Altered B cell activation contributes to the immunopathogenesis of childhood arthritis-associated uveitis

Domain: neuroscience
Published: 2026-02-03
Editorial outcome: minor_revision

Abstract
In Juvenile Idiopathic Arthritis (JIA), the most common childhood rheumatic disease, many patients also develop uveitis (JIA-uveitis), risking life-long vision loss. The mechanisms driving uveitis development in JIA remain understudied. Here, we demonstrate that peripheral blood CD19+ IgD-CD27- double negative type 1 (DN1) B cells are elevated in JIA-uveitis compared to JIA patients without eye disease (JIA). The B cell receptor (BCR) repertoire was also more clonal and somatically hypermutated in JIA-uveitis and antigen-activated B cells infiltrated chronically inflamed JIA-uveitis eyes. Features of heightened B cell activation were recapitulated in experimental autoimmune uveoretinitis (EAU) and disrupting B and T cell interactions using monoclonal antibodies and transgenic mice suppresses uveitis. Together, these findings support a conceptual shift that uveitis is a primarily T cell driven disease and provide evidence for potential new therapeutic strategies that also consider B cells as drivers in disease pathology.

Introduction
Juvenile Idiopathic Arthritis (JIA) is an umbrella term encompassing a group of arthritides that develop in children under 16 years old. In the most common forms of the disease (oligo-articular and rheumatoid factor (RF) neg poly-articular JIA) up to 30% of patients can develop chronic anterior uveitis1. The connection between eye inflammation (uveitis) and joint inflammation (arthritis) in JIA remains unclear2. Response to therapy is heterogeneous, with some children developing a treatment-refractory form of disease, leading to life-long sight-loss2. Up to 30% of JIA-uveitis patients have lost vision in at least one eye by the age of 183. Rapid control of inflammation is crucial for preventing uveitis-associated visual disability1–3. However, therapeutics are still applied in a stepwise approach. Currently, methotrexate is the first-line therapy for children whose disease remains uncontrolled following treatment with topical steroids, while biologics (e.g., Tumour Necrosis Factor alpha-TNFα-blockade) are given to children whose disease is resistant to both topical steroids and methotrexate2. It is estimated that over 25% of children will not respond adequately to TNFα therapy, leading to permanent sight-loss3. New studies are needed to understand why uveitis develops in only some JIA patients and whether newly uncovered mechanisms can be used to identify novel therapeutic strategies aiming to improve long-term outcomes in more patients.

Although current therapeutic strategies for JIA-uveitis are based upon treatments which block inflammatory mediators such as TNFα and most experimental medicine studies have focused on the role that T cells play in uveitis4,5, there is evidence that B cells may also be important in disease pathogenesis. The primary risk factors for uveitis development in JIA patients is anti-nuclear autoantibody (ANA) positivity6, a hallmark for a breakdown in B cell tolerance7, and early age of arthritis onset, which is associated with a prominent B cell transcriptional signature within total peripheral blood mononuclear cells (PBMC) when compared to children with later disease-onset8. In patients with JIA-uveitis who develop complications associated with treatment non-response and vision loss requiring surgical intervention, ocular samples provide preliminary evidence that B cells may infiltrate the ocular compartment. Antibody-producing plasma cells can be found within the inflamed iris of ANA+ patients with childhood-onset uveitis, including those with a JIA diagnosis9,10 and there is intraocular upregulation of B cell-encoded gene expression (e.g., Marginal Zone B and B1 specific protein–MZB1) and B cell-activating and survival factors (e.g., B cell activating factor–BAFF, A proliferation inducing ligand–APRIL) in the aqueous humour of JIA-uveitis patients when compared to uveitis-free patients11. Recent experimental uveitis studies in mice have also demonstrated that depletion of B cells using monoclonal antibodies suppresses disease severity12.

In recent years, there has been a growing interest in understanding how double-negative B cell subsets, or DN B cells, are linked to autoimmune pathogenesis. In healthy adults, DN B cells account for only about 5% of the total B cell population, making them relatively rare13–15. Broadly characterised by the absence of CD27 and IgD expression, these cells represent a heterogeneous population with four distinct subtypes. Briefly, DN1 B cells, which constitute the majority of DN B cells in healthy individuals, are characterised as CD11c-CXCR5+CD21+16,17. Data has described this subset both as a precursor of switched memory B cells and recent germinal center (GC) emigrants16 and as an novel durable subset of memory B cells potentially derived following extrafollicular (EF) activation of B cells18. Recent studies have linked an expansion of CD27-CD21+DN1-like B cells to dysregulated GC responses in adult conditions such as IgA nephropathy19, and a trend for increased DN1/DN2 ratio has been described as a feature of defective GC selection in Primary Antiphospholipid Syndrome20. In contrast, DN2 B cells (CD11c+CXCR5-CD21-Tbet+) are a less common subset that are suspected to solely originate from the EF response and serve as precursors of plasma cells. In systemic lupus erythematosus (SLE), DN2 B cells are expanded, and the presence of these cells has been correlated with disease severity13. Remaining minority subsets of DN B cells include CD11c-CD21-DN3 B cells, with trajectory analysis suggesting that they may serve as precursors of DN2 cells, and DN4 cells, which serve as precursors of IgE class-switched memory B cells in allergic situations21. In the context of JIA, DN2 B cells have been shown to accumulate in the inflamed joints of ANA+ patients22, but no studies have explored the phenotype and function of the breadth of DN B cell subsets across multiple immune cell compartments in JIA or JIA-uveitis.

Unlike other autoimmune conditions, the anti-TNFα Adalimumab remains the only NICE-approved biologic for JIA-uveitis treatment3. An unmet need for evidence-based studies that aim to uncover specific uveitic-driving mechanisms which can be exploited for therapeutic innovation remains. To address this gap, we stratified a large cohort of JIA patients recruited to the CLUSTER JIA consortium23 based on their uveitis status and irrespective of international league of associations for rheumatology (ILAR) subtype24. In this cohort, we found that JIA-uveitis patients can be distinguished from JIA patients with arthritis alone (JIA) by a significant expansion of CD19+IgD-CD27- DN B cells, and specifically DN1 B cells, in the peripheral blood. We found that this increase in DN1 B cells was associated with an increase in developmentally-linked memory B cells and that the B cell receptor (BCR) repertoire showed higher clonality and increased levels of somatic hypermutation in JIA-uveitis compared to JIA. Using a resource of ocular samples from patients with treatment-resistant, severe JIA-uveitis, we found that antigen-activated B cell subsets, and particularly plasmablasts, can be found infiltrating the eye of JIA-uveitis patients. In the mouse model of experimental autoimmune uveoretinitis (EAU), we found evidence of heightened B cell activation, including an increase in GC B cells and plasmablasts in the spleen of mice with EAU compared to naïve controls and an infiltration of B cells into the inflamed ocular compartment of mice with severe EAU. Disrupting B and T cell interactions using antagonistic anti-CD40L monoclonal antibodies25,26 and BCL6fl/flCD4cre mice, which are deficient in T follicular helper (Tfh) cells27, we found that uveitis incidence and severity was dramatically reduced in both conditions. Importantly, this study provides the first evidence that anti-CD40L antagonism, which shows efficacy in clinical trials for other B cell mediated autoimmune conditions25,28, may be beneficial for the treatment of JIA-uveitis and potentially other forms of childhood and adult-onset uveitis.

Results
DN B cells, and particular DN1 B cells, are expanded in the peripheral blood of JIA-uveitis patients compared to JIA patients with no eye disease

To address whether the B cell compartment of JIA-uveitis patients is altered compared to JIA patients who do not develop uveitis, a large cohort of JIA (n=158) patients recruited to the CLUSTER consortium were stratified based on their uveitis status. ‘JIA’ samples (n=116) had no history of uveitis, whilst 44 ‘JIA-uveitis’ samples were categorised by either having previous uveitis or active eye inflammation at the time of sample (Table 1). Assessment of the differential phenotype of 34 different immune cell populations (Supplementary Table S1) in the peripheral blood of JIA-uveitis and JIA patients demonstrated that there was an expanded population of CD19+CD27-IgD- DN B cells, and particularly CD11c- DN B cells, in JIA-uveitis compared to JIA patients (Fig. 1A–F and Supplementary Figs. 1–4). No other immune subset was found to be significantly altered between these patient groups after corrections for multiple testing. These differences were specific to the peripheral blood as no differences in immunophenotype could be found when comparing synovial fluid mononuclear cells from JIA-uveitis versus JIA patients (Supplementary Fig. 5). To determine whether these observed increases in DN and CD11c- DN B cells were being driven by uveitis activity, we stratified our JIA-uveitis patients into those with active and inactive eye inflammation at the time of sample. The increases in DN and CD11c- DN B cells were observed in JIA-uveitis patients regardless of uveitis disease activity (Fig. 1G, H). In a small cohort of JIA patients who went on to develop uveitis after the sample date, we also saw that this increase in DN B cells did not precede uveitis development (Supplementary Fig. 6).

It should be noted that this JIA-uveitis cohort is enriched for ANA-positive oligoarticular JIA patients, reflecting the established epidemiological pattern where this subgroup of JIA carries the highest risk for uveitis development29. Thus, we next performed a subgroup analysis restricted to oligoarticular patients only to ensure our findings were not driven by subtype heterogeneity (Fig. 1I, J). This analysis replicated the findings of increased CD19+CD27-IgD- DN and CD11c- DN B cells in JIA-uveitis patients, though with reduced statistical power due to smaller sample sizes (DN: p=0.0161, DN1: p=0.0083). To exclude that the increase in CD11c- DN B cells in peripheral blood was due to the impact of various clinical factors further to an oligoarticular subtype that increase the risk of developing JIA-uveitis, such as a positive ANA titre or a young age of arthritis onset or presenting with polyarticular-RF-negative as well as oligoarticular arthritis subtype (together sometimes referred to as ‘polygo’ types of JIA) which are known to impact B cell phenotype30,31, a multiple linear regression analysis was performed. As expected, variables such as ethnicity, ‘polygo’ subtype, methotrexate treatment and ANA status had a significant association with the CD11c- DN B cell expansion. However, this analysis demonstrated that the strongest driving factor behind the observed increase in CD11c- DN B was a positive uveitis status (Fig. 1K).

DN B cells are a heterogeneous group of cells which can be generated through different B cell activation pathways with described DN B cell subsets including CD11c-CXCR5+ DN1 B cells, CD11c+CXCR5- DN2 B cells, CD11c-CXCR5- DN3 B cells, and a minority subset of IgE+ CXCR5+ DN4 B cells17. Thus, an in-depth exploration of the phenotype of CD11c- DN B cells was performed on a subset of our original full JIA and JIA-uveitis patient cohort. The expanded cells were CXCR5+CD11c-, confirming that DN1 B cells, and no other DN B cell subset, were expanded in JIA-uveitis compared to JIA (Fig. 2A–D). The expanded DN1 B cells in JIA-uveitis peripheral blood were also CD86+, a key activation marker involved in B:T cell interactions32, and CXCR3+, which controls migration of B cells into the inflamed site and the nervous system33 (Fig. 2E–H).

The expansion in DN1 B cells in JIA-uveitis is associated with an increase of developmentally linked memory B cells and a more clonal B cell repertoire

DN1 B cells have been previously postulated to readily differentiate into memory B cells17. Accordingly, in the full cohort of JIA and JIA-uveitis patients we found a highly significant and strong positive correlation between the proportions of CD11c-DN1-like B cells and CD24hiCD38- memory B cells (Fig. 3A). In a penalised regression ‘LASSO’ regression model where all the phenotypic flow cytometry data from all enumerated 34 immune cell populations were inputted alongside all the recorded clinical demographic information for each patient, the model also deemed that factors most influencing the likelihood of a JIA patient having uveitis were the increase in CD11c-DN1-like B cells alongside CD24hiCD38- memory B cells followed by decreased in CCR6-CXCR3- Th2 T cells, a positive ANA status and an oligoarticular JIA subtype (Fig. 3B). Interestingly, when comparing model accuracy, the variables identified by the LASSO regression (area under curve, AUC 0.82) outperformed both a multiple logistic regression model incorporating key clinical risk factors (JIA subtype, ANA status, age, and sex) (AUC 0.74) and a model using ANA as the sole predictor (AUC 0.64) (Fig. 3C). Previous studies interrogating the developmental connection between DN1 B cells and memory B cells have been performed in adults and recent studies have suggested that DN1 B cells may also act as a long-lived memory population derived via EF activation pathways18. Thus, to interrogate B cell developmental pathways in children and specifically JIA patients, we next performed trajectory analysis on the integrated CD11c- B cell compartment from the peripheral blood of a subset of JIA and JIA-uveitis patients. We delineated 9 clusters of peripheral blood B cells, including transitional B cells, naïve B cells, CXCR3- and CXCR3+ unswitched memory B cells, CXCR3- and CXCR3+ class-switched B cells, DN1 B cells, plasmablasts and plasma cells (Fig. 3D, E). Trajectory analysis inferred that there were three lineages by which B cells could differentiate using these data and included two pathways by which DN1 B cells could further differentiate. In lineage 1, DN1 B cells differentiated into class-switched memory B cells, which represented the end of the differentiation pathway. In lineage 2, DN1 B cells differentiated into plasma cells/plasmablasts with a minority population of class-switched memory B cells acting as an intermediate step. A third pathway (lineage 3) excluded DN1 B cells and was the provenance of a population of potentially long-lived unswitched memory B cells (Fig. 3E).

To next address whether the expansion in B cell subsets had impacted both B cell receptor repertoire and transcriptional profile of B cells in JIA-uveitis compared to JIA patients, bulk CD19+ RNA seq data from JIA (n=101) and JIA-uveitis patients (n=33) from the CLUSTER cohort was used to perform both BCR repertoire analysis and differential gene expression analysis34. Although there were limited differences in the total B cell transcriptome when comparing JIA and JIA-uveitis patients (Supplementary Fig. 7), there were differences in the BCR repertoire. More specifically, when adjusted for age and sex, the JIA-uveitis group exhibited a reduced diversity score compared to JIA, suggesting a more clonal B cell receptor repertoire (Fig. 3F). When assessing BCR mutational load, which reflects somatic hypermutation levels, we also found that JIA-uveitis patients exhibited higher mean mutational frequency than JIA patients (Fig. 3G). Of note, these changes in BCR diversity were not accompanied by differences in the length of the CDR3 amino acid sequence (Supplementary Fig. 7C) or in the utilisation of functional V genes (Supplementary Fig. 7D). However, there was a significant enrichment in the V pseudogene IGHV3.60 compared to JIA alone (Supplementary Fig. 7D).

B cells can be found in the ocular compartment of JIA-uveitis patients and are mainly of a plasma cell phenotype

Despite our observed changes in the B cell compartment between JIA and JIA-uveitis patients in the periphery, to fully elucidate any potential disease mechanisms it is necessary to interrogate the active disease site – the eye. To address this, we analysed rare aqueous humour (AqH) samples collected from JIA-uveitis patients (n=2) undergoing cataract surgery using spectral flow cytometry (Supplementary Table 5). This demonstrated that leucocyte proportions were similar between these patients (Fig. 4A). Using unsupervised clustering, we were able to identify six clear immune cell populations within the AqH-infiltrating leucocytes including CD4+ T cells, CD8+ T cells, innate-like lymphocytes, granulocytes, monocytes and a minority subset of CD19+ B cells in all patients (Fig. 4B). Although the minority subset, further characterisation of the phenotype of AqH-infiltrating B cells demonstrated that B cells present within the AqH were mainly of a class-switch memory (CD19+CD27+IgD-CD38-CD20+) or plasmablast (CD19+CD27+IgD-CD38+CD20+) phenotype (Fig. 4C, D). It has been previously shown that whilst there is a limited number of CD19+ B cells in the synovial fluid of JIA patients, that there is a larger B cell infiltrate of mainly plasma cells in the synovial tissue itself35–37. To address if this was also the case in JIA-uveitis, we analysed archival H&E stained tissue from enucleated eyes collected from JIA-uveitis patients (Supplementary Table 6). In all samples (n=3, patient 1, 2 & 3), there was clear evidence of plasma cell infiltration based on their classic morphology, including clockface nuclei and large cytoplasmic domains (Fig. 4E–G). Infiltration of plasma cells varied in location depending on the sample, but evidence of plasma cell infiltration was found in the iris of patients 1 & 2, the cornea of patient 2 and the choroid of patient 3 (Fig. 4E–G).

Disruption of B and T cell interactions suppresses experimental autoimmune uveitis severity

Although our human studies suggested that B-cell activation pathways are dysregulated in JIA-uveitis patients, they do not address whether these pathways are directly contributing to uveitis pathogenesis. Thus, we next sought to understand whether the animal model of experimental autoimmune uveoretinitis (EAU) could be used to perform mechanistic studies to understand the direct contribution of B cells to uveitis pathology. Although EAU does not fully replicate JIA-uveitis as it lacks joint inflammation (arthritis), it does serve as a valuable tool to model the breakdown of the blood-retina barrier, which occurs in human uveitis38. As EAU is primarily characterised as a T cell driven disease4, we first assessed how the peripheral and ocular B cell immunophenotype was altered in mice with EAU compared to controls. This showed that there a significant increase in multiple splenic B cell subsets in mice with EAU versus controls including CD95+GL7+ germinal centre (GC) B cells and CD138+Blimp-1+ plasmablasts (Fig. 5A–D). Notably, the frequency of PD1+CXCR5+ Tfh cells was also increased in the spleens of EAU mice compared to controls (Fig. 5E, F). We also observed that there was a significant increase in CD19+ B cells in the ocular compartment of mice with EAU compared to controls, with a specific infiltration of B cells into the ocular compartment of mice with severe disease (Fig. 6A–D). We next assessed the efficacy of anti-CD40L, which has been previously used to block T cell-dependent B cell activation39, in modulating EAU disease severity. This demonstrated that mice treated with antagonistic anti-CD40L were resistant to EAU induction and had a significantly reduced frequency of GC B cells and Tfh T cells when compared to isotype control treated mice (Fig. 7A–G). Secondly, we assessed the severity of in BCL6fl/flCD4cre mice, which are deficient in Tfh cells, leading to impaired B cell responses27. The severity of EAU was also significantly reduced in BCL6fl/flCD4cre mice compared to control mice, and there was a reduction in the frequency of GC B cells and Tfh T cells (Fig. 8A–G).

Methods
Human participants
JIA and JIA-uveitis patients were recruited through three main pathways, through the CLUSTER consortium (https://www.clusterconsortium.org.uk/), Childhood Ocular Inflammatory Disease (CHOIR) Research Tissue biobank (https://www.hra.nhs.uk/planning-and-improving-research/application-summaries/research-summaries/choir-biobank/), and via the Moorfield Pathology Diagnosis Archive. All research involving human participants was conducted in accordance with the declaration of Helsinki and was approved by the relevant institutional and national research committees. Written informed consent was obtained for all samples used in this study.

CLUSTER consortium cohort
Patients within the CLUSTER consortium were recruited at Great Ormond Street Hospital (GOSH) London, UK with parent or legal guardian consent and age appropriate assent for paediatric participants. Recruitment, PBMC and SFMC sampling was approved by the London-Bloomsbury Research Ethics (reference: 95RU04 & 04RU07). Patient demographics can be found in Table 1. Please note, for patient ancestry data the original terminology as recorded in source studies has been reported to ensure data integrity and avoid any misclassification. Where possible, all demographic data has been reported, and the proportion of missing data (such as HLA-B27 status) is declared in the demographic tables.

CHOIR biobank
All patients within the CHOIR Biobank were recruited between 2023 and 2024 to GOSH, London, UK with parent or legal guardian consent and age-appropriate assent for paediatric participants. Study recruitment was approved by the London-Bloomsbury Research Ethics (reference 22:/LO/0575). Patient demographics can be found in Supplementary Table 5.

Moorfields pathology diagnostic archive
Enucleated eyes obtained as part of standard clinical care from JIA-associated uveitis patients were provided via Moorfields Eye Hospital Biobank under the approval of South-West Central Bristol Research Ethics Committee (Ethics reference 20:/SW/0031-2022ETR84). All donors provided written informed consent for biobanking and approved research use. Use of these samples in the present study is covered by the ethical approvals listed above. Patient demographics can be found in Supplementary Table 6.

Human sample collection (blood, synovial fluid and aqueous humour)
Venous blood for PBMCs was collected in PFH (preservative-free heparin) coated monovettes (Sarstedt, cat no: 03.1628.100). Synovial fluid was collected in 20ml falcons containing PFH following arthrocentesis. Aqueous humour (AqH) was collected in sterile 1.5ml Eppendorf’s containing 200ul PBS (Sigma-Aldrich, cat no: P4474) with EDTA (2mM, Sigma Aldrich, cat no: 574795). Peripheral blood mononuclear cells (PBMCs) were isolated via density centrifugation as previously described72. Synovial fluid mononuclear cells (SFMCs) were processed identically as PBMC except they were mixed 1:1 with complete media (RPMI-1640 supplemented with glutamine, penicillin and streptomycin (Sigma Aldrich, cat no: 12352207) and 10% Foetal Calf Serum (Gibco, cat no: A5256701) and treated with Hyaluronidase (10μ/ml, Sigma-Aldrich, cat no: HX0514) for 30min at 37°C prior to density centrifugation. Following density centrifugation, PBMC and SFMC were cryopreserved at −196°C in freezing media (10% Dimethyl sulfoxide (DMSO, Sigma-Aldrich, cat no: D2653), 90% Foetal Calf Serum (FCS, Gibco) and stored until use. AqH samples were collected in sterile tubes and arrived on ice. Immediately after receival, AqH samples were centrifuged at 300xg for 10min and processed for analysis.

H&E histological analysis of enucleated eye tissue
JIA-uveitis eyes were sourced from formalin-fixed, paraffin-embedded whole enucleated eyes in blocks created between 1998 and 2000 and obtained from Moorfields Biobank (Ethics reference 20/SW/0031). FFPE sections were cut at 4µm thick and stained with haematoxylin and eosin. Sections were imaged using a Thunder Imager Live Cell microscope (Leica Microsystems) with a 5x objective for the whole eye overview and 63x objective in areas of interest. Image processing was carried out using Fiji ImageJ73, with plasma cells identified morphologically and confirmed by an expert clinical histopathologist.

Sex as a biological variable
Human studies included both male and female participants. The influence of sex on immunophenotyping data was explored using linear and LASSO regression models. For all transcriptomic analyses, sex and age were treated as biological variables, and data were adjusted accordingly. In mouse studies, female mice were used for all experiments according to standard protocols for the EAU model (66). Females are preferentially used in EAU experiments as uveitis, including JIA-uveitis, is more prevalent in the females versus male human population.

Animal strains and husbandry
Female C57BL/6 mice were purchased from Envigo at 6 weeks old (stock no: 000664). Experiments were initiated when mice were between 6 and 8 weeks of age, unless otherwise stated and housed in UCL biological service units. Mice were kept in individually ventilated specific pathogen-free cages in a controlled environment with a 12h light/dark cycle and a temperature of 22+/−2°C. All mice were fed a standard diet and had access to water ad libitum. Control mice were housed in separate cages to prevent inadvertent exposure to IRBP and CFA used for EAU induction. For all animal experiments, our previous studies have demonstrated that a minimum group size of 8 is needed for a significance level of 0.05 (5%), power of 80%, and an estimated effect size of approximately 10% to identify differences between groups. For all experiments where mice were given treatments, mice were randomised, and the handler was blinded. Mice were euthanised using Schedule 1 methods. All experiments were approved by the Animal Welfare and Ethical Review Body of University College London and authorised by the United Kingdom Home Office. All animal experiments were performed in accordance with the ARVO statement of ‘The Use of Animals in Ophthalmic and Vision Research’. BCL6fl/flCD4cre mice were kindly donated by Professor Richard Jenner, UCL. Briefly, parental strains (BCL6fl/fl mice, Jackson strain no: 023727) and (CD4cre mice, Jackson strain no: 022071) were bred to give BCL6fl/flCD4cre mice as previously described27. EAU was initiated in female BCL6fl/flCD4cre mice which were between 6-8 weeks old. For EAU controls’ C57BL/6 or BCL6fl/fl mice were used as appropriate.

Induction and clinical evaluation of experimental autoimmune uveitis (EAU)
IRBP 1-20 (peptide sequence: GPTHLFQPSLVLDMAKVLLD) was purchased and synthesised by Cambridge peptides, UK (cat no: HY-P1861A-10mg). IRBP 1–20 was reconstituted in 10% DMSO in PBS (Sigma Aldrich can no: P4474 and D2653) to achieve a final concentration of 20mg/ml and stored at −80°C prior to use. On the day of EAU initiation, a stock solution containing of IRBP antigen was prepared by diluting with PBS to give a concentration of 10mg/ml and mixed 1:1 with Complete Freund’s Adjuvant (Sigma Aldrich, cat no: F5881) supplemented with 1.5mg/ml Mycobacterium tuberculosis (Difco, cat no: 231141). 50μl of IRBP CFA mix was injected subcutaneously (SC) into the right and left flanks of the mouse – resulting in a total dose of 100μl at 500mg per mouse. Mice also received an intraperitoneal injection of 1.5mg of Bordetella Pertussis Toxin (Tocris, cat no: 3097), which was diluted in PBS to make a 0.1mg/ml stock. Humane endpoints were predefined according to the institutional ethical approval. Specifically, any mouse that lost more than 15% of its pre-procedure body weight, showed dyspnoea, ruffled fur, weakness, dehydration, persistent hunching, or exhibited ulcers >3mm at the injection site or infection not resolving within 48h was immediately euthanised by a Schedule 1 method. Mice were scored based on fundus images obtained using a Micron III or Micron V imaging system (Pheonix Research Labs). The scoring system was adapted based on the previously published method74. Briefly, images were scored by evaluating the ocular inflammation based on the following metrics: 1, Optic disc swelling (margin of the optic disc becomes blurred as inflammation occurs); 2, Retinal vasculitis (engorged vessels with cuffing of white infiltrates around edges); 3, Retinal tissue infiltrates (white lesions that occur separately from vessels); 4, Structural damage (retinal atrophy with scarring); 5, Posterior Synechiae. All experiments were terminated on day 21 post-disease induction.

Disruption of B:T cell interactions using CD40L antibody
Anti-CD40L (clone: MR-1; IgG) and IgG isotype control antibodies were purchased from BioXcell (cat no: BE0017-1 and BE0091). Four days after the initiation of EAU, C57BL/6 mice were injected intraperitoneally (I.P) three times per week (Monday–Wednesday–Friday) with 500μg of anti-CD40L or isotype as a control. Mice were imaged and euthanised on day-21 post induction.

Dissection of mouse tissues and sample preparation
The retina and spleen were taken from sacrificed mice for further analysis. Briefly, mice were enucleated, and the retina was removed using a dissecting microscope. Retinal samples were manually dissociated. Spleens were removed and placed into complete media. Splenocytes were disaggregated by pushing through a 70μm cell strainer (Sigma Aldrich, cat no: CLS352340). For spleen samples, red blood cells were lysed using lysis buffer (Sigma Aldrich, cat no: R7757), and then lysis was stopped by washing in RPMI 1640 media supplemented with 10% FCS (Sigma Aldrich, RPMI cat no 12352207 and Gibco FCS cat no: A5256701). Both tissues were then processed for downstream flow cytometry analysis.

Flow cytometry
Flow cytometry protocols were performed identically for both human and mouse samples and for both conventional and spectral flow cytometry unless otherwise stated. Isolated cells were stained for multi-colour flow cytometry with fluorochrome-conjugated antibodies as previously described75. Briefly, cells were plated in a 96-well round bottom plate and stained with 50ml viability dye Live/Dead fixable blue stain or Zombie NIR viability dye (Biolegend, cat no: 423105). Cells were then washed and stained for surface markers with fluorochrome-conjugated antibodies and fixed by incubation in 2% paraformaldehyde (PFA, Sigma Aldrich, cat no: 394513). For intracellular staining, instead of PFA, cells were incubated with Transcription Factor Staining Buffer (eBiosciences, cat no: 00-5523-00). Cells were then washed and incubated with fluorochrome-conjugated antibodies in perm buffer (eBiosciences, cat no: 00-5523-00). Following staining, all samples were resuspended in 200µl FACS buffer for analysis on an LSRII cell analyser (BD Biosciences) or Sony spectral cell analyser ID7000 (Sony Biotechnology). For the details of antibodies used for human standard panels, see Supplementary Table 7; for mouse standard panels see Supplementary Table 8; for human spectral panels see Supplementary Table 9. Flow cytometric data were analysed using FlowJo version 10 and R studio version 4.4.1. Gating strategies can be found in Supplementary Figs. 8–11.

Cell sorting, RNA isolation and library preparation
Cells were stained with fluorochrome-conjugated antibodies (Supplementary Table 10) in MACs buffer (PBS, 0.5% FCS, 0.5mM EDTA) and filtered through polypropylene tubes with 70m filter caps (Falcon, cat no: 352235) and DAPI, (Thermo Fischer Scientific, cat no: D1306) was added to each sample. CD19+ cells were sorted using a FACSAria (BD Biosciences) into 1.5ml RNA-free Eppendorf’s containing 100ml RNAse free PBS. Purity cheques were performed on sorted populations and reached 98% purity. Sorted cells were kept on ice and immediately processed for RNA isolation to prevent RNA degradation. On average 330,976 B cells were sorted per sample, though this varied considerably (range: 42,125–2,592,752). RNA was isolated from sorted cell populations using the PicoPure kit (Thermo Fisher, cat no: KIT0204) according to the manufacturer’s instructions. The quality of RNA was then assessed using a NanoDrop spectrophotometer, and samples with a Nanodrop scored were submitted to UCL Genomics Facility. Total RNA quantification and integrity was confirmed using Agilent’s 4200 Tapestation (Standard Total RNA assay). RINs values were confirmed to all be >7.0, indicating good to high integrity RNA suitable for library preparation. For each sample, 50ng of total RNA were processed using a commercial mRNA library preparation kit according to the manufacturer’s instructions. Two different kits were used: the KAPA mRNA HyperPrep Kit (Roche, cat no: KK8580) and the TruSeq Stranded mRNA Library prep kit (Illumina, cat no: 20020492).

RNA sequencing and data analysis
High yield, adaptor-dimer free libraries were confirmed on the Agilent TapeStation 4200 (High Sensitivity Agilent DNA 1000 assay). Samples were quantified using the Qubit High Sensitivity DNA assay and normalised to 10nM. An equal volume of each library was pooled together and re-quantified by Qubit. Samples were sequenced on the NovaSeq instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75. At the same time, the unique molecular index was moved to the read header for downstream analysis. FASTQ files were then aligned to the human genome UCSC hg38 using STAR software76. Aligned reads were then UMI deduplicated using Je-suite (2.0.2.RC)77 and reads per transcript were counted by FeatureCounts78 in order to produce a digital output of gene expression. Differential expression analysis was performed using the R package DESeq279. All annotation and sequences were obtained from Illumina iGenomes (http://emea.support.illumina.com/sequencing/sequencing_software/igenome.html). Genes were considered differentially expressed if they had an adjusted p-value of less than 0.05 and a log2 fold change greater than 1.

B cell receptor repertoire analysis
B cell receptor (BCR) repertoire analysis was performed on bulk CD19+ RNA sequencing data using the MiXCR software (v4.5.0)34,80. The MiXCR analyse command was used to execute its optimised pipeline for the analysis of bulk RNA sequencing data. This pipeline involves: 1. The alignment of sequencing reads from transcripts to immunoglobulin light and heavy genes. 2. The assembly of reads, containing only fragments of CDR3, into longer contigs covering the full or nearly full CDR3 region. 3. Clustering assembled sequences to the clonotypes based on their CDR3 region. 4. The export of clonotypes and corresponding abundance information. The MiXCR individual post-analysis module was then used for downsampling, normalisation and calculation of the CDR3 diversity and gene segment data for each sample. BCR clonotype data was tested for differences between samples from JIA and JIA-uveitis patients. The effects of uveitis status on BCR clonality (Shannon-weiner diversity), CDR3 amino acid length, individual isotype usages, and individual IGHV gene usages were tested separately using multiple linear regression models (ordinary least squares), controlling for age and sex. P-values for each were derived from two-sided t tests of the regression coefficients. For the sake of displaying differences in IGHV gene usage, where some genes are not found in some samples, a pseudocount equal to the minimum proportion seen in the table was added, then log transformed the data. Log transformation means that the coefficients estimated by the linear model can be interpreted as estimations of log fold change.

B cell receptor somatic hypermutation analysis
Assembled contigs containing the full CDR3 region, which are output by the MiXCR pipeline, were converted into .fasta format, which were used as input for the standard Immcantation change-O pathway: assignGenes.py, then MakeDB.py, then CreateGermlines.py. We have included a Bash script with details on this linking between the MiXCR and Immcantation pipelines (mixcr_to_immcantation_script.sh) within the supplementary data file 1. Following this, the observed Mutations function from the Immcantation shazam package was used to calculate mutational frequencies in the V segment for each contig in RStudioTM. For each sample, the mean of mutational frequences across all heavy chain contigs was calculated. Only contigs assembled from more than one read were included in the calculation of the mean mutational frequency. The effect of uveitis status on mean mutational load (per sample) was tested using a multiple linear regression model (ordinary least squares), controlling for age and sex. A p-value was derived from a two-sided t test of the regression coefficient.

Unsupervised clustering
Unsupervised clustering analysis was performed using two different R packages. For human aqueous humour analysis, the CATALYST package was used to identify 6 different clusters using FlowSOM (6037 cells total) from n=2 patients81. For JIA and JIA-Uveitis peripheral blood mononuclear cells, the Spectre R package was used82. Briefly, 19 fcs files from CD11c- CD19+ B cells were clustered using Spectre and FlowSOM to identify 15 different B cell clusters. Phenotypically similar clusters were manually merged to avoid over-clustering, resulting in 9 individual populations (38976 cells total).

Pseudotime analysis
The 9 populations of CD11c- CD19+ B cell clusters were analysed using the slingshot package83. Transitional B cells were specified as the initial cluster, but no endpoint was provided. Visualisation was achieved by plotting pseudotime values across all cell subsets within the 3 inferred lineages using ggplot.

Data visualisation
Plots were generated using GraphPad Prism (version 10.2.1) and R. In R, visualisations were created with the ggplot2 and clusterProfiler packages. Schematic diagrams were designed with BioRender (2023), and all figures were finalised and curated using Adobe Illustrator (AI) 2023.

Statistics
Only biological replicates were used with no technical replicates. Data are presented as group means ± SD or medians ± IQR with individual participants/animals shown as symbols. All data were analysed using GraphPad Prism version 9 and R Studio version 4.4.1. Normal distribution of samples was tested using the D’Agostino & Pearson test. For experiments containing only two independent groups, normally distributed data were analysed using a two-tailed unpaired t-test, while when data did not pass normality tests, a two-tailed Mann-Whitney test was used to determined differences between groups. For experiments that included more than two groups and were non-normally distributed, Kruskal-Wallis tests were used, followed by Dunn’s post-hoc test for pairwise comparisons. Lines on summary dot plots represent mean ± SD for normally distributed groups and median ± IQR for non-normal groups. To account for multiple testing, the significance threshold of <0.05 was adjusted by dividing it by the total number of comparisons being made using Bonferroni’s correction for multiple testing unless otherwise stated. Multiple linear regression analysis was performed using the R function lm, and only cases with no missing data were included in the analysis. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed using the R package glmnet.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is a well-conceived, comprehensive study that addresses a significant gap in our understanding of the immunopathogenesis of juvenile idiopathic arthritis-associated uveitis (JIA-uveitis). The work provides compelling evidence for a pathogenic role of heightened germinal center (GC) reactions and DN1 B-cell expansion in JIA-uveitis, challenging the prevailing notion of a predominantly T cell-driven disease. The inclusion of both human and animal model data strengthens the translational impact of the findings.\nMajor Comments\n1. The authors should more clearly articulate how their findings compare to recent reports in other pediatric autoimmune diseases (e.g., SLE, IgA nephropathy) where DN1 B cells and GC dysregulation have been implicated. A comparative discussion will enhance the translational relevance.\n2. Patient Cohort Heterogeneity:\nThe clinical heterogeneity (JIA subtypes, treatments, stage of disease) is acknowledged. Please expand on how these variables were controlled statistically, and discuss potential confounding effects more explicitly.\n3. Gene Expression Data Interpretation:\nWhile bulk RNA-seq of CD19+ B-cells showed limited differential gene expression between groups, it is possible that signals from rare, disease-driving subsets (e.g., DN1 cells) were masked. Single-cell RNA-seq or subset-specific transcriptomics would further validate nuances in B-cell activity.\n4. Clinical Metrics and Outcome Correlation:\nAre DN1 B-cell frequency and BCR clonality correlated with clinical severity, progression, or treatment response in JIA-uveitis? Such data would strengthen the case for clinical utility.\n5. Animal Model Limitations:\nAs acknowledged by the authors, the EAU model does not fully recapitulate human JIA-uveitis (joint disease is absent, uveitis type differs). Discuss how these differences may affect translational projection and therapeutic expectations.\n6. Future Directions:\nDiscuss the feasibility and potential timeline for clinical translation of CD40L antagonists in pediatric uveitis. Are there safety concerns or ongoing trials in related conditions that may expedite development?\nMinor Comments\n• The sample size for AqH and enucleated tissues is understandably limited, but more information about patient clinical characteristics (disease duration, therapy, ANA status) should be provided in supplementary tables.\n• Certain figures (e.g., flow cytometry plots, gating strategies) could benefit from additional annotation for improved clarity.\n• Ensure all references, especially those relating to novel B-cell subsets, are up-to-date and provide context for readers outside the field.\n• Summary tables comparing patient demographics and major findings may aid reader comprehension.\n• At times, DN1 B cells are referred to as “recent GC emigrants” and elsewhere as “potential long-lived memory.” The manuscript should consistently clarify the developmental interpretation adopted by the authors.", "reviewer_2": "The authors have explored the role of B cells in uveitis seen in patients with JIA. They demonstrate and increase in DN B cell in children with uveitis, which had good correlation with memory B cells and suggest early GC immigrants. In 3 eye tissue got at the time of enucleation, they showed infiltration by plasma cell however the B cell infiltration is not clear. Though single cell RNA seq of B cells did not show much difference in clonality between those with and without uveitis they found some minor differences\nFurther in IRBP induced uveitis model the authors showed B cell infiltration which is reduced on treatment with CD40L antagonist and as well as in bc6 KO mice that has less of GC reaction as GC reaction is probably the source of DN B cells.\nBased on these , The authors speculate if CD40 blockade may be useful in uveitis.\nIt is an interesting piece of data but due to samples having been collected long ago for another study the population is very heterogeneous. An ideal study should have included prospectively enrolled oligo JIA patients (all ANA positive) with and without uveitis at an early stage of disease so that the drugs do not impact the immune cells. Follow up of those patients would have provided if the number of DN1 cells comes down on treatment or not.\nIt is surprising that no significant difference in BCR was found between the two groups. How many CD19+ B cells sequenced? Could this be due to many different types of JIA as each has a different immunopathogenesis.\nFurther the distribution of most patient variables are different between those with and without uveitis as each of this variable can affect immune cells. There was a stark gender difference in 2 groups in children especially near puberty the immune cells numbers etc can be different between the boys and girls. Can the authors do a sensitivity analysis and compare the oligo JIA patients who form the majority in both groups (with and without uveitis) to see if that shows the same findings in B cell or not?\nThe average duration of disease is 4 years but what is the duration of uveitis? Significant number of patients were on steroids and or TNF therapy?\nThe animal model used is not an ideal model as it is a T cell mediated model while the authors want to show the role of B cells. Could another model using organoids given better results?\nHowever despite these caveats the paper shows some involvement of B cells in uveitis of JIA. Use of animal model though not perfect adds credence to their hypothesis.", "reviewer_3": "The authors investigate B cell populations and phenotypes in JIA-associated uveitis and conclude that B cells play a pronounced role in uveitis pathophysiology. This is an elegant study with important findings.\nMain concerns of this referee are linked to conclusions and clarity of data presentation/discussion.\nThe conclusion that antibody positivity argues for a role of B cells in uveitis is maybe slightly strong. Rheumatoid factor positive patients, for instance, respond to rituximab but are 'protected' from uveitis (while then more severe, RF positive patients less frequently experience uveitis). This should be considered and mentioned/discussed in the manuscript.\nIn this referee's opinion the findings are important, indeed, exciting and definitely worth reporting and following but do not deliver proof of the 'upstream' involvement of B cells in disease pathology. B cell activation and proliferation may still be the result of pathological T cell activation and function. Proof (and even this would be 'incomplete' because of the complex interactions of immune cells) could be added by depleting B cells in uveitis, which is currently not commonly used in JIA-associated uveitis.\nHowever, the authors propose that B cells play a key role as an early disease mechanism. They reduce inflammation in a mouse model by blocking CD40L. As CD40L also activated CD8+ T cells, this conclusion may be challenged. Would it be helpful to include B cell depletion through targeting of B cells specific surface antigens in this model?\nTable 1: The percentage of HLA-B27 positive participants among uveitis patients appears very low (general population: 8-10%). Does this mean that 2% were positive among all tested participants or among all while not tested were counted as negative? While this likely is not meaningful for any conclusions here, the numbers/proportions appear confusing.\nThis referee does not understand how 5% of patients can be classed as RF+ JIA but 9% were RF positive int eh wider cohort? Were some incorrectly classified? Were some patients only tested once and RF were low titer and maybe false positive (after an infection, etc.)?\nNomenclature is somewhat outdated. The authors may want to talk about 'ancestry' (not ethnicity) and use the term \"European ancestry' instead of 'Caucasian'.\nTaken together, this is an interesting paper with some beautiful data. However, there should be more focus on alternative explanations, including T cell pathology. From the beginning, the authors could be more transparent about the over-representation of ANA positive oligo JIA patients in their cohort. There may well be differential pathomechanisms involved across the JIA subforms. This could be reflected in the title even."}, "author_response": "We would like to thank the reviewers for their insightful comments and suggestions on\nour manuscript. We were pleased they thought our manuscript was ‘well-conceived’\n‘interesting’ and ‘elegant’ and ‘addresses a significant gap in the understanding of the\nimmunopathogenesis of JIA-Uveitis’. Based on their comments, we have carefully re-\nworded the text, added a more in-depth discussion of certain points and added new\ndata. Please see below for our point-by-point response to individual queries below in\nblue and changes to the manuscript highlighted in grey.\nReviewer #1 (Remarks to the Author)\nThis is a well-conceived, comprehensive study that addresses a significant gap in our\nunderstanding of the immunopathogenesis of juvenile idiopathic arthritis-associated\nuveitis (JIA-uveitis). The work provides compelling evidence for a pathogenic role of\nheightened germinal center (GC) reactions and DN1 B-cell expansion in JIA-uveitis,\nchallenging the prevailing notion of a predominantly T cell-driven disease. The\ninclusion of both human and animal model data strengthens the translational impact\nof the findings.\nMajor Comments (1 - 6)\n1. The authors should more clearly articulate how their findings compare to recent\nreports in other paediatric autoimmune diseases (e.g., SLE, IgA nephropathy) where\nDN1 B cells and GC dysregulation have been implicated. A comparative discussion\nwill enhance the translational relevance.\nWe would like to thank the reviewer for this important suggestion. We have now\nprovided additional information regarding how our findings relate to previous findings\nthat DN1 B cells are expanded in primary anti-phospholipid syndrome (PAPS)1 and\nIgA nephropathy2 (see lines 429-446).\n2. Patient Cohort Heterogeneity. The clinical heterogeneity (JIA subtypes, treatments,\nstage of disease) is acknowledged. Please expand on how these variables were\ncontrolled statistically and discuss potential confounding effects more explicitly.\nThe reviewer correctly identifies the substantial clinical heterogeneity present in our\ncohorts which reflects the complex nature of JIA as a disease. We have addressed\nthis heterogeneity through multiple statistical approaches: 1) Multiple Linear\nRegression Analysis (Figure 1.K) where CD11c- (DN1) B cells were inputted as the\ndependent variable and key clinical characteristics (age, sex, ANA status, 'polygo'\nsubtype, and treatment) as independent variables. This analysis demonstrated that\nuveitis status had the most significant effect on changes in CD11c- DN1 B cells, even\nafter controlling for these confounders; 2) Penalised LASSO Regression (Figure 3.B)\nincorporating all available clinical demographic information (treatments, sex, age etc.)\nalongside 34 different immune cell populations. This unbiased approach identified\nCD11c- DN B cells, memory B cells, reduced Th2 T cells, positive ANA status and\noligoarticular JIA subtype as the most important factors associated with uveitis\npresence and excluded 43 other clinical variables and immune cell proportions; 3.\nAdjustment in Bulk RNAseq Analysis of the B cell receptor repertoire and\n1\ndifferentially expressed genes (Figure 3. F-G and Supplementary Figure 6) for\nage and sex. We have also now added new data which includes further subgroup\nanalysis of JIA patients with oligoarticular arthritis only and have demonstrated the\nsame effect in DN1 B cell expansion in JIA patients with uveitis patients (Figure 1 I-J).\nAs suggested by the reviewer we have now extended our discussion of these\nconfounders and how we have adjusted for them in our different analyses more\nexplicitly (see lines 347-360 and 381-385).\n3. Gene Expression Data Interpretation. While bulk RNA-seq of CD19+ B-cells\nshowed limited differential gene expression between groups, it is possible that signals\nfrom rare, disease-driving subsets (e.g., DN1 cells) were masked. Single-cell RNA-\nseq or subset-specific transcriptomics would further validate nuances in B-cell activity.\nWe fully agree with the reviewer that single-cell RNA sequencing (scRNAseq) would\nbe extremely valuable for unmasking transcriptomic differences in rare B-cell subsets,\nparticularly DN1 cells, that may be diluted in bulk RNAseq analysis. However, logistical\nconstraints have prevented this approach in our current study. The paediatric blood\nsamples analysed in this study were collected over a 20+ year period as part of a\ncollaborative consortium effort (www.clusterconsortium.org.uk) and were originally\nanalysed between 2019-2023 by flow cytometry and bulk RNA sequencing of CD4+ T\ncells, CD8+ T cells, CD14+ monocytes and CD19+ B cells to understand how to predict\ntreatment response in JIA based on arthritic (joint) inflammation). We have now utilised\nthis rich dataset retrospectively and re-analysed the data to explore the\nimmunopathogenesis of JIA-uveitis. Unfortunately, this means that there are no\nremaining blood samples from this current cohort, and replenishment of the biobank\nfor new studies has been severely impacted by the COVID-19 pandemic, which limits\nour ability to perform subset-specific transcriptomics or scRNAseq at this time. Over\nthe course of the next 2-3 years, we are aiming to recruit JIA-uveitis patients as part\nof our newly funded uveitis-prospective study (created in response to the data\ngathered in this study) and JIA patients with joint involvement alone as part of ongoing\nbio-sampling of this population to perform this experiment. Unfortunately, due to the\nrelative rarity of JIA-uveitis, we cannot collect these samples to perform a powered\nanalysis any quicker. We hope that this reviewer agrees that timely publication of our\ncurrent findings preclude the inclusion of further transcriptomic data in this manuscript\nand have now addressed how these data would strengthen future studies in two\nsections in the discussion (see lines 404-407 and 443-446). In addition, after reflection\nregarding how we could use our current data to strengthen our findings and to further\nvalidate differences in cell activity, we have performed additional analysis of the BCR\nrepertoire to assess the levels of somatic hypermutation (SHM) alongside clonality.\nThese new data demonstrate that the BCR of JIA-uveitis patients is significantly more\nmutated than that of JIA patients with arthritis alone (p=0.0005). As SHM takes place\nuniquely within the germinal centre, these new data provide further evidence that GC\nresponses are dysregulated in JIA-uveitis (Figure 3.G).\n4. Clinical Metrics and Outcome Correlation: Are DN1 B-cell frequency and BCR\nclonality correlated with clinical severity, progression, or treatment response in JIA-\nuveitis? Such data would strengthen the case for clinical utility.\nMany thanks for this suggestion. Unfortunately, these samples were originally\ncollected for JIA-arthritis studies through rheumatology clinics, which only recorded\n2\nuveitis status as previous/current/no without additional ophthalmological details such\nas disease severity or uveitis-specific treatment responses. While treatment response\ndata exists for the JIA arthritis patients in this study3, this means that no equivalent\ndata is available for the uveitis as patients were recruited to this study based on joint\nrather than eye inflammation at time of sampling. This limitation is now discussed\nfurther in the manuscript (see lines 562-570). Interestingly, stratification based on\navailable data (whether patients had active or inactive uveitis at time of sample)\ndemonstrates that DN1 B cell frequency remains high in patients with inactive disease\n(of which the majority are on systemic medications) suggesting that current\nmedications do not impact this underlying defect (see Figure 1G-H and\nSupplementary Table 2). We have also revisited a subset of patients excluded from\nour original analysis due to their small sample size, who went on to develop uveitis\nafter sample date (n=10). These data demonstrate that the increase in DN1 B cell\ndoes not precede uveitis development, these data are included below for the\nreviewer’s interest (Figure R1).\nFigure R1. Dot plot showing the proportion of DN and CD11c- DN B cells CD19+ B cells\nin the peripheral blood (PB) of JIA patients with uveitis (n=44), and patients who went\non to develop uveitis after the time of sample - future uveitis (n=10). Lines in dot plots\nrepresent median ± IQR. Kruskal-wallis test was used to determine significance of difference\nbetween groups.\n5. Animal Model Limitations: As acknowledged by the authors, the EAU model does\nnot fully recapitulate human JIA-uveitis (joint disease is absent, uveitis type differs).\nDiscuss how these differences may affect translational projection and therapeutic\nexpectations.\nWe acknowledge the differences between EAU and human JIA-uveitis will affect\ntranslational interpretation and this is now expanded on in the discussion (see lines\n522-549). We appreciate that results should be interpreted as proof-of-concept that\ntargeting dysregulated GC reactions to treat JIA-uveitis could be a future therapeutic\navenue. However, it is important to note that the EAU model has previously provided\nvaluable mechanistic evidence that has informed therapeutic design for human uveitis\nincluding the use of cyclosporin, TNF and IFNa4–7 underlying the potential important\n‘first step’ in the path to translation our data represents. We have now highlighted this\n3\nin the text and provided new examples that would strengthen translation potential of\nour findings including organoids systems as part of future studies (see lines 581-592).\n6. Future Directions: Discuss the feasibility and potential timeline for clinical translation\nof CD40L antagonists in paediatric uveitis. Are there safety concerns or ongoing trials\nin related conditions that may expedite development?\nEarly CD40L antagonists were associated with serious thromboembolic complications\ndue to platelet binding and activation8. However, next-generation CD40L inhibitors\nhave been specifically engineered with reduced platelet binding affinity, substantially\nimproving their safety profile and resulting in their trial in various adult autoimmune\nconditions. There are 2 phase 3 trials underway in multiple sclerosis (MS): using\nFrexalimab to treat relapsing remitting MS in adults (trial numbers: NCT06141473 and\nNCT06141486). In sjogrens, a phase 2 Dazodalibep trial showed promising results9.\nand in SLE and type I Diabetes, Frexalimab is currently being trialled10. We have\nadded a line to the discussion to highlight how these agents (as they have passed\nsafety testing) could be applied to JIA-uveitis (lines 542-549).\nMinor Comments\n• The sample size for AqH and enucleated tissues is understandably limited, but more\ninformation about patient clinical characteristics (disease duration, therapy, ANA status)\nshould be provided in supplementary tables.\nWe agree with the reviewer regarding the importance of providing detailed patient\nclinical characteristics. We have detailed demographic tables for both aqueous\nhumour and enucleated eye samples which are in the supplementary material\n(Supplementary Table 4-5). We have added disease duration where possible. For\nthe enucleated tissues, we had obtained histopathological records which were usually\nlinked to patient clinical records. However, as these samples were collected historically\nas part of routine clinical care (prior to the year 2000), minimal demographic or clinical\ninformation was recorded electronically. Furthermore, all three patient therapies were\nlikely carried out in various private institutions, further complicating access to clinical\nrecords. Due to data protection laws and anonymisation, these records are\nunfortunately inaccessible. This represents a limitation inherent to archived tissue\nstudies and is now noted in the text (see line 562-570).\n• Certain figures (e.g., flow cytometry plots, gating strategies) could benefit from\nadditional annotation for improved clarity.\nWe have added additional annotation to population labels within flow cytometry figures\nand included a detailed gating strategy including markers and associated\nfluorochromes to the supplementary figures for clarity (Supplementary Figures 7-10).\n• Ensure all references, especially those relating to novel B-cell subsets, are up-to-\ndate and provide context for readers outside the field.\nMany thanks for this suggestion, we have updated the manuscript accordingly.\n4\n• Summary tables comparing patient demographics and major findings may aid reader\ncomprehension.\nA summary table comparing patients demographics can be found in Table 1 (Blood\nPatient demographics), Supplementary Table 2 (JIA-uveitis patient demographics\nactive/inactive), Supplementary Table 3 (JIA and JIA-uveitis patients with\noligoarticular arthritis demographics), Supplementary Table 4 (Aqueous humour\npatient demographics), Supplementary Table 5 (Enucleated eye patient\ndemographics). We have also included a graphical abstract in our new version of the\nmanuscript to aid reader comprehension (Figure 7).\n• At times, DN1 B cells are referred to as “recent GC emigrants” and elsewhere as\n“potential long-lived memory.” The manuscript should consistently clarify the\ndevelopmental interpretation adopted by the authors.\nWe apologise for the inconsistent terminology regarding DN1 B cells and their\ndevelopmental status. We have revised the manuscript to address this issue. These\nterms are now only quoted in the introduction where we introduce the literature of DN1\nB cells and the different terminology’s used by others in the field (lines 88-91 and 215).\nReviewer #2 (Remarks to the Author) (1 - 5)\n1. The authors have explored the role of B cells in uveitis seen in patients with JIA.\nThey demonstrate and increase in DN B cell in children with uveitis, which had good\ncorrelation with memory B cells and suggest early GC immigrants. In 3 eye tissue got\nat the time of enucleation, they showed infiltration by plasma cell however the B cell\ninfiltration is not clear. Though single cell RNA seq of B cells did not show much\ndifference in clonality between those with and without uveitis they found some minor\ndifferences. Further in IRBP induced uveitis model the authors showed B cell\ninfiltration which is reduced on treatment with CD40L antagonist and as well as in bc6\nKO mice that has less of GC reaction as GC reaction is probably the source of DN B\ncells. Based on these, the authors speculate if CD40 blockade may be useful in uveitis.\nIt is an interesting piece of data but due to samples having been collected long ago for\nanother study the population is very heterogeneous. An ideal study should have\nincluded prospectively enrolled oligo JIA patients (all ANA positive) with and without\nuveitis at an early stage of disease so that the drugs do not impact the immune cells.\nFollow up of those patients would have provided if the number of DN1 cells comes\ndown on treatment or not.\nWe thank the reviewer for their thoughtful summary of our findings and constructive\nsuggestions regarding study design. We appreciate their recognition that sample\nheterogeneity reflects the challenges inherent in studying rare diseases over extended\ncollection periods. We agree that the heterogeneous nature of our cohorts, collected\nover 20+ years as part of broader JIA studies represents a limitation. The reviewer\ncorrectly identifies that prospective enrolment with standardised protocols would be\nideal for future investigations. Based on our data in this manuscript, we have recently\nestablished a uveitis-specific prospective cohort study that will record comprehensive\nophthalmological data over time, allowing us to address many of the limitations\n5\nidentified and will provide a rich resource for future studies. These studies will enable\nlongitudinal tracking of DN1 B-cell frequencies in relation to treatment responses and\ndisease progression, which as this reviewer correctly identifies is a fundamental next\nstep. We have added a line in the discussion to address this important point (see lines\n562-570).\nAs requested by this reviewer below (point 3), we have now performed subgroup\nanalysis comparing DN and DN1 B cell frequencies in oligoarticular JIA patients only\n(n=45 JIA-alone vs n=38 JIA-uveitis). This analysis is presented in Figure 1I-J and\nshows the same findings as using the full cohort that DN (p=0.0161) and DN1\n(p=0.0083) B cells are increased in patients with uveitis. These data provide further\nevidence that the increase in DN and DN1 B cells is driven by uveitis status rather\nthan differences in subtype between group. While we understand the reviewer's\nsuggestion to focus exclusively on oligoarticular ANA-positive patients, we maintain a\nbroader approach based on emerging evidence that ILAR clinical subtypes do not\ncompletely represent underlying \"molecular pathotypes\"11. Our data supports this\nconcept, showing that immunological signatures may not fully align with traditional\nILAR classifications, and uveitis (while more common in ANA+ oligoarticular patients)\ncan also develop in ANA- patients and patients with other subtypes of JIA. In further\nsupport of this concept, our recent study focusing on the immune cell infiltrate in JIA\nsynovial tissue biopsies which demonstrates that age-of-onset or severity seem to\nhave a greater impact on synovial tissue immunobiology in JIA rather than ILAR\nsubtype and that JIA synovial tissue pathology is more homogenous that adult\narthritides12. Restricting studies to isolated ILAR categories may limit our\nunderstanding of shared pathogenic mechanisms that lead to uveitis regardless of\nsubtype and exclude patients from clinical trials who could also benefit from new\ntherapies.\n2. It is surprising that no significant difference in BCR was found between the two\ngroups. How many CD19+ B cells sequenced? Could this be due to many different\ntypes of JIA as each has a different immunopathogenesis.\nApologies for the confusion regarding our BCR data. To clarify, although we found no\nsignificant differences in differentially expressed genes between CD19+ B-cells from\nJIA-alone and JIA-uveitis patients (Supplementary Figure 6), we found significant\ndifferences in the clonal diversity of the B cell receptor (BCR) repertoire. Since our\ninitial submission, we have also added new data demonstrating that the BCR\nrepertoire in JIA-uveitis is significantly more somatically hypermutated compared to\nJIA-alone patients (Figure 3G). These data demonstrate that changes to the BCR\nrepertoire are a more faithful representation of the history of B cells antigen-activation\ndynamics due to the transient nature of transcriptional regulation. Of note, we have\nalso performed additional analysis comparing DEG between oligo patients alone and\nfind no significant differences between groups (data not shown). We sequenced an\naverage of ~300,000 B cells per patient (ranging from 50,000-500,000), this\ninformation has now been added to the methodology section (lines 838-840).\n3. Further the distribution of most patient variables are different between those with\nand without uveitis as each of this variable can affect immune cells. There was a stark\ngender difference in 2 groups in children especially near puberty the immune cells\nnumbers etc can be different between the boys and girls. Can the authors do a\n6\nsensitivity analysis and compare the oligo JIA patients who form the majority in both\ngroups (with and without uveitis) to see if that shows the same findings in B cell or not?\nWe appreciate the reviewer highlighting sex differences between groups. This\ndistribution reflects the well-established epidemiology of JIA-associated uveitis, where\nfemales have a 2:1 higher risk of developing uveitis compared to males13. However,\nregarding the reviewer's concern about pubertal effects on immune cell populations,\nwe have recently published that B cell subsets profiles in peripheral blood remain\nsimilar in children regardless of sex until puberty14. Given that our cohort consists\nprimarily of pre-adolescent15 (the majority of which will be pre-pubertal) children\n(median age 8.7 for JIA-uveitis and 9.1 for JIA), sex-related immune differences should\nbe minimal. This is supported by our multiple linear regression model which identifies\nno association between sex and DN1 B cell frequency, and all our transcriptional and\nBCR data is adjusted for both age and sex. For the data from the subgroup analysis,\nplease see our reply above (reviewer 2, point 1).\n4. The average duration of disease is 4 years but what is the duration of uveitis?\nUnfortunately, as noted in our response to reviewer 1, these patients were originally\nrecruited for JIA arthritis that was limited to basic disease status (active/inactive/no)\nfrom rheumatology notes and detailed ophthalmological data including uveitis duration\nwas could not be collected. This limitation is now extensively discussed in the\nmanuscript (see lines 562-570).\n4. Significant number of patients were on steroids and or TNF therapy?\nTNF therapy use was minimal in our cohorts (5% of JIA-uveitis patients and 0% of\nJIA-alone patients). We acknowledge the higher prevalence of systemic steroid use:\n32% of JIA-uveitis patients versus 4% of JIA-alone patients. To address potential\nconfounding effects, we included systemic steroid treatment as a variable in both our\nmultiple linear regression (Figure 1.K) and penalised LASSO regression (Figure 3.B)\nanalyses, as detailed in our response to reviewer 1 (point 3). Both statistical models\ndemonstrated that systemic steroids did not significantly contribute to CD11c⁻ DN1 B-\ncell increases and were not identified as an important factor affecting uveitis likelihood.\nThese analyses confirm that our key findings regarding DN1 B-cell elevation are\nindependent of steroid treatment effects. For clarity, we have standardised our\nterminology throughout the manuscript to ensure all references to systemic steroids\nuse consistent language and apologise for any confusion in the original submission\nwhen oral steroid and systemic steroid were used interchangeably.\n5. The animal model used is not an ideal model as it is a T cell mediated model while\nthe authors want to show the role of B cells. Could another model using organoids\ngiven better results? However, despite these caveats the paper shows some\ninvolvement of B cells in uveitis of JIA. Use of animal model though not perfect adds\ncredence to their hypothesis.\nWe acknowledge that the EAU model is traditionally viewed as T-cell mediated.\nHowever, we are not the first group to investigate B-cell contributions in this model.\nPrevious studies have demonstrated important B cell roles in EAU pathogenesis16–18,\nand B/plasma cell-targeted therapies have shown therapeutic efficacy19,20. We also\n7\nbelieve that because EAU is primarily driven by T cells, the observation of a strong B\ncell component provides more compelling evidence of B cell involvement than would\nbe obtained from a model inherently designed around B cell activation. These findings\nsupport the relevance of studying B cell biology, and specifically for our study germinal\ncentre reactions, in this established uveitis model. Regarding organoid models, uveal\norganoid systems, which are the most appropriate model for interrogating the anterior\nuveitic disease observed in JIA-uveitis patients, are currently not available with no\nstandardised methodology, and there are no published studies for uveal organoids\nwith immune cell co-cultures. We agree this would be an exciting future direction for\nthe field and have highlighted this in the discussion (see lines 588-592).\nReviewer #3 (Remarks to the Author)\nThe authors investigate B cell populations and phenotypes in JIA-associated uveitis\nand conclude that B cells play a pronounced role in uveitis pathophysiology. This is an\nelegant study with important findings.\nMain concerns of this referee are linked to conclusions and clarity of data\npresentation/discussion. The conclusion that antibody positivity argues for a role of B\ncells in uveitis is maybe slightly strong. Rheumatoid factor positive patients, for\ninstance, respond to rituximab but are 'protected' from uveitis (while then more severe,\nRF positive patients less frequently experience uveitis). This should be considered and\nmentioned/discussed in the manuscript.\nWe thank the reviewer for highlighting this important observation regarding the\ncomplexity of the role of B cells and antibodies in uveitis. We have added a discussion\nof the RF+ poly uveitis paradox (lines 480-487) where we acknowledge that it is likely\nthat different B cell subsets or activation states may have different roles in arthritis and\nuveitis.\nIn this referee's opinion the findings are important, indeed, exciting and definitely worth\nreporting and following but do not deliver proof of the 'upstream' involvement of B cells\nin disease pathology. B cell activation and proliferation may still be the result of\npathological T cell activation and function. Proof (and even this would be 'incomplete'\nbecause of the complex interactions of immune cells) could be added by depleting B\ncells in uveitis, which is currently not commonly used in JIA-associated uveitis\nHowever, the authors propose that B cells play a key role as an early disease\nmechanism. They reduce inflammation in a mouse model by blocking CD40L. As\nCD40L also activated CD8+ T cells, this conclusion may be challenged. Would it be\nhelpful to include B cell depletion through targeting of B cells specific surface antigens\nin this model?\nWe thank the reviewer for these important observations, which for clarity we have\nanswered together. We appreciate the wide range of effects of CD40L, which are now\nmore broadly discussed (lines 534-537). These broad ranging effects are exactly why\nwe complemented these experiments with data demonstrating that CD4creBCL6fl/fl\nmice develop significantly less severe disease than EAU controls (see Figure F-J).\nCD4creBCL6fl/fl mice are well published to serve as a T follicular helper cell knockout\nmouse (leaving the rest of the T cell compartment ‘intact’) and cannot mount GC\nreactions. Thus, we used this system as a model to specifically investigate the role of\n8\nthe germinal centre in the pathology of EAU based on our human findings further to\nthe broad impact of anti-CD40L. As rightly pointed out by this reviewer, these\nexperiments provide evidence that changes to T cell:B cell interaction within the\ngerminal centre influence EAU pathology and potentially human uveitis. Throughout\nthe manuscript we have rewritten the text to provide a more balanced perspective\nfocusing on the communication between cell types, how this can drive uveitis\npathology and the role the B cell may take in this dynamic relationship. We have also\nadded data to our mouse studies highlighting the impact of EAU and our interventions\non T follicular helper cells (see Figure 5 E-F, Figure 6 F-G and M-N) to ensure a more\nbalanced assessment of both B-T cells within the geminal centre. In addition, while\nresearching the best methodology to perform the B cell specific depletion suggested\nby this reviewer, we have excitingly found a recently published report in Nature\nCommunications19 that has performed this exact experiment demonstrating that\ndepletion of B cell post-EAU onset, but not prior to onset, suppresses EAU severity\n(paper figure 5 f-g). These data support our pilot analyses that the increase in DN1 B\ncells does not precede disease development (see response to reviewer 1, point 4) and\nrather that B cells seem to be driving part of the active disease process. For the sake\nof novelty, we have not repeated this experiment but have made sure this reference is\nhighlighted in the introduction (see lines 77-80) and have now extensively discussed\nhow the findings from this previous paper complements our own alongside the\ntranslational relevance of both studies (see lines 500-503, 522-532). Our apologies for\nmissing this important reference from our original submission.\nTable 1: The percentage of HLA-B27 positive participants among uveitis patients\nappears very low (general population: 8-10%). Does this mean that 2% were positive\namong all tested participants or among all while not tested were counted as negative?\nWhile this likely is not meaningful for any conclusions here, the numbers/proportions\nappear confusing.\nWe thank the reviewer for this observation and have corrected a calculation error. In\nTable 1 in our JIA-uveitis cohort, only 13 of 44 patients (30%) were tested for HLA-\nB27, with 1 testing positive (8%). In the JIA group, 53 of 116 patients (46%) were tested,\nwith 16 testing positive (30%). The large amount of missing HLA-B27 in our cohort is\nbecause the HLA-B27 test is not used routinely in all JIA patients at the centre these\nsamples were collected from. We acknowledge this is a limitation of our study and\nhave discussed this in the methods (lines 716-718).\nThis referee does not understand how 5% of patients can be classed as RF+ JIA but\n9% were RF positive int eh wider cohort? Were some incorrectly classified? Were\nsome patients only tested once and RF were low titer and maybe false positive (after\nan infection, etc.)?\nWe thank the reviewer for seeking clarification on this point and apologies for any\nconfusion. We believe there may have been a misreading of Table 1. In the JIA-uveitis\ngroup, 7% had missing RF data, therefore 4 out of 37 tested patients were RF+ (10.8%,\nrounded to 11%). In the JIA group, 9% had missing RF data, so 11 out of 107 tested\npatients were RF+ (10.3%, rounded to 10%).\nNomenclature is somewhat outdated. The authors may want to talk about 'ancestry'\n(not ethnicity) and use the term \"European ancestry' instead of 'Caucasian'.\nWhile we agree that terms like 'European ancestry' are preferable to 'Caucasian,' we\nhave maintained the original terminology as recorded in the source studies (dating\nfrom the late 1990s to 2019) to ensure data integrity and avoid potential\nmisclassification. We have however changed the subheading from ‘ethnicity’ to\n‘ancestry’ in our demographic tables. We now highlight this limitation in our methods\nsection (lines 712-714).\nTaken together, this is an interesting paper with some beautiful data. However, there\nshould be more focus on alternative explanations, including T cell pathology. From the\nbeginning, the authors could be more transparent about the over-representation of\nANA positive oligo JIA patients in their cohort. There may well be differential\npathomechanisms involved across the JIA subforms. This could be reflected in the title\neven.\nWe have added upfront acknowledgment of the oligoarticular ANA+ patient\noverrepresentation in our results section (lines 164-167), as this reflects the natural\nepidemiology of JIA-uveitis 21 and as correctly noted by this reviewer, it is important to\nhighlight this from the outset of our manuscript. We have also added discussion on the\nrole of T cells/autoantibody producing cells throughout the manuscript and expanded\non the idea that JIA-uveitis pathology may be driven by T cell:B cell interactions within\nthe GC (lines 603-611)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This revised manuscript by Jebson et al. represents a significant advance in understanding the immunopathogenesis of JIA-associated uveitis. The authors have thoroughly addressed all reviewer comments with additional analyses, clearer discussion, and improved data presentation.", "reviewer_2": "The authors has now presented a much revised manuscript and all my queries have been addressed satisfactorily", "reviewer_3": "The authors have sufficiently addressed my comments and concerns."}, "author_response": ""}]

Decision letter