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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".