Apical membrane insertion of CFTR is increased by protein kinase C activation
Bibliographic record
Abstract
CFTR channels regulate ion and fluid transport across respiratory epithelium, however the signaling pathways that govern their translocation and insertion into the apical membrane are poorly understood. We tested the hypothesis that PKC phosphorylation, which modulates responsiveness of the CFTR channel to PKA, also influences its insertion into the apical membrane of polarized Calu‐3 cells. Monolayers were cultured on porous supports and biotinylated by apical exposure to sulfo‐NHS‐SS‐biotin before, or after, activation of PKC or PKA using PMA or forskolin, respectively. Apical membrane proteins were recovered on streptavidin beads and the amount of CFTR in the pulldowns was assessed by Western blotting. The physiological agonist VIP, which stimulates both PKA and PKC activities through a G protein coupled receptor, was also tested. PMA increased the amount of CFTR in the pulldowns whereas forskolin had no effect. Moreover, the increase in surface expression induced by PMA was blocked by the PKC inhibitor Bis I, and was correlated with larger short‐circuit current responses to forskolin+IBMX stimulation in parallel Ussing chamber studies. Bis I also reduced current responses to VIP. We conclude that PKC activation increases functional CFTR protein in the apical membrane as part of an integrated response to VIP and perhaps other secretagogues. Supported by the Breathe program of the Canadian CF Foundation and CIHR
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".