Interaction between 2 extracellular loops influences the activity of the cystic fibrosis transmembrane conductance regulator chloride channel
Bibliographic record
Abstract
Activity of the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel is thought to be controlled by cytoplasmic factors. However, recent evidence has shown that overall channel activity is also influenced by extracellular anions that interact directly with the extracellular loops (ECLs) of the CFTR protein. Very little is known about the structure of the ECLs or how substances interacting with these ECLs might affect CFTR function. We used patch-clamp recording to investigate the accessibility of cysteine-reactive reagents to cysteines introduced throughout ECL1 and 2 key sites in ECL4. Furthermore, interactions between ECL1 and ECL4 were investigated by the formation of disulfide crosslinks between cysteines introduced into these 2 regions. Crosslinks could be formed between R899C (in ECL4) and a number of sites in ECL1 in a manner that was dependent on channel activity, suggesting that the relative orientation of these 2 loops changes on activation. Formation of these crosslinks inhibited channel function, suggesting that relative movement of these ECLs is important to normal channel function. Implications of these findings for the effects of mutations in the ECLs that are associated with cystic fibrosis and interactions with extracellular substances that influence channel activity are discussed.
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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".