Serotonin Activates S6 Kinase in a Rapamycin-Sensitive Manner in<i>Aplysia</i>Synaptosomes
Bibliographic record
Abstract
The identification of tags that can specifically mark activated synapses is important for understanding how long-term synaptic changes can be restricted to specific synapses. The maintenance of synapse-specific facilitation inAplysiasensory to motor neuron cultures can be blocked by inhibitors of translation and by the drug rapamycin, which specifically blocks a signaling pathway that regulates phosphorylation of translational regulators. One important target of rapamycin is the phosphorylation and subsequent activation of S6 kinase. To test whether S6 kinase is the target for the ability of rapamycin to block synapse-specific facilitation inAplysia, we clonedAplysiaS6 kinase, its substrate S6, and the S6 kinase kinase phosphoinositide-dependent kinase 1 (PDK-1). Serotonin, which induces synapse-specific facilitation, increased phosphorylation ofAplysiaS6 kinase at threonine 399 in a rapamycin-sensitive manner inAplysiasynaptosomes. The phosphorylation of threonine 399 by 5-HT was independent of phosphoinositide-3 kinase, dependent on PKA and PKC, and occluded by the phosphatase inhibitor calyculin-A. 5-HT also increased S6 kinase activity and led to increased phosphorylation of S6 in synaptosomes. 5-HT increased levels of S6 in synaptosomes because of a rapamycin-sensitive increase in translation–stabilization of S6.AplysiaPDK-1 bound to and phosphorylatedAplysiaS6 kinase but only modulated phosphorylation of threonine 399 indirectly. These results suggest a mechanism by which the levels of translation factors can be increased specifically at activated synapses generating a long-lasting synaptic tag.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".