Protective effects and regulatory pathways of melatonin in traumatic brain injury mice model: Transcriptomics and bioinformatics analysis

Traumatic brain injury (TBI) is the leading cause of disability and mortality globally. Melatonin (Mel) is a neuroendocrine hormone synthesized from the pineal gland that protects against TBI. Yet, the precise mechanism of action is not fully understood. In this study, we examined the protective effect and regulatory pathways of melatonin in the TBI mice model using transcriptomics and bioinformatics analysis. The expression profiles of mRNA, long non-coding RNA (lncRNA), microRNA (miRNA), and circular RNA (circRNA) were constructed using the whole transcriptomes sequencing technique. In total, 93 differentially expressed (DE) mRNAs (DEmRNAs), 48 lncRNAs (DElncRNAs), 59 miRNAs (DEmiRNAs), and 59 circRNAs (DEcircRNAs) were identified by the TBI mice with Mel treatment compared to the group without drug intervention. The randomly selected coding RNAs and non-coding RNAs (ncRNAs) were identified by quantitative real-time polymerase chain reaction (qRT-PCR). To further detect the biological functions and potential pathways of those differentially expressed RNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were executed. In our research, the regulatory network was constructed to show the relationship of lncRNA-RBPs. The lncRNA-mRNA co-expression network was established based on the Pearson coefficient to indicate the expression correlations. Moreover, the DEcircRNA–DEmiRNA–DEmRNA and DElncRNA–DEmiRNA–DEmRNA regulatory networks were constructed to demonstrate the regulatory relationship between ncRNAs and mRNA. Finally, to further verify our predicted results, cytoHubba was used to find the hub gene in the synaptic vesicle cycle pathway, and the expression level of SNAP-25 and VAMP-2 after melatonin treatment were detected by Western blotting and immunofluorescence. To sum up, these data offer a new insight regarding the molecular effect of melatonin treatment after TBI and suggest that the high-throughput sequencing and analysis of transcriptomes are useful for studying the drug mechanisms in treatment after TBI.