Reduced passive force in skeletal muscles lacking protein arginylation
Bibliographic record
Abstract
Arginylation is a posttranslational modification that plays a global role in mammals. Mice lacking the enzyme arginyltransferase in skeletal muscles exhibit reduced contractile forces that have been linked to a reduction in myosin cross-bridge formation. The role of arginylation in passive skeletal myofibril forces has never been investigated. In this study, we used single sarcomere and myofibril measurements and observed that lack of arginylation leads to a pronounced reduction in passive forces in skeletal muscles. Mass spectrometry indicated that skeletal muscle titin, the protein primarily linked to passive force generation, is arginylated on five sites located within the A band, an important area for protein-protein interactions. We propose a mechanism for passive force regulation by arginylation through modulation of protein-protein binding between the titin molecule and the thick filament. Key points are as follows: 1) active and passive forces were decreased in myofibrils and single sarcomeres isolated from muscles lacking arginyl-tRNA-protein transferase (ATE1). 2) Mass spectrometry revealed five sites for arginylation within titin molecules. All sites are located within the A-band portion of titin, an important region for protein-protein interactions. 3) Our data suggest that arginylation of titin is required for proper passive force development in skeletal muscles.
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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) | 0.001 | 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) | 0.002 | 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".