Plasma membrane depolarization‐induced ERK activation: role in regulating paracellular permeability of the proximal tubular epithelium
Bibliographic record
Abstract
Both physiological and pathological conditions, including apical ion transport, hypoxia, oxidative stress, and ATP depletion can alter the membrane potential of epithelial cells. We have previously shown that membrane depolarization induces Rho‐Rho kinase (ROK) mediated phosphorylation of myosin light chain (MLC) in proximal tubule epithelial cells and elevates paracellular permeability. Here we show that the permeability increase was absent in cells stably expressing a non‐phosphorylatable, dominant negative MLC. Depolarization, elicited by high extracellular [K + ],– or ouabain, an inhibitor of the Na + /K + ATPase, also activated the Ras‐Raf‐MEK‐ERK pathway. ERK activation was fast, reversible and independent of intracellular Ca 2+ . Immunofluorescent staining revealed that ERK translocates to the cell periphery. The MEK inhibitor PD98059 reduced the depolarization‐induced rise in paracellular permeability. Interestingly, PD98059 also mitigated the depolarization‐induced MLC phosphorylation, raising the possibility that ERK affects paracellular permeability by contributing to increased contractility. In summary, our data suggest that cell contractility, regulated by the Rho‐ROK and the Ras‐ERK pathway might play a key role in epithelial barrier dysfunction caused by sustained depolarization. Support: NSERC, Banting Foundation and Kidney Foundation of Canada.
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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 | 0.000 |
| 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".