Abstract

The etiological spectrum of ultra-rare developmental disorders remains to be fully defined. Chromatin regulatory mechanisms maintain cellular identity and function, where misregulation may lead to developmental defects. Here, we report pathogenic variations in MSL3, which encodes a member of the chromatin-associated male-specific lethal (MSL) complex responsible for bulk histone H4 lysine 16 acetylation (H4K16ac) in flies and mammals. These variants cause an X-linked syndrome affecting both sexes. Clinical features of the syndrome include global developmental delay, progressive gait disturbance, and recognizable facial dysmorphism. MSL3 mutations affect MSL complex assembly and activity, accompanied by a pronounced loss of H4K16ac levels in vivo. Patient-derived cells display global transcriptome alterations of pathways involved in morphogenesis and cell migration. Finally, we use histone deacetylase inhibitors to rebalance acetylation levels, alleviating some of the molecular and cellular phenotypes of patient cells. Taken together, we characterize a syndrome that allowed us to decipher the developmental importance of MSL3 in humans.

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Data availability

RNA-Seq data have been deposited to the Gene Expression Omnibus under accession GSE102250. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/) via the PRIDE partner repository72 with dataset identifier PXD009317. The damaging variants reported as disease causing in this article were deposited in the ClinVar database under study accession SUB2871008.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We are grateful to the patients and their families for their cooperation and interest in the study. We thank N. Iovino, B. Sheikh, and I. Ilik for critical reading of the manuscript. We also thank C. Pessoa Rodrigues and A. Karoutas for technical help, insightful discussion, and advice. We thank V. Bhardwaj for advice and consulting on RNA-Seq analysis, and S. Kübart, A. Schröer, J. Wirth, and H.-G. Nothwang for help with inversion breakpoint mapping. We thank L. Wells for patient recruitment and clinical data collection. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust, Department of Health, and Wellcome Trust Sanger Institute (grant number WT098051). The views expressed in this publication are those of the authors and not necessarily those of the Wellcome Trust or Department of Health. This study has UK Research Ethics Committee (REC) approval (10/H0305/83, granted by the Cambridge South REC; and GEN/284/12, granted by the Republic of Ireland REC). The research team acknowledges the support of the National Institute for Health Research through the Comprehensive Clinical Research Network. This study makes use of DECIPHER (see URLs), which is funded by Wellcome. Sequencing for patient 12 was provided by the Center for Mendelian Genomics at the Broad Institute of MIT and Harvard, and was funded by the National Human Genome Research Institute, National Eye Institute, and National Heart, Lung and Blood Institute grant UM1 HG008900 to D. MacArthur and H. Rehm. This work was supported by CRC992, CRC1140, and CRC746 (awarded to A.A.). It was also supported by the council of Burgundy, German Human Genome Program (grant number 01KW99087) and National Genome Research Network (project numbers 01GR0105 and 01GS08160), awarded to V.M.K. and A.R., respectively. C.I.K.V. was supported by a Human Frontier Science Program long-term fellowship (000233/2014-L).

Author information

Author notes

  1. These authors contributed equally: Ange-Line Bruel, Giuseppe Semplicio, Claudia Isabelle Keller Valsecchi, Tuğçe Aktaş.

  2. A list of members and affiliations appears in the Supplementary Note.

Affiliations

  1. Max Planck Institute of Immunobiology and Epigenetics, Freiburg im Breisgau, Germany

    • M. Felicia Basilicata
    • , Giuseppe Semplicio
    • , Claudia Isabelle Keller Valsecchi
    • , Tuğçe Aktaş
    • , Tobias Rumpf
    • , Witold G. Szymanski
    • , Gerhard Mittler
    •  & Asifa Akhtar
  2. Inserm UMR 1231 GAD, Genetics of Developmental disorders and Centre de Référence Maladies Rares Anomalies du Développement et syndromes malformatifs FHU TRANSLAD, Université de Bourgogne-Franche Comté, Dijon, France

    • Ange-Line Bruel
    • , Yannis Duffourd
    • , Salima El Chehadeh
    • , Christel Thauvin-Robinet
    • , Laurence Faivre
    •  & Julien Thevenon
  3. West Midlands Regional Clinical Genetics Service and Birmingham Health Partners, Birmingham Women’s Hospital NHS Foundation Trust, Birmingham, UK

    • Jenny Morton
  4. Department of Clinical Genetics, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark

    • Iben Bache
    •  & Maria Kirchhoff
  5. Wilhelm Johannsen Centre for Functional Genome Research, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark

    • Iben Bache
  6. Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands

    • Christian Gilissen
    • , Ineke van der Burgt
    • , Rolph Pfundt
    •  & Han G. Brunner
  7. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium

    • Olivier Vanakker
    • , Björn Menten
    •  & Sarah Vergult
  8. Department of Clinical Genetics, United Laboratories, Tartu University Hospital and Institute of Clinical Medicine, University of Tartu, Tartu, Estonia

    • Katrin Õunap
    •  & Sander Pajusalu
  9. Service de Génétique Médicale, Hôpital de Hautepierre, Strasbourg, France

    • Salima El Chehadeh
  10. GeneDx, Gaithersburg, MD, USA

    • Megan T. Cho
  11. Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Royal Children’s Hospital, University of Melbourne Department of Paediatrics, Parkville, VIC, Australia

    • Tiong Yang Tan
  12. Division of Genetics and Metabolism, Phoenix Children’s Hospital, Phoenix, AZ, USA

    • Kristin Lindstrom
  13. Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    • André Reis
  14. Sheffield Clinical Genetics Service, Sheffield Children’s NHS Foundation Trust, Sheffield, UK

    • Diana S. Johnson
  15. Department of Clinical Genetics, Liverpool Women’s NHS Foundation Trust, Liverpool, UK

    • Alan Fryer
    •  & Victoria McKay
  16. Northern Genetics Service, Teesside Genetics Unit, The James Cook University Hospital, Middlesbrough, UK

    • Richard B. Fisher
  17. Cytogenetic Laboratory, Victorian Clinical Genetics Services, Murdoch Children’s Research Institute, Royal Children’s Hospital, Melbourne, Victoria, Australia

    • David Francis
  18. Neuroscience Research Australia, Sydney, New South Wales, Australia

    • Tony Roscioli
  19. Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia

    • Tony Roscioli
  20. Department of Medical Genetics, Sydney Children’s Hospital, Sydney, New South Wales, Australia

    • Tony Roscioli
  21. Department of Clinical Genomics, Ambry Genetics, Aliso Viejo, CA, USA

    • Kelly Radtke
  22. Division of Genetics, Cooper University Hospital and Cooper Medical School at Rowan University, Camden, NJ, USA

    • Jaya Ganesh
  23. Department of Clinical Genetics and School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands

    • Han G. Brunner
  24. Department of Clinical Genetics, Children’s Hospital at Westmead, Disciplines of Genetic Medicine and Child and Adolescent Health, University of Sydney, Sydney, New South Wales, Australia

    • Meredith Wilson
  25. Research Group Development and Disease, Max Planck Institute for Molecular Genetics, Berlin, Germany

    • Vera M. Kalscheuer
  26. CNRS UMR 5309, INSERM, U1209, Institute of Advanced Biosciences, Université Grenoble-Alpes CHU Grenoble, Grenoble, France

    • Julien Thevenon

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Consortia

  1. DDD Study

    Contributions

    M.F.B., A.A., and J.T. conceived the project and planned the experiments. The review of phenotypes and sample collection were performed by J.M., I.B., M.K., H.G.B., O.V., K.Õ., B.M., T.Y.T., S.V., K.L., A.R., D.S.J., A.F., V.M., R.B.F., V.M.K., A.-L.B., L.F., S.E.C., J.T., C.T.R., I.v.d.B., M.T.C., and M.W. Fibroblast isolation, tissue section preparation and processing, cell culture, protein analysis, FACS, immunostaining, microscopy, drug treatment experiments, ChIP, RNA expression analysis, and IPA pathway analyses were performed by M.F.B. A.-L.B. initiated data sharing and compiled patients’ clinical data. G.S. performed deep-sequencing data analysis and manuscript editing. C.I.K.V. contributed to the design, analysis, and interpretation of the experiments and data. T.A. performed cloning of the MSL3 expression constructs and co-immunoprecipitations. W.G.S. and G.M. performed LC-MS sample preparation and analysis. T.R. purified recombinant proteins, and performed HAT assays and in silico modeling. Ethical consultation was provided by L.F. Data analysis for exome and frequency calculation was performed by Y.D., S.P., J.T., A.-L.B., C.G., and R.P. The manuscript was written and edited by C.I.K.V., M.F.B., A.A., and J.T. All authors reviewed, edited, and approved the paper.

    Competing interests

    The authors declare no competing interests.

    Corresponding authors

    Correspondence to Julien Thevenon or Asifa Akhtar.

    Integrated supplementary information

    1. Supplementary Figure 1 MSL3 variants cause a novel syndrome.

      a, Representation of the MSL3 patient countries of origin. b, Sanger sequencing confirming the skipping of MSL3 exon 11 in P1 cDNA. The allelic ratio was in favor of random X inactivation. Right, cropped agarose gel picture of RT–PCR products. c, Schematic representation of X chromosome inversion of P16. d, Patient pedigrees. No MSL3 variant could be detected in the parents of patients P1–P11 (the father of patient P10 could not be tested). P16’s mother is reported as mildly affected with no clinical details provided. Her X chromosome inversion occurred de novo. e, Heat map representing RNA-Seq data for MSL3 in the developing human brain compared to other members of the MSL complex. Data were retrieved from https://www.ebi.ac.uk/gxa/home/. f, Amino acid sequence alignment of MSL3 orthologs showing that the mutated residues are highly conserved throughout evolution.

    2. Supplementary Figure 2 Characterization of MSL3 patient-derived fibroblasts.

      a, FFPE skin sections from Control (ctrl) and P1/P2/P14 stained with H&E. The dashed line distinguishes dermis and epidermis layers. Architectural skin layers are demarcated. Scale bar, 20 μm. Patients donated n= 1 skin sample; at least two sections per slide were analyzed. SB, stratum basale; SL, stratum lucidum; SS, stratum spinosum; SC, stratum corneum. b, Immunostaining for H3K27me3 (red) in primary HDFs. Scale bar, 5 µm. The staining was repeated twice with similar results. c, Cropped immunoblots for H4K16ac and total histone H3 as well as H3 and H4 pan-acetylation in additional HDF lines. The experiment was repeated twice with similar results. d, Sashimi plot derived from MiSeq results showing exon skipping in P1 but not P2 or Control (ctrl) HDF cDNA. e, Distribution of identified protein intensities measured in LC-MS/MS experiments before (left) and after (right) normalization. Protein intensities as well as modified site intensities were normalized and scaled by adjusting the centers of the distributions around zero to account for loading differences in SDS-PAGE. Box plots are centered on the median with the lower and upper hinges corresponding to the first and third quartiles. Normalized values were used for further statistical analysis as described in Supplementary Table 2. f, Heat map representing all acetyl (K), mono- and trimethyl (R-K) histone modification normalized intensities detected over the bulk histone background level as in Supplementary Table 2. g, Proliferation curve in P1, P2 and P14 compared to Control (ctrl). The center value at each time point represents the mean of n= 2 independent experiments. h, FACS cell cycle analysis of Control (ctrl) andP1/P2/P14 HDFs, Propidium iodide was used to define cell cycle phases. Bar plots represent the mean of n= 2 independent experiments with overlaid data points. i, RT–qPCR analysis of senescence markers P16-INK4A and P21-WAF displayed as dot plots. Expression levels were normalized to RPLP0 and expressed relative to Control (ctrl). Each data point represents an independent experiment (n) with the center line representing the mean ± s.e.m. when applicable. P values were determined by ordinary one-way ANOVA followed by Bonferroni multiple-test correction. Further details and statistical test values are provided in Supplementary Table 5. j, Representative DIC images of β-galactosidase activity assays performed in Control (ctrl) and P1/P2/P14 HDFs. The experiment was repeated three times with similar results. k, Representative FACS analysis of MKI67 (x axis) and H4K16ac (y axis) in Control (ctrl) and P1/P2/P14 HDFs. Quadrants show the percentage of cells with relative abundance of cell populations. The experiment was repeated twice with similar results.

    3. Supplementary Figure 3 Validation of transcriptional responses in MSL3 patients.

      a, MA plot comparing the mean of the normalized counts versus the log2[fold-change] obtained from RNA-Seq of patients versus Control (ctrl) HDFs (n= 2 passages of Control were compared with n= 2 passages of P1, P2 and P14 each). DE genes (FDR cutoff of 0.05) are marked in red. b, H4K16ac ChIP–qPCR analysis of H3F3B and respective expression levels from RNA-Seq (normalized read counts) displayed as dot plots. H4K16ac ChIP–qPCR enrichment values were calculated relative to input and expressed as a fold change enrichment over the negative control, KLK3. Each data point represents an independent experiment (n) with the center line representing the mean ± s.e.m. where applicable. c, RT–qPCR expression analysis in HDFs displayed as dot plots. Expression levels were normalized to RPLP0 and expressed relative to Control (ctrl). Each data point represents an independent experiment (n) with the center line representing the  mean ± s.e.m. where applicable. P values were determined by ordinary one-way ANOVA followed by Bonferroni multiple-test correction. Further details and statistical test values are provided in Supplementary Table 5. d, Immunohistochemistry for the serotonin receptor HTR7 and netrin receptor UNC5B on Control (ctrl) and patient-derived FFPE skin sections. Similar staining results were obtained in n= 2 sections per slide. e, RT–qPCR expression analysis in male and female HDFs upon MSL3 knockdown (KD) displayed as bar plots representing the mean  ± s.e.m. Expression levels were normalized to RPLP0 and expressed relative to scrambled siRNA (scramble). The same data points for ZNF185 and SPON2 are also shown in Fig. 3 and are illustrated again for comparative purposes. Each overlaid data point represents the number (n) of independent experiments.

    4. Supplementary Figure 4 Response to HDACi in MSL3 patient cells.

      a, Heat map representing histone modification changes upon HDACi treatments (dataset from Nat. Biotechnol. 10.1038/nbt.3130, 2015). b, Cropped immunoblot for H4K16ac, pan-acetylated H3 and H4, H3K27me3, H3K4me1 and H3 for nuclear extracts of Control HDFs treated with HDACi. The same extracts were separated on a Coomassie-stained gel serving as loading control. The experiment was repeated twice with similar results. c, RT–qPCR of acetylation-sensitive targets in control HDFs. The bar plot represents the mean of n= 2 independent experiments with overlaid individual data points. d, Cropped immunoblot of P1/P2/P14 HDF nuclear extracts upon LBH-589 treatment. The experiment was repeated twice with similar results. e, RT–qPCR of P1/P2/P14 HDFs treated with four different HDACi: SAHA (vorinostat, HDAC class I and II inhibitor), LBH-589 (panobinostat, pan-HDACi) and MGCD0103 (mocetinostat, HDACi class I and IV). Expression levels are normalized to RPLP0, calculated relative to Control (ctrl without treatment) and shown as dot plots. Each data point represents (n) independent experiments with the center line representing the mean ± s.e.m. where applicable. f, Scheme representing the number of DE genes upon LBH-589 treatment. g, Heat map representing z-scores on the MSL3 patients DE downregulated (left; n= 196) and DE upregulated (right; n= 323) genes upon LBH-589 treatment obtained by RNA-Seq (P1/P2/P14, 2 passages). h, Dot plots of normalized RNA-Seq read counts for DNA damage and cell cycle marker genes before and after treatment with LBH-589. The center line represents the mean of n= 2 independent experiments. i, Representative DIC images upon LBH-589 treatment of Control (ctrl) and P1/P2/P14 HDFs at 0, 24 and 48 h after creating a gap area.

    5. Supplementary Figure 5 Uncropped western blots, agarose and Coomassie gel pictures, and gating strategy example.

      To illustrate molecular weight markers, epi-white and chemiluminescence pictures were merged in the display (respective blots are marked with an asterisk). Actual figure panels represent only chemiluminescence and not the merged pictures. Cropped regions are framed. Representative gating strategy for excluding debris (top) and doublets (bottom) in flow cytometry analysis of 1 × 104 events. Singlets are defined on the forward scatter (height) versus forward scatter (area) dot plot.

    Supplementary information

    1. Supplementary Text and Figures

      Supplementary Figures 1–5 and Supplementary Note

    2. Reporting Summary

    3. Supplementary Table 1

      Clinical description of the 16 patients reported in this study

    4. Supplementary Table 2

      Normalized LC-MS limma analysis results in patients versus controls (n = 3 controls, n = 6 patients)

    5. Supplementary Table 3

      DE gene lists of control versus MSL3 patient HDFs, KEGG (IPA) pathway analysis for disease and Molecular Function of DE genes, LBH-589 treatment effect on DE genes, and OMIM tables for down, up and rescued transcripts

    6. Supplementary Table 4

      DE gene lists upon LBH-589 treatment and KEGG (IPA) pathways scores for Disease and Function

    7. Supplementary Table 5

      Primer list and details of statistical analyses

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    DOI

    https://doi.org/10.1038/s41588-018-0220-y