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

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Abstract

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.

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Acknowledgements

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?”

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Authors and Affiliations

  1. Inserm, U1016, Institut Cochin, Paris, France

    Léo Mietton, Nicolas Lebrun, Benjamin Saintpierre, Juliette Hamroune, Pierre Billuart & Thierry Bienvenu

  2. CNRS, UMR8104, Paris, France

    Léo Mietton, Nicolas Lebrun, Benjamin Saintpierre, Juliette Hamroune, Pierre Billuart & Thierry Bienvenu

  3. Université Paris Descartes, Sorbonne Paris Cité, Paris, France

    Léo Mietton, Nicolas Lebrun, Benjamin Saintpierre, Juliette Hamroune, Pierre Billuart & Thierry Bienvenu

  4. Institut de Psychiatrie et de Neurosciences de Paris, Inserm U894, 102 rue de la santé, 75014, Paris, France

    Nicolas Lebrun, Pierre Billuart & Thierry Bienvenu

  5. U.F. de Génétique moléculaire, Hôpital Armand Trousseau, Assistance Publique - Hôpitaux de Paris, Paris, France

    Irina Giurgea

  6. INSERM UMR S933, Faculté de médecine Sorbonne Universités, 75012, Paris, France

    Irina Giurgea

  7. Service de génétique, CHU de Rouen et Inserm U1079, Université de Rouen, Centre Normand de Génomique Médicale et Médecine Personnalisée, Rouen, France

    Alice Goldenberg

  8. Service de génétique et embryologie médicales, Centre de référence Maladie du cervelet, CHU Paris Est - Hôpital d’Enfants Armand-Trousseau, Paris, France

    Alexandra Afenjar

  9. Laboratoire de Génétique et Biologie Moléculaires, Hôpital Cochin, HUPC, AP-HP, Paris, France

    Thierry Bienvenu

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  1. Léo Mietton
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12017_2018_8502_MOESM1_ESM.docx

Figure S1: Genomic positions of primers used for chromatin immunoprecipitation assays: ChIP-Seq data of NHDF-Ad (adult dermal fibroblasts) H3K4me3 Histone (from https://www.encodeproject.org/experiments/ENCSR000APR/) (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

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

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

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)

Table S1: Primers sequence used for chromatin immunoprecipitation (ChIP) assays. (XLS 36 KB)

12017_2018_8502_MOESM6_ESM.xls

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

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

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

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

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)

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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

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 (http://www.geneontology.org/) (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

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

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

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

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)

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Mietton, L., Lebrun, N., Giurgea, I. et al. RNA Sequencing and Pathway Analysis Identify Important Pathways Involved in Hypertrichosis and Intellectual Disability in Patients with Wiedemann–Steiner Syndrome. Neuromol Med 20, 409–417 (2018). https://doi.org/10.1007/s12017-018-8502-1

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