Modulation of NMDA Receptors by Pituitary Adenylate Cyclase Activating Peptide in CA1 Neurons Requires Gα<sub>q</sub>, Protein Kinase C, and Activation of Src
Bibliographic record
Abstract
At CA1 synapses, activation of NMDA receptors (NMDARs) is required for the induction of both long-term potentiation and depression. The basal level of activity of these receptors is controlled by converging cell signals from G-protein-coupled receptors and receptor tyrosine kinases. Pituitary adenylate cyclase activating peptide (PACAP) is implicated in the regulation of synaptic plasticity because it enhances NMDAR responses by stimulating Galphas-coupled receptors and protein kinase A (Yaka et al., 2003). However, the major hippocampal PACAP1 receptor (PAC1R) also signals via Galphaq subunits and protein kinase C (PKC). In CA1 neurons, we showed that PACAP38 (1 nM) enhanced synaptic NMDA, and evoked NMDAR, currents in isolated CA1 neurons via activation of the PAC1R, Galphaq, and PKC. The signaling was blocked by intracellular applications of the Src inhibitory peptide Src(40-58). Immunoblots confirmed that PACAP38 biochemically activates Src. A Galphaq pathway is responsible for this Src-dependent PACAP enhancement because it was attenuated in mice lacking expression of phospholipase C beta1, it was blocked by preventing elevations in intracellular Ca2+, and it was eliminated by inhibiting either PKC or cell adhesion kinase beta [CAKbeta or Pyk2 (proline rich tyrosine kinase 2)]. Peptides that mimic the binding sites for either Fyn or Src on receptor for activated C kinase-1 (RACK1) also enhanced NMDAR in CA1 neurons, but their effects were blocked by Src(40-58), implying that Src is the ultimate regulator of NMDARs. RACK1 serves as a hub for PKC, Fyn, and Src and facilitates the regulation of basal NMDAR activity in CA1 hippocampal 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".