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Identification of unique DNA methylation sites in Kabuki syndrome using whole genome bisulfite sequencing and targeted hybridization capture followed by enzymatic methylation sequencing

Abstract

Background

Kabuki syndrome (KS) is a congenital malformation syndrome caused by mutations in the KMT2D and KDM6A genes that encode histone modification enzymes. Although KS is considered a single gene disorder, its symptoms vary widely. Recently, disease-specific DNA methylation patterns, or episignatures, have been recognized and used as a diagnostic tool for KS. Because of various crosstalk mechanisms between histone modifications and DNA methylation, DNA methylation analysis may have high potential for investigations into the pathogenesis of KS.

Results

In this study, we investigated altered CpG-methylation sites that were specific to KS to find important genes associated with the various phenotypes or pathogenesis of KS. Whole genome bisulfite sequencing (WGBS) was performed to select target CpG islands, and enzymatic conversion technology was applied after hybridization capture to confirm KS-specific episignatures of 130 selected differently methylated target regions (DMTRs) in DNA samples from the 65 participants, 31 patients with KS and 34 unaffected individuals, in this study. We identified 26 candidate genes in 22 DMTRs that may be associated with KS. Our results indicate that disease-specific methylation sites can be identified from a small number of WGBS samples, and hybridization capture followed by enzymatic methylation sequencing can simultaneously test the sites.

Conclusions

Although DNA methylation can be tissue-specific, our results suggest that methylation profiling of DNA extracted from peripheral blood may be a powerful approach to study the pathogenesis of diseases.

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Data availability

All data generated and analyzed during this study are included in this published article and its supplementary information files. The datasets used and analyzed during this study are available from the corresponding author on reasonable request.

References

  1. Niikawa N, Matsuura N, Fukushima Y, Ohsawa T, Kajii T. Kabuki make-up syndrome: a syndrome of mentalretardation, unusual facies, large and protruding ears, and postnatal growth deficiency. J Pediatrics. 1981;99:565–9.

    Article  CAS  Google Scholar 

  2. Kuroki Y, Suzuki Y, Chyo H, Hata A, Matsui I. A new malformation syndrome of long palpebralfissures, large ears, depressed nasal tip, and skeletal anomalies associated with postnatal dwarfism and mental retardation. J Pediatrics. 1981;99:570–3.

    Article  CAS  Google Scholar 

  3. Niikawa N, Kuroki Y, Kajii T, Matsuura N, Ishikiriyama S, Tonoki H, et al. Kabuki make‐up (Niikawa‐Kuroki) syndrome: A study of 62 patients. Am J Med Genet. 1988;31:565–89.

    Article  PubMed  CAS  Google Scholar 

  4. Ng SB, Bigham AW, Buckingham KJ, Hannibal MC, McMillin MJ, Gildersleeve HI, et al. Exome sequencing identifies MLL2 mutations as a cause of Kabuki syndrome. Nat Genet. 2010;42:790–3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Miyake N, Mizuno S, Okamoto N, Ohashi H, Shiina M, Ogata K, et al. KDM 6 A Point Mutations Cause K abuki Syndrome. Hum Mutat. 2013;34:108–10.

    Article  PubMed  CAS  Google Scholar 

  6. Bögershausen N, Wollnik B. Unmasking kabuki syndrome. Clin Genet. 2013;83:201–11.

    Article  PubMed  Google Scholar 

  7. Bögershausen N, Gatinois V, Riehmer V, Kayserili H, Becker J, Thoenes M, et al. Mutation update for Kabuki syndrome genes KMT2D and KDM6A and further delineation of X‐linked Kabuki syndrome subtype 2. Hum Mutat. 2016;37:847–64.

    Article  PubMed  Google Scholar 

  8. Ang S-Y, Uebersohn A, Spencer CI, Huang Y, Lee J-E, Ge K, et al. KMT2D regulates specific programs in heart development via histone H3 lysine 4 di-methylation. Development. 2016;143:810–21.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Froimchuk E, Jang Y, Ge K. Histone H3 lysine 4 methyltransferase KMT2D. Gene . 2017;627:337–42.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Fahrner JA, Lin W-Y, Riddle RC, Boukas L, DeLeon VB, Chopra S, et al. Precocious chondrocyte differentiation disrupts skeletal growth in Kabuki syndrome mice. JCI insight. 2019;4:e129380.

  11. Pilarowski GO, Cazares T, Zhang L, Benjamin JS, Liu K, Jagannathan S, et al. Abnormal Peyer patch development and B-cell gut homing drive IgA deficiency in Kabuki syndrome. J Allergy Clin Immunol. 2020;145:982–92.

    Article  PubMed  CAS  Google Scholar 

  12. Banka S, Veeramachaneni R, Reardon W, Howard E, Bunstone S, Ragge N, et al. How genetically heterogeneous is Kabuki syndrome?: MLL2 testing in 116 patients, review and analyses of mutation and phenotypic spectrum. Eur J Hum Genet. 2012;20:381–8.

    Article  PubMed  CAS  Google Scholar 

  13. Miyake N, Koshimizu E, Okamoto N, Mizuno S, Ogata T, Nagai T, et al. MLL2 and KDM6A mutations in patients with Kabuki syndrome. Am J Med Genet Part A. 2013;161:2234–43.

    Article  CAS  Google Scholar 

  14. Schenkel LC, Kernohan KD, McBride A, Reina D, Hodge A, Ainsworth PJ, et al. Identification of epigenetic signature associated with alpha thalassemia/mental retardation X-linked syndrome. Epigenetics Chromatin. 2017;10:1–11.

    Article  Google Scholar 

  15. Aref-Eshghi E, Rodenhiser DI, Schenkel LC, Lin H, Skinner C, Ainsworth P, et al. Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes. Am J Hum Genet. 2018;102:156–74.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lee J-E, Wang C, Xu S, Cho Y-W, Wang L, Feng X, et al. H3K4 mono-and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation. Elife. 2013;2:e01503.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhang J, Dominguez-Sola D, Hussein S, Lee J-E, Holmes AB, Bansal M, et al. Disruption of KMT2D perturbs germinal center B cell development and promotes lymphomagenesis. Nat Med. 2015;21:1190.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Micale L, Augello B, Maffeo C, Selicorni A, Zucchetti F, Fusco C, et al. Molecular Analysis, Pathogenic Mechanisms, and Readthrough Therapy on a Large Cohort of K abuki Syndrome Patients. Hum Mutat. 2014;35:841–50.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Aref-Eshghi E, Kerkhof J, Pedro VP, France GD, Barat-Houari M, Ruiz-Pallares N, et al. Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 Mendelian neurodevelopmental disorders. Am J Hum Genet. 2020;106:356–70.

  20. Aref-Eshghi E, Schenkel LC, Lin H, Skinner C, Ainsworth P, Paré G, et al. The defining DNA methylation signature of Kabuki syndrome enables functional assessment of genetic variants of unknown clinical significance. Epigenetics. 2017;12:923–33.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Butcher DT, Cytrynbaum C, Turinsky AL, Siu MT, Inbar-Feigenberg M, Mendoza-Londono R, et al. CHARGE and Kabuki syndromes: gene-specific DNA methylation signatures identify epigenetic mechanisms linking these clinically overlapping conditions. Am J Hum Genet. 2017;100:773–88.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Sobreira N, Brucato M, Zhang L, Ladd-Acosta C, Ongaco C, Romm J, et al. Patients with a Kabuki syndrome phenotype demonstrate DNA methylation abnormalities. Eur J Hum Genet. 2017;25:1335–44.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Barau J, Teissandier A, Zamudio N, Roy S, Nalesso V, Hérault Y, et al. The DNA methyltransferase DNMT3C protects male germ cells from transposon activity. Science. 2016;354:909–12.

    Article  PubMed  CAS  Google Scholar 

  24. Neri F, Rapelli S, Krepelova A, Incarnato D, Parlato C, Basile G, et al. Intragenic DNA methylation prevents spurious transcription initiation. Nature. 2017;543:72–7.

    Article  PubMed  CAS  Google Scholar 

  25. Shukla S, Kavak E, Gregory M, Imashimizu M, Shutinoski B, Kashlev M, et al. CTCF-promoted RNA polymerase II pausing links DNA methylation to splicing. Nature. 2011;479:74–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Machida S, Takizawa Y, Ishimaru M, Sugita Y, Sekine S, Nakayama J-I, et al. Structural basis of heterochromatin formation by human HP1. Mol Cell. 2018;69:385–97. e8.

    Article  PubMed  CAS  Google Scholar 

  27. Petruk S, Sedkov Y, Johnston DM, Hodgson JW, Black KL, Kovermann SK, et al. TrxG and PcG proteins but not methylated histones remain associated with DNA through replication. Cell. 2012;150:922–33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Weinberg DN, Papillon-Cavanagh S, Chen H, Yue Y, Chen X, Rajagopalan KN, et al. The histone mark H3K36me2 recruits DNMT3A and shapes the intergenic DNA methylation landscape. Nature. 2019;573:281–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. McInnes L, Healy J, Melville J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426. 2018.

  30. Aref-Eshghi E, Bend EG, Colaiacovo S, Caudle M, Chakrabarti R, Napier M, et al. Diagnostic utility of genome-wide DNA methylation testing in genetically unsolved individuals with suspected hereditary conditions. Am J Hum Genet. 2019;104:685–700.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 2008;9:1–15.

    Article  Google Scholar 

  32. Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F. Evaluation of the Infinium Methylation 450K technology. Epigenomics. 2011;3:771–84.

    Article  PubMed  CAS  Google Scholar 

  33. Touleimat N, Tost J. Complete pipeline for Infinium® Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation. Epigenomics. 2012;4:325–41.

    Article  PubMed  CAS  Google Scholar 

  34. Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, et al. ChAMP: 450k chip analysis methylation pipeline. Bioinformatics. 2014;30:428–30.

    Article  PubMed  CAS  Google Scholar 

  35. Clark C, Palta P, Joyce CJ, Scott C, Grundberg E, Deloukas P, et al. A comparison of the whole genome approach of MeDIP-seq to the targeted approach of the Infinium HumanMethylation450 BeadChip® for methylome profiling. PloS one. 2012;7:e50233.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Williams L, Bei Y, Church HE, Dai N, Dimalanta ET, Ettwiller LM, et al. Enzymatic Methyl-seq: the next generation of methylome analysis. 2019. https://international.neb.com/tools-and-resources/feature-articles/enzymatic-methyl-seq-the-next-generation-of-methylome-analysis. Accessed 21 Sep. 2022

  37. Feng S, Zhong Z, Wang M, Jacobsen SE. Efficient and accurate determination of genome-wide DNA methylation patterns in Arabidopsis thaliana with enzymatic methyl sequencing. Epigenetics Chromatin. 2020;13:1–17.

    Article  Google Scholar 

  38. Arima T, Kamikihara T, Hayashida T, Kato K, Inoue T, Shirayoshi Y, et al. ZAC, LIT1 (KCNQ1OT1) and p57 KIP2 (CDKN1C) are in an imprinted gene network that may play a role in Beckwith–Wiedemann syndrome. Nucleic Acids Res. 2005;33:2650–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Topham JT. Comprehensive and integrative analysis of the KMT2D regulome [master's thesis]. Vancouver: University of British Columbia. 2017. https://doi.org/10.14288/1.0349091.

  40. Lan F, Bayliss PE, Rinn JL, Whetstine JR, Wang JK, Chen S, et al. A histone H3 lysine 27 demethylase regulates animal posterior development. Nature. 2007;449:689–94.

    Article  PubMed  CAS  Google Scholar 

  41. Agger K, Cloos PA, Christensen J, Pasini D, Rose S, Rappsilber J, et al. UTX and JMJD3 are histone H3K27 demethylases involved in HOX gene regulation and development. Nature. 2007;449:731–4.

    Article  PubMed  CAS  Google Scholar 

  42. The Trim Galore package. Accessed 5 Sep 2020. https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/.

  43. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.

    Article  Google Scholar 

  44. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27:1571–2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gao S, Zou D, Mao L, Liu H, Song P, Chen Y, et al. BS-SNPer: SNP calling in bisulfite-seq data. Bioinformatics. 2015;31:4006–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  47. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13:1–9.

    Article  Google Scholar 

  48. the Rubystats library. Accessed 19 Aug 2019. https://github.com/phillbaker/rubystats.

  49. PCAtools. Accessed 19 Jan 2021. https://github.com/kevinblighe/PCAtools.

  50. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9.

    Article  PubMed  CAS  Google Scholar 

  51. Dreos R, Ambrosini G, Cavin Périer R, Bucher P. EPD and EPDnew, high-quality promoter resources in the next-generation sequencing era. Nucleic Acids Res. 2013;41:D157–D64.

    Article  PubMed  CAS  Google Scholar 

  52. Mishima H, Aerts J, Katayama T, Bonnal RJ, Yoshiura K-I. The Ruby UCSC API: accessing the UCSC genome database using Ruby. BMC Bioinforma. 2012;13:1–6.

    Article  Google Scholar 

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Acknowledgements

We thank all the participants in this study. We especially thank Hayashida C, Fukushima A, and Niiya A for their help with the experiments. We acknowledge all the facilities that provided samples. We thank Margaret Biswas, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

Funding

This work was supported by the Practical Research Project for Rare/Intractable Diseases (No. 19ek0109234h0003, to KY) from the Agency for Medical Research and Development, and the Grant-in-Aid for Scientific Research (B) (to KY) from the Japan Society for the Promotion of Science.

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Contributions

KY conceived the project and planned the study. YH conducted the experiments. YH, HM, TK, SS, and KH analyzed the data. YH and HM wrote the manuscript. AK and KY edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Koh-ichiro Yoshiura.

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The experimental procedures were approved by the Committee for Ethical Issues on Human Genome and Gene Analysis at Nagasaki University (permission: #20140803). Written informed consent was obtained from all the participants.

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Hamaguchi, Y., Mishima, H., Kawai, T. et al. Identification of unique DNA methylation sites in Kabuki syndrome using whole genome bisulfite sequencing and targeted hybridization capture followed by enzymatic methylation sequencing. J Hum Genet 67, 711–720 (2022). https://doi.org/10.1038/s10038-022-01083-4

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