Regulation of Tyrosine Phosphatase STEP61 by Protein Kinase A during Motor Skill Learning in Mice
Bibliographic record
Abstract
Recently, striatal-enriched protein tyrosine phosphatase (STEP) and its upstream regulator protein kinase A (PKA) have been suspected to play a role in the intracellular mechanisms of fear conditioning and spatial memory. However, whether they contribute to the learning and memory of motor skills is totally unknown. In this study, we have investigated the role of STEP and PKA activities during motor skill learning associated with the accelerating rotarod task. We observed that learning the rotarod task differentially modulated the levels of phosphorylated STEP61 at serine 221, a site directly regulated by PKA, in the hippocampus, motor cortex and striatum. In a second set of experiments, we have pharmacologically inhibited PKA by the injection of Rp-cAMPS directly into the dorsal striatum of mice before rotarod trainings. PKA phosphorylation of STEP prevents the dephosphorylation of STEP substrates, whereas inhibition of PKA promotes STEP activity. Striatal PKA inhibitions dose-dependently impaired mice performances on the accelerating rotarod task. General motor abilities testing revealed an intact motor control in mice treated with 5 and 20 µg of Rp-cAMPS, but not at the highest dose of 40 µg. This suggested that motor learning was selectively affected by PKA inhibition at lower doses. Most notably, striatal inhibition of PKA reduced the levels of phosphorylated STEP61 at serine 221. Our data support that inactivation of STEP61 by the PKA activity is part of the molecular process associated with 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".