The glycosylation of the extracellular loop of<i>β</i>2 subunits diversifies functional phenotypes of BK Channels
Bibliographic record
Abstract
- and voltage-activated potassium (MaxiK or BK) channels are composed of a pore-forming α subunit (Slo) and 4 types of auxiliary β subunits or just a pore-forming α subunit. Although multiple N-linked glycosylation sites in the extracellular loop of β subunits have been identified, very little is known about how glycosylation influences the structure and function of BK channels. Using a combination of site-directed mutagenesis, western blot and patch-clamp recordings, we demonstrated that 3 sites in the extracellular loop of β2 subunit are N-glycosylated (N-X-T/S at N88, N96 and N119). Glycosylation of these sites strongly and differentially regulate gating kinetics, outward rectification, toxin sensitivity and physical association between the α and β2 subunits. We constructed a model and used molecular dynamics (MD) to simulate how the glycosylation facilitates the association of α/β2 subunits and modulates the dimension of the extracellular cavum above the pore of the channel, ultimately to modify biophysical and pharmacological properties of BK channels. Our results suggest that N-glycosylation of β2 subunits plays crucial roles in imparting functional heterogeneity of BK channels, and is potentially involved in the pathological phenotypes of carbohydrate metabolic diseases.
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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".