Identification of miRNA-mRNA Network in Autism Spectrum Disorder Using a Bioinformatics Method.
Rezvan NorooziMarcel E DingerRazieh FatehiMohammad TaheriSoudeh Ghafouri-FardPublished in: Journal of molecular neuroscience : MN (2020)
Autism spectrum disorder (ASD) includes a heterogeneous group of disorders with different contributing genetics and epigenetics factors. Aberrant expression of miRNAs has been detected in ASD children compared with normally developed children. Due to the heterogeneity of this disorder, there is no consensus on ASD-associated miRNAs; thus, it is necessary to develop a model for comprehensive assessment of the role of miRNAs in ASD. We interrogated the PubMed, Google Scholar, and Web of Science databases until the end of 2019 to identify ASD-associated miRNAs. In addition, mRNA-coding genes that contribute to the pathogenesis of ASD were downloaded from the SFARI GENE ( https://gene.sfari.org/ ). The obtained 201 miRNAs and 478 target mRNAs were imported into the Cytoscape software suite to construct a miRNA-mRNA network. A protein-protein interaction network was constructed for target mRNAs using the CluPedia program in Cytoscape. Using this approach, we detected five modules that were associated with neurexins and neuroligins, glutamatergic synapse, cell adhesion molecules, NOTCH, MECP2 and circadian clock pathways, L1CAM interactions, and neurotransmitter release cycle. Taken together, functional analysis of these genes led to determination of critical pathways related to CNS disorders. Thus, the suggested approach in the current study resulted in the identification of the most relevant pathways in the pathogenesis of ASD that can be used as biomarkers or therapeutic targets.
Keyphrases
- autism spectrum disorder
- attention deficit hyperactivity disorder
- intellectual disability
- genome wide
- protein protein
- genome wide identification
- bioinformatics analysis
- genome wide analysis
- young adults
- cell adhesion
- small molecule
- binding protein
- copy number
- blood brain barrier
- wastewater treatment
- transcription factor
- machine learning
- artificial intelligence
- poor prognosis
- single cell
- solid phase extraction
- deep learning
- working memory
- network analysis
- gene expression