B CELL RECEPTOR SEQUENCING REVEALS DISTINCT SELECTION OF AUTOREACTIVE AGE/AUTOIMMUNITY-ASSOCIATED B CELLS IN PATIENTS WITH SLE
Notice bibliographique
Résumé
PT021 / #495 Topic: AS01 - Adaptive Immunity POSTER TOUR 05: SLE PATHOGENESIS 24-05-2025 10:00 AM - 10:20 AM Background/Purpose Autoreactive B cells that recognize nuclear antigens are normally present in healthy individuals and patients with systemic lupus erythematosus (SLE). Age/autoimmunity-associated B cells (ABCs) are a recently characterized subset of B cells that are reported to be enriched in autoreactivity and differentiate into plasmablasts or plasma cells. The selection process and regulation of autoreactive B cells and ABCs in patients with SLE has not been completely understood. To gain insights into tolerance checkpoints and the developmental trajectories of autoreactive clones, we studied the BCR sequences from thousands of antinuclear antigens (ANA) positive and ANA-negative B cells from patients with SLE. Methods We included 13 patients with SLE. From peripheral blood samples, we identified and sorted ANA+ and ANA- cells from 3 different B cell subsets: naïve, memory, and ABCs, as well as from total plasmablasts. ANA+ cells were identified by flow cytometry using a novel method based on their binding to nuclear extract. We performed bulk B cell receptor sequencing from genomic DNA. We mapped and sequenced B cell receptor (BCR) regions and investigated the features of the immunoglobulin heavy chain (IgH) repertoire of the sorted subsets. Statistical analysis: To compare CDR3 length and SHM at clone level, we used a generalized mixed-effects model design in which patient of origin was included as a random effect and B cell subsets or patient disease activity status as fixed effects. To analyze the patterns of V gene usage of the most prevalent genes across different B cell subsets, we performed Principal Component Analysis (PCA) with scaled and centered data. Results Ten (77%) patients were female. Mean ± SD age of 38.3 ± 11.5 years. According to the PGA score, 8 patients had at least mild activity (PGA ≥ 0.5), and 5 were inactive. ANA reactivity was similar in ABCs (median 8.3%, IQR 4.8-11.9%) and naive B cells (8.3%; 6-9.8%) and higher in both than in memory B cells (4.1%; 3.3-6.3; p<0.05 both comparisons). We observed preferential usage of some VH (IGHV1-18, IGHV3-21, IGHV3-23|3-23D, IGHV4-34, IGHV4-39 and IGHV4-59) and VJ genes (IGHJ4 and IGHJ6) in our cohort. ANA+ naïve and ANA+ ABCs used different gene segments (Figure 1 top panel) and have longer CDR3 regions (Figure 1, lower panel) than ANA+ memory B cells and ANA- subsets, which suggests a close relationship between these 2 subsets. ANA+ ABCs and memory B cells have lower frequency of somatic hypermutation (SHM) compared with their ANA- counterparts (Figure 2, left panel). This suggests extrafollicular (EF) generation of ANA+ antigen experienced B cells. Patients with active disease have a lower frequency of SHM in ANA+ ABCs and memory B cells and ANA- ABCs (Figure 2, right panel), suggesting increased EF activation in patients with active SLE. Figure 1. Figure 2. Conclusions Compared to memory B cells, ABCs are enriched in autoreactivity. ANA+ ABCs have evidence of a different selection process than memory B cells, and are probably directly derived from ANA+ naïve B cells. Our data support that ANA+ B cells, and particularly ANA+ ABCs can contribute to the generation of autoantibodies in patients with SLE through an EF pathway, and that in patients with active SLE there is more EF activation.
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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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 0,000 |
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 ».