B CELL RECEPTOR SEQUENCING REVEALS DISTINCT SELECTION OF AUTOREACTIVE AGE/AUTOIMMUNITY-ASSOCIATED B CELLS IN PATIENTS WITH SLE
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".