NEW IGG AND IGA AUTOANTIBODY SPECIFICITIES TARGETING DNA- AND RNA-BINDING PROTEINS DIFFERENTIATE SYSTEMIC LUPUS ERYTHEMATOSUS FROM HEALTHY INDIVIDUALS AND OTHER AUTOIMMUNE DISEASES
Bibliographic record
Abstract
PV226 / #259 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Systemic lupus erythematosus (SLE) is characterized by the production of autoantibodies (AAbs), the specificities of which remain largely unknown and their contribution to disease pathogenesis remains poorly understood. Currently used AAbs either demonstrate high sensitivity across connective tissue diseases (eg, ANA) or high specificity yet low sensitivity (eg, anti-dsDNA). To address the urgent unmet needs of heterogeneity, unpredictability, and diagnostic delay in patients with SLE, we screened for circulating IgG and IgA autoantibodies against 1,609 proteins. Methods Plasma samples from patients with SLE, Sjögren’s disease (SjD) and systemic sclerosis (SSc), and healthy controls (HC) were obtained from 2 independent cohorts (discovery and validation) within the European PRECISESADS consortium (NTC02890121). The discovery cohort comprised 199 patients with SLE, 115 patients with SjD, 115 patients with SSc, and 111 HC. The validation cohort included 30 patients with SLE, 31 patients with SjD, 24 patients with SSc, and 84 HC from an independent inception cohort. Plasma samples were analyzed for IgG and IgA autoantibody specificities against a comprehensive panel of 1,609 human proteins, utilizing the i-Ome Discovery protein microarray (Sengenics). Conventional autoantibodies (IgG anti-dsDNA, IgG anti-Smith, IgG and IgM anti-cardiolipin, IgG and IgM anti-b2GPI) were measured using an automated chemiluminescent immunoanalyser. Differentially abundant AAb (daAAb) analysis was performed with the limma R package after adjustments for age, recruiting center, batch, and polyspecific antibody reactivity (PSA) following diagnostic performance. Results In 2 independent cohorts, we identified and validated 5 IgG (anti-LIN28A, anti-HNRNPA2B1, anti-HMG20B, anti-HMGB2, and anti-TFCP2) and 4 IgA (anti-LIN28A, anti-HMG20B, anti-SUB1, and anti-TFCP2) autoantibodies that demonstrated high specificity for SLE, along with consistent and robust positivity frequencies. Levels of some, notably anti-LIN28A, varied over time and exhibited metrics that outperformed those of traditional autoantibody markers such as anti-dsDNA. We identified 5 patient subgroups based on SLE-specific IgG autoantibodies and 5 based on IgA autoantibodies. One subgroup exhibited broad reactivity against numerous antigens, 3 subgroups showed varying reactivity patterns, and 1 was completely seronegative for the specificities screened for. SLE patients with positive autoantibody levels for conventional autoantibody markers were similarly distributed across the clusters. Differentially abundant autoantibody targets pointed to RNA- and DNA-binding and transcription functions, with considerable overlap across patient subgroups stratified by IgG and IgA reactivity patterns. Conclusions We described and validated novel IgG and IgA autoantibody specificities. The observation of IgA seroreactivity is novel and provides implications for the importance of mucosal immunity in SLE pathogenesis. Certain autoantibodies were significantly more abundant in SLE compared to healthy controls and other autoimmune disease comparators, showing promise for improved diagnostics and aiding in the molecular characterization of individuals with SLE. These findings could support more informed and personalized therapeutic strategies. Both IgG and IgA anti-LIN28A demonstrated high specificity and sensitivity in distinguishing SLE from healthy individuals and other autoimmune diseases, outperforming conventional autoantibodies in diagnostic metrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".