Heme Oxygenase-1 Inhibits the Expression of Adhesion Molecules Associated with Endothelial Cell Activation via Inhibition of NF-κB <i>RelA</i> Phosphorylation at Serine 276
Bibliographic record
Abstract
Heme oxygenase-1 (HO-1; encoded by the Hmox1 gene) catalyzes the degradation of free heme into biliverdin, via a reaction that releases iron (Fe) and carbon monoxide. We report that HO-1 down-regulates the proinflammatory phenotype associated with endothelial cell (EC) activation by reducing intracellular nonprotein-bound Fe (labile Fe). EC isolated from Hmox1(-/-) mice have higher levels of intracellular labile Fe and reactive oxygen species (ROS) as compared with EC isolated from Hmox1(+/+) mice. Basal and TNF-induced expression of VCAM-1, ICAM-1, and E-selectin were increased in Hmox1(-/-) vs Hmox1(+/+) EC, an effect reversed by Fe chelation using deferoxamine mesylate (DFO). Fe chelation inhibits TNF-driven transcription of Vcam-1, Icam-1, and E-selectin, as assessed using luciferase reporter assays. This effect is associated with inhibition of the transcription factor NF-kappaB via a mechanism that is not associated with the inhibition of IkappaBalpha phosphorylation/degradation or NF-kappaB (i.e., RelA) nuclear translocation, although it affects very modestly NF-kappaB binding to DNA kappaB consensus sequences in the Vcam-1 and E-selectin promoters. HO-1 inhibits NF-kappaB (i.e., RelA) phosphorylation at Ser(276), a phosphoacceptor that is critical to sustain TNF-driven NF-kappaB activity in EC. This effect was mimicked by Fe chelation as well as by antioxidants (N-acetylcysteine). In conclusion, we demonstrate a novel mechanism via which HO-1 down-modulates the proinflammatory phenotype of activated EC, i.e., the inhibition of RelA phosphorylation at Ser(276).
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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".