Distinct Effector Cytokine Profiles of Memory and Naive Human B Cell Subsets and Implication in Multiple Sclerosis
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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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".