Abnormal B‐cell cytokine responses a trigger of T‐cell–mediated disease in MS?
Bibliographic record
Abstract
OBJECTIVE: To study antibody-independent contributions of B cells to inflammatory disease activity, and the immune consequences of B-cell depletion with rituximab, in patients with multiple sclerosis (MS). METHODS: B-Cell effector-cytokine responses were compared between MS patients and matched controls using a 3-signal model of activation. The effects of B-cell depletion on Th1/Th17 CD4 and CD8 T-cell responses in MS patients were assessed both ex vivo and in vivo, together with pharmacokinetic/pharmacodynamic studies as part of 2 rituximab clinical trials in relapsing-remitting MS. RESULTS: B Cells of MS patients exhibited aberrant proinflammatory cytokine responses, including increased lymphotoxin (LT):interleukin-10 ratios and exaggerated LT and tumor necrosis factor (TNF)-alpha secretion, when activated in the context of the pathogen-associated TLR9-ligand CpG-DNA, or the Th1 cytokine interferon-gamma, respectively. B-Cell depletion, both ex vivo and in vivo, resulted in significantly diminished proinflammatory (Th1 and Th17) responses of both CD4 and CD8 T cells. Soluble products from activated B cells of untreated MS patients reconstituted the diminished T-cell responses observed following in vivo B-cell depletion in the same patients, and this effect appeared to be largely mediated by B-cell LT and TNFalpha. INTERPRETATION: We propose that episodic triggering of abnormal B-cell cytokine responses mediates 'bystander activation' of disease-relevant proinflammatory T cells, resulting in new relapsing MS disease activity. Our findings point to a plausible mechanism for the long-recognized association between infections and new MS relapses, and provide novel insights into B-cell roles in both health and disease, and into mechanisms contributing to therapeutic effects of B-cell depletion in human autoimmune diseases, including MS.
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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".