Metformin alters Airway Epithelial Tight Junction Protein Abundance
Bibliographic record
Abstract
Glucose concentration in the airway surface liquid (ASL) is normally ~0.4mM. Raised blood glucose elevates ASL glucose (up to 4.0mM) which increases the risk of respiratory infection, particularly with methicillin‐resistant Staphylococcus aureus and Pseudomonas aeruginosa . Previously, in an H441/ S. aureus co‐culture model we showed that glucose diffused across the epithelium into the ASL via paracellular pathways and was utilised by the bacteria to support their growth. Treatment with metformin reduced paracellular permeability to glucose and supressed glucose‐induced S. aureus growth. In this study, we investigated the effect of metformin on airway epithelial tight junction protein abundance in epithelial/bacterial co‐cultures using western blot and immunocytochemical analysis. S. aureus (8325‐4) addition to the apical surface of H441 monolayers for 7 hours decreased E‐cadherin (p<0.001,n=13) and occludin abundance (p<0.05,n=5), but had no effect on claudin‐1 (p>0.05,n=3). Pre‐treatment of H441 monolayers with metformin (1 mM; 18 hours) prior to S. aureus addition, had no effect on E‐cadherin, but enhanced occludin abundance (p<0.05,n=5). P. aeruginosa (PA01) addition to the surface of Calu‐3 airway epithelial monolayers reduced transepithelial resistance (TEER) (from 669±27 to 489±20Ω.cm 2 ; p<0.05, n=5). Metformin attenuated this effect (551±16Ω.cm) and inhibited glucose‐induced bacterial growth (both p<0.05, n=4). P. aeruginosa had no effect on E‐cadherin or claudin‐1 abundance in Calu‐3 monolayers, but abolished occludin expression (p<0.0001, n=3). Metformin did not reverse the effect on occludin but increased claudin abundance in the presence and absence of P. aeruginosa (p<0.01, n=3)
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".