MiR-365-3p is a negative regulator in IL-17-mediated asthmatic inflammation
Bibliographic record
Abstract
Background: Interleukin-17, the major proinflammatory cytokine secreted by Th17 cells, makes essential contribution to pathogenesis of severe asthma, while the detailed mechanisms, especially the involvement of microRNAs which are also important participants in asthma progression, remains largely unclear. Methods: In this study, we established a house dust mite (HDM) extract-induced murine asthmatic models and the miRNA expression in the lung tissues of mice were profiled by miRNA microarray assay. The effect of miR-365-3p on IL-17-mediated inflammation was examined by qRT-PCR and immunoblotting analysis. The involvement of ARRB2 as target gene of miR-365-3p was verified by overexpression or RNA interference. Results: HDM extract-induced asthmatic inflammation was proved to be IL17-mediated and miR-365-3p was screened out to be the only miRNA exclusively responsive to IL-17. miR-365-3p, whose expression was significantly downregulated upon IL-17 stimulation, was demonstrated to exert remarkable anti-inflammatory effect to decrease IL-17-provoked inflammatory cytokines (KC/IL-8 and IL-6) in both airway epithelial cells and macrophages of murine and human origins, verifying its universal antagonizing activity against IL-17-initiated inflammation across the two species. ARRB2 was characterized as the key target of miR-365-3p to negate IL-17-induced inflammatory cytokines. Conclusion: Taken together, our data supported the notion that miR-365-3p, which was diminished by IL-17 in murine and human asthmatic pathogenesis, functioned as an essential negative mediator in IL-17-stimuated inflammatory response by targeting ARRB2, which would shed new light to the understanding and therapeutics thereof of asthmatic inflammation.
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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 | 0.001 |
| 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 | 0.001 |
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".