Synapse formation changes the rules for desensitization of PKC translocation in<i>Aplysia</i>
Bibliographic record
Abstract
Protein kinase Cs (PKCs) are activated by translocating from the cytoplasm to the membrane. We have previously shown that serotonin-mediated translocation of PKC to the plasma membrane in Aplysia sensory neurons was subject to desensitization, a decrease in the ability of serotonin to induce translocation after previous application of serotonin. In Aplysia, changes in the strength of the sensory-motor neuron synapse are important for behavioral sensitization and PKC regulates a number of important aspects of this form of synaptic plasticity. We have previously suggested that the desensitization of PKC translocation in Aplysia sensory neurons may partially explain the differences between spaced and massed training, as spaced applications of serotonin, a cellular analog of spaced training, cause greater desensitization of PKC translocation than one massed application of serotonin, a cellular analog of massed training. Our previous studies were performed in isolated sensory neurons. In the present study, we monitored translocation of fluorescently-tagged PKC to the plasma membrane in living sensory neurons that were co-cultured with motor neurons to allow for synapse formation. We show that desensitization now becomes similar during spaced and massed applications of serotonin. We had previously modeled the signaling pathways that govern desensitization in isolated sensory neurons. We now modify this mathematical model to account for the changes observed in desensitization dynamics following synapse formation. Our study shows that synapse formation leads to significant changes in the molecular signaling networks that underlie desensitization of PKC translocation.
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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 | 0.000 |
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".