Phosphorylation of protein kinase C sites in NBD1 and the R domain control CFTR channel activation by PKA
Bibliographic record
Abstract
Activation of the cystic fibrosis transmembrane conductance regulator (CFTR) channel by protein kinase A (PKA) is enhanced by protein kinase C (PKC). However, the mechanism of modulation is not known and it remains uncertain whether PKC acts directly on CFTR or through phosphorylation of an ancillary protein. Using excised patches that had been pre-treated with phosphatases, we found that PKC exposure results in much larger PKA-activated currents and shifts the PKA concentration dependence. To examine if these effects are mediated by direct PKC phosphorylation of CFTR, a mutant was constructed in which serines or threonines at nine PKC consensus sequences on CFTR were replaced by alanines (i.e. the '9CA' mutant T582A/T604A/S641A/T682A/S686A/S707A/S790A/T791A/S809A). In excised patches, 9CA channels had greatly reduced responses to PKA (i.e. 5-10 % that of wild-type), which were not enhanced by PKC pre-treatment, although the mutant channels were still functional according to iodide efflux assays. Stimulation of iodide efflux by chlorophenylthio-cAMP (cpt-cAMP) was delayed in cells expressing 9CA channels, and a similar delay was observed when cells expressing wild-type CFTR were treated with the PKC inhibitor chelerythrine. This suggests that weak activation by PKA in excised patches and slow stimulation of iodide efflux from intact cells are specifically due to the loss of PKC phosphorylation. Finally, PKC caused a slight activation of wild-type channels when added to excised patches after phosphatase pre-treatment but had no effect on the mutant. We conclude that direct phosphorylation of CFTR at one or more of the nine sites mutated in 9CA is required for both the partial activation by PKC and for its modulation of CFTR responses to PKA.
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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 | 0.001 |
| Scholarly communication | 0.000 | 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".