IL-15 expression on B cells and myeloid cells is distinctly modulated by inflammatory factors in MS and healthy donors
Bibliographic record
Abstract
Abstract Although the salience of the immune system in multiple sclerosis (MS) pathology has been established, the mechanisms involved are incompletely characterized. We have identified IL-15 as a relevant contributor to MS neuropathology; a greater proportion of antigen presenting cells (APC) (B cells and myeloid cells) from MS patients express IL-15 vs. controls. Our aim is to identify factors and mechanisms that contribute to elevated IL-15 levels in the context of MS. The addition of CpG, a TLR9 agonist, to CD40L-cultured human B cells increased the proportion of IL-15+ B cells. Whereas other TLR ligands (e.g. polyI:C) did not alter B cell IL-15 levels, they nevertheless increased CD40 expression. Monocytes from MS patients and age/sex-matched healthy controls (HC) were differentiated into M0 macrophages prior to M1 or M2 polarization. M2 polarization significantly enhanced the percentage of IL-15+macrophages; in contrast, M1 polarization did not. GM-CSF, which is produced by encephalitogenic T cells, induced a major increase in the proportion of IL-15+ M0 and M1 macrophages and decreased the proportion of IL-15+ monocytes from HC and MS donors. While IL-15+ M2 macrophages from HC are increased upon GM-CSF stimulation, IL-15+ M2 from MS donors are not. GM-CSF did not alter IL-15 expression on microglia. Pharmacological inhibitors of STAT5 (Pimozide) and JAK2 (AG490) phosphorylation decreased the GM-CSF-induced increase of IL-15 on M0 macrophages. Thus, GM-CSF is a potent trigger for IL-15 expression on human M0, M1 and M2 macrophages, but not monocytes or microglia. Our data suggest that specific stimuli mediate IL-15 expression on human APC and that cells from MS donors exhibit altered susceptibility to IL-15 modulators compared to HC.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".