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Germline Chd8 haploinsufficiency alters brain development in mouse


The chromatin remodeling gene CHD8 represents a central node in neurodevelopmental gene networks implicated in autism. We examined the impact of germline heterozygous frameshift Chd8 mutation on neurodevelopment in mice. Chd8+/del5 mice displayed normal social interactions with no repetitive behaviors but exhibited cognitive impairment correlated with increased regional brain volume, validating that phenotypes of Chd8+/del5 mice overlap pathology reported in humans with CHD8 mutations. We applied network analysis to characterize neurodevelopmental gene expression, revealing widespread transcriptional changes in Chd8+/del5 mice across pathways disrupted in neurodevelopmental disorders, including neurogenesis, synaptic processes and neuroimmune signaling. We identified a co-expression module with peak expression in early brain development featuring dysregulation of RNA processing, chromatin remodeling and cell-cycle genes enriched for promoter binding by Chd8, and we validated increased neuronal proliferation and developmental splicing perturbation in Chd8+/del5 mice. This integrative analysis offers an initial picture of the consequences of Chd8 haploinsufficiency for brain development.

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Figure 1: Chd8+/del5 mouse model.
Figure 2: Chd8+/del5 mice exhibit cognitive deficits but no ASD-relevant social or repetitive phenotypes.
Figure 3: Chd8 haploinsufficiency drives megalencephaly and cognitive impairment in mouse.
Figure 4: Differential gene expression in Chd8+/del5 neurodevelopment.
Figure 5: DE genes with correlated expression patterns across brain development reveal perturbations to early and later neurodevelopmental pathways.
Figure 6: An early neurodevelopmental expression network (M.1) regulated by Chd8 haploinsufficiency involved in chromatin modification, RNA processing and cell cycle.
Figure 7: Chd8+/del5 mutants exhibit altered cortical neurogenesis at E14.5.
Figure 8: RNA processing pathways are enriched for differentially expressed genes in Chd8+/del5 mice.

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  1. 1

    Ho, L. & Crabtree, G.R. Chromatin remodelling during development. Nature 463, 474–484 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Chen, T. & Dent, S.Y.R. Chromatin modifiers and remodellers: regulators of cellular differentiation. Nat. Rev. Genet. 15, 93–106 (2014).

    CAS  PubMed  Google Scholar 

  3. 3

    Ronan, J.L., Wu, W. & Crabtree, G.R. From neural development to cognition: unexpected roles for chromatin. Nat. Rev. Genet. 14, 347–359 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Sanders, S.J. First glimpses of the neurobiology of autism spectrum disorder. Curr. Opin. Genet. Dev. 33, 80–92 (2015).

    CAS  PubMed  Google Scholar 

  5. 5

    McCarthy, S.E. et al. De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol. Psychiatry 19, 652–658 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Vissers, L.E.L.M., Gilissen, C. & Veltman, J.A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9–18 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209–215 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216–221 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Bernier, R. et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158, 263–276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Nishiyama, M. et al. Early embryonic death in mice lacking the beta-catenin-binding protein Duplin. Mol. Cell. Biol. 24, 8386–8394 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Sugathan, A. et al. CHD8 regulates neurodevelopmental pathways associated with autism spectrum disorder in neural progenitors. Proc. Natl. Acad. Sci. USA 111, E4468–E4477 (2014).

    CAS  PubMed  Google Scholar 

  12. 12

    Cotney, J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment. Nat. Commun. 6, 6404 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Katayama, Y. et al. CHD8 haploinsufficiency results in autistic-like phenotypes in mice. Nature 537, 675–679 (2016).

    CAS  PubMed  Google Scholar 

  14. 14

    Platt, R.J. et al. Chd8 Mutation Leads to autistic-like behaviors and impaired striatal circuits. Cell Rep. 19, 335–350 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Durak, O. et al. Chd8 mediates cortical neurogenesis via transcriptional regulation of cell cycle and Wnt signaling. Nat. Neurosci. 19, 1477–1488 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Silverman, J.L., Yang, M., Lord, C. & Crawley, J.N. Behavioural phenotyping assays for mouse models of autism. Nat. Rev. Neurosci. 11, 490–502 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Silverman, J.L., Babineau, B.A., Oliver, C.F., Karras, M.N. & Crawley, J.N. Influence of stimulant-induced hyperactivity on social approach in the BTBR mouse model of autism. Neuropharmacology 68, 210–222 (2013).

    CAS  PubMed  Google Scholar 

  18. 18

    Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

    CAS  Google Scholar 

  19. 19

    Sanders, S.J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 1215–1233 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Hormozdiari, F., Penn, O., Borenstein, E. & Eichler, E.E. The discovery of integrated gene networks for autism and related disorders. Genome Res. 25, 142–154 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Willsey, A.J. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155, 997–1007 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Darnell, J.C. et al. FMRP stalls ribosomal translocation on mRNAs linked to synaptic function and autism. Cell 146, 247–261 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Voineagu, I. et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474, 380–384 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Parikshak, N.N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016).

    CAS  PubMed  Google Scholar 

  25. 25

    Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    PubMed  PubMed Central  Google Scholar 

  26. 26

    Parikshak, N.N. et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155, 1008–1021 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Jensen, L.J. et al. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 37, D412–D416 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Alrahbeni, T. et al. Full UPF3B function is critical for neuronal differentiation of neural stem cells. Mol. Brain 8, 33 (2015).

    PubMed  PubMed Central  Google Scholar 

  29. 29

    Laumonnier, F. et al. Mutations of the UPF3B gene, which encodes a protein widely expressed in neurons, are associated with nonspecific mental retardation with or without autism. Mol. Psychiatry 15, 767–776 (2010).

    CAS  PubMed  Google Scholar 

  30. 30

    Katz, Y., Wang, E.T., Airoldi, E.M. & Burge, C.B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Zhang, X. et al. Cell-type-specific alternative splicing governs cell fate in the developing cerebral cortex. Cell 166, 1147–1162.e15 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Tipps, M.E., Raybuck, J.D., Buck, K.J. & Lattal, K.M. Delay and trace fear conditioning in C57BL/6 and DBA/2 mice: issues of measurement and performance. Learn. Mem. 21, 380–393 (2014).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Brunner, D. et al. Comprehensive analysis of the 16p11.2 deletion and null Cntnap2 mouse models of autism spectrum disorder. PLoS One 10, e0134572 (2015).

    PubMed  PubMed Central  Google Scholar 

  34. 34

    Ellegood, J. et al. Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity. Mol. Psychiatry 20, 118–125 (2015).

    CAS  PubMed  Google Scholar 

  35. 35

    Etherton, M.R., Blaiss, C.A., Powell, C.M. & Südhof, T.C. Mouse neurexin-1alpha deletion causes correlated electrophysiological and behavioral changes consistent with cognitive impairments. Proc. Natl. Acad. Sci. USA 106, 17998–18003 (2009).

    CAS  PubMed  Google Scholar 

  36. 36

    Orosco, L.A. et al. Loss of Wdfy3 in mice alters cerebral cortical neurogenesis reflecting aspects of the autism pathology. Nat. Commun. 5, 4692 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Hagerman, R., Au, J. & Hagerman, P. FMR1 premutation and full mutation molecular mechanisms related to autism. J. Neurodev. Disord. 3, 211–224 (2011).

    PubMed  PubMed Central  Google Scholar 

  38. 38

    Lee, J.-A. et al. Cytoplasmic Rbfox1 regulates the expression of synaptic and autism-related genes. Neuron 89, 113–128 (2016).

    CAS  PubMed  Google Scholar 

  39. 39

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Du, C., Ma, X., Meruvu, S., Hugendubler, L. & Mueller, E. The adipogenic transcriptional cofactor ZNF638 interacts with splicing regulators and influences alternative splicing. J. Lipid Res. 55, 1886–1896 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Yoo, S.-W., Motari, M.G. & Schnaar, R.L. Agenesis of the corpus callosum in Nogo receptor deficient mice. J. Comp. Neurol. 525, 291–301 (2017).

    CAS  PubMed  Google Scholar 

  42. 42

    Yeung, J., Ha, T.J., Swanson, D.J. & Goldowitz, D. A novel and multivalent role of Pax6 in cerebellar development. J. Neurosci. 36, 9057–9069 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Nguyen, K.H., Yao, X.-H., Erickson, A.G., Mishra, S. & Nyomba, B.L.G. Glucose intolerance in aging male IGFBP-3 transgenic mice: differential effects of human IGFBP-3 and its mutant IGFBP-3 devoid of IGF binding ability. Endocrinology 156, 462–474 (2015).

    PubMed  Google Scholar 

  44. 44

    Galazo, M.J., Emsley, J.G. & Macklis, J.D. Corticothalamic projection neuron development beyond subtype specification: Fog2 and intersectional controls regulate intraclass neuronal diversity. Neuron 91, 90–106 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Espuny-Camacho, I. et al. Hallmarks of Alzheimer's disease in stem-cell-derived human neurons transplanted into mouse brain. Neuron 93, 1066–1081.e8 (2017).

    CAS  PubMed  Google Scholar 

  46. 46

    Huang, Z., Yu, Y., Shimoda, Y., Watanabe, K. & Liu, Y. Loss of neural recognition molecule NB-3 delays the normal projection and terminal branching of developing corticospinal tract axons in the mouse. J. Comp. Neurol. 520, 1227–1245 (2012).

    CAS  PubMed  Google Scholar 

  47. 47

    Lloyd-Burton, S. & Roskams, A.J. SPARC-like 1 (SC1) is a diversely expressed and developmentally regulated matricellular protein that does not compensate for the absence of SPARC in the CNS. J. Comp. Neurol. 520, 2575–2590 (2012).

    CAS  PubMed  Google Scholar 

  48. 48

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Google Scholar 

  49. 49

    Silverman, J.L. et al. GABAB receptor agonist R-baclofen reverses social deficits and reduces repetitive behavior in two mouse models of autism. Neuropsychopharmacology 40, 2228–2239 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Flannery, B.M. et al. Behavioral assessment of NIH Swiss mice acutely intoxicated with tetramethylenedisulfotetramine. Neurotoxicol. Teratol. 47, 36–45 (2015).

    CAS  PubMed  Google Scholar 

  51. 51

    Silverman, J.L. et al. Negative allosteric modulation of the mGluR5 receptor reduces repetitive behaviors and rescues social deficits in mouse models of autism. Sci. Transl. Med. 4, 131ra51 (2012).

    PubMed  PubMed Central  Google Scholar 

  52. 52

    Silverman, J.L. et al. Low stress reactivity and neuroendocrine factors in the BTBR T+tf/J mouse model of autism. Neuroscience 171, 1197–1208 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Wöhr, M. & Scattoni, M.L. Behavioural methods used in rodent models of autism spectrum disorders: current standards and new developments. Behav. Brain Res. 251, 5–17 (2013).

    PubMed  Google Scholar 

  54. 54

    Chadman, K.K. et al. Minimal aberrant behavioral phenotypes of neuroligin-3 R451C knockin mice. Autism Res. 1, 147–158 (2008).

    PubMed  PubMed Central  Google Scholar 

  55. 55

    Rogers, D.C. et al. Behavioral and functional analysis of mouse phenotype: SHIRPA, a proposed protocol for comprehensive phenotype assessment. Mamm. Genome 8, 711–713 (1997).

    CAS  PubMed  Google Scholar 

  56. 56

    Yang, M., Lewis, F.C., Sarvi, M.S., Foley, G.M. & Crawley, J.N. 16p11.2 Deletion mice display cognitive deficits in touchscreen learning and novelty recognition tasks. Learn Mem. 22, 622–632 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Thomas, A.M., Bui, N., Perkins, J.R., Yuva-Paylor, L.A. & Paylor, R. Group I metabotropic glutamate receptor antagonists alter select behaviors in a mouse model for fragile X syndrome. Psychopharmacology (Berl.) 219, 47–58 (2012).

    CAS  Google Scholar 

  58. 58

    Thomas, A. et al. Marble burying reflects a repetitive and perseverative behavior more than novelty-induced anxiety. Psychopharmacology (Berl.) 204, 361–373 (2009).

    CAS  Google Scholar 

  59. 59

    Henderson, C. et al. Reversal of disease-related pathologies in the fragile X mouse model by selective activation of GABAB receptors with arbaclofen. Sci. Transl. Med. 4, 152ra128 (2012).

    PubMed  Google Scholar 

  60. 60

    Yang, M., Silverman, J.L. & Crawley, J.N. Automated three-chambered social approach task for mice. in Current Protocols in Neuroscience (eds. Gerfen, C.R., Holmes, A., Sibley, D., Skolnick, P. & Wray, S.) Chapter 8, Unit 8.26–8.26.16 (Wiley, 2011).

  61. 61

    Scattoni, M.L., Ricceri, L. & Crawley, J.N. Unusual repertoire of vocalizations in adult BTBR T+tf/J mice during three types of social encounters. Genes Brain Behav. 10, 44–56 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Bales, K.L. et al. Long-term exposure to intranasal oxytocin in a mouse autism model. Transl. Psychiatry 4, e480 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Skrzypiec-Spring, M., Grotthus, B., Szelag, A. & Schulz, R. Isolated heart perfusion according to Langendorff---still viable in the new millennium. J. Pharmacol. Toxicol. Methods 55, 113–126 (2007).

    CAS  Google Scholar 

  64. 64

    Cahill, L.S. et al. Preparation of fixed mouse brains for MRI. Neuroimage 60, 933–939 (2012).

    PubMed  Google Scholar 

  65. 65

    Bock, N.A. et al. In vivo magnetic resonance imaging and semiautomated image analysis extend the brain phenotype for cdf/cdf mice. J. Neurosci. 26, 4455–4459 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Nieman, B.J. et al. Fast spin-echo for multiple mouse magnetic resonance phenotyping. Magn. Reson. Med. 54, 532–537 (2005).

    PubMed  Google Scholar 

  67. 67

    Jones, D.K., Horsfield, M.A. & Simmons, A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42, 515–525 (1999).

    CAS  PubMed  Google Scholar 

  68. 68

    Dorr, A.E., Lerch, J.P., Spring, S., Kabani, N. & Henkelman, R.M. High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. Neuroimage 42, 60–69 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Collins, D.L., Neelin, P., Peters, T.M. & Evans, A.C. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 18, 192–205 (1994).

    CAS  PubMed  Google Scholar 

  70. 70

    Avants, B.B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).

    PubMed  Google Scholar 

  71. 71

    Avants, B.B., Epstein, C.L., Grossman, M. & Gee, J.C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008).

    CAS  PubMed  Google Scholar 

  72. 72

    Lerch, J.P. et al. Cortical thickness measured from MRI in the YAC128 mouse model of Huntington's disease. Neuroimage 41, 243–251 (2008).

    PubMed  Google Scholar 

  73. 73

    Nieman, B.J., Flenniken, A.M., Adamson, S.L., Henkelman, R.M. & Sled, J.G. Anatomical phenotyping in the brain and skull of a mutant mouse by magnetic resonance imaging and computed tomography. Physiol. Genomics 24, 154–162 (2006).

    CAS  PubMed  Google Scholar 

  74. 74

    Steadman, P.E. et al. Genetic effects on cerebellar structure across mouse models of autism using a magnetic resonance imaging atlas. Autism Res. 7, 124–137 (2014).

    PubMed  Google Scholar 

  75. 75

    Ullmann, J.F.P., Janke, A.L., Reutens, D. & Watson, C. Development of MRI-based atlases of non-human brains. J. Comp. Neurol. 523, 391–405 (2015).

    PubMed  Google Scholar 

  76. 76

    Genovese, C.R., Lazar, N.A. & Nichols, T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870–878 (2002).

    PubMed  Google Scholar 

  77. 77

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Liao, Y., Smyth, G.K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    CAS  Google Scholar 

  80. 80

    Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Google Scholar 

  81. 81

    Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Nord, A.S. et al. Rapid and pervasive changes in genome-wide enhancer usage during mammalian development. Cell 155, 1521–1531 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84

    Feng, J., Liu, T. & Zhang, Y. Using MACS to identify peaks from ChIP-seq data. in Current Protocols in Bioinformatics (eds. Bateman, A., Draghici, S., Khurana, E, Orchard, S. & Pearson, W.R.) Chapter 2, Unit 2.14 (Wiley, 2011).

  85. 85

    Chen, E.Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Medina-Rivera, A. et al. RSAT 2015: regulatory sequence analysis tools. Nucleic Acids Res. 43, W50-6 (2015).

    PubMed  Google Scholar 

  87. 87

    Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, e17 (2005).

    Google Scholar 

  88. 88

    Van Laere, S. et al. Distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene-expression analysis. Br. J. Cancer 97, 1165–1174 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Young, M.D., Wakefield, M.J., Smyth, G.K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    PubMed  PubMed Central  Google Scholar 

  90. 90

    Rossin, E.J. et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 7, e1001273 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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Sequencing was performed at the UC Berkeley and UC Davis DNA cores. This work was supported by institutional funds from the UC Davis Center for Neuroscience, by the UC Davis MIND Institute Intellectual and Developmental Disabilities Research Center (U54 HD079125) and by NIGMS R35 GM119831. L.S.-F. was supported by the UC Davis Floyd and Mary Schwall Fellowship in Medical Research and by grant number T32-GM008799 from NIGMS-NIH. A.A.W. was supported by Training Grant number T32-GM007377 from NIH-NIGMS. R.C.-P. was supported by a Science Without Borders Fellowship from CNPq (Brazil). A.V., L.A.P. and D.E.D. were supported by National Institutes of Health grants R24HL123879, U01DE024427, R01HG003988, U54HG006997 and UM1HL098166. Research conducted at the E.O. Lawrence Berkeley National Laboratory was performed under Department of Energy Contract DE-AC02-05CH11231, University of California. J.E. and J.P.L. were supported by the Canadian Institute for Health Research, Brain Canada and the Ontario Brain Institute.

Author information




A.L.G., L.S.-F., J.E., N.A.C. and M.A.R. are listed as joint first authors, as each led components of the experiments and analysis. A.L.G., L.S.-F., J.E., M.A.R., N.A.C., J.P.L., J.N.C., J.L.S., K.S.Z. and A.S.N. designed the experiments. Generation of mouse model: A.S.N., D.E.D., A.V., L.A.P., B.J.M., I.P.-F. and V.A.; mouse behavior: N.A.C., M.C.P., M.D.S., J.N.C. and J.L.S.; mouse MRI: J.E. and J.P.L.; genomics and molecular genetics: L.S.-F., A.L.G., I.Z., A.A.W., R.C.-P., S.L., B.J.M. and A.S.N.; neuroanatomy: A.L.G., M.A.R., T.W.S., I.Z., G.K., K.S.Z. A.L.G., L.S.-F., J.E., N.A.C., M.A.R., K.S.Z., J.N.C., J.L.S. and A.S.N. drafted the manuscript. All authors contributed to manuscript revisions.

Corresponding author

Correspondence to Alex S Nord.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Raw western blots.

Western blots of Chd8 and Gapdh protein in adult 5-bp del and 14-bp del Chd8 mutant lines show decreased protein levels in Chd8+/del5 mice from both alleles. WT n = 3 for 280 kDa, WT n = 4 for 110 kDa, 5-bp n = 3, 14-bp n = 3. There is a significant difference in Chd8 protein abundance in Chd8+/del5 mice at 280 kDa (ANOVA *P = 0.0118, 5-bp *P = 0.0319; 14-bp *P = 0.0328) but not at 110 kDa (ANOVA P = 0.1676, 5-bp P = 0.1086, 14-bp P = 0.4509). B. Chd8 and Gapdh protein expression in WT and Chd8+/del5 (HT) mice at P0. There is a significant difference in Chd8 protein abundance in Chd8+/del5 mice at 280 kDa (n: WT = 9, Chd8+/del5 = 9; *P = 0.0089) and 110 kDa (**P = 0.001). C. Chd8 and Gapdh protein expression in Chd8+/del5 (HT) and WT mice at E14.5. There is a significant difference in Chd8 protein abundance in Chd8+/del5 mice at 280 kDa (n: WT = 6, Chd8+/del5 = 6; *P = 0.02) but not at 110 kDa (P = 0.9903). D. Raw Hnrnpa2b1 western blot from P0 forebrain. One-way ANOVA followed by unpaired post hoc t-tests used in A. Unpaired t-tests used for B-C. Error bars represent mean ± s.e.m.

Supplementary Figure 2 Chd8+/del5 mice from the replication cohort exhibit cognitive deficits, but no ASD social or repetitive phenotypes.

Chd8+/del5 mice exhibit deficits in learning and memory, including reduced freezing after fear conditioning in both context (A; t(1, 40) = 1.6607, P = 0.1046) and cued assays (B; t(1, 40) = 1.9593, P = 0.0571). Novel Object: Chd8+/del5 mice fail to show significant difference in exploration between a novel and familiarized object (C; WT F(1, 20) = 4.5583, P = 0.0453; Chd8+/del5 F(1, 19) = 0.7921, P = 0.3846). Social Approach: Chd8+/del5 mice do not exhibit differences relative to WT littermates in time spent in chamber with novel mouse (D; WT: F(1, 20) = 10.438, P = 0.0042; Chd8+/del5: F(1, 20) = 15.470, P = 0.0008), time sniffing a novel mouse (E; WT: F(1, 20) = 20.3750, P = 0.0002; Chd8+/del5: F(1, 20) = 30.3946, P = 0.00002), or chamber entries (F; F(1, 40) = 0.307, P = 0.583). Male-Female Social Interactions: Chd8+/del5 mice exhibit no differences between WT littermates in time spent engaged in nose to nose sniffs (G; t(1, 20) = 0.1520, P = 0.8807), nose to anogenital sniffs (H; t(1, 20) = 0.2492, P = 0.8057), time following (I; t(1, 20) = 1.6396, P = 0.1167), or ultrasonic vocalizations (J; t(1, 20) = 0.7010, P = 0.4914) with an estrus female. Repetitive Behavior: Chd8+/del5 mice do not exhibit any differences in time spent self-grooming (K; t(1, 40) = 0.2489, P = 0.8047). Open Field: Chd8+/del5 mice do not exhibit any differences in distance traveled (L; t(1, 40) = 0.9799, P = 0.3328). All data shown from 2nd cohort. Male mice were used in G-J; males and females were used in all other panels. Unpaired t-tests used for A-B, G-L; within genotype repeated measures ANOVA used for C-E; one-way ANOVA used for F. Error bars represent mean ± s.e.m.

Supplementary Figure 3 Fractional anisotropy (FA) differences in several coronal slices throughout the brain.

Highlighted are FA differences measured as effect size between the Chd8+/del5 brains and corresponding WT brains thresholded at 1.0. Of interest is the lateral decrease and corresponding medial increase in FA surrounding the cerebral peduncle. It should be noted that these differences are only indicative of trends as no significant differences were found in our FA measures.

Supplementary Figure 4 qRT-PCR validation of differential mRNA expression at P0.

A-J. Data from 7 WT and 5 Chd8+/del5 (HT) P0 forebrains (with the exception of G, which used 7 Chd8+/del5 P0 forebrains), with reactions performed in triplicate. For each target, the one highest and one lowest values were removed from analysis to reduce noise. P-values from Student’s t-test. Primers reported in Table S4. A-J WT n: 6, 4, 6, 6, 6, 6, 7, 6, 6, 6; Chd8+/del5 n: 7, 4, 5, 5, 4, 5, 7, 5, 5, 4.

Supplementary Figure 5 Chd8 RNA-seq and ChIP-seq analyses.

A. Results from RSAT de novo motif analysis on our Chd8 ChIP-seq peaks. Highlighted are three representative results for significant YY1, NRF1, and NFYB motifs (left), and corresponding predicted motif sites within Chd8 ChIP-seq peaks (middle) and the identified motif (right). B-C. Comparison of DE genes in our RNA-seq data with two recent Chd8 models, Katayama et al. (2016) (germline heterozygous Chd8), and Durak et al. (2016) (Chd8 knockdown). Dots represent genes passing FDR < 0.05 in our data and in either Katayama et al. (2016) or Durak et al. (2016) compared to our model (n = 37 and 77, respectively). The red dot indicates Chd8. DE genes in our data are significantly enriched in both datasets, but overlap between our data is stronger with Durak et al. (2016) than with Katayama et al. (2016). D. Differential expression of example synaptic genes. Top panels show expression (log2RPKM) across stages, bottom shows first four early developmental stages only. From left to right: Scn2b, Cacna1e, Cacna2d1, Cacna1b. Solid line = mean, dashed line = ±1 s.e.m. Black: WT; Red: Chd8+/del5.

Supplementary Figure 6 P0 and P7 coronal sections reveal no obvious perturbations to cortical lamination.

Single optical sections of wild type (A-D, I-M) and Chd8+/del5 (E-H, N-R) brains were imaged at 4X and paired based on corresponding anatomical depth of section. Immunofluorescence corresponding to Ctip2 (green; B, F, J, O), Tbr1 (red; C, G, K, P), and Brn2 (magenta; L, Q) was collected via sequential imaging and merged with DAPI (blue; D, H, M, R). Scale bars in E and N are 500 μm. P0: Tbr1 thickness P = 0.34, Ctip2 thickness P = 0.81, P7: Tbr1 thickness P = 0.88, Ctip2 thickness P = 0.69, Brn2 thickness P = 0.32. P-values derived from Student’s t-test. P0: WT n = 8, Chd8+/del5 n = 10; P7: WT n = 7, Chd8+/del5 n = 10.

Supplementary Figure 7 Changes in splicing in Chd8+/del5 mice.

A. DE down-regulated genes are enriched in DS genes, but not up-regulated DE genes. DS genes from our data overlap significantly with DS genes identified in a recent postmortem ASD cortex study25. B. (Left) Genome Browser representation of the alternatively spliced Srsf7 intron (chr17:80,604,601-80,603,728). (Right) Isoform-specific qRT-PCR analysis of the Srsf7 intron across development in WT and e17.5 Chd8+/del5 (HT) mice. n: e12.5 = 7, e14.5 = 9, e17.5 Chd8+/del5 = 7, e17.5 WT = 7, P0 = 11. Student’s t-test, e14.5 WT to e17.5 Chd8+/del5, P = 0.30, e17.5 Chd8+/del5 to e17.5 WT, P = 0.62. Linear regression across developmental time points, adjusted R2 = 0.37, P = 1.32E-05. Error bars represent mean ± s.e.m.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1819 kb)

Supplementary Methods Checklist (PDF 721 kb)

Supplementary Table 1

Summary of MRI volume by regions (XLSX 61 kb)

Supplementary Table 2

RNA sample and library data (XLSX 45 kb)

Supplementary Table 3

Differential expression summary table (XLSX 12110 kb)

Supplementary Table 4

Primers used for qPCR experiments (XLSX 47 kb)

Supplementary Table 5

Summary of GO enrichment (XLSX 94 kb)

Supplementary Table 6

Enrichment summary for Reactome pathways (XLSX 285 kb)

Supplementary Table 7

WGCNA results (XLSX 48 kb)

Supplementary Table 8

MISO results (XLSX 2669 kb)

Supplementary Software

Analysis scripts (ZIP 8 kb)

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Gompers, A., Su-Feher, L., Ellegood, J. et al. Germline Chd8 haploinsufficiency alters brain development in mouse. Nat Neurosci 20, 1062–1073 (2017).

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