A previously unrecognized 22q13.2 microdeletion syndrome that encompasses <i>TCF20</i> and <i>TNFRSF13C</i>

Upadia, Jariya; Gonzales, Patrick R.; Atkinson, T. Prescott; Schroeder, Harry W.; Robin, Nathaniel H.; Rudy, Natasha; Mikhail, Fady M.

doi:10.1002/ajmg.a.40492

“…The reduction of cell cycle exiting leads to a reduction in neuronal output and these reasons cause small brain size, which is similar to a previous report of insm1 knockout mice . Besides, there are several reports showed that some patients with TCF20 mutations have microcephaly, which is constant to our mouse model . We further performed RNA‐seq to explore the possible target of TCF20.…”

Section: Discussionsupporting

confidence: 86%

“…TCF20 was also found to play a role as transcriptional coactivator in modulating the transcriptional activity of Sp1, c-Jun, Est1, and RNF4 [18][19][20]. TCF20 is located in the chromosome 22q13.2 region, which is close to the Phelan-McDermid syndrome (PMS) candidate gene shank3 [21]. PMS, known as 22q13.3 deletion syndrome, involves a range of phenotypes, including global developmental delay, intellectual disability, neonatal hypotonia, autism, and autistic-like behaviors, which has caused PMS to be considered a syndromic form of ASD [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…PMS, known as 22q13.3 deletion syndrome, involves a range of phenotypes, including global developmental delay, intellectual disability, neonatal hypotonia, autism, and autistic‐like behaviors, which has caused PMS to be considered a syndromic form of ASD . TCF20 mutations or microdeletions were also found in PMS patients . All these studies suggest that TCF20 dysfunction could be related to ASDs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TCF 20 dysfunction leads to cortical neurogenesis defects and autistic‐like behaviors in mice

Feng

¹

,

Zhao

²

,

Ji

³

et al. 2020

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Recently, de novo mutations of transcription factor 20 (TCF20) were found in patients with autism by large-scale exome sequencing. However, how TCF20 modulates brain development and whether its dysfunction causes ASD remain unclear. Here, we show that TCF20 deficits impair neurogenesis in mouse. TCF20 deletion significantly reduces the number of neurons, which leads to abnormal brain functions. Furthermore, transcriptome analysis and ChIP-qPCR reveal that the DNA demethylation factor TDG is a downstream target gene of TCF20. As a nonspecific DNA demethylation factor, TDG potentially affects many genes. Combined TDG ChIP-seq and GO analysis of TCF20 RNA-Seq identifies T-cell factor 4 (TCF-4) as a common target. TDG controls the DNA methylation level in the promoter area of TCF-4, affecting TCF-4 expression and modulating neural differentiation. Overexpression of TDG or TCF-4 rescues the deficient neurogenesis of TCF20 knockdown brains. Together, our data reveal that TCF20 is essential for neurogenesis and we suggest that defects in neurogenesis caused by TCF20 loss are associated with ASD.

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“…Intriguingly, TCF20 was one of the highest ranking candidate autism risk genes (category 2) according to the most recent version of the SFARI Gene resource. Mutations in TCF20 were also implicated in Phelan-McDermid syndrome ( Upadia et al, 2018 ), developmental disorders ( Deciphering Developmental Disorders Study, 2017 ), and schizophrenia ( Smeland et al, 2017 ). FBXO11 was prioritized as a strong ASD candidate gene ( Ji et al, 2016 ), and was recently reported to be associated with a variable neurodevelopmental disorder ( Gregor et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates

Lin

¹

,

Afshar

²

,

Rajadhyaksha

³

et al. 2020

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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be down-expressed in ASD brains, especially in frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, NBEA , HERC1 , and TCF20 ), but also indicated potentially novel candidates, such as, MYCBP2 and CAND1 , which are involved in protein ubiquitination. We also showed that our method outperformed state-of-the-art scoring systems for ranking curated ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.

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“…Intriguingly, TCF20 was one of the highest ranking candidate autism risk genes (category 2) according to the most recent version of the SFARI Gene resource. Mutations in TCF20 were also implicated in Phelan-McDermid syndrome (40), developmental disorders (41), and schizophrenia (42). Our ranking system also successfully predicted another ASD gene FBXO11 (ranked sixth).…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

Lin

¹

,

Rajadhyaksha

²

,

Potash³

et al. 2018

Preprint

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Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis.The role of de novo mutations in ASD has been well established, but the set of genes implicated to date is still far from complete. The current study employs a machine learning-based approach to predict ASD risk genes using features from spatiotemporal gene expression patterns in human brain, gene-level constraint metrics, and other gene variation features. The genes identified through our prediction model were enriched for independent sets of ASD risk genes, and tended to be differentially expressed in ASD brains, especially in the frontal and parietal cortex. The highest-ranked genes not only included those with strong prior evidence for involvement in ASD (for example, TCF20 and FBOX11), but also indicated potentially novel candidates, such as DOCK3, MYCBP2 and CAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-theart scoring systems for ranking ASD candidate genes. Gene ontology enrichment analysis of our predicted risk genes revealed biological processes clearly relevant to ASD, including neuronal signaling, neurogenesis, and chromatin remodeling, but also highlighted other potential mechanisms that might underlie ASD, such as regulation of RNA alternative splicing and ubiquitination pathway related to protein degradation. Our study demonstrates that human brain spatiotemporal gene expression patterns and gene-level constraint metrics can help predict ASD risk genes. Our gene ranking system provides a useful resource for prioritizing ASD candidate genes.One approach is based on the concept of guilt-by-association, i.e., assuming that genes that confer risk for ASD are likely to be functionally related, and that they thus converge on molecular networks and biological pathways implicated in disease (12, 13). For example, one study showed that ASD genes with de novo mutations converged on pathways related to chromatin remodeling and synaptic function (14). To leverage these functional relationships, several studies have explored integrating known risk genes using a protein-protein interaction (PPI) network to identify novel genes involved in ASD (15-18). However, a PPI network is built upon general protein-protein interactions without reference to tissue or cell type specificity, and this approach may not fully capture the brain-centric functional relationships among ASD genes. Accordingly, a brain-specific network-based approach, which considered relationships within the context of the brain, was proposed to predict ASD genes, (19, 20). Studies employing this paradigm, however, did not consider the dynamic patterns of gene relationships during brain development, thereby limiting their potential for discovery, given the possibility that genes might only be functionally related within a specific developmental stage. Evidence for this comes from Willsey et al., who showed, using 4 spatiotemporal gene expression data from human ...

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A previously unrecognized 22q13.2 microdeletion syndrome that encompasses TCF20 and TNFRSF13C

Cited by 12 publications

References 17 publications

TCF 20 dysfunction leads to cortical neurogenesis defects and autistic‐like behaviors in mice

TCF 20 dysfunction leads to cortical neurogenesis defects and autistic‐like behaviors in mice

A Machine Learning Approach to Predicting Autism Risk Genes: Validation of Known Genes and Discovery of New Candidates

A machine learning approach to predicting autism risk genes: Validation of known genes and discovery of new candidates

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