NeuroMolecular Medicine

, Volume 20, Issue 3, pp 409–417 | Cite as

RNA Sequencing and Pathway Analysis Identify Important Pathways Involved in Hypertrichosis and Intellectual Disability in Patients with Wiedemann–Steiner Syndrome

  • Léo Mietton
  • Nicolas Lebrun
  • Irina Giurgea
  • Alice Goldenberg
  • Benjamin Saintpierre
  • Juliette Hamroune
  • Alexandra Afenjar
  • Pierre Billuart
  • Thierry BienvenuEmail author
Original Paper


A growing number of histone modifiers are involved in human neurodevelopmental disorders, suggesting that proper regulation of chromatin state is essential for the development of the central nervous system. Among them, heterozygous de novo variants in KMT2A, a gene coding for histone methyltransferase, have been associated with Wiedemann–Steiner syndrome (WSS), a rare developmental disorder mainly characterized by intellectual disability (ID) and hypertrichosis. As KMT2A is known to regulate the expression of multiple target genes through methylation of lysine 4 of histone 3 (H3K4me), we sought to investigate the transcriptomic consequences of KMT2A variants involved in WSS. Using fibroblasts from four WSS patients harboring loss-of-function KMT2A variants, we performed RNA sequencing and identified a number of genes for which transcription was altered in KMT2A-mutated cells compared to the control ones. Strikingly, analysis of the pathways and biological functions significantly deregulated between patients with WSS and healthy individuals revealed a number of processes predicted to be altered that are relevant for hypertrichosis and intellectual disability, the cardinal signs of this disease.


Wiedemann–Steiner syndrome KMT2A Hypertrichosis RNA sequencing Pathway analysis 



We thank the families for their enthusiastic participation. We thank Patrick Nusbaum and Arnaud Hubas for providing primary cultures of fibroblasts. We also thank Franck Letourneur (Genomics Platform at the Institut Cochin, Paris, France) for assistance with RNA sequencing and analysis. This work was supported by the Université Paris Descartes and the Labex “Who I am?”

Compliance with Ethical Standards

Conflict of interest

The authors have declared no conflicting interests.

Supplementary material

12017_2018_8502_MOESM1_ESM.docx (87 kb)
Figure S1: Genomic positions of primers used for chromatin immunoprecipitation assays: ChIP-Seq data of NHDF-Ad (adult dermal fibroblasts) H3K4me3 Histone (from (Rosenbloom et al. 2010) were used to design ChIP-qPCR primers. Mapping of amplicons (”Your Seq”) was represented for the different H3K4Me3 target regions. (DOCX 87 KB)
12017_2018_8502_MOESM2_ESM.pptx (410 kb)
Figure S2: PCA plot for control (Te03, Te04, Te05, F298 and F157) (green) and WSS samples (F70274, F66867, F66262 and LEB) (red). (PPTX 409 KB)
12017_2018_8502_MOESM3_ESM.pptx (984 kb)
Figure S3: An MA-Plot of changes due to KMT2A pathogenic variant. The log2 fold change is plotted on the y-axis and the average of the normalized counts is shown on the x-axis. Each gene is represented with a dot. DEGs are shown in red. (PPTX 983 KB)
12017_2018_8502_MOESM4_ESM.pptx (88 kb)
Figure S4. Validation of RNA-Seq data by qRT-PCR. Correlation plot of log2 fold changes obtained by RNA-Seq (x-axis) and by RT-qPCR (y-axis) for selected genes with transcriptional changes (black dots). Direction of change correlates fully with r2 for a linear regression model at 0.85. Black line represents the linear regression line and r2 represents the correlation coefficient. Correlation obtained by RNA-Seq and qRT-PCR using the primary samples (on top) and correlation obtained by RNA-Seq and qRT-PCR using other independent fibroblast cultures (on bottom). (PPTX 88 KB)
12017_2018_8502_MOESM5_ESM.xls (36 kb)
Table S1: Primers sequence used for chromatin immunoprecipitation (ChIP) assays. (XLS 36 KB)
12017_2018_8502_MOESM6_ESM.xls (198 kb)
Table S2: Differentially expressed genes (DEG)s with a p value <0.05 identified from RNA-Seq. by RNA-Seq in fibroblasts from WSS patients sorted by p value. (XLS 198 KB)
12017_2018_8502_MOESM7_ESM.xlsx (25 kb)
Table S3: Differentially expressed genes (DEG)s with a p value <0.01 and a Fold Change >2 (with > 10 reads) identified by RNA-Seq in fibroblasts from WSS patients sorted by p value. (XLSX 24 KB)
12017_2018_8502_MOESM8_ESM.xls (32 kb)
Table S4: Top 10 of the differentially expressed genes (DEG)s (down-regulated genes in grey and up-regulated genes in white) exhibiting the higher fold changes. (XLS 32 KB)
12017_2018_8502_MOESM9_ESM.xlsx (11 kb)
Table S5: Top 20 of the differentially expressed genes (DEG)s (down-regulated genes in grey and up-regulated genes in white) exhibiting the strongest p values. (XLSX 11 KB)
12017_2018_8502_MOESM10_ESM.xlsx (9 kb)
Table S6: mRNA expression estimated by number of KMT2A normalized reads obtained by RNA sequencing in controls and in KMT2A mutated patients (in yellow). (XLSX 8 KB)
12017_2018_8502_MOESM11_ESM.xlsx (10 kb)
Table S7: List of differentially expressed genes assessed by quantitative reverse transcription – PCR. Log2 Fold change results obtained in RNA-Seq samples and in replicated samples are indicated for each analyzed gene. (XLSX 10 KB)
12017_2018_8502_MOESM12_ESM.xls (26 kb)
Table S8: Canonical pathways enriched in DEGs sorted by p value of enrichment (nd = non-determined)(-log(p value)=1.3 ~ p value=0.05)). Using the IPA software in the GO database ( (Blake et al. 2013), the physiological functions and biological processes of the DEGs were classified into the different categories. Notably, some DEGs were significantly enriched in the eNOS signaling, and Axonal Guidance Signaling pathway. (XLS 25 KB)
12017_2018_8502_MOESM13_ESM.xlsx (9 kb)
Table S9: Upstream analysis (Activated/inhibited diseases and functions enriched in DEGs) using the list of DEGs with a p value <0.05 and a minimum fold change of 1.2 (and with more than 10 reads) showing that KMT2A activity was predicted to be inhibited. (XLSX 8 KB)
12017_2018_8502_MOESM14_ESM.xls (30 kb)
Table S10: Significantly deregulated IPA-networks using the list of DEGs with a p value <0.01 and a fold change >2. Network analysis showed string association of modulated gene expressions with skin and brain networks, represented by high network scores. (XLS 29 KB)
12017_2018_8502_MOESM15_ESM.xlsx (11 kb)
Table S11: Top 10 of diseases and functions (with a positive activation z score) enriched in genes using the list of DEGs with a p value <0.01, showing an overrepresentation of hyperplasia of skin (positive activation z score) (XLSX 10 KB)
12017_2018_8502_MOESM16_ESM.xlsx (10 kb)
Table S12: Top 10 of diseases and functions (with a negative activation z score) enriched in genes using the list of DEGs with a p value <0.01, showing an overrepresentation of brain functions (negative activation z score). (XLSX 10 KB)


  1. Aggarwal, A., Rodriguez, D. F., & Northrup, H. (2017). Wiedemann-Steiner syndrome: Novel pathogenic variant and review of literature. European Journal of Medical Genetics, 60, 285–288.CrossRefPubMedGoogle Scholar
  2. Blake, J. A., et al. (2013). Gene Ontology annotations and resources. Nucleic Acids Research, 41(Database issue), D530–D535.PubMedGoogle Scholar
  3. Brookes, E., et al. (2015). Mutations in the intellectual disability gene KDM5C reduce protein stability and demethylase activity. Human Molecular Genetics, 24(10), 2861–2872.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Chen, J., et al. (2005). Endothelial nitric oxide synthase regulates brain-derived neurotrophic factor expression and neurogenesis after stroke in mice. Journal of Neurosciences, 25(9), 2366–2375.Google Scholar
  5. Chen, T., & Dent, S. Y. (2014). Chromatin modifiers and remodellers: regulators of cellular differentiation. Nature Review Genetics, 15(2), 93–106.CrossRefGoogle Scholar
  6. Cosgrove, M. S., & Patel, A. (2010). Mixed lineage leukemia: a structure-function perspective of the MLL1 protein. FEBS Journal, 277(8), 1832–1842.CrossRefPubMedGoogle Scholar
  7. de Hoon, M. J., Imoto, S., Nolan, J., & Miyano, S. (2004). Open source clustering software. Bioinformatics, 20(9), 1453–1454.CrossRefPubMedGoogle Scholar
  8. Del Rizzo, P. A., & Trievel, R. C. (2011). Substrate and product specificities of SET domain methyltransferases. Epigenetics, 6(9), 1059–1067.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Diehl, F., et al. (2007). The histone methyltransferase MLL is an upstream regulator of endothelial-cell sprout formation. Blood, 109(4), 1472–1478.CrossRefPubMedGoogle Scholar
  10. Dobin, A., et al. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.CrossRefPubMedGoogle Scholar
  11. Gao, J., et al. (2016). Transcription factor Six2 mediates the protection of GDNF on 6-OHDA lesioned dopaminergic neurons by regulating Smurf1 expression. Cell Death Disease, 7, e2217.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Guenther, M. G., et al. (2005). Global and Hox-specific roles for the MLL1 methyltransferase. Proceedings of the National Academy of Sciences USA, 102(24), 8603–8608.CrossRefGoogle Scholar
  13. Hellsten, Y., et al. (2012). Vasodilator interactions in skeletal muscle blood flow regulation. Journal of Physiology, 590(24), 6297–6305.CrossRefPubMedGoogle Scholar
  14. Herman, A., & Herman, A. P. (2016). Mechanism of action of herbs and their active constituents used in hair loss treatment. Fitoterapia, 114, 18–25.CrossRefPubMedGoogle Scholar
  15. Jones, W. D., et al. (2012). De novo mutations in MLL cause Wiedemann-Steiner syndrome. American Journal of Human Genetics, 91(2), 358–364.CrossRefPubMedPubMedCentralGoogle Scholar
  16. Katada, S., & Sassone-Corsi, P. (2010). The histone methyltransferase MLL1 permits the oscillation of circadian gene expression. Nature Structural & Molecular Biology, 17(12), 1414–1421.CrossRefGoogle Scholar
  17. Kerimoglu, C., et al. (2017). KMT2A and KMT2B mediate memory function by affecting distinct genomic regions. Cell Reports, 20(3), 538–548.CrossRefPubMedGoogle Scholar
  18. Kouzarides, T. (2007). Chromatin modifications and their function. Cell, 128(4), 693–705.CrossRefPubMedGoogle Scholar
  19. Lebrun, N., et al. (2017). Molecular and cellular issues of KMT2A variants involved in Wiedemann-Steiner syndrome. European Journal of Human Genetics, 26(1), 107–116.CrossRefPubMedGoogle Scholar
  20. Li, Z., et al. (2008). Inhibitory effect of D1-like and D3 dopamine receptors on norepinephrine-induced proliferation in vascular smooth muscle cells. American Journal of Physiology Heart and Circulation Physiology, 294(6), H2761–H2768.CrossRefGoogle Scholar
  21. Liao, Y., Smyth, G. K., & Shi, W. (2014). FeatureCounts: an efficient general-purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930.CrossRefPubMedGoogle Scholar
  22. Lin, W. H., et al. (2015). Fibroblast growth factors stimulate hair growth through β-catenin and Shh expression in C57BL/6 mice. BioMed Research International, 2015, 730139.PubMedPubMedCentralGoogle Scholar
  23. Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Malik, S., & Bhaumik, S. R. (2010). Mixed lineage leukemia: histone H3 lysine 4 methyltransferases from yeast to human. FEBS Journal, 277(8), 1805–1821.CrossRefPubMedGoogle Scholar
  25. Merzdorf, C. S., & Sive, H. L. (2006). The zic1 gene is an activator of Wnt signaling. The International Journal of Developmental Biology, 50(7), 611–617.CrossRefPubMedGoogle Scholar
  26. Milne, T. A., et al. (2005). Menin and MLL cooperatively regulate expression of cyclin-dependent kinase inhibitors. Proceedings of the National Academy of Sciences USA, 102(3),749–754.CrossRefGoogle Scholar
  27. Miyake, N., et al. (2016). Delineation of clinical features in Wiedemann-Steiner syndrome caused by KMT2A mutations. Clinical Genetics, 89(1), 115–119.CrossRefPubMedGoogle Scholar
  28. Mize, R. R., et al. (1998). The role of nitric oxide in development of the patch-cluster system and retinocollicular pathways in the rodent superior colliculus. Progress in Brain Research, 118, 133–152.CrossRefPubMedGoogle Scholar
  29. Ohuchi, H., et al. (2003). Fibroblast growth factor 10 is required for proper development of the mouse whiskers. Biochemical and Biophysical Research Communications, 302(3), 562–567.CrossRefPubMedGoogle Scholar
  30. Oyama, T., et al. (2013). Cleavage of TFIIA by Taspase1 activates TRF2-specified mammalian male germ cell programs. Developmental Cell, 27(2), 188–200.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Parkel, S., Lopez-Atalaya, J. P., & Barco, A. (2013). Histone H3 lysine methylation in cognition and intellectual disability disorders. Learning & Memory, 20(10), 570–579.CrossRefGoogle Scholar
  32. Pezzani, L., Milani, D., & Tadini, G. (2015). Intellectual disability: When the hypertrichosis is a clue. Journal of Pediatrics Genetics, 4(3), 154–158.CrossRefGoogle Scholar
  33. Pontén, F., Jirström, K., & Uhlen, M. (2008). The Human protein atlas—a tool for pathology. Journal of Pathology, 216(4), 387–393.CrossRefPubMedGoogle Scholar
  34. Pourebrahim, R., et al. (2007). ZIC1 gene expression is controlled by DNA and histone methylation in mesenchymal proliferations. FEBS Letters, 581(26), 5122–5126.CrossRefPubMedGoogle Scholar
  35. Powell, B. C., et al. (1998). The Notch signalling pathway in hair growth. Mechanisms of Development, 78(1–2), 189–192.CrossRefPubMedGoogle Scholar
  36. Rishikaysh, P., et al. (2014). Signaling involved in hair follicle morphogenesis and development. International Journal of Molecular Sciences, 15(1), 1647–1670.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Ronan, J. L., Wu, W., & Crabtree, G. R. (2013). From neural development to cognition: unexpected roles for chromatin. Nature Reviews Genetics, 14(5), 347–359.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Rosenbloom, K. R., et al. (2010). ENCODE whole-genome data in the UCSC Genome Browser. Nucleic Acids Research, 38(Database issue), D620–D625.CrossRefPubMedGoogle Scholar
  39. Saldanha, A. J. (2004). Java TreeView–extensible visualization of microarray data. Bioinformatics, 20(17), 3246–3248.CrossRefPubMedGoogle Scholar
  40. Subramanian, A., et al (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545–15550.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Sun, Y., et al. (2017). Further delineation of the phenotype of truncating KMT2A mutations: The extended Wiedemann-Steiner syndrome. American Journal of Medical Genetics A, 173(2), 510–514.CrossRefGoogle Scholar
  42. Twigg, S. R., et al. (2015). Gain-of-Function Mutations in ZIC1 Are Associated with Coronal Craniosynostosis and Learning Disability. American Journal of Human Genetics, 97(3), 378–388.CrossRefPubMedPubMedCentralGoogle Scholar
  43. Vallianatos, C. N., & Iwase, S. (2015). Disrupted intricacy of histone H3K4 methylation in neurodevelopmental disorders. Epigenomics, 7(3), 503–519.CrossRefPubMedPubMedCentralGoogle Scholar
  44. Wang, D., et al. (2013). MicroRNA-205 controls neonatal expansion of skin stem cells by modulating the PI(3)K pathway. Nature Cell Biology, 15(10), 1153–1163.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Wang, X., & Zhang, X. (2011). Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite. BMC Systems Biology, 5, S3.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Wiersma, M., et al. (2016). Protein kinase Msk1 physically and functionally interacts with the KMT2A/MLL1methyltransferase complex and contributes to the regulation of multiple target genes. Epigenetics & Chromatin, 9, 52.CrossRefGoogle Scholar
  47. Zhang, P., Bergamin, E., & Couture, J. F. (2013). The many facets of MLL1 regulation. Biopolymers, 99(2), 136–145.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Léo Mietton
    • 1
    • 2
    • 3
  • Nicolas Lebrun
    • 1
    • 2
    • 3
    • 4
  • Irina Giurgea
    • 5
    • 6
  • Alice Goldenberg
    • 7
  • Benjamin Saintpierre
    • 1
    • 2
    • 3
  • Juliette Hamroune
    • 1
    • 2
    • 3
  • Alexandra Afenjar
    • 8
  • Pierre Billuart
    • 1
    • 2
    • 3
    • 4
  • Thierry Bienvenu
    • 1
    • 2
    • 3
    • 4
    • 9
    Email author
  1. 1.Inserm, U1016, Institut CochinParisFrance
  2. 2.CNRS, UMR8104ParisFrance
  3. 3.Université Paris Descartes, Sorbonne Paris CitéParisFrance
  4. 4.Institut de Psychiatrie et de Neurosciences de Paris, Inserm U894ParisFrance
  5. 5.U.F. de Génétique moléculaire, Hôpital Armand Trousseau, Assistance Publique - Hôpitaux de ParisParisFrance
  6. 6.INSERM UMR S933, Faculté de médecine Sorbonne UniversitésParisFrance
  7. 7.Service de génétique, CHU de Rouen et Inserm U1079Université de Rouen, Centre Normand de Génomique Médicale et Médecine PersonnaliséeRouenFrance
  8. 8.Service de génétique et embryologie médicales, Centre de référence Maladie du cerveletCHU Paris Est - Hôpital d’Enfants Armand-TrousseauParisFrance
  9. 9.Laboratoire de Génétique et Biologie MoléculairesHôpital Cochin, HUPC, AP-HPParisFrance

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