Selective Phosphorylation of AMPA Receptor Contributes to the Network of Long-Term Potentiation in the Anterior Cingulate Cortex
Bibliographic record
Abstract
Phosphorylation of AMPA receptor GluA1 plays important roles in synaptic potentiation. Most previous studies have been performed in the hippocampus, while the roles of GluA1 phosphorylation in the cortex remain unknown. Here we investigated the involvement of the phosphorylation of GluA1 in the LTP in the anterior cingulate cortex (ACC) using mice with a GluA1 knock-in mutation at the PKA phosphorylation site serine 845 (s845A) or CaMKII/PKC phosphorylation site serine 831 (s831A). The network LTP, which is constructed by multiple recordings of LTP at different locations within the ACC, was also investigated. We found that the expression of LTP and network LTP was significantly impaired in the s845A mice, but not in the s831A mice. By contrast, basal synaptic transmission and NMDA receptor-mediated responses were not affected. Furthermore, to uncover potential information under the current acquired data, a new method for reconstruction and better visualization of the signals was developed to observe the spatial localizations and dynamic temporal changes of fEPSP signals and multiple LTP responses within the ACC circuit. Our results provide strong evidence that PKA phosphorylation of the GluA1 is important for the network LTP expression in the ACC. SIGNIFICANCE STATEMENT Previous studies have shown that PKA and PKC phosphorylation of AMPA receptor GluA1 plays critical roles in LTP in the hippocampus, while the roles of GluA1 phosphorylation in the cortex remain unknown. In the present study, by combining a 64-channel multielectrode system and a novel analysis and visualization method, we observed the accurate spatial localization and dynamic temporal changes of network fEPSP signals and LTP responses within the ACC circuit and found that PKA phosphorylation, but not PKC phosphorylation, of the GluA1 is required for LTP in the ACC.
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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) | 0.001 | 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".