Regulation of Eukaryotic Initiation Factor 4AII by MyoD during Murine Myogenic Cell Differentiation
Bibliographic record
Abstract
Gene expression during muscle cell differentiation is tightly regulated at multiple levels, including translation initiation. The PI3K/mTOR signalling pathway exerts control over protein synthesis by regulating assembly of eukaryotic initiation factor (eIF) 4F, a heterotrimeric complex that stimulates recruitment of ribosomes to mRNA templates. One of the subunits of eIF4F, eIF4A, supplies essential helicase function during this phase of translation. The presence of two cellular eIF4A isoforms, eIF4AI and eIF4AII, has long thought to impart equivalent functions to eIF4F. However, recent experiments have alluded to distinct activities between them. Herein, we characterize distinct regulatory mechanisms between the eIF4A isoforms during muscle cell differentiation. We find that eIF4AI levels decrease during differentiation whereas eIF4AII levels increase during myofiber formation in a MyoD-dependent manner. This study characterizes a previously undefined mechanism for eIF4AII regulation in differentiation and highlights functional differences between eIF4AI and eIF4AII. Finally, RNAi-mediated alterations in eIF4AI and eIF4AII levels indicate that the myogenic process can tolerate short term reductions in eIF4AI or eIF4AII levels, but not both.
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 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 | 0.001 | 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 | 0.001 |
| 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".