Down‐regulation of <scp>IL</scp>‐8 by high‐dose vitamin <scp>D</scp> is specific to hyperinflammatory macrophages and involves mechanisms beyond up‐regulation of <scp><i>DUSP1</i></scp>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: There is current interest in vitamin D as a potential anti-inflammatory treatment for chronic inflammatory lung disease, including cystic fibrosis (CF). Vitamin D transcriptionally up-regulates the anti-inflammatory gene DUSP1, which partly controls production of the inflammatory chemokine IL-8. IL-8 is overabundant in CF airways, potentially due to hyperinflammatory responses of CF macrophages. We tested the ability of vitamin D metabolites to down-regulate IL-8 production in CF macrophages. EXPERIMENTAL APPROACH: CF and healthy monocyte-derived macrophages (MDM) were treated with two vitamin D metabolites, 25-hydroxyvitamin D3 (25OHD3 ) and 1,25-dihydroxyvitamin D3 (1,25(OH)2 D3 ), or paricalcitol, synthetic analogue of 1,25(OH)2 D3 . 25OHD3 was tested at doses of 25-150 nM, whereas 1,25(OH)2 D3 and paricalcitol at doses of up to 100 nM. IL-8 was stimulated by bacterial virulence factors. As potential anti-inflammatory mechanism of vitamin D metabolites, we assessed up-regulation of DUSP1. KEY RESULTS: MDM from patients with CF and some healthy donors showed excessive production of stimulated IL-8, highlighting their hyperinflammatory phenotype. Vitamin D metabolites down-regulated stimulated IL-8 only in those hyperinflammatory MDM, and only when used at high doses (>100 nM for 25OHD3 , or >1 nM for 1,25(OH)2 D3 and paricalcitol). The magnitude of IL-8 down-regulation by vitamin D metabolites or paricalcitol was moderate (∼30% vs. >70% by low-dose dexamethasone). Transcriptional up-regulation of DUSP1 by vitamin D metabolites was seen in all tested MDM, regardless of IL-8 down-regulation. CONCLUSIONS AND IMPLICATIONS: Vitamin D metabolites and their analogues moderately down-regulate IL-8 in hyperinflammatory macrophages, including those from CF. This down-regulation appears to go through DUSP1-independent mechanisms.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".