Regulation of Internal Ribosome Entry Site-mediated Translation by Eukaryotic Initiation Factor-2α Phosphorylation and Translation of a Small Upstream Open Reading Frame
Bibliographic record
Abstract
Adaptation to amino acid deficiency is critical for cell survival. In yeast, this adaptation involves phosphorylation of the translation eukaryotic initiation factor (eIF) 2alpha by the kinase GCN2. This leads to the increased translation of the transcription factor GCN4, which in turn increases transcription of amino acid biosynthetic genes, at a time when expression of most genes decreases. Here it is shown that translation of the arginine/lysine transporter cat-1 mRNA increases during amino acid starvation of mammalian cells. This increase requires both GCN2 phosphorylation of eIF2alpha and the translation of a 48-amino acid upstream open reading frame (uORF) present within the 5'-leader of the transporter mRNA. When this 5'-leader was placed in a bicistronic mRNA expression vector, it functioned as an internal ribosomal entry sequence and its regulated activity was dependent on uORF translation. Amino acid starvation also induced translation of monocistronic mRNAs containing the cat-1 5'-leader, in a manner dependent on eIF2alpha phosphorylation and translation of the 48-amino acid uORF. This is the first example of mammalian regulation of internal ribosomal entry sequence-mediated translation by eIF2alpha phosphorylation during amino acid starvation, suggesting that the mechanism of induced Cat-1 protein synthesis is part of the adaptive response of cells to amino acid limitation.
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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".