GSE172009
RNA-sequencing of CD4+ T cells in Relapsing-Remitting Multiple Sclerosis patients at relapse; deciphering the involvement of novel genes and pathways
Objective: CD4+ T cells known as a noteworthy potential modulator of inflammation in multiple sclerosis (MS). In the current study, we proposed a systems biology approach to investigate the transcriptome profile of CD4+ T cells in patients at relapse phase.   Materials and Methods: We performed RNA sequencing of CD4+ T cells isolated from four relapsing-remitting MS (RRMS) patients at relapse phase and four age- and sex-matched healthy controls. The edgeR statistical method was employed to determine differential expression genes (DEGs). The gene set enrichment analysis was subsequently performed. Applying physical interaction network, genes with higher degrees were selected as hub genes.    Results: A total of 1278 genes and 1034 gens were defined at significantly higher or lower levels, respectively, in CD4+ T cells of RRMS patients as compared with healthy controls. The top up- and down-regulated gene were JAML and KDM3A. The detected DEGs were remarkably on chromosomes 1 and 2, respectively. The DEGs were mainly enriched in pathways such as 'regulation of transcription, DNA-templated', 'regulation of B cell receptor signaling pathway', 'protein phosphorylation', 'epidermal growth factor receptor signaling pathway', and 'positive regulation of neurogenesis'. Moreover, 16 KEGG pathways mostly associated with the immune system and viral infections were enriched. In the constructed physical interaction networks, UBA52 and TP53 were illustrated as the most highly ranked hub genes among up- and down-regulated genes, correspondingly.   Conclusions: By applying global transcriptome profiling of CD4+ T cells, we deciphers the involvement of several novel genes and pathways in MS pathogenesis. The present results need to be affirmed by in vivo and in vitro studies. Overall design: Investigating transcriptome profile of CD4+ T cells in RRMS patients at relapse phase compared to healthy controls