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The contribution of de novo coding mutations to meningomyelocele

Nature volume 641, pages 419–426 (2025)Cite this article

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Abstract

Meningomyelocele (also known as spina bifida) is considered to be a genetically complex disease resulting from a failure of the neural tube to close. Individuals with meningomyelocele display neuromotor disability and frequent hydrocephalus, requiring ventricular shunting. A few genes have been proposed to contribute to disease susceptibility, but beyond that it remains unexplained1. We postulated that de novo mutations under purifying selection contribute to the risk of developing meningomyelocele2. Here we recruited a cohort of 851 meningomyelocele trios who required shunting at birth and 732 control trios, and found that de novo likely gene disruption or damaging missense mutations occurred in approximately 22.3% of subjects, with 28% of such variants estimated to contribute to disease risk. The 187 genes with damaging de novo mutations collectively define networks including actin cytoskeleton and microtubule-based processes, Netrin-1 signalling and chromatin-modifying enzymes. Gene validation demonstrated partial or complete loss of function, impaired signalling and defective closure of the neural tube in Xenopus embryos. Our results indicate that de novo mutations make key contributions to meningomyelocele risk, and highlight critical pathways required for neural tube closure in human embryogenesis.

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Fig. 1: Enrichment of damaging DNMs in MM versus control.
Fig. 2: Functional convergence of genes implicated by damaging DNMs.
Fig. 3: Functional modules that contribute to MM risk.
Fig. 4: Functional validation of damaging DNMs related to actin polymerization.

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Data availability

The WES and WGS sequencing data used in this study are available in publicly accessible databases for the 1,146 subjects in the database of Genotypes and Phenotypes (phs003746.v1.p1 and phs002591.v1.p1). Pedigree information with database of Genotypes and Phenotypes identifiers is available in the Supplementary Data. Sequencing data for the remaining subjects cannot be deposited in public repositories because they were enrolled in the study with consent forms that did not conform to current data-sharing requirements. Summary data for these subjects are available on request from the corresponding author (J.J.G.) on reasonable request. Source data are provided with this paper.

Code availability

The computational codes used in this study are available at GitHub (https://github.com/Gleeson-Lab/Publications/tree/main/MM_DNM).

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Acknowledgements

We thank the individuals with meningomyelocele and their families who participated in this study; K. James, R. George, B. Copeland, V. Stanley, C. Shen and J. Venneri from the Spina Bifida Sequencing Consortium for recruitment and data technical support; staff at the UCSD Laboratory for Pediatric Brain Disease for clinical and technical support; B. Rosenthal and K. Fisch for statistical modelling; staff at the Broad Institute, the Yale Genetic Center, the Regeneron Genetics Center, the UCSD Institute for Genomic Medicine, the UC Irvine Sequencing Center and the Rady Children Institute for Genomics Medicine for sequencing support; B. Craddock for functional analysis of TNK2; and the Spina Bifida Association for recruitment. This work was supported by the Center for Inherited Disease Research (grant HHSN268201700006I), the Yale Center for Genomic Analysis, the Broad Institute, the UC Irvine Genomics Core, the UCSD Institute for Genomic Medicine, the UCSD Imaging Core (grants X01HD100698, X01HD110998, HD114132, P01HD104436 and U54OD030187); the Howard Hughes Medical Institute, the Dickinson Foundation and Rady’s Children Institute for Genomic Medicine to J.G.G.; the National Research Foundation of Korea, funded by the Ministry of Science and ICT (MSIT) (RS-2023-00278314) and the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare (RS-2024-00438443, RS-2024-00405260), to Y.-J.J.H. and S.K.; the Science and Technology Development Fund (STDF) of Egypt (33650) with ethical approval 20105 to M.M.N., A.M.S.S., MY.I., and a VA Merit Award (I01 BX006248) to W.T.M.

Author information

Authors and Affiliations

  1. Rady Children’s Institute for Genomic Medicine, San Diego, CA, USA

    Yoo-Jin Jiny Ha, Ashna Nisal, Isaac Tang, Ishani Jhamb, Cassidy Wallace, Robyn Howarth, Sarah Schroeder, Keng Ioi Vong, Naomi Meave, Fiza Jiwani, Chelsea Barrows, Sangmoon Lee, Nan Jiang, Arzoo Patel, Krisha Bagga, Niyati Banka, Liana Friedman, Hui Su Jeong, Stephen F. Kingsmore & Joseph G. Gleeson

  2. Department of Neurosciences and Pediatrics, University of California, San Diego, San Diego, CA, USA

    Yoo-Jin Jiny Ha, Ashna Nisal, Isaac Tang, Ishani Jhamb, Cassidy Wallace, Robyn Howarth, Sarah Schroeder, Keng Ioi Vong, Naomi Meave, Fiza Jiwani, Chelsea Barrows, Sangmoon Lee, Nan Jiang, Arzoo Patel, Krisha Bagga, Niyati Banka, Liana Friedman, Hui Su Jeong & Joseph G. Gleeson

  3. Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea

    Yoo-Jin Jiny Ha & Sangwoo Kim

  4. Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Republic of Korea

    Yoo-Jin Jiny Ha, Seyoung Yu, Heon Yung Gee & Sangwoo Kim

  5. Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, USA

    Chanjae Lee & John B. Wallingford

  6. Department of Neuroscience, Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA

    Francisco A. Blanco & Kimberley F. Tolias

  7. Department of Pharmacology, Yonsei University College of Medicine, Seoul, Republic of Korea

    Seyoung Yu & Heon Yung Gee

  8. Department of Systems Biology, College of Life Science and Biotechnology, Yonsei University, Seoul, Republic of Korea

    Soeun Rhee & Donghyuk Shin

  9. Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea

    Hui Su Jeong & Ji Eun Lee

  10. Department of Cell Biology and Physiology, Washington University in St Louis, St Louis, MO, USA

    Isaac Plutzer & Michael B. Major

  11. INSERM UMR-S 1193, UFR de Pharmacie, University Paris-Saclay, Orsay, France

    Béatrice Benoit & Christian Poüs

  12. Biochimie-Hormonologie, Assistance Publique - Hôpitaux de Paris Université Paris-Saclay, Clamart, France

    Christian Poüs

  13. The Jackson Laboratory, Bar Harbor, ME, USA

    Caleb Heffner & Stephen A. Murray

  14. Department of Neurosciences, Research Center of CHU Sainte Justine, University of Montreal, Montreal, Quebec, Canada

    Zoha Kibar

  15. Neurosurgery Division, Department of Surgery, Jos University Teaching Hospital, Jos, Nigeria

    Gyang Markus Bot

  16. Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston and Children’s Memorial Hermann Hospital, Houston, TX, USA

    Hope Northrup, Kit Sing Au & Kit Sing Au

  17. Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA

    Madison Strain, Allison E. Ashley-Koch & Allison E. Ashley Koch

  18. Center for Precision Environmental Health, Departments of Molecular and Human Genetics, Molecular and Cellular Biology and Medicine, Baylor College of Medicine, Houston, TX, USA

    Richard H. Finnell

  19. Rady Children’s Hospital, San Diego, CA, USA

    Joan T. Le, Hal S. Meltzer, Hal S. Meltzer, Joan T. Le & David D. Gonda

  20. Department of Surgery and Anatomy, Ribeirão Preto Medical School, University of São Paulo, Ribeirao Preto, Brazil

    Camila Araujo, Helio R. Machado & Camila Araújo

  21. J. C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, SC, USA

    Roger E. Stevenson

  22. Catedrática de Ciencias Ómicas, Facultad de Medicina, Universidad Francisco Marroquín, Guatemala City, Guatemala

    Anna Yurrita

  23. National University of Medical Sciences, Rawalpindi, Pakistan

    Sara Mumtaz

  24. University of Concepcion, Concepcion, Chile

    Awais Ahmed

  25. Children’s Hospital, Pakistan Institute of Medical Sciences, Islamabad, Pakistan

    Mulazim Hussain Khara

  26. Department of Genetics, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico

    Osvaldo M. Mutchinick

  27. Clínica Santa Sofia, Caracas, Venezuela

    José Ramón Medina-Bereciartu

  28. Division of Nephrology, Boston Children’s Hospital, Boston, MA, USA

    Friedhelm Hildebrandt

  29. Department of Pediatrics, MediClubGeorgia Medical Center, Tbilisi, Georgia

    Gia Melikishvili

  30. Division of Pediatric General, Thoracic and Fetal Surgery, Department of Surgery, University of Missouri School of Medicine, Columbia, MO, USA

    Ahmed I. Marwan

  31. Genomics and Clinical Genetics Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy

    Valeria Capra

  32. Department of Pediatrics, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt

    Mahmoud M. Noureldeen & Aida M. S. Salem

  33. Clinical Genetics Department, Human Genetics and Genome Research Institute, National Research Centre, Cairo, Egypt

    Mahmoud Y. Issa & Maha S. Zaki

  34. Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA

    Libin Xu

  35. Regeneron Genetics Center, Tarrytown, NY, USA

    Anna Alkelai & Alan R. Shuldiner

  36. Department of Physiology and Biophysics, Stony Brook University, Stony Brook, NY, USA

    W. Todd Miller

  37. VA Medical Center, Northport, NY, USA

    W. Todd Miller

  38. POSTECH Biotechnology Center, Pohang University of Science and Technology, Pohang, Republic of Korea

    Sangwoo Kim

  39. Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

    Philip J. Lupo

  40. Mekelle University, Mekelle, Ethiopia

    Tony Magana

  41. Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Caroline M. Kolvenbach & Shirlee Shril

  42. NHO Shizuoka Institute of Epilepsy and Neurological Disorders, Shizuoka, Japan

    Yukitoshi Takahashi

  43. Department of Pediatrics, University Hospital Cologne, University of Cologne, Cologne, Germany

    Hormos Salimi-Dafsari

  44. Department of Pediatric Neurosurgery, Stanford University, Palo Alto, CA, USA

    H. Westley Phillips

  45. Children’s Hospital of Orange County, Orange, CA, USA

    Brian Hanak

  46. Department of Child Health and Diseases, Department of Child Neurology, Umuttepe Campus, Kocaeli University, Kocaeli, Turkey

    Bülent Kara & Ayfer Sakarya Güneş

  47. Division of Woman and Child Health, Human Development Program, The Aga Khan University, Karachi, Pakistan

    Salman Kirmani

  48. Department of Molecular and Medical Genetics, Tbilisi State Medical University, Tbilisi, Georgia

    Tinatin Tkemaladze

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Consortia

Spina Bifida Sequencing Consortium

  • Allison E. Ashley Koch
  • , Hal S. Meltzer
  • , Joan T. Le
  • , Kit Sing Au
  • , Hope Northrup
  • , Gyang Markus Bot
  • , Valeria Capra
  • , Richard H. Finnell
  • , Zoha Kibar
  • , Philip J. Lupo
  • , Helio R. Machado
  • , Camila Araújo
  • , Tony Magana
  • , Ahmed I. Marwan
  • , Gia Melikishvili
  • , Osvaldo M. Mutchinick
  • , Roger E. Stevenson
  • , Anna Yurrita
  • , Maha S. Zaki
  • , Sara Mumtaz
  • , José Ramón Medina-Bereciartu
  • , Caroline M. Kolvenbach
  • , Shirlee Shril
  • , Friedhelm Hildebrandt
  • , Mahmoud M. Noureldeen
  • , Aida M. S. Salem
  • , Yukitoshi Takahashi
  • , Hormos Salimi-Dafsari
  • , H. Westley Phillips
  • , Brian Hanak
  • , Bülent Kara
  • , Ayfer Sakarya Güneş
  • , David D. Gonda
  • , Salman Kirmani
  • , Tinatin Tkemaladze
  •  & Joseph G. Gleeson

Contributions

Y.-J.J.H., C.W., N.M., F.J., K.I.V., C.B., S.S., S.L., N.J., A.P., K.B., N.B. and L.F. recruited subjects and performed genetic analysis. I.J. and R.H. performed the MERFISH analysis. C.L. and J.B.W. generated Xenopus data. A.N., I.T., J.E.L., I.P., M.B.M., F.A.B., K.F.T., S.Y., H.S.J., B.B., W.T.M., C.H., S.A.M., H.Y.G., C.P. and L.X. performed functional analysis; Z.K., G.M.B., H.N., K.S.A., M.S., A.A.-K., R.H.F., J.L., H.M., C.A., H.R.M., R.E.S., A.Y., S.M., A. Ahmed, M.H.K., O.M.M., J.R.M.-B., F.H., G.M., A.I.M., V.C., M.M.N., A.M.S.S., M.Y.I. and M.S.Z. recruited families. A. Alkelai, A.R.S. and S.F.K. performed sequencing. D.S. and S.R. conducted computational prediction. Y.-J.J.H., S.K. and J.G.G. performed analysis, wrote drafts, and incorporated feedback from coauthors.

Corresponding authors

Correspondence to Sangwoo Kim or Joseph G. Gleeson.

Ethics declarations

Competing interests

A. Alkelai and A.R.S. are full-time employees of Regeneron Genetics Center. S.K. is a cofounder of AIMA, which seeks to develop techniques for early cancer diagnosis based on circulating tumour DNA. R.H.F. previously led TeratOmic Consulting, which is now defunct, and received travel funds for Reproductive and Developmental Medicine editorial board meetings.

Peer review

Peer review information

Nature thanks Erica Davis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Power calculation of estimating a cohort size for DNM detection.

Power calculation showing potential number of discovered genes compared with cohort size (350 trios), for two different v values (enrichment ratio of loss of function variants in case versus control) and two different k values (assumed number of risk genes). For instance, if there are 50 genes to discover (k = 50), a cohort of 400 trios will identify 16 genes if LOF variants are 2.5x more common in affected (v = 2.5). All calculations manage a conservative false discovery rate (FDR). Gray dash: FDR.

Extended Data Fig. 2 Spatial expression of DNM genes with MERFISH in E9.5 mouse embryos.

Spatial expression of the MM genes with damaging DNMs. a, Gene expression of marker genes for seven selected cell types (neuron, neural progenitor, pre-epithelial to mesenchymal transition neural crest progenitor (NC progenitor), neural crest, mesoderm, dorsal root ganglia, and blood), in two embryonic replicates. b, Six spatial expression pattern of damaging DNM genes with specific (left) and broad (right) expression. Full MERFISH image of 36 genes can be found in the GitHub (https://github.com/Gleeson-Lab/Publications/tree/main/MM_DNM).

Extended Data Fig. 3 Cell type expression of the DNM genes in MERFISH.

a, Expression at E9.5 of the 36 damaging DNMs in seven cell types: neuron, neural progenitor, pre-epithelial to mesenchymal transition neural crest progenitor (Pre-EMT-NCP), neural crest, mesoderm, dorsal root ganglia, and blood. Indeterminate refers to the cells that were not specified with the marker genes designed for the seven cell types. b, Expression of marker genes used for specifying the cell types in MERFISH. Marker genes are shown within the cell type category which they represent.

Extended Data Fig. 4 A human protein network constructed with damaging DNMs contributing to MM risk.

By using the 187 damaging MM DNM genes, a propagated network was generated with NetColoc69 with a background protein network PCNet70, incorporating 439 nodes and 2,447 edges. Big blue circle: damaging DNM genes, Small purple circle: propagated gene, green border: known mouse NTD genes. The network is visualized with Cytoscape with STRING database.

Extended Data Fig. 5 H1149P patient mutation impairs TIAM1 activity and PLCE1 patient mutation E623Q leads to diminished GTP-bound RhoA.

a, H1149P mutation is located within the Dbl homology (DH) domain responsible for GEF activity. TIAM1 contains an N-terminal pleckstrin homology (PH), coiled-coiled (CC), extension (Ex), RAS binding (RBD), PDZ, Dbl-homology (DH) and PH domains, with the patient mutation falling within the DH domain. b, Schematic of PLCE1 protein with domains annotated. Patient E623Q mutation is located in the Ras GEF domain. PLCE1 contains a Guanine nucleotide exchange factor for Ras-like small GTPases (RAS GEF), Pleckstrin Homology (PH), Phospholipase C catalytic domain X (PLCX), Phospholipase C catalytic domain Y (PLCY), Protein Kinase C conserved region 2 (C2), RAS association domain 1 (RA1), and RAS association domain 2 (RA2). c, Construct expression H1149P (n = 76) is equivalent to wildtype (n = 85) in Phalloidin quantification. d, Construct expression H1149P in constitutive active (C.A.) (n = 53). Src Rac1 Förster resonance energy transfer (FRET) is equivalent to wildtype. P value adjusted with Bonferroni. Kruskal-Wallis followed by a two-sided pairwise Wilcoxon test, P value adjusted with Bonferroni. Data shown with Hampel filter. Error bar: standard error of the mean. P values: ns: not significant. e, Active GTP-bound form of RhoA precipitated from HEK293 expressing Myc-tagged PLCE1 using a GST-rhotekin pulldown assay. Overexpression of WT PLCE1 resulted in a substantial decrease in relative RhoA activity compared with mock cells. Compared to WT, cells transfected with variant forms of PLCE1 exhibited marked differences in GTP-bound RhoA.

Extended Data Fig. 6 The P168L patient mutation impairs TNK2 activity and intronic mutation at the splice acceptor site before exon 47 leads to alternative splicing of DNAH5.

a, P168L mutation is located within the kinase domain. TNK2 contains sterile alpha motif (SAM), Src homology 3 (SH3), CDS42 and RAC-interactive binding (CRIB), Mig6 homology region (MHR), and ubiquitin-associated domain (UBA). b, Blots for the A156T kinase dead, wild-type, and the patient mutation P168L. Lysates were probed with pY284-Ack1 (top), Ack1-flag (middle), and gamma-tubulin (bottom). Repeated independently with similar results four times. Ack1 refers to TNK2. c, TNK2 P168L patient mutation impaired WASP phosphorylation from immunoprecipitation (IP) kinase assay, compared to WT and kinase dead A156T. d, Location of chr5:13807727 T > G patient mutation in DNAH5 gene. e, Primer design for detecting altered splicing in DNAH5 cDNA - pair (i) spanning exons 46–48, pair (ii) spanning exons 46–49 and control pair (iii) spanning exons 1–4. f, RT-PCR results using primers listed in e showing altered splicing for exon 46–48 and 46–49 in patient cDNA, but not in controls.

Extended Data Fig. 7 KDM1A R332C patient mutation and VWA8b patient mutation R230G significantly reduces protein expression levels.

a, Schematic of KDM1A protein domains - R332C patient mutation is located in the amino-oxidase domain of KDM1A protein. b, Protein levels of WT and R332C KDM1A detected by western blot from HEK293T cells transfected with pEGFP-C2-KDM1A WT or R332C plasmids; c, Quantification of GFP-KDM1A band intensities from b normalized with β-actin loading control (n = 3). Bar: median, Error bar: interquartile range. Two-tailed unpaired t test with Welch’s correction, P value * = 0.0316. d, Schematic of protein domains for human VWA8b consisting of NTPase, Walker A (WA), ATP binding, and Walker B (WB) domains and patient mutation R230G in the NTPase domain. e, Protein levels of HA-tagged mVwa8b empty vector (EV), WT and R230 overexpressed in HEK293T cells detected using anti-HA antibody; alpha tubulin used as loading control. f, Quantification of HA band intensity from panel b normalized to loading control (n = 3). Bar: mean, Error bar: standard deviation of mean (SEM). one-way ANOVA ****: P value < 0.0001.

Extended Data Fig. 8 Validation of Spen and Mink1 knockdown.

a, Schematics of SPEN and MINK1 protein with domains annotated and patient mutations. RRM, RNA Recognition motif. MINT, Mxs2-interacting protein. SPOC, Spen paralogue and orthologue SPOC. CNH, Citron homology domain. b, Dorsal views of Xenopus laevis embryos subjected to in situ hybridization for Pax3 to visualize the neural folds in Spen morphants. c, RT-PCR confirmed that Spen MO reduced the amount of normally spliced Spen transcript. d. Dorsal views of Xenopus l. embryos subjected to in situ hybridization for Pax3 in Mink1 morphants. e, Validation of Mink1 morpholinos by RT-PCR. c,e, Each experiment was performed independently at least twice with similar results. f, Dorsal views of embryos injected with Spen gRNAs only or gRNAs with Cas9, with the accompanying chromatogram showing Sanger sequencing at the CRISPR target site. Control embryos injected with gRNAs only (#1 and #2) developed normally and exhibited an intact sequence, while embryos injected with Spen gRNAs and Cas9 (#3-#6) displayed neural tube defects and mosaic mutations at the CRISPR target site. g, Dorsal views of embryos injected with Mink1 gRNAs only or Mink1 gRNAs with Cas9, with the accompanying chromatogram showing Sanger sequencing at the CRISPR target site. Mink1 crispants (#2-#4) exhibited neural tube defect phenotypes and mosaic mutations at the CRISPR target site in both the L and S alleles of Mink1.

Extended Data Fig. 9 Validation of Whamm knockdown.

a, Schematics of SPEN and MINK1 protein with domains annotated and patient mutations. JMY, Junction-mediating and WASP homolog-associated domain, JMY_N, N-terminal of JMY. WH2, WASP-homology 2 domain b, The neural tube closure defect phenotype induced by Whamm MO (10 ng) was rescued through the injection of Whamm mRNA (700 pg). Embryos injected only with mRNA showed no significant phenotype. c, Dorsal views of Xenopus embryos at Stage 19, quantified with Pax3 for in situ hybridization to visualize the neural folds. d, Quantification of the average distance between neural folds in Whamm MO with rescue Whamm mRNA. The rescue experiment was repeated independently with similar results, with multiple independent experiments; Control (n = 16), Whamm MO (n = 20), Whamm MO + mRNA (n = 19), mRNA (n = 14). Box plot indicates the median (center line), the interquartile range (bounds of the box), and the whiskers represent the minimum and maximum. P-values by one-way ANOVA, followed by Tukey’s multiple comparison test: **** < 0.0001, ns: not significant. e, RT-PCR confirmed that Whamm MO reduced the amount of normally spliced Whamm transcript. f, Schematic showing gRNA regions designed to target Whamm gene and primer sites for genotyping. g, Control embryos (#1-#5) developed normally, while crispants (#6-#10) displayed neural tube defects. h, Genotyping in the target area. PCR products from control embryos (#1-#5) are approximately 631 bp, while those from Whamm crispants (#6-#10, except #8) are around 331, indicating a deletion of approximately 300 bp. e,h, Each experiment was performed independently at least twice with similar results. i, Comparison of sequence between control (#4 in panel g is shown) and crispant (#8 in panel g is shown) at the Whamm CRISPR target site. Although the #8 embryo did not exhibit the 300 bp deletion, Sanger sequencing result shows it has mosaic mutations.

Extended Data Fig. 10 Validation of Nostrin knockdown and synergistic effect with Whamm.

a, Schematics of NOSTRIN protein with domains annotated and patient mutations. FCH, Fes/CIP4, and EFC/F-BAR homology domain. F-BAR, Fes/CIP4 homology – Bin-Amphiphysin-Rvs domain. HR1, REM-1 domain. SH3, Src homology 3 domain. b, RT-PCR confirmed that the splice-blocking MO for Nostrin S reduced the amount of normally spliced Nostrin transcript. Experiment was performed independently at least twice with similar results c, Dorsal views of embryos injected with Nostrin gRNAs only or Nostrin gRNAs with Cas9, with the accompanying chromatogram showing Sanger sequencing at the CRISPR target site. Control embryos injected with gRNAs only (#1) developed normally and exhibited intact sequences, while embryos injected with Nostrin gRNAs and Cas9 (#2-#4) displayed neural tube defects and mosaic mutations at the CRISPR target site. d, Neural folds visualized by in situ hybridization for Pax3 in Nostrin splice-blocking MO and/or Wham MO. e, Dorsal views of late neurula embryos injected with Nostrin translation-blocking MO and/or Whamm MO.

Extended Data Table 1 De novo SNV and Indel rates (×10−8) of WGS in coding and noncoding regions
Full size table
Extended Data Table 2 Damaging DNM genes with functional validation
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Notes, Supplementary Tables 1–4 and Supplementary Figs. 1–9.

Reporting Summary

Peer Review File

Supplementary Data

This file contains the following: MM pedigree information (individual and family identities of each sample described, along with data deposition information); damaging DNMs in MM (information on de novo mutations, including validation status); WGS SNVs and indels with high impact a (list of SNVs and indels detected from WGS with high impact); MERFISH marker genes (markers used for MERFISH, listed with annotated cell types); genes in the propagated network (a list of 439 genes in the MM propagated network); known mouse NTD genes (374 known mouse NTD genes used for the hypergeometric test); submodule enrichment (results of enrichment analysis with the five functional modules); gene pLI scores of the five submodules (pLI scores for genes in the five submodules, annotated as observed DNMs or propagated); and a list of primer sequences used in the study.

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Ha, YJ., Nisal, A., Tang, I. et al. The contribution of de novo coding mutations to meningomyelocele. Nature 641, 419–426 (2025). https://doi.org/10.1038/s41586-025-08676-x

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