Modulation of IRP Binding Activity by Oxygen in Primary Erythroid Cells.
Bibliographic record
Abstract
Abstract Iron regulatory proteins 1 and 2 (IRP1 and IRP2) are cytoplasmic RNA-binding proteins that target specific stem-loop RNA structures known as iron responsive elements (IRE). Binding of IRPs to IREs inhibits translation of ferritin mRNA and stabilizes transferrin receptor (TfR) mRNA. Various factors have been reported to regulate binding activity of IRPs, such as iron, phosphorylation, nitric oxide and hypoxia. While there is a consistent agreement on the negative effect of iron on the interaction between IRPs and IREs, reports regarding the influence of hypoxia on the IRE-binding activity of IRPs vary in a species and cell specific manner. It was the aim of this work to study the effect of hypoxic (3% oxygen) and normoxic (20% oxygen) conditions on IRP binding activity in primary erythroid cells. The cells were induced for differentiation and incubated under physiological, low (Desferrioxamine) and high (ferric ammonium citrate) iron conditions. Binding activity of IRPs and protein levels of ferritin and TfR as well as cell proliferation and differentiation parameters were determined to analyze the regulation of iron metabolism during terminal differentiation. The data show, that in developing red blood cells binding activities of IRP1 and IRP2 are reduced at 3% oxygen. This reduction correlates with increased ferritin protein levels and decreased TfR protein levels. Moreover, incubation under hypoxia strongly decreased cell expansion and reduces hemoglobinization. These results suggest that terminal erythroid differentiation in the bone marrow might occur under normoxic rather than hypoxic conditions.
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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".