Distinct Effector Cytokine Profiles of Memory and Naive Human B Cell Subsets and Implication in Multiple Sclerosis
Why this work is in the frame
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Bibliographic record
Abstract
Although recent animal studies have fuelled growing interest in Ab-independent functions of B cells, relatively little is known about how human B cells and their subsets may contribute to the regulation of immune responses in either health or disease. In this study, we first confirm that effector cytokine production by normal human B cells is context dependent and demonstrate that this involves the reciprocal regulation of proinflammatory and anti-inflammatory cytokines. We further report that this cytokine network is dysregulated in patients with the autoimmune disease multiple sclerosis, whose B cells exhibit a decreased average production of the down-regulatory cytokine IL-10. Treatment with the approved chemotherapeutic agent mitoxantrone reciprocally modulated B cell proinflammatory and anti-inflammatory cytokines, establishing that the B cell cytokine network can be targeted in vivo. Prospective studies of human B cells reconstituting following in vivo depletion suggested that different B cell subsets produced distinct effector cytokines. We confirmed in normal human B cell subsets that IL-10 is produced almost exclusively by naive B cells while the proinflammatory cytokines lymphotoxin and TNF-alpha are largely produced by memory B cells. These results point to an in vivo switch in the cytokine "program" of human B cells transitioning from the naive pool to the memory pool. We propose a model that ascribes distinct and proactive roles to memory and naive human B cell subsets in the regulation of memory immune responses and in autoimmunity. Our findings are of particular relevance at a time when B cell directed therapies are being applied to clinical trials of several autoimmune diseases.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it