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Molecular Autism
Published in: Molecular Autism 1/2017

Open Access 01-12-2017 | Research

Neurogenetic analysis of childhood disintegrative disorder

Authors: Abha R. Gupta, Alexander Westphal, Daniel Y. J. Yang, Catherine A. W. Sullivan, Jeffrey Eilbott, Samir Zaidi, Avery Voos, Brent C. Vander Wyk, Pam Ventola, Zainulabedin Waqar, Thomas V. Fernandez, A. Gulhan Ercan-Sencicek, Michael F. Walker, Murim Choi, Allison Schneider, Tammy Hedderly, Gillian Baird, Hannah Friedman, Cara Cordeaux, Alexandra Ristow, Frederick Shic, Fred R. Volkmar, Kevin A. Pelphrey

Published in: Molecular Autism | Issue 1/2017

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Abstract

Background

Childhood disintegrative disorder (CDD) is a rare form of autism spectrum disorder (ASD) of unknown etiology. It is characterized by late-onset regression leading to significant intellectual disability (ID) and severe autism. Although there are phenotypic differences between CDD and other forms of ASD, it is unclear if there are neurobiological differences.

Methods

We pursued a multidisciplinary study of CDD (n = 17) and three comparison groups: low-functioning ASD (n = 12), high-functioning ASD (n = 50), and typically developing (n = 26) individuals. We performed whole-exome sequencing (WES), copy number variant (CNV), and gene expression analyses of CDD and, on subsets of each cohort, non-sedated functional magnetic resonance imaging (fMRI) while viewing socioemotional (faces) and non-socioemotional (houses) stimuli and eye tracking while viewing emotional faces.

Results

We observed potential differences between CDD and other forms of ASD. WES and CNV analyses identified one or more rare de novo, homozygous, and/or hemizygous (mother-to-son transmission on chrX) variants for most probands that were not shared by unaffected sibling controls. There were no clearly deleterious variants or highly recurrent candidate genes. Candidate genes that were found to be most conserved at variant position and most intolerant of variation, such as TRRAP, ZNF236, and KIAA2018, play a role or may be involved in transcription. Using the human BrainSpan transcriptome dataset, CDD candidate genes were found to be more highly expressed in non-neocortical regions than neocortical regions. This expression profile was similar to that of an independent cohort of ASD probands with regression. The non-neocortical regions overlapped with those identified by fMRI as abnormally hyperactive in response to viewing faces, such as the thalamus, cerebellum, caudate, and hippocampus. Eye-tracking analysis showed that, among individuals with ASD, subjects with CDD focused on eyes the most when shown pictures of faces.

Conclusions

Given that cohort sizes were limited by the rarity of CDD, and the challenges of conducting non-sedated fMRI and eye tracking in subjects with ASD and significant ID, this is an exploratory study designed to investigate the neurobiological features of CDD. In addition to reporting the first multimodal analysis of CDD, a combination of fMRI and eye-tracking analyses are being presented for the first time for low-functioning individuals with ASD. Our results suggest differences between CDD and other forms of ASD on the neurobiological as well as clinical level.
Appendix
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Literature
1.
American Psychiatric Association. Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders: DSM-5. 5th ed. Washington: American Psychiatric Association; 2013. p. 50–9.CrossRef
2.
Kanner L. Autistic disturbances of affective contact. Nerv Child. 1943;2:217–50.
3.
Heller T. Dementia infantilis. Zeitschrift fur die Erforschung und Behandlung des Jugenlichen, Schwachsinns. 1908;2:17–28.
4.
Westphal A, Schelinski S, Volkmar F, Pelphrey K. Revisiting regression in autism: Heller’s dementia infantilis. J Autism Dev Disord. 2013;43:265–71.CrossRefPubMed
5.
World Health Organization. The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. Geneva: World Health Organization; 1992.
6.
American Psychiatric Association. Pervasive Developmental Disorders. In: Diagnostic and statistical manual of mental disorders: DSM-IV. 4th ed. Washington: American Psychiatric Association; 1994. p. 65–71.
7.
Hendry CN. Childhood disintegrative disorder: should it be considered a distinct diagnosis? Clin Psychol Rev. 2000;20:77–90.CrossRefPubMed
8.
Rosman NP, Bergia BM. Childhood disintegrative disorder: distinction from autistic disorder and predictors of outcome. J Child Neurol. 2013;28:1587–98.CrossRefPubMed
9.
Mouridsen SE, Rich B, Isager T. A comparative study of genetic and neurobiological findings in disintegrative psychosis and infantile autism. Psychiatry Clin Neurosci. 2000;54:441–6.CrossRefPubMed
10.
Volkmar FR, Cohen DJ. Disintegrative disorder or “late onset” autism. J Child Psychol Psychiatry. 1989;30:717–24.CrossRefPubMed
11.
Volkmar FR, Rutter M. Childhood disintegrative disorder: results of the DSM-IV autism field trial. J Am Acad Child Adolesc Psychiatry. 1995;34:1092–5.CrossRefPubMed
12.
13.
Malhotra S, Gupta N. Childhood disintegrative disorder: re-examination of the current concept. Eur Child Adolesc Psychiatry. 2002;11:108–14.CrossRefPubMed
14.
Kurita H, Osada H, Miyake Y. External validity of childhood disintegrative disorder in comparison with autistic disorder. J Autism Dev Disord. 2004;34:355–62.CrossRefPubMed
15.
Homan KJ, Mellon MW, Houlihan D, Katusic MZ. Brief report: childhood disintegrative disorder: a brief examination of eight case studies. J Autism Dev Disord. 2011;41:497–504.CrossRefPubMed
16.
Barger BD, Campbell JM, McDonough JD. Prevalence and onset of regression within autism spectrum disorders: a meta-analytic review. J Autism Dev Disord. 2013;43:817–28.CrossRefPubMed
17.
Kurita H, Koyama T, Setoya Y, Shimizu K, Osada H. Validity of childhood disintegrative disorder apart from autistic disorder with speech loss. Eur Child Adolesc Psychiatry. 2004;13:221–6.CrossRefPubMed
18.
Baio J. Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2010. MMWR Surveill Summ. 2014;63:1–21.
19.
20.
21.
Tottenham N, Tanaka JW, Leon AC, McCarry T, Nurse M, Hare TA, et al. The NimStim set of facial expressions: judgments from untrained research participants. Psychiatry Res. 2009;168:242–9.CrossRefPubMedPubMedCentral
22.
De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515:209–15.CrossRefPubMedPubMedCentral
23.
Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–21.CrossRefPubMedPubMedCentral
24.
Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron. 2015;87:1215–33.CrossRefPubMedPubMedCentral
25.
26.
Ronemus M, Iossifov I, Levy D, Wigler M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat Rev Genet. 2014;15:133–41.CrossRefPubMed
27.
Bunik V, Kaehne T, Degtyarev D, Shcherbakova T, Reiser G. Novel isoenzyme of 2-oxoglutarate dehydrogenase is identified in brain, but not in heart. FEBS J. 2008;275:4990–5006.CrossRefPubMed
28.
Allison T, Ginter H, McCarthy G, Nobre AC, Puce A, Luby M, et al. Face recognition in human extrastriate cortex. J Neurophysiol. 1994;71:821–5.PubMed
29.
Kanwisher N, McDermott J, Chun MM. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neurosci. 1997;17:4302–11.PubMed
30.
Pitcher D, Walsh V, Duchaine B. The role of the occipital face area in the cortical face perception network. Exp Brain Res. 2011;209:481–93.CrossRefPubMed
31.
Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F, et al. Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry. 2000;57:331–40.CrossRefPubMed
32.
Pelphrey KA, Sasson NJ, Reznick JS, Paul G, Goldman BD, Piven J. Visual scanning of faces in autism. J Autism Dev Disord. 2002;32:249–61.CrossRefPubMed
33.
Klin A, Jones W, Schultz R, Volkmar F, Cohen D. Visual fixation patterns during viewing of naturalistic social situations as predictors of social competence in individuals with autism. Arch Gen Psychiatry. 2002;59:809–16.CrossRefPubMed
34.
Papagiannopoulou EA, Chitty KM, Hermens DF, Hickie IB, Lagopoulos J. A systematic review and meta-analysis of eye-tracking studies in children with autism spectrum disorders. Soc Neurosci. 2014;9:610–32.PubMed
35.
Campbell DJ, Shic F, Macari S, Chawarska K. Gaze response to dyadic bids at 2 years related to outcomes at 3 years in autism spectrum disorders: a subtyping analysis. J Autism Dev Disord. 2014;44:431–42.CrossRefPubMedPubMedCentral
36.
Norbury CF, Brock J, Cragg L, Einav S, Griffiths H, Nation K. Eye-movement patterns are associated with communicative competence in autistic spectrum disorders. J Child Psychol Psychiatry. 2009;50:834–42.CrossRefPubMed
37.
Lewkowicz DJ, Hansen-Tift AM. Infants deploy selective attention to the mouth of a talking face when learning speech. Proc Natl Acad Sci U S A. 2012;109:1431–6.CrossRefPubMedPubMedCentral
38.
Norbury CF, Griffiths H, Nation K. Sound before meaning: word learning in autistic disorders. Neuropsychologia. 2010;48:4012–9.CrossRefPubMed
39.
Morton J, Johnson MH. CONSPEC and CONLERN: a two-process theory of infant face recognition. Psychol Rev. 1991;98:164–81.CrossRefPubMed
40.
41.
42.
Tarpey PS, Smith R, Pleasance E, Whibley A, Edkins S, Hardy C, et al. A systematic, large-scale resequencing screen of X-chromosome coding exons in mental retardation. Nature. 2009;41:535–43.
43.
Rauch A, Wieczorek D, Graf E, Wieland T, Endele S, Schwarzmayr T, et al. Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study. Lancet. 2012;380:1674–82.CrossRefPubMed
44.
Hu H, Haas SA, Chelly J, Van Esch H, Raynaud M, de Brouwer AP, et al. X-exome sequencing of 405 unresolved families identifies seven novel intellectual disability genes. Mol Psychiatry. 2016;21:133–48.CrossRefPubMed
45.
Kirov G, Rees E, Walters JTR, Escott-Price V, Georgieva L, Richards AL, et al. The penetrance of copy number variations for schizophrenia and developmental delay. Biol Psychiatry. 2014;75:378–85.CrossRefPubMed
46.
Mulle JG, Dodd AF, McGrath JA, Wolyniec PS, Mitchell AA, Shetty AC, et al. Microdeletions of 3q29 confer high risk for schizophrenia. Am J Hum Genet. 2010;87:229–36.CrossRefPubMedPubMedCentral
47.
Allen AS, Berkovic SF, Cossette P, Delanty N, Dlugos D, Eichler EE, et al. De novo mutations in epileptic encephalopathies. Nature. 2013;501:217–21.CrossRefPubMed
48.
Xu B, Ionita-Laza I, Roos JL, Boone B, Woodrick S, Sun Y, et al. De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia. Nat Genet. 2012;44:1365–9.CrossRefPubMedPubMedCentral
49.
Fitzgerald TW, Gerety SS, Jones WD, van Kogelenberg M, King DA, McRae J, et al. Large-scale discovery of novel genetic causes of developmental disorders. Nature. 2015;519:223–8.CrossRef
50.
Ahn K, Gotay N, Andersen TM, Anvari AA, Gochman P, Lee Y, et al. High rate of disease-related copy number variations in childhood onset schizophrenia. Mol Psychiatry. 2014;19:568–72.CrossRefPubMed
51.
Lim ET, Raychaudhuri S, Sanders SJ, Stevens C, Sabo A, MacArthur DG, et al. Rare complete knockouts in humans: population distribution and significant role in autism spectrum disorders. Neuron. 2013;77:235–42.CrossRefPubMedPubMedCentral
52.
Piton A, Gauthier J, Hamdan FF, Lafreniere RG, Yang Y, Henrion E, et al. Systematic resequencing of X-chromosome synaptic genes in autism spectrum disorder and schizophrenia. Mol Psychiatry. 2011;16:867–80.CrossRefPubMed
53.
Niranjan TS, Skinner C, May M, Turner T, Rose R, Stevenson R, et al. Affected kindred analysis of human X chromosome exomes to identify novel X-linked intellectual disability genes. PLoS One. 2015;10:e0116454.CrossRefPubMedPubMedCentral
54.
Fromer M, Pocklington AJ, Kavanagh DH, Williams HJ, Dwyer S, Gormley P, et al. De novo mutations in schizophrenia implicate synaptic networks. Nature. 2014;506:179–84.CrossRefPubMedPubMedCentral
55.
de Ligt J, Willemsen MH, van Bon BW, Kleefstra T, Yntema HG, Kroes T, et al. Diagnostic exome sequencing in persons with severe intellectual disability. N Engl J Med. 2012;367:1921–9.CrossRefPubMed
Metadata
Title
Neurogenetic analysis of childhood disintegrative disorder
Authors
Abha R. Gupta
Alexander Westphal
Daniel Y. J. Yang
Catherine A. W. Sullivan
Jeffrey Eilbott
Samir Zaidi
Avery Voos
Brent C. Vander Wyk
Pam Ventola
Zainulabedin Waqar
Thomas V. Fernandez
A. Gulhan Ercan-Sencicek
Michael F. Walker
Murim Choi
Allison Schneider
Tammy Hedderly
Gillian Baird
Hannah Friedman
Cara Cordeaux
Alexandra Ristow
Frederick Shic
Fred R. Volkmar
Kevin A. Pelphrey
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Molecular Autism / Issue 1/2017
Electronic ISSN: 2040-2392
DOI
https://doi.org/10.1186/s13229-017-0133-0

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