Disease-specific T cell receptors maintain pathogenic T helper cell responses in postinfectious Lyme arthritis

Abstract

BACKGROUNDAntibiotic-Refractory Lyme Arthritis (ARLA) involves a complex interplay of T cell responses targeting Borrelia burgdorferi antigens progressing toward autoantigens by epitope spreading. However, the precise molecular mechanisms driving the pathogenic T cell response in ARLA remain unclear. Our aim was to elucidate the molecular program of disease-specific Th cells.METHODSUsing flow cytometry, high-throughput T cell receptor (TCR) sequencing, and scRNA-Seq of CD4+ Th cells isolated from the joints of patients with ARLA living in Europe, we aimed to infer antigen specificity through unbiased analysis of TCR repertoire patterns, identifying surrogate markers for disease-specific TCRs, and connecting TCR specificity to transcriptional patterns.RESULTSPD-1hiHLA-DR+CD4+ effector T cells were clonally expanded within the inflamed joints and persisted throughout disease course. Among these cells, we identified a distinct TCR-β motif restricted to HLA-DRB1*11 or *13 alleles. These alleles, being underrepresented in patients with ARLA living in North America, were unexpectedly prevalent in our European cohort. The identified TCR-β motif served as surrogate marker for a convergent TCR response specific to ARLA, distinguishing it from other rheumatic diseases. In the scRNA-Seq data set, the TCR-β motif particularly mapped to peripheral T helper (TPH) cells displaying signs of sustained proliferation, continuous TCR signaling, and expressing CXCL13 and IFN-γ.CONCLUSIONBy inferring disease-specific TCRs from synovial T cells we identified a convergent TCR response in the joints of patients with ARLA that continuously fueled the expansion of TPH cells expressing a pathogenic cytokine effector program. The identified TCRs will aid in uncovering the major antigen targets of the maladaptive immune response.FUNDINGSupported by the German Research Foundation (DFG) MO 2160/4-1; the Federal Ministry of Education and Research (BMBF; Advanced Clinician Scientist-Program INTERACT; 01EO2108) embedded in the Interdisciplinary Center for Clinical Research (IZKF) of the University Hospital Würzburg; the German Center for Infection Research (DZIF; Clinical Leave Program; TI07.001_007) and the Interdisciplinary Center for Clinical Research (IZKF) Würzburg (Clinician Scientist Program, Z-2/CSP-30).

Keywords: Arthritis; Autoimmune diseases; Autoimmunity; Infectious disease; T cell receptor.

Figure 1. Expansion of PD-1^hiHLA-DR⁺CD4⁺ effector T cells in the joints of patients with ARLA throughout disease course.

(A) Representative dot plots showing PD-1 and HLA-DR expression on peripheral blood CD4⁺ T cells from healthy controls and matched peripheral blood and SF CD4⁺ T cells from patient ARLA05. (B) Compiled data from 9 people in the healthy control (HC) group and 6 patients with ARLA indicating PD-1^hiHLA-DR⁺ T cell frequencies in peripheral blood (PB) and matched SF. Bars represent mean frequency ± SD. Unpaired (left) and paired (right) 2-tailed Student’s t test, ***P < 0.001. (C) Immunofluorescence images using MICS technology of 3 regions of interest (ROI) of synovial tissue sections from ARLA01 with indicated stain markers. Scale bars: 100 μm (left image); 20 μm (2 top right images and 1 bottom left); 30 μm (bottom right image). (D) Spatial mapping of 3 CD4⁺ T cell subsets gated based on their PD1 and HLA-DR expression and projected on segmented data of ROIs. Scale bar: 100 μm (E) Average minimum distance of each segmented CD20⁺ B cell to segmented CD4⁺ T cell subsets. 1-way ANOVA with Tukey’s multiple comparisons test; ***P < 0.001. (F) Dot plots showing PD-1 and HLA-DR expression on SF CD4⁺ T cells from patient ARLA02 at different time points. (G) Distribution of SF PD-1^hiHLA-DR⁺CD4⁺ T cell frequencies in 5 ARLA patients observed throughout the disease course; intraarticular corticosteroids (IACS), TNF-α inhibitor (TNFi), methotrexate (MTX). (H) Dot plots demonstrating PD-1 and HLA-DR expression on SF CD4⁺ T cells from patients with JIA and ARLA. (I) Distribution of SF PD-1^hiHLA-DR⁺CD4⁺ T cell frequencies stratified based on disease subgroup and/or antinuclear antibody (ANA) status of patients with JIA and ARLA. ERA, enthesitis-related arthritis. Bars indicate mean frequency ± SD; Dunnett’s multiple comparisons test from ordinary 1-way ANOVA, ***P < 0.001.

Figure 2. Convergent and ongoing T cell responses in the joints of patients with ARLA.

(A) Schematic work-flow illustrating the analysis process for identification of TCR similarities and clustering of TCRs into groups based on their probable specificity. (B) Network representation displaying TCR specificity groups enriched by GLIPH2 in SF PD-1^hiHLA-DR⁺CD4⁺ T cells from 5 patients with ARLA with at least 1 HLA-DRB1*11 allele. Only specificity groups containing sequences from multiple patients are shown. Motifs are represented by small black circles, and corresponding CDR3-β sequences as colored circles; colors correspond to the sourcing individual sizes indicate the absolute abundancies of unique CDR3 amino acid (aa) sequences in all patients. (C) Tracking of occupied repertoire space within SF PD-1^hiHLA-DR⁺CD4⁺ T cells using sequences containing CDR3 aa motifs from the specificity cluster at various time points in 3 patients. Each color corresponds to an unique CDR3 aa sequence. (D) Ratio comparison of the occupied repertoire space by sequences containing CDR3 aa motifs from the specificity cluster, defined in B, against the occupied repertoire space by CDR3 aa sequences in the ‘specificity cluster’ at time point 1 (as depicted in Supplemental Figure 2A).

Figure 3. A combined CDR1β / CDR3β surrogate marker defines a common disease-associated TCRs motif.

(A) The distribution of TRBV-TRBJ gene segment pairings is depicted in circos plots, showing unique TCRβ chain sequences derived from SF PD-1^hiHLA-DR⁺CD4⁺ T cells collected from 5 ARLA patients. The upper circle delineates sequences belonging to the ‘specificity cluster,’ as illustrated in Figure 2A, while the lower circle represents the remaining sequences. The TRBV7-2.TRBJ2-7 and TRBV18.TRBJ2-7 pairing are highlighted in red and blue, respectively. (B) Sequence plots depicting the amino acid sequences in CDR1-3β derived from sequences within the specificity cluster. For the generation of sequence plots, TCR sequences were filtered to include the most abundant length of each CDR. Potential surrogate markers, such as GH in CDR1-β (CDR-1β motif) and SL/SV in CDR3-β (CDR3β motif), are outlined in red. (C) The frequencies of the indicated surrogate markers are compared between sequences within and outside the specificity cluster; P values determined by multiple paired t tests are adjusted for multiple testing by Holm-Šídák method; *P < 0.05, ***P < 0.001 Bars indicate mean ± SD. (D) Alluvial plot of TRAV-TRAJ-TRBV-TRBJ combinations (determined by paired TCR-α/β–sequencing of CD4⁺ SF T cells from 3 patients with ARLA) in unique clones containing motifs from the specificity cluster in the CDR3-β. Alluvials from clones with the CDR3-β motif (SL/SV at IMGT position 111/112 in CDR3-β) are highlighted in coral. (E) Frequency of clones with TRAV23/DV6 gene segment usage within all clones or within subsets filtered for the indicated properties of the TCR-β chain. The number of clones in each subset is indicated at each bar. Circles represent individual patients, error bars indicate SD. Significances were calculated by 1-way ANOVA and multiple comparisons (to all clones) corrected with Dunnetts formula; *P < 0.05, **P < 0.01.

Figure 4. Surrogate markers for ARLA-associated TCRs are disease specific and HLA-DRB1 restricted.

(A) Network representation of TCR specificity groups enriched by GLIPH2 in SF PD-1^hiCD4⁺ T cells from 12 patients with ARLA, 6 with JIA and 3 with RA. Only specificity groups containing sequences from multiple patients are included and only networks with at least 50 members are shown. Motifs are represented by small black circles and corresponding CDR3 sequences by colored circles. The circle sizes reflect the absolute abundances of unique CDR3 amino acid (aa) sequences across all patients. (B) Sequence plots showcasing the aa sequences in CDR1-3β, derived from sequences within the highlighted network on the left, are displayed. To generate these sequence plots, sequences were filtered for the most abundant length of each CDR. (C) Frequencies of indicated surrogate markers in TCR-β sequences of FACS-sorted SF PD-1^hiHLA-DR⁺CD4⁺ cells from children with JIA (n = 6) and ARLA (n = 12) determined by bulk sequencing. Patients exhibiting Serine at position 13 (Ser13) of HLA-DRB1 on at least 1 allele are denoted by filled circles. Bars indicate mean ± SD. 1-way ANOVA and multiple comparisons (to ARLA +) corrected with Dunnetts formula; the Ser13– JIA group was excluded from the statistical analysis due to the small sample size of n = 2; NS: P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 5. The synovial CD4⁺ T cell landscape in ARLA is dominated by clonally expanded T_PH cells.

(A) UMAP representations of cell clustering via scRNA-Seq analysis of SF CD4⁺ T cells from 3 patients with ARLA. (B) Mean frequency of cells allocated to the respective clusters. Symbols represent individual patients, error bars indicate SD. (C) 2-D dot plot showing the expression of selected marker genes. The area of the dots indicates the percentage of cells within the cluster expressing the gene, the color represents the average expression level. (D) Representative dot blots showing cytokine expression in SF PD-1^hi (upper row) and PD-1^lo CD4⁺ cells, assessed by flow cytometry. (E) Clonal expansion is depicted based on the size of individual clones, determined through paired TCR-α/β sequencing, represented on the UMAP plot from A. (F) Clonal connectivities between individual clusters are illustrated by arrows, with darker colors indicating a higher proportion of clones originating from the starting cluster. (G) Possible developmental trajectories projected onto the UMAP representation from A, inferred by RNA velocity analysis.

Figure 6. ARLA-specific T cell clones map to the T_PH cluster and show signs of TCR-driven activation.

(A) UMAP representations of cell clustering via scRNA-Seq analysis of SF CD4⁺ T cells from 3 ARLA patients (from Figure 5A). In the center, cells with viral TCR motifs are highlighted, while on the right those with the ARLA motifs (CDR1-β and CDR3-β motifs) are highlighted. (B) Relative distribution of cells within each cluster, categorized by color as depicted in A, considering all cells or cells containing either viral or ARLA TCR motifs. Fisher’s exact test between indicated groups; ****P < 0.0001. (C) Normalized enrichment scores (NES) from Gene Set Enrichment Analysis (GSEA) analysis using Reactome pathways with differentially expressed genes between T_PH cells (cluster 0 and 3) with either viral or ARLA TCR motifs. Pathways are ranked based on their NES, with circle size corresponding to negative log₁₀ (adjusted P value). Red circles represent pathways with an adjusted P value < 0.05. The significantly enriched pathways are identified by name. (D) Genes associated with TCR signaling-related GSEA pathways were used as input for the AddModuleScore function from Seurat. Resulting scores per cell are plotted and compared between T_PH cells containing no motifs, viral or ARLA TCR motifs. (E) Expression of selected activation and effector genes in the same groups as in D. 1-way ANOVA with Tukey’s multiple comparisons test; P values < 0.1 are shown, *P < 0.05, **P < 0.01, ***P < 0.001.