Coupling between apical and paracellular transport processesThis paper is one of a selection of papers published in this Special Issue, entitled CSBMCB — Membrane Proteins in Health and Disease.
Bibliographic record
Abstract
Transcellular transport affects the paracellular flux through 2 distinct mechanisms: by determining the driving force and by altering the permeability of the paracellular pathway. Such coordination ensures efficient transepithelial transport by preventing the build-up of large electrical and osmotic gradients. The regulation of paracellular permeability was originally recognized as increased paracellular flux of water and solutes upon the activation of the intestinal Na+-coupled glucose uptake. Despite great advances in the molecular characterization of the tight junctions that form the structural basis of epithelial barrier functions, the mechanisms whereby apical transporters alter the paracellular pathways remains unresolved. Recent studies suggest that myosin-based contractility is central to this coupling. In this minireview, we summarize our current knowledge of paracellular permeability, its regulation by contractility, and the various signaling events that link apical Na+-glucose cotransport to myosin phosphorylation. While the role of myosin phosphorylation appears to be universal, the mechanism(s) whereby apical transport triggers this process is likely cell specific. The current model suggests that in intestinal cells, a key factor is a p38 MAP kinase-induced Na+/H+-exchanger-mediated alkalinization. We propose an alternative, nonexclusive mechanism in kidney tubular cells, in which the key event may be a Na+-cotransport-triggered plasma membrane depolarization, which in turn leads to Rho-mediated myosin phosphorylation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".