Ablation of PRMT6 reveals a role as a negative transcriptional regulator of the p53 tumor suppressor
Bibliographic record
Abstract
Arginine methylation of histones is a well-known regulator of gene expression. Protein arginine methyltransferase 6 (PRMT6) has been shown to function as a transcriptional repressor by methylating the histone H3 arginine 2 [H3R2(me2a)] repressive mark; however, few targets are known. To define the physiological role of PRMT6 and to identify its targets, we generated PRMT6(-/-) mouse embryo fibroblasts (MEFs). We observed that early passage PRMT6(-/-) MEFs had growth defects and exhibited the hallmarks of cellular senescence. PRMT6(-/-) MEFs displayed high transcriptional levels of p53 and its targets, p21 and PML. Generation of PRMT6(-/-); p53(-/-) MEFs prevented the premature senescence, suggesting that the induction of senescence is p53-dependent. Using chromatin immunoprecipitation assays, we observed an enrichment of PRMT6 and H3R2(me2a) within the upstream region of Trp53. The PRMT6 association and the H3R2(me2a) mark were lost in PRMT6(-/-) MEFs and an increase in the H3K4(me3) activator mark was observed. Our findings define a new regulator of p53 transcriptional regulation and define a role for PRMT6 and arginine methylation in cellular senescence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".