Project description:Tuberous Sclerosis Complex (TSC), an autosomal dominant condition, is engendered by heterozygous mutations in either TSC1 or TSC2 genes, manifesting in systemic growth of benign tumors. In addition to brain lesions, neurologic sequelae represent the greatest morbidity in TSC patients. Investigations utilizing TSC1/2-knockout animal or human stem cell models suggest that TSC deficiency-causing hyper-activation of mTOR signaling might precipitate anomalous neurodevelopmental processes. However, how the pathogenic variants of TSC1/2 genes identified in TSC patients affect the trajectory of human brain development and how they contribute to the neurological manifestations in TSC remain largely unexplored. Here, we employed 3-dimensional cortical organoids derived from induced pluripotent stem cells (iPSCs) from TSC patients harboring TSC2 mutations, alongside organoids from age- and sex-matched healthy individuals as controls. Through comprehensively longitudinal molecular and cellular analysis of TSC organoids, including transcriptomics and single cell transcriptomics, we found that TSC2 pathogenic variants led to dysregulated neurogenesis, synaptogenesis, and gliogenesis, particularly for reactive astrogliosis. The altered developmental trajectory of TSC organoids significantly resembles the molecular signatures of neuropsychiatric disorders, including autism spectrum disorders, epilepsy, and intellectual disability. Through cell-cell communication analysis at the single cell level, we identified that TSC2 pathogenic variants disrupted the cell-cell communications, in particular, neuron-astrocyte interactions within the NLGN-NRXN signaling network. Furthermore, cellular and electrophysiological assessments of TSC cortical organoids, along with proteomics and phosphoproteomics analyses of synaptosomes, revealed that pathogenic TSC2 variants precipitate perturbations in mitochondrial translational integrity, neurofilament formation, synaptic transmission, and neuronal network activity. Intriguingly, the increased neu

Project description:Copy number variations (CNVs) account for a substantial proportion of human genomic variation, and have been shown to cause neurodevelopmental disorders. We sought to determine the relevance of CNVs to the aetiology of schizophrenia. Whole genome, high resolution, tiling path BAC array comparative genomic hybridization (array CGH) was employed to test DNA from 91 individuals with DSM-IV schizophrenia. Common DNA copy number changes that are unlikely to be directly pathogenic in schizophrenia were identified by comparison to a reference dataset of 372 control individuals analysed in our laboratory, and a screen against the Database of Genomic Variants. The remaining aberrations were tested for inheritance from the parents, and validated with Affymetrix 250K SNP arrays or 244K Agilent oligo-arrays. Thirteen aberrations satisfied our criteria. Two of them are very likely to be pathogenic. A deletion at 2p16.3 disrupts NRXN1 and was present in an affected sibling. A de novo duplication at 15q13.1 spans APBA2. The proteins of these two genes interact directly and play a role in synaptic development and function. Both genes have been affected by CNVs in other neurodevelopmental disorders. Keywords: Array CGH We undertook a systematic search for CNVs in patients with schizophrenia using high resolution, whole genome tiling path BAC arrays. We selected 45 male and 48 female unrelated proband-parent trios from our sample of ~600 Bulgarian SZ trios recruited as described previously. In all cases IQ was > 70. The mean age of probands was 33.8 years (SD = 10.1, range 13-57 years). The mean age at onset of psychotic symptoms was 22.1 years (SD = 6.6, range 11-44 years). Sonicated patient and reference DNA was labelled by random priming (Bioprime Array CGH, Invitrogen, Carlsbad, CA) with Cy3 and Cy5 (Amersham Biosciences, Piscataway, NJ), respectively, and hybridized onto a tiling path BAC array, consisting of ~36,000 BAC clones obtained from several sources as described on our website (http://www.molgen.mpg.de/~abt_rop/molecular_cytogenetics/). All protocols are also provided on that website. For the analysis and visualization of array CGH data, our software-package CGHPRO was employed. For the assessment of copy number gains and losses, we used conservative log2 ratio thresholds of 0.3 and -0.3, respectively. Deviant signal intensity ratios involving three or more neighboring BAC clones were considered to be potentially pathogenic, unless they were covered by more than one known DNA copy number variant, as listed in the Database of Genomic Variants. No dye swap was done. Array CGH analysis was successful in 91 cases.

Project description:Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders.

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