c-Myc and eIF4F Are Components of a Feedforward Loop that Links Transcription and Translation
Bibliographic record
Abstract
The Myc/Max/Mad family of transcription factors and the eukaryotic initiation factor 4F (eIF4F) complex play fundamental roles in regulating cell growth, proliferation, differentiation, and oncogenic transformation. eIF4F is involved in the recruitment of ribosomes to mRNAs and is thought to generally be the rate-limiting phase of translation. Here, we show that c-Myc directly activates transcription of the three subunits of eIF4F (eIF4E, eIF4AI, and eIF4GI). These transcriptional effects are mediated through canonical E-boxes (5'CACGTG3') present in the promoters of these genes. In addition, the c-Myc antagonist Mad1 down-regulates the expression of eIF4F subunits. We also show that MycER activation stimulates protein synthesis at the level of translation initiation. Increased eIF4F levels result in stimulation of c-Myc mRNA translation specifically, as assessed by quantitative reverse transcription-PCR. We use a murine model of lymphomagenesis to show the expression of eIF4F subunits is also up-regulated by c-Myc in vivo. Our results suggest the presence of a feedforward loop involving c-Myc and eIF4F that serves to link transcription and translation and that could contribute to the effects of c-Myc on cell proliferation and neoplastic growth.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".