Lipid Binding Regulates Synaptic Targeting of PICK1, AMPA Receptor Trafficking, and Synaptic Plasticity
Bibliographic record
Abstract
The targeting and surface expression of membrane proteins are critical to their functions. In neurons, synaptic targeting and surface expression of AMPA-type glutamate receptors were found to be critical for synaptic plasticity such as long-term potentiation and long-term depression (LTD). PICK1 (protein interacting with C kinase 1) is a cytosolic protein that interacts with many membrane proteins, including AMPA receptors via its PDZ (postsynaptic density-95/Discs large/zona occludens-1) domain. Its interactions with membrane proteins regulate their subcellular targeting and surface expression. However, the mechanism by which PICK1 regulates protein trafficking has not been fully elucidated. Here, we show that PICK1 directly binds to lipids, mainly phosphoinositides, via its BAR (Bin/amphiphysin/Rvs) domain. Lipid binding of the PICK1 BAR domain is positively regulated by its PDZ domain and negatively regulated by its C-terminal acidic domain. Mutation of critical residues of the PICK1 BAR domain eliminates its lipid-binding capability. Lipid binding of PICK1 controls the subcellular localization of the protein, because BAR domain mutant of PICK1 has diminished synaptic targeting compared with wild-type PICK1. In addition, the BAR domain mutant of PICK1 does not cluster AMPA receptors. Moreover, wild-type PICK1 enhances synaptic targeting of AMPA receptors, whereas the BAR domain mutant of PICK1 fails to do so. The BAR domain mutant of PICK1 loses its ability to regulate surface expression of the AMPA receptors and impairs expression of LTD in hippocampal neurons. Together, our findings indicate that the lipid binding of the PICK1 BAR domain is important for its synaptic targeting, AMPA receptor trafficking, and synaptic plasticity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".