The ubiquitin proteasome pathway in neuropsychiatric disorders

Cheon, Solmi; Dean, Milan; Chahrour, Maria H.

doi:10.1016/j.nlm.2018.01.012

“…GO enrichment analysis demonstrated that the predicted risk genes were enriched in GO terms related to neuronal signaling, neurogenesis, chromatin remodeling, and histone modification, all of which are important biological processes implicated in ASD. In addition, among our top ten ranked genes, we found that five were related to the ubiquitination pathway ( HERC1, CAND1, ZYG11B, FBXO11, and MYCBP2 ), which is consistent with the significant enrichment of protein ubiquitination process in our GO enrichment analysis (GO_PROTEIN_UBIQUITINATION, p corrected = 9 × 10 −6 ), supporting the merging role of ubiquitin signaling in ASD (46, 53). Our analyses also highlighted other biological mechanisms that may underlie ASD.…”

Section: Discussionsupporting

confidence: 83%

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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“…GO enrichment analysis demonstrated that the predicted risk genes were enriched in GO terms related to neuronal signaling, neurogenesis, chromatin remodeling, and histone modification, all of which are important biological processes implicated in ASD. In addition, among our top 10 ranked genes, we found that five were related to the protein ubiquitination pathway ( HERC1 , CAND1 , ZYG11B , HECTD1 , and MYCBP2 ), which is consistent with the significant enrichment of protein ubiquitination process in our GO enrichment analysis (GO_PROTEIN_UBIQUITINATION, OR = 2.3, p corrected = 1.9 × 10 –6 ), supporting the merging role of ubiquitin signaling in ASD ( Mabb and Ehlers, 2010 ; Cheon et al, 2018 ). Our analyses also highlighted other biological mechanisms that may underlie ASD.…”

Section: Discussionsupporting

confidence: 83%

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

View full text Add to dashboard Cite

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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“…Ubiquitin-processing enzymes are fundamental for neuronal development and function (43,44) and dysregulation of these enzymes can cause neurodevelopmental (2,3,45) and neurodegenerative diseases (4,46). E3 ligases determine the substrate specificity of the ubiquitination reaction, as they directly interact with proteins targeted for ubiquitination (47).…”

Section: Discussionmentioning

confidence: 99%

“…Ubiquitination is a highly coordinated multi-step enzymatic cascade that requires the concerted action of three key factors: E1 ubiquitin-activating enzymes, E2 ubiquitinconjugating enzymes, and E3 ubiquitin ligases (1), many of which are strongly implicated the pathogenesis of neurodevelopmental disorders (2)(3)(4). E3 ubiquitin ligases have received particular attention, as they confer substrate specificity of the ubiquitination reaction by catalyzing the transfer of ubiquitin to proteins destined for degradation or other ubiquitin-dependent fates (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

The X-linked intellectual disability gene product and E3 ubiquitin ligase KLHL15 degrades doublecortin proteins to constrain neuronal dendritogenesis

Song

¹

,

Merrill

²

,

Usachev

³

et al. 2020

Preprint

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Proper brain development and function requires finely controlled mechanisms for protein turnover and disruption of genes involved in proteostasis is a common cause of neurodevelopmental disorders. Kelch-like 15 (KLHL15) is a substrate adaptor for cullin3 (Cul3)-containing E3 ubiquitin ligases and KLHL15 gene mutations were recently described as a cause of severe X-linked intellectual disability. Here, we used a bioinformatics approach to identify a family of neuronal microtubule-associated proteins (MAPs) as KLHL15 substrates, which are themselves critical for early brain development. We biochemically validated doublecortin (DCX), also an X-linked disease gene, and doublecortin-like kinases 1 and 2 (DCLK1/2) as bona fide KLHL15 interactors and mapped KLHL15 interaction regions to their tandem DCX domains. Shared with two previously identified KLHL15 substrates, a FRY tripeptide at the C-terminal edge of the second DCX domain is necessary for KLHL15-mediated ubiquitination of DCX and DCLK1/2 and subsequent proteasomal degradation. Conversely, silencing endogenous KLHL15 markedly stabilizes these DCX domain-containing proteins and prolongs their half-life. Functionally, overexpression of KLHL15 in the presence of wild-type DCX reduces dendritic complexity of cultured hippocampal neurons, whereas neurons expressing FRY-mutant DCX are resistant to KLHL15. Collectively, our findings highlight the critical importance of the E3 ubiquitin ligase adaptor KLHL15 in proteostasis of neuronal MAPs and identify a regulatory network important for development of the mammalian nervous system.

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The ubiquitin proteasome pathway in neuropsychiatric disorders

Cited by 28 publications

References 89 publications

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

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

The X-linked intellectual disability gene product and E3 ubiquitin ligase KLHL15 degrades doublecortin proteins to constrain neuronal dendritogenesis

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