Biology subjects

Antaki, D.

Publications and source records attributed to Antaki, D..

10 recordsLinked to original sources

bioRxiv2022

Comprehensive multiomic profiling of somatic mutations in malformations of cortical development

Malformations of cortical development (MCD) are neurological conditions displaying focal disruption of cortical architecture and cellular organization arising during embryogenesis, largely from somatic mosaic mutations. Identifying the genetic causes of MCD has been a challenge, as mutations remain at low allelic fractions in brain tissue resected to treat epilepsy. Here, we report a genetic atlas from 317 brain resections, identifying 69 mutated genes through intensive profiling of somatic mutations, combining whole-exome and targeted-amplicon sequencing with functional validation and single-cell sequencing. Genotype-phenotype correlation analysis elucidated specific MCD gene sets associating distinct pathophysiological and clinical phenotypes. The unique spatiotemporal expression patterns identified by comparing single-nucleus transcriptional sequences of mutated genes in control and patient brains implicate critical roles in excitatory neurogenic pools during brain development, and in promoting neuronal hyperexcitability after birth.

genetics↗

bioRxiv2020

DeepMosaic: Control-independent mosaic single nucleotide variant detection using deep convolutional neural networks

Introductory paragraphMosaic variants (MVs) reflect mutagenic processes during embryonic development1 and environmental exposure2, accumulate with aging, and underlie diseases such as cancer and autism3. The detection of MVs has been computationally challenging due to sparse representation in non-clonally expanded tissues. While heuristic filters and tools trained on clonally expanded MVs with high allelic fractions are proposed, they show relatively lower sensitivity and more false discoveries4-9. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs, and a convolutional neural networks-based classification module for control-independent MV detection. DeepMosaic achieved higher accuracy compared with existing methods on biological and simulated sequencing data, with a 96.34% (158/164) experimental validation rate. Of 932 mosaic variants detected by DeepMosaic in 16 whole genome sequenced samples, 21.89-58.58% (204/932-546/932) MVs were overlooked by other methods. Thus, DeepMosaic represents a highly accurate MV classifier that can be implemented as an alternative or complement to existing methods.

genomics↗

bioRxiv2020

Temporal stability of human sperm mosaic mutations results in life-long threat of transmission to offspring

Every newborn harbors scores of new single nucleotide variants (SNVs) that may impact health and disease1-4; the majority of these are contributed by the paternal germ cells5. In some cases, these mutations are identifiable in a subset of the parents cells--a phenomenon called mosaicism, which is capable of producing disease recurrence6-8. Here, we provide a comprehensive analysis of male gonadal mosaic mutations, employing 300x whole genome sequencing (WGS) of blood and sperm in 17 healthy individuals, including assessment across multiple samples and age groups. Approximately 1 in 15 healthy males is predicted to harbor a transmissible, likely pathogenic exonic variant that is mosaic in his sperm. In general, only a third of sperm mosaic mutations were detectable in blood cells, all were remarkably stable over the course of months to years, and 23% were present in 5% or more of sperm cells. There was no evidence of age-dependent clonal expansion or collapse, as seen in hematopoiesis. Thus, despite the observed increase of mutations in offspring of men with advanced paternal age, detectable sperm mosaicism remains stable, represents a life-long transmission risk to offspring, and suggests a testis stem cell niche that prevents widespread clonality.

genomics↗

bioRxiv2020

Somatic mosaicism in the mature brain reveals clonal cellular distributions during cortical development

The structure of the human neocortex underlies species-specific features and is a reflection of intricate developmental programs. Here we analyzed neocortical cellular lineages through a comprehensive assessment of brain somatic mosaicism--which acts as a neutral recorder of lineage history. We employed deep whole genome and variant sequencing in a single postmortem neurotypical human brain across 25 anatomic regions and three distinct modalities: bulk geographies, sorted cell types, and single nuclei. We identified 259 mosaic variants, revealing remarkable differences in localization, clonal abundance, cell type specificity, and clade distribution. We identified a set of hierarchical cellular diffusion barriers, whereby the left-right axis separation of the neocortex occurs prior to anterior-posterior and dorsal-ventral axis separation. We also found that stochastic distribution is a driver of clonal dispersion, and that rules regarding cellular lineages and anatomical boundaries are often ignored. Our data provides a comprehensive analysis of brain somatic mosaicism across the human cerebral cortex, deconvolving clonal distributions and migration patterns in the human embryo. One Sentence SummaryComprehensive evaluation of brain somatic mosaicism in the adult human identifies rules governing cellular distribution during embryogenesis.

genomics↗

bioRxiv2020

Cortical Organoids Model Early Brain Development Disrupted by 16p11.2 Copy Number Variants in Autism

Reciprocal deletion and duplication of 16p11.2 region is the most common copy number variation (CNV) associated with Autism Spectrum Disorders. We generated cortical organoids from skin fibroblasts of patients with 16p11.2 CNV to investigate impacted neurodevelopmental processes. We show that organoid size recapitulates macrocephaly and microcephaly phenotypes observed in the patients with 16p11.2 deletions and duplications. The CNV has mirror-opposite effect on neuronal maturation, proliferation, and synapse number, in concordance with its effect on brain growth in humans. We demonstrate that 16p11.2 CNV alters the ratio of neurons to neural progenitors in organoids during early neurogenesis, with excess of neurons and depletion of neural progenitors observed in deletions, and mirror phenotypes in duplications. Transcriptomic and proteomic profiling revealed multiple dysregulated pathways, including defects in neuron migration. Inhibition of activity of the small GTPase RhoA rescued migration deficits. This study provides insights into potential neurobiological mechanisms behind the 16p11.2 CNV during neocortical development.

neuroscience↗

bioRxiv2020

Importance of polymorphic SNPs, short tandem repeats and structural variants for differential gene expression among inbred C57BL/6 and C57BL/10 substrains

Mouse substrains are an invaluable model for understanding disease. We compared C57BL/6J, which is the most commonly used inbred mouse strain, with 8 C57BL/6 and 5 C57BL/10 closely related inbred substrains. Whole genome sequencing and RNA-sequencing analysis yielded 352,631 SNPs, 109,096 INDELs, 150,344 short tandem repeats (STRs), 3,425 structural variants (SVs) and 2,826 differentially expressed genes (DEGenes) among these 14 strains. 312,981 SNPs (89%) distinguished the B6 and B10 lineages. These SNPs were clustered into 28 short segments that are likely due to introgressed haplotypes rather than new mutations. Outside of these introgressed regions, we identified 53 SVs, protein-truncating SNPs and frameshifting INDELs that were associated with DEGenes. Our results can be used for both forward and reverse genetic approaches, and illustrate how introgression and mutational processes give rise to differences among these widely used inbred substrains.

genomics↗

bioRxiv2017

Quantification of autism recurrence risk by direct assessment of paternal sperm mosaicism

De novo genetic mutations represent a major contributor to pediatric disease, including autism spectrum disorders (ASD), congenital heart disease, and muscular dystrophies1,2, but there are currently no methods to prevent or predict them. These mutations are classically thought to occur either at low levels in progenitor cells or at the time of fertilization1,3 and are often assigned a low risk of recurrence in siblings4,5. Here, we directly assess the presence of de novo mutations in paternal sperm and discover abundant, germline-restricted mosaicism. From a cohort of ASD cases, employing single molecule genotyping, we found that four out of 14 fathers were germline mosaic for a putatively causative mutation transmitted to the affected child. Three of these were enriched or exclusively present in sperm at high allelic fractions (AF; 7-15%); and one was recurrently transmitted to two additional affected children, representing clinically actionable information. Germline mosaicism was further assessed by deep (>90x) whole genome sequencing of four paternal sperm samples, which detected 12/355 transmitted de novo single nucleotide variants that were mosaic above 2% AF, and more than two dozen additional, non-transmitted mosaic variants in paternal sperm. Our results demonstrate that germline mosaicism is an underestimated phenomenon, which has important implications for clinical practice and in understanding the basis of human disease. Genetic analysis of sperm can assess individualized recurrence risk following the birth of a child with a de novo disease, as well as the risk in any male planning to have children.

genetics↗

bioRxiv2017

Multi-platform discovery of haplotype-resolved structural variation in human genomes

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, and strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three human parent-child trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs ([≥]50 bp) per human genome. We also discover 156 inversions per genome--most of which previously escaped detection. Fifty-eight of the inversions we discovered intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The method and the dataset serve as a gold standard for the scientific community and we make specific recommendations for maximizing structural variation sensitivity for future large-scale genome sequencing studies.

genomics↗

bioRxiv2017

Paternally inherited noncoding structural variants contribute to autism

The genetic architecture of autism spectrum disorder (ASD) is known to consist of contributions from gene-disrupting de novo mutations and common variants of modest effect. We hypothesize that the unexplained heritability of ASD also includes rare inherited variants with intermediate effects. We investigated the genome-wide distribution and functional impact of structural variants (SVs) through whole genome analysis ([≥]30X coverage) of 3,169 subjects from 829 families affected by ASD. Genes that are intolerant to inactivating variants in the exome aggregation consortium (ExAC) were depleted for SVs in parents, specifically within fetal-brain promoters, UTRs and exons. Rare paternally-inherited SVs that disrupt promoters or UTRs were over-transmitted to probands (P = 0.0013) and not to their typically-developing siblings. Recurrent functional noncoding deletions implicate the gene LEO1 in ASD. Protein-coding SVs were also associated with ASD (P = 0.0025). Our results establish that rare inherited SVs predispose children to ASD, with differing contributions from each parent.

genomics↗

bioRxiv2017

SV2: Accurate Structural Variation Genotyping and De Novo Mutation Detection

MotivationStructural Variation (SV) detection from short-read whole genome sequencing is error prone, presenting significant challenges for population or family-based studies of disease.\n\nResultsHere we describe SV2, a machine-learning algorithm for genotyping deletions and duplications from paired-end sequencing data. SV2 can rapidly integrate variant calls from multiple structural variant discovery algorithms into a unified call set with high genotyping accuracy and capability to detect de novo mutations.\n\nAvailability and ImplementationSV2 is freely available on GitHub (https://github.com/dantaki/SV2)

bioinformatics↗