Clinical and Experimental Immunology

Tissue-based assays provide broad screening for neural autoantibodies but remain vulnerable to technical and reader-dependent variability. We evaluated an integrated workflow combining automated 3,3'-diaminobenzidine tissue-based assay processing, digital whole-slide imaging, and interpretable deep learning. Model development used 2,357 retrospective serum and cerebrospinal fluid images referred from more than 80 institutions and processed in a centralized laboratory, with reference labels established by expert-consensus tissue-based assay interpretation. A prospective validation cohort included 200 images from 200 unique patients (154 positive and 46 negative; 100 serum and 100 cerebrospinal fluid). The same images were evaluated by two expert and two novice readers in a fixed-order two-round study, with unaided interpretation followed by artificial-intelligence-assisted interpretation after a washout of at least two weeks. The standalone binary classifier achieved an area under the receiver operating characteristic curve of 0.922, accuracy of 86.5%, sensitivity of 85.7%, and specificity of 89.1% in prospective validation. Pooled reader accuracy was 90.4% in the unaided round and 97.4% in the artificial-intelligence-assisted round (McNemar P < 0.001), with specificity of 82.6% and 99.5%, respectively. Novice-reader accuracy was 83.3% unaided and 96.5% with artificial-intelligence assistance (P < 0.001), approaching unaided expert accuracy of 97.5%. Among tissue-based-assay-positive cases, reader accuracy for cytoplasmic/nuclear antigen-associated versus neural surface antigen-associated staining-pattern subclassification was 88.8% unaided and 94.0% with artificial-intelligence assistance (P < 0.001). These findings support further multicentre, cross-platform evaluation of artificial-intelligence-assisted neural autoantibody tissue interpretation.

Objective: The principal diagnostic test for rheumatoid arthritis (RA) relies on the detection of anti-cyclic citrullinated peptide (CCP) antibodies. The CCP test exhibits moderate sensitivity and specificity but does not adequately detect a high proportion of cases or effectively distinguish RA from related conditions such as systemic lupus erythematosus (SLE). To discover improved diagnostic biomarkers for RA, we performed a comprehensive proteomic analysis of antibody-bound antigens present in RA, SLE, and healthy control (HC) sera and identified proteins associated with transforming growth factor-beta-1 (TGF-beta-1) signaling and assessed the sensitivity and specificity of antibodies against these antigens in comparison to CCP. Methods: Total antibodies were immunoprecipitated from pooled sera of HC individuals (n=3) and patients with clinically diagnosed RA (n=3) or SLE (n=3). Antibody-bound antigens were identified using multidimensional protein identification technology (MudPIT) shotgun proteomics. Candidate antigens associated with TGF-beta-1 signaling were selected for evaluation, and corresponding serum autoantibodies were assessed for their ability to classify RA using a POC lateral-flow immunochromatographic format. Results: Proteins present in antibody immunoprecipitations included TGF-beta-1-induced protein (TGFBI), thrombospondin 1 (TSP1), serum amyloid A1 (SAA), and fibronectin (FN). POC assays using these candidates, as well as TGF-beta-1 itself, as capture antigens were used to detect corresponding serum autoantibodies in 107 subjects with clinically diagnosed RA, 42 subjects with other rheumatic diseases (non-RA), and 121 HC subjects. Receiver operating characteristic curves were constructed using RA and HC groups to assess diagnostic performance, and optimal cutpoints identified using the Youden Index (maximizing the sum of sensitivity and specificity). CCP test results were determined using previously established thresholds. Using an analysis et of 169 subjects in the HC and RA groups who had values for TGFBI, SAA, and CCP, TGFBI autoantibodies achieved a sensitivity of 82%, specificity of 98.8%, positive likelihood ratio (PLR) of 65.6, and NLR of 0.2. SAA autoantibodies achieved a sensitivity of 92.1%, specificity of 85%, PLR of 6.1, and NLR of 0.1. Both outperformed CCP (sensitivity of 68.5%, specificity of 95%, PLR of 13.7, and NLR of 0.3). Using an analysis set of 120 subjects in the RA and non-RA groups who had values for TGFBI, SAA, and CCP, TGFBI and SAA autoantibodies exhibited sensitivities and specificities of 82 and 92.1% and 74.2 and 45.2%, respectively, compared to 68.5 and 74.2% for CCP. Autoantibodies to TGF-beta-1, TSP1, and FN exhibited only modest diagnostic performance. Conclusion: RA sera contain autoantibodies targeting multiple proteins associated with the TGF-beta-1 signaling pathway that is implicated in inflammatory processes in RA. Among the candidates tested, TGFBI and SAA autoantibodies exhibited the highest diagnostic performance in a POC lateral-flow format, supporting their potential utility as biomarkers for RA diagnosis. These findings also support further investigation of the TGF-beta-1 signaling pathway as a potential therapeutic target in RA.

Using an analysis pipeline that combines machine learning-based feature selection with classical statistical modelling, we aimed to predict incident inflammatory bowel disease (IBD) and to identify candidate proteins associated with disease development. We used UK Biobank data in an observational cohort study of 52,331 participants with proteomics data and without a history of IBD. The cohort was split into an 80/20 train/test dataset, and three separate extreme gradient boosting models were trained on the training dataset. Using Shapley values, we ranked the predictive importance of 2,912 proteins for incident IBD, Crohn's disease (CD), and ulcerative colitis (UC). The top 20 proteins per outcome served as features in multivariate Cox models predicting 350 incident cases over 14 years. All three proteomics Cox models showed moderate discrimination in the test dataset, with the UC model performing the best (C-index: 0.68), followed by IBD (C-index: 0.65), and CD (C-index: 0.60). All proteomics models outperformed clinical models. Smoking remained the only significant clinical predictor in the combined proteomics + clinical model. 48 unique proteins were identified across the three models, with 16 reaching statistical significance (p< 0.05). Machine learning-based feature selection with statistical survival modelling identifies plasma proteins that predict incident IBD and highlight biologically plausible, potentially targetable pathways in the preclinical phase of IBD.

Systemic lupus erythematosus (SLE) is the prototypical systemic autoimmune disease defined by the production of autoantibodies directed against diverse self-antigens. Antinuclear antibodies (ANA) are central to SLE diagnosis, and anti-double-stranded DNA (anti-dsDNA) antibodies are clinically used to track disease activity. However, these biomarkers provide only partial insight into the full spectrum of autoimmune changes in patients. To expand the known landscape of SLE autoreactivity shared among patients and associated with disease activity, we performed an unbiased characterization of 1,268 SLE plasma samples from 695 SLE patients and 301 healthy controls using phage immunoprecipitation and sequencing (PhIP-seq). This technique preferentially displays linear epitopes, and thus provides a complementary set of autoreactivities to previously discovered autoreactive targets. Despite the marked clinical heterogeneity of SLE, machine learning applied to this data surfaced a shared, reproducible signature that robustly discriminates between SLE and healthy controls in two validation cohorts. Autoreactivities in SLE were significantly enriched for RNA-binding protein targets. Autoreactive proteins containing Basic Acidic Dipeptides (BAD) repeat domains, features associated with biomolecular condensate formation, distinguished patients with high disease activity (by SLEDAI score) from patients with no disease activity. Among all shared autoreactivities, a peptide derived from YTHDF2 emerged as the most prevalent target across SLE cohorts occurring in ~25% of SLE patients and displayed striking sequence similarity to a peptide derived from the Epstein Barr virus (EBV) lytic switch protein BZLF1. Competition assays demonstrated higher-affinity binding to the viral peptide, supporting a model in which antiviral humoral responses to EBV may drive secondary epitope spreading toward molecularly mimicked self-antigens. Remarkably, orthogonal validation using the Luminex bead platform containing a panel of only 10 peptides, including BZLF1, demonstrated strong discrimination between SLE and healthy controls (AUC = 0.95). Together, these findings identify a rich, greatly expanded landscape of autoreactivities in SLE, candidate biomarkers associated with high disease activity, and provide mechanistic insight linking EBV-driven immunity to epitope-specific autoimmunity in lupus.

BACKGROUND & OBJECTIVE Tofacitinib, a Janus kinase (JAK) inhibitor, is approved for ulcerative colitis (UC). We investigated baseline and longitudinal transcriptomics and proteomics in intestinal biopsies, peripheral blood, and serum from UC patients starting tofacitinib to characterise treatment-associated effects and identify which molecular compartment harbours the most predictive response-associated biomarkers. METHODS In this prospective, open-label study, patients with moderately to severely active UC started tofacitinib 10 mg BID for eight weeks. Clinical, biochemical, endoscopic, and histological data were collected at baseline and week 8. The primary outcome, histo-endoscopic mucosal improvement (HEMI), was defined as endoscopic Mayo subscore [&le;]1 with histologic remission (Robarts Histopathology Index [&le;]3, no mucosal neutrophils). Mucosal biopsies and peripheral blood were collected at baseline and week 8 for bulk RNA-sequencing (RNA-seq); biopsies and serum underwent proteomic profiling using Olink Explore 384 (Cardiometabolic, Inflammation, Neurology, Oncology panels). RESULTS In total, 127 paired multi-omic samples were collected longitudinally from 40 UC patients: 15 (38%) achieved HEMI at week 8 (responders), 13 (32.5%) were late responders, and 12 (30%) were non-responders. Pretreatment mucosal biopsies from future responders showed elevated interferon, IL-6-family, IL-2/common-{gamma}-chain, and T-cell-receptor signalling gene expression, which declined on treatment only in responders. A responder-specific JAK-STAT-dependent mechanism spanned interferon, cytokine-receptor, T-cell-receptor, and NF-{kappa}B signalling, with concordant gene- and protein-level reductions. Compared with a public infliximab dataset, JAK-STAT/interferon downregulation was shared and response-independent, while non-canonical NF-{kappa}B downregulation was tofacitinib-specific. In blood, shared pathway labels reflected distinct genes from mucosal effectors. CONCLUSION Response to tofacitinib depends on a JAK-tractable mucosal pathway absent in non-responders, suggesting a pretreatment biomarker for predicting treatment response.

Systemic lupus erythematosus (SLE) features a broad array of autoantibodies against nuclear components, but the mechanisms driving this diversity are poorly understood. We investigated whether posttranslational arginine methylation creates common neo-epitopes that could unify this response. Using methylome-wide peptide arrays, we identified anti-symmetric dimethylarginine (SDMA) autoantibodies in 30-40% of SLE patients, with individualized reactivity fingerprints. A single anti-SDMA monoclonal antibody from MRL-lpr mice recognized multiple SDMA-modified antigens, and EBNA-1, an Epstein-Barr virus protein, bound SLE plasma in an SDMA-dependent manner, pointing to potential molecular mimicry. In a validation cohort of 201 patients, anti-SDMA titers correlated with SLEDAI-2K scores and inversely with C3/C4; seropositivity was overrepresented in younger patients, those with renal involvement, and anti-Sm-positive individuals. These results establish that anti-SDMA responses are common in SLE, target methylated arginine motifs across self and viral proteins, and serve as markers of active, complement-consuming disease. The patient-specific recognition of SDMA epitopes may underlie the serological heterogeneity that defines SLE.

While the immune reset induced by CD19 CAR T therapies has shown striking promise in systemic lupus erythematosus (SLE), the nature and duration of benefit mediated by B cell depletion remains unclear. Loss of tolerance in SLE is driven by extrafollicular and germinal center interactions between dendritic cells and self-reactive T and B cells. We hypothesized that CD19 CAR-engineered regulatory T cells (CAR19 EngTreg), capable of suppressing multiple autoimmune effector cell types, might promote long-term tolerance without producing high-levels of inflammatory mediators. To generate CAR19 EngTreg, we combined CD19 CAR delivery with homology directed repair at the FOXP3 locus to drive constitutive, stable FOXP3 expression in CD4 T cells. CAR19 EngTreg expressed canonical Treg markers; exhibited minimal inflammatory cytokine production; and blocked human B cell differentiation into antibody secreting plasma cells. In humanized mouse models, transfer of CAR19 EngTreg led to a reduction in B cell numbers with low inflammatory cytokine production relative to CD19 CAR Teff, and reduced tissue inflammation. In a short-term, in vivo imiquimod-induced inflammatory model, murine CAR19 EngTreg reduced skin pathology, spleen size, and inflammatory cytokines. Finally, in two distinct mouse models of lupus, CAR19 EngTreg persisted for >4 months leading to reduced autoimmune T and B effector populations, sustained reduction in multiple autoantibodies and improved renal function and histopathology. Thus, CAR19 EngTreg may offer a promising and durable therapeutic avenue for B cell-mediated autoimmune disorders capable of long-term regulation of both autoreactive B and T cells.

CD11c+CD21- B cells are overrepresented in rheumatoid arthritis (RA). Here we delineate their functional state at site of inflammation and its molecular basis. Phenotypic profiling of paired blood and synovial fluid from patients with anti-citrullinated protein antibody (ACPA) seropositive RA (N=12), seronegative RA (N=10) and spondyloarthritis (N=8), demonstrated a striking enrichment of CD11c+CD21- B cells in synovial fluid in all three diseases (average of CD19+ cells: 67%, 49% and 51%). In ACPA-positive RA we identified increased proportions of CD11c+IgG+CD27low cells and several stages of pre-plasmablasts. The CD11c+CD21- cells had a phenotype indicating BCR activation, proliferation, and antigen presentation. This B-cell profile was mirrored by a LAG3+CTLA4+TIGIT+ peripheral helper T-cell program in ACPA-positive RA, contrasting a CCR6+ Th17-skewing in seronegative RA. Single-cell spatial proteomics revealed a unique CD11chighCD21 low B-cell surface architecture including a BCR co-receptor-tetraspanin signature with CD72-CD81 co-localization, which was elevated in RA. CD11c+ cells also displayed CD44-CD52/CD59 and MHCII-CD40/CD84 immune-modules absent in other B-cell subsets. In summary, CD11c+CD21- B cells are highly diverse, and while an activated antigen-experienced synovial profile was shared between disease groups, ACPA+ RA showed evidence of increased extrafollicular plasmablast differentiation. Surface receptor organization implicated a favoured BCR responsiveness, complement resistance and antigen presentation.

Transcriptional risk scores, an integrated score of genomic susceptibility variants with expression Quantitative Trait Loci (eQTL) data, may more accurately distinguish clinical phenotypes compared to polygenic risk scores. This approach has not been applied to multiple sclerosis. We integrated data from the largest GWAS of MS, involving 14,802 MS patients, with eQTL data from 7,466 blood donors and 5,494 brain donors, using summary-data-based Mendelian randomization (SMR) followed by the heterogeneity in dependent instruments (HEIDI) test. MS SNVs with a significant eQTL were integrated with single-cell RNA sequencing data from 1,27 million peripheral blood mononuclear cells from 927 donors and 750,614 single nucleus RNA sequencing central nervous system cells from 192 individuals using Bayesian colocalization. Derived polygenic and cell-line specific transcriptional risk scores were compared with age-related MS severity score (ARMSS) in a cohort of 1,077 MS patients from the South Wales MS Registry and with MRI-derived age-adjusted total brain volumes of 272 MS patients within the UK Biobank. Cell-line specific transcriptional risk scores were associated with MS outcomes in two independent cohorts. Our results identified MS-associated eGenes enriched across several biological pathways, providing insights into pathophysiological mechanisms in MS that could facilitate precision medicine and identify novel druggable pathways.

Progressive multiple sclerosis (PMS) lacks blood-based biomarkers of the neurodegenerative processes driving disability. We measured synaptopodin, a postsynaptic protein, in neuronal-enriched extracellular vesicles (NEVs) across two independent cohorts: the SPRINT-MS trial (n=184) and a real-world validation cohort (n=268). A one SD increase in baseline synaptopodin was associated with ~32% lower risk of confirmed disability progression in PMS (p=0.011), with the strongest effect in secondary PMS (SPMS) (~57% lower odds, p=0.019). This SPMS association was replicated in the validation cohort (~60% lower odds, p=0.009). Higher baseline synaptopodin was associated with future slower whole-brain atrophy and preserved microstructural integrity across MRI metrics (p=0.007-0.013). Longitudinal changes in NEV synaptopodin were associated with disability worsening (p=0.007) and paralleled MRI microstructural changes. NEV synaptopodin, but not NfL, was associated with confirmed disability progression in PMS participants with both biomarkers measured (HR 0.71 vs 1.12, p=0.007 vs 0.22 for NEV synaptopodin and NfL, respectively) and SPMS (HR 0.58 vs 1.10, p=0.003 vs 0.50). Adding synaptopodin, but not NfL, to a base model improved discrimination for disability progression in SPMS in both cohorts (e.g. validation AUC 0.755 to 0.838, p=0.005). These findings identify NEV synaptopodin as a prognostic and longitudinal, blood biomarker of synaptic integrity in PMS, particularly SPMS, complementing NfL.

Objectives. Before-and-after transcriptomics in rheumatic disease often yields no reproducible treatment signal, though disease-state stratification is robust. We asked whether exposure and response-prediction labels fall below the detection floor of the designs defining them, while matched disease-state labels do not. Methods. Eight public datasets were analysed (paired juvenile dermatomyositis n = 10; RA before/after methotrexate, n = 28 pairs; lupus nephritis n = 10; controlled exposures; a single-cell dose series). Response labels in current use (one benchmark trajectory, five published signatures) were audited in every analysable source cohort, using effect sizes (dz) against each design's exact minimum detectable effect. Results. Disease-state labels were detectable: the interferon signature declined in 10/10 juvenile dermatomyositis patients (p = 0.002; 59.5% of score variance). No exposure label cleared its design floor in any clinical cohort; a drug-matched methotrexate prior was nominally significant in juvenile dermatomyositis yet did not clear its floor (dz = -0.80; CI spanning it). Exposure became detectable only where prior-context and intensity were jointly favourable (multi-day target tissue dz = 1.24, q = 0.018; well-level pseudobulk dz = +3.70, floor 1.80). Audited response labels failed in 14 of 15 cohorts (93%; descriptive binomial 95% CI 68-100): eleven below their floors, three sign-reversed; the largest training cohort pooled to zero (dz = +0.07; I2 = 69%). Effect size ordered reported AUCs monotonically. Conclusion. Under conventional treatment intensities, exposure and response-prediction labels are either undetectable below the design floor or detected with a sign that does not replicate; disease-state labels are detectable. Part of the negative literature reflects a design floor.

Objective: To assess the accuracy of codes and algorithms used to identify selected connective tissue diseases (CTDs) in electronic health records (EHRs) and administrative databases. Methods: We searched MEDLINE, Embase, and CENTRAL databases for studies that validated case definitions in EHRs against a reference standard, including rheumatologist-confirmed diagnosis or clinical classification criteria. Title/abstract screening, full-text review, data extraction, and quality appraisal were independently performed in duplicate. Findings were synthesised narratively and through a bivariate random-effects meta-analysis of sensitivity and specificity. Results: A total of 38 studies were included. Systemic lupus erythematosus (SLE) was the most frequently studied disease, followed by systemic sclerosis (SSc). Across diseases, progressively restrictive algorithms generally improved positive predictive value (PPV), although the optimal strategy varied by disease. In SLE, multiple diagnostic codes provided the most favorable overall performance, whereas adding clinical data provided limited incremental benefit. In SSc, disease-specific clinical features, particularly Raynaud's phenomenon, improved case identification, while specialist or inpatient-based algorithms performed better for polymyositis/dermatomyositis, although evidence was limited. For SLE, meta-analysis yielded pooled sensitivity of 0.90 (95% Cis 0.70-0.97), specificity of 0.83 (95% CIs 0.54-0.95), and PPV of 0.75 (95% CIs 0.59-0.86). Evidence for Sjogren's Disease was limited to one study, and no studies evaluated MCTD/UCTD. Conclusions: Optimal EHR case-identification strategies vary by disease and should be tailored to disease-specific clinical and diagnostic characteristics rather than algorithm complexity alone. Future research should develop and externally validate robust, standardised algorithms in representative populations, using rigorous validation methods, and assess the added value of longitudinal EHR data, biomarkers, and computational phenotyping approaches.

Objective: Autoantibodies are central for pathology of rheumatoid arthritis (RA) and diabetes mellitus (DM) carrying high predictive and diagnostic value. Here, we explore the frequency and functional potential of DM-related antibodies (DMab) in RA. Methods: DMab targeting glutamic acid decarboxylase (GAD65ab), tyrosine phosphatase-like islet antigen 2 (IA2ab) and zinc transporter 8 (ZnT8ab) were measured in 326 DM-free RA patients (260 females, 66 males), using specific ELISAs. The DMab profile was analysed in relation to 30 clinical and serological variables. ZnT8+ cells were detailed by a public single-cell transcriptome in RA synovial tissue. Results: DMab production was more frequent in females, dominated by ZnT8ab (86%), followed by GAD65ab (26.4%) and IA2ab (7.0%). DMab-positive patients, and specifically ZnT8ab-positive women, had higher levels of insulin and HOMA-IR index and lower serum IL-6, IFN{gamma}, and IL-9. Multivariable logistic regression estimating ZnT8ab, IA2ab, GAD65ab production revealed that BDNF, insulin and DAS28 were positive predictors of ZnT8ab. Increasing ZnT8ab levels were inversely related to RAab in the mixed interaction model. CD14+ cell transcriptome identified association between an activated secretory cell type and increasing ZnT8ab levels. Additionally, ZnT8ab levels were in direct proportion with ZnT8 production in RA CD4+ T cells. Single-cell transcriptome of RA synovial tissue revealed ZnT8+ cells among the lubricin producing lining layer fibroblasts programmed for invasive behaviour. Conclusion: This study reveals frequent production of ZnT8ab in non-diabetic RA patients and identifies their antigenic target expressed in lining synovial fibroblasts of RA joints.

Giant cell arteritis (GCA) is an immune-mediated vasculitis that can cause serious morbidity including sight loss, stroke and aortic aneurysm. There is an unmet need for blood-based biomarkers and novel therapeutics. We measured plasma protein levels using the SomaScan v4.1 platform, comprising 7,273 protein measurements (SOMAmers) reflecting 6,596 unique protein targets. We profiled the proteome in 113 patients who presented acutely with possible GCA. 56 had biopsy-proven GCA and 57 did not have GCA (non-GCA). We found 144 SOMAmers increased and 151 reduced in GCA. Dysregulated biological pathways included IL6-JAK-STAT3 signalling, chemokines for both myeloid (e.g. CXCL2, CXCL3, CCL18, CCL23) and lymphoid cells (e.g. CXCL9, CXCL13), and the complement system (e.g. C9, CFHR5, C5a, Factor B). Enzyme-Linked Immunosorbent Assays (ELISAs) confirmed elevated plasma levels of C5a, the terminal complement complex, Factor B and CFHR5 in GCA. We developed a 26-protein signature that distinguished GCA from non-GCA (area-under-the curve, AUC, 0.93) and validated the diagnostic signature in external datasets. Our data implicate terminal complement pathway activation in GCA, raising the possibility of complement-directed therapies. We show proof-of-principle that a multiplex protein biomarker could be used as a diagnostic adjunct in suspected GCA.

The skull bone marrow axis has recently emerged as an important source of immune cells during neuroinflammation, including in the experimental autoimmune encephalomyelitis (EAE) , the mouse model of multiple sclerosis. The cribriform plate bone marrow (cpBM), is a portion of the ethmoid bone of the skull of mice, and is positioned next to nasal regions. At the cribriform plate olfactory nerves pass through the skull and recent evidence supports that it represents an important immune niche with links to CSF outflow and connections to cpBM. Previous data supports cellular composition of the ethmoid bone marrow in mice is altered in neuroinflammation suggesting it senses inflammatory cues, but the exact nature of these changes is still unclear. Here using scRNAseq and flow cytometry of the ethmoid bone marrow region we test how the region responds to EAE. In addition we also compare the cpBM region with systemic bone marrow in the femur to understand region specific differences. We report that neuroinflammation creates a regulatory response at these sites and implicates the cpBM in controlling these effects. A better understanding of the cribriform niche may offer new treatments for neuroinflammatory disease

Background. Persistent SARS-CoV-2 antigen is a leading hypothesis for post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID. Impaired Fc-mediated antiviral antibody function could contribute to inefficient clearance of infected cells and viral antigen despite preserved antibody levels. Therefore, we tested whether PASC is associated with altered Fc{gamma} receptor-mediated antibody function and differences in IgG1 Fc fucosylation, a major regulator of Fc effector function. Methods. Serum samples from RECOVER-Adult participants with prior SARS-CoV-2 infection (99 PASC, 139 non-PASC) were analyzed for anti-SARS-CoV-2 spike receptor-binding domain (RBD) IgG and subclasses, Fc{gamma}RIIIa- and Fc{gamma}RIIa-mediated reporter activity as surrogate measures of antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP), anti-RBD IgG1 Fc glycosylation, and circulating fucose-modifying enzymes. Results. Despite higher anti-RBD IgG and IgG1 abundance, PASC sera showed lower ADCC- and ADCP-associated Fc{gamma}R reporter activity, including after normalization for antibody abundance. Anti-RBD IgG1 showed greater fucosylation in PASC samples (30 PASC, 34 non-PASC). Serum FUCA2 was lower in PASC samples (24 PASC, 34 non-PASC), whereas FUCA1 and soluble CD16a levels did not differ between the two groups. Conclusion. PASC is associated with impaired antiviral IgG Fc function, increased IgG1 Fc fucosylation, and reduced circulating FUCA2, identifying a glycoimmune axis that may contribute to impaired Fc-dependent antigen clearance and represents a potentially targetable pathway in Long COVID.

Post-treatment Lyme disease syndrome (PTLDS) is characterized by persistent symptoms after antibiotic therapy for Borrelia burgdorferi infection. The mechanisms underlying these symptoms remain incompletely understood and have been attributed to ongoing low-level infection, autoimmunity, or immune dysregulation driven by residual bacterial antigens. We investigated whether Borrelia burgdorferi protein antigen persists in long-lived monocyte subsets in PTLDS patients and is associated with broader immune dysregulation. CD14CD16 monocytes from 10 patients with PTLDS were analyzed by mass spectrometry; plasma from all 10 was profiled on a 14-plex cytokine panel against 50 healthy donors; PBMCs from 6 patients (3 antigen-positive and 3 antigen-negative) underwent RT-qPCR and bulk RNA-seq with immune-compartment profiling. FlaB and OspC were detected in CD14CD16 monocytes months to years after treatment, whereas Borrelia burgdorferi transcript was not detected. Plasma IFN-{gamma} exceeded the healthy donor median in all 10 participants. Antigen-positive samples showed an antigen-associated inflammatory transcriptional profile, including increased TNFSF14/LIGHT, GSDMD, GBP5, and KCNJ2 expression, together with a compartment-level pattern of monocyte-skewed myeloid reconfiguration, higher neutrophil-related features, and selective T-cell-state changes. These findings link residual Borrelia burgdorferi protein in CD14CD16 monocytes to monocyte-skewed immune remodeling in PTLDS, paralleling observations in long COVID and supporting a model in which post-infectious syndromes are sustained by antigen-fueled immune remodeling rather than ongoing infection.

Objective To examine whether prospectively recorded maternal BMI in early pregnancy is associated with offspring multiple sclerosis (MS) risk, including childhood-onset and adult-onset MS. Design Nationwide population-based cohort study. Setting Swedish national health and demographic registers. Participants 1,524,863 singleton children born in Sweden from 1992 to 2007 and followed through 2025. Exposures Maternal BMI recorded at the first antenatal visit, categorised as underweight (<18.5), normal weight (18.5-24.9), overweight (25.0-29.9), and obesity ([&ge;]30.0), and also modelled continuously. Main outcome measures MS in offspring, identified as at least three ICD-10 G35 codes recorded on separate dates. Cox models estimated adjusted hazard ratios (HR) and 95% confidence intervals (CI) adjusting for confounders. Results During follow-up, 1539 offspring (0.1%) were diagnosed with MS at a median age of 24 years (interquartile range, 20-27 years). MS incidence rates by maternal BMI category were 0.37 per 10,000 person-years for both underweight, and normal weight, 0.42 for overweight, and 0.45 for obesity. Compared with normal weight women, maternal overweight and obesity were associated with higher hazards of offspring MS, with adjusted HRs of 1.20 (95% CI, 1.05 to 1.37) and 1.35 (95% CI, 1.11 to 1.64), respectively. Onset-specific analyses showed similar findings for adult-onset MS and less precise but directionally consistent estimates for paediatric-onset MS. Each 1-unit increase in maternal BMI was associated with a 3%, 4%, and 3% higher hazard of overall, paediatric-onset, and adult-onset offspring MS, respectively. Conclusions In this population-based cohort study, higher maternal BMI were associated with an increased rate of MS in offspring. The findings provide large-scale prospective evidence that maternal adiposity may be related to offspring MS risk and support further investigation of the prenatal metabolic environment in the early-life origins of MS.

BackgroundPost-acute COVID-19 syndrome (PACS) can cause persistent disability involving fatigue, post-exertional malaise, neurocognitive symptoms, and autonomic manifestations. A clinically overlapping condition has been reported following COVID-19 vaccination and termed post-acute COVID-19 vaccination syndrome (PACVS), but its underlying mechanisms remain poorly understood. We investigated whether immune dysregulation, autoreactivity, antigen persistence, and viral reactivation--mechanisms implicated in PACS--are also present in PACVS. MethodsThis cross-sectional study included 28 PACS patients, 29 PACVS patients, and 16 convalescent controls (CCG). Clinical manifestations were assessed using ME/CFS-related symptom questionnaires and the Bell Score. T-cell phenotypes and T cells reactive to SARS-CoV-2 Spike, {beta}1/{beta}2-adrenergic receptor, and M3/M4 muscarinic receptor antigens were analyzed by multicolor flow cytometry. Circulating Spike protein and GPCR autoantibodies were quantified by ELISA, and EBV load by qPCR. ResultsPACS and PACVS showed substantial clinical overlap, although several symptoms were more severe in PACS. GPCR autoantibody profiles were comparable between patient groups, while {beta}2-adrenergic receptor autoantibodies were increased compared with CCG. Only three of 57 conventional immunophenotyping parameters differed between PACS and PACVS in unadjusted analyses, whereas both groups showed multiple alterations compared with CCG. Circulating Spike protein was detected in both patient groups, with the highest prevalence in PACVS. {beta}1/{beta}2- and M3/M4-reactive CD4+ T cells were increased, whereas Spike-specific CD4+ T cells were reduced compared with CCG. EBV reactivation was rare. ConclusionsPACS and PACVS exhibit substantial clinical and immunological overlap, including GPCR-directed autoreactivity and altered receptor-reactive T-cell responses. These shared immune-mediated features suggest overlapping pathophysiological mechanisms and warrant investigation of whether therapeutic strategies currently explored for PACS may also be relevant to PACVS.

Systemic sclerosis (SSc) is a rare, heterogeneous autoimmune disease characterized by immune dysregulation, vasculopathy and fibrosis. While typically sporadic, SSc can in a minority of cases show familial clustering. Whole exome sequencing (WES), performed on six families with two affected members each, identified a total of 253 genes with rare, disease co-segregating variants predicted to affect protein function. Of these, we prioritized PRKD2, one of only three genes identified in two families. PRKD2 encodes Protein kinase D2: an intracellular serine-threonine kinase that amplifies T cell receptor (TCR) signaling. Both familial SSc-associated PRKD2 missense variants increased kinase activation, exerting overlapping, activating effects on TCR signaling in heterozygous knock-in Jurkat cells. A pan-PRKD inhibitor suppressed T cell activation in peripheral blood mononuclear cells from both familial SSc cases and controls. The enrichment of PRKD2 rare variants in a sporadic SSc cohort provides further support for its relevance to SSc pathogenesis. Together, these findings identify PRKD2 rare variants as potential genetic contributors to SSc, and PRKD2-mediated TCR signaling as a candidate disease-relevant and potentially targetable pathway.