Angiopoietin-1 Upregulates De Novo Expression of Il-1β and Il1-Ra, and the Exclusive Release of Il1-Ra from Human Neutrophils
Bibliographic record
Abstract
The expression of the angiopoietin (Ang) receptor, Tie2, on both endothelial and inflammatory cells supports the idea that Ang signaling may play a fundamental role in initiating and maintaining the inflammatory response. We have previously shown that Ang1 and/or Ang2 alter the innate immune response by enhancing human neutrophil survival, chemotaxis and production of inflammatory cytokine interleukin-8 (IL-8) in vitro. Thus, we hypothesized that Ang1 and Ang2 could modulate other inflammatory signals in neutrophils, a possibility we explored through a gene-based assay looking at changes in the mRNA expression of 84 inflammatory cytokines and their receptors. We observed that Ang1 (10(-8) M), but not Ang2, increased mRNA expression of prominent pro-inflammatory cytokine IL-1β and its natural antagonist IL-1RA, by up to 32.6- and 10.0-fold respectively, compared to PBS-control. The effects of Ang1 extended to the proteins, as Ang1 increased intracellular levels of precursor and mature IL-1β, and extracellular levels of IL-1RA proteins, by up to 4.2-, 5.0- and 4.4-fold respectively, compared to PBS-control. Interestingly, Ang1 failed at inducing IL-1β protein release or at increasing intracellular IL-1RA, but the ratio of IL-1RA to mature IL-1β remained above 100-fold molar excess inside and outside the cells. The above-noted effects of Ang1 were mediated by MAP kinases, whereby inhibiting MEK1/2 lead to up to 70% effect reduction, whereas the blockade of p38MAPK activity doubled Ang1's effect. These findings suggest that Ang1 selectively alters the balance of neutrophil-derived inflammatory cytokines, favoring the blockade of IL-1 activity, a consideration for future therapies of inflammatory diseases.
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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".