Plasma membrane depolarization: a possible coupling factor between epithelial transcellular and paracellular transport
Bibliographic record
Abstract
The coordinated transport through transcellular and paracellular pathways ensures efficient transepithelial flux of water and solutes. Despite the fact that the coupling between these processes is of vital importance, their link is not well understood. To gain insight into the mechanism we asked whether 1) depolarization, induced by apical electrogenic transport processes, can serve as a signal coupling transcellular and paracellular transport; 2) increased cell contractility might play a role in this process. Paracellular permeability across confluent monolayer of proximal tubular (LLC‐PK1) cells was followed as apical‐to‐basolateral transport of 4 kDa FITC‐dextran. We show that depolarization, elicited either by high extracellular [K + ], or by glucose or alanine, substrates of apical electrogenic transporters, substantially increased paracellular permeability. The effect of glucose required Na + ‐dependent cotransport, since 2‐deoxy‐glucose, which is not a substrate of the Na + /glucose cotransporter, failed to increase paracellular permeability. Importantly, depolarization was accompanied with Rho activation and Rho kinase (ROK)‐dependent MLC phosphorylation. Inhibitors of ROK and myosin ATPase (Y27632, and blebbistatin) partially prevented the depolarization‐induced rise in paracellular permeability, suggesting that the Rho‐ROK‐MLC pathway and the consequent increase in contractility contribute to the effect. Thus, plasma membrane depolarization is a good candidate as a coupling signal between apical and paracellular transport. Supported by Canadian Institute of Health Research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".