Src inhibition reduces NR2B surface expression and synaptic plasticity in the amygdala
Bibliographic record
Abstract
The Src protein tyrosine kinase plays a central role in the regulation of N-methyl-d-aspartate receptor (NMDAR) activity by regulating NMDAR subunit 2B (NR2B) surface expression. In the amygdala, NMDA-dependent synaptic plasticity resulting from convergent somatosensory and auditory inputs contributes to emotional memory; however, the role of Src tyrosine kinase has not been investigated. We have synthesized a Src-derived peptide, Tat-Src (40-58), that crosses the blood-brain barrier following injection and accumulates intracellularly. Tat-Src (40-58) blocks the interaction of Src with NMDA receptors. Following injection, mice demonstrate impaired amygdala-dependent cued fear conditioning, as well as impairments in an amygdala-dependent nonassociative social recognition task. The Src inhibitor decreased NR2B phosphorylation in amygdala tissue and reduced NR2B surface expression in cultured amygdala neurons with a concomitant reduction in NMDA multimer-containing dendritic puncta. In addition, preincubation of this inhibitory peptide blocked amygdalar long-term potentiation in the lateral to basolateral pathway in vitro. These results indicate that Src is a key regulator of NMDAR trafficking in the amygdala. Furthermore, Src-dependent phosphorylation of NR2B supports amygdala plasticity and amygdalar-dependent learning.
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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".