Claudin-4 forms a paracellular barrier, revealing the interdependence of claudin expression in the loose epithelial cell culture model opossum kidney cells
Bibliographic record
Abstract
The effect of claudins on paracellular fluxes has been predominantly studied in either Madin-Darby canine kidney (MDCK) or LLCPK cells. Neither model system has a very low transepithelial resistance (TER) as observed in leaky epithelia. Moreover, results from one model system are not always consistent with another. Opossum kidney (OK) cells form tight junctions yet have a very low TER. We therefore set out to characterize the paracellular transport properties of this cell culture model. Ussing chamber dilution potential measurements revealed that OK cells exhibit a very low TER (11.7 ± 1.4 Ω·cm(2)), slight cation selectivity (P(Na)/P(Cl) = 1.10 ± 0.01), and the Eisenman permeability sequence IV; the permeability of monovalent cations ranking K(+) > Cs(+) > Rb(+) > Na(+) > Li(+). Quantitative real-time PCR studies found that OK cells endogenously express claudin-4 > -1 > -6 > -20 > -9 > -12 > -11 > -15. Overexpression of claudin-4 significantly increased TER, decreased Na(+) and Cl(-) permeability, and increased levels of claudin-1, -6, and -9 mRNA. Knockdown of claudin-4 in the overexpressing cells significantly decreased TER without altering claudin expression; thus claudin-4 forms a barrier in OK cells. Knockdown of endogenous claudin-4 decreased claudin-1, -9, and -12 expression without altering TER. Claudin-2 overexpression decreased TER, significantly increased Na(+) and Cl(-) permeability, and decreased claudin-12 and -6 expression. Together these results demonstrate that claudin expression is tightly coupled in OK cells.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".