Abstract 518: Differential Effects of Alcohol and Its Primary Metabolite Acetaldehyde on Vascular Smooth Muscle Cell Notch Signaling and Growth
Bibliographic record
Abstract
Moderate consumption of alcohol (ethanol/EtOH) is a negative risk factor for atherosclerosis while binge drinking and chronic alcohol abuse is associated with increased cardiovascular disease and mortality. As the balance between the levels of EtOH itself and its primary metabolite, acetaldehyde, is likely different in these two drinking groups, we assessed the hypothesis that differential effects of EtOH and acetaldehyde on vascular smooth muscle cell (SMC) Notch signaling and growth may be key in mediating the effects of various drinking patterns on cardiovascular disease. Human coronary artery smooth muscle cells (HCASMC) were treated (24 h) with either EtOH (25 mM) or acetaldehyde (10 or 25 μM). We confirmed our previously reported inhibitory effects of EtOH on Notch signaling and subsequently on the growth of HCASMC. In contrast, acetaldehyde treatment stimulated HCASMC proliferation (determined by cell counts) and increased proliferating cell nuclear antigen expression, concomitant with stimulation of Notch signaling as determined by increased Notch target gene (HRT 1-3) mRNA levels. Interaction of a ligand (e.g., Dll4) with the Notch receptor initiates proteolytic cleavage by γ-secretase, resulting in the release of the active Notch-intracellular domain (NICD). A fluorogenic peptide cleavage assay demonstrated almost complete inhibition by EtOH of Dll4-stimulated γ-secretase activity in a solubilized membrane fraction from HCASMC (similar to the effect of the control inhibitor, DAPT), but no effect of acetaldehyde treatment. Similarly, EtOH, but not acetaldehyde, affected the association and distribution of the γ-secretase catalytic subunit, presenilin-1, with lipid rafts as determined by dual fluorescent labeling and confocal microscopic visualization (Alexa 488-conjugated cholera toxin B subunit for lipid rafts, and Alexa 594-conjugated anti-presenilin-1 antibody). In conclusion, EtOH and acetaldehyde have opposite effects on SMC Notch signaling and growth. These differential effects of alcohol and its metabolite may be relevant in mediating the opposing effects of different drinking patterns (e.g., daily moderate vs weekend binge) on cardiovascular disease.
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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) | 0.003 | 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 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".