Effects of nitrogen monoxide and carbon monoxide on molecular and cellular iron metabolism: mirror-image effector molecules that target iron
Bibliographic record
Abstract
Many effector functions of nitrogen monoxide (NO) and carbon monoxide (CO) are mediated through their high-affinity for iron (Fe). In this review, the roles of NO and CO are examined in terms of their effects on the molecular and cellular mechanisms involved in Fe metabolism. Both NO and CO avidly form complexes with a plethora of Fe-containing molecules. The generation of NO and CO is mediated by the nitric oxide synthase and haem oxygenase (HO) families of enzymes respectively. The effects of NO on Fe metabolism have been well characterized, whereas knowledge of the effects of CO remains within its infancy. In terms of the role of NO in Fe metabolism, one of the best characterized interactions includes its effect on the iron regulatory proteins. These molecules are mRNA-binding proteins that control the expression of the transferrin receptor 1 and ferritin, molecules that are involved in Fe uptake and storage respectively. Apart from this, activated macrophages impart their cytotoxic activity by generating NO, which results in marked Fe mobilization from tumour-cell targets. This deprives the cell of the Fe that is required for DNA synthesis and energy production. Considering that HO degrades haem, resulting in the release of CO, Fe(II) and biliverdin, it is suggested that a CO-Fe complex will form. This may account for the rapid Fe mobilization observed from macrophages after haemoglobin catabolism. Intriguingly, overexpression of HO results in cellular Fe mobilization, suggesting that CO has a similar effect to NO on Fe trafficking. Preliminary evidence suggests that, like NO, CO plays important roles in Fe metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 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".