miR-146a-5p plays an essential role in the aberrant epithelial–fibroblast cross-talk in COPD
Bibliographic record
Abstract
We previously reported that epithelial-derived interleukin (IL)-1α drives fibroblast-derived inflammation in the lung epithelial–mesenchymal trophic unit. Since miR-146a-5p has been shown to negatively regulate IL-1 signalling, we investigated the role of miR-146a-5p in the regulation of IL-1α-driven inflammation in chronic obstructive pulmonary disease (COPD). Human bronchial epithelial (16HBE14o-) cells were co-cultured with control and COPD-derived primary human lung fibroblasts (PHLFs), and miR-146a-5p expression was assessed with and without IL-1α neutralising antibody. Genomic DNA was assessed for the presence of the single nucleotide polymorphism (SNP) rs2910164. miR-146a-5p mimics were used for overexpression studies to assess IL-1α-induced signalling and IL-8 production by PHLFs. Co-culture of PHLFs with airway epithelial cells significantly increased the expression of miR-146a-5p and this induction was dependent on epithelial-derived IL-1α. miR-146a-5p overexpression decreased IL-1α-induced IL-8 secretion in PHLFsviadownregulation of IL-1 receptor-associated kinase-1. In COPD PHLFs, the induction of miR-146a-5p was significantly less compared with controls and was associated with the SNP rs2910164 (GG allele) in the miR-146a-5p gene. Our results suggest that induction of miR-146a-5p is involved in epithelial–fibroblast communication in the lungs and negatively regulates epithelial-derived IL-1α induction of IL-8 by fibroblasts. The decreased levels of miR-146a-5p in COPD fibroblasts may induce a more pro-inflammatory phenotype, contributing to chronic inflammation in COPD.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".