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Loss of δ-catenin function in severe autism

Nature volume 520, pages 51–56 (2015)Cite this article

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Abstract

Autism is a multifactorial neurodevelopmental disorder affecting more males than females; consequently, under a multifactorial genetic hypothesis, females are affected only when they cross a higher biological threshold. We hypothesize that deleterious variants at conserved residues are enriched in severely affected patients arising from female-enriched multiplex families with severe disease, enhancing the detection of key autism genes in modest numbers of cases. Here we show the use of this strategy by identifying missense and dosage sequence variants in the gene encoding the adhesive junction-associated δ-catenin protein (CTNND2) in female-enriched multiplex families and demonstrating their loss-of-function effect by functional analyses in zebrafish embryos and cultured hippocampal neurons from wild-type and Ctnnd2 null mouse embryos. Finally, through gene expression and network analyses, we highlight a critical role for CTNND2 in neuronal development and an intimate connection to chromatin biology. Our data contribute to the understanding of the genetic architecture of autism and suggest that genetic analyses of phenotypic extremes, such as female-enriched multiplex families, are of innate value in multifactorial disorders.

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Figure 1: Genetic features of a sex-dependent multifactorial model.
Figure 2: Missense variants in human δ-catenin and their effect on protein function in vivo.
Figure 3: CNV in human CTNND2.
Figure 4: δ-Catenin is critical for maintaining functional neuronal networks.
Figure 5: Gene expression correlation between CTNND2 and known autism genes.

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References

  1. Kogan, M. D. et al. Prevalence of parent-reported diagnosis of autism spectrum disorder among children in the US, 2007. Pediatrics 124, 1395–1403 (2009)

    PubMed  Google Scholar 

  2. Jorde, L. B. et al. Complex segregation analysis of autism. Am. J. Hum. Genet. 49, 932–938 (1991)

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Abrahams, B. S. & Geschwind, D. H. Advances in autism genetics: on the threshold of a new neurobiology. Nature Rev. Genet. 9, 341–355 (2008)

    CAS  PubMed  Google Scholar 

  4. Ronemus, M., Iossifov, I., Levy, D. & Wigler, M. The role of de novo mutations in the genetics of autism spectrum disorders. Nature Rev. Genet. 15, 133–141 (2014)

    CAS  PubMed  Google Scholar 

  5. Carter, C. O. Genetics of common disorders. Br. Med. Bull. 25, 52–57 (1969)

    CAS  PubMed  Google Scholar 

  6. Chakravarti, A. 2013 William Allan Award: my multifactorial journey. Am. J. Hum. Genet. 94, 326–333 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Emison, E. S. et al. Differential contributions of rare and common, coding and noncoding Ret mutations to multifactorial Hirschsprung disease liability. Am. J. Hum. Genet. 87, 60–74 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Arikkath, J. et al. δ-Catenin regulates spine and synapse morphogenesis and function in hippocampal neurons during development. J. Neurosci. 29, 5435–5442 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Bareiss, S., Kim, K. & Lu, Q. δ-Catenin/NPRAP: a new member of the glycogen synthase kinase-3β signaling complex that promotes β-catenin turnover in neurons. J. Neurosci. Res. 88, 2350–2363 (2010)

    CAS  PubMed  Google Scholar 

  10. Weiss, L. A., Arking, D. E., Daly, M. J. & Chakravarti, A. A genome-wide linkage and association scan reveals novel loci for autism. Nature 461, 802–808 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Talkowski, M. E. et al. Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell 149, 525–537 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Jho, E. H. et al. Wnt/β-catenin/Tcf signaling induces the transcription of Axin2, a negative regulator of the signaling pathway. Mol. Cell. Biol. 22, 1172–1183 (2002)

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Itoh, K. & Sokol, S. Y. Graded amounts of Xenopus dishevelled specify discrete anteroposterior cell fates in prospective ectoderm. Mech. Dev. 61, 113–125 (1997)

    CAS  PubMed  Google Scholar 

  14. Nojima, H. et al. Genetic evidence for involvement of maternally derived Wnt canonical signaling in dorsal determination in zebrafish. Mech. Dev. 121, 371–386 (2004)

    CAS  PubMed  Google Scholar 

  15. Lu, Q. et al. δ-Catenin, an adhesive junction-associated protein which promotes cell scattering. J. Cell Biol. 144, 519–532 (1999)

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Penzes, P., Cahill, M. E., Jones, K. A., VanLeeuwen, J. E. & Woolfrey, K. M. Dendritic spine pathology in neuropsychiatric disorders. Nature Neurosci. 14, 285–293 (2011)

    CAS  PubMed  Google Scholar 

  17. Kim, H. et al. δ-Catenin-induced dendritic morphogenesis. An essential role of p190RhoGEF interaction through Akt1-mediated phosphorylation. J. Biol. Chem. 283, 977–987 (2008)

    CAS  PubMed  Google Scholar 

  18. Israely, I. et al. Deletion of the neuron-specific protein δ-catenin leads to severe cognitive and synaptic dysfunction. Curr. Biol. 14, 1657–1663 (2004)

    CAS  PubMed  Google Scholar 

  19. Matter, C., Pribadi, M., Liu, X. & Trachtenberg, J. T. δ-Catenin is required for the maintenance of neural structure and function in mature cortex in vivo. Neuron 64, 320–327 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Abu-Elneel, K. et al. A δ-catenin signaling pathway leading to dendritic protrusions. J. Biol. Chem. 283, 32781–32791 (2008)

    CAS  PubMed  Google Scholar 

  21. Wang, M., Dong, Q., Zhang, D. & Wang, Y. Expression of δ-catenin is associated with progression of human astrocytoma. BMC Cancer 11, 514 (2011)

    CAS  PubMed  Google Scholar 

  22. Rodova, M., Kelly, K. F., VanSaun, M., Daniel, J. M. & Werle, M. J. Regulation of the rapsyn promoter by kaiso and δ-catenin. Mol. Cell. Biol. 24, 7188–7196 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Kim, S. W. et al. Non-canonical Wnt signals are modulated by the Kaiso transcriptional repressor and p120-catenin. Nature Cell Biol. 6, 1212–1220 (2004)

    CAS  PubMed  Google Scholar 

  24. Koutras, C., Lessard, C. B. & Levesque, G. A nuclear function for the presenilin 1 neuronal partner NPRAP/δ-catenin. J. Alzheimer’s Dis. 27, 307–316 (2011)

    CAS  Google Scholar 

  25. Zhang, J. et al. Isoform- and dose-sensitive feedback interactions between paired box 6 gene and δ-catenin in cell differentiation and death. Exp. Cell Res. 316, 1070–1081 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Umeda, T. et al. Evaluation of Pax6 mutant rat as a model for autism. PLoS ONE 5, e15500 (2010)

    ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  27. Davis, L. K. et al. Pax6 3′ deletion results in aniridia, autism and mental retardation. Hum. Genet. 123, 371–378 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Duparc, R. H., Boutemmine, D., Champagne, M. P., Tetreault, N. & Bernier, G. Pax6 is required for δ-catenin/neurojugin expression during retinal, cerebellar and cortical development in mice. Dev. Biol. 300, 647–655 (2006)

    CAS  PubMed  Google Scholar 

  29. Pinto, D. et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466, 368–372 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. Medina, M., Marinescu, R. C., Overhauser, J. & Kosik, K. S. Hemizygosity of δ-catenin (CTNND2) is associated with severe mental retardation in cri-du-chat syndrome. Genomics 63, 157–164 (2000)

    CAS  PubMed  Google Scholar 

  31. Vrijenhoek, T. et al. Recurrent CNVs disrupt three candidate genes in schizophrenia patients. Am. J. Hum. Genet. 83, 504–510 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhou, J. et al. Presenilin 1 interaction in the brain with a novel member of the armadillo family. Neuroreport 8, 2085–2090 (1997)

    CAS  PubMed  Google Scholar 

  33. Fujita, T. et al. δ-catenin/NPRAP (neural plakophilin-related armadillo repeat protein) interacts with and activates sphingosine kinase 1. Biochem. J. 382, 717–723 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Martinez, M. C., Ochiishi, T., Majewski, M. & Kosik, K. S. Dual regulation of neuronal morphogenesis by a δ-catenin-cortactin complex and Rho. J. Cell Biol. 162, 99–111 (2003)

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Silverman, J. B. et al. Synaptic anchorage of AMPA receptors by cadherins through neural plakophilin-related arm protein AMPA receptor-binding protein complexes. J. Neurosci. 27, 8505–8516 (2007)

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Laura, R. P. et al. The Erbin PDZ domain binds with high affinity and specificity to the carboxyl termini of δ-catenin and ARVCF. J. Biol. Chem. 277, 12906–12914 (2002)

    CAS  PubMed  Google Scholar 

  37. Kim, K. et al. Dendrite-like process formation and cytoskeletal remodeling regulated by δ-catenin expression. Exp. Cell Res. 275, 171–184 (2002)

    CAS  PubMed  Google Scholar 

  38. Daniel, J. M. & Reynolds, A. B. The catenin p120(ctn) interacts with Kaiso, a novel BTB/POZ domain zinc finger transcription factor. Mol. Cell. Biol. 19, 3614–3623 (1999)

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Yoon, H. G., Chan, D. W., Reynolds, A. B., Qin, J. & Wong, J. N-CoR mediates DNA methylation-dependent repression through a methyl CpG binding protein Kaiso. Mol. Cell 12, 723–734 (2003)

    CAS  PubMed  Google Scholar 

  40. Brigidi, G. S. et al. Palmitoylation of δ-catenin by DHHC5 mediates activity-induced synapse plasticity. Nature Neurosci. 17, 522–532 (2014)

    CAS  PubMed  Google Scholar 

  41. De Rubeis, S. et al. CYFIP1 coordinates mRNA translation and cytoskeleton remodeling to ensure proper dendritic spine formation. Neuron 79, 1169–1182 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Matsuoka, R. L. et al. Class 5 transmembrane semaphorins control selective mammalian retinal lamination and function. Neuron 71, 460–473 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. He, W. Z. et al. Analysis of de novo copy number variations in a family affected with autism spectrum disorders using high-resolution array-based comparative genomic hybridization. Chin. J. Med. Genet. 29, 266–269 (2012)

    CAS  Google Scholar 

  44. Harvard, C. et al. A variant Cri du Chat phenotype and autism spectrum disorder in a subject with de novo cryptic microdeletions involving 5p15.2 and 3p24.3-25 detected using whole genomic array CGH. Clin. Genet. 67, 341–351 (2005)

    CAS  PubMed  Google Scholar 

  45. Marshall, C. R. et al. Structural variation of chromosomes in autism spectrum disorder. Am. J. Hum. Genet. 82, 477–488 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Qiao, Y. et al. Phenomic determinants of genomic variation in autism spectrum disorders. J. Med. Genet. 46, 680–688 (2009)

    CAS  PubMed  Google Scholar 

  47. Sanders, S. J. et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Gai, X. et al. Rare structural variation of synapse and neurotransmission genes in autism. Mol. Psychiatry 17, 402–411 (2012)

    CAS  PubMed  Google Scholar 

  49. Geschwind, D. H. et al. The autism genetic resource exchange: a resource for the study of autism and related neuropsychiatric conditions. Am. J. Hum. Genet. 69, 463–466 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Homer, N., Merriman, B. & Nelson, S. F. BFAST: an alignment tool for large scale genome resequencing. PLoS ONE 4, e7767 (2009)

    ADS  PubMed  PubMed Central  Google Scholar 

  51. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010)

    PubMed  PubMed Central  Google Scholar 

  52. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010)

    PubMed  PubMed Central  Google Scholar 

  54. Brewer, G. J. Serum-free B27/neurobasal medium supports differentiated growth of neurons from the striatum, substantia nigra, septum, cerebral cortex, cerebellum, and dentate gyrus. J. Neurosci. Res. 42, 674–683 (1995)

    CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge the participation of all of the families in the AGRE, NIMH and SSC studies that have been a model of public participatory research. The AGRE is a program of Autism Speaks and is supported, in part, by grant 1U24MH081810 from the National Institute of Mental Health. The SSC used here was developed by the following principal investigators: A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, E. Wijsman. We thank the Allen Brain Atlas for use of their publicly available developing human brain expression data. Finally, we thank V. Kustanovich (AGRE) for helping with access to Autism Diagnostic Observation Schedule severity score data, D. Arking for sharing DNA from the SSC for Taqman genotyping, S. Maragh for zebrafish complementary DNA (cDNA) libraries and eef1a1l1 primers, A. Kapoor for discussions, Q. Jiang for the translation of ref. 43, and J. A. Rosenfeld, L. G. Shaffer, Y. Shen and B.-L. Wu for sharing CNV data sets. Sequencing services were provided by the Johns Hopkins University Next Generation Sequencing Center, Sidney Kimmel Comprehensive Cancer Center, Illumina Sequencing Services and the Johns Hopkins University Genetic Resources Core Facility. E.C.O. is a National Alliance for Research on Schizophrenia and Depression young investigator. N.K. is a Distinguished George W. Brumley Professor. This work was funded by grants from the Simons Foundation to A.C. and to N.K., NIMH grant MH095867 to M.E.T., NIMH grants 5R25MH071584-07 and MH19961-14 to D.M.D.L. (Malison), National Institutes of Health grant RO1MH074090 to C.L.M., NIMH grant R01MH081754 to A.C. and an Autism Speaks Dennis Weatherstone pre-doctoral fellowship (number 7863) to T.T.

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Authors and Affiliations

  1. Center for Complex Disease Genomics, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Tychele N. Turner, Maria X. Sosa, Dallas R. Auer, Vasyl Pihur & Aravinda Chakravarti

  2. Predoctoral Training Program in Human Genetics and Molecular Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Tychele N. Turner

  3. National Institute of Mental Health (NIMH) Autism Centers of Excellence (ACE) Genetics Consortium at the University of California, Los Angeles, Los Angeles, 90095, California, USA

    Tychele N. Turner, Maria X. Sosa, Dallas R. Auer, Stephan J. Sanders, Daniel Moreno-De-Luca, Vasyl Pihur, Christa Lese Martin, Matthew W. State & Aravinda Chakravarti

  4. Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, 21205, Maryland, USA

    Kamal Sharma & Richard Huganir

  5. Center for Human Disease Modeling, Duke University, Durham, 27710, North Carolina, USA

    Edwin C. Oh, Yangfan P. Liu & Nicholas Katsanis

  6. Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, Massachusetts, USA

    Ryan L. Collins, Harrison Brand & Michael E. Talkowski

  7. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, Massachusetts, USA

    Harrison Brand & Michael E. Talkowski

  8. Department of Psychiatry, University of California, San Francisco, San Francisco, California 94158, USA,

    Stephan J. Sanders & Matthew W. State

  9. Department of Psychiatry, Yale University, New Haven, 06511, Connecticut, USA

    Daniel Moreno-De-Luca

  10. Leidos Biomedical Research, Inc., Frederick, 21702, Maryland, USA

    Teri Plona, Kristen Pike & Daniel R. Soppet

  11. National Human Genome Research Institute, Bethesda, 20892, Maryland, USA

    Michael W. Smith

  12. Baylor College of Medicine, Houston, 77030, Texas, USA

    Sau Wai Cheung

  13. Autism & Developmental Medicine Institute, Geisinger Health System, Lewisburg, 17837, Pennsylvania, USA

    Christa Lese Martin

  14. University of Illinois at Chicago, Chicago, 60608, Illinois, USA

    Edwin Cook

Authors
  1. Tychele N. Turner
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Contributions

Designed the study and wrote the manuscript (T.T., A.C.); edited the manuscript (all authors); examined phenotype data for the female autism patients (T.T., E.C.); MECP2/CTNND2 sequencing and TaqMan genotyping (T.T., M.X.S., T.P., K.P., D.S., M.W.S.); autism exome sequencing (T.T.); Simons exome sequencing analysis (S.S., M.S.); CNV analysis (S.W.C., C.L.M., D.M.D., S.S., R.C.C., H.B., M.E.T, M.S., T.T.); CTNND2 molecular biology (T.T., M.X.S.); zebrafish gastrulation and protein-protein interaction studies (Y.P.L., E.O., N.K.); primary hippocampal neuron experiments and expression analysis (K.S., T.T., D.A.); bioinformatics analyses (T.T.,V.P.).

Corresponding author

Correspondence to Aravinda Chakravarti.

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The authors declare no competing financial interests.

Additional information

All sequence data have been deposited in the National Database for Autism Research in NDAR Study 367 and are available at http://dx.doi.org/10.15154/1171641.

Extended data figures and tables

Extended Data Figure 1 Exome sequencing workflow and quality control.

a, Workflow of exome analysis in this study. b, Variant recalibration metrics exhibiting why a 99% cutoff was used for truth sensitivity. c, Variant recalibration specificity versus sensitivity.

Extended Data Figure 2 Sanger sequencing chromatograms.

a, G34S variant in this study; b, R713C variants in this study; c, G34S in NA19020.

Extended Data Figure 3 Principal component analysis of 6,211 shared autosomal single nucleotide polymorphisms in CEU, YRI, CHB/JPT, autism and NA19020 samples.

Extended Data Figure 4 Read and amplicon metrics in CTNND2 sequencing.

a, Histogram of reads per sample. b, Average quality scores across the read across all samples with each sample represented by a separate line. c, Boxplot of coverage per amplicon.

Extended Data Figure 5 Validation of deletions.

a, In AU066818; b, in AU075604; c, in AU1178301 and AU1178202; d, in AU051503.

Extended Data Figure 6 CTNND2 CNVs from patients with neurodevelopment disorders studied using methods published in ref. 11.

Extended Data Figure 7 Wnt defects in ctnnd2b zebrafish morphant embryos.

a, Relative axin2 mRNA level in the ten-somite stage in control versus morphant embryos. b, Wholemount RNA in situ hybridization of chordin. Dorsal view in upper panels with the anterior aspect at the apex. The dorsal axis is marked with a red dashed line, and regions with high expression are marked (arrows) in control embryos. Lateral view in lower panels, length (L) and width (W) of chordin expression domains were measured. c, Quantification of chordin expression domains (length:width ratio) in injected embryos. d, Immunoblot showing a macromolecular interaction between Flag-tagged CTNNB1 and GFP-tagged CTNND2 with the corresponding variants. Two-sided t-tests were conducted: P < 0.05, P < 0.01 and P < 0.001, respectively. Sample size (n) is marked for each condition.

Extended Data Figure 8 Functional in vitro modelling of δ-catenin missense variants in embryonic rat hippocampal neurons.

a, Representation of spines along the dendrite in control and overexpression GFP vectors (empty or fused with wild-type or variant allele containing CTNND2 (G34S, R713C, A482T (control)). Cell counts for each construct were as follows: GFP (N = 32), GFP–WT (N = 27), GFP–G34S (N = 29), GFP–R713C (N = 26) and GFP–A482T (N = 29). b, Quantification of dendritic spine numbers and statistical comparisons by Tukey’s honestly significant test following ANOVA. Both P < 0.05 than GFP and significantly different from wild type, respectively.

Extended Data Figure 9 Gene expression of CTNND2 and co-expression with known autism genes.

a, Expression of CTNND2 in various human fetal and adult tissues, shown as fold difference relative to adult brain. b, RNA-Seq-based CTNND2 gene expression in the developing human brain (http://www.brainspan.org); shown are log2(RPKM expression) values at time-points from 8 weeks after conception to 40 years of age, with the lowest to highest expression coloured from navy blue to red. Controls for high expression, low to no expression and known autism genes are GAPDH, CFTR and MECP2, respectively.

Extended Data Figure 10 Analysis of overexpression of transiently transfected neurons.

Representation of average intensity of five individual regions of interest from a selected dendritic region. Quantitative comparison does not reveal a significant difference in expression levels of different variants of CTNND2.

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Turner, T., Sharma, K., Oh, E. et al. Loss of δ-catenin function in severe autism. Nature 520, 51–56 (2015). https://doi.org/10.1038/nature14186

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