Volume regulation in in situ human airway epithelial (calu‐3) cells: Focal Adhesion Kinase (FAK) and NKCC1
Bibliographic record
Abstract
Ion transport by FAK‐activated Na + ‐K + ‐2Cl − co‐transporter (NKCC1) and cystic fibrosis transmembrane conductance regulators (CFTR) is essential in cell regulatory volume responses in teleost epithelia (Marshall et al. 2005,2009). FAK‐related protein tyrosine kinase 2 (PYK2) activates CFTR in Calu‐3 cells (Liang et al. 2011), however, FAK regulation of NKCC or CFTR has not been investigated in a mammalian system. We aim to examine the possible role of FAK in regulating NKCC or CFTR ion‐transport during cell volume regulation in human airway (Calu‐3) cells. To model in vivo airway conditions, Calu‐3 cells were cultured into confluent monolayers on glass coverslips in minimal essential medium (MEM) at 37°C with 5% CO 2 /95% air. Cell volume regulation was examined using, 4–7 day‐old monolayers stained with BODIPY‐ceramide vital dye (1:1000 in isotonic MEM solution for 1hr) and mounted in a perfusion chamber. Monolayers were perfused with combinations of isotonic MEM 285 mOsm/kg (control), and NaCl‐equilibrated 360 mOsm/kg hypertonic MEM (hypersaline lung lavages use 3–7% NaCl aq ). Cell height (volume) changes were recorded over 1hr using XZt confocal micrographs. Hypertonic shock caused Calu‐3 cells shrinkage over 1–7 mins followed by regulatory volume increase (RVI) (contrasting previous reports) over 45‐mins (n=18 cells; 6 monolayers). To determine if NKCC1 or FAK‐facilitated ion transport facilitated RVI, monolayers were treated with NKCC1 inhibitor bumetanide and a FAK‐inhibitor TAE226. Inhibition of NKCC1 and FAK altered volume regulation patterns suggesting a critical mediation of airway cell volume regulation by NKCC1 and FAK. Funded by NSERC and NSHRF.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".