F-Box Proteins Elongate Translation During Stress Recovery
Bibliographic record
Abstract
Protein synthesis is energetically costly and is tightly regulated by evolutionarily conserved mechanisms. Under restrictive growth conditions and in response to various stresses, such as DNA damage, cells inhibit protein synthesis to redirect available adenosine triphosphate to more essential processes. Conversely, proliferating cells, such as cancer cells, increase protein synthetic rates to support growth-related anabolic processes. mRNA translation occurs in three separate phases, consisting of initiation, elongation, and termination. Although all three phases are highly regulated, most of the translational control occurs at the rate-limiting initiation step. New evidence has described a molecular mechanism involved in the regulation of translation elongation. DNA damage initially slowed down elongation rates by activating the eukaryotic elongation factor 2 kinase (eEF2K) through an adenosine monophosphate (AMP)-activated protein kinase (AMPK)-dependent mechanism. However, during checkpoint recovery, the SCF (Skp, Cullin, F-box-containing) βTrCP (β-transducin repeat-containing protein) E3 ubiquitin ligase promoted degradation of eEF2K, thereby allowing the restoration of peptide chain elongation. These findings establish an important link between DNA damage signaling and the regulation of translation elongation.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".