Analysis of Cap-binding Proteins in Human Cells Exposed to Physiological Oxygen Conditions
Bibliographic record
Abstract
Translational control is a focal point of gene regulation, especially during periods of cellular stress. Cap-dependent translation via the eIF4F complex is by far the most common pathway to initiate protein synthesis in eukaryotic cells, but stress-specific variations of this complex are now emerging. Purifying cap-binding proteins with an affinity resin composed of Agarose-linked m7GTP (a 5' mRNA cap analog) is a useful tool to identify factors involved in the regulation of translation initiation. Hypoxia (low oxygen) is a cellular stress encountered during fetal development and tumor progression, and is highly dependent on translation regulation. Furthermore, it was recently reported that human adult organs have a lower oxygen content (physioxia 1-9% oxygen) that is closer to hypoxia than the ambient air where cells are routinely cultured. With the ongoing characterization of a hypoxic eIF4F complex (eIF4FH), there is increasing interest in understanding oxygen-dependent translation initiation through the 5' mRNA cap. We have recently developed a human cell culture method to analyze cap-binding proteins that are regulated by oxygen availability. This protocol emphasizes that cell culture and lysis be performed in a hypoxia workstation to eliminate exposure to oxygen. Cells must be incubated for at least 24 hr for the liquid media to equilibrate with the atmosphere within the workstation. To avoid this limitation, pre-conditioned media (de-oxygenated) can be added to cells if shorter time points are required. Certain cap-binding proteins require interactions with a second base or can hydrolyze the m7GTP, therefore some cap interactors may be missed in the purification process. Agarose-linked to enzymatically resistant cap analogs may be substituted in this protocol. This method allows the user to identify novel oxygen-regulated translation factors involved in cap-dependent translation.
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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 | 0.001 |
| 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".