Alternatively spliced protein arginine methyltransferase 1 isoform PRMT1v2 promotes the survival and invasiveness of breast cancer cells
Bibliographic record
Abstract
Protein arginine methylation is catalyzed by protein arginine methyltransferases (PRMTs) and plays an important role in many cellular processes. Aberrant PRMT expression has been observed in several common cancer types; however, their precise contribution to the cell transformation process is not well understood. We previously reported that the PRMT1 gene generates several alternatively spliced isoforms, and our initial biochemical characterization of these isoforms revealed that they exhibit distinct substrate specificity and subcellular localization. We focus here on the PRMT1v2 isoform, which is the only predominantly cytoplasmic isoform, and we have found that its relative expression is increased in breast cancer cell lines and tumors. Specific depletion of PRMT1v2 using RNA interference caused a significant decrease in cancer cell survival due to an induction of apoptosis. Furthermore, depletion of PRMT1v2 in an aggressive cancer cell line significantly decreased cell invasion. We also demonstrate that PRMT1v2 overexpression in a non-aggressive cancer cell line was sufficient to render them more invasive. Importantly, this novel activity is specific to PRMT1v2, as overexpression of other isoforms did not enhance invasion. Moreover, this activity requires both proper subcellular localization and methylase activity. Lastly, PRMT1v2 overexpression altered cell morphology and reduced cell-cell adhesion, a phenomenon that we convincingly linked with reduced β-catenin protein expression. Overall, we demonstrate a specific role for PRMT1v2 in breast cancer cell survival and invasion, underscoring the importance of identifying and characterizing the distinct functional differences between PRMT1 isoforms.
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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".