Nerve‐derived agrin cooperates with neural activity to potentiate translational control of skeletal muscle growth via p70 S6K
Bibliographic record
Abstract
The trophic mechanisms by which motor nerves impart growth of skeletal muscle targets are rudimentary. We have shown that muscles exposed to increased neural activation and contractile loading fail to hypertrophy if not provided with suitable trophic chemical support from their motor nerves. When trophically‐challenged and overloaded muscles are treated with recombinant neural agrin, hypertrophic growth is rescued via p70 S6K signaling. Agrin therapy induced extensive phosphorylation of p70 S6K, but not via Akt or PDK‐1 as upstream modulators, suggesting neural agrin binding to its receptor activates p70 S6K, either directly, or indirectly via other yet to be identified signaling elements. We also assessed the contribution of neural activity and nerve trophic factors on p70 S6K phosphorylation in muscles either denervated or paralyzed by TTX‐inactivation of the sciatic nerve. We found p70 S6K to be fully dephosphorylated after denervation but not after TTX, suggesting nerve trophic factors to be major modulators of this signaling pathway. Considering muscle growth is regulated in part by translational mechanisms downstream of Akt/mTOR and by transcriptional mechanisms associated with muscle activation, our data suggest agrin may cooperate with these signaling pathways to converge upon p70 S6K to potentiate translational control of muscle growth. Supported by NSERC and the CRC to RNM.
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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".