Proteinase‐activated receptors induce interleukin‐8 expression by intestinal epithelial cells through ERK/RSK90 activation and histone acetylation
Bibliographic record
Abstract
Proteinase-activated receptors (PARs) are involved in both inflammation and tumorigenesis in epithelial cells. Interleukin (IL)-8 is a potent chemoattractant and is also involved in angiogenesis. The molecular mechanism whereby PARs induce epithelial IL-8 expression is not known. In HT-29 colonic epithelial cells, PAR(1) or PAR(2) agonists stimulated the expression of IL-8 through a NF-kappaB-dependent pathway without inducing IkappaB degradation and disassociation of IkappaB from NF-kappaB. Further studies revealed that PAR activation induced the phosphorylation of p65 at Ser-276 in the nucleus, which increased the recruitment of histone acetyltransferase (HAT) p300 to p50. Inhibition of ERK activation completely blocked PAR-induced IL-8 expression, phosphorylation of p65 and HAT activity. We also demonstrated that RSK p90 was the downstream kinase that mediated ERK-induced nuclear p65 phosphorylation. In conclusion, activation of either PAR(1) or PAR(2) stimulated the transcriptional up-regulation of IL-8 in HT-29 colonic epithelial cells through a pathway that involved ERK/RSK p90, NF-kappaB phosphorylation, and HAT activity. These studies provide evidence of a new role for serine proteinases and PARs in the regulation of gene expression in colonic inflammation and tumorigenesis.
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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".