scholarly journals Cell Type-Specific Gene Network-Based Analysis Depicts the Heterogeneity of Autism Spectrum Disorder

2020 ◽  
Vol 14 ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Guoli Ji
Keyword(s):  
Autism Spectrum Disorder ◽  
Gene Network ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Specific Gene ◽  
Cell Type ◽  
Cell Type Specific

scholarly journals Cell type-specific predictive models for prioritizing genes and gene sets associated with autism spectrum disorder

2020 ◽  
Author(s):  
Jinting Guan ◽  
Yang Wang ◽  
Yiping Lin ◽  
Qingyang Yin ◽  
Yibo Zhuang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Predictive Models ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Gene Sets ◽  
Cell Type Specific ◽  
Protoplasmic Astrocytes

Abstract Background Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity. Although bulk transcriptomic analyses revealed convergence of disease pathology on common pathways, the brain cell type-specific molecular pathology of ASD is still needed to study. Different gene functions may be dysregulated and causal genes may be distinct among different brain cells in ASD. Gene expression profiling-based machine learning studies can be conducted for the diagnosis of ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions.Methods To characterize the cell type heterogeneity of ASD and to take advantage of the potential of gene expression signature as diagnostic biomarkers for ASD, we construct multiple kinds of classification models for ASD based on the recently available human brain nucleus gene expression data of ASD and controls. Firstly, we construct cell type-specific predictive models based on individual genes to screen cell type-specific genes associated with ASD. Then from the view of gene set, we construct cell type-specific gene set-based predictive models to screen cell type-specific gene sets associated with ASD. These two kinds of predictive models can be applied to predict the diagnosis of a given nucleus with known cell type. Lastly, we further construct a multi-label predictive model for predicting the cell type and diagnosis of a given nucleus at the same time.Results It is found that the functions of genes with predictive power for ASD are not consistent and the top important genes are distinct among different cells, demonstrating the cell type heterogeneity of ASD. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. Gene BCYRN1 and CCK are prioritized in excitatory neurons, and HSPA1A is of note in protoplasmic astrocytes.Limitations Our study utilized methods of machine learning to identify biomarkers of ASD, while it is more convincing if subsequent experiments could be conducted to validate the results.Conclusions The results show that it may be feasible to use single cell/nucleus gene expression for ASD detection and the constructed predictive models can promote the diagnosis of ASD. Our analytical pipeline prioritizes ASD-associated cell type-specific genes and gene sets, which may be used as potential biomarkers of ASD.

scholarly journals An analytical method for the identification of cell type-specific disease gene modules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Yang Wang ◽  
Junchao Gao ◽  
Guoli Ji
Keyword(s):  
Disease Gene ◽  
Gene Interaction ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Disease ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

scholarly journals Gene Module Analysis Reveals Cell-Type Specificity and Potential Target Genes in Autism’s Pathogenesis

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 410
Author(s):  
Guoli Ji ◽  
Shuchao Li ◽  
Lishan Ye ◽  
Jinting Guan
Keyword(s):  
Target Genes ◽  
Neurodevelopmental Disorder ◽  
Expression Patterns ◽  
Autism Spectrum ◽  
Anterior Cingulate ◽  
Specific Gene ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Type Specific ◽  
Gene Modules

Multiple genetic factors contribute to the pathogenesis of autism spectrum disorder (ASD), a kind of neurodevelopmental disorder. Genes were usually studied separately for their associations with ASD. However, genes associated with ASD do not act alone but interact with each other in a network module. The identification of these modules is the basis for the systematic understanding of the pathogenesis of ASD. Moreover, ASD is characterized by highly pathogenic heterogeneity, and gene modules associated with ASD are cell-type-specific. In this study, based on the single-nucleus RNA sequencing data of 41 post-mortem tissue samples from the prefrontal cortex and anterior cingulate cortex of 19 ASD patients and 16 control individuals, we applied sparse module activity factorization, a matrix decomposition method consistent with the multi-factor and heterogeneous characteristics of ASD pathogenesis, to identify cell-type-specific gene modules. Then, statistical procedures were performed to detect highly reproducible cell-type-specific ASD-associated gene modules. Through the enrichment analysis of cell markers, 31 cell-type-specific gene modules related to ASD were further screened out. These 31 gene modules are all enriched with curated ASD risk genes. Finally, we utilized the expression patterns of these cell-type-specific ASD-associated gene modules to build predictive models for ASD. The excellent predictive performance also proved the associations between these gene modules and ASD. Our study confirmed the multifactorial and cell-type-specific characteristics of ASD pathogeneses. The results showed that excitatory neurons such as L2/3, L4, and L5/6-CC play essential roles in ASD’s pathogenic processes. We identified the potential ASD target genes that act together in cell-type-specific modules, such as NRG3, KCNIP4, BAI3, PTPRD, LRRTM4, and LINGO2 in the L2/3 gene modules. Our study offers new potential genomic targets for ASD and provides a novel method to study gene modules involved in the pathogenesis of ASD.

scholarly journals Cortical overgrowth in a preclinical forebrain organoid model of CNTNAP2-associated autism spectrum disorder

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Job O. de Jong ◽  
Ceyda Llapashtica ◽  
Matthieu Genestine ◽  
Kevin Strauss ◽  
Frank Provenzano ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Cellular Proliferation ◽  
Cell Types ◽  
Cell Number ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Signature Genes ◽  
Truncating Mutation ◽  
Cell Type Specific ◽  
Induced Pluripotent

AbstractWe utilized forebrain organoids generated from induced pluripotent stem cells of patients with a syndromic form of Autism Spectrum Disorder (ASD) with a homozygous protein-truncating mutation in CNTNAP2, to study its effects on embryonic cortical development. Patients with this mutation present with clinical characteristics of brain overgrowth. Patient-derived forebrain organoids displayed an increase in volume and total cell number that is driven by increased neural progenitor proliferation. Single-cell RNA sequencing revealed PFC-excitatory neurons to be the key cell types expressing CNTNAP2. Gene ontology analysis of differentially expressed genes (DEgenes) corroborates aberrant cellular proliferation. Moreover, the DEgenes are enriched for ASD-associated genes. The cell-type-specific signature genes of the CNTNAP2-expressing neurons are associated with clinical phenotypes previously described in patients. The organoid overgrowth phenotypes were largely rescued after correction of the mutation using CRISPR-Cas9. This CNTNAP2-organoid model provides opportunity for further mechanistic inquiry and development of new therapeutic strategies for ASD.

Development of Neural Structure and Function in Autism Spectrum Disorder: Potential Implications for Learning Language

2020 ◽  
Vol 29 (4) ◽  
pp. 1783-1797
Author(s):  
Kelly L. Coburn ◽  
Diane L. Williams
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Development ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Language Intervention ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Neural Structure ◽  
Complex Information ◽  
And Function

Purpose Neurodevelopmental processes that begin during gestation and continue throughout childhood typically support language development. Understanding these processes can help us to understand the disruptions to language that occur in neurodevelopmental conditions, such as autism spectrum disorder (ASD). Method For this tutorial, we conducted a focused literature review on typical postnatal brain development and structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, and electroencephalography studies of the neurodevelopmental differences that occur in ASD. We then integrated this knowledge with the literature on evidence-based speech-language intervention practices for autistic children. Results In ASD, structural differences include altered patterns of cortical growth and myelination. Functional differences occur at all brain levels, from lateralization of cortical functions to the rhythmic activations of single neurons. Neuronal oscillations, in particular, could help explain disrupted language development by elucidating the timing differences that contribute to altered functional connectivity, complex information processing, and speech parsing. Findings related to implicit statistical learning, explicit task learning, multisensory integration, and reinforcement in ASD are also discussed. Conclusions Consideration of the neural differences in autistic children provides additional scientific support for current recommended language intervention practices. Recommendations consistent with these neurological findings include the use of short, simple utterances; repetition of syntactic structures using varied vocabulary; pause time; visual supports; and individualized sensory modifications.

Parent Education in Studies With Nonverbal and Minimally Verbal Participants With Autism Spectrum Disorder: A Systematic Review

2020 ◽  
Vol 29 (2) ◽  
pp. 890-902
Author(s):  
Lynn Kern Koegel ◽  
Katherine M. Bryan ◽  
Pumpki Lei Su ◽  
Mohini Vaidya ◽  
Stephen Camarata
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Parent Education ◽  
Autism Spectrum ◽  
Verbal Communication ◽  
Fidelity Of Implementation ◽  
Spectrum Disorder ◽  
Education Programs ◽  
Minimally Verbal ◽  
Parent Education Programs

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.

The Influence of Language Context on Repetitive Speech Use in Children With Autism Spectrum Disorder

2020 ◽  
Vol 29 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Allison Gladfelter ◽  
Cassidy VanZuiden
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Individual Characteristics ◽  
Receptive Vocabulary ◽  
Spectrum Disorder ◽  
School Age Children ◽  
Social Responsiveness ◽  
Contributing Factors ◽  
Children With Asd ◽  
Language Context

Purpose Although repetitive speech is a hallmark characteristic of autism spectrum disorder (ASD), the contributing factors that influence repetitive speech use remain unknown. The purpose of this exploratory study was to determine if the language context impacts the amount and type of repetitive speech produced by children with ASD. Method As part of a broader word-learning study, 11 school-age children with ASD participated in two different language contexts: storytelling and play. Previously collected language samples were transcribed and coded for four types of repetitive speech: immediate echolalia, delayed echolalia, verbal stereotypy, and vocal stereotypy. The rates and proportions of repetitive speech were compared across the two language contexts using Wilcoxon signed-ranks tests. Individual characteristics were further explored using Spearman correlations. Results The children produced lower rates of repetitive speech during the storytelling context than the play-based context. Only immediate echolalia differed between the two contexts based on rate and approached significance based on proportion, with more immediate echolalia produced in the play-based context than in the storytelling context. There were no significant correlations between repetitive speech and measures of social responsiveness, expressive or receptive vocabulary, or nonverbal intelligence. Conclusions The children with ASD produced less immediate echolalia in the storytelling context than in the play-based context. Immediate echolalia use was not related to social skills, vocabulary, or nonverbal IQ scores. These findings offer valuable insights into better understanding repetitive speech use in children with ASD.

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