Modulation of Ca<sub>v</sub>3.2 T-type calcium channel permeability by asparagine-linked glycosylation
Bibliographic record
Abstract
Low-voltage-gated T-type calcium channels are expressed throughout the nervous system where they play an essential role in shaping neuronal excitability. Defects in T-type channel expression have been linked to various neuronal disorders including neuropathic pain and epilepsy. Currently, little is known about the cellular mechanisms controlling the expression and function of T-type channels. Asparagine-linked glycosylation has recently emerged as an essential signaling pathway by which the cellular environment can control expression of T-type channels. However, the role of N-glycans in the conducting function of T-type channels remains elusive. In the present study, we used human Cav3.2 glycosylation-deficient channels to assess the role of N-glycosylation on the gating of the channel. Patch-clamp recordings of gating currents revealed that N-glycans attached to hCav3.2 channels have a minimal effect on the functioning of the channel voltage-sensor. In contrast, N-glycosylation on specific asparagine residues may have an essential role in the conducting function of the channel by enhancing the channel permeability and / or the pore opening of the channel. Our data suggest that modulation of N-linked glycosylation of hCav3.2 channels may play an important physiological role, and could also support the alteration of T-type currents observed in disease states.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".