Protein arginine methyltransferases PRMT1, PRMT4/CARM1 and PRMT5 have distinct functions in control of osteoblast differentiation
Bibliographic record
Abstract
Osteogenic differentiation of mesenchymal cells is controlled by epigenetic enzymes that regulate post-translational modifications of histones. Compared to acetyl or methyltransferases, the physiological functions of protein arginine methyltransferases (PRMTs) in osteoblast differentiation remain minimally understood. Therefore, we surveyed the expression and function of all nine mammalian PRMT members during osteoblast differentiation. RNA-seq gene expression profiling shows that Prmt1, Prmt4/Carm1 and Prmt5 represent the most prominently expressed PRMT subtypes in mouse calvarial bone and MC3T3 osteoblasts as well as human musculoskeletal tissues and mesenchymal stromal cells (MSCs). Based on effects of siRNA depletion, it appears that PRMT members have different functional effects: (i) loss of Prmt1 stimulates and (ii) loss of Prmt5 decreases calcium deposition of mouse MC3T3 osteoblasts, while (iii) loss of Carm1 is inconsequential for calcium deposition. Decreased Prmt5 suppresses expression of multiple genes involved in mineralization (e.g., Alpl, Ibsp, Phospho1) consistent with a positive role in osteogenesis. Depletion of Prmt1, Carm1 and Prmt5 has intricate but modest time-dependent effects on the expression of a panel of osteoblast differentiation and proliferation markers but does not change mRNA levels for select epigenetic regulators (e.g., Ezh1, Ezh2, Brd2 and Brd4). Treatment with the Class I PRMT inhibitor GSK715 enhances extracellular matrix mineralization of MC3T3 cells, while blocking formation of H3R17me2a but not H4R3me2a marks. In sum, Prmt1, Carm1 and Prmt5 have distinct biological roles during osteoblast differentiation, and different types histone H3 and H4 arginine methylation may contribute to the chromatin landscape during osteoblast differentiation.
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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 | 0.000 |
| 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".