MicroRNA Expression in Human Airway Smooth Muscle Cells
Bibliographic record
Abstract
Defining mechanisms by which differentiated, contractile smooth muscle cells become proliferative and secretory in response to mechanical and environmental stress is crucial for determining the contribution of airway smooth muscle (ASM) to inflammatory responses that result in airway disease. Regulation by microRNAs (miRNAs) has emerged as an important post-transcriptional mechanism regulating gene expression that may modulate ASM phenotype, but little is known about the expression and functions of miRNA in smooth muscle. In the present study we used microarrays to determine whether miRNAs in human ASM cells are altered by a proinflammatory stimulus. In ASM cells exposed to IL-1beta, TNF-alpha, and IFN-gamma, we found 11 miRNAs to be significantly down-regulated. We verified decreased expression of miR-25, miR-140*, mir-188, and miR-320 by quantitative PCR. Analysis of miR-25 expression indicates that it has a broad role in regulating ASM phenotype by modulating expression of inflammatory mediators such as RANTES, eotaxin, and TNF-alpha; genes involved in extracellular matrix turnover; and contractile proteins, most notably myosin heavy chain. miRNA binding algorithms predict that miR-25 targets Krüppel-like factor 4 (KLF4), a potent inhibitor of smooth muscle-specific gene expression and mediator of inflammation. Our study demonstrates that inhibition of miR-25 in cytokine-stimulated ASM cells up-regulates KLF4 expression via a post-transcriptional mechanism. This provides novel evidence that miR-25 targets KLF4 in ASM cells and proposes that miR-25 may be an important mediator of ASM phenotype.
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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 | 0.001 |
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".