PKC Enhances the Capacity for Secretion by Rapidly Recruiting Covert Voltage-Gated Ca<sup>2+</sup>Channels to the Membrane
Bibliographic record
Abstract
It is unknown whether neurons can dynamically control the capacity for secretion by promptly changing the number of plasma membrane voltage-gated Ca(2+) channels. To address this, we studied peptide release from the bag cell neurons of Aplysia californica, which initiate reproduction by secreting hormone during an afterdischarge. This burst engages protein kinase C (PKC) to trigger the insertion of a covert Ca(2+) channel, Apl Cav2, alongside a basal channel, Apl Cav1. The significance of Apl Cav2 recruitment to secretion remains undetermined; therefore, we used capacitance tracking to assay secretion, along with Ca(2+) imaging and Ca(2+) current measurements, from cultured bag cell neurons under whole-cell voltage-clamp. Activating PKC with the phorbol ester, PMA, enhanced Ca(2+) entry, and potentiated stimulus-evoked secretion. This relied on channel insertion, as it was occluded by preventing Apl Cav2 engagement with prior whole-cell dialysis or the cytoskeletal toxin, latrunculin B. Channel insertion reduced the stimulus duration and/or frequency required to initiate secretion and strengthened excitation-secretion coupling, indicating that Apl Cav2 accesses peptide release more readily than Apl Cav1. The coupling of Apl Cav2 to secretion also changed with behavioral state, as Apl Cav2 failed to evoke secretion in silent neurons from reproductively inactive animals. Finally, PKC also acted secondarily to enhance prolonged exocytosis triggered by mitochondrial Ca(2+) release. Collectively, our results suggest that bag cell neurons dynamically elevate Ca(2+) channel abundance in the membrane to ensure adequate secretion during the afterdischarge.
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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 | 0.001 | 0.001 |
| 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".