Background There have been lacking reliable serum biomarkers in assessing the disease activity of Takayasu’s arteritis (TAK). This study aimed to assess the disease activity of TAK by assayed gene expression levels in peripheral mononuclear cells (PBMCs). Methods The expression level of genes that essential in T cell activation in PBMCs in active TAK patients, inactive TAK patients, and healthy controls were detected by real-time fluorescence quantitative polymerase chain reaction, including TCR, CD28, CD40, CD40L, PD-1, PD-L1, PD-L2, CTLA4, TIGIT, TIM3, LAG3, CCL5, T-bet, RORC, and FOXP3. Gene co-expression network was established, and the signature of the topology structure in active TAK patients compared to the inactive TAK patients were extracted and described by formulas. Respectively, the disease activity was assessed by the routine serum biomarkers, including ESR, CRP, IL-6, and TNF-α, the gene expression level of TCR, CD28, T-bet, and RORC, as well as the signature of the topology structure, and the diagnostic efficacies were compared. Results Compared with the inactive TAK patient group, the active TAK patient group had a greater clustering coefficient in the network consisting of genes that essential in T cell activation. When assessing the disease activity used this signature of topology structure, the sensitivity was 90.9%, the specificity was 100%, and the AUC was 0.98, which was greater than the AUCs of these biomarkers. Conclusions The signature of the topology structure could distinguish the active TAK patients from inactive TAK patients. This maybe is a novel evaluation algorithm of disease activity.

To explore the relationships between Toll-like receptors (TLRs) and the activation and differentiation of T-cells in Takayasu’s arteritis (TAK), using real-time fluorescence quantitative polymerase chain reaction, mRNA abundance of 29 target genes in peripheral blood mononuclear cells (PBMCs) were detected from 27 TAK patients and 10 healthy controls. Compared with the healthy control group, the untreated TAK group and the treated TAK group had an increased mRNA level of TLR2 and TLR4. A sample-to-sample matrix revealed that 80% of healthy controls could be separated from the TAK patients. Correlation analysis showed that the inactive-treated TAK group exhibited a unique pattern of inverse correlations between the TLRs gene clusters (including TLR1/2/4/6/8, BCL6, TIGIT, NR4A1, etc) and the gene cluster associated with T-cell activation and differentiation (including TCR, CD28, T-bet, GATA3, FOXP3, CCL5, etc). The dynamic gene co-expression network indicated the TAK groups had more active communication between TLRs and T-cell activation than healthy controls. BCL6, CCL5, FOXP3, GATA3, CD28, T-bet, TIGIT, IκBα, and NR4A1 were likely to have a close functional relation with TLRs at the inactive stage. The co-expression of TLR4 and TLR6 could serve as a biomarker of disease activity in treated TAK (the area under curve/sensitivity/specificity, 0.919/100%/90.9%). The largest gene co-expression cluster of the inactive-treated TAK group was associated with TLR signaling pathways, while the largest gene co-expression cluster of the active-treated TAK group was associated with the activation and differentiation of T-cells. The miRNA sequencing of the plasma exosomes combining miRDB, DIANA-TarBase, and miRTarBase databases suggested that the miR-548 family miR-584, miR-3613, and miR-335 might play an important role in the cross-talk between TLRs and T-cells at the inactive stage. This study found a novel relation between TLRs and T-cell in the pathogenesis of autoimmune diseases, proposed a new concept of TLR-co-expression signature which might distinguish different disease activity of TAK, and highlighted the miRNA of exosomes in TLR signaling pathway in TAK.