Mouse Models of Overexpression Reveal Distinct Oncogenic Roles for Different Type I Protein Arginine Methyltransferases
Bibliographic record
Abstract
Abstract Protein arginine methyltransferases (PRMT) are generally not mutated in diseased states, but they are overexpressed in a number of cancers, including breast cancer. To address the possible roles of PRMT overexpression in mammary gland tumorigenesis, we generated Cre-activated PRMT1, CARM1, and PRMT6 overexpression mouse models. These three enzymes are the primary type I PRMTs and are responsible for the majority of the asymmetric arginine methylation deposited in the cells. Using either a keratin 5-Cre recombinase (K5-Cre) cross or an MMTV-NIC mouse, we investigated the impact of PRMT overexpression alone or in the context of a HER2-driven model of breast cancer, respectively. The overexpression of all three PRMTs induced hyper-branching of the mammary glands and increased Ki-67 staining. When combined with the MMTV-NIC model, these in vivo experiments provided the first genetic evidence implicating elevated levels of these three PRMTs in mammary gland tumorigenesis, albeit with variable degrees of tumor promotion and latency. In addition, these mouse models provided valuable tools for exploring the biological roles and molecular mechanisms of PRMT overexpression in the mammary gland. For example, transcriptome analysis of purified mammary epithelial cells isolated from bigenic NIC-PRMT1Tg and NIC-PRMT6Tg mice revealed a deregulated PI3K–AKT pathway. In the future, these PRMTTg lines can be leveraged to investigate the roles of arginine methylation in other tissues and tumor model systems using different tissue-specific Cre crosses, and they can also be used for testing the in vivo efficacy of small molecule inhibitors that target these PRMT. Significance: These findings establish Cre-activated mouse models of three different arginine methyltransferases, PRMT1, CARM1, and PRMT6, which are overexpressed in human cancers, providing a valuable tool for the study of PRMT function in tumorigenesis. See related commentary by Watson and Bitler, p. 3
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".