Visualizing the CFTR and ENaC association in living cells
Bibliographic record
Abstract
The molecular events underlying CFTR‐ENaC coupling, two major transport proteins implicated in pathophysiology of Cystic Fibrosis, is obscure. In an attempt to examine the intermolecular interaction of these proteins we used fluorescence resonance energy transfer (FRET) microscopy. FRET is distance‐dependent imaging approach to measure protein‐protein interactions. To measure FRET we used acceptor bleaching, and monitored the donor's fluorescence lifetime by frequency domain fluorescence lifetime imaging microscopy (FLIM). These FRET measurements were complemented by coimmunoprecipitation experiments. Apparent FRET efficiencies ( E ) averaged ~8% but values as high as ~17% were observed, significant when compared to the negative control and comparable to E reported in other FRET studies. No appreciable FRET signal was observed when ECFP‐ClC1 was co‐transfected with αβ γ‐ENaC (<1%). When CFTR and ENaC subunits were over‐expressed in HEK293T cells, we found that β‐ENaC could be co‐immunoprecipitated with CFTR. Under the same condition, we were unable to co‐immunoprecipitate β‐ENaC with ECFP‐ClC1. Our results place CFTR and ENaC in close proximity to each other, suggestive of direct interaction between these two proteins. Supported by NIH 2RO1‐DK37206‐15, NIH P50 DK53090‐05.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".