TRPC channels underlie cholinergic plateau potentials and persistent activity in entorhinal cortex
Bibliographic record
Abstract
Persistent neuronal activity lasting seconds to minutes has been proposed to allow for the transient storage of memory traces in entorhinal cortex and thus could play a major role in working memory. Nonsynaptic plateau potentials induced by acetylcholine account for persistent firing in many cortical and subcortical structures. The expression of these intrinsic properties in cortical neurons involves the recruitment of a non-selective cation conductance. Despite its functional importance, the identity of the cation channels remains unknown. Here we show that, in layer V of rat medial entorhinal cortex, muscarinic receptor-evoked plateau potentials and persistent firing induced by carbachol require phospholipase C activation, decrease of PIP(2) levels, and permissive intracellular Ca(2+) concentrations. Plateau potentials and persistent activity were suppressed by the generic nonselective cation channel blockers FFA (100 μM) and 2-APB (100 μM), as well as by the TRPC channel blocker SKF-96365 (50 μM). However, plateau potentials were not affected by the TRPV channel blocker ruthenium red (40 μM). The TRPC3/6/7 activator OAG did not induce or enhance persistent firing evoked by carbachol. Voltage clamp recordings revealed a carbachol-activated, nonselective cationic current with a heteromeric TRPC-like phenotype. Moreover, plateau potentials and persistent firing were inhibited by intracellular application of the peptide EQVTTRL that disrupts interactions between the C-terminal domain of TRPC4/5 subunits and associated PDZ proteins. Altogether, our data suggest that TRPC cation channels mediating persistent muscarinic currents significantly contribute to the firing and mnemonic properties of projection neurons in the entorhinal cortex.
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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) | 0.001 | 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".