Competition between translation initiation factor eIF5 and its mimic protein 5MP determines non-AUG initiation rate genome-wide
Bibliographic record
Abstract
In the human genome, translation initiation from non-AUG codons plays an important role in various gene regulation programs. However, mechanisms regulating the non-AUG initiation rate remain poorly understood. Here, we show that the non-AUG initiation rate is nearly consistent under a fixed nucleotide context in various human and insect cells. Yet, it ranges from <1% to nearly 100% compared to AUG translation, depending on surrounding sequences, including Kozak, and possibly additional nucleotide contexts. Mechanistically, this range of non-AUG initiation is controlled in part, by the eIF5-mimic protein (5MP). 5MP represses non-AUG translation by competing with eIF5 for the Met-tRNAi-binding factor eIF2. Consistently, eIF5 increases, whereas 5MP decreases translation of NAT1/EIF4G2/DAP5, whose sole start codon is GUG. By modulating eIF5 and 5MP1 expression in combination with ribosome profiling we identified a handful of previously unknown non-AUG initiation sites, some of which serve as the exclusive start codons. If the initiation rate for these codons is low, then an AUG-initiated downstream ORF prevents the generation of shorter, AUG-initiated isoforms. We propose that the homeostasis of the non-AUG translatome is maintained through balanced expression of eIF5 and 5MP.
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 | 0.000 | 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".