Alternative ferritin mRNA translation via internal initiation
Bibliographic record
Abstract
Ferritin stores and detoxifies an excess of intracellular iron, and thereby plays an important role in the metabolism of this metal. As unshielded iron promotes oxidative stress, ferritin is crucial in maintaining cellular redox balance and may also modulate cell growth, survival, and apoptosis. The expression of ferritin is controlled by transcriptional and post-transcriptional mechanisms. In light of the well-established transcriptional induction of ferritin by inflammatory signals, we examined how ferritin mRNA translation responds to stress conditions. We first used HT1080 fibrosarcoma cells engineered for coumermycin-inducible expression of PKR, a stress kinase that inhibits protein synthesis in virus-infected cells by phosphorylating eIF2α. In this setting, iron triggered partial ferritin mRNA translation despite a PKR-induced global shutdown in protein synthesis. Moreover, iron-mediated ferritin synthesis was evident in cells infected with an attenuated strain of poliovirus; when eIF4GI was cleaved, eIF2α was phosphorylated, and host protein synthesis was inhibited. Under global inhibition of protein synthesis or specific inhibition of ferritin mRNA translation in cells overexpressing PKR or IRP1, respectively, we demonstrate association of ferritin mRNA with heavy polysomes. Further experiments revealed that the 5' untranslated region (5' UTR) of ferritin mRNA contains a bona fide internal ribosomal entry site (IRES). Our data are consistent with the existence of an alternative, noncanonical mechanism for ferritin mRNA translation, which may primarily operate under stress conditions to protect cells from oxidative stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".