Reduced expression of let‐7f activates <scp>TGF</scp>‐β/<scp>ALK</scp>5 pathway and leads to impaired ischaemia‐induced neovascularization after cigarette smoke exposure
Bibliographic record
Abstract
This study sought to determine the potential role of microRNAs (miRNAs) in the detrimental effects of cigarette smoke on angiogenesis and neovascularization. Using large-scale miRNA profiling and qRT-PCR analyses, we identified let-7f as a pro-angiogenic miRNA which expression is significantly reduced in HUVECs treated with cigarette smoke extracts (CSE), and in the ischemic muscles of mice that are exposed to cigarette smoke (MES). In a mouse model of hindlimb ischaemia, intramuscular injection of let-7f mimic restored ischaemia-induced neovascularization in MES. Doppler flow ratios and capillary density in ischemic muscles were significantly improved in MES treated with let-7f mimic. Clinically, this was associated with reduced ambulatory impairment and hindlimb ischaemic damage. Treatment with let-7f mimic could also rescue pro-angiogenic cell (PAC) number and function (attachment, proliferation, migration) in MES. ALK5 (TGF-βR1), an important modulator of angiogenesis, is a target of let-7f. Here we show that ALK5 is increased in HUVECs exposed to CSE and in the ischaemic muscles of MES. This is associated with a downstream activation of the anti-angiogenic factors SMAD2/3 and PAI-1. Importantly, treatment with let-7f mimic reduces the expression of ALK5, SMAD2/3 and PAI-1 both in vitro and in vivo. Moreover, let-7f overexpression or ALK5 inhibition can rescue angiogenesis in HUVECs exposed to CSE. Cigarette smoke exposure is associated with reduced expression of let-7f and activation of the anti-angiogenic TGF-β/ALK5 pathway. Overexpression of let-7f using a miRNA mimic could constitute a novel therapeutic strategy to improve ischaemia-induced neovascularization in pathological conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".