A Mouse <i>PRMT1</i> Null Allele Defines an Essential Role for Arginine Methylation in Genome Maintenance and Cell Proliferation
Bibliographic record
Abstract
Protein arginine methyltransferase 1 (PRMT1) is the major enzyme that generates monomethylarginine and asymmetrical dimethylarginine. We report here a conditional null allele of PRMT1 in mice and that the loss of PRMT1 expression leads to embryonic lethality. Using the Cre/lox-conditional system, we show that the loss of PRMT1 in mouse embryonic fibroblasts (MEFs) leads to the loss of arginine methylation of substrates harboring a glycine-arginine rich motif, including Sam68 and MRE11. The loss of PRMT1 in MEFs leads to spontaneous DNA damage, cell cycle progression delay, checkpoint defects, aneuploidy, and polyploidy. We show using a 4-hydroxytamoxifen-inducible Cre that the loss of PRMT1 in MEFs leads to a higher incidence of chromosome losses, gains, structural rearrangements, and polyploidy, as documented by spectral karyotyping. Using PRMT1 small interfering RNA in U2OS cells, we further show that PRMT1-deficient cells are hypersensitive to the DNA damaging agent etoposide and exhibit a defect in the recruitment of the homologous recombination RAD51 recombinase to DNA damage foci. Taken together, these data show that PRMT1 is required for genome integrity and cell proliferation. Our findings also suggest that arginine methylation by PRMT1 is a key posttranslational modification in the DNA damage response pathway in proliferating mammalian cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".