DDX54 regulates transcriptome dynamics during DNA damage response
Bibliographic record
Abstract
The cellular response to genotoxic stress is mediated by a well-characterized network of DNA surveillance pathways. The contribution of post-transcriptional gene regulatory networks to the DNA damage response (DDR) has not been extensively studied. Here, we systematically identified RNA-binding proteins differentially interacting with polyadenylated transcripts upon exposure of human breast carcinoma cells to ionizing radiation (IR). Interestingly, more than 260 proteins, including many nucleolar proteins, showed increased binding to poly(A)+RNA in IR-exposed cells. The functional analysis of DDX54, a candidate genotoxic stress responsive RNA helicase, revealed that this protein is an immediate-to-early DDR regulator required for the splicing efficacy of its target IR-induced pre-mRNAs. Upon IR exposure, DDX54 acts by increased interaction with a well-defined class of pre-mRNAs that harbor introns with weak acceptor splice sites, as well as by protein–protein contacts within components of U2 snRNP and spliceosomal B complex, resulting in lower intron retention and higher processing rates of its target transcripts. Because DDX54 promotes survival after exposure to IR, its expression and/or mutation rate may impact DDR-related pathologies. Our work indicates the relevance of many uncharacterized RBPs potentially involved in the DDR.
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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) | 0.000 | 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) | 0.002 | 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".