A novel long non-coding RNA, lnc-RNU12, influences the T-cell cycle via c-JUN and CCNL2 in rheumatoid arthritis.

OBJECTIVES: Long non-coding RNAs (lncRNAs) play important roles in RA pathogenesis. However, specific lncRNAs that regulate gene expression in RA pathogenesis are poorly known. This study was undertaken to characterize a novel lncRNA (lnc-RNU12) that has a lower-than-normal expression level in RA patients. METHODS: We performed initial genome-wide lncRNA microarray screening in peripheral blood mononuclear cells from 28 RA cases and 18 controls. Multiple methods were used to validate the detected associations between lncRNAs and RA. Furthermore, we identified the source and characteristics of the highlighted lncRNAs, detected the target genes, and determined the functional effect on immune cells through lncRNA knock-down in Jurkat T cell lines. RESULTS: lnc-RNU12 was downregulated in peripheral blood mononuclear cells and T cell subtypes of RA patients and was genetically associated with RA risk. lnc-RNU12 mediates the effect of microbiome alterations on RA risk. Activation of T cells caused low expression of lnc-RNU12. Knock-down of lnc-RNU12 in Jurkat T cells caused cell cycle S-phase arrest and altered the expression of protein-coding genes related to the cell cycle and apoptosis (e.g. c-JUN, CCNL2, CDK6, MYC, RNF40, PKM, VPS35, DNAJB6 and FLCN). Finally, c-JUN and CCNL2 were identified as target genes of lnc-RNU12 at the mRNA and protein expression levels. RNA-binding protein immunoprecipitation assays verified the interaction between lnc-RNU12 and the two proteins (c-Jun and cyclin L2) in Jurkat cells. CONCLUSIONS: Our study suggested that lnc-RNU12 was involved in the pathogenesis of RA by influencing the T cell cycle by targeting c-JUN and CCNL2.

Integrative analysis highlighted susceptibility genes for rheumatoid arthritis.

Genetic factors underlying susceptibility to rheumatoid arthritis (RA) are largely unknown. The aim of this study was to identify potential genes for RA. We conducted summary statistic data-based Mendelian randomization (SMR) analysis to detect potential causal genes for RA. Further, we performed additional bioinformatics analysis to show the potential relevance of the identified genes to RA. We identified 140 genes that showed causal association with RA. Among these genes, 24 have not been reported to be associated with RA (e.g., IFNAR2, FLOT1, ITPR3, PPP2R3C and SLC35B2). The unreported genes were highly connected with some well-known RA-related genes (e.g., HLA-DQB1, CD226, PTPN22, CD40, IFNGR2, BLK, TRAF1, SYNGR1 and CCR6) that were also found to be causally associated with RA. The identified genes were involved in the significant enriched RA-related biological pathways. We found integrative evidence in support of IFNAR2 as a potential causal gene of RA in SMR, differential expression, weighted gene co-expression network, protein-protein interaction and functional enrichment analyses. The present study highlights a list of potential causal genes for RA. The findings provide new insights into the mechanism underlying known genome-wide associated RA susceptibility loci.