Differential Pattern of Inflammatory Molecule Regulation in Intestinal Epithelial Cells Stimulated with IL-1
Bibliographic record
Abstract
To better predict the consequences of blocking signal transduction pathways as a means of controlling intestinal inflammation, we are characterizing the pathways up-regulated by IL-1 in intestinal epithelial cells (IEC). IL-1beta induced increased mRNA levels of MIP-2, MCP-1, RANTES, inducible NO synthase (iNOS), and cyclooxygenase-2 (COX-2) in the IEC-18 cell line. IL-1beta activated NF-kappaB but not ERK or p38. Infecting cells with adenovirus expressing a mutated gene for IkappaBalpha (IkappaBAA) blocked IL-1-induced mRNA increases in MIP-2, MCP-1, and iNOS but not COX-2 or RANTES. Expression of IkappaBAA attenuated the IL-1-induced increase in COX-2 protein. Unexpectedly, RANTES mRNA increased, and protein was secreted by cells expressing IkappaBAA in the absence of IL-1. Adenovirus-expressing IkappaBAA, blocking protein synthesis, and IL-1beta all resulted in activation of JNK. The JNK inhibitor SP600125 prevented the RANTES increases by all three stimuli. A human enterocyte line was similarly examined, and both NF-kappaB and JNK regulate IL-1-induced RANTES secretion. We conclude that in IEC-18, IL-1beta-induced increases in mRNA for MIP-2, MCP-1, and iNOS are NF-kappaB-dependent, whereas regulation of RANTES mRNA is independent of NF-kappaB but is positively regulated by JNK. IL-1beta-induced mRNA increases in COX-2 mRNA are both NF-kappaB- and MAPK-independent but the translation of COX-2 protein is NF-kappaB-dependent. This pattern of signaling due to a single stimulus exposed the complexities of regulating inflammatory genes in IEC.
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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 | 0.000 |
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".