Unraveling transcriptional heterogeneity and the labyrinthine nature of neurodevelopment can probe insights into neuropsychiatric disorders. It is noteworthy that adult neurogenesis is restricted to the subventricular and subgranular zones of… Click to show full abstract
Unraveling transcriptional heterogeneity and the labyrinthine nature of neurodevelopment can probe insights into neuropsychiatric disorders. It is noteworthy that adult neurogenesis is restricted to the subventricular and subgranular zones of the brain. Recent studies suggest long non-coding RNAs (lncRNAs) as an avant-garde class of regulators implicated in neurodevelopment. But, paucity exists in the knowledge regarding lncRNAs in neurogenesis and their associations with neurodevelopmental defects. To address this, we extensively reviewed the existing literature databases as well as performed relevant in-silico analysis. We utilized Allen Brain Atlas (ABA) differential search module and generated a catalogue of ∼30,000 transcripts specific to the neurogenic zones, including coding and non-coding transcripts. To explore the existing lncRNAs reported in neurogenesis, we performed extensive literature mining and identified 392 lncRNAs. These degenerate lncRNAs were mapped onto the ABA transcript list leading to detection of 20 lncRNAs specific to neurogenic zones (Dentate gyrus/Lateral ventricle), among which 10 showed associations to several neurodevelopmental disorders following in-silico mapping onto brain disease databases like Simons Foundation Autism Research Initiative, AutDB, and lncRNADisease. Notably, using ABA correlation module, we could establish lncRNA-to-mRNA coexpression networks for the above 10 candidate lncRNAs. Finally, pathway prediction revealed physical, biochemical, or regulatory interactions for nine lncRNAs. In addition, ABA differential search also revealed 54 novel significant lncRNAs from the null set (∼30,000). Conclusively, this review represents an updated catalogue of lncRNAs in neurogenesis and neurological diseases, and overviews the field of OMICs-based data analysis for understanding lncRNome-based regulation in neurodevelopment.
               
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