Impact of the Autism-Associated Long Noncoding RNA MSNP1AS on Neuronal Architecture and Gene Expression in Human Neural Progenitor Cells

DeWitt, Jessica; Grepo, Nicole; Wilkinson, Brent; Evgrafov, Oleg V.; Knowles, James A.; Campbell, Jerry L.

doi:10.3390/genes7100076

Cited by 29 publications

(27 citation statements)

References 38 publications

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“…Recently, an antisense noncoding RNA of the moesin pseudogene 1 (MSNP1AS) was shown to be transcribed from the locus harboring rs4307059. Alterations in this pseudogene were postulated to contribute to ASD [73][74][75].…”

Section: Discussionmentioning

confidence: 99%

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

Wang

Jia

et al. 2020

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

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

Wang

Jia

et al. 2020

Preprint

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“…In the cluster 5p14.1, to where important common variants associated with ASD map, the moesin pseudogene 1 ( MSNP1 ) and its antisense lncRNA MSNP1-AS may also be found. MSNP1-AS overexpression was found in post-mortem ASD cerebral cortex and in individuals with the rs4307059 ASD risk allele [ 195 , 196 ]. The overexpression of this lncRNA decreased neurite number and length in human neuronal progenitor cells and dysregulated the expression of genes involved in protein synthesis and chromatin remodeling.…”

Section: Long Non-coding Rnas In Complexes Diseasesmentioning

confidence: 99%

“…The overexpression of this lncRNA decreased neurite number and length in human neuronal progenitor cells and dysregulated the expression of genes involved in protein synthesis and chromatin remodeling. MSNP1AS knockdown altered the expression of several genes, mainly those involved in chromatin remodeling and immune response [ 196 ].…”

Section: Long Non-coding Rnas In Complexes Diseasesmentioning

confidence: 99%

Long Non-Coding RNAs in Multifactorial Diseases: Another Layer of Complexity

Cipolla

Oliveira

Salviano-Silva

et al. 2018

ncRNA

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Multifactorial diseases such as cancer, cardiovascular conditions and neurological, immunological and metabolic disorders are a group of diseases caused by the combination of genetic and environmental factors. High-throughput RNA sequencing (RNA-seq) technologies have revealed that less than 2% of the genome corresponds to protein-coding genes, although most of the human genome is transcribed. The other transcripts include a large variety of non-coding RNAs (ncRNAs), and the continuous generation of RNA-seq data shows that ncRNAs are strongly deregulated and may be important players in pathological processes. A specific class of ncRNAs, the long non-coding RNAs (lncRNAs), has been intensively studied in human diseases. For clinical purposes, lncRNAs may have advantages mainly because of their specificity and differential expression patterns, as well as their ideal qualities for diagnosis and therapeutics. Multifactorial diseases are the major cause of death worldwide and many aspects of their development are not fully understood. Recent data about lncRNAs has improved our knowledge and helped risk assessment and prognosis of these pathologies. This review summarizes the involvement of some lncRNAs in the most common multifactorial diseases, with a focus on those with published functional data.

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“…But most evidence, which characterized lncRNA dysregulation as an integral component of the transcriptomic signature of ASD, was derived from gene expression profiles of individuals with ASD [16][17][18]. Only several lncRNAs associated with ASD have been identified by genome-derived evidence, and further explored in action mechanisms by loss-of-function and gain-of-function experiments, such as SHANK2-AS, MSNP1-AS and BDNF-AS etc [19,20]. In consideration of spatiotemporal-specific expression patterns, lncRNAs execute different functions and have unique gene expression patterns in distinct cellular conditions, therefore differentially expressed lncRNAs in a specific tissue couldn't reflect the global effects of dysregulated lncRNAs [21].…”

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confidence: 99%

Identification and functional analysis of long non-coding RNAs in autism spectrum disorders

Tong

Zhou

Wang

2020

Preprint

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BackgroundGenetic and environmental factors, alone or in combination, contribute to the pathogenesis of autism spectrum disorder (ASD). Although many protein-coding genes have now been identified as disease risk genes for ASD, a detailed illustration of long non-coding RNAs (lncRNAs) associated with ASD remains elusive. In this study, our aim was to identify ASD-related lncRNAs and explore their functions and associated biological pathways in autism. MethodsASD-related lncRNAs were identified based on genomic variant data of individuals with ASD from a twin study, and further validated using an independent copy number variant (CNV) dataset.The functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of three different types of functional neighbor genes (i.e. genomic neighbors, competing endogenous RNA (ceRNA) neighbors and gene co-expression neighbors in the cortex).The differential functions of ASD-related lncRNAs in distinct brain regions were demonstrated by using gene co-expression network analysis based on tissue-specific gene expression profiles.Moreover, a functional network analysis were conducted for highly reliable functional neighbor genes of ASD-related lncRNAs. Finally, several potential drugs were predicted based on the enrichment of drug-induced pathway sets in ASD-altered biological pathway list. ResultsIn total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs 2 were further validated by a copy number variant (CNV) dataset. Most of functional neighbor genes of ASD-related lncRNAs were enriched in several functions and biological pathways, including nervous system development, inflammatory response and transcriptional regulation. As a set, ASD-related lncRNAs were mainly associated with nervous system development and dopaminergic synapse in the cortex, but associated with transcriptional regulation in the cerebellum. Moreover, all highly reliable functional neighbor genes were connected in a single functional network. Finally, several potential drugs were predicted and partly supported by the previous reports. ConclusionsWe concluded that ASD-related lncRNAs participate in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. And detailed investigation of ASD-related lncRNAs also provided clues for developing potential ASD diagnosis biomarker and therapy.

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Impact of the Autism-Associated Long Noncoding RNA MSNP1AS on Neuronal Architecture and Gene Expression in Human Neural Progenitor Cells

Cited by 29 publications

References 38 publications

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

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

Long Non-Coding RNAs in Multifactorial Diseases: Another Layer of Complexity

Identification and functional analysis of long non-coding RNAs in autism spectrum disorders

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