Lidocaine Promotes the Trafficking and Functional Expression of Na<sub>v</sub>1.8 Sodium Channels in Mammalian Cells
Bibliographic record
Abstract
Nociceptive neurons of the dorsal root ganglion (DRG) express a combination of rapidly gating TTX-sensitive and slowly gating TTX-resistant Na currents, and the channels that produce these currents have been cloned. The Na(v)1.7 and Na(v)1.8 channels encode for the rapidly inactivating TTX-sensitive and slowly inactivating TTX-resistant Na currents, respectively. Although the Na(v)1.7 channel expresses well in cultured mammalian cell lines, attempts to express the Na(v)1.8 channel using similar approaches has been met with limited success. The inability to heterologously express Na(v)1.8 has hampered detailed characterization of the biophysical properties and pharmacology of these channels. In this study, we investigated the determinants of Na(v)1.8 expression in tsA201 cells, a transformed variant of HEK293 cells, using a combination of biochemistry, immunochemistry, and electrophysiology. Our data indicate that the unusually low expression levels of Na(v)1.8 in tsA201 cells results from a trafficking defect that traps the channel protein in the endoplasmic reticulum. Incubating the cultured cells with the local anesthetic lidocaine dramatically enhanced the cell surface expression of functional Na(v)1.8 channels. The biophysical properties of the heterologously expressed Na(v)1.8 channel are similar but not identical to those of the TTX-resistant Na current of native DRG neurons, recorded under similar conditions. Our data indicate that the lidocaine acts as a molecular chaperone that promotes efficient trafficking and increased cell surface expression of Na(v)1.8 channels.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".