Parvalbumin protein controls inhibitory tone in the spinal cord
Bibliographic record
Abstract
Abstract The nervous system processes sensory information by relying on the precise coordination of neuronal networks and their specific synaptic firing patterns. In the spinal cord, disturbances to the firing pattern of the tonic firing parvalbumin (PV)-expressing inhibitory interneuron (PV neurons) disrupt the ability of the dorsal horn to integrate touch information and may result in pathological phenotypes. The parvalbumin protein (PVp) is a calcium (Ca 2+ )-binding protein that buffers the accumulation of Ca 2+ following a train of action potential to allow for tonic firing. Here, we find that peripheral nerve injury causes a decrease in PVp expression in PV neurons and makes them transition from tonic to adaptive firing. We also show that reducing the expression of PVp causes otherwise healthy adult mice to develop mechanical allodynia and causes their PV neurons to lose their high frequency firing pattern. We show that this frequency adaptation is mediated by activation of SK channels on PV neurons. Further, we show their tonic firing can be partially restored after nerve injury by selectively inhibiting the SK2 channels of PV neurons. We also reveal that a decrease in the transcriptional coactivator, PGC-1α, causes decrease PVp expression and the development of mechanical allodynia. By preventing the decrease in PVp expression before nerve injury, we were able to protect mice from developing mechanical allodynia. Our results indicate an essential role for PVp-mediated calcium buffering in PV neuron firing activity and the development of mechanical allodynia after nerve injury.
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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".