Role of miR‐27a Mediated Regulation of VAV3 in Sepsis‐induced ARDS
Bibliographic record
Abstract
Mechanisms of the salutary effects of Mesenchymal stem cell (MSC) administration in sepsis-induced ARDS are unclear. We profiled mRNA and miRNA in septic lungs from MSC-treated vs non-treated mice (1) and identified differential expression of miR-27a and its putative target gene VAV3. Our objectives are to verify the regulation of VAV3 by miR-27 and determine an observable phenotype in vitro. Methods: In vitro – Human Pulmonary Microvascular Endothelial Cells (HPMECs) treated with 10ng/mL TNFα. In vivo – C57BL/6J instilled intratracheally with lipopolysaccharide (LPS) for 8 hours, and lungs are harvested. Differential expression of miR-27a and VAV3 at RNA and protein levels are measured by qPCR and Western blot. To verify regulation of VAV3 by miR-27a, we treat HPMECs with miR-27a specific inhibitor and mimic from QIAGEN. Scratch migration assay on HPMECs treated with miR-27a specific inhibitor and mimic, with and without TNFα. Results: VAV3 is down-regulated in both in vitro and in vivo, concomitant to increased levels of miR-27a. MiR-27a mimic demonstrates increased miR-27a expression and down-regulation of VAV3, and vice-versa upon treating with miR-27a inhibitor. In scratch migration assays, both TNFα and the miR-27a mimic are observed to decrease cellular migration, while the miR-27a inhibitor attenuates the decrease from TNFα stimulation. Conclusion In our models, miR-27a functions as a post-transcriptional regulator of VAV3. A decrease in VAV3 results in decreased cell migration, which may affect pulmonary wound healing and inflammation. (1) Mei, S. H. et al. AJRCCM, 2010. Research Funding: Grant # MOP-130331 to CCDS, Queen Elizabeth to LZ
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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".