Dexamethasone inhibits the action of TNF on ENaC expression and activity
Bibliographic record
Abstract
We have reported that TNF, a proinflammatory cytokine present in several lung pathologies, decreases the expression and activity of the epithelial Na(+) channel (ENaC) by approximately 70% in alveolar epithelial cells. Because dexamethasone has been shown to upregulate ENaC mRNA expression and is well known to downregulate proinflammatory genes, we tested if it could alleviate the effect of TNF on ENaC expression and activity. In cotreatment with TNF, we found that dexamethasone reversed the inhibitory effect of TNF and upregulated alpha, beta, and gammaENaC mRNA expression. When the cells were pretreated for 24 h with TNF before cotreatment, dexamethasone was still able to increase alphaENaC mRNA expression to 1.8-fold above control values. However, in these conditions, beta and gammaENaC mRNA expression was reduced to 47% and 14%, respectively. The potential role of TNF and dexamethasone on alphaENaC promoter activity was tested in A549 alveolar epithelial cells. TNF decreased luciferase (Luc) expression by approximately 25% in these cells, indicating that the strong diminution of alphaENaC mRNA must be related to posttranscriptional events. Dexamethasone raised Luc expression by fivefold in the cells and augmented promoter activity by 2.77-fold in cotreatment with TNF. In addition to its effect on alphaENaC gene expression, dexamethasone was able to maintain amiloride-sensitive current as well as the liquid clearance abilities of TNF-treated cells within the normal range. All these results suggest that dexamethasone alleviates the downregulation of ENaC expression and activity in TNF-treated alveolar epithelial cells.
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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".