Persistently activated, proliferative memory autoreactive B cells promote inflammation in rheumatoid arthritis
Bibliographic record
Abstract
Autoreactive B cells mediate autoimmune pathology, but exactly how remains unknown. A hallmark of rheumatoid arthritis (RA), a common autoimmune disease, is the presence of disease-specific anticitrullinated protein antibodies (ACPAs). Here, we showed that ACPA-positive B cells in patients with RA strongly expressed T cell-stimulating ligands, produced abundant proinflammatory cytokines, and were proliferative while escaping inhibitory signals. This activated state was found at different degrees in different stages of disease: highest in patients with recent-onset RA, moderate in patients with established RA, and far less pronounced in ACPA-positive individuals "at risk" for developing disease. The activated autoreactive B cell response persisted in patients who achieved clinical remission with conventional treatment. ACPA-positive B cells in blood and synovial fluid secreted increased amounts of the chemoattractant interleukin-8, which attracted neutrophils, the most abundant immune cell in arthritic joints. Tetanus toxoid-specific B cells from the same patients exhibited properties of memory B cells without the activation and proliferation phenotype, but these cells transiently acquired a similar proliferative phenotype upon booster vaccination. Together, these data indicated that continuous antigenic triggering of autoreactive B cells occurs in human autoimmune disease and support the emerging concept of immunological activity that persists under treatment even in clinical remission, which may revise our current concept of treatment targets for future therapeutic interventions. In addition, our data pointed to a pathogenic role of ACPA-positive B cells in the inflammatory disease process underlying RA and favor approaches that aim at their antigen-specific inactivation or depletion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".