mTOR signaling contributes to motor skill learning in mice
Bibliographic record
Abstract
The mammalian target of rapamycin (mTOR) kinase is a critical regulator of mRNA translation and is suspected to be involved in various long-lasting forms of synaptic and behavioral plasticity. However, its role in motor learning and control has never been examined. This study investigated, in mice, the implication of mTOR in the learning processes associated with the accelerating rotarod task. We first observed that the rotarod learning did not alter the levels of total mTOR in the striatum, hippocampus, cerebellum, and anterior cortex of trained mice. However, it increased the levels of phosphorylated mTOR in the striatum and hippocampus exclusively during the first session of training; no change was observed at the second and third sessions. In order to further investigate the potential role of mTOR during motor skill learning, we performed systemic and intrastriatal inhibitions of mTOR using the pharmacological inhibitor rapamycin, as well as a genetic knockdown of striatal mTOR using intrastriatal infusion of mTOR siRNA. These three independent approaches were all associated with a significant reduction in rotarod performances that were reminiscent of impaired consolidation processes. Notably, these treatments did not affect the capacity of mice to execute the pole test, suggesting that mTOR activity was mainly controlling motor learning rather than motor abilities. Moreover, all treatments decreased the levels of phosphorylated 4EBP1 and P70S6K, two molecular downstream targets of mTORC1. Our findings demonstrate that striatal mTOR kinase, via the phosphorylation of 4EBP1 and P70S6K, plays an important role in the cellular and molecular processes involved in motor skill learning.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".