Tumor Necrosis Factor‐α Increases Claudin‐1, 4, and 7 Expression in Tubular Cells: Role in Permeability Changes
Bibliographic record
Abstract
cells TNFα caused a biphasic change in transepithelial resistance (TER) consisting of an early drop and recovery, followed by a late increase. However, the underlying mechanisms and the role of specific claudins in the TER effect remained incompletely understood. Here we sought to define how TNFα affects claudins 1, 4, and 7 in tubular cells and to correlate their changes with the TER effect. We show that TNFα elevates total and surface levels of Cldn-1, 4, and 7, and increases their mRNA expression through the ERK and JNK pathways. Further, JNK is also important for TNFα-induced changes in claudin-2 expression. Continuous monitoring of TER using Electric cell-substrate impedance sensing (ECIS) reveals that the two phases of the TNFα effect are differently regulated. Specifically, inhibition of the ERK or JNK pathways prevent the late TER increase, but not the early TER effect. Silencing experiments also show that Cldn-1 is necessary for the early TNFα-induced TER change, while all three claudins appear to contribute to the late TER increase. In summary, we define a central role for ERK and JNK in TNFα-induced altered claudin expression and barrier tightening. Together, our current and previous works show that the TNFα-induced early TER effect requires claudin-1, while claudin-2 decrease is a significant mediator of the late TER increase, and elevation in claudin-1, 4, and 7 contribute to a smaller extent. J. Cell. Physiol. 232: 2210-2220, 2017. © 2016 Wiley Periodicals, Inc.
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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".