A novel p53 regulator, C16ORF72/TAPR1, buffers against telomerase inhibition
Bibliographic record
Abstract
Telomere erosion in cells with insufficient levels of the telomerase reverse transcriptase (TERT), contributes to age-associated tissue dysfunction and senescence, and p53 plays a crucial role in this response. We undertook a genome-wide CRISPR screen to identify gene deletions that sensitized p53-positive human cells to telomerase inhibition. We uncovered a previously unannotated gene, C16ORF72, which we term Telomere Attrition and p53 Response 1 (TAPR1), that exhibited a synthetic-sick relationship with TERT loss. A subsequent genome-wide CRISPR screen in TAPR1-disrupted cells reciprocally identified TERT as a sensitizing gene deletion. Cells lacking TAPR1 or TERT possessed elevated p53 levels and transcriptional signatures consistent with p53 upregulation. The elevated p53 response in TERT- or TAPR1-deficient cells was exacerbated by treatment with the MDM2 inhibitor and p53 stabilizer nutlin-3a and coincided with a further reduction in cell fitness. Importantly, the sensitivity to treatment with nutlin-3a in TERT- or TAPR1-deficient cells was rescued by loss of p53. These data suggest that TAPR1 buffers against the deleterious consequences of telomere erosion or DNA damage by constraining p53. These findings identify C16ORF72/TAPR1 as new regulator at the nexus of telomere integrity and p53 regulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".