NEW IGG AND IGA AUTOANTIBODY SPECIFICITIES TARGETING DNA- AND RNA-BINDING PROTEINS DIFFERENTIATE SYSTEMIC LUPUS ERYTHEMATOSUS FROM HEALTHY INDIVIDUALS AND OTHER AUTOIMMUNE DISEASES
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».