MicroRNA‐10a controls airway smooth muscle cell proliferation <i>via</i> direct targeting of the PI3 kinase pathway
Bibliographic record
Abstract
Airway smooth muscle (ASM) cells play important physiological roles in the lung, and abnormal proliferation of ASM directly contributes to the airway remodeling during development of lung diseases such as asthma. MicroRNAs are small yet versatile gene tuners that regulate a variety of cellular processes, including cell growth and proliferation; however, little is known about the precise role of microRNAs in the proliferation of the ASM. Here we report that a specific microRNA (miR-10a) controls ASM proliferation through directly inhibiting the phosphoinositide 3-kinase (PI3K) pathway. Next-generation sequencing identified miR-10a as the most abundant microRNA expressed in primary human airway smooth muscle (HASM) cells, accounting for > 20% of all small RNA reads. Overexpression of miR-10a reduced mitogen-induced HASM proliferation by ∼50%, whereas inhibition of miR-10a increased HASM proliferation by ∼40%. Microarray profiling of HASM cells expressing miR-10a mimics identified 52 significantly down-regulated genes as potential targets of miR-10a, including the catalytic subunit α of PI3K (PIK3CA), the central component of the PI3K pathway. MiR-10a directly suppresses PIK3CA expression by targeting the 3'-untranslated region (3'-UTR) of the gene. Inhibition of PIK3CA by miR-10a reduced V-akt murine thymoma viral oncogene homolog 1 (AKT) phosphorylation and blunted the expression of cyclins and cyclin-dependent kinases that are required for HASM proliferation. Together, our study identifies a novel microRNA-mediated regulatory mechanism for PI3K signaling and ASM proliferation and further suggests miR-10a as a potential therapeutic target for lung diseases whose etiology resides in abnormal ASM proliferation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".