Aldehyde dehydrogenase 2 and PARP1 interaction modulates hepatic HDL biogenesis by LXRα-mediated ABCA1 expression
Bibliographic record
Abstract
HDL cholesterol (HDL-C) predicts risk of cardiovascular disease (CVD), but the factors regulating HDL are incompletely understood. Emerging data link CVD risk to decreased HDL-C in 8% of the world population and 40% of East Asians who carry an SNP of aldehyde dehydrogenase 2 (ALDH2) rs671, responsible for alcohol flushing syndrome; however, the underlying mechanisms remain unknown. We found significantly decreased HDL-C with increased hepatosteatosis in ALDH2-KO (AKO), ALDH2/LDLR-double KO (ALKO), and ALDH2 rs671-knock-in (KI) mice after consumption of a Western diet. Metabolomics identified ADP-ribose as the most significantly increased metabolites in the ALKO mouse liver. Moreover, ALDH2 interacted with poly(ADP-ribose) polymerase 1 (PARP1) and attenuated PARP1 nuclear translocation to downregulate poly(ADP-ribosyl)ation of liver X receptor α (LXRα), leading to an upregulation of ATP-binding cassette transporter A1 (ABCA1) and HDL biogenesis. Conversely, AKO or ALKO mice exhibited lower HDL-C with ABCA1 downregulation due to increased nuclear PARP1 and upregulation of LXRα poly(ADP-ribosyl)ation. Consistently, PARP1 inhibition rescued ALDH2 deficiency-induced fatty liver and elevated HDL-C in AKO mice. Interestingly, KI mouse or human liver tissues showed ABCA1 downregulation with increased nuclear PARP1 and LXRα poly(ADP-ribosyl)ation. Our study uncovered a key role of ALDH2 in HDL biogenesis through the LXRα/PARP1/ABCA1 axis, highlighting a potential therapeutic strategy in CVD.
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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 | 0.000 | 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) | 0.000 | 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".