MicroRNA-146a and microRNA-146b expression and anti-inflammatory function in human airway smooth muscle
Bibliographic record
Abstract
MicroRNA (miR)-146a and miR-146b are negative regulators of inflammatory gene expression in lung fibroblasts, epithelial cells, monocytes, and endothelial cells. The abundance of cyclooxygenase-2 (COX-2) and IL-1β is negatively regulated by the miR-146 family, suggesting miR-146a and/or miR-146b might modulate inflammatory mediator expression in airway smooth muscle thereby contributing to pathogenesis of asthma. To test this idea we compared miR-146a and miR-146b expression in human airway smooth muscle cells (hASMCs) from nonasthmatic and asthmatic subjects treated with cytomix (IL-1β, TNF-α, and IFNγ) and examined the miRNAs' effects on COX-2 and IL-1β expression. We found that cytomix treatment elevated miR-146a and miR-146b abundance. Induction with cytomix was greater than induction with individual cytokines, and asthmatic cells exhibited higher levels of miR-146a expression following cytomix treatment than nonasthmatic cells. Transfection of miR-146a or miR-146b mimics reduced COX-2 and IL-1β expression. A miR-146a inhibitor increased COX-2 and IL-1β expression, but a miR-146b inhibitor was ineffective. Repression of COX-2 and IL-1β expression by miR-146a correlated with reduced abundance of the RNA-binding protein human antigen R. These results demonstrate that miR-146a and miR-146b expression is inducible in hASMCs by proinflammatory cytokines and that miR-146a expression is greater in asthmatic cells. Both miR-146a and miR-146b can negatively regulate COX-2 and IL-1β expression at pharmacological levels, but loss-of-function studies showed that only miR-146a is an endogenous negative regulator in hASMCs. The results suggest miR-146 mimics may be an attractive candidate for further preclinical studies as an anti-inflammatory treatment of asthma.
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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".