The emergence of protein arginine methyltransferases in skeletal muscle and metabolic disease
Bibliographic record
Abstract
Protein arginine methyltransferases (PRMTs) are a family of enzymes that catalyze the methylation of arginine residues on target proteins and thus alter the stability, localization, or activity of the substrate. In doing so, PRMTs mediate a variety of intracellular functions that are essential for survival. Additionally, PRMT dysregulation is involved in a number of the most prevalent health disorders, including cancer and neurodegenerative and cardiovascular diseases, as well as in the aging process. Investigations of PRMT biology in skeletal muscle cells began in 2002, and since then these enzymes have emerged as regulators of skeletal muscle phenotype determination, maintenance, and remodeling. Specifically, more recent in vivo studies have revealed that PRMTs impact multiple aspects of skeletal muscle biology, including satellite cell function and phenotypic plasticity in response to exercise and disuse. Skeletal muscle plays critically important roles in regulating whole body metabolism, and recent investigations have also begun elucidating PRMT expression and function under conditions of metabolic dysfunction. The goals of this review are to 1) summarize the literature on PRMT biology in skeletal muscle with a particular emphasis on the in vivo evidence and 2) survey PRMTs in metabolic disorders, namely, obesity and type 2 diabetes mellitus. We also identify notable knowledge gaps therein and present opportunities to further expand our understanding of these enzymes so critical to health and disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".