Dataset Information

Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders.

ABSTRACT: Genetic 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, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% of these ASD-related lncRNAs were further validated by an independent copy number variant (CNV) dataset. Then, the functions and associated biological pathways of ASD-related lncRNAs were explored by enrichment analysis of their 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 results have shown that most of the functional neighbor genes of ASD-related lncRNAs were enriched in nervous system development, inflammatory responses, and transcriptional regulation. Moreover, we explored the differential functions of ASD-related lncRNAs in distinct brain regions by using gene co-expression network analysis based on tissue-specific gene expression profiles. 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. In addition, a functional network analysis was conducted for the highly reliable functional neighbor genes of ASD-related lncRNAs. We found that all the highly reliable functional neighbor genes were connected in a single functional network, which provided additional clues for the action mechanisms of ASD-related lncRNAs. Finally, we predicted several potential drugs based on the enrichment of drug-induced pathway sets in the ASD-altered biological pathway list. Among these drugs, several (e.g., amoxapine, piperine, and diflunisal) were partly supported by the previous reports. In conclusion, ASD-related lncRNAs participated in the pathogenesis of ASD through various known biological pathways, which may be differential in distinct brain regions. Detailed investigation into ASD-related lncRNAs can provide clues for developing potential ASD diagnosis biomarkers and therapy.

SUBMITTER: Tong Z

PROVIDER: S-EPMC7525012 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders.

Tong Zhan Z Zhou Yuan Y Wang Juan J

Frontiers in genetics 20200916

Genetic 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, we first identified ASD-related lncRNAs based on genomic variant data of individuals with ASD from a twin study. In total, 532 ASD-related lncRNAs were identified, and 86.7% ...[more]

PMID: 33193567

Similar Datasets

Project description:BackgroundAutism spectrum disorders (ASD) manifest with neurodevelopmental phenotypes including communicative, social and behavioral impairments that affect as many as 1 in 88 children. The majority of autism cases have no known genetic cause, suggesting complex genetics of the disorder, but a few genes of large effect have been identified.MethodsIn order to identify novel ASD genetic correlates, we investigated non-protein coding RNAs (ncRNAs) which are abundantly transcribed from the human genome, enriched in the brain, and have been implicated in neurodevelopmental disorders. Using an algorithm that we developed, we examined a publicly available transcriptomics database, AceView, to identify the natural antisense transcripts (NATs) that overlap with known autism-related genes. We validated the presence and differential expression of NATs in different brain regions of ASD and control brains using qRT-PCR. Additionally, we investigated the subcellular localization of these transcripts in a neuronal cell line using RNA-sequencing (RNA-seq).ResultsWe found noncoding antisense RNA transcripts at approximately 40% of loci previously implicated in ASD. We confirmed the expression of 10 antisense RNAs in different postmortem human brain tissues. The expression of five antisense transcripts was found to be region-specific, suggesting a role for these ncRNAs in the development and function of specific brain regions. Some antisense RNAs overlapping suspected ASD genes exhibited concordant expression relative to their sense protein-coding genes, while other sense-antisense pairs demonstrate a discordant relationship. Interestingly, the antisense RNA corresponding to the SYNGAP1 locus (SYNGAP1-AS) was found to be differentially expressed in brain regions of patients with ASD compared to control individuals. RNA-seq analysis of subcellular compartments from SH-SY5Y human neuroblastoma cells demonstrated that antisense RNAs to ASD candidate genes are predominantly expressed in the nucleoplasmic or chromatin compartments, implying their involvement in nuclear-associated processes.ConclusionsOur data suggests that NATs are abundantly expressed from ASD-related loci and provide evidence for their roles in target gene regulation, neurodevelopment and autism pathogenesis. This class of RNA should therefore be considered in functional studies aimed at understanding genetic risk factors for ASD.

Project description:Abnormal regulation of long non-coding RNAs (lncRNAs) appears to be a primary feature of numerous types of human cancer. However, the association between the dysregulation of lncRNAs and functional alterations in gastric cancer (GC) remains unclear. In previous studies, we applied microarray and bioinformatics analyses to screen for key lncRNAs from the tumor tissues and matched adjacent non-tumor tissues of 10 patients with GC. There were seven key lncRNAs demonstrated to be significantly different between carcinoma tissues and adjacent non-tumor tissues. In the present study, the expression of these seven selected lncRNAs were validated in 82 patients with GC to further investigate the association between lncRNAs and GC clinical characterization. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) results demonstrated that RP5-919F19, MCPH1 antisense RNA 1 (CTD-2541M15) and urothelial carcinoma-associated 1 (UCA1) exhibited consistent upregulation in cancer compared with adjacent non-tumor tissues, whereas AP000459, LOC101928316, tumor suppressor candidate 8 (LINC01071) and maternally expressed 3 (MEG3) showed consistent downregulation. The results from the microarray and RT-qPCR experiments achieved 100% agreement. A correlation analysis indicated that RP5-919F19, LOC101928316 and MEG3 were significantly associated with tumor differentiation degree, RP5-919F19, UCA1 and MEG3 were significantly associated with lymph node metastasis, and RP5-919F19, CTD-2541M15 and UCA1 were significantly associated with tumor-node-metastasis stage (P<0.05). In addition, it was identified that the differential expression of LINC01071 and LOC101928316 significantly correlated with the age and gender of the GC patients, respectively (P<0.05). The results suggest that the lncRNAs RP5-919F19, LOC101928316, CTD-2541M15, UCA1 and MEG3 are closely associated with the invasion and metastasis of GC, which reveals these indicators as potential specificity biomarkers for the diagnosis, prognosis and classification of GC. Thus, these lncRNAs merit further study as novel candidate biomarkers for the clinical diagnosis of GC and as potential targets for therapy.

Dataset Information

Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders.

Publications

Identification and Functional Analysis of Long Non-coding RNAs in Autism Spectrum Disorders.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets