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.

Background:The activation of self-specific T cells is essential in pathogenesis of Takayasu arteritis (TAK). Dendritic cell (DC) plays an indispensable role as the only antigen presenting cell for initial T cell, and Toll-like receptors (TLRs) are common source of activation signals for DCs. Then we speculate that there are activation of TLRs in TAK patients.Objectives:To investigate the activation of TLRs in TAK patients.Methods:Twenty-seven TAK patients were enrolled during April to October in 2019, with diagnosis met the 1990 criteria of American College of Rheumatology. Patient were divided into groups by the disease activity and medication history. Disease activity was assessed by the 1994 NIH criteria. Quantitative Real-time Polymerase Chain Reaction (RT-qPCR) was used to analyze the mRNA relative abundance of 28 target genes in peripheral blood mononuclear cells (PBMCs). Differences between groups and correlation between any two genes were analyzed.Results:The demographic data and clinical features of TAK patients were shown in Table 1. (1) Compared with health control (HC) group, mRNA abundance ofTLR2, TLR4, P50, P65, IκBα, CTLA4, CD3,andBCL6in untreated TAK group was upregulated (<0.05), whereas mRNA abundance ofCD40was downregulated (p <0.05). (2) Compared with HC group, mRNA abundance ofTLR2, TLR4, IκBα, PD-1 and BCL6in treated TAK group was upregulated (p <0.05), whereas mRNA abundance ofLAG3, CD40andTCRwas downregulated (p <0.05). (3) Compared with untreated TAK group, mRNA abundance ofP50, P65, CD28, CTLA4, TLR2, TLR4, IκBα, PD-1 and RORCwas upregulated in treated TAK group (p <0.05). (4) Compared with non-active treated TAK group, mRNA abundance ofp50, CD28, TCR, GATA3, RORC and FOXP3was upregulated in nonactive treated TAK group (p <0.05). BCL6 showed correlation with the TLRs-NFκB pathway. (Figure 1~2, Table 2)Table 1.Demographic data and clinical features of patients with TAKAge (year)Gender (male/ female)Disease duration* (months)ESR (mm/h)hs-CRP* (mg/L)Interleukin 6 (pg/mL)TNFα(pg/mL)Prednisoneused/ non-usedDosage (mg/d)Treated (n=20)39.37±9.271/1943 (12, 103)14.60±8.941 (0.55, 5.625)2.1 (2, 3.95)7.56±4.3918/210 (10, 32.5) Active (n=11)39.30±7.8891/10118 (16, 166.5)16.82±10.815.63 (1.49, 8.33)3.15 (2.025, 5.775)8.42±5.5710/110 (10, 15) Nonactive (n=9)39.44±10.590/940 (12, 44)11.89±4.610.84 (0.31, 1)2 (2, 2.4)6.60±2.118/18.75 (6.875, 16.25) Pvalue0.89—0.160.340.020.080.65—0.37Untreated (n=7) Active (n=4)  1 31 M — 91 140.72 — ——0  2 25 F — 19 11.28 6.3 5.2—0  3 23 M — 71 77.36 6.3 6.2—0  4 29 F — 127 113.62 22.2 8.4—0 Nonactive (n=3)  5 34 F — 7 0.34 2 4.3—0  6 27 F — 14 0.16 25.7 4—0  7 38 F — 5 0.32 3 4—0* median (min, max)Table 2.Genes expressed abnormally in PBMCs of TAK patientsAbnormally expressed in untreated TAKAbnormally expressed in treated TAKInfluenced by treatmentAssociated with the TAK activityupregulateddownregulatedupregulateddownregulatedupregulateddownregulatedUpregulateddownregulatedGenes associated with the TLRs-NFκB pathwayTLR2, TLR4, p50, p65, IκBα—TLR2, TLR4, IκBα——p50, p65p50—Positive and negative costimulatory molecules and their ligandsCTLA4CD40PD-1CD40, LAG3—CD28, CTLA4CD28—Genes associated with the activation or differentiation of T cell or B cellCD3, BCL6—BCL6TCR—CD3, TCR, RORCTCR, GATA3, RORC, FOXP3—Conclusion:TLRs-NFκB pathway may be activated in TAK patients, with upregulation ofBCL6, and there may be deficiency ofCD40.TLR2, TLR4, PD-1, LAG3, CD40andBCL6may play roles in the pathogenesis of TAK.p50, CD28, TCR, GATA3, RCRCandFOXP3may be related to the disease activity of TAK.Disclosure of Interests:Yixiao Tian: None declared, Jing Li: None declared, Xinping Tian: None declared, Xiaofeng Zeng Consultant of: MSD Pharmaceuticals