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Clinical MedicineAutoimmunityInfectious disease
Open Access | 
10.1172/JCI179391
1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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1Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Würzburg, Germany.
2German Centre for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
3Institute of Pathology, University of Würzburg, Würzburg, Germany.
4Department of Pediatric Immunology, Rheumatology, and Infectiology, Hospital for Children and Adolescents, Leipzig University, Leipzig, Germany.
5Children’s Hospital, Vivantes Klinikum im Friedrichshain, Berlin, Germany.
6Pediatric Hematology, Oncology and Stem Cell Transplantation, University Hospital Würzburg, Würzburg, Germany.
7Computational Systems Virology and Bioinformatics, Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany.
8Faculty for Informatics and Data Science, University of Regensburg, Regensburg, Germany.
Address correspondence to: Henner Morbach, Pediatric Inflammation Medicine, Department of Pediatrics, University Hospital Würzburg, Josef-Schneider-St. 2, 97080 Würzburg, Germany. Phone: 49.931.201.27728; Email: Morbach_H@ukw.de.
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Published July 4, 2024 - More info
Published in Volume 134, Issue 17 on September 3, 2024
Related article:
Abstract
Lyme disease, caused by Borrelia burgdorferi (Bb), can progress to Lyme arthritis (LA). While most patients with LA respond successfully to antibiotic therapy, a small percentage fail to improve, a condition known as antibiotic-refractory Lyme arthritis (ARLA). While T cell responses are known to drive ARLA, molecular mechanisms for ARLA remain unknown. In this issue of the JCI, Dirks et al. isolated disease-specific Th cells from patients with ARLA residing in Germany. A distinct TCR-β motif distinguished ARLA from other rheumatic diseases. Notably, the TCR-β motif was linked predominantly to HLA-DRB1*11 or 13 alleles, which differed from alleles in patients from North America. It also mapped primarily to T peripheral helper (Tph) cells, as opposed to classical Th1 cells. These findings provide a roadmap explaining how T cell responses necessary for control of an infection can, despite antibiotic therapy, drive a disadvantageous T cell response, resulting in a postinfectious, inflammatory arthritis.
Authors
Allen C. Steere, Jacob E. Lemieux
× AbstractBACKGROUND. Antibiotic-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.
METHODS. Using 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.
RESULTS. PD-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.
CONCLUSION. By 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.
FUNDING. Supported 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).
Graphical Abstract
Introduction
Lyme disease, caused by the tick-transmitted spirochete Borrelia (B.) burgdorferi sensu lato, is the most prevalent vector-borne illness in North America and Europe, posing a significant public health concern (1, 2). In North America, B. burgdorferi sensu stricto (s.s.) is the main species, while in Europe, additional species such as B. afzelii and B. garinii contribute to disease burden (3). Among the late manifestations, Lyme Arthritis (LA) is the most commonly reported (4). While the majority of cases resolve after antibiotic treatment, a small subgroup of individuals experiences persistent joint inflammation despite adequate antibiotic treatment, this condition is referred to as postinfectious or antibiotic-refractory LA (ARLA) (5–8).
ARLA is characterized by an exaggerated proinflammatory immune response that persists after the initial infection and results in synovitis and synovial hyperplasia. Infections caused by more virulent Borrelia burgdorferi strains, especially those belonging to the RST1 (OspC type A) genotype, are correlated with the development of ARLA (9). These strains exhibit genetic traits conducive to heightened expression of outer surface lipoproteins, which are suggested to be implicated in inducing exaggerated immune reactions in susceptible individuals (10). In line with this, the Borrelia burgdorferi lipoprotein outer surface protein A (OspA) has been identified as a well-established antigen triggering this dysregulated immune response (11–14). The risk of developing ARLA has been linked to specific HLA-DRB1 alleles that efficiently present OspA-derived peptides to CD4+ T cells (13, 15). In a North American study, HLA-DRB1 alleles *01:01, *04:01, and *15:01 were associated with a higher risk, while *08:01, *11:01, *11:04, and *13:02 were associated with a potential protective capacity (15). In individuals at risk, OspA appears to trigger an excessive immune response creating a highly inflammatory milieu that subsequently promotes epitope spreading of the immune response toward autoantigens (7). This autoimmune response seems to be mediated by T-bet and/or ROR-γt–expressing Th cells and targeted extracellular matrix proteins as well as vascular autoantigens (16–20).
While this may point toward Th1 and/or Th17 cells as drivers of arthritis, the detailed function and molecular program of pathogenic Th cells in patients with ARLA remain unclear. Moreover, the concept of an OspA-mediated dysregulated immune response might not directly extend to regions outside of North America where the composition of Borrelia species causing LA differs. This discrepancy is evident since immune responses against OspA are noticeable in many patients in North America, especially those experiencing ARLA, but they are seldom observed in patients with LA in Europe (21, 22).
Hence, our objective was to ascertain the molecular program of disease-specific Th cells in patients with ARLA, link T cell receptor (TCR) specificity to cellular function, and track clonal evolution of these cells at the site of inflammation throughout disease progression. Acknowledging the uncertainty of potential target antigens among patients outside of North America, we implemented an unbiased approach focusing on the host’s immune response. The objective was to infer putative TCR specificity through the clustering of TCRs displaying biological similarities. For this, we employed high-throughput TCR sequencing of activated CD4+ Th cells from the joints of patients in conjunction with scRNA-Seq.
Using this approach, we identified disease-specific TCRs in inflamed joints of patients with ARLA living in Central Europe, mainly restricted to HLA-DRB1*11 or HLA-DRB1*13 alleles, which were unexpectedly prevalent in our cohort despite their underrepresentation in the North American cohort. These ARLA-specific T cells exhibited sustained proliferation, likely due to locally induced TCR signaling, and expressed a pathogenic effector program resembling peripheral Th (TPH) cells, marked by CXCL13 and IFN-γ expression.
ResultsPD-1hiHLA-DR+CD4+ effector cells are expanded in the joints of patients with ARLA throughout disease course. We studied a cohort of 13 pediatric patients with ARLA (mean ± SD age of onset 13.6 ± 2.2 years; 38.5% female and 61.5% male) residing throughout Germany, from whom synovial fluid (SF) was preserved following therapeutic joint injections after they had completed antibiotic therapy. Antibiotic treatment was initiated within 2 months after onset of arthritis in 7 of these 13 patients (median 2.0 months, range 7 days to 10 months). Despite prior antibiotic treatment, all patients presented with chronic arthritis and received intraarticular steroid injection. All patients were followed for at least 12 months, with a median followup of 21 months (range 12 to 60 months). All but a single patient experienced a recurrence of arthritis after intraarticular steroid injection and required treatment with DMARDs, along with additional intraarticular steroid injections (Supplemental Figure 1 and Supplemental Table 1; supplemental material available online with this article; https://doi.org/10.1172/JCI179391DS1).
All patients exhibited a broad serological response against multiple Borrelia antigens; however, the majority did not show detectable responses to OspA (Supplemental Table 1). Some patients carried the well-known ARLA HLA-DRB1 risk alleles *01:01, *04:01, and *15:01, including the only patient with a detectable antibody response against OspA who was homozygous for the *15:01 allele. However, 5 out of the 13 patients (38.5%) carried at least 1 HLA-DRB1*11 allele, which was underrepresented in the North American ARLA cohort (Supplemental Table 1) (8). In the European Cohort, the cumulative frequency of the group of known HLA-DRB1 “risk alleles” tended to be lower in comparison with the reported North American cohort (23.1% versus 38.1%, P = 0.18), whereas the frequency of “protective alleles” was significantly higher (42.5% versus 11.9%, P < 0.01; Supplemental Table 2). Thus, despite the similarity in HLA-DRB1 allele distribution among geographically and ethnically matched control cohorts (Supplemental Table 2), these allele groups exhibited contrasting patterns between the 2 patient cohorts.
Effector T cells present an instructive cell subset for investigating ongoing immune responses, as they arise from the division of antigen-specific T cells upon TCR activation and are enriched with antigen-specific clones. Recent evidence suggests that disease-specific effector Th cells can be distinguished based on the expression of distinct activation markers (23). To analyze the distribution of recently antigen-activated effector CD4+ T cells in the inflamed joints of patients with ARLA, we utilized flow cytometry and employed high expression levels of PD-1 and HLA-DR as surrogate markers. A significant proportion of PD-1hiHLA-DR+CD4+ T cells was observed in the SF but not in the peripheral blood of patients with ARLA, and these cells exhibited enrichment in CD45RO+CCR7– effector memory phenotype (Figure 1A, and B, and Supplemental Figure 2A). Analyzing the clonal diversity by sequencing the TCR-β repertoire within bulk-sorted SF PD-1hiHLA-DR+ and PD-1loHLA-DR– CD4+ T cells revealed a more restricted repertoire in the PD-1hiHLA-DR+ subset as an indicator of oligoclonal expansion (Supplemental Figure 2B). Additionally, the PD-1hiHLA-DR+CD4 T cell subset demonstrated a significant enrichment in cells expressing Ki-67 as sign of ongoing proliferation (Supplemental Figure 2, C and D).
Figure 1Expansion of PD-1hiHLA-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-1hiHLA-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-1hiHLA-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-1hiHLA-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.
PD1hiHLA-DR+CD4+ T cells were observed in comparable frequencies in both SF and matched synovial tissue samples, with a substantial clonal overlap evident between these compartments (Supplemental Figure 3, A–C). IHC analysis revealed synovial hyperplasia and synovial infiltration of PD-1–expressing CD4+ T cells as well as CD20+ B cells (Supplemental Figure 3D). To track PD1hiHLA-DR+CD4+ T cells in their spatial context at single-cell resolution, we employed MACSima Imaging Cycling Staining (MICS) to analyze 3 distinct regions of interest (ROI) across 1 tissue section from a representative patient (ARLA01). We observed a dense infiltrate of CD4+ T cells dispersed across the synovial sublining layer (Figure 1C). Within this infiltrate, few scattered CD20+ B cells were present, while CD138+ plasma cells were located in the synovial stroma. Additionally, several lymphoid aggregates were identified, comprised of CD4+ T cells with elevated PD-1 expression and CD20+ B cells (Figure 1C). Next, we quantified the spatial distribution of CD4+ T cells based on the expression levels of PD-1 and HLA-DR in segmented cells. PD-1–HLA-DR– cells were loosely scattered, whereas PD-1+HLA-DR+ were localized at the centers of the lymphoid aggregates and surrounded by PD-1+HLA-DR–CD4+ T cells (Figure 1D). Among these 3 CD4+ T cell subsets, PD1+HLADR+ and PD-1+HLA-DR– cells exhibited shorter average minimum distance to CD20+ B cells compared with PD-1–HLA-DR– cells, suggesting ongoing T/B cell interaction in theses lymphoid aggregates (Figure 1E).
We were able to track the expansion of PD-1hiHLA-DR+CD4+ T cells during the disease course in 5 patients with ARLA. Remarkably, a high frequency of PD-1hiHLA-DR+CD4+ T cells persisted in their joints for up to 2.5 years after the onset of arthritis, correlating with ongoing synovitis despite prior antibiotic treatment and concurrent antiinflammatory medication (Figure 1, F and G, and Supplemental Figure 1). The frequencies of PD-1hiHLA-DR+CD4+ T cells in the SF of patients with ARLA were found to be significantly higher compared with age-matched patients with various subtypes of juvenile idiopathic arthritis (JIA), which served as disease controls (Figure 1, H and I). Notably, the frequency of PD-1hiHLA-DR+CD4+ T cells in ARLA SF even exceeded that observed in patients with antinuclear antibody–positive JIA, a condition in which a local autoimmune response is suggested to drive T effector cell expansion (24).
Collectively, our data characterize the PD-1+HLA-DR+ CD4+ T cell subset in the joints of patients with ARLA as oligoclonally expanded effector cells that may localize within lymphoid aggregates within the synovia. These findings underscore their role as locally induced effector cells and highlight their potential utility in dissecting the pathogenic T cell response within the inflamed joints of patients with ARLA.
The TCR repertoire of PD-1hiHLA-DR+ CD4+ T cells in the joints of patients with ARLA displays signs of an ongoing and convergent T cell response. We speculated whether the sustained expansion of PD-1hiHLA-DR+CD4+ T cells in patients with ARLA throughout the disease course might be driven by persistent recognition of disease-specific antigens. To investigate this, we first analyzed the TCR repertoire of these cells for indicative markers. Considering HLA restriction and the unexpectedly high frequency of the HLA-DRB1*11 allele in our ARLA cohort, we initially focused our analysis on patients who carried at least 1 HLA-DRB1*11 allele (Supplemental Tables 1 and 3). To comprehensively assess the TCR repertoire on a larger scale, we sorted SF PD-1hiHLA-DR+CD4+ T cells from 5 patients and analyzed their TCR-β repertoire by bulk sequencing. Each sample yielded between 529 and 4,774 distinct clones, with minimal clonal overlap observed among the analyzed individuals (0, 0, or 5 shared clonotypes between 5, 4, or 3 individuals, respectively). As this approach failed to reveal significant numbers of shared clonotypes indicative of a disease-specific TCR response, we next utilized GLIPH2 (Grouping of Lymphocyte Interactions by Paratope Hotspots, version 2) (25). This algorithm clusters TCRs based on shared sequence similarities rather than identities, predicting them to bind the same MHC-restricted peptide antigen. By applying this method to the combined set of bulk TCR-β sequences, we identified 593 different specificity groups overlapping between at least 2 individuals (Figure 2A). Performing a network visualization of those sequences and their specificity groups, we identified a cluster of significantly enriched TCRs that were closely linked by similar specificity groups (highlighted as ‘Specificity Cluster’ in Figure 2B). This cluster comprised 3.0% to 6.7% of the total clonal space and was enriched with specificity groups that shared similar local motifs located in the part of the CDR3-β not coded for by germline TRBV and TRBJ segments (therefore n–/p– nucleotides, Table 1).
Figure 2Convergent 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-1hiHLA-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-1hiHLA-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).
Table 1CDR3-β sequence and VJ gene usage contained within the specificity cluster
We next sought to investigate the longitudinal kinetics of the specificity cluster within the PD-1hiHLA-DR+ cell subset in SF samples from 3 patients with ARLA collected at various time points during disease course. TCRs carrying specificity cluster motifs remained detectable in subsequent samples at frequencies comparable to those observed during the initial sampling, with clonal persistence even after intervals of up to 16 months between analysis time points (Figure 2C and Supplemental Figure 4A). In addition, nearly half of the TCRs associated with any specificity cluster motifs in the follow-up samples could not be identified in the initial sample, suggesting that new clones with similar TCRs are recruited into the specificity cluster over time (Figure 2D and Supplemental Figure 4A). Exploring deeper into the CDR3-β sequences corresponding with the most frequent specificity groups in the cluster, we uncovered greater nucleotide diversity compared with the amino acid (aa) level as a characteristic sign of a convergent T cell response (Supplemental Figure 4, B and C).
Thus, within SF PD-1hiHLA-DR+CD4+ T cells of patients with ARLA exhibiting a distinct HLA-DRB1 background, we identified a persistent cluster of TCR specificity groups that endured throughout the disease course. This cluster was partially replenished over time by different clones with identical TCR motifs. This observation aligns with a continually ongoing T cell response in the joints of these patients with ARLA, triggered by and converging toward a set of antigens.
A HLA-DRB1–restricted TCR-β amino acid motif functions as surrogate marker for ARLA-specific TCRs. To facilitate the identification of disease-specific TCRs in ARLA, our objective was to uncover surrogate markers capable of comprehensively identifying these TCRs with minimal effort. To achieve this, we conducted a detailed analysis of the fundamental molecular patterns exhibited by the TCRs within the previously defined specificity cluster.
Analysis of the TCR-β VJ pairings revealed a significant increase of the TRBV7-2.TRBJ2-7 and TRBV18.TRBJ2-7 combinations in TCRs within the cluster compared with all others (74.8% versus 1.5%, respectively; P < 0.0001 by χ2 with Yate’s correction; Figure 3A). In addition, TCR-β sequences contributed by patient ARLA06 to the specificity cluster displayed a distinct VJ pairing with predominance of TRBV5-4.J2-3 (Supplemental Figure 5A). The TCR-β sequences from all patients in the cluster almost exclusively used the aa doublet ‘SL’ or ‘SV’ within the hypervariable part of the CDR3 region at IMGT position 111 and 112, which were not encoded by germline template and displayed high variability on the nucleotide level (Table 1, Figure 3B, Supplemental Figure 4, B and C, and Supplemental Figure 5B). Additionally, the TCR-β sequences were characterized by usage of ‘GH’ at IMGT position 28 and 29 within the CDR1 region, which reflects the use of the above mentioned Vβ segments 7–2, 18, and 5–4 that inherit this motif in their germline configuration (Figure 3B). The simple CDR3-β motif (‘SV or SL’) — alone or in combination with the CDR1-β motif (‘GH’) — could, within the 5 analyzed patients, identify between 55%–100% of all TCRs that belonged to the cluster (Figure 3C). Notably, the frequencies of these markers also remained unchanged in SF PD-1hiHLA-DR+CD4+ T cells of patients with ARLA during the disease course and were found at similar frequencies in matched synovial tissue (Supplemental Figure 6).
Figure 3A 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-1hiHLA-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.
We then aimed at including the TCR-α chain into our analysis. To obtain paired TCR-α/β sequences we performed scRNA-Seq of SF CD4+ T cells from 3 patients (Supplemental Table 3). Clones that could be linked to the specificity cluster by TCR-β specificity groups revealed a significant enrichment toward usage of the gene segment TRAV23/DV6 compared with all other clones, which rather displayed random usage of TRAV segments (64% versus 4.7% of the clones respectively, P < 0.0001 by Fisher’s exact test; Figure 3D and Supplemental Figure 7A). This significant enrichment of TRAV23/DV6 usage was also observed when filtering the clones for the above delineated surrogate marker combinations of the TCR-β chain (Figure 3E and Supplemental Figure 7, B and C). Notably, whereas the CDR3-β motif alone identified clones using TRAV23/DV6 with a frequency of 52% to 62% among analyzed patients, the combination of the CDR1-β and CDR3-β motifs increased this frequency to 67%–89% without substantial loss of identified clone numbers (Figure 3E). Hence, the combination of the CDR3-β motif (‘SL’ or ‘SV’ at IMGT positions 111 and 112) with the CDR1-β motif (‘GH’ at IMGT positions 28 and 29), hereafter termed the ‘ARLA motif,’ demonstrated the highest specificity without comprising sensitivity as a surrogate marker for identifying T cell clones that make up the identified specificity cluster in patients with ARLA.
To investigate the specificity of the ARLA motif, we explored the CD4+ TCR repertoire within various disease conditions for its presence. Initially, we assessed published TCR-β sequences known to target microbial antigens or autoantigens for the defined surrogate markers (n = 2,094 from VDJdb (26), n = 12 (27)). The distinct ARLA-associated CDR3-β motif was found in a few sequences, however, the specific combination of this motif together with the distinct VJ combination was not detected in any of the sequences (Supplemental Figure 8; CDR1-β motif frequencies could not be assessed due to missing information in databases). The CDR3-β motif was similarly low in the SF CD4+ TCR repertoire from 2 patients in the North American cohort with LA and could not be identified in published TCR-β sequences from HLA-DRB1*04:01 restricted OspA163-174-specific T cell clones (Supplemental Figure 8B and Supplemental Figure 9) (27, 28). Additionally, the ‘ARLA motif’ was almost absent in the SF CD4+ T cell repertoire in patients with JIA and rheumatoid arthritis (RA) (Supplemental Figure 9) (28–30).
We next extended our analysis from the 5 patients with at least 1 HLA-DRB1*11 allele to all 12 patients with ARLA with available TCR sequences and performed GLIPH2 on TCR-β sequences derived from bulk sorted PD-1hiCD4+ T cells from all patients with ARLA as well as on published data from patients with RA or JIA (input: 20,486, 25,095, and 8,698 sequences from ARLA, JIA, and RA respectively (
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