Abstract 644: Role of Micro RNA LET-7F in Cigarette Smoke-Induced Impairment of Neovascularization
Bibliographic record
Abstract
Background: Exposure to cigarette smoke is associated with impaired neovascularization in response to ischemia. The precise mechanisms involved in that process remain to be determined. Micro RNA (miR) are emerging as key regulators of several physiological processes, including angiogenesis. Here we investigated the potential role of miRs for the modulation of neovascularization in the context of cigarette smoking. Methods and Results: Human Umbilical Vascular Endothelial Cells (HUVECs) were exposed or not to cigarette smoke extracts (CSE). Using Affimetrix GeneChip miRNA array analysis, we found that the pro-angiogenic miR let-7f was downregulated by 40% in HUVECs exposed to CSE. Using an inhibitor of let-7f, we demonstrated reduced migration and tube formation in HUVECs, reproducing the phenotype induced by CSE. A let-7f mimic could rescue cellular migration and tube formation in HUVECs exposed to CSE. Moreover, the expression of let-7f is significantly reduced in the ischemic muscles of mice exposed to cigarette smoke (CS). In vivo, hindlimb ischemia was surgically provoked by femoral artery removal to mice exposed (SMK) or not to CS for two weeks with a local injection of a control or a let-7f mimic. Let-7f mimic could rescue blood flow recuperation and capillary density in ischemic muscles 21 days post-ischemia associated with improved mobility. We found that CS was associated with reduced number of endothelial progenitor cells (EPCs) and impairment of angiogenic activities. Importantly, let-7f mimic rescued EPC number and EPC functional activities in SMK group. TGF-β-RI and HIF1AN are predicted to be targeted by let-7f and both are increased in SMK mice, whereas the expression of HIF-1a and VEGF are reduced in these mice. Interestingly, SMK mice injected with a let-7f mimic have decreased muscle expression of TGF-β-RI and HIF1AN associated with normalized HIF-1 and VEGF expression. Conclusion: Our results suggest that a reduction in the expression of let-7f could be involved in the cigarette smoke-induced inhibition of angiogenesis through modulation of TGF-β-RI and HIF1AN. Overexpression of let-7f using a miR mimic could constitute a novel therapeutic strategy to improve ischemia-induced neovascularization in pathological conditions.
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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) | 0.002 | 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".