Vitamin C Inhibits NF-κB Activation by TNF Via the Activation of p38 Mitogen-Activated Protein Kinase
Bibliographic record
Abstract
The transcription factor NF-kappaB is a central mediator of altered gene expression during inflammation, and is implicated in a number of pathologies, including cancer, atherosclerosis, and viral infection. We report in this study that vitamin C inhibits the activation of NF-kappaB by multiple stimuli, including IL-1 and TNF in the endothelial cell line ECV304 and in primary HUVECs. The induction of a NF-kappaB-dependent gene, IL-8, by TNF was also inhibited. The effect requires millimolar concentrations of vitamin C, which occur intracellularly in vivo, particularly during inflammation. Vitamin C was not toxic to cells, did not inhibit another inducible transcription factor, STAT1, and had no effect on the DNA binding of NF-kappaB. Inhibition by vitamin C was not simply an antioxidant effect, because redox-insensitive pathways to NF-kappaB were also blocked. Vitamin C was shown to block IL-1- and TNF-mediated degradation and phosphorylation of I-kappaBalpha (inhibitory protein that dissociates from NF-kappaB), due to inhibition of I-kappaB kinase (IKK) activation. Inhibition of TNF-driven IKK activation was mediated by p38 mitogen-activated protein kinase, because treatment of cells with vitamin C led to a rapid and sustained activation of p38, and the specific p38 inhibitor SB203580 reversed the inhibitory effect of vitamin C on IKK activity, I-kappaBalpha phosphorylation, and NF-kappaB activation. The results identify p38 as an intracellular target for high dose vitamin C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".