Dimethyl Fumarate Treatment Mediates an Anti-Inflammatory Shift in B Cell Subsets of Patients with Multiple Sclerosis
Bibliographic record
Abstract
The therapeutic mode of action of dimethyl fumarate (DMF), approved for treating patients with relapsing-remitting multiple sclerosis, is not fully understood. Recently, we and others demonstrated that Ab-independent functions of distinct B cell subsets are important in mediating multiple sclerosis (MS) relapsing disease activity. Our objective was to test whether and how DMF influences both the phenotype and functional responses of disease-implicated B cell subsets in patients with MS. High-quality PBMC were obtained from relapsing-remitting MS patients prior to and serially after initiation of DMF treatment. Multiparametric flow cytometry was used to monitor the phenotype and functional response-profiles of distinct B cell subsets. Total B cell counts decreased following DMF treatment, largely reflecting losses of circulating mature/differentiated (but not of immature transitional) B cells. Within the mature B cell pool, DMF had a greater impact on memory than naive B cells. In keeping with these in vivo effects, DMF treatment in vitro remarkably diminished mature (but not transitional B cell) survival, mediated by inducing apoptotic cell death. Although DMF treatment (both in vivo and in vitro) minimally impacted B cell IL-10 expression, it strongly reduced B cell expression of GM-CSF, IL-6, and TNF-α, resulting in a significant anti-inflammatory shift of B cell response profiles. The DMF-mediated decrease in B cell proinflammatory cytokine responses was further associated with reduced phosphorylation of STAT5/6 and NF-κB in surviving B cells. Together, these data implicate novel mechanisms by which DMF may modulate MS disease activity through shifting the balance between pro- and anti-inflammatory B cell responses.
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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".