A Synaptic Perspective of Fragile X Syndrome and Autism Spectrum Disorders

Bagni, Claudia; Zukin, R. Suzanne

doi:10.1016/j.neuron.2019.02.041

Cited by 250 publications

(286 citation statements)

References 289 publications

(354 reference statements)

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“…As illustrated above, several of the themes identified have evidence for a joint role in ASD. In support to this, in Fragile X, a well-known syndrome associated with ASD, evidence has been published for all pathways mentioned above: from dysregulation of calcium signaling, synaptic structures, actin to inflammation, and changes in the retinoid and coagulation pathways [63][64][65][66][67][68][69].…”

Section: Discussionmentioning

confidence: 98%

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Sardaar

Dionne‐Laporte

et al. 2020

BMC Psychiatry

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Background: Machine learning (ML) algorithms and methods offer great tools to analyze large complex genomic datasets. Our goal was to compare the genomic architecture of schizophrenia (SCZ) and autism spectrum disorder (ASD) using ML. Methods: In this paper, we used regularized gradient boosted machines to analyze whole-exome sequencing (WES) data from individuals SCZ and ASD in order to identify important distinguishing genetic features. We further demonstrated a method of gene clustering to highlight which subsets of genes identified by the ML algorithm are mutated concurrently in affected individuals and are central to each disease (i.e., ASD vs. SCZ "hub" genes).Results: In summary, after correcting for population structure, we found that SCZ and ASD cases could be successfully separated based on genetic information, with 86-88% accuracy on the testing dataset. Through bioinformatic analysis, we explored if combinations of genes concurrently mutated in patients with the same condition ("hub" genes) belong to specific pathways. Several themes were found to be associated with ASD, including calcium ion transmembrane transport, immune system/inflammation, synapse organization, and retinoid metabolic process. Moreover, ion transmembrane transport, neurotransmitter transport, and microtubule/ cytoskeleton processes were highlighted for SCZ. Conclusions: Our manuscript introduces a novel comparative approach for studying the genetic architecture of genetically related diseases with complex inheritance and highlights genetic similarities and differences between ASD and SCZ.

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Section: Discussionmentioning

confidence: 98%

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Sardaar

Dionne‐Laporte

et al. 2020

BMC Psychiatry

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“…Also present are TRIO (SZ, Katrancha et al, 2017), which promotes reorganization of the actin cytoskeleton during neurite outgrowth (Bateman et al, 2000;van Haren et al, 2014), and FMR1 (FMRP) (fragile-X syndrome, Verkerk et al, 1991;ASD, Darnell et al, 2011;SZ, Pardiñas et al, 2018). While discussion of FMRP in disease has tended to focus on its role in plasticity at mature synapses (Bagni and Zukin, 2019, Darnell et al, 2011, it is also present in the axons and dendrites of developing neurons where it controls growth cone morphology and motility (Antar et al, 2006). Reflecting this role, targets of FMRP are highly over-represented in early-stable -/-( Figure 4E).…”

Section: Disruption Of Neurogenic Transcriptional Programs In Neurodementioning

confidence: 99%

DLG2 knockout reveals neurogenic transcriptional programs underlying neuropsychiatric disorders and cognition

Sanders

D’Andrea

Collins³

et al. 2020

Preprint

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Genetic studies robustly implicate perturbation of DLG2-scaffolded mature postsynaptic signalling complexes in schizophrenia. Here we study in vitro cortical differentiation of DLG2 -/human embryonic stem cells via integrated phenotypic, gene expression and disease genetic analyses. This uncovers a developmental role for DLG2 in the regulation of neural stem cell proliferation and adhesion, and the activation of transcriptional programs during early excitatory corticoneurogenesis. Down-regulation of these programs in DLG2 -/lines delays expression of cell-type identity and causes marked deficits in neuronal migration, morphology and active properties. Genetic risk factors for neuropsychiatric and neurodevelopmental disorders converge on these neurogenic programs, each disorder displaying a distinct pattern of enrichment. These data unveil an intimate link between neurodevelopmental and mature signalling deficits contributing to disease -suggesting a dual role for known synaptic risk genes -and reveal a common pathophysiological framework for studying the neurodevelopmental origins of Mendelian and genetically complex mental disorders. Results Knockout generation and validationTwo DLG2 -/lines were created from H7 hESCs using the CRISPR/Cas9-D10A nickase system targeting the first PDZ domain ( Figure S1). Sequencing revealed no off-target mutations (see Methods, Figure S2 & Table S1). A significant decrease in DLG2 mRNA was observed for exons spanning the first PDZ domain, with a similar decrease inferred for PDZ-containing transcripts, indicating degradation of DLG2 -/transcripts via nonsense-mediated decay ( Figure S3A, B). Quantitative mass spectrometry-based proteomic analysis of peptide-affinity pulldowns using the NMDA receptor NR2 subunit PDZ peptide ligand (Husi and Grant, 2001) confirmed the presence of DLG2 in pulldowns from WT but not DLG2 -/lines ( Figure S3C-F & Table S2). Genotyping revealed no CNVs in either DLG2 -/line relative to WT ( Figure S4A). Both DLG2 -/lines expressed pluripotency markers OCT4, SOX2 and NANOG at 100% of WT levels ( Figure S4B-E). Cells were extensively characterised for their cortical identity using western blotting and immunocytochemistry from day 20 to 60 ( Figure S5). Over 90% of day 20 cells were positive for FOXG1, PAX6 and SOX2 confirming their dorsal forebrain fate ( Figure S5A-B). DLG2 regulates neural stem-cell proliferation and adhesionRNA was extracted in triplicate from each line at 4 timepoints spanning cortical excitatory neuron development (Figure 1A, B) and gene expression quantified. To robustly identify genes dysregulated by DLG2 knockout, expression data from the 2 DLG2 -/lines were pooled and compared to a WT sister line at each timepoint (see Methods). Disruption of DLG2 had a profound effect: of the >13,000 protein-coding genes expressed at each timepoint, ~7% were differentially expressed between DLG2 -/and WT at day 15, rising to 40-60% between days 20 and 30 then decreasing to ~25% by day 60 ( Figure 1C). Strikingly, the 3 genes with the strongest ...

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“…Chromatin modelling is essential for the establishment and maintenance of gene expression profiles to support neuronal differentiation, structural brain organisation, and flexibility of neuronal circuitry for learning (13,14). Synaptic transmission, its upstream regulation and downstream signalling, are fundamental to dynamic neurophysiological processes supporting perception, memory and action (15). Our hypothesis was that these functional networks could be associated with different dimensional autism characteristics, reflecting distinct underlying mechanisms.…”

Section: Introductionmentioning

confidence: 98%

Gene functional networks and autism spectrum characteristics in young people with intellectual disability

Brkić

Ng‐Cordell

O’Brien

et al. 2020

Preprint

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BackgroundGenes associated with Intellectual disability (ID) can be grouped into networks according to gene function. This study asked whether individuals with ID show differences in autism spectrum characteristics (ASC), depending on the functional network membership of their rare, pathogenic de novo genetic variants. MethodsChildren and young people with ID of known genetic origin were allocated to two broad functional network groups: synaptic physiology (n=29) or chromatin regulation (n=23). We applied principle components analysis to the Social Responsiveness Scale to map the structure of ASC in this population, and identified three components -Inflexibility, Social Understanding and Social Motivation. We then used Akaike Information Criterion (AIC) to test the best fitting models for predicting ASC components, including demographic factors (age, gender), non-ASC behavioural factors (global adaptive function, anxiety, hyperactivity, inattention) and gene functional networks. ResultsWe found that, when other factors are accounted for, the chromatin regulation group showed higher levels of Inflexibility. We also observed contrasting predictors of ASC within each network group.Within the chromatin regulation group, Social Understanding was associated with inattention, and Social Motivation was predicted by hyperactivity. Within the synaptic group, Social Understanding was associated with hyperactivity, and Social Motivation was linked to anxiety. ConclusionWe report that gene functional networks can predict Inflexibility, but not other ASC dimensions.Contrasting behavioural associations within each group suggests network-specific developmental pathways from genomic variation to autism. Simple classification of neurodevelopmental disorder genes as high risk or low risk for autism is unlikely to be valid or useful.Affiliations:

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A Synaptic Perspective of Fragile X Syndrome and Autism Spectrum Disorders

Cited by 250 publications

References 289 publications

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

DLG2 knockout reveals neurogenic transcriptional programs underlying neuropsychiatric disorders and cognition

Gene functional networks and autism spectrum characteristics in young people with intellectual disability

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