Ca<sup>2+</sup>removal by the plasma membrane Ca<sup>2+</sup>-ATPase influences the contribution of mitochondria to activity-dependent Ca<sup>2+</sup>dynamics in<i>Aplysia</i>neuroendocrine cells
Bibliographic record
Abstract
After Ca(2+) influx, mitochondria can sequester Ca(2+) and subsequently release it back into the cytosol. This form of Ca(2+)-induced Ca(2+) release (CICR) prolongs Ca(2+) signaling and can potentially mediate activity-dependent plasticity. As Ca(2+) is required for its subsequent release, Ca(2+) removal systems, like the plasma membrane Ca(2+)-ATPase (PMCA), could impact CICR. Here we examine such a role for the PMCA in the bag cell neurons of Aplysia californica CICR is triggered in these neurons during an afterdischarge and is implicated in sustaining membrane excitability and peptide secretion. Somatic Ca(2+) was measured from fura-PE3-loaded cultured bag cell neurons recorded under whole cell voltage clamp. Voltage-gated Ca(2+) influx was elicited with a 5-Hz, 1-min train, which mimics the fast phase of the afterdischarge. PMCA inhibition with carboxyeosin or extracellular alkalization augmented the effectiveness of Ca(2+) influx in eliciting mitochondrial CICR. A Ca(2+) compartment model recapitulated these findings and indicated that disrupting PMCA-dependent Ca(2+) removal increases CICR by enhancing mitochondrial Ca(2+) loading. Indeed, carboxyeosin augmented train-evoked mitochondrial Ca(2+) uptake. Consistent with their role on Ca(2+) dynamics, cell labeling revealed that the PMCA and mitochondria overlap with Ca(2+) entry sites. Finally, PMCA-dependent Ca(2+) extrusion did not impact endoplasmic reticulum-dependent Ca(2+) removal or release, despite the organelle residing near Ca(2+) entry sites. Our results demonstrate that Ca(2+) removal by the PMCA influences the propensity for stimulus-evoked CICR by adjusting the amount of Ca(2+) available for mitochondrial Ca(2+) uptake. This study highlights a mechanism by which the PMCA could impact activity-dependent plasticity in the bag cell neurons.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".