Genome-scale mapping of DNA damage suppressors through phenotypic CRISPR-Cas9 screens
Bibliographic record
Abstract
To maintain genome integrity, cells must accurately duplicate their genome and repair DNA lesions when they occur. To uncover genes that suppress DNA damage in human cells, we undertook flow-cytometry-based CRISPR-Cas9 screens that monitored DNA damage. We identified 160 genes whose mutation caused spontaneous DNA damage, a list enriched in essential genes, highlighting the importance of genomic integrity for cellular fitness. We also identified 227 genes whose mutation caused DNA damage in replication-perturbed cells. Among the genes characterized, we discovered that deoxyribose-phosphate aldolase DERA suppresses DNA damage caused by cytarabine (Ara-C) and that GNB1L, a gene implicated in 22q11.2 syndrome, promotes biogenesis of ATR and related phosphatidylinositol 3-kinase-related kinases (PIKKs). These results implicate defective PIKK biogenesis as a cause of some phenotypes associated with 22q11.2 syndrome. The phenotypic mapping of genes that suppress DNA damage therefore provides a rich resource to probe the cellular pathways that influence genome maintenance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".