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Atypical behaviour and connectivity in SHANK3-mutant macaques

Abstract

Mutation or disruption of the SH3 and ankyrin repeat domains 3 (SHANK3) gene represents a highly penetrant, monogenic risk factor for autism spectrum disorder, and is a cause of Phelan–McDermid syndrome. Recent advances in gene editing have enabled the creation of genetically engineered non-human-primate models, which might better approximate the behavioural and neural phenotypes of autism spectrum disorder than do rodent models, and may lead to more effective treatments. Here we report CRISPR–Cas9-mediated generation of germline-transmissible mutations of SHANK3 in cynomolgus macaques (Macaca fascicularis) and their F1 offspring. Genotyping of somatic cells as well as brain biopsies confirmed mutations in the SHANK3 gene and reduced levels of SHANK3 protein in these macaques. Analysis of data from functional magnetic resonance imaging revealed altered local and global connectivity patterns that were indicative of circuit abnormalities. The founder mutants exhibited sleep disturbances, motor deficits and increased repetitive behaviours, as well as social and learning impairments. Together, these results parallel some aspects of the dysfunctions in the SHANK3 gene and circuits, as well as the behavioural phenotypes, that characterize autism spectrum disorder and Phelan–McDermid syndrome.

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Fig. 1: Generation and germline transmission of macaques with SHANK3 mutations.
Fig. 2: Sleep disturbances and altered home-cage activity in SHANK3-mutant macaques.
Fig. 3: Impaired social interaction and reduced vocalization in SHANK3 mutants.
Fig. 4: Eye-tracking properties in SHANK3-mutant macaques.
Fig. 5: Dysregulated global and local connectivity in SHANK3-mutant macaques.

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All data are available in the main text or the Supplementary Information. All sequencing data, images, code, and materials used in the analysis are available to researchers for the purpose of reproducing or extending the analyses.

References

  1. Naisbitt, S. et al. Shank, a novel family of postsynaptic density proteins that binds to the NMDA receptor/PSD-95/GKAP complex and cortactin. Neuron 23, 569–582 (1999).

    Article  CAS  Google Scholar 

  2. Jiang, Y. H. & Ehlers, M. D. Modeling autism by SHANK gene mutations in mice. Neuron 78, 8–27 (2013).

    Article  CAS  Google Scholar 

  3. Moessner, R. et al. Contribution of SHANK3 mutations to autism spectrum disorder. Am. J. Hum. Genet. 81, 1289–1297 (2007).

    Article  CAS  Google Scholar 

  4. Phelan, K. & McDermid, H. E. The 22q13.3 deletion syndrome (Phelan–McDermid Syndrome). Mol. Syndromol. 2, 186–201 (2012).

    CAS  PubMed  Google Scholar 

  5. Betancur, C. & Buxbaum, J. D. SHANK3 haploinsufficiency: a “common” but underdiagnosed highly penetrant monogenic cause of autism spectrum disorders. Mol. Autism 4, 17 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  7. Leblond, C. S. et al. Meta-analysis of SHANK mutations in autism spectrum disorders: a gradient of severity in cognitive impairments. PLoS Genet. 10, e1004580 (2014).

    Article  Google Scholar 

  8. Frank, Y. et al. A prospective study of neurological abnormalities in Phelan–McDermid syndrome. J. Rare Disord. 5, 1–13 (2017).

    Google Scholar 

  9. Chen, J. A., Peñagarikano, O., Belgard, T. G., Swarup, V. & Geschwind, D. H. The emerging picture of autism spectrum disorder: genetics and pathology. Annu. Rev. Pathol. 10, 111–144 (2015).

    Article  CAS  Google Scholar 

  10. Gauthier, J. et al. De novo mutations in the gene encoding the synaptic scaffolding protein SHANK3 in patients ascertained for schizophrenia. Proc. Natl Acad. Sci. USA 107, 7863–7868 (2010).

    Article  ADS  CAS  Google Scholar 

  11. Peça, J. et al. Shank3 mutant mice display autistic-like behaviours and striatal dysfunction. Nature 472, 437–442 (2011).

    Article  ADS  Google Scholar 

  12. Jennings, C. G. et al. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat. Neurosci. 19, 1123–1130 (2016).

    Article  Google Scholar 

  13. Bauman, M. D. & Schumann, C. M. Advances in nonhuman primate models of autism: integrating neuroscience and behavior. Exp. Neurol. 299, 252–265 (2018).

    Article  CAS  Google Scholar 

  14. Chang, S. W. et al. Neuroethology of primate social behavior. Proc. Natl Acad. Sci. USA 110, 10387–10394 (2013).

    Article  ADS  CAS  Google Scholar 

  15. Platt, M. L., Seyfarth, R. M. & Cheney, D. L. Adaptations for social cognition in the primate brain. Phil. Trans. R. Soc. Lond. B 371, 20150096 (2016).

    Article  Google Scholar 

  16. Izpisua Belmonte, J. C. et al. Brains, genes, and primates. Neuron 86, 617–631 (2015).

    Article  Google Scholar 

  17. Sclafani, V. et al. Early predictors of impaired social functioning in male rhesus macaques (Macaca mulatta). PLoS ONE 11, e0165401 (2016).

    Article  Google Scholar 

  18. Liu, Z. et al. Autism-like behaviours and germline transmission in transgenic monkeys overexpressing MeCP2. Nature 530, 98–102 (2016).

    Article  ADS  CAS  Google Scholar 

  19. Chen, Y. et al. Modeling Rett syndrome using TALEN-edited MECP2 mutant cynomolgus monkeys. Cell 169, 945–955 (2017).

    Article  CAS  Google Scholar 

  20. Sasaki, E. et al. Generation of transgenic non-human primates with germline transmission. Nature 459, 523–527 (2009).

    Article  ADS  CAS  Google Scholar 

  21. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    Article  ADS  CAS  Google Scholar 

  22. Niu, Y. et al. Generation of gene-modified cynomolgus monkey via Cas9/RNA-mediated gene targeting in one-cell embryos. Cell 156, 836–843 (2014).

    Article  CAS  Google Scholar 

  23. Zhao, H. et al. Altered neurogenesis and disrupted expression of synaptic proteins in prefrontal cortex of SHANK3-deficient non-human primate. Cell Res. 27, 1293–1297 (2017).

    Article  CAS  Google Scholar 

  24. Tu, Z. et al. CRISPR/Cas9-mediated disruption of SHANK3 in monkey leads to drug-treatable autism-like symptoms. Hum. Mol. Genet. 28, 561–571 (2019).

    Article  Google Scholar 

  25. Durand, C. M. et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat. Genet. 39, 25–27 (2007).

    Article  CAS  Google Scholar 

  26. Zhou, Y. et al. Mice with Shank3 mutations associated with ASD and schizophrenia display both shared and distinct defects. Neuron 89, 147–162 (2016).

    Article  CAS  Google Scholar 

  27. Speed, H. E. et al. Autism-associated insertion mutation (InsG) of Shank3 exon 21 causes impaired synaptic transmission and behavioral deficits. J. Neurosci. 35, 9648–9665 (2015).

    Article  CAS  Google Scholar 

  28. Bae, S., Park, J. & Kim, J. S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014).

    Article  CAS  Google Scholar 

  29. Jiang, Y. & Platt, M. L. Oxytocin and vasopressin flatten dominance hierarchy and enhance behavioral synchrony in part via anterior cingulate cortex. Sci. Rep. 8, 8201 (2018).

    Article  ADS  Google Scholar 

  30. Falck-Ytter, T., Bölte, S. & Gredebäck, G. Eye tracking in early autism research. J. Neurodev. Disord. 5, 28 (2013).

    Article  Google Scholar 

  31. Mosher, C. P., Zimmerman, P. E. & Gothard, K. M. Videos of conspecifics elicit interactive looking patterns and facial expressions in monkeys. Behav. Neurosci. 125, 639–652 (2011).

    Article  Google Scholar 

  32. Daluwatte, C. et al. Atypical pupillary light reflex and heart rate variability in children with autism spectrum disorder. J. Autism Dev. Disord. 43, 1910–1925 (2013).

    Article  Google Scholar 

  33. Maestripieri, D. & Wallen, K. T. Affiliative and submissive communication in rhesus macaques. Primates 38, 127–138 (1997).

    Article  Google Scholar 

  34. Hinde, R. A. & Rowell, T. E. Communication by postures and facial expressions in the rhesus monkey (Macaca mulatta). J. Zool. 138, 1–21 (1962).

    Google Scholar 

  35. Gothard, K. M., Battaglia, F. P., Erickson, C. A., Spitler, K. M. & Amaral, D. G. Neural responses to facial expression and face identity in the monkey amygdala. J. Neurophysiol. 97, 1671–1683 (2007).

    Article  CAS  Google Scholar 

  36. Parr, L. A. & Heintz, M. Facial expression recognition in rhesus monkeys, Macaca mulatta. Anim. Behav. 77, 1507–1513 (2009).

    Article  Google Scholar 

  37. Wass, S. V. et al. Shorter spontaneous fixation durations in infants with later emerging autism. Sci. Rep. 5, 8284 (2015).

    Article  CAS  Google Scholar 

  38. Tabet, A. C. et al. A framework to identify contributing genes in patients with Phelan–McDermid syndrome. NPJ Genom. Med. 2, 32 (2017).

    Article  Google Scholar 

  39. Rudie, J. D. et al. Altered functional and structural brain network organization in autism. Neuroimage Clin. 2, 79–94 (2013).

    Article  Google Scholar 

  40. Emerson, R. W. et al. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci. Transl. Med. 9, eaag2882 (2017).

    Article  Google Scholar 

  41. Lewis, J. D., Theilmann, R. J., Townsend, J. & Evans, A. C. Network efficiency in autism spectrum disorder and its relation to brain overgrowth. Front. Hum. Neurosci. 7, 845 (2013).

    Article  Google Scholar 

  42. Buckner, R. L., Andrews-Hanna, J. R. & Schacter, D. L. The brain’s default network: anatomy, function, and relevance to disease. Ann. NY Acad. Sci. 1124, 1–38 (2008).

    Article  ADS  Google Scholar 

  43. Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).

    Article  Google Scholar 

  44. Goldman, S. E. et al. Defining the sleep phenotype in children with autism. Dev. Neuropsychol. 34, 560–573 (2009).

    Article  Google Scholar 

  45. Adolphs, R. The social brain: neural basis of social knowledge. Annu. Rev. Psychol. 60, 693–716 (2009).

    Article  Google Scholar 

  46. Arnsten, A. F. Stress signalling pathways that impair prefrontal cortex structure and function. Nat. Rev. Neurosci. 10, 410–422 (2009).

    Article  CAS  Google Scholar 

  47. Guénolé, F. et al. Melatonin for disordered sleep in individuals with autism spectrum disorders: systematic review and discussion. Sleep Med. Rev. 15, 379–387 (2011).

    Article  Google Scholar 

  48. Just, M. A., Keller, T. A., Malave, V. L., Kana, R. K. & Varma, S. Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neurosci. Biobehav. Rev. 36, 1292–1313 (2012).

    Article  Google Scholar 

  49. Moeller, S., Nallasamy, N., Tsao, D. Y. & Freiwald, W. A. Functional connectivity of the macaque brain across stimulus and arousal states. J. Neurosci. 29, 5897–5909 (2009).

    Article  CAS  Google Scholar 

  50. Vincent, J. L. et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86 (2007).

    Article  ADS  CAS  Google Scholar 

  51. Ke, Q. et al. TALEN-based generation of a cynomolgus monkey disease model for human microcephaly. Cell Res. 26, 1048–1061 (2016).

    Article  CAS  Google Scholar 

  52. Sri Kantha, S. & Suzuki, J. Sleep quantitation in common marmoset, cotton top tamarin and squirrel monkey by non-invasive actigraphy. Comp. Biochem. Physiol. A 144, 203–210 (2006).

    Article  Google Scholar 

  53. Freund, J. et al. Emergence of individuality in genetically identical mice. Science 340, 756–759 (2013).

    Article  ADS  CAS  Google Scholar 

  54. Bei, D. M. & Lafferty J. D. Dynamic topic models. In Proc. 23rd International Conference Machine Learning (2006).

  55. Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Engineer. 82, 34–45 (1960).

    Google Scholar 

  56. Harlow, H. F. & Bromer, J. A. A test apparatus for monkeys. Psychol. Rec. 2, 434–436 (1938).

    Article  Google Scholar 

  57. Harlow, H. F. The development of learning in the rhesus monkey. Am. Sci. 47, 459–479 (1959).

    Google Scholar 

  58. Levin, E. D. & Bowman, R. E. The effect of pre- or postnatal lead exposure on Hamilton Search Task in monkeys. Neurobehav. Toxicol. Teratol. 3, 391–394 (1983).

    Google Scholar 

  59. Frey, S. et al. An MRI based average macaque monkey stereotaxic atlas and space (MNI monkey space). Neuroimage 55, 1435–1442 (2011).

    Article  Google Scholar 

  60. Ashburner, J. SPM: a history. Neuroimage 62, 791–800 (2012).

    Article  Google Scholar 

  61. Behzadi, Y., Restom, K., Liau, J. & Liu T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).

    Article  Google Scholar 

  62. Deshpande, G., LaConte, S., Peltier, S. & Hu X. Integrated local correlation: a new measure of local coherence in fMRI data. Hum. Brain Mapp. 30, 13–23 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank L. Harp McGovern and the late P. J. McGovern for their vision and support; F. Zhang for advice and reagents for CRISPR; D. G. Amaral for sharing image resources for creating eye-tracking stimuli; J. Bachevalier for advice on behavior testing; E. A. Murray for guidance on the Wisconsin General Test Apparatus assay; G. Genovese and R. Rosario for support with statistical and bioinformatics analysis; S. Sharma, S. Lall and S. Krol for critical reading of the manuscript; L. Dennis, N. Nien-Chu Espinoza, S. Yang, A. Chakrabarti, N. Joshi and Y. Fukumura for behavioral scoring; X. Wu, X. Ding, L. Cheng and X. Liu for technical support; the veterinary team of Blooming-Spring for excellent colony management and technical support; and S. E. Hyman (Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard), N. Sanjana (NYU) and L. Cong (Stanford University) and members of the Feng laboratory at MIT for critical discussion on this project. This work was supported by National Key R&D Program of China (2017YFC1307500); Shenzhen Overseas Innovation Team Project (KQTD20140630180249366); Guangdong Innovative and Entrepreneurial Research Team Program (2014ZT05S020). S.Y. and Q.K. was supported by Frontier and Innovation of Key Technology Project in Science and Technology Department of Guangdong Province (2014B020225007 and 2019B020235002); and Program for New Century Excellent Talents in University of Ministry of Education of the People’s Republic of China (NCET-12-1078). This work was also supported by the National Key R&D Program of China (2018YFA0107203 and 2017YFA0103802 to A.P.X., 2017YFA0103802 to W.L.); the External Cooperation Program of Chinese Academy of Sciences (172644KYSB20160026); International Partnership Program of Chinese Academy of Sciences (172644KYS820170004 to L.W., 172644KYSB20160175 to H.Z.); the Patrick J. McGovern Foundation; Hundred Talent Program of Chinese Academy of Sciences to H.Z.; the National Natural Science Foundation of China (81425016 to A.P.X., 31671119 to Z.L.); Shenzhen Science and Technology Innovation Commission grants (JCYJ20151030140325151 to H.Z.; GJHZ20160229200136090, JCYJ20170413165053031 to T.Y.; JCYJ20170413162938668 to Z.L.). Y. Zhou was supported by postdoctoral fellowships from the Simons Center for the Social Brain at MIT and Nancy Lurie Marks Family Foundation. G.F. is supported by the McGovern Institute for Brain Research at MIT, James and Patricia Poitras Center for Psychiatric Disorders Research at MIT, the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard, the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT, and Edward and Kay Poitras. L.W. is also supported by Guangdong Provincial Key Laboratory of Brain Connectome and Behavior 2017B030301017, Shenzhen Discipline Construction Project for Neurobiology DRCSM [2016]1379, and Shenzhen-Hong Kong Institute of Brain Science.

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Nature thanks Thomas Bourgeron, Michael Platt and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

Authors

Contributions

G.F., S.Y. and Y. Zhou conceived the study, and R.D., G.F. and H.Z. provided ongoing guidance on the design. S.Y. and A.P.X. oversaw the generation of mutant monkeys. H.Z. oversaw the characterization of mutant monkeys. Y. Zhou carried out CRISPR design and validation. S.Y., Q.K., H.C., Y. Zhou, J.Y., D.X., Y.H. and A.P.X. generated mutant monkeys. Y. Zhou and D.W. designed and performed molecular, protein, sequencing and off-target analyses. R.L., J.S., Y. Zhou, G.F. and R.D. designed and analysed behavioural experiments and MRI assays. H.Z., L.W., Z.L., T.Y., Y. Zou, M.J., W.J., Y.B., W.M., T.A., Y.L., X.L., W.L., L.H., S.A.A. and M.S. participated in the design or execution of some of the behavioural experiments. R.L., D.S.H., J.W.F. III, J.B.H., A.F.-K., O.M. and S.P. managed and performed behavioural scoring. Y. Zhou, R.L., R.D., J.S. and G.F. wrote the manuscript with input from all authors.

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Correspondence to Huihui Zhou, Andy Peng Xiang, Guoping Feng or Shihua Yang.

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Extended data figures and tables

Extended Data Fig. 1 Original images for western blots and DNA gel.

Original images for western blots and DNA gel electrophoresis corresponding to specific figure panels as indicated are presented without cropping or further processing, such as adjusting of brightness and contrast.

Extended Data Fig. 2 Summary of founder and germline-transmitted SHANK3 mutations.

a, Schematic showing the structure of the wild-type macaque SHANK3 gene and magnified panels with the annotated sequence of the gRNA and protospacer adjacent motif (PAM) for both strands within exon 21. b, SURVEYOR assay showing SpCas9-mediated indels in cultured cynomolgus monkey primary skin fibroblasts with indicated gRNAs. c, Genotyping PCR results of individual monkey embryos injected with a mixture of SpCas9 mRNA, SHANK3 gRNA no. 1 and gRNA no. 2. Asterisks indicate effectively edited embryos. d, Number of injected embryos, transferred recipients and newborn macaques in this study. e, SHANK3-mutant macaques have similar body weights to those of age-matched wild-type controls. f, Pie charts of genotype (indels) of DNA from semen from mutant macaques M2 and M3 show a similar pattern to their respective blood samples.

Source Data

Extended Data Fig. 3 Alignment of partial SHANK3 sequence genotyped from skin DNA.

ae, Alignment of ten representative reads of SHANK3 sequence genotyped from a skin biopsy of each mutant monkey with reference SHANK3 sequence from wild-type monkey.

Source Data

Extended Data Fig. 4 Statistical analysis of western blots using brain lysates prepared from V1 biopsy of macaques.

a, b, Quantification of blots was based on five technical repeats using the same V1 protein sample with N-terminal (a) and C-terminal (b) antibodies. Values were normalized to those of the C1 control monkey. α-Tubulin, as loading control, was run on the same gel. Data are presented as mean ± s.e.m., n = 5 technical repeats using sample for the 2 controls and 5 SHANK3 mutants, *P < 0.05, **P < 0.01, ***P < 0.001; ns, not significant; one-way analysis of variance (ANOVA) with Bonferroni post hoc test.

Source Data

Extended Data Fig. 5 Representative traces of overall activity.

a–l, Representative and enlarged traces of overall activity recorded by motion watches across multiple days from a control macaque and all five SHANK3 mutants. A.U, arbitrary units.

Source Data

Extended Data Fig. 6 Behavioural parameters of monkeys during the first and second five minutes of interaction.

a, Schematic showing the two interconnected cages used for habituation of individual macaques and subsequent paired social-interaction assay. bl, Separate behavioural parameters of monkeys in control and SHANK3-mutant groups during the first five minutes of interaction. m, No difference in social behaviours (including chasing, following, circling, fleeing and play) during the second five minutes of interaction. In all panels, n = 6 macaques for control group; n = 5 macaques for the SHANK3-mutant group. Data are presented as mean ± s.e.m., two-tailed Mann–Whitney U-test.

Extended Data Fig. 7 Behavioural parameters of probe macaques when paired with wild-type or SHANK3-mutant monkeys during the first five minutes of interaction.

ak, Total durations of chasing (a), following (b), circling (c), fleeing (d), play (e), attacking (f), anogenital inspection (g), rump presentation (h), mounting (i), receiving grooming (j) and giving grooming (k). In all panels, n = 10 probe monkeys paired individually with 6 wild-type macaques from the control group and 5 macaques from the SHANK3-mutant group. Data are presented as mean ± s.e.m., *P < 0.05, **P < 0.01; two-tailed Mann–Whitney U-test.

Extended Data Fig. 8 Performance of control and mutant monkeys in the discrimination and reversal tasks using WGTA.

a, Task design. b, c, Total days (b) and total trials (c) required for macaques to pass the black–white discrimination test of the WGTA. d, e, Total days (d) and total trials (e) required for macaques to pass the black–white reversal test of the WGTA (>75%-correct trial). f, A graphical model for Bayesian nonparametric multitarget tracking. Priors omitted for brevity. Arrows pointing to ellipses indicate continuation to the next time step. g, Diagram of the eye-tracking box. In be, n = 6 macaques for control group; n = 3 macaques for the SHANK3-mutant group. Data are presented as mean ± s.e.m.; Mann–Whitney U-test. Coloured squares indicate individual macaques with SHANK3 mutations.

Extended Data Fig. 9 Performance of controls and SHANK3 mutants in the Hamilton search task.

a, Schematic and flow chart of the Hamilton search task. b, Performance of macaques in the ‘set-breaking’ test of the Hamilton search task. M3 showed no improvement (delta value = 0). ‘Delta’ is set to measure the learning of the monkey across five test days, calculated by increase of the number of trials in which the monkey opened the correct well on the first try. c, Percentage of correct trials on the ‘forced set-breaking’ test of the Hamilton search task, from monkeys across five test days. d, Number of monkeys that reached a 75%-correct rate on the fifth day of the forced set-breaking test. *P < 0.05, Two-tailed χ2 test (P = 0.023) was applied to determine the statistical difference between groups.

Extended Data Fig. 10 Structural MRI and seed-based functional MRI analysis of macaque brains.

ac, No difference in white matter volume (a) and cerebrospinal fluid volume (b), but a reduced volume of grey matter (c), in SHANK3 mutants, relative to control macaques. In ac, n = 6 macaques for control group; n = 5 macaques for SHANK3-mutant group. Data are presented as mean ± s.e.m., **P < 0.01, Mann–Whitney U-test. Coloured squares indicate individual mutant macaques. d, e, Sagittal, coronal and axial views of averaged functional MRI image from six control macaques (d) and five SHANK3 mutants (e), using the putative posterior cingulate cortex as seed region. f, Sagittal, coronal and axial views of averaged functional MRI image show blood-oxygen-level-dependent signals in the posterior cingulate cortex that are greater in mutants than in controls. In df, the putative posterior cingulate cortex regions are highlighted by arrows.

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1-3. Supplemental Table 1: Off target analysis of SHANK3 gRNA #1 and gRNA #2. Supplemental Table 2: Ethogram example from the mutant monkey M5. Supplemental Table 3: Power analysis of data sets with statistical significance.

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Zhou, Y., Sharma, J., Ke, Q. et al. Atypical behaviour and connectivity in SHANK3-mutant macaques. Nature 570, 326–331 (2019). https://doi.org/10.1038/s41586-019-1278-0

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