Human Cells Cultured under Physiological Oxygen Utilize Two Cap-binding Proteins to recruit Distinct mRNAs for Translation
Bibliographic record
Abstract
Translation initiation is a focal point of translational control and requires the binding of eIF4E to the 5' cap of mRNA. Under conditions of extreme oxygen depletion (hypoxia), human cells repress eIF4E and switch to an alternative cap-dependent translation mediated by a homolog of eIF4E, eIF4E2. This homolog forms a complex with the oxygen-regulated hypoxia-inducible factor 2α and can escape translation repression. This complex mediates cap-dependent translation under cell culture conditions of 1% oxygen (to mimic tumor microenvironments), whereas eIF4E mediates cap-dependent translation at 21% oxygen (ambient air). However, emerging evidence suggests that culturing cells in ambient air, or "normoxia," is far from physiological or "normal." In fact, oxygen in human tissues ranges from 1-11% or "physioxia." Here we show that two distinct modes of cap-dependent translation initiation are active during physioxia and act on separate pools of mRNAs. The oxygen-dependent activities of eIF4E and eIF4E2 are elucidated by observing their polysome association and the status of mammalian target of rapamycin complex 1 (eIF4E-dependent) or hypoxia-inducible factor 2α expression (eIF4E2-dependent). We have identified oxygen conditions where eIF4E is the dominant cap-binding protein (21% normoxia or standard cell culture conditions), where eIF4E2 is the dominant cap-binding protein (1% hypoxia or ischemic diseases and cancerous tumors), and where both cap-binding proteins act simultaneously to initiate the translation of distinct mRNAs (1-11% physioxia or during development and stem cell differentiation). These data suggest that the physioxic proteome is generated by initiating translation of mRNAs via two distinct but complementary cap-binding proteins.
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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) | 0.002 | 0.001 |
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".