The Epigenetic Drug 5-Azacytidine Interferes with Cholesterol and Lipid Metabolism
Bibliographic record
Abstract
DNA methylation and histone acetylation inhibitors are widely used to study the role of epigenetic marks in the regulation of gene expression. In addition, several of these molecules are being tested in clinical trials or already in use in the clinic. Antimetabolites, such as the DNA-hypomethylating agent 5-azacytidine (5-AzaC), have been shown to lower malignant progression to acute myeloid leukemia and to prolong survival in patients with myelodysplastic syndromes. Here we examined the effects of DNA methylation inhibitors on the expression of lipid biosynthetic and uptake genes. Our data demonstrate that, independently of DNA methylation, 5-AzaC selectively and very potently reduces expression of key genes involved in cholesterol and lipid metabolism (e.g. PCSK9, HMGCR, and FASN) in all tested cell lines and in vivo in mouse liver. Treatment with 5-AzaC disturbed subcellular cholesterol homeostasis, thereby impeding activation of sterol regulatory element-binding proteins (key regulators of lipid metabolism). Through inhibition of UMP synthase, 5-AzaC also strongly induced expression of 1-acylglycerol-3-phosphate O-acyltransferase 9 (AGPAT9) and promoted triacylglycerol synthesis and cytosolic lipid droplet formation. Remarkably, complete reversal was obtained by the co-addition of either UMP or cytidine. Therefore, this study provides the first evidence that inhibition of the de novo pyrimidine synthesis by 5-AzaC disturbs cholesterol and lipid homeostasis, probably through the glycerolipid biosynthesis pathway, which may contribute mechanistically to its beneficial cytostatic properties.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".