Post-Transcriptional Regulation of DNA Damage-Responsive Gene Expression
Bibliographic record
Abstract
SIGNIFICANCE: Production of proteins requires the synthesis, maturation, and export of mRNAs before their translation in the cytoplasm. Endogenous and exogenous sources of DNA damage pose a challenge to the co-ordinated regulation of gene expression, because the integrity of the DNA template can be compromised by DNA lesions. Cells recognize and respond to this DNA damage through a variety of DNA damage responses (DDRs). Failure to deal with DNA damage appropriately can lead to genomic instability and cancer. RECENT ADVANCES: The p53 tumor suppressor plays a dominant role in DDR-dependent changes in gene expression, but this transcription factor is not solely responsible for all changes. Recent evidence indicates that RNA metabolism is integral to DDRs as well. In particular, post-transcriptional processes are emerging as important contributors to these complex responses. CRITICAL ISSUES: Transcriptional, post-transcriptional, and translational regulation of gene expression is subject to changes in response to DNA damage. How these processes are intertwined in the unfolding of DDR is not fully understood. FUTURE DIRECTIONS: Many complex regulatory responses combine to determine cell fate after DNA damage. Understanding how transcriptional, post-transcriptional, and translational processes interdigitate to create a web of regulatory interactions will be one of the key challenges to fully understand DDRs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".