Roles of LRRC26 as an auxiliary γ1-subunit of large-conductance Ca<sup>2+</sup>-activated K<sup>+</sup> channels in bronchial smooth muscle cells
Bibliographic record
Abstract
concentration. Although leucine-rich repeat-containing (LRRC) proteins have been identified as novel auxiliary γ-subunits of the BK channel (BKγ) in several cell types, its physiological roles in SMCs are unclear. The BKγ expression patterns in selected SM tissues were examined using real-time PCR analyses and Western blotting. The functional contribution of BKγ1 to BK channel activity was examined by whole cell patch-clamp in SMCs and heterologous expression systems. BKγ1 expression in mouse bronchial SMCs (mBSMCs) was higher than in other several SMC types. Coimmunoprecipitation and total internal reflection fluorescence imaging analyses revealed molecular interaction between BKα and BKγ1 in mBSMCs. Under voltage-clamp, steady-state activation of BK channel currents at pCa 8.0 in mBSMCs occurred in a voltage range comparable to that of reconstituted BKα/BKγ1 complex. However, this range was much more negative than in mouse aortic SMCs (mASMCs) or in HEK293 cells expressing BKα alone and β-subunit (BKβ1). Mallotoxin, a selective activator of BK channel that lacks BKγ1, dose-dependently activated BK currents in mASMCs but not in mBSMCs. The abundant expression of BKγ1 in mBSMCs extensively facilitates BK channel activity to keep the resting membrane potential at negative values and prevents contraction under physiological conditions.
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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) | 0.001 | 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".