article PubMed Google Scholar

Simonetti, L. et al. Intelligence quotient variability in klinefelter syndrome is associated with GTPBP6 expression under regulation of X-chromosome inactivation pattern. Front. Genet. 12, 724625. https://doi.org/10.3389/fgene.2021.724625 (2021).

Article CAS PubMed PubMed Central Google Scholar

Rheault, M. N. et al. X-inactivation modifies disease severity in female carriers of murine X-linked Alport syndrome. Nephrol. Dial. Transpl. 25, 764–769. https://doi.org/10.1093/ndt/gfp551 (2009).

Article CAS Google Scholar

Juan-Mateu, J. et al. Prognostic value of X-chromosome inactivation in symptomatic female carriers of dystrophinopathy. Orphanet J. Rare Dis. 7, 82. https://doi.org/10.1186/1750-1172-7-82 (2012).

Article PubMed PubMed Central Google Scholar

Minamikawa, S. et al. Development of ultra-deep targeted RNA sequencing for analyzing X-chromosome inactivation in female Dent disease. J. Hum. Genet. 63, 589–595. https://doi.org/10.1038/s10038-018-0415-1 (2018).

Article CAS PubMed Google Scholar

Zhang, X. et al. A new sex-specific underlying mechanism for female schizophrenia: Accelerated skewed X chromosome inactivation. Biol. Sex Differ. 11, 39. https://doi.org/10.1186/s13293-020-00315-6 (2020).

Article CAS PubMed PubMed Central Google Scholar

Garagiola, I. et al. X Chromosome inactivation: A modifier of factor VIII and IX plasma levels and bleeding phenotype in Haemophilia carriers. Eur. J. Hum. Genet. 29, 241–249. https://doi.org/10.1038/s41431-020-00742-4 (2021).

Article CAS PubMed Google Scholar

Fahim, A. T. et al. X-chromosome inactivation is a biomarker of clinical severity in female carriers of RPGR-associated X-linked retinitis pigmentosa. Ophthalmol. Retina 4, 510–520. https://doi.org/10.1016/j.oret.2019.11.010 (2020).

Article PubMed Google Scholar

Zhao, Y. et al. X chromosome inactivation pattern and pregnancy outcome of female carriers of pathogenic heterozygous X-linked deletions. Front. Genet. https://doi.org/10.3389/fgene.2021.782629 (2021).

Article PubMed PubMed Central Google Scholar

Amos-Landgraf, J. M. et al. X chromosome-inactivation patterns of 1005 phenotypically unaffected females. Am. J. Hum. Genet. 79, 493–499. https://doi.org/10.1086/507565 (2006).

Article CAS PubMed PubMed Central Google Scholar

Shvetsova, E. et al. Skewed X-inactivation is common in the general female population. Eur. J. Hum. Genet. 27, 455–465. https://doi.org/10.1038/s41431-018-0291-3 (2019).

Article CAS PubMed Google Scholar

Knudsen, G. P., Pedersen, J., Klingenberg, O., Lygren, I. & Ørstavik, K. H. Increased skewing of X chromosome inactivation with age in both blood and buccal cells. Cytogenet. Genome Res. 116, 24–28. https://doi.org/10.1159/000097414 (2007).

Article CAS PubMed Google Scholar

Busque, L. et al. Nonrandom X-inactivation patterns in normal females: Lyonization ratios vary with age. Blood 88, 59–65 (1996).

Article CAS PubMed Google Scholar

Zito, A. et al. Heritability of skewed X-inactivation in female twins is tissue-specific and associated with age. Nat. Commun. 10, 5339. https://doi.org/10.1038/s41467-019-13340-w (2019).

Article ADS PubMed PubMed Central Google Scholar

Kristiansen, M. et al. Twin study of genetic and aging effects on X chromosome inactivation. Eur. J. Hum. Genet. 13, 599–606. https://doi.org/10.1038/sj.ejhg.5201398 (2005).

Article CAS PubMed Google Scholar

Vickers, M. A., McLeod, E., Spector, T. D. & Wilson, I. J. Assessment of mechanism of acquired skewed X inactivation by analysis of twins. Blood 97, 1274–1281. https://doi.org/10.1182/blood.v97.5.1274 (2001).

Article CAS PubMed Google Scholar

Gao, X., Jia, M., Zhang, Y., Breitling, L. P. & Brenner, H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: A systematic review of DNA methylation studies. Clin. Epigenet. 7, 113. https://doi.org/10.1186/s13148-015-0148-3 (2015).

Article CAS Google Scholar

Allen, R. C., Zoghbi, H. Y., Moseley, A. B., Rosenblatt, H. M. & Belmont, J. W. Methylation of HpaII and HhaI sites near the polymorphic CAG repeat in the human androgen-receptor gene correlates with X chromosome inactivation. Am. J. Hum. Genet. 51, 1229–1239 (1992).

CAS PubMed PubMed Central Google Scholar

Machado, F. B. et al. 5meCpG epigenetic marks neighboring a primate-conserved core promoter short tandem repeat indicate X-chromosome inactivation. PLoS One 9, e103714. https://doi.org/10.1371/journal.pone.0103714 (2014).

Article ADS CAS PubMed PubMed Central Google Scholar

Wolf, S. F., Jolly, D. J., Lunnen, K. D., Friedmann, T. & Migeon, B. R. Methylation of the hypoxanthine phosphoribosyltransferase locus on the human X chromosome: Implications for X-chromosome inactivation. Proc. Natl. Acad. Sci. U. S. A. 81, 2806–2810. https://doi.org/10.1073/pnas.81.9.2806 (1984).

Article ADS CAS PubMed PubMed Central Google Scholar

Keith, D. H., Singer-Sam, J. & Riggs, A. D. Active X chromosome DNA is unmethylated at eight CCGG sites clustered in a guanine-plus-cytosine-rich island at the 5’ end of the gene for phosphoglycerate kinase. Mol. Cell. Biol. 6, 4122–4125. https://doi.org/10.1128/mcb.6.11.4122 (1986).

Article CAS PubMed PubMed Central Google Scholar

Multer, G. L. & Boynton, K. A. PCR bias in amplification of androgen receptor alleles, a trinucleotide repeat marker used in clonality studies. Nucleic Acids Res. 23, 1411–1418. https://doi.org/10.1093/nar/23.8.1411 (1995).

Article Google Scholar

Yang, Y. et al. Quantitative and multiplexed DNA methylation analysis using long-read single-molecule real-time bisulfite sequencing (SMRT-BS). BMC Genom. 16, 350. https://doi.org/10.1186/s12864-015-1572-7 (2015).

Article CAS Google Scholar

Rupert, J. L., Brown, C. J. & Willard, H. F. Direct detection of non-random X chromosome inactivation by use of a transcribed polymorphism in the XIST gene. Eur. J. Hum. Genet. 3, 333–343. https://doi.org/10.1159/000472322 (1995).

Article CAS PubMed Google Scholar

Höijer, I. et al. Detailed analysis of HTT repeat elements in human blood using targeted amplification-free long-read sequencing. Hum. Mutat. 39, 1262–1272. https://doi.org/10.1002/humu.23580 (2018).

Article CAS PubMed PubMed Central Google Scholar

Gilpatrick, T. et al. Targeted nanopore sequencing with Cas9-guided adapter ligation. Nat. Biotechnol. 38, 433–438. https://doi.org/10.1038/s41587-020-0407-5 (2020).

Article CAS PubMed PubMed Central Google Scholar

López-Girona, E. et al. CRISPR-Cas9 enrichment and long read sequencing for fine mapping in plants. Plant Methods 16, 121. https://doi.org/10.1186/s13007-020-00661-x (2020).

Article CAS PubMed PubMed Central Google Scholar

McDonald, T. L. et al. Cas9 targeted enrichment of mobile elements using nanopore sequencing. Nat. Commun. 12, 3586. https://doi.org/10.1038/s41467-021-23918-y (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Giesselmann, P. et al. Analysis of short tandem repeat expansions and their methylation state with nanopore sequencing. Nat. Biotechnol. 37, 1478–1481. https://doi.org/10.1038/s41587-019-0293-x (2019).

Article CAS PubMed Google Scholar

Gudmundsson, S. et al. TAF1, associated with intellectual disability in humans, is essential for embryogenesis and regulates neurodevelopmental processes in zebrafish. Sci. Rep. 9, 10730. https://doi.org/10.1038/s41598-019-46632-8 (2019).

Article ADS CAS PubMed PubMed Central Google Scholar

Simpson, J. T. et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat. Methods 14, 407–410. https://doi.org/10.1038/nmeth.4184 (2017).

Article CAS PubMed Google Scholar

Carrette, L. L. G., Blum, R., Ma, W., Kelleher, R. J. & Lee, J. T. Tsix–Mecp2 female mouse model for Rett syndrome reveals that low-level MECP2 expression extends life and improves neuromotor function. Proc. Natl. Acad. Sci. 115, 8185–8190. https://doi.org/10.1073/pnas.1800931115 (2018).

Article ADS CAS PubMed PubMed Central Google Scholar

Bhatnagar, S. et al. Genetic and pharmacological reactivation of the mammalian inactive X chromosome. Proc. Natl. Acad. Sci. 111, 12591–12598. https://doi.org/10.1073/pnas.1413620111 (2014).

Article ADS CAS PubMed PubMed Central Google Scholar

Carrette, L. L. G. et al. A mixed modality approach towards Xi reactivation for Rett syndrome and other X-linked disorders. Proc. Natl. Acad. Sci. 115, E668–E675. https://doi.org/10.1073/pnas.1715124115 (2018).

Article CAS PubMed Google Scholar

Aguilar, R. et al. Targeting Xist with compounds that disrupt RNA structure and X inactivation. Nature 604, 160–166. https://doi.org/10.1038/s41586-022-04537-z (2022).

Article ADS CAS PubMed Google Scholar

Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to imageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).

Article CAS PubMed PubMed Central Google Scholar

Labun, K. et al. CHOPCHOP v3: Expanding the CRISPR web toolbox beyond genome editing. Nucleic Acids Res. 47, W171-w174. https://doi.org/10.1093/nar/gkz365 (2019).

Article CAS PubMed PubMed Central Google Scholar

Andrew, S. E., Goldberg, Y. P. & Hayden, M. R. Rethinking genotype and phenotype correlations in polyglutamine expansion disorders. Hum. Mol. Genet. 6, 2005–2010. https://doi.org/10.1093/hmg/6.12.2005 (1997).

Article CAS PubMed Google Scholar

Lee, I. et al. Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing. Nat. Methods 17, 1191–1199. https://doi.org/10.1038/s41592-020-01000-7 (2020).

Article CAS PubMed PubMed Central Google Scholar

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We wish to acknowledge the family for participating in this study. Thanks to Mai-Britt Mosbech and colleagues at Uppsala Genome Center, National Genomics Infrastructure/SciLifeLab, Uppsala university for valuable technical support in the DNA extraction procedure. Thanks to the analyst team at Klinisk genetik, Uppsala University hospital for input on the FLA analysis.

Open access funding provided by Uppsala University. This study was supported by grants from Magnus Bergvalls Foundation, The Swedish Society of Medicine, Nilsson-Ehle Foundation, Swedish Society for Medical Research (SSMF), Åke Wiberg Foundation, Jeansson Foundation, Svenska läkare sällskapet (SLS) and Tore Nilsson Foundation. J.J and S.G was supported by grants from Sävstaholm stiftelse. S.L, M.L, M.W and J.J were supported by grants from “Avtal mellan svenska staten och vissa landsting om samarbete om grundutbildning av läkare, medicinsk forskning och utveckling av hälso- och sjukvården” (ALF). S.G was supported by the Knut and Alice Wallenberg foundation.

Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Husargatan 3, Box 815, SE-751 08, Uppsala, Sweden

Josefin Johansson, Sarah Lidéus, Ida Höijer, Adam Ameur, Göran Annerén, Marie-Louise Bondeson & Maria Wilbe

Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Sanna Gudmundsson

Division of Genetics and Genomics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

Sanna Gudmundsson

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J.J., M.-L.B. and M.W. conceived the study. J.J., M.W., M.-L.B. and A.A. contributed with experimental and analytic design. M.-L.B. and G.A. contributed with patient material. J.J., S.L., I.H. and S.G. performed the experiments. J.J., S.L., M.E. and M.W. performed the analysis. J.J. illustrated and prepared the figures. J.J. and M.W. drafted the manuscript. All authors read and approved the final manuscript.

Correspondence to Maria Wilbe.

J.J, M.W and M-L.B: Patent application pending “QUANTIFICATION OF X-CHROMOSOME INACTIVATION” (Application number 2250254-6). S.L, G.A, S.G, A.A, M.E, I.H: No competing interests.

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Johansson, J., Lidéus, S., Höijer, I. et al. A novel quantitative targeted analysis of X-chromosome inactivation (XCI) using nanopore sequencing. Sci Rep 13, 12856 (2023). https://doi.org/10.1038/s41598-023-34413-3

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Received: 12 April 2022

Accepted: 29 April 2023

Published: 08 August 2023

DOI: https://doi.org/10.1038/s41598-023-34413-3

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