Protein arginine methyltransferase expression, localization, and activity during disuse-induced skeletal muscle plasticity
Bibliographic record
Abstract
Protein arginine methyltransferase 1 (PRMT1), PRMT4, and PRMT5 catalyze the methylation of arginine residues on target proteins. Previous work suggests that these enzymes regulate skeletal muscle plasticity. However, the function of PRMTs during disuse-induced muscle remodeling is unknown. The purpose of our study was to determine whether denervation-induced muscle disuse alters PRMT expression and activity in skeletal muscle, as well as to contextualize PRMT biology within the early disuse-evoked events that precede atrophy, which remain largely undefined. Mice were subjected to 6, 12, 24, 72, or 168 h of unilateral hindlimb denervation. Muscle mass decreased by ~30% after 72 or 168 h of neurogenic disuse, depending on muscle fiber type composition. The expression, localization, and activities of PRMT1, PRMT4, and PRMT5 were modified, exhibiting changes in gene expression and activity that were PRMT-specific. Rapid alterations in canonical muscle atrophy signaling such as forkhead box protein O1, muscle RING-finger protein-1, as well as peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) content, AMP-activated protein kinase (AMPK) and p38 mitogen-activated protein kinase, were observed before measurable decrements in muscle mass. Denervation-induced modifications in AMPK-PRMT1 and PGC-1α-PRMT1 binding revealed a novel, putative PRMT1-AMPK-PGC-1α signaling axis in skeletal muscle. Here, PGC-1α-PRMT1 binding was elevated after 6 h of disuse, whereas AMPK-PRMT1 interactions were reduced following 168 h of denervation. Our data suggest that PRMT biology is integral to the mechanisms that precede and initiate skeletal muscle atrophy during conditions of neurogenic disuse. This study furthers our understanding of the role of PRMTs in governing skeletal muscle plasticity.
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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".