A Novel MicroRNA-132-Surtuin-1 Axis Underlies Aberrant B-cell Cytokine Regulation in Patients with Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
Clinical trial results demonstrating that B-cell depletion substantially reduces new relapses in patients with multiple sclerosis (MS) have established that B cells play a role in the pathophysiology of MS relapses. The same treatment appears not to impact antibodies directed against the central nervous system, which underscores the contribution of antibody-independent functions of B cells to disease activity. One mechanism by which B cells are now thought to contribute to MS activity is by over-activating T cells, including through aberrant expression of B cell pro-inflammatory cytokines. However, the mechanisms underlying the observed B cell cytokine dysregulation in MS remain unknown. We hypothesized that aberrant expression of particular microRNAs might be involved in the dysregulated pro-inflammatory cytokine responses of B cells of patients with MS. Through screening candidate microRNAs in activated B cells of MS patients and matched healthy subjects, we discovered that abnormally increased secretion of lymphotoxin and tumor necrosis factor α by MS B cells is associated with abnormally increased expression of miR-132. Over-expression of miR-132 in normal B cells significantly enhanced their production of lymphotoxin and tumor necrosis factor α. The over-expression of miR-132 also suppressed the miR-132 target, sirtuin-1. We confirmed that pharmacological inhibition of sirtuin-1 in normal B cells induces exaggerated lymphotoxin and tumor necrosis factor α production, while the abnormal production of these cytokines by MS B cells can be normalized by resveratrol, a sirtuin-1 activator. These results define a novel miR-132-sirtuin-1 axis that controls pro-inflammatory cytokine secretion by human B cells, and demonstrate that a dysregulation of this axis underlies abnormal pro-inflammatory B cell cytokine responses in patients with MS.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".