scholarly journals Identification of CDH11 as an ASD risk gene by matched-gene co-expression analysis and mouse behavioral studies

2020 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Expression Analysis ◽  
Expression Profiles ◽  
Autism Spectrum ◽  
Molecular Pathways ◽  
Behavioral Alterations ◽  
High Confidence ◽  
Risk Genes ◽  
Risk Gene ◽  
Behavioral Studies ◽  
Mrna Length

AbstractIn the study of autism spectrum disorder (ASD) by gene co-expression analysis (GCA), we found that four gene features, including gene size, mRNA length, mRNA abundance, and guanine-cytosine content, profoundly affect gene co-expression profiles. To circumvent the potential interference of these confounding factors on GCA, we developed the “matched-gene co-expression analysis” (MGCA) to investigate gene co-expression relationships. This method demonstrated the convergent expression profile of high confidence ASD risk genes and effectively revealed convergent molecular pathways of ASD risk genes. Application of MGCA to two ASD candidate genes CDH11 and CDH9 showed association of CDH11, but not CDH9, with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations. This study confirmed that CDH11 is an important ASD risk gene and demonstrated the importance of considering matched gene features in the analysis of gene co-expression.

scholarly journals Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

2021 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Jing-Yan Jia ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Autism Spectrum Disorder ◽  
Expression Analysis ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Developmental Expression ◽  
Behavioral Alterations ◽  
Risk Genes ◽  
Behavioral Studies

AbstractA large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features – gene size, mRNA abundance, and guanine-cytosine content – affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

scholarly journals Identification of CDH11 as an ASD Risk Gene by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

2020 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Jing-Yan Jia ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Expression Analysis ◽  
Gene Knockout ◽  
Expression Profiles ◽  
Cell Types ◽  
Developmental Expression ◽  
Molecular Pathways ◽  
Behavioral Tests ◽  
Risk Genes ◽  
Risk Gene ◽  
Behavioral Studies

Abstract Background: Gene co-expression analysis (GCA) has emerged as an important tool to identify convergent molecular pathways of ASD risk genes. The aim of this study is to identify ASD-relevant genes at the whole-genome level using GCA with consideration of the effect of confounding factors on GCA, including the size, expression level, and guanine-cytosine content of genes. Methods: Pearson’s correlation coefficient was computed to indicate the co-expression of a gene pair based on the BrainSpan human brain transcriptome dataset. Whether a gene is significantly co-expressed with a group of high-confidence ASD risk genes (hcASDs) was determined by statistically comparing the co-expression of this gene with the hcASD gene set to that of this gene with permuted gene sets of matched gene features. This method is referred to as "matched-gene co-expression analysis" (MGCA). Gene ontology (GO) analysis and construction of integrated GO enrichment networks were performed to reveal convergent pathways of co-expressed genes. Behavioral tests were carried out in gene knockout mice. Results: Gene size, mRNA length, mRNA abundance, and guanine-cytosine content were found to affect co-expression profiles of ASD genes. Using the MGCA method, we confirmed the convergence in the developmental expression profiles of hcASDs. MGCA also effectively revealed convergent molecular pathways of ASD risk genes and determined that CDH11, but not CDH9, is associated with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations.Limitations: The use of tissue-derived transcriptomes instead of single-cell transcriptomes may have detected coincident expression of some functionally irrelevant genes in different cell types. Some ASD risk genes may have been missed due to the highly stringent statistical standard of MGCA. Another limitation is the relatively small number of animals analyzed in behavioral tests. Conclusions: Results of this study revealed the importance of considering matched gene features in GCA. CDH11 was confirmed to be an important ASD risk gene and Cdh11-null mice were found to be a very useful animal model for investigation of ASD.

scholarly journals Methyl-CpG binding domain 2 (Mbd2) is an Epigenetic Regulator of Autism-Risk Genes and Cognition

2018 ◽  
Author(s):  
Elad Lax ◽  
Sonia DoCarmo ◽  
Yehoshua Enuka ◽  
Daniel M. Sapozhnikov ◽  
Lindsay A. Welikovitch ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Autism Spectrum ◽  
Methylation Pattern ◽  
Binding Domain ◽  
Behavioral Alterations ◽  
Risk Genes ◽  
A Genome ◽  
Epigenetic Regulator ◽  
Neuronal Genes ◽  
Regulatory Dna

AbstractThe Methyl-CpG-Binding Domain Protein family has been implicated in neurodevelopmental disorders. The Methyl-CpG-binding domain 2 (Mbd2) binds methylated DNA and was shown to play an important role in cancer and immunity. Some evidence linked this protein to neurodevelopment. However, its exact role in neurodevelopment and brain function is mostly unknown.Here we show that Mbd2-deficiency in mice (Mbd2−/−) results in deficits in cognitive, social and emotional functions. Mbd2 binds regulatory DNA regions of neuronal genes in the hippocampus and loss of Mbd2 alters the expression of hundreds of genes with a robust down-regulation of neuronal gene pathways. Further, a genome-wide DNA methylation analysis found an altered DNA methylation pattern in regulatory DNA regions of neuronal genes in Mbd2−/− mice. Differentially expressed genes significantly overlap with gene-expression changes observed in brains of Autism Spectrum Disorder (ASD) individuals. Notably, down-regulated genes are significantly enriched for human ortholog ASD risk-genes. Observed hippocampal morphological abnormalities were similar to those found in individuals with ASD and ASD rodent models. Hippocampal Mbd2 knockdown partially recapitulates the behavioral phenotypes observed in Mbd2−/− mice.These findings suggest Mbd2 is a novel epigenetic regulator of genes that are associated with ASD in humans. Mbd2 loss causes behavioral alterations that resemble those found in ASD individuals.

scholarly journals Forecasting risk gene discovery in autism with machine learning and genome-scale data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Leo Brueggeman ◽  
Tanner Koomar ◽  
Jacob J. Michaelson
Keyword(s):  
Machine Learning ◽  
Gene Discovery ◽  
Autism Spectrum ◽  
Sufficient Evidence ◽  
Pathway Enrichment Analysis ◽  
Risk Genes ◽  
Risk Gene ◽  
New Genes ◽  
Genome Scale ◽  
Scale Data

AbstractGenetics has been one of the most powerful windows into the biology of autism spectrum disorder (ASD). It is estimated that a thousand or more genes may confer risk for ASD when functionally perturbed, however, only around 100 genes currently have sufficient evidence to be considered true “autism risk genes”. Massive genetic studies are currently underway producing data to implicate additional genes. This approach — although necessary — is costly and slow-moving, making identification of putative ASD risk genes with existing data vital. Here, we approach autism risk gene discovery as a machine learning problem, rather than a genetic association problem, by using genome-scale data as predictors to identify new genes with similar properties to established autism risk genes. This ensemble method, forecASD, integrates brain gene expression, heterogeneous network data, and previous gene-level predictors of autism association into an ensemble classifier that yields a single score indexing evidence of each gene’s involvement in the etiology of autism. We demonstrate that forecASD has substantially better performance than previous predictors of autism association in three independent trio-based sequencing studies. Studying forecASD prioritized genes, we show that forecASD is a robust indicator of a gene’s involvement in ASD etiology, with diverse applications to gene discovery, differential expression analysis, eQTL prioritization, and pathway enrichment analysis.

scholarly journals Predicting the Risk Genes of Autism Spectrum Disorders

2021 ◽  
Vol 12 ◽  
Author(s):  
Yenching Lin ◽  
Srinivasulu Yerukala Sathipati ◽  
Shinn-Ying Ho
Keyword(s):  
Wide Spectrum ◽  
Gene Prediction ◽  
Characteristic Curve ◽  
Brain Structures ◽  
Autism Spectrum ◽  
Support Vector ◽  
Test Accuracy ◽  
Risk Genes ◽  
Risk Gene ◽  
The Brain

Autism spectrum disorder (ASD) refers to a wide spectrum of neurodevelopmental disorders that emerge during infancy and continue throughout a lifespan. Although substantial efforts have been made to develop therapeutic approaches, core symptoms persist lifelong in ASD patients. Identifying the brain temporospatial regions where the risk genes are expressed in ASD patients may help to improve the therapeutic strategies. Accordingly, this work aims to predict the risk genes of ASD and identify the temporospatial regions of the brain structures at different developmental time points for exploring the specificity of ASD gene expression in the brain that would help in possible ASD detection in the future. A dataset consisting of 13 developmental stages ranging from 8 weeks post-conception to 8 years from 26 brain structures was retrieved from the BrainSpan atlas. This work proposes a support vector machine–based risk gene prediction method ASD-Risk to distinguish the risk genes of ASD and non-ASD genes. ASD-Risk used an optimal feature selection algorithm called inheritable bi-objective combinatorial genetic algorithm to identify the brain temporospatial regions for prediction of the risk genes of ASD. ASD-Risk achieved a 10-fold cross-validation accuracy, sensitivity, specificity, area under a receiver operating characteristic curve, and a test accuracy of 81.83%, 0.84, 0.79, 0.84, and 72.27%, respectively. We prioritized the temporospatial features according to their contribution to the prediction accuracy. The top identified temporospatial regions of the brain for risk gene prediction included the posteroventral parietal cortex at 13 post-conception weeks feature. The identified temporospatial features would help to explore the risk genes that are specifically expressed in different brain regions of ASD patients.

scholarly journals The K-Cl co-transporter 2 is a point of convergence for multiple autism spectrum disorder and epilepsy risk gene products

2020 ◽  
Author(s):  
Joshua L. Smalley ◽  
Georgina Kontou ◽  
Catherine Choi ◽  
Qiu Ren ◽  
David Albrecht ◽  
...  
Keyword(s):  
Protein Complexes ◽  
Fragile X ◽  
Critical Role ◽  
Autism Spectrum ◽  
Risk Genes ◽  
Neurodevelopmental Delay ◽  
Risk Gene ◽  
Binding Partners ◽  
Mental Retardation Protein ◽  
Neuronal Plasma Membrane

ABSTRACTKCC2 plays a critical role in determining the efficacy of synaptic inhibition and deficits in its activity lead to epilepsy and neurodevelopmental delay. Here we use unbiased proteomic analyses to demonstrate that KCC2 forms stable protein complexes in the neuronal plasma membrane with 96 autism and/or epilepsy risk gene (ASD/Epi) products including ANKB, ANKG, CNTN1, ITPR1, NCKAP1, SCN2A, SHANK3, SPTAN1, and SPTBN1. Many of these proteins are also targets of Fragile-X mental retardation protein (FMRP), the inactivation of which is the leading monogenic cause of autism. Accordingly, the expression of a subset of these KCC2-binding partners was decreased in Fmr1 knockout mice. Fmr1 knockout compromised KCC2 phosphorylation, a key regulatory mechanism for transporter activity and the postnatal development of GABAergic inhibition. Thus, KCC2 is a point of convergence for multiple ASD/Epi risk genes and therapies targeting this transporter may have broad utility in alleviating these heterogeneous disorders and their associated epilepsies.

scholarly journals Transcriptome-(phospho)proteome characterization of brain of a germline model of cytoplasmic-predominant Pten expression with autism-like phenotypes

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Stetson Thacker ◽  
Charis Eng
Keyword(s):  
Autism Spectrum ◽  
Pten Expression ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Animal Modeling ◽  
Network Analyses ◽  
Phosphorylated Proteins ◽  
Mass Spectrometry Technology ◽  
Neurological Processes

AbstractPTEN has a strong Mendelian association with autism spectrum disorder (ASD), representing a special case in autism’s complex genetic architecture. Animal modeling for constitutional Pten mutation creates an opportunity to study how disruption of Pten affects neurobiology and glean potential insight into ASD pathogenesis. Subsequently, we comprehensively characterized the neural (phospho)proteome of Ptenm3m4/m3m4 mice, which exhibits cytoplasmic-predominant Pten expression, by applying mass spectrometry technology to their brains at two-weeks- (P14) and six-weeks-of-age (P40). The differentially expressed/phosphorylated proteins were subjected to gene enrichment, pathway, and network analyses to assess the affected biology. We identified numerous differentially expressed/phosphorylated proteins, finding greater dysregulation at P40 consistent with prior transcriptomic data. The affected pathways were largely related to PTEN function or neurological processes, while scant direct overlap was found across datasets. Network analysis pointed to ASD risk genes like Pten and Psd-95 as major regulatory hubs, suggesting they likely contribute to initiation or maintenance of cellular and perhaps organismal phenotypes related to ASD.

scholarly journals Correction to: Absence of parvalbumin increases mitochondria volume and branching of dendrites in inhibitory Pvalb neurons in vivo: a point of convergence of autism spectrum disorder (ASD) risk gene phenotypes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucia Janickova ◽  
Karin Farah Rechberger ◽  
Lucas Wey ◽  
Beat Schwaller
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Risk Gene

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals “Guilt by association” is not competitive with genetic association for identifying autism risk genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Biological Data ◽  
Disease Genes ◽  
Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

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