Non‐ionotropic NMDAR signalling activates Panx1 to induce P2X4R‐dependent long‐term depression in the hippocampus
Bibliographic record
Abstract
Abstract In recent years, evidence supporting non‐ionotropic signalling by the NMDA receptor (niNMDAR) has emerged, including roles in long‐term depression (LTD). Here, we investigated whether niNMDAR‐pannexin‐1 (Panx1) contributes to LTD at the CA3–CA1 hippocampal synapse. Using whole‐cell, patch clamp electrophysiology in rat hippocampal slices, we show that a low‐frequency stimulation (3 Hz) of the Schaffer collaterals produces LTD that is blocked by continuous but not transient application of the NMDAR competitive antagonist, MK‐801. After transient MK‐801, LTD involved pannexin‐1 and sarcoma (Src) kinase. We show that pannexin‐1 is not permeable to Ca2+, but probably releases ATP to induce LTD via P2X4 purinergic receptors because LTD after transient MK‐801 application was prevented by 5‐BDBD. Thus, we conclude that niNMDAR activation of Panx1 can link glutamatergic and purinergic pathways to produce LTD following low frequency synaptic stimulation when NMDARs are transiently inhibited. image Key points Differential effect of short‐term D‐APV and MK‐801 application on long‐term depression (LTD) suggests that the NMDA receptor (niNMDAR) contributes to later phases of synaptic depression. niNMDAR LTD involved sarcoma (Src) kinase and pannexin‐1 (Panx1), which is a pathway previously identified to be active during excitotoxicity. Panx1 was not calcium permeable but may contribute to late phase LTD via ATP release. Panx1 blockers prevent LTD, and this was rescued with exogenous ATP application. Inhibition of LTD with 5‐BDBD suggests the downstream involvement of postsynaptic P2X4 receptors.
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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) | 0.002 | 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".