Abstract 19834: MicroRNA-148a Inhibits Expression of Plasminogen Activator Inhibitor-1: Mechanistic Implications for Obesity-associated Thrombosis
Bibliographic record
Abstract
Obesity is an epidemic in developed countries and is associated with an increased incidence of arterial and venous thrombosis. The mechanisms of obesity-induced thrombosis, however, are not well defined. In the current study, we tested the hypothesis that diet-induced obesity causes alterations in microRNA expression that may predispose to thrombosis. To induce obesity, C57Bl/6J mice were fed a 60% high fat diet. Compared with lean mice, obese mice had elevated fasting levels of glucose and insulin resistance, and exhibited acceleration of carotid artery thrombosis after photochemical injury (P<0.01) and increased susceptibility to venous thrombosis induced by inferior vena cava ligation (P<0.05). Plasma levels of plasminogen activator inhibitor-1 (PAI-1), an inhibitor of fibrinolysis secreted by adipocytes and endothelial cells, were elevated 3-fold in obese mice, suggesting a potential mechanism of thrombosis in diet-induced obesity. TaqMan low-density microRNA arrays detected downregulation of 41 microRNAs in plasma exosomes from obese mice (P<0.05). Bioinformatic analysis of the downregulated microRNAs in relation to the PAI-1 3’-UTR identified microRNA-148a as a potential inhibitor of PAI-1 expression in obesity. We found that levels of PAI-1 mRNA were increased 4.5-fold (P<0.05) and levels of microRNA-148a decreased 4-fold (P<0.05) in white adipose tissue from obese mice compared with lean mice. Transfection of primary adipocytes or a murine endothelial cell line (MS-1) with a microRNA-148a mimetic resulted in 50-70% downregulation of PAI-1 expression (P<0.05), demonstrating a causal inverse relationship between microRNA-148a and PAI-1 mRNA. Our findings suggest that downregulation of microRNA-148a in diet-induced obesity may promote thrombosis through loss of repression of PAI-1 expression at the mRNA level.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".