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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Molecular Autism 1/2017

Neurogenetic analysis of childhood disintegrative disorder

Zeitschrift:
Molecular Autism > Ausgabe 1/2017
Autoren:
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
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s13229-017-0133-0) contains supplementary material, which is available to authorized users.

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.
Zusatzmaterial
Additional file 1: Supplementary information includes supplementary methods, references, and figures. (PDF 700 kb)
13229_2017_133_MOESM1_ESM.pdf
Additional file 2: Table S1. CDD families for genetic analysis. Table S2. Rare non-synonymous and synonymous variants from WES unique to probands or unaffected sibling controls. Table S3. Rates of variants from WES in CDD probands and unaffected sibling controls. Table S4. Genes represented once in the core probe set (Kang et al. 2011) and used for expression analysis. Table S5. Median expression values (Log2-transformed signal intensity) for CDD candidate genes by time period and brain region. Table S6. Difference in median expression values (Log2-transformed signal intensity) between non-neocortical and neocortical regions for gene sets by time period. Table S7. Pearson correlation coefficients for all pairwise combinations of CDD candidate genes. Table S8. Clinical characteristics of subjects studied by neuroimaging. Table S9. Features of CDD, LFASD, HFASD, and TD cohorts for neuroimaging analysis. Table S10. List of brain regions where TD:discovery exhibits significant faces > houses activation. Table S11. Mean % signal change (faces > houses) for each cohort in Fig.  4b. Table S12. Mean % signal change (faces > houses) for each cohort in Figure S2. Table S13. List of brain regions where CDD exhibits significant faces > houses activation. Table S14. Mean % signal change (faces > houses) for each cohort in Fig.  5b. Table S15. Clinical characteristics of subjects studied by eye tracking. Table S16. Features of CDD, LFASD, HFASD, and TD cohorts in eye-tracking analysis. Table S17. Whole-exome sequencing quality metrics. Table S18. Features of SSC probands with and without regression by IQ and autism severity. (XLSX 364 kb)
Literatur
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