Tumour necrosis factor alpha and interferon gamma induce transcriptional downregulation of aquaporin 3 RNA expression through distinct mechanisms (893.28)
Bibliographic record
Abstract
Although aquaporins (AQP) are known to be involved in water movement, the role of AQP3 in the barrier dysfunction that characterizes inflammatory bowel diseases remains unknown. We hypothesized that TNFα and IFNγ were involved in the transcriptional inhibition of AQP3 RNA expression in intestinal inflammation. Methods: AQP3 expression was assessed in C57Bl/6 mice administered dextran sodium sulfate (DSS) to induce colonic inflammation. In vitro , the human adenocarcinoma cell line HT29 was treated with either TNFα or IFNγ and AQP3 pre‐mRNA and mRNA expression were assessed by real‐time RT‐PCR. Cytokine‐responsive transcriptional elements were elucidated using AQP3 promoter driven firefly luciferase expression. Results: AQP3 was decreased early in DSS colitis, with diminished basolateral membrane staining in epithelial cells lining colonic crypts. Similarly, TNFα or IFNγ‐treated HT29 cells had decreased AQP3 pre‐mRNA expression at 2 hr, followed by decreased mRNA expression at 6‐12 hr. IFNγ‐induced transcriptional inhibition of AQP3 expression was reversed using a broad‐spectrum JAK inhibitor (JAK Inhibitor I, 10 µM), but not a JAK2 specific inhibitor (JAK2 Inhibitor II, 10 µM). Truncated AQP3 promoter constructs revealed an IFNγ‐responsive transcriptional element in the 251bp region upstream of the TATA box and a transcriptional suppressor in the 251 ‐ 666bp region upstream of the TATA box. In contrast, TNFα‐induced transcriptional inhibition of AQP3 expression was not reversed by inhibitors of the PI3K, AKT, NF‐κB, ERK/MAPK and p38 MAPK pathways (LY294002, 10 µM; Triciribine, 1 µM; BAY11‐7082, 30 µM; U0126, 10 µM and SB203580, 10 µM respectively). Grant Funding Source : CCFC, CIHR & AIHS
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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) | 0.002 | 0.001 |
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".