Regulation of Kv1.2 Redox-Sensitive Gating by the Transmembrane Lectin LMAN2
Bibliographic record
Abstract
Voltage gated potassium (Kv)1.2 channels influence excitability and action potential propagation in the nervous system. Unlike closely related Kv1 channels, Kv1.2 exhibits highly variable voltage-dependence of gating, attributed to regulation by unidentified extrinsic factors. Variability of Kv1.2 gating is strongly influenced by the extracellular redox potential, and we demonstrate that Kv1.2 currents in dorsal root ganglion sensory neurons exhibit similar variability and redox sensitivity as observed when the channel is heterologously expressed in cell lines. We used a functional screening approach to test the effects of candidate regulatory proteins on Kv1.2 gating, using patch clamp electrophysiology. Among 52 candidate genes tested, we observed that co-expression with the transmembrane lectin LMAN2 led to a pronounced gating shift of Kv1.2 activation to depolarized voltages in CHO and L(tk-) cell lines, accompanied by deceleration of activation kinetics. Overexpression of LMAN2 promoted a slow gating mode of Kv1.2 that mimics the functional outcomes of extracellular reducing conditions, and enhanced sensitivity to extracellular reducing agents. In contrast, shRNA-mediated knockdown of endogenous LMAN2 in cell lines reduced Kv1.2 redox sensitivity and gating variability. Kv1.2 sensitivity to LMAN2 is abolished by mutation of neighboring residues F251 and T252 in the intracellular S2-S3 linker, and these also abolish redox-dependent gating changes, suggesting that LMAN2 influences the same pathway as redox for Kv1.2 modulation. In conclusion, we identified LMAN2 as a candidate regulatory protein that influences redox-dependent modulation of Kv1.2, and clarified the structural elements of the channel that are required for sensitivity.
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 | 0.000 | 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".