scholarly journals Dissecting Autism Genetic Risk Using Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Siying Chen ◽  
Xueya Zhou ◽  
Eve Byington ◽  
Samuel L. Bruce ◽  
Haicang Zhang ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Sequencing ◽  
De Novo ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Supervised Machine Learning ◽  
Specific Gene ◽  
Rna Seq ◽  
Risk Genes ◽  
Sequencing Studies

AbstractAutism spectrum disorder (autism) is a condition with strong but heterogenous genetic contribution. Recent exome and genome sequencing studies have uncovered many new risk genes through de novo variants. However, a large fraction of enrichment of de novo variants observed in cases are not accounted for by known or candidate risk genes, suggesting that the majority of risk genes are still unknown. Here we hypothesize that autism risk genes share a few common cell-type specific gene expression patterns during brain development, and such information can be quantified to improve statistical power of detecting new risk genes. We obtained large-scale single-cell RNA-seq data from human fetal brain collected through a range of developmental stages, and developed a supervised machine-learning approach “A-risk” (Autism risk), to predict the plausibility of autism risk genes across the genome. Using data from recent exome sequencing studies of autism, A-risk achieves better performance in prioritizing de novo variants than other methods, especially for genes that are less intolerant of loss of function variants. We stratified genes based on A-risk and mutation intolerance metrics to improve estimation of priors in extTADA and identified 71 candidate risk genes. In particular, CLCN4, PRKAR1B, and NR2F1 are potentially new risk genes with further support from neurodevelopmental disorders. Expression patterns of both known and candidate risk genes reveals the important role of deep-layer excitatory neurons from adult human cortex in autism etiology. With the unprecedented revolution of single-cell transcriptomics and expanding autism cohorts with exome or genome sequencing, our method will facilitate systematic discovery of novel risk genes and understanding of biological pathogenesis in autism.

scholarly journals Varying intolerance of gene pathways to mutational classes explain genetic convergence across neuropsychiatric disorders

2016 ◽  
Author(s):  
Shahar Shohat ◽  
Eyal Ben-David ◽  
Sagiv Shifman
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
De Novo ◽  
Expression Patterns ◽  
Neuropsychiatric Disorders ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Missense Mutations ◽  
Loss Of Function ◽  
Brain Gene Expression

AbstractGenetic susceptibility to Intellectual disability (ID), autism spectrum disorder (ASD) and schizophrenia (SCZ) often arises from mutations in the same genes, suggesting that they share common mechanisms. We studied genes with de novo mutations in the three disorders and genes implicated by SCZ genome-wide association study (GWAS). Using biological annotations and brain gene expression, we show that mutation class explains enrichment patterns more than specific disorder. Genes with loss of function mutations and genes with missense mutations were enriched with different pathways, shared with genes intolerant to mutations. Specific gene expression patterns were found for each disorder. ID genes were preferentially expressed in fetal cortex, ASD genes also in fetal cerebellum and striatum, and genes associated with SCZ were most significantly enriched in adolescent cortex. Our study suggests that convergence across neuropsychiatric disorders stems from vulnerable pathways to genetic variations, but spatiotemporal activity of genes contributes to specific phenotypes.

scholarly journals Rare coding variation illuminates the allelic architecture, risk genes, cellular expression patterns, and phenotypic context of autism

2021 ◽  
Author(s):  
Jack M. Fu ◽  
F. Kyle Satterstrom ◽  
Minshi Peng ◽  
Harrison Brand ◽  
Ryan L. Collins ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Developmental Disorders ◽  
De Novo ◽  
Expression Patterns ◽  
Allelic Diversity ◽  
Autism Spectrum ◽  
Functional Variation ◽  
Risk Genes ◽  
Missense Variants ◽  
Cellular Expression

Individuals with autism spectrum disorder (ASD) or related neurodevelopmental disorders (NDDs) often carry disruptive mutations in genes that are depleted of functional variation in the broader population. We build upon this observation and exome sequencing from 154,842 individuals to explore the allelic diversity of rare protein-coding variation contributing risk for ASD and related NDDs. Using an integrative statistical model, we jointly analyzed rare protein-truncating variants (PTVs), damaging missense variants, and copy number variants (CNVs) derived from exome sequencing of 63,237 individuals from ASD cohorts. We discovered 71 genes associated with ASD at a false discovery rate (FDR) ≤ 0.001, a threshold approximately equivalent to exome-wide significance, and 183 genes at FDR ≤ 0.05. Associations were predominantly driven by de novo PTVs, damaging missense variants, and CNVs: 57.4%, 21.2%, and 8.32% of evidence, respectively. Though fewer in number, CNVs conferred greater relative risk than PTVs, and repeat-mediated de novo CNVs exhibited strong maternal bias in parent-of-origin (e.g., 92.3% of 16p11.2 CNVs), whereas all other CNVs showed a paternal bias. To explore how genes associated with ASD and NDD overlap or differ, we analyzed our ASD cohort alongside a developmental delay (DD) cohort from the deciphering developmental disorders study (DDD; n=91,605 samples). We first reanalyzed the DDD dataset using the same models as the ASD cohorts, then performed joint analyses of both cohorts and identified 373 genes contributing to NDD risk at FDR ≤ 0.001 and 662 NDD risk genes at FDR ≤ 0.05. Of these NDD risk genes, 54 genes (125 genes at FDR ≤ 0.05) were unique to the joint analyses and not significant in either cohort alone. Our results confirm overlap of most ASD and DD risk genes, although many differ significantly in frequency of mutation. Analyses of single-cell transcriptome datasets showed that genes associated predominantly with DD were strongly enriched for earlier neurodevelopmental cell types, whereas genes displaying stronger evidence for association in ASD cohorts were more enriched for maturing neurons. The ASD risk genes were also enriched for genes associated with schizophrenia from a separate rare coding variant analysis of 121,570 individuals, emphasizing that these neuropsychiatric disorders share common pathways to risk.

scholarly journals Prediction of single-cell mechanisms for disease progression in hypertrophic remodelling by a trans-omics approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Momoko Hamano ◽  
Seitaro Nomura ◽  
Midori Iida ◽  
Issei Komuro ◽  
Yoshihiro Yamanishi
Keyword(s):  
Heart Failure ◽  
Single Cell ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Marker Genes ◽  
Specific Gene ◽  
Rna Seq ◽  
Aortic Constriction ◽  
Multiple Risk Factors

AbstractHeart failure is a heterogeneous disease with multiple risk factors and various pathophysiological types, which makes it difficult to understand the molecular mechanisms involved. In this study, we proposed a trans-omics approach for predicting molecular pathological mechanisms of heart failure and identifying marker genes to distinguish heterogeneous phenotypes, by integrating multiple omics data including single-cell RNA-seq, ChIP-seq, and gene interactome data. We detected a significant increase in the expression level of natriuretic peptide A (Nppa), after stress loading with transverse aortic constriction (TAC), and showed that cardiomyocytes with high Nppa expression displayed specific gene expression patterns. Multiple NADH ubiquinone complex family, which are associated with the mitochondrial electron transport system, were negatively correlated with Nppa expression during the early stages of cardiac hypertrophy. Large-scale ChIP-seq data analysis showed that Nkx2-5 and Gtf2b were transcription factors characteristic of high-Nppa-expressing cardiomyocytes. Nppa expression levels may, therefore, represent a useful diagnostic marker for heart failure.

scholarly journals Single-Cell RNA-Seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-Deficiency

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A753-A754
Author(s):  
Jingfei Chen ◽  
Jialei Duan ◽  
Alina Montalbano ◽  
Boxun Li ◽  
Gary Hon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Immune Cell ◽  
Expression Patterns ◽  
Critical Role ◽  
Fetal Lung ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Clusters ◽  
Pregnant Mice

Abstract Single-cell RNA-seq of Mouse Decidual Leukocytes Reveals Intriguing Gestational Changes in the Immune Cell Landscape and Effects of SRC-1/-2 Double-DeficiencyOur previous findings suggest that the fetus signals the mother when it is ready to be born through secretion of surfactant components/immune modulators, surfactant protein A (SP-A) and platelet-activating factor (PAF) by the fetal lung into amniotic fluid (AF). This occurs with increased proinflammatory cytokine expression by fetal AF macrophages (Mϕ), increased myometrial NF-κB activation and contractile (CAP) gene expression. Steroid receptor coactivator (Src)-1 and Src-2 are critical for developmental induction SP-A and PAF by the fetal lung. The finding that pregnant wild-type (WT) mice carrying Src-1 and Src-2 double-deficient fetuses (Src-1/-2d/d) manifested a marked delay (~38h) in parturition, further suggests that signals for parturition arise from the fetus and that Src-1 and Src-2 serve a critical role. Infiltrating leukocytes at the maternal-fetal interface (MFI)/decidua are known to play a central role in pregnancy maintenance and parturition timing. However, there is limited knowledge regarding gestational changes in immune cell composition toward term. To analyze gestational changes in the composition of immune cells within decidua and effects of Src-1/-2d/d, we conducted single-cell RNA-seq of ~17,000 decidual leukocytes (CD45+) from pregnant mice at 15.5 and 18.5 dpc carrying WT or Src-1/-2d/d fetuses. Unsupervised clustering identified 22 distinct decidual immune cell clusters, comprised of Mϕ, B cells, natural killer cells, neutrophils, dendritic cells and monocytes. Significant differences in cell type composition and transcriptional profiles were found across study groups (WT @ 15.5 dpc vs. 18.5 dpc; Src-1/-2d/dvs. WT @15.5 dpc and 18.5 dpc). Interestingly, in deciduas of pregnant mice carrying WT fetuses, Mϕ and B cell clusters markedly increased between 15.5 and 18.5 dpc, whereas, neutrophils declined; however, these gestational changes did not occur in pregnant mice carrying Src-1/-2d/d fetuses. Differential gene expression and gene ontology enrichment analyses revealed specific gene expression patterns distinguishing these immune cell subtypes to uncover their putative functions. These findings highlight the complexity and dynamics of the decidual immune cell landscape during the transition from myometrial quiescence to contractility, and the fetal-maternal interactions leading to parturition. Support: NIH grants R01-HL050022 (CRM) and P01-HD087150 (CRM), Burroughs Wellcome Preterm Birth Grant #1019823 (CRM).

scholarly journals Finding cell-specific expression patterns in the early Ciona embryo with single-cell RNA-seq

2017 ◽  
Author(s):  
Garth R. Ilsley ◽  
Ritsuko Suyama ◽  
Takeshi Noda ◽  
Nori Satoh ◽  
Nicholas M. Luscombe
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Stage ◽  
Specific Expression ◽  
Temporal Gene Expression

AbstractSingle-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

scholarly journals Whole genome sequencing in multiplex families reveals novel inherited and de novo genetic risk in autism

2018 ◽  
Author(s):  
Elizabeth K. Ruzzo ◽  
Laura Pérez-Cano ◽  
Jae-Yoon Jung ◽  
Lee-kai Wang ◽  
Dorna Kashef-Haghighi ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
De Novo ◽  
Autism Spectrum ◽  
Whole Genome ◽  
Single Nucleotide Variants ◽  
Risk Genes ◽  
Protein Protein Interaction ◽  
Genetics Research ◽  
Multiplex Families

AbstractGenetic studies of autism spectrum disorder (ASD) have revealed a complex, heterogeneous architecture, in which the contribution of rare inherited variation remains relatively un-explored. We performed whole-genome sequencing (WGS) in 2,308 individuals from families containing multiple affected children, including analysis of single nucleotide variants (SNV) and structural variants (SV). We identified 16 new ASD-risk genes, including many supported by inherited variation, and provide statistical support for 69 genes in total, including previously implicated genes. These risk genes are enriched in pathways involving negative regulation of synaptic transmission and organelle organization. We identify a significant protein-protein interaction (PPI) network seeded by inherited, predicted damaging variants disrupting highly constrained genes, including members of the BAF complex and established ASD risk genes. Analysis of WGS also identified SVs effecting non-coding regulatory regions in developing human brain, implicating NR3C2 and a recurrent 2.5Kb deletion within the promoter of DLG2. These data lend support to studying multiplex families for identifying inherited risk for ASD. We provide these data through the Hartwell Autism Research and Technology Initiative (iHART), an open access cloud-computing repository for ASD genetics research.

scholarly journals Single cell RNA-seq study of wild type and Hox9,10,11 mutant developing uterus

2018 ◽  
Author(s):  
Michael L. Mucenski ◽  
Robert Mahoney ◽  
Mike Adam ◽  
Andrew S. Potter ◽  
S. Steven Potter
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Hox Genes ◽  
Expression Patterns ◽  
Wild Type Mouse ◽  
Cell Types ◽  
Specific Gene ◽  
Rna Seq ◽  
Wild Type ◽  
Mouse Uterus

AbstractThe uterus is a remarkable organ that must guard against infections while maintaining the ability to support growth of a fetus without rejection. The Hoxa10 and Hoxa11 genes have previously been shown to play essential roles in uterus development and function. In this report we show that the Hoxc9,10,11 genes play a redundant role in the formation of uterine glands. In addition, we use single cell RNA-seq to create a high resolution gene expression atlas of the developing wild type mouse uterus. Cell types and subtypes are defined, for example dividing endothelial cells into arterial, venous, capillary, and lymphatic, while epithelial cells separate into luminal and glandular subtypes. Further, a surprising heterogeneity of stromal and myocyte cell types are identified. Transcription factor codes and ligand/receptor interactions are characterized. We also used single cell RNA-seq to globally define the altered gene expression patterns in all developing uterus cell types for two Hox mutants, with 8 or 9 mutant Hox genes. The mutants show a striking disruption of Wnt signaling as well as the Cxcl12/Cxcr4 ligand/receptor axis.Summary statementA single cell RNA-seq study of the developing mouse uterus defines cellular heterogeneities, lineage specific gene expression programs and perturbed pathways in Hox9,10,11 mutants.

scholarly journals In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with Autism risk genes

2019 ◽  
Author(s):  
Xin Jin ◽  
Sean K. Simmons ◽  
Amy X. Guo ◽  
Ashwin S. Shetty ◽  
Michelle Ko ◽  
...  
Keyword(s):  
De Novo ◽  
Disease Risk ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Functional Evaluation ◽  
Loss Of Function ◽  
Risk Genes ◽  
Biologically Relevant ◽  
Relevant Context

AbstractThe thousands of disease risk genes and loci identified through human genetic studies far outstrip our current capacity to systematically study their functions. New experimental approaches are needed for functional investigations of large panels of genes in a biologically relevant context. Here, we developed a scalable genetic screen approach, in vivo Perturb-Seq, and applied this method to the functional evaluation of 35 autism spectrum disorder (ASD) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing brain in utero, and then performed single-cell RNA-Seq in the postnatal brain. We identified cell type-specific gene signatures from both neuronal and glial cell classes that are affected by genetic perturbations, and pointed at elements of both convergent and divergent cellular effects across this cohort of ASD risk genes. In vivo Perturb-Seq pioneers a systems genetics approach to investigate at scale how diverse mutations affect cell types and states in the biologically relevant context of the developing organism.

scholarly journals A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies

2018 ◽  
Vol 102 (6) ◽  
pp. 1031-1047 ◽  
Author(s):  
Yuwen Liu ◽  
Yanyu Liang ◽  
A. Ercument Cicek ◽  
Zhongshan Li ◽  
Jinchen Li ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
De Novo ◽  
Whole Genome ◽  
De Novo Mutations ◽  
Risk Genes ◽  
Statistical Framework ◽  
Sequencing Studies

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

2018 ◽  
Author(s):  
Ying Lin ◽  
Anjali M. Rajadhyaksha ◽  
James B. Potash ◽  
Shizhong Han
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Human Brain ◽  
Candidate Genes ◽  
De Novo ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Gene Expression Patterns ◽  
Risk Genes ◽  
Gene Level

AbstractAutism spectrum disorder (ASD) is a complex neurodevelopmental condition with a strong genetic basis. The role ofde novomutations 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,TCF20andFBOX11), but also indicated potentially novel candidates, such asDOCK3,MYCBP2andCAND1, which are all involved in neuronal development. Through extensive validations, we also showed that our method outperformed state-of-the-art 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.

Sign in / Sign up

Export Citation Format

Share Document