Prostaglandin E<sub>2</sub>modulates TNF-α-induced MCP-1 synthesis in pancreatic acinar cells in a PKA-dependent manner
Bibliographic record
Abstract
Cyclooxygenase (COX)-2 is increased in human chronic pancreatitis. We recently demonstrated in a model of chronic pancreatitis (WBN/Kob rat) that inhibition of COX-2 activity reduces and delays pancreatic inflammation and fibrosis. Monocyte chemoattractant protein (MCP)-1 mRNA and PGE(2) were significantly reduced, correlating with a decreased infiltration of macrophages. MCP-1 plays an important role in the recruitment of macrophages to the site of tissue injury. The aim of our study is to identify mechanisms by which macrophages and acinar cells maintain an inflammatory reaction. The expression profile of E prostanoid receptors EP(1-4) and MCP-1 was analyzed by RT-PCR from pancreatic specimens and AR42J cells. MCP-1 secretion was detected by ELISA from rat pancreatic lobuli. We determined EP(1-4) mRNA levels in WBN/Kob rats with chronic pancreatic inflammation. Individual isoforms were highly increased in rat pancreas, concurrent with MCP-1 mRNA expression. In supernatants of pancreatic lobuli and AR42J cells, MCP-1 was detectable by ELISA. In the presence of TNF-alpha, MCP-1 was upregulated. Coincubation with PGE(2) enhanced the TNF-alpha-induced MCP-1 synthesis significantly. Similarly, TNF-alpha mRNA was synergistically upregulated by TNF-alpha and PGE(2). Furthermore, the synergistic effect of TNF-alpha and PGE(2) was abolished by inhibition of PKA but not of PKC. We conclude that EP receptors are upregulated during chronic pancreatic inflammation. PGE(2) modulates the TNF-alpha-induced MCP-1 synthesis and secretion from acinar cells. This synergistic effect is controlled by PKA. This mechanism might explain the COX-2-dependent propagation of pancreatic inflammation.
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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 | 0.001 |
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".