Tumor necrosis factor-α induces a biphasic change in claudin-2 expression in tubular epithelial cells: role in barrier functions
Bibliographic record
Abstract
The inflammatory cytokine tumor necrosis factor-α (TNF-α) is a pathogenic factor in acute and chronic kidney disease. TNF-α is known to alter expression of epithelial tight junction (TJ) proteins; however, the underlying mechanisms and the impact of this effect on epithelial functions remain poorly defined. Here we describe a novel biphasic effect of TNF-α on TJ protein expression. In LLC-PK1 tubular cells, short-term (1-6 h) TNF-α treatment selectively elevated the expression of the channel-forming TJ protein claudin-2. In contrast, prolonged (>8 h) TNF-α treatment caused a marked downregulation in claudin-2 and an increase in claudin-1, -4, and -7. The early increase and the late decrease in claudin-2 expression involved distinct mechanisms. TNF-α slowed claudin-2 degradation through ERK, causing the early increase. This increase was also mediated by the EGF receptor and RhoA and Rho kinase. In contrast, prolonged TNF-α treatment reduced claudin-2 mRNA levels and promoter activity independent from these signaling pathways. Electric Cell-substrate Impedance Sensing measurements revealed that TNF-α also exerted a biphasic effect on transepithelial resistance (TER) with an initial decrease and a late increase. Thus there was a good temporal correlation between TNF-α-induced claudin-2 protein and TER changes. Indeed, silencing experiments showed that the late TER increase was at least in part caused by reduced claudin-2 expression. Surprisingly, however, claudin-2 silencing did not prevent the early TER drop. Taken together, the TNF-α-induced changes in claudin-2 levels might contribute to TER changes and could also play a role in newly described functions of claudin-2 such as proliferation regulation.
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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 | 0.001 |
| 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".